{"meta":{"query_hash":"a80d432d834b","filters":{"topic":"Neural Networks and Applications"},"cohort_total":2372,"direct_labels_cover":1,"predictions_cover":2372,"exported":2372,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/a80d432d834b","api":"https://metacan.xera.ac/api/v1/cohort?topic=Neural+Networks+and+Applications"},"results":[{"id":"W1010659188","doi":"10.1017/cbo9780511791277.020","title":"Multivariate Gaussian from maximum entropy","year":2005,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Multivariate statistics; Principle of maximum entropy; Gaussian; Mathematics; Multivariate analysis; Statistics; Statistical physics; Physics; Chemistry; Computational chemistry","score_opus":0.011376925886241392,"score_gpt":0.24371898630847053,"score_spread":0.23234206042222913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1010659188","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006945605,0.007186415,0.9415027,0.0033049218,0.00074514287,0.0000711884,0.002378506,0.0008178438,0.03704765],"genre_scores_gemma":[0.4722969,0.02416812,0.42734566,0.0020298546,0.0057938835,0.00060263916,0.008664814,0.0025359937,0.056562133],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990109,0.00040342996,0.000052464835,0.00016179553,0.0002974587,0.00007378364],"domain_scores_gemma":[0.9975967,0.0013818749,0.00014462741,0.00043271342,0.00032838515,0.00011580833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023800125,0.0008706834,0.0014067757,0.002486441,0.0006019599,0.0020651123,0.0012677949,0.0013044514,0.01870068],"category_scores_gemma":[0.01244129,0.0005167819,0.0010846531,0.0023210184,0.0015388262,0.0037808463,0.0032250998,0.0028687813,0.004254587],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009692636,0.000021014956,0.00045323902,0.0003219962,0.00006662966,0.00010135673,0.00008162438,0.033435423,0.0006927017,0.82518536,0.028022738,0.111521],"study_design_scores_gemma":[0.000012535544,0.00001348655,0.0005642351,0.0000779155,0.000016425603,0.00008830996,0.000012550305,0.11081255,0.00054378854,0.87454695,0.013282738,0.00002850921],"about_ca_topic_score_codex":0.0017905214,"about_ca_topic_score_gemma":0.002252489,"teacher_disagreement_score":0.01870068,"about_ca_system_score_codex":0.0013905818,"about_ca_system_score_gemma":0.0011768047,"threshold_uncertainty_score":0.06256002},"labels":[],"label_agreement":null},{"id":"W107074470","doi":"10.1007/978-3-319-06483-3_21","title":"Ensemble of Multiple Kernel SVM Classifiers","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Ottawa","funders":"","keywords":"Multiple kernel learning; Generalization; Computer science; Support vector machine; Kernel (algebra); Artificial intelligence; Machine learning; Ensemble learning; Pattern recognition (psychology); Kernel method; Mathematics","score_opus":0.020985478821517,"score_gpt":0.2404894999563194,"score_spread":0.2195040211348024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W107074470","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038689848,0.0044913674,0.9466629,0.0002469754,0.0009268664,0.000080115744,0.00021645862,0.0015588589,0.007126607],"genre_scores_gemma":[0.54133934,0.002660331,0.43514723,0.00026734945,0.000756633,0.00015576949,0.0016891042,0.00027404437,0.017710235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988172,0.0002115276,0.00009843019,0.0002603286,0.00049987884,0.000112653666],"domain_scores_gemma":[0.9981628,0.000322327,0.00008435067,0.00038309325,0.0009628121,0.00008455201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018054922,0.00087634317,0.0024463558,0.0012913991,0.0005132405,0.0015054088,0.0013571022,0.0011442374,0.0025554749],"category_scores_gemma":[0.002874885,0.00055385317,0.0012797695,0.0017217302,0.00020655872,0.0025069665,0.0013264978,0.0015699952,0.002194313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002239543,0.00019325152,0.0021039895,0.00016672943,0.00048258863,0.00006518461,0.000051095623,0.06875927,0.009993591,0.003172916,0.00979728,0.90499026],"study_design_scores_gemma":[0.000009865792,0.000118972894,0.0013819872,0.000031175652,0.00015413603,0.00014275304,0.00003117625,0.9821545,0.0054957243,0.0042282944,0.0062280595,0.000023428403],"about_ca_topic_score_codex":0.000893318,"about_ca_topic_score_gemma":0.0018714855,"teacher_disagreement_score":0.0025554749,"about_ca_system_score_codex":0.0003428493,"about_ca_system_score_gemma":0.00059876574,"threshold_uncertainty_score":0.009548485},"labels":[],"label_agreement":null},{"id":"W112486021","doi":"","title":"Enhancing the Performance of Neurofuzzy Predictors by Emotional Learning Algorith.","year":2003,"lang":"en","type":"article","venue":"Informatica (slovenia)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Adaptive neuro fuzzy inference system; Artificial intelligence; Generalization; Nonlinear system; Machine learning; Fuzzy logic; Artificial neural network; Inference; Adaptability; Time series; Series (stratigraphy); Computational complexity theory; Algorithm; Fuzzy control system; Mathematics","score_opus":0.006122637572448104,"score_gpt":0.20266844177072194,"score_spread":0.19654580419827383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W112486021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13524748,0.0010301311,0.855947,0.0003986827,0.00012682422,0.00006172413,0.000040200834,0.0008391328,0.0063086846],"genre_scores_gemma":[0.9219002,0.00026507824,0.07622228,0.000071819624,0.00003790827,0.00003822837,0.000043891196,0.000023188955,0.0013973579],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973327,0.00009562901,0.000019602077,0.000035336205,0.00008528375,0.00003088599],"domain_scores_gemma":[0.99839056,0.0010749093,0.00012118487,0.0000848175,0.00030131274,0.000027278313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014948231,0.0006135309,0.00039253128,0.00035975908,0.00021629418,0.00061409024,0.0003828926,0.0007357281,0.0010331622],"category_scores_gemma":[0.0058402736,0.0002123238,0.00026588936,0.00028258745,0.0002997075,0.00092090265,0.00045050663,0.000600654,0.00038665437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042347988,0.00017900001,0.0036230362,0.00014658738,0.00008431229,0.00016992759,0.00014398262,0.5961484,0.015590669,0.0058818646,0.0012571502,0.37635162],"study_design_scores_gemma":[0.0000046844043,0.000042074367,0.00039921317,0.0000068033446,0.000008177229,0.000018613055,0.0000061165115,0.99561703,0.0029976428,0.0006736298,0.00022230484,0.0000037420596],"about_ca_topic_score_codex":0.0013431439,"about_ca_topic_score_gemma":0.0013333643,"teacher_disagreement_score":0.0014948231,"about_ca_system_score_codex":0.00024040895,"about_ca_system_score_gemma":0.00030518684,"threshold_uncertainty_score":0.007905483},"labels":[],"label_agreement":null},{"id":"W112565442","doi":"10.1007/978-3-642-25832-9_3","title":"Semi-Supervised Classification Using Tree-Based Self-Organizing Maps","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Artificial neural network; Cardinality (data modeling); Pattern recognition (psychology); Tree (set theory); Training set; Machine learning; Data mining; Mathematics","score_opus":0.0442433781530636,"score_gpt":0.24654343320559793,"score_spread":0.20230005505253434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W112565442","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028411916,0.0005008892,0.9665917,0.00008786089,0.000113071,0.00013774821,0.0003089785,0.0021679208,0.0016799695],"genre_scores_gemma":[0.43102455,0.00039187734,0.5622651,0.000113079586,0.0001606989,0.00040208627,0.0020409042,0.00025395225,0.0033477547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998621,0.00031711836,0.00013119364,0.00036184883,0.00045727278,0.000111651905],"domain_scores_gemma":[0.9972516,0.0012220346,0.00018357518,0.00032319082,0.00093017676,0.00008956114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016706977,0.0007653918,0.001508723,0.0016408408,0.0006486146,0.0016261023,0.0015801821,0.00095328875,0.001384053],"category_scores_gemma":[0.0033114497,0.00043780243,0.001475709,0.0016272585,0.00045261433,0.0015896192,0.0011062467,0.0008529693,0.0013167133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031661498,0.00027344463,0.002288511,0.000292379,0.00023542071,0.00013524722,0.00025412938,0.07468925,0.012269761,0.004655808,0.008158354,0.89643115],"study_design_scores_gemma":[0.00001059368,0.00006104119,0.00072644296,0.000018843773,0.000032248976,0.00009000447,0.000044838733,0.9884135,0.0039284006,0.0056630396,0.0009944285,0.000016640533],"about_ca_topic_score_codex":0.0019791205,"about_ca_topic_score_gemma":0.0027993345,"teacher_disagreement_score":0.0019791205,"about_ca_system_score_codex":0.00041189505,"about_ca_system_score_gemma":0.00085374666,"threshold_uncertainty_score":0.008835614},"labels":[],"label_agreement":null},{"id":"W1139486","doi":"","title":"An automated hammerstein recurrent neural network for dynamic applications","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Recurrent neural network; Artificial intelligence","score_opus":0.014595633733850392,"score_gpt":0.3061609408638437,"score_spread":0.29156530712999335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1139486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029705027,0.0016892469,0.9585486,0.00044726234,0.00021417918,0.000110716544,0.00023773221,0.0055846195,0.0034626455],"genre_scores_gemma":[0.6745351,0.0008245701,0.30884686,0.00034195362,0.00016209007,0.00039172918,0.0010013559,0.00022492754,0.013671376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959356,0.0000877693,0.00003194825,0.00013911093,0.00007879416,0.00006882795],"domain_scores_gemma":[0.9993605,0.00030739163,0.000050154755,0.00005366906,0.00020173391,0.000026542559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012084473,0.001015007,0.00090524636,0.00076592265,0.00054618536,0.0011400366,0.0017358698,0.0014839676,0.004848945],"category_scores_gemma":[0.0026037944,0.00061315595,0.0006582425,0.0006736215,0.00038691674,0.0011396214,0.0007960253,0.0013388328,0.0015012517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024217957,0.00013760643,0.0007530965,0.000117929216,0.00009918757,0.00012693337,0.000045601242,0.5111776,0.005811684,0.0032278171,0.004682071,0.4735783],"study_design_scores_gemma":[0.000004569048,0.000013551787,0.0000626737,0.0000034774537,0.0000055678247,0.000006314403,0.0000025858512,0.99869615,0.00037584218,0.00054796174,0.00027815995,0.0000030178314],"about_ca_topic_score_codex":0.023697197,"about_ca_topic_score_gemma":0.02269458,"teacher_disagreement_score":0.023697197,"about_ca_system_score_codex":0.0010964851,"about_ca_system_score_gemma":0.0011681458,"threshold_uncertainty_score":0.047118545},"labels":[],"label_agreement":null},{"id":"W116068320","doi":"10.1007/978-3-642-24412-4_3","title":"On the Expressive Power of Deep Architectures","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":180,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Expressive power; Deep neural networks; Electronic circuit; Computer architecture; Theoretical computer science; Electrical engineering","score_opus":0.01627985766662682,"score_gpt":0.23056476091546363,"score_spread":0.2142849032488368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W116068320","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025547707,0.0139071,0.8664644,0.0066764774,0.0004931057,0.000031577893,0.000543342,0.0011475737,0.08518885],"genre_scores_gemma":[0.69629526,0.01591841,0.24403363,0.0028146768,0.0017097248,0.00020434456,0.0009205232,0.0009658996,0.037137512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99906856,0.00027678814,0.0000782376,0.00013369217,0.00034467576,0.000098054385],"domain_scores_gemma":[0.9947305,0.004116452,0.00013401115,0.00072776614,0.00020749938,0.00008374967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016792679,0.0009705936,0.0008543021,0.000728168,0.0006431527,0.0031249926,0.0016244728,0.0012808479,0.0069908476],"category_scores_gemma":[0.008553319,0.0010025067,0.00077167625,0.0010929169,0.0036721113,0.010052786,0.003223866,0.0058284136,0.0012282904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009626856,0.000021538348,0.00020902169,0.0001852308,0.00003551324,0.00008050707,0.00013213744,0.026407162,0.0027526289,0.8872958,0.004804249,0.07797991],"study_design_scores_gemma":[0.000006866838,0.000008121997,0.0000502156,0.000035314988,0.00000810639,0.000029964696,0.0000114197255,0.045369104,0.00067571644,0.9500756,0.0037229662,0.000006564235],"about_ca_topic_score_codex":0.0013924814,"about_ca_topic_score_gemma":0.0021348777,"teacher_disagreement_score":0.0069908476,"about_ca_system_score_codex":0.0009688215,"about_ca_system_score_gemma":0.00043742094,"threshold_uncertainty_score":0.023386717},"labels":[],"label_agreement":null},{"id":"W117143849","doi":"10.1007/0-387-24555-3_5","title":"Bias in Estimating the Variance of K-Fold Cross-Validation","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Estimator; Generalization; Cross-validation; Mathematics; Variance (accounting); Covariance matrix; Variance decomposition of forecast errors; Covariance; Eigenvalues and eigenvectors; Statistics; Computation; Applied mathematics; Degrees of freedom (physics and chemistry); Generalization error; Algorithm; Computer science; Artificial intelligence","score_opus":0.061456586886874554,"score_gpt":0.30315410285264466,"score_spread":0.2416975159657701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W117143849","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039255144,0.0019852722,0.991867,0.00021958609,0.0002675424,0.00003802252,0.00006645192,0.00078754895,0.00084296253],"genre_scores_gemma":[0.15235223,0.0016529706,0.83430123,0.0008218088,0.0007049301,0.00056684256,0.00081319356,0.001707627,0.007079153],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97976893,0.013273862,0.0012606223,0.0017906764,0.0034668487,0.00043900136],"domain_scores_gemma":[0.92626166,0.058794327,0.0012787303,0.0061902893,0.0071981046,0.00027688895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031423137,0.0016428729,0.0026661644,0.0018187419,0.0010354281,0.0023239749,0.00385384,0.0033629013,0.0028032481],"category_scores_gemma":[0.11650964,0.0013572345,0.0013955049,0.0020420875,0.002184199,0.0042306455,0.0025538853,0.005137705,0.002128907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050806743,0.00018800398,0.009103108,0.00076299784,0.0008955539,0.00015458252,0.0003749532,0.26393566,0.0043486184,0.06876162,0.0156543,0.6353125],"study_design_scores_gemma":[0.00005552023,0.00009734546,0.0028529232,0.00026168078,0.00011814206,0.00022121596,0.00005344331,0.9205751,0.00645705,0.06507038,0.0041610007,0.00007618846],"about_ca_topic_score_codex":0.004844412,"about_ca_topic_score_gemma":0.0067553576,"teacher_disagreement_score":0.031423137,"about_ca_system_score_codex":0.0016257926,"about_ca_system_score_gemma":0.0017924305,"threshold_uncertainty_score":0.16618341},"labels":[],"label_agreement":null},{"id":"W120526399","doi":"","title":"Nonlinear dynamics and chaos in information processing neural networks","year":2001,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Artificial neural network; CHAOS (operating system); Computer science; Artificial intelligence; Nonlinear system; Information processing; Physics; Psychology; Neuroscience","score_opus":0.00800094340883237,"score_gpt":0.22928034573378253,"score_spread":0.22127940232495016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W120526399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071705274,0.08272094,0.730271,0.013952965,0.0011839378,0.00010683885,0.00022634334,0.00029173013,0.09954094],"genre_scores_gemma":[0.8297375,0.04493599,0.09964035,0.0012859271,0.0019320461,0.00024890827,0.0001184903,0.000075603646,0.022025233],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999759,0.00008773187,0.000013946774,0.000033244967,0.00008030837,0.00002578161],"domain_scores_gemma":[0.9995441,0.0003270715,0.00004214006,0.000024221463,0.00004468276,0.000017712724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049120956,0.0003719564,0.00048886775,0.0005676091,0.00042054456,0.0011545302,0.0004236797,0.001102839,0.0018770313],"category_scores_gemma":[0.002034758,0.00017971706,0.0002469954,0.0007127903,0.0017782926,0.0016260914,0.00059840665,0.0008626583,0.0002937518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011027684,0.000008761202,0.0002180376,0.00013944747,0.000014880752,0.00012983191,0.00017049865,0.05689454,0.0015725418,0.9254368,0.001623488,0.013780217],"study_design_scores_gemma":[0.000007143574,0.000015623697,0.00020763426,0.000040058923,0.0000059437316,0.00006445467,0.00003220981,0.13256271,0.0004322203,0.8561608,0.0104601905,0.000010950456],"about_ca_topic_score_codex":0.0009689776,"about_ca_topic_score_gemma":0.00087585655,"teacher_disagreement_score":0.0018770313,"about_ca_system_score_codex":0.0005992211,"about_ca_system_score_gemma":0.00034491782,"threshold_uncertainty_score":0.0062793493},"labels":[],"label_agreement":null},{"id":"W1226178539","doi":"10.1167/15.12.473","title":"Modelling probability summation for the detection of multiple stimuli under the assumptions of signal detection theory","year":2015,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Monte Carlo method; Summation; Weibull distribution; SIGNAL (programming language); Mathematics; Applied mathematics; Computer science; Statistics","score_opus":0.07886108583782751,"score_gpt":0.3096834821559823,"score_spread":0.23082239631815477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1226178539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031158663,0.00018131471,0.9662662,0.00016399974,0.000020638468,0.00007834977,0.00005652366,0.00025573082,0.0018186317],"genre_scores_gemma":[0.67645913,0.0005978774,0.3159894,0.00025066716,0.00007835351,0.00061827106,0.00023352161,0.0002869976,0.005485734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978962,0.00063873356,0.00017691495,0.00043012662,0.00068030966,0.0001776765],"domain_scores_gemma":[0.9840889,0.013067255,0.001078713,0.00074104074,0.0008686172,0.00015550162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005510128,0.00081939704,0.0012008094,0.0012710007,0.0004314112,0.0013810082,0.0024611708,0.0017103618,0.0025662428],"category_scores_gemma":[0.034075305,0.00073932874,0.0016530306,0.0012269252,0.0015900619,0.0038665119,0.0014956233,0.0017235867,0.0007594412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002671461,0.0001408378,0.004948448,0.0003955126,0.00012862255,0.0005646704,0.0008149905,0.7920429,0.020151103,0.1175185,0.0010664561,0.06196075],"study_design_scores_gemma":[0.000008925623,0.000033009303,0.0008202119,0.0000123085165,0.000013013969,0.00017495707,0.000011808932,0.96702355,0.0010685966,0.030490782,0.0003249991,0.00001787986],"about_ca_topic_score_codex":0.0054181996,"about_ca_topic_score_gemma":0.0027628462,"teacher_disagreement_score":0.005510128,"about_ca_system_score_codex":0.0015299916,"about_ca_system_score_gemma":0.0010299124,"threshold_uncertainty_score":0.029140651},"labels":[],"label_agreement":null},{"id":"W1274597083","doi":"10.1167/15.12.391","title":"Stimulus-specific regularities as a basis for perceptual induction","year":2015,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Rule induction; Stimulus (psychology); Learning rule; Perception; Artificial intelligence; Sequence learning; Sequence (biology); Mathematics; Pattern recognition (psychology); Psychology; Computer science; Cognitive psychology; Artificial neural network; Biology","score_opus":0.05403714166850158,"score_gpt":0.31413736375728785,"score_spread":0.26010022208878625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1274597083","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86014974,0.00040701678,0.11698776,0.0008305866,0.00013042224,0.00023255643,0.00017876265,0.0009821713,0.020101007],"genre_scores_gemma":[0.97745436,0.00010652799,0.020907259,0.00018123027,0.000030341776,0.00009092247,0.0001837091,0.00016767903,0.0008779378],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981054,0.00034176288,0.00013744498,0.00063528767,0.0005712063,0.00020883458],"domain_scores_gemma":[0.99073166,0.0044001364,0.0010608647,0.002651771,0.00067889446,0.00047675104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019934212,0.00033186056,0.0005937984,0.0005211679,0.00042106866,0.0015405692,0.0014109798,0.00085973204,0.004103971],"category_scores_gemma":[0.0133810155,0.0008214731,0.00076849206,0.0002642507,0.0018147618,0.0024318194,0.0018317482,0.0021369339,0.0005756313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017319068,0.000787729,0.031801157,0.0008887846,0.00028004387,0.0006462966,0.0014444632,0.028892776,0.69470125,0.08135275,0.0018178028,0.15565503],"study_design_scores_gemma":[0.00041030964,0.0014848284,0.18649359,0.00012087991,0.00023832546,0.0010957818,0.00036331595,0.22579263,0.24607842,0.33088994,0.006779756,0.0002522029],"about_ca_topic_score_codex":0.00066102867,"about_ca_topic_score_gemma":0.00057461177,"teacher_disagreement_score":0.004103971,"about_ca_system_score_codex":0.00080410583,"about_ca_system_score_gemma":0.00065138866,"threshold_uncertainty_score":0.013729095},"labels":[],"label_agreement":null},{"id":"W136578677","doi":"10.1007/978-3-642-25126-9_41","title":"Modeling Slowly Changing Dimensions in ORM","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Theoretical computer science","score_opus":0.029832396267620284,"score_gpt":0.2430094971162429,"score_spread":0.21317710084862262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W136578677","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054588277,0.0015289903,0.934972,0.000684818,0.00020681607,0.000035899197,0.00035593295,0.0005302687,0.0070970897],"genre_scores_gemma":[0.73215914,0.0012019654,0.25029722,0.0003341346,0.00019306512,0.00013831377,0.0006308506,0.00023593733,0.014809313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996594,0.00013081942,0.000014766379,0.000078588615,0.000059447415,0.000056950284],"domain_scores_gemma":[0.99881005,0.00079675514,0.00010545852,0.00012056406,0.00011489501,0.000052220897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008771327,0.00050881907,0.0007109356,0.00045064473,0.0003827417,0.0011041531,0.0010972363,0.0011773239,0.003683941],"category_scores_gemma":[0.00430096,0.0004477988,0.0006949781,0.00081419945,0.00057976367,0.001845172,0.0012145605,0.0016534942,0.000577333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067015106,0.000037186295,0.001816017,0.000091321424,0.000050798757,0.00007575355,0.00012016001,0.8189406,0.0010535765,0.12645786,0.003003976,0.048285704],"study_design_scores_gemma":[0.000001957105,0.0000049387536,0.00009321136,0.0000047183566,0.0000029508708,0.0000072135426,0.000006734709,0.9826089,0.00007880227,0.016618308,0.0005691222,0.0000031813363],"about_ca_topic_score_codex":0.006769646,"about_ca_topic_score_gemma":0.008167745,"teacher_disagreement_score":0.006769646,"about_ca_system_score_codex":0.00085291645,"about_ca_system_score_gemma":0.00045360017,"threshold_uncertainty_score":0.013460517},"labels":[],"label_agreement":null},{"id":"W138102449","doi":"","title":"Music and Cognition: What cognitive science can learn from music cognition","year":2006,"lang":"en","type":"article","venue":"UvA-DARE (University of Amsterdam)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cognition; Cognitive science; Music psychology; Tonality; Psychology; Perception; Music perception; Cognitive psychology; Musicology; Musical; Art; Visual arts; Neuroscience","score_opus":0.01702935254790139,"score_gpt":0.19585301637244662,"score_spread":0.17882366382454523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W138102449","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02440134,0.2989065,0.025965432,0.5121396,0.0070560393,0.00003256428,0.00020756511,0.00020115005,0.13108982],"genre_scores_gemma":[0.70589006,0.20976241,0.016767543,0.042276543,0.0105542485,0.00011395925,0.0002692793,0.000149978,0.01421598],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99808264,0.0009825192,0.0000883485,0.00026963776,0.0003901127,0.00018672456],"domain_scores_gemma":[0.99416286,0.0038263726,0.00033044524,0.0005284303,0.00055969856,0.00059221644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004150607,0.00093588996,0.0010256334,0.0023869947,0.0024112666,0.009738458,0.0012810437,0.0035446333,0.0055816853],"category_scores_gemma":[0.0076103443,0.00038718164,0.00094226486,0.0017281603,0.027675511,0.021138178,0.0028841347,0.0048506088,0.0013173411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010857891,0.00008773177,0.0037583713,0.0012341295,0.00013865698,0.00021521187,0.013359454,0.0011928098,0.0008474634,0.8229521,0.036098383,0.12000715],"study_design_scores_gemma":[0.000008778474,0.000021176238,0.0020472235,0.00053210586,0.000016442444,0.00012190838,0.0031451022,0.00033337018,0.000116216564,0.9345927,0.059033293,0.000031675332],"about_ca_topic_score_codex":0.0057672416,"about_ca_topic_score_gemma":0.005093312,"teacher_disagreement_score":0.009738458,"about_ca_system_score_codex":0.002960607,"about_ca_system_score_gemma":0.0030080846,"threshold_uncertainty_score":0.021950781},"labels":[],"label_agreement":null},{"id":"W138361265","doi":"","title":"The Consolidation of Neural Network Task Knowledge.","year":2003,"lang":"en","type":"article","venue":"International Conference on Machine Learning and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Forgetting; Computer science; Domain knowledge; Task (project management); Knowledge transfer; Artificial neural network; Artificial intelligence; Transfer of learning; Knowledge engineering; Task analysis; Machine learning; Knowledge management; Engineering; Psychology; Cognitive psychology","score_opus":0.022115917049588138,"score_gpt":0.3032427455940909,"score_spread":0.28112682854450277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W138361265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1723676,0.0074416418,0.7824416,0.0027620206,0.000432159,0.00024742266,0.0005432619,0.0011461437,0.032618187],"genre_scores_gemma":[0.8857806,0.0031308832,0.100071624,0.00026150094,0.00009635048,0.00023663016,0.00073740806,0.00009030751,0.009594698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954057,0.000099379846,0.000032945405,0.00013589833,0.00014078945,0.00005048766],"domain_scores_gemma":[0.9974751,0.00089496974,0.00037618756,0.0007357749,0.0003941128,0.00012389648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012040398,0.00040313357,0.0003899942,0.00049194664,0.00040420913,0.0019607001,0.0016826647,0.00082184345,0.0033777857],"category_scores_gemma":[0.010118207,0.00032493268,0.0003965565,0.0006426962,0.0012039957,0.0054568183,0.001312707,0.0012886027,0.0012269077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002698917,0.00025390738,0.0060979915,0.00093614333,0.00020154784,0.00027797796,0.0010048678,0.07943652,0.045302633,0.16543871,0.0054402016,0.6953395],"study_design_scores_gemma":[0.00004266243,0.0004578678,0.009127179,0.00027088448,0.00014872116,0.00058002083,0.00052733417,0.48243207,0.057140876,0.41797608,0.031208355,0.00008804968],"about_ca_topic_score_codex":0.001631701,"about_ca_topic_score_gemma":0.0012853759,"teacher_disagreement_score":0.0033777857,"about_ca_system_score_codex":0.0010665373,"about_ca_system_score_gemma":0.0009739372,"threshold_uncertainty_score":0.011299849},"labels":[],"label_agreement":null},{"id":"W1408639475","doi":"","title":"Learning Recurrent Neural Networks with Hessian-Free Optimization","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":534,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Recurrent neural network; Hessian matrix; Sequence (biology); Computer science; Artificial intelligence; Artificial neural network; Term (time); Scheme (mathematics); Optimization problem; Interpretation (philosophy); Algorithm; Mathematics","score_opus":0.020625453449305137,"score_gpt":0.21150795947534742,"score_spread":0.1908825060260423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1408639475","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015635895,0.000260803,0.9821745,0.00015723746,0.000031407686,0.000022079697,0.000034391327,0.00061244855,0.0010712385],"genre_scores_gemma":[0.5103866,0.00032857788,0.4824223,0.0002981048,0.000116240386,0.0001934634,0.00041179013,0.00036867106,0.005474244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995554,0.00019796082,0.000026477897,0.00008775901,0.00009682867,0.0000354821],"domain_scores_gemma":[0.99883646,0.00073065277,0.00012220386,0.00011068347,0.00016056854,0.00003947685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001514278,0.0010602186,0.0010212667,0.00041324185,0.00031273504,0.0006274505,0.0012995438,0.0013423095,0.0014030682],"category_scores_gemma":[0.00480813,0.0006698712,0.00052524504,0.0004766549,0.0007487337,0.001417595,0.0011030119,0.001341167,0.00072605396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003565283,0.00002533566,0.000302051,0.00005543879,0.000039521296,0.00005312089,0.000045160396,0.9413835,0.0016234968,0.012218662,0.0011042188,0.043113846],"study_design_scores_gemma":[0.0000020986215,0.000006428596,0.000013421645,0.000001356398,0.0000012627982,0.0000030068104,8.448557e-7,0.9980274,0.00013433945,0.0017307935,0.00007736332,0.0000016507008],"about_ca_topic_score_codex":0.0034125862,"about_ca_topic_score_gemma":0.0055831783,"teacher_disagreement_score":0.0034125862,"about_ca_system_score_codex":0.00055928295,"about_ca_system_score_gemma":0.00082470645,"threshold_uncertainty_score":0.008008301},"labels":[],"label_agreement":null},{"id":"W14115579","doi":"10.1007/3-540-44732-6_18","title":"Feature Based Decision Fusion","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Voting; Classifier (UML); Majority rule; Artificial intelligence; Machine learning; Random subspace method; Data mining","score_opus":0.015027019635253385,"score_gpt":0.2493096760238658,"score_spread":0.23428265638861243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W14115579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010457753,0.0012043959,0.9808859,0.00021977785,0.00024460655,0.000050188115,0.00019082926,0.0007562286,0.005990333],"genre_scores_gemma":[0.5394612,0.0010585823,0.44073364,0.0003382253,0.00030886917,0.00013948002,0.0011617984,0.00015753378,0.016640645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928385,0.00015601364,0.00004860084,0.00016007342,0.00026447026,0.00008697539],"domain_scores_gemma":[0.99927384,0.000273295,0.000042276777,0.00013279876,0.00025096186,0.000026787624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011584609,0.00069211615,0.0014168439,0.00084568246,0.0005123375,0.0013607176,0.0010292351,0.0010319897,0.0067245117],"category_scores_gemma":[0.002037919,0.00035823832,0.0006941563,0.0012182832,0.00043033162,0.001419059,0.0012151449,0.0009930979,0.0023253572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054667366,0.00012294781,0.0004981424,0.00011419335,0.00014392861,0.00008810858,0.00004107371,0.07052143,0.018795056,0.024009103,0.008207981,0.8769114],"study_design_scores_gemma":[0.000022421453,0.00014094537,0.000795558,0.000023087077,0.00007651317,0.00015524394,0.000018702729,0.9409838,0.019889908,0.031731173,0.006126281,0.000036334655],"about_ca_topic_score_codex":0.0008514747,"about_ca_topic_score_gemma":0.0011655015,"teacher_disagreement_score":0.0067245117,"about_ca_system_score_codex":0.00046101332,"about_ca_system_score_gemma":0.00044378478,"threshold_uncertainty_score":0.022495687},"labels":[],"label_agreement":null},{"id":"W143173872","doi":"10.1007/3-211-27389-1_115","title":"Learning Image Filtering from a Gold Sample Based on Genetic Optimization of Morphological Processing","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Noise (video); Image (mathematics); Filter (signal processing); Sample (material); Distortion (music); Computer vision; Image processing; Context (archaeology); Mathematical morphology; Genetic algorithm; Pattern recognition (psychology); Machine learning","score_opus":0.022018282321920952,"score_gpt":0.23300163786037378,"score_spread":0.21098335553845282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W143173872","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018287038,0.000106218366,0.97988087,0.00006195514,0.00003111298,0.0000259445,0.000018103166,0.0004763206,0.0011124559],"genre_scores_gemma":[0.15406448,0.00014519917,0.8420228,0.00006901892,0.000023996132,0.000055939723,0.000095188625,0.0001495435,0.0033739174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997906,0.000021529679,0.000011560022,0.000065241824,0.00008132968,0.00002977418],"domain_scores_gemma":[0.999553,0.00018480464,0.00004407675,0.000063103565,0.00013490405,0.000020129582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079116994,0.00059672137,0.0012616375,0.00084594154,0.00035617367,0.0008245478,0.00093795604,0.0013105097,0.0022603795],"category_scores_gemma":[0.0015313872,0.00040633304,0.000990047,0.0007081284,0.0009306135,0.0008461847,0.0006018345,0.00082987937,0.00075672485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024320249,0.000122044294,0.0014298315,0.00020758472,0.00011062225,0.00016350701,0.00018340159,0.2764464,0.10266104,0.025779048,0.0022153924,0.59043795],"study_design_scores_gemma":[0.000011138336,0.00007417946,0.00038368048,0.000014281913,0.000027572749,0.000095466836,0.000017439439,0.9767786,0.016319163,0.0053398693,0.0009270545,0.0000115162175],"about_ca_topic_score_codex":0.0028012493,"about_ca_topic_score_gemma":0.0036937655,"teacher_disagreement_score":0.0028012493,"about_ca_system_score_codex":0.0006456311,"about_ca_system_score_gemma":0.0007017864,"threshold_uncertainty_score":0.0075617433},"labels":[],"label_agreement":null},{"id":"W143396975","doi":"","title":"RECOGNITION OF HANDWRITTEN NUMERALS BY STRUCTURAL PROBABILISTIC NEURAL NETWORKS","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Numeral system; Computer science; Probabilistic logic; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Subspace topology; Feature extraction; Feature (linguistics); Property (philosophy); Probabilistic neural network; Time delay neural network","score_opus":0.02221190066769923,"score_gpt":0.23195424260336403,"score_spread":0.2097423419356648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W143396975","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14628209,0.0004228245,0.8466337,0.00018353379,0.000054017684,0.00003865915,0.0002842822,0.0024594082,0.0036415649],"genre_scores_gemma":[0.7718965,0.00035407275,0.22355713,0.00005933011,0.000040320985,0.000040464514,0.0004930633,0.000085586296,0.0034735336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970573,0.0000701509,0.000013647691,0.000082631785,0.00010236608,0.000025382415],"domain_scores_gemma":[0.9995443,0.00017206163,0.000102386555,0.00007452397,0.00009357898,0.0000132033165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029218066,0.00047225758,0.00045044802,0.0005439509,0.00016297896,0.00062886614,0.0005056905,0.00039553616,0.0014218985],"category_scores_gemma":[0.001378893,0.00030922535,0.00046144263,0.0006905477,0.0005224187,0.00083914446,0.00039241943,0.00046540488,0.00055871555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034674903,0.00008044868,0.0015182613,0.00017319719,0.00007643221,0.00020818914,0.0000912303,0.33660126,0.097391516,0.006081678,0.0022347784,0.5551963],"study_design_scores_gemma":[0.0000058873147,0.000024258406,0.0013132462,0.0000052074333,0.000008888792,0.00007037454,0.000009438916,0.98247886,0.012491977,0.002889664,0.0006900277,0.000012079975],"about_ca_topic_score_codex":0.003015428,"about_ca_topic_score_gemma":0.005823357,"teacher_disagreement_score":0.003015428,"about_ca_system_score_codex":0.00034763615,"about_ca_system_score_gemma":0.00038088046,"threshold_uncertainty_score":0.0059957504},"labels":[],"label_agreement":null},{"id":"W145032828","doi":"10.1007/978-3-540-35488-8_11","title":"High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dirichlet distribution; Artificial neural network; Bayesian probability; Data set; Data mining; Computer science; Set (abstract data type); Diffusion; Artificial intelligence; Pattern recognition (psychology); Machine learning; Mathematics; Physics","score_opus":0.031840167301238356,"score_gpt":0.2560280284749311,"score_spread":0.22418786117369277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W145032828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009781784,0.0029959746,0.9838786,0.0007979393,0.00010071749,0.00002446443,0.00007808992,0.0002088434,0.0021335592],"genre_scores_gemma":[0.41032967,0.005092504,0.5723722,0.00046279558,0.0009025418,0.0002331616,0.00056307093,0.00017600936,0.009868086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978688,0.0009661423,0.00015112855,0.0003206365,0.00060213736,0.00009119118],"domain_scores_gemma":[0.9915194,0.0066342843,0.00042166235,0.0006491937,0.00066318584,0.00011228732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004490746,0.000769635,0.0020248957,0.0020265067,0.00090820226,0.0028692875,0.0021583254,0.0022081903,0.0021112412],"category_scores_gemma":[0.019811392,0.0009615046,0.0013045024,0.0035239442,0.0021198017,0.006650071,0.0020656667,0.0038581172,0.0007022347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013106223,0.00008029787,0.0015929538,0.0002202766,0.00016139212,0.000064010375,0.0003098318,0.39359608,0.00079718174,0.32537735,0.006084869,0.2715847],"study_design_scores_gemma":[0.0000044136855,0.0000055305504,0.00015597434,0.000018740076,0.000008981337,0.000018263154,0.000010913719,0.76340944,0.00018655208,0.23558782,0.0005832989,0.000010101938],"about_ca_topic_score_codex":0.003678675,"about_ca_topic_score_gemma":0.0036447,"teacher_disagreement_score":0.004490746,"about_ca_system_score_codex":0.0020061973,"about_ca_system_score_gemma":0.0008019258,"threshold_uncertainty_score":0.02374965},"labels":[],"label_agreement":null},{"id":"W145098650","doi":"10.1007/978-3-642-34481-7_2","title":"Time Series Prediction Method Based on LS-SVR with Modified Gaussian RBF","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Gaussian function; Kernel (algebra); Gaussian; Series (stratigraphy); Radial basis function kernel; Radial basis function; Mathematics; Support vector machine; Algorithm; Function (biology); Applied mathematics; Computer science; Kernel method; Mathematical optimization; Artificial intelligence; Artificial neural network; Physics","score_opus":0.015378322034223586,"score_gpt":0.24211858492055036,"score_spread":0.22674026288632676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W145098650","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020447195,0.00039795684,0.9769091,0.00009571535,0.00014404605,0.000025244886,0.000045615947,0.0007993642,0.0011356852],"genre_scores_gemma":[0.6654473,0.00067514466,0.32519087,0.00011831819,0.00017733772,0.00014606626,0.00035691608,0.00017404357,0.00771398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966526,0.00006768001,0.000025149593,0.00010295719,0.00010854718,0.000030444016],"domain_scores_gemma":[0.99960965,0.000118635035,0.00003138345,0.00003509595,0.00018620494,0.00001905984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007486067,0.000585456,0.0012412268,0.00039375102,0.00035993234,0.0005787539,0.0010591863,0.0008869899,0.0015068464],"category_scores_gemma":[0.0012700296,0.00033275905,0.00081138295,0.0006542781,0.0002130638,0.0009970954,0.00046762437,0.0010524783,0.00053726515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003530631,0.00016387938,0.001908087,0.00019970367,0.00017523572,0.00012835127,0.00008989909,0.5822885,0.020014137,0.006428047,0.003973654,0.3842775],"study_design_scores_gemma":[0.0000023203518,0.000011420355,0.00009893731,0.0000014367081,0.00000596851,0.000008561621,0.0000016278375,0.9989091,0.0005856285,0.00020841199,0.0001629616,0.0000035586788],"about_ca_topic_score_codex":0.0055464176,"about_ca_topic_score_gemma":0.0036591075,"teacher_disagreement_score":0.0055464176,"about_ca_system_score_codex":0.00031447873,"about_ca_system_score_gemma":0.00066096004,"threshold_uncertainty_score":0.01102829},"labels":[],"label_agreement":null},{"id":"W1474208048","doi":"10.1016/j.ifacol.2015.06.070","title":"A Predictive Preference Model for Maintenance of a Heating Ventilating and Air Conditioning System","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"HVAC; Air conditioning; Artificial neural network; Decision tree; Computer science; Filter (signal processing); Differential pressure; Reliability engineering; Machine learning; Engineering","score_opus":0.05601462601537983,"score_gpt":0.26722911533954546,"score_spread":0.21121448932416564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1474208048","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70650315,0.0005145468,0.28783315,0.0004894781,0.00008655133,0.00008288066,0.0005035937,0.0002557417,0.003730875],"genre_scores_gemma":[0.9930153,0.000088955,0.005012825,0.000021670308,0.000014367963,0.00004676494,0.000115687166,0.0000052245214,0.0016791676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996948,0.00009562844,0.000021330608,0.000072081704,0.00005639528,0.00005980093],"domain_scores_gemma":[0.99937063,0.0003396586,0.00007496955,0.000018024273,0.00016244294,0.000034233988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093159283,0.00051297445,0.00061910565,0.0006268878,0.00032903693,0.00067290384,0.0010642861,0.00086498936,0.0024485409],"category_scores_gemma":[0.001658344,0.0002694586,0.00054349605,0.00061387516,0.0002839264,0.0006196709,0.00032259303,0.0008031287,0.00021471386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013785204,0.00009183888,0.003764292,0.000037121623,0.00003816796,0.000090375404,0.000032050106,0.97946036,0.0005948831,0.0015650804,0.00029765622,0.013890237],"study_design_scores_gemma":[0.000002127916,0.00001274301,0.0003248856,9.412147e-7,0.0000035977846,0.000003842011,0.0000030683925,0.9994092,0.000037900958,0.00018210582,0.00001804745,0.0000015889915],"about_ca_topic_score_codex":0.02604759,"about_ca_topic_score_gemma":0.019428257,"teacher_disagreement_score":0.02604759,"about_ca_system_score_codex":0.00089397875,"about_ca_system_score_gemma":0.0007061764,"threshold_uncertainty_score":0.051791966},"labels":[],"label_agreement":null},{"id":"W147665587","doi":"10.3233/978-1-60750-661-4-147","title":"Neural Symbolic Decision Making: A Scalable and Realistic Foundation for Cognitive Architectures","year":2010,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Foundation (evidence); Computer science; Scalability; Cognition; Cognitive science; Artificial intelligence; Psychology; Neuroscience; Political science","score_opus":0.03533986577583653,"score_gpt":0.3035423842445864,"score_spread":0.2682025184687499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W147665587","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01878642,0.00092775334,0.94041216,0.0019554668,0.00013101929,0.00007689618,0.00032293462,0.0007180453,0.036669288],"genre_scores_gemma":[0.3789965,0.0014227261,0.6059006,0.00024043664,0.00012558105,0.0003781746,0.0004995444,0.0001897366,0.012246745],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99979776,0.000039536935,0.000013262829,0.000039206217,0.00008874669,0.000021505188],"domain_scores_gemma":[0.9996427,0.00015843143,0.000021250396,0.00009543951,0.0000491288,0.00003313826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048049501,0.0005506208,0.0005813102,0.00026915382,0.00058022444,0.0019475891,0.00216168,0.001193144,0.0071450183],"category_scores_gemma":[0.00144993,0.00049770024,0.0008631796,0.0004227208,0.0017317218,0.0031158095,0.0011653762,0.0022761445,0.0013462417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003234026,0.00002593958,0.00014193109,0.00011039687,0.00002692581,0.000086564854,0.000106132815,0.16545704,0.0024862594,0.8027211,0.0022589976,0.026546352],"study_design_scores_gemma":[0.000014889038,0.000013797714,0.00006666207,0.000025213933,0.000008262495,0.00003296694,0.000012529948,0.46575043,0.0008386106,0.52553266,0.0076947524,0.000009270239],"about_ca_topic_score_codex":0.0026465345,"about_ca_topic_score_gemma":0.0027951594,"teacher_disagreement_score":0.0071450183,"about_ca_system_score_codex":0.0012361094,"about_ca_system_score_gemma":0.001154489,"threshold_uncertainty_score":0.023902476},"labels":[],"label_agreement":null},{"id":"W1480531085","doi":"10.1109/mci.2006.1626492","title":"Strategies for unsupervised multimedia processing","year":2006,"lang":"en","type":"article","venue":"IEEE Computational Intelligence Magazine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Artificial neural network; Multimedia; Data science; Asset (computer security); Image processing; Machine learning; Image (mathematics); Computer security","score_opus":0.03135880305248429,"score_gpt":0.2945008055974157,"score_spread":0.2631420025449314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1480531085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060124714,0.00061101426,0.96048576,0.00090077217,0.00010940224,0.0001011978,0.00006712909,0.0005204591,0.031191824],"genre_scores_gemma":[0.29022634,0.0015510237,0.6702721,0.0009059617,0.0003176198,0.00055750285,0.00031910217,0.00045497392,0.035395343],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952686,0.00011785762,0.000023697317,0.00014780134,0.00013616202,0.000047564594],"domain_scores_gemma":[0.9992861,0.00028891893,0.000046863064,0.00020068101,0.00013050956,0.000046920708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007542432,0.0007498544,0.00048480515,0.0009875336,0.0008966659,0.0020646427,0.0016722407,0.0011126874,0.010417394],"category_scores_gemma":[0.0029434897,0.0003498836,0.00070413214,0.0006708148,0.001522418,0.0032641105,0.0026687675,0.0011960919,0.0031631074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050816496,0.00006119208,0.0002932754,0.00016999469,0.00006251549,0.00016302924,0.00036945063,0.02789772,0.010453258,0.7787336,0.007585491,0.17415968],"study_design_scores_gemma":[0.000021811642,0.000046592162,0.00018505627,0.00004880531,0.00002286785,0.00020622087,0.00014419966,0.25285923,0.0055415086,0.7096339,0.031259343,0.000030392352],"about_ca_topic_score_codex":0.0009045678,"about_ca_topic_score_gemma":0.0015725205,"teacher_disagreement_score":0.010417394,"about_ca_system_score_codex":0.0006644213,"about_ca_system_score_gemma":0.0007326667,"threshold_uncertainty_score":0.034849703},"labels":[],"label_agreement":null},{"id":"W1480751171","doi":"","title":"Comparaison entre réseau de neurones et LMS à référence filtrée appliqués au contrôle actif de bruit","year":2000,"lang":"fr","type":"article","venue":"Canadian acoustics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Physics; Humanities; Philosophy","score_opus":0.02474565823869927,"score_gpt":0.2667339367166132,"score_spread":0.24198827847791396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1480751171","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6193738,0.0023046017,0.3602514,0.00040769242,0.00020135703,0.00010771629,0.00013042326,0.0020016073,0.015221388],"genre_scores_gemma":[0.9113053,0.0005649825,0.07946953,0.0000556597,0.000019732615,0.00007961624,0.00014610383,0.00022119819,0.0081377765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999509,0.00006808651,0.000030096766,0.000101017824,0.00022909047,0.00006267221],"domain_scores_gemma":[0.9981298,0.0009829751,0.000100087374,0.00015234675,0.0005723476,0.00006252823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007462028,0.00059274386,0.00045437284,0.0006491319,0.00035314573,0.0007166779,0.0006539181,0.00089123886,0.0030041458],"category_scores_gemma":[0.003924119,0.00018925813,0.00040068442,0.00048185798,0.00043379088,0.0009442605,0.00033040336,0.0004820656,0.00059459923],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016885693,0.00012219991,0.0041974513,0.00063680793,0.00015507074,0.00025872584,0.00058387785,0.36225176,0.26638573,0.0074147163,0.0010717442,0.35523337],"study_design_scores_gemma":[0.000050981955,0.00077940896,0.009362885,0.000044080327,0.000067043155,0.00019882823,0.00021010221,0.81170994,0.16969472,0.0020984802,0.005727927,0.000055681743],"about_ca_topic_score_codex":0.0062034936,"about_ca_topic_score_gemma":0.0062237303,"teacher_disagreement_score":0.0062034936,"about_ca_system_score_codex":0.00083138444,"about_ca_system_score_gemma":0.0004565698,"threshold_uncertainty_score":0.012334764},"labels":[],"label_agreement":null},{"id":"W1482832823","doi":"","title":"Algorithmes a base de reseaux de neurones pour la commande de systemes dynamiques non lineaires et leurs modeles d'integration sur silicium (french text)","year":2004,"lang":"fr","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Humanities; Philosophy; Physics","score_opus":0.0675459428000701,"score_gpt":0.30688713920619765,"score_spread":0.23934119640612755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1482832823","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025338769,0.0009267065,0.96148956,0.0004206415,0.00018977362,0.00009242462,0.0001093082,0.0014297917,0.0100030005],"genre_scores_gemma":[0.4386633,0.0020504997,0.52027595,0.00030492622,0.000072637544,0.0005476165,0.00037061592,0.0005332583,0.03718125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981314,0.000031542466,0.000014472931,0.00004696229,0.00006911632,0.00002480211],"domain_scores_gemma":[0.99966073,0.00015649846,0.000030033003,0.00004447548,0.000088590445,0.000019716383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046904918,0.0005483491,0.00058818987,0.00034475228,0.00048191813,0.0011275385,0.0015829636,0.0010732715,0.009008808],"category_scores_gemma":[0.001256548,0.00034027928,0.0009850791,0.0003967151,0.0006435816,0.0012739507,0.000599729,0.0010711112,0.0019386082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001565603,0.000068551024,0.00111524,0.00035689364,0.00010574445,0.00029871182,0.00024248626,0.7505574,0.035329744,0.09175541,0.003039447,0.11697381],"study_design_scores_gemma":[0.000016654349,0.000061067985,0.0002751333,0.000026907055,0.000023751078,0.00009088756,0.00004112814,0.9733329,0.00872839,0.010868428,0.0065175905,0.000017290537],"about_ca_topic_score_codex":0.007584251,"about_ca_topic_score_gemma":0.0072921733,"teacher_disagreement_score":0.009008808,"about_ca_system_score_codex":0.0010185209,"about_ca_system_score_gemma":0.0010180488,"threshold_uncertainty_score":0.03013742},"labels":[],"label_agreement":null},{"id":"W1484315154","doi":"10.1109/icsmc.2004.1399791","title":"Effect of sharing training patterns on the performance of classier ensemble","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Disjoint sets; Classifier (UML); Cluster analysis; Machine learning; Space partitioning; Data mining; Training set; Artificial intelligence; Algorithm","score_opus":0.025018720847994412,"score_gpt":0.25715835544985477,"score_spread":0.23213963460186035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484315154","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98113865,0.00042388798,0.01658768,0.00019662153,0.00005540795,0.000041374708,0.000060160713,0.00038975012,0.001106413],"genre_scores_gemma":[0.9948343,0.000060766604,0.004603257,0.000059003833,0.000021897311,0.000029833724,0.0000879048,0.000036088415,0.00026689365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9920135,0.0037204174,0.00082080916,0.0011896704,0.001225964,0.0010296485],"domain_scores_gemma":[0.9250876,0.04837564,0.004044174,0.014810372,0.0058108945,0.0018713772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012721534,0.001179996,0.001616459,0.00069180754,0.0010624486,0.0011607407,0.0013156453,0.0019302581,0.00078784913],"category_scores_gemma":[0.06517609,0.0006040516,0.00055709446,0.0007471353,0.0014411673,0.0029993467,0.0024910755,0.0011573773,0.00037436764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0071768165,0.0011996346,0.044657767,0.00019502154,0.00088815985,0.0004013826,0.00067870563,0.6669025,0.028919693,0.0011530531,0.0013758135,0.24645153],"study_design_scores_gemma":[0.00033887321,0.005883457,0.030996518,0.00007350608,0.0007395882,0.00060003443,0.00060698215,0.8791741,0.07642594,0.0038601237,0.0011502805,0.0001506588],"about_ca_topic_score_codex":0.0017109213,"about_ca_topic_score_gemma":0.0013689493,"teacher_disagreement_score":0.012721534,"about_ca_system_score_codex":0.0006529706,"about_ca_system_score_gemma":0.0010090792,"threshold_uncertainty_score":0.06727874},"labels":[],"label_agreement":null},{"id":"W1486106859","doi":"10.1109/ijcnn.1993.714269","title":"Stable dynamic backpropagation using constrained learning rate algorithm","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Backpropagation; Computer science; Algorithm; Scheme (mathematics); Process (computing); Stability (learning theory); Binary number; Artificial neural network; Point (geometry); Adaptive learning; Artificial intelligence; Machine learning; Mathematics","score_opus":0.014315905003071429,"score_gpt":0.25602162107615456,"score_spread":0.24170571607308314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486106859","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004249134,0.000104757964,0.9946637,0.000051477567,0.000015395713,0.000020247051,0.0000065225495,0.00011194976,0.0007767478],"genre_scores_gemma":[0.5520906,0.00059829466,0.4402886,0.000127912,0.00005497556,0.00033403593,0.000110883724,0.00018528542,0.006209532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994487,0.00015756466,0.00003247731,0.00008704435,0.00022856864,0.000045612305],"domain_scores_gemma":[0.99896455,0.000567124,0.00008983193,0.000076729746,0.00026996125,0.000031785934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013326127,0.0008596527,0.0009677339,0.000607545,0.0004662588,0.0011010462,0.0014870847,0.0013117202,0.0015114481],"category_scores_gemma":[0.0037735398,0.0004989583,0.00048560643,0.0007238714,0.00086405105,0.0016141735,0.0011219521,0.0010872176,0.00049124705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078293175,0.000046388323,0.00025601932,0.000093124974,0.00005429347,0.00010740701,0.00007821764,0.83151495,0.0067084716,0.08244202,0.0011032588,0.07751753],"study_design_scores_gemma":[0.000004667348,0.00000794404,0.0000145124195,0.0000020930681,0.000002074745,0.000008251423,0.0000011785721,0.9951197,0.00062477734,0.003975628,0.00023615173,0.0000030210026],"about_ca_topic_score_codex":0.004184262,"about_ca_topic_score_gemma":0.0024370179,"teacher_disagreement_score":0.004184262,"about_ca_system_score_codex":0.0007611938,"about_ca_system_score_gemma":0.001225257,"threshold_uncertainty_score":0.008319855},"labels":[],"label_agreement":null},{"id":"W1486783062","doi":"10.1002/0470018860.s00022","title":"Symbolic versus Subsymbolic","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Cognitive Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.012538224904216414,"score_gpt":0.28032687724042454,"score_spread":0.26778865233620813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486783062","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051113956,0.0065325326,0.5430605,0.011662693,0.0006295314,0.000057282243,0.0007611383,0.0016897642,0.38449258],"genre_scores_gemma":[0.92646736,0.0031157641,0.05059583,0.0006834472,0.0003908955,0.00007333293,0.00038385217,0.00018225978,0.018107226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999166,0.00026983832,0.00003832633,0.00014092364,0.00033314008,0.000051791914],"domain_scores_gemma":[0.99839884,0.0006706346,0.00018621296,0.0003712757,0.00025900887,0.000114011025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006846873,0.00061104813,0.00058009283,0.0018565898,0.000535922,0.0041709514,0.0014608573,0.00079509325,0.017952241],"category_scores_gemma":[0.0040201847,0.0001832217,0.0004278137,0.0019041908,0.005271866,0.007458342,0.0018084558,0.0014061627,0.002521103],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027626327,0.0000088538945,0.00014050413,0.00008687801,0.000009671605,0.000037027738,0.00017771535,0.0031529428,0.00031318274,0.97704744,0.0017539158,0.017244192],"study_design_scores_gemma":[0.000007724536,0.000009279092,0.00009738397,0.000057366855,0.000011811075,0.00007098327,0.0000950624,0.019970927,0.00050966,0.9662162,0.012944784,0.000008724511],"about_ca_topic_score_codex":0.0024277281,"about_ca_topic_score_gemma":0.001883193,"teacher_disagreement_score":0.017952241,"about_ca_system_score_codex":0.0015160844,"about_ca_system_score_gemma":0.00096284295,"threshold_uncertainty_score":0.06005621},"labels":[],"label_agreement":null},{"id":"W1488184357","doi":"10.1007/10719871_8","title":"Dynamical Recurrent Networks for Sequential Data Processing","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Symbol (formal); Preprocessor; Computer science; Construct (python library); Recurrent neural network; Identification (biology); Sequence (biology); Task (project management); Algorithm; Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science; Engineering; Programming language","score_opus":0.04048973937762545,"score_gpt":0.29651291048291256,"score_spread":0.2560231711052871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488184357","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015235156,0.0015533249,0.99441445,0.0001301755,0.00018002326,0.000017569535,0.000108736764,0.00064633886,0.0014259324],"genre_scores_gemma":[0.19653983,0.0044435454,0.7731208,0.00026611492,0.0006578461,0.00032805197,0.0012384745,0.0006589147,0.022746447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997036,0.00007582993,0.000032659733,0.00008342198,0.000087453256,0.000017039092],"domain_scores_gemma":[0.99929667,0.00045147422,0.00004099208,0.00011121538,0.00008628352,0.000013379941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005618599,0.0007771864,0.000980535,0.00039028842,0.00017561608,0.0007952834,0.0009273267,0.0008867342,0.0070546274],"category_scores_gemma":[0.002540855,0.0004307936,0.00053460925,0.0007537758,0.00039508997,0.0013797632,0.0006730491,0.0012186911,0.0018141692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013758095,0.00006665542,0.00042552178,0.00056329486,0.0001496611,0.00018373487,0.00010844097,0.22303002,0.009233041,0.2198325,0.01812005,0.5281495],"study_design_scores_gemma":[0.000009630849,0.000018353096,0.00010463469,0.000015261288,0.000018230894,0.00003893748,0.000005256831,0.9329686,0.0011672437,0.05998784,0.005656231,0.000009745248],"about_ca_topic_score_codex":0.00143836,"about_ca_topic_score_gemma":0.0019477125,"teacher_disagreement_score":0.0070546274,"about_ca_system_score_codex":0.00037131394,"about_ca_system_score_gemma":0.00031467667,"threshold_uncertainty_score":0.023600101},"labels":[],"label_agreement":null},{"id":"W1488215951","doi":"10.1002/0471427950.ch3","title":"Neural Units: Concepts, Models, and Learning","year":2003,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Sigmoid function; Adaptation (eye); Computer science; Artificial intelligence; Element (criminal law); Cognitive science; Neuroscience; Psychology","score_opus":0.029485331743030384,"score_gpt":0.258227819043399,"score_spread":0.22874248730036859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488215951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006559194,0.065852866,0.8404713,0.0050421995,0.0012363555,0.000077365235,0.00029641067,0.00059383473,0.07987056],"genre_scores_gemma":[0.3169718,0.11739393,0.37626472,0.0021287946,0.0036181533,0.00055493216,0.0007271678,0.00050535484,0.18183514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99974877,0.00005437208,0.000018523866,0.00006121501,0.00010219383,0.000014994834],"domain_scores_gemma":[0.9997416,0.00012426058,0.000019675714,0.000030504234,0.00007336634,0.000010478214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041501105,0.0007637252,0.0006179862,0.00061460404,0.0003343162,0.002080951,0.0012772463,0.0014718986,0.008969974],"category_scores_gemma":[0.0014404133,0.00043740156,0.0005010926,0.0015111285,0.0017367913,0.0038901374,0.0006528278,0.0020116542,0.002428464],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029563842,0.000026962069,0.00018217866,0.0003471846,0.000018649505,0.00006412576,0.00016262534,0.016481182,0.0013925812,0.8429285,0.015915869,0.12245063],"study_design_scores_gemma":[0.000007750595,0.00002542961,0.00023514811,0.0001643287,0.000015550339,0.00017826095,0.00007562721,0.059131343,0.0014105457,0.86728877,0.07144662,0.000020550971],"about_ca_topic_score_codex":0.0015143423,"about_ca_topic_score_gemma":0.0015686055,"teacher_disagreement_score":0.008969974,"about_ca_system_score_codex":0.00096085266,"about_ca_system_score_gemma":0.00057866616,"threshold_uncertainty_score":0.030007541},"labels":[],"label_agreement":null},{"id":"W1490702503","doi":"10.1038/73865","title":"The dynamics inside the box","year":2000,"lang":"en","type":"article","venue":"Nature Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Black box; Neuroscience; Dynamics (music); Computer science; Cognitive science; Spike (software development); Computational neuroscience; Decoding methods; Sensory system; Network dynamics; Psychology; Artificial intelligence; Telecommunications; Mathematics","score_opus":0.005645370580664727,"score_gpt":0.23912771406488567,"score_spread":0.23348234348422095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1490702503","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059125666,0.007238342,0.27471045,0.0397206,0.005116451,0.00008437412,0.0006569065,0.0007333876,0.61261386],"genre_scores_gemma":[0.78989995,0.004593445,0.03057957,0.004045878,0.0022940375,0.00022196703,0.00032212038,0.0005284953,0.16751455],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996443,0.00009119135,0.0000090534995,0.000095673684,0.00010962757,0.000050144223],"domain_scores_gemma":[0.9993912,0.00023323254,0.00003661941,0.00013201275,0.00010415229,0.00010283379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005079911,0.000381136,0.0006258171,0.0003865278,0.0010309779,0.0022315735,0.0007423674,0.001741272,0.030359399],"category_scores_gemma":[0.0027996632,0.00030374792,0.00044994336,0.0002544282,0.0026189452,0.007269698,0.0016723864,0.0025361327,0.0040097525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001139251,0.000004762884,0.000051692365,0.000016467067,0.0000036490624,0.00002179598,0.0000682541,0.0013253257,0.0005260795,0.9903903,0.0042572594,0.0033230372],"study_design_scores_gemma":[0.000008954659,0.000008379343,0.00011626829,0.000026584054,0.000002507855,0.00003019962,0.00006362743,0.0076004937,0.00018893277,0.96732384,0.024620378,0.000009868649],"about_ca_topic_score_codex":0.001351738,"about_ca_topic_score_gemma":0.00055506395,"teacher_disagreement_score":0.030359399,"about_ca_system_score_codex":0.0006663231,"about_ca_system_score_gemma":0.00078895653,"threshold_uncertainty_score":0.10156226},"labels":[],"label_agreement":null},{"id":"W1493832124","doi":"","title":"Continuous Neural Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial neural network; Uncountable set; Hyperparameter; Parametrization (atmospheric modeling); Computer science; Kernel (algebra); Simple (philosophy); Regularization (linguistics); Stochastic neural network; Mathematics; Kernel method; Algorithm; Artificial intelligence; Applied mathematics; Recurrent neural network; Discrete mathematics; Support vector machine","score_opus":0.010237778275747948,"score_gpt":0.24122500854206166,"score_spread":0.2309872302663137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493832124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00965031,0.0047474625,0.9674407,0.0010584266,0.0004633772,0.000043920438,0.0003676456,0.00055193563,0.015676161],"genre_scores_gemma":[0.6799931,0.007084612,0.283333,0.0005967235,0.0007610961,0.0002911263,0.0011993059,0.00017151293,0.026569411],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992042,0.0002230975,0.00005342797,0.0002427046,0.00021031252,0.000066200184],"domain_scores_gemma":[0.9984694,0.0008558546,0.00013060353,0.00021196336,0.00026293242,0.0000692344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010008157,0.0008403719,0.000966671,0.0007094966,0.00043971319,0.0020938194,0.0017829745,0.0018529155,0.0056617814],"category_scores_gemma":[0.0064859954,0.00038277585,0.00057670247,0.0010323396,0.0013623143,0.0025164115,0.00139899,0.0020142328,0.001402039],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010010171,0.0000472645,0.0009637586,0.00039675582,0.00011666892,0.00022100423,0.000090002184,0.4248911,0.0023719056,0.42906642,0.0069099627,0.134825],"study_design_scores_gemma":[0.000009415871,0.0000151322865,0.00018640111,0.000037318354,0.0000119922415,0.000048806363,0.000010962936,0.8266123,0.00042308433,0.16630927,0.006323058,0.000012252419],"about_ca_topic_score_codex":0.0017836909,"about_ca_topic_score_gemma":0.0016443164,"teacher_disagreement_score":0.0056617814,"about_ca_system_score_codex":0.00093618245,"about_ca_system_score_gemma":0.0006742522,"threshold_uncertainty_score":0.018940568},"labels":[],"label_agreement":null},{"id":"W1494689342","doi":"10.1007/978-3-540-27868-9_67","title":"Bounds for the Average Generalization Error of the Mixture of Experts Neural Network","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Generalization; Artificial neural network; Generalization error; Upper and lower bounds; Approximation error; Modular design; Computer science; Algorithm; Mathematics; Artificial intelligence","score_opus":0.018631484583932854,"score_gpt":0.24961199831087835,"score_spread":0.2309805137269455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1494689342","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009797187,0.012786686,0.95965576,0.003366858,0.000848431,0.000102027836,0.00045229463,0.0011014469,0.011889343],"genre_scores_gemma":[0.33453453,0.016719623,0.6074283,0.003989189,0.0053844214,0.0015542533,0.0020197204,0.0024659375,0.025904136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9894281,0.0039612143,0.00070874096,0.0021225086,0.002860247,0.00091915176],"domain_scores_gemma":[0.89856917,0.08151453,0.0026475207,0.006563544,0.008829987,0.001875303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028927345,0.0053762696,0.004932632,0.0049212566,0.0018892131,0.0046984656,0.009336464,0.008452953,0.009633361],"category_scores_gemma":[0.115204886,0.0020426053,0.003952322,0.0036474438,0.005433879,0.013775255,0.009483621,0.014417858,0.0032602747],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086703454,0.00019280697,0.0020817693,0.0014361719,0.00093580934,0.00029314213,0.00049713027,0.45859134,0.004448865,0.37266135,0.020074563,0.13792002],"study_design_scores_gemma":[0.000023617671,0.00008733312,0.0008741958,0.00028955252,0.00015585049,0.00016366514,0.00004553759,0.8081834,0.0014539947,0.18589234,0.0027575805,0.00007288888],"about_ca_topic_score_codex":0.004523312,"about_ca_topic_score_gemma":0.004098841,"teacher_disagreement_score":0.028927345,"about_ca_system_score_codex":0.006249587,"about_ca_system_score_gemma":0.0028647862,"threshold_uncertainty_score":0.1529842},"labels":[],"label_agreement":null},{"id":"W1495850371","doi":"10.1109/iconip.2002.1198971","title":"Learning aggregation for combining classifier ensembles","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Classifier (UML); Computer science; Random subspace method; Artificial intelligence; Machine learning; Training set; Cascading classifiers; Ensemble learning; Architecture; Pattern recognition (psychology)","score_opus":0.027496324594507165,"score_gpt":0.26025406645095306,"score_spread":0.2327577418564459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495850371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0126627665,0.00046273507,0.98367137,0.00012256867,0.00009147215,0.00013344466,0.0000542555,0.0011988586,0.0016025291],"genre_scores_gemma":[0.26884845,0.000567273,0.7269333,0.00017513418,0.00027174674,0.00045083457,0.00047682194,0.00016332149,0.0021132056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99537414,0.0015905006,0.00042103097,0.0008194527,0.0014842327,0.00031058263],"domain_scores_gemma":[0.99222434,0.003252092,0.00058923755,0.0019742115,0.0017384473,0.0002216809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009205477,0.0016194498,0.0025576719,0.0028508352,0.0011722852,0.0021323378,0.0017326312,0.0013662551,0.0018186866],"category_scores_gemma":[0.01668752,0.00086466054,0.0018196668,0.0028060633,0.0008080531,0.0035264501,0.003428638,0.0022245042,0.0010465507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015312732,0.0001581936,0.0035732256,0.00023915766,0.0005525697,0.00013168149,0.0003488976,0.2808162,0.0075680297,0.016273435,0.0037693805,0.6864161],"study_design_scores_gemma":[0.000020026597,0.00018201406,0.0010360301,0.000049162987,0.0001637957,0.000121505975,0.00007340068,0.9514749,0.00652611,0.03429022,0.006024205,0.000038602608],"about_ca_topic_score_codex":0.0019701463,"about_ca_topic_score_gemma":0.0029151067,"teacher_disagreement_score":0.009205477,"about_ca_system_score_codex":0.000860116,"about_ca_system_score_gemma":0.0010037668,"threshold_uncertainty_score":0.048683763},"labels":[],"label_agreement":null},{"id":"W1495918329","doi":"10.1109/icnn.1994.374498","title":"On the multilayered Hopfield neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Computer science; Hopfield network; Convergence (economics); Stability (learning theory); Content-addressable memory; Simple (philosophy); Artificial intelligence; Equilibrium point; Computation; Physical neural network; Associative property; Extension (predicate logic); Recurrent neural network; Types of artificial neural networks; Algorithm; Machine learning; Mathematics","score_opus":0.03190701136787043,"score_gpt":0.22240075706781018,"score_spread":0.19049374569993976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495918329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010205603,0.004971225,0.96987647,0.0005123922,0.00023773541,0.000038160255,0.00010757299,0.00016820405,0.013882646],"genre_scores_gemma":[0.62561786,0.015943937,0.3353331,0.00046145273,0.0009249678,0.00019143372,0.00031119588,0.00008987476,0.02112617],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99960786,0.000097836164,0.000025715517,0.00007499078,0.00015622118,0.000037390742],"domain_scores_gemma":[0.99974376,0.00009379128,0.000027726712,0.000028589342,0.000087879154,0.000018215747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006592102,0.00052222086,0.0004655258,0.0007014083,0.00036445836,0.00097446016,0.000928729,0.00096701126,0.0020774116],"category_scores_gemma":[0.0019131218,0.00034719615,0.00061113783,0.000857989,0.00095885986,0.0020633796,0.0009295886,0.0011318,0.0004912358],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008618577,0.000026461283,0.0005942101,0.0002615768,0.00008348521,0.00022490922,0.0001728158,0.3854723,0.0074546323,0.4651654,0.00307282,0.13738517],"study_design_scores_gemma":[0.00001587026,0.000055580284,0.00032055672,0.000069118825,0.00003121014,0.00009328492,0.0000157736,0.7430688,0.0017433511,0.24242257,0.012130825,0.00003297545],"about_ca_topic_score_codex":0.0050653955,"about_ca_topic_score_gemma":0.002843453,"teacher_disagreement_score":0.0050653955,"about_ca_system_score_codex":0.00085841876,"about_ca_system_score_gemma":0.00064065575,"threshold_uncertainty_score":0.010071814},"labels":[],"label_agreement":null},{"id":"W1496278981","doi":"10.1109/mwscas.1990.140708","title":"Pulse arithmetic in switched capacitor neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Switched capacitor; Content-addressable memory; Artificial neural network; Computer science; Multiplication (music); Multiplier (economics); Very-large-scale integration; Hopfield network; Capacitor; Arithmetic; Adder; Electronic engineering; Voltage; Mathematics; Electrical engineering; Artificial intelligence; Engineering; Telecommunications; Embedded system","score_opus":0.022066496674530058,"score_gpt":0.22259335753412252,"score_spread":0.20052686085959245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496278981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047038425,0.0026849345,0.91414416,0.0010743729,0.00025677565,0.00005280039,0.00015424775,0.0006849211,0.03390945],"genre_scores_gemma":[0.8897033,0.002219598,0.08272319,0.00025524243,0.00015714808,0.0001771189,0.00011937422,0.00008095873,0.024564127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99985313,0.000040838255,0.0000055040755,0.000022304033,0.000057820267,0.000020390491],"domain_scores_gemma":[0.9997644,0.00013406345,0.000019916532,0.000015389187,0.00005122938,0.000015044285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002843528,0.0003378367,0.00037104485,0.00030740057,0.00039368085,0.0009465714,0.00086032454,0.0012078334,0.0035541973],"category_scores_gemma":[0.0012269025,0.00022798135,0.00031283766,0.00058272365,0.0008979133,0.0013837051,0.0003474763,0.0009345921,0.0004529814],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007089895,0.000021595406,0.00023401866,0.000075713404,0.000018506194,0.00020078124,0.000094989795,0.66882014,0.0033065116,0.30849972,0.001996923,0.016660191],"study_design_scores_gemma":[0.0000070185943,0.000012333957,0.00003920557,0.000006214158,0.000003594881,0.000025174853,0.000006515053,0.9437031,0.0003180402,0.05450786,0.0013663829,0.0000044392036],"about_ca_topic_score_codex":0.004414927,"about_ca_topic_score_gemma":0.0030855983,"teacher_disagreement_score":0.004414927,"about_ca_system_score_codex":0.00089241914,"about_ca_system_score_gemma":0.00047478345,"threshold_uncertainty_score":0.011889994},"labels":[],"label_agreement":null},{"id":"W1496508179","doi":"10.1002/0471427950.ch5","title":"Advanced Methods for Learning and Adaptation in MFNNs","year":2003,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Adaptation (eye); Computer science; Psychology; Neuroscience","score_opus":0.02964605262494453,"score_gpt":0.36034724543746444,"score_spread":0.3307011928125199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496508179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016615328,0.00041030464,0.9965443,0.00006998256,0.000041552954,0.00000909262,0.000010130983,0.00008226426,0.001170811],"genre_scores_gemma":[0.1664851,0.0014783089,0.8124648,0.00016160839,0.00035373666,0.00024277675,0.00012358552,0.00023131324,0.018458728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944276,0.00017471529,0.00003360521,0.00006939965,0.00025530157,0.000024159031],"domain_scores_gemma":[0.99896896,0.00050943054,0.000050585797,0.0001286163,0.0003197761,0.000022688077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001379379,0.0006326908,0.00069072546,0.0007491648,0.00029796443,0.0009528502,0.0010963639,0.0011664868,0.003995163],"category_scores_gemma":[0.0048389803,0.00034150088,0.00071646005,0.0006013076,0.0007164075,0.0017119625,0.00094159937,0.0016652166,0.00086865947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043680076,0.000038106544,0.0002726248,0.00017373104,0.00006182266,0.00005129836,0.00010698023,0.5026771,0.0062064514,0.2333894,0.002407208,0.2545716],"study_design_scores_gemma":[0.0000030519952,0.0000109977855,0.00009270427,0.0000138813075,0.0000047962726,0.000019122965,0.0000038955504,0.96317357,0.0008539658,0.033644293,0.0021726904,0.000006961326],"about_ca_topic_score_codex":0.0017862746,"about_ca_topic_score_gemma":0.0020582792,"teacher_disagreement_score":0.003995163,"about_ca_system_score_codex":0.00069513306,"about_ca_system_score_gemma":0.00044879576,"threshold_uncertainty_score":0.01336509},"labels":[],"label_agreement":null},{"id":"W1496663493","doi":"10.1007/11785231_6","title":"Nonlinear Function Learning by the Normalized Radial Basis Function Networks","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Radial basis function; Computer science; Empirical risk minimization; Nonlinear system; Convergence (economics); Regularization (linguistics); Radial basis function network; Basis (linear algebra); Covariance; Function (biology); Minification; Consistency (knowledge bases); Basis function; Applied mathematics; Algorithm; Artificial neural network; Mathematical optimization; Artificial intelligence; Mathematics; Statistics; Mathematical analysis","score_opus":0.009200502120929585,"score_gpt":0.21148984377833502,"score_spread":0.20228934165740542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496663493","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044687446,0.0013738387,0.9910766,0.00011231555,0.00009222734,0.000013044553,0.00003312151,0.00032827407,0.0025018482],"genre_scores_gemma":[0.24536467,0.0030243713,0.7348281,0.00013257579,0.00022871417,0.00011923261,0.0002570696,0.00033216045,0.015713098],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996044,0.00014475382,0.000019066103,0.00007815573,0.00013207302,0.000021576372],"domain_scores_gemma":[0.9995486,0.00016183352,0.00004188019,0.00006374492,0.00016848143,0.000015445847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011039653,0.00057463255,0.0007538649,0.00041203893,0.00021283397,0.0007709676,0.00096743106,0.00073235435,0.0020961233],"category_scores_gemma":[0.0029723647,0.0003824778,0.0005685057,0.0008156504,0.0006685733,0.0016384833,0.0008418855,0.0010040265,0.0014159139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016115834,0.000032446507,0.00037389732,0.00021286572,0.000073662464,0.00004469184,0.00004733264,0.4362683,0.010780841,0.0638435,0.0063661854,0.48179516],"study_design_scores_gemma":[0.0000030730855,0.000011968067,0.00010595262,0.000009494701,0.00000611142,0.000028893466,0.0000026630667,0.9864746,0.0016990073,0.0095802,0.0020696884,0.000008427545],"about_ca_topic_score_codex":0.0021741115,"about_ca_topic_score_gemma":0.0018841083,"teacher_disagreement_score":0.0021741115,"about_ca_system_score_codex":0.0005452688,"about_ca_system_score_gemma":0.00052779476,"threshold_uncertainty_score":0.0070121884},"labels":[],"label_agreement":null},{"id":"W1497940321","doi":"10.1007/3-540-70659-3","title":"Structural, syntactic, and statistical pattern recognition : joint IAPR International Workshops SSPR 2002 and SPR 2002, Windsor, Ontario, Canada, August 6-9, 2002 : proceedings","year":2002,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Windsor; Joint (building); Geography; Library science; Engineering; Computer science; Geology; Civil engineering","score_opus":0.023544396719772414,"score_gpt":0.2126339897196951,"score_spread":0.1890895929999227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1497940321","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021743717,0.054506645,0.7020221,0.012299256,0.0069990684,0.00057364855,0.013938574,0.037154052,0.15076295],"genre_scores_gemma":[0.037016694,0.032823455,0.26348433,0.0010661735,0.0011546002,0.00029191014,0.03133576,0.0032537067,0.62957335],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951255,0.000054983535,0.000028015433,0.00011304495,0.00023898912,0.000052427444],"domain_scores_gemma":[0.99852765,0.00022385683,0.00004027519,0.00023235308,0.0008496245,0.00012617298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001274061,0.0014944553,0.0017217114,0.0017094152,0.0009768691,0.00319248,0.0021589233,0.0009224197,0.03974574],"category_scores_gemma":[0.0019442082,0.000791513,0.0007598266,0.002606224,0.0013093188,0.002238478,0.0013796687,0.0011791616,0.033234622],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008717503,0.000085296066,0.00053169404,0.0002606515,0.000030974967,0.00006868755,0.00009846547,0.0013707739,0.00680704,0.003501669,0.40600345,0.5811542],"study_design_scores_gemma":[0.000076874014,0.00014907066,0.009061767,0.00033158957,0.00016764717,0.00097346085,0.00051080005,0.04143144,0.024888804,0.032852143,0.88946116,0.000095214426],"about_ca_topic_score_codex":0.051010545,"about_ca_topic_score_gemma":0.10748672,"teacher_disagreement_score":0.051010545,"about_ca_system_score_codex":0.0015507014,"about_ca_system_score_gemma":0.003954158,"threshold_uncertainty_score":0.13296276},"labels":[],"label_agreement":null},{"id":"W1498553108","doi":"10.1109/ijcnn.2005.1556112","title":"A self-organizing map for concept classification in information retrieval","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Information retrieval; Field (mathematics); Process (computing); Representation (politics); Human–computer information retrieval; Core (optical fiber); Prime (order theory); Self-organizing map; Concept search; Artificial intelligence; Natural language processing; Search engine; Artificial neural network; Web search query","score_opus":0.03476381803468095,"score_gpt":0.26314017269059586,"score_spread":0.2283763546559149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1498553108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0103449905,0.000984346,0.9856989,0.00036010702,0.00011134758,0.00015357029,0.0001516742,0.0008851142,0.001309984],"genre_scores_gemma":[0.29556996,0.000990682,0.6983954,0.00027365584,0.0002369012,0.0008120756,0.00056090957,0.00013961492,0.0030208218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986975,0.00050531246,0.00008075243,0.00027558513,0.00036774462,0.00007304779],"domain_scores_gemma":[0.9987062,0.00072245946,0.000077940575,0.00015102563,0.0003007298,0.000041601583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021384424,0.0007318928,0.0008793385,0.0023211355,0.00084836525,0.0018559166,0.0019691675,0.0014928856,0.0017086676],"category_scores_gemma":[0.0059286575,0.00041397798,0.0013075711,0.002329514,0.0011883485,0.0030053414,0.0011785048,0.0012642184,0.0011416276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029366114,0.00033585457,0.002890448,0.00050125504,0.00027096583,0.00022830431,0.00068108196,0.23221219,0.007641058,0.07269097,0.014326012,0.6679282],"study_design_scores_gemma":[0.00001502908,0.000055292872,0.00051843125,0.000020133064,0.000017934335,0.000078561614,0.000057232515,0.9533339,0.0015817067,0.041607678,0.0026851639,0.000028885055],"about_ca_topic_score_codex":0.0052256254,"about_ca_topic_score_gemma":0.0035195765,"teacher_disagreement_score":0.0052256254,"about_ca_system_score_codex":0.0012395262,"about_ca_system_score_gemma":0.0011904644,"threshold_uncertainty_score":0.011309266},"labels":[],"label_agreement":null},{"id":"W1500616748","doi":"10.1109/tnn.2003.811562","title":"A Kohonen-like decomposition method for the euclidean traveling salesman problem - KNIES_DECOMPOSE","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Travelling salesman problem; Bottleneck traveling salesman problem; Euclidean geometry; 2-opt; Self-organizing map; Heuristic; Euclidean distance; Partition (number theory); Mathematical optimization; Computer science; Artificial neural network; Traveling purchaser problem; Mathematics; Decomposition; Algorithm; Artificial intelligence; Combinatorics","score_opus":0.020380633936201206,"score_gpt":0.28915941299335646,"score_spread":0.26877877905715525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1500616748","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021962505,0.00020287593,0.9950119,0.00008685518,0.00005281757,0.00004277166,0.00003801572,0.00015745495,0.0022111088],"genre_scores_gemma":[0.069279425,0.00036806893,0.92542905,0.00010798722,0.000046078498,0.00016208731,0.00019185337,0.00009472988,0.004320772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968433,0.00006626294,0.000021585136,0.000056991255,0.00013718143,0.000033647073],"domain_scores_gemma":[0.99982125,0.000055917233,0.0000146043085,0.000021087642,0.00007044737,0.000016640844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005164829,0.00068329234,0.0006110552,0.0005500981,0.0005070606,0.00078674173,0.0011649672,0.0008958516,0.0036298505],"category_scores_gemma":[0.0010282383,0.0004125546,0.0009865236,0.0007814555,0.0003972154,0.0013835442,0.0010443019,0.0011568812,0.0008129377],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009512797,0.00010790139,0.00047715416,0.00034516805,0.00012484033,0.00009872217,0.00015489687,0.528926,0.008142227,0.05682757,0.007817183,0.39688322],"study_design_scores_gemma":[0.000012829515,0.000023964874,0.000089341585,0.000013691781,0.000013045067,0.000043781245,0.00003079508,0.9797426,0.0013411365,0.013226996,0.0054491423,0.000012737047],"about_ca_topic_score_codex":0.004968675,"about_ca_topic_score_gemma":0.0073186057,"teacher_disagreement_score":0.004968675,"about_ca_system_score_codex":0.00047941535,"about_ca_system_score_gemma":0.0010548503,"threshold_uncertainty_score":0.0121430755},"labels":[],"label_agreement":null},{"id":"W1502394320","doi":"10.3968/j.pam.1925252820120302.1255","title":"On the Power Efficiency of Artificial Neural Network (ANN) and the Classical Regression Model","year":2012,"lang":"en","type":"article","venue":"Progress in applied mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Computer science; Regression; Regression analysis; Variance (accounting); Artificial intelligence; Field (mathematics); Key (lock); Machine learning; Statistics; Mathematics","score_opus":0.028172062642568968,"score_gpt":0.27703234647864117,"score_spread":0.2488602838360722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502394320","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06609763,0.017866991,0.88531923,0.00147256,0.00029388885,0.000031485783,0.00007398782,0.00020612101,0.028638132],"genre_scores_gemma":[0.8269135,0.016082315,0.14404303,0.00027115433,0.0005941462,0.00007718551,0.00019365796,0.0002889848,0.011536117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976562,0.001161123,0.00008631523,0.00027661418,0.00072356133,0.00009606376],"domain_scores_gemma":[0.9911907,0.0074385777,0.00025732338,0.00046113037,0.0006088262,0.000043371667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039254124,0.0007387717,0.0008144314,0.001154466,0.00024165523,0.0012610116,0.0008771114,0.00088656606,0.0018965075],"category_scores_gemma":[0.015888935,0.0002733107,0.00057445565,0.0012609792,0.0012605747,0.0031861737,0.0009737859,0.0009715007,0.0005890811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002300304,0.00010059582,0.0035219665,0.000548194,0.0002481131,0.00024465087,0.00013329425,0.4880012,0.0037297176,0.28327078,0.0019897802,0.21798171],"study_design_scores_gemma":[0.000011797975,0.00009256475,0.0015826097,0.000085667125,0.000063997075,0.00017465628,0.00003860716,0.91975814,0.0024444438,0.07090152,0.0048239348,0.000022126671],"about_ca_topic_score_codex":0.0015945375,"about_ca_topic_score_gemma":0.00090671517,"teacher_disagreement_score":0.0039254124,"about_ca_system_score_codex":0.00067067833,"about_ca_system_score_gemma":0.00048359382,"threshold_uncertainty_score":0.020759821},"labels":[],"label_agreement":null},{"id":"W1502479874","doi":"10.1109/acssc.1995.540851","title":"On the classification of moving objects in image sequences using 3D adaptive recursive tracking filters and neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Computer vision; Trajectory; Passband; Filter (signal processing); Intersection (aeronautics); Artificial neural network; Perceptron; Noise (video); Image (mathematics); Physics; Band-pass filter","score_opus":0.064452394381,"score_gpt":0.2612980340309522,"score_spread":0.1968456396499522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502479874","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027139314,0.00084609597,0.9705639,0.00022729783,0.000045514484,0.00002960323,0.000049010185,0.00033542808,0.0007638521],"genre_scores_gemma":[0.380804,0.0022749037,0.611833,0.0002783478,0.00019193723,0.00018516005,0.00034358326,0.00006777528,0.00402128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996519,0.0000889626,0.000032386226,0.00008664988,0.00009444369,0.000045526827],"domain_scores_gemma":[0.99884784,0.0006596564,0.00016180893,0.00006276757,0.00024191034,0.000025937245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010641189,0.00069615437,0.0006664415,0.0012917429,0.0003730235,0.0009281854,0.0008721727,0.0014005824,0.00072143046],"category_scores_gemma":[0.0038405582,0.00033202258,0.00085882406,0.0013590117,0.00083169976,0.0012080513,0.00043355298,0.0006268621,0.00033271432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016134864,0.00006934437,0.0016485994,0.000121072255,0.00009173339,0.00016038687,0.00019645633,0.5482272,0.013188703,0.015893407,0.0012510878,0.4189907],"study_design_scores_gemma":[0.0000024823569,0.000012113136,0.0003917628,0.000008729101,0.00000806995,0.000019018476,0.000006733545,0.9955187,0.00094878464,0.0028054714,0.00027084857,0.000007334709],"about_ca_topic_score_codex":0.017418312,"about_ca_topic_score_gemma":0.013806371,"teacher_disagreement_score":0.017418312,"about_ca_system_score_codex":0.0009150949,"about_ca_system_score_gemma":0.0005786099,"threshold_uncertainty_score":0.034633875},"labels":[],"label_agreement":null},{"id":"W1508427692","doi":"10.1007/978-3-642-35512-7","title":"Long-Memory Processes","year":2013,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":430,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science","score_opus":0.014472245708664616,"score_gpt":0.22331816324862255,"score_spread":0.20884591753995793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1508427692","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01900283,0.034599856,0.19738474,0.0048593665,0.003203411,0.00005264018,0.00043091402,0.0010204122,0.73944587],"genre_scores_gemma":[0.26891857,0.02446447,0.03193916,0.0009721458,0.0029065844,0.00010056682,0.0008085855,0.00046212232,0.6694278],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998994,0.000012142016,0.0000049108685,0.000024144312,0.000044821663,0.00001462488],"domain_scores_gemma":[0.99972147,0.00010358978,0.000018347566,0.00006545374,0.00007286318,0.000018269553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024852992,0.00066209614,0.00044672846,0.0005202844,0.00044280675,0.0016675323,0.00078778487,0.00073240354,0.027529443],"category_scores_gemma":[0.0012457637,0.0002007121,0.0002996792,0.00076446135,0.0009458754,0.0037922931,0.00082646,0.0014331889,0.00793838],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002678788,0.000021962771,0.00011306065,0.00017481875,0.000020399602,0.00008131921,0.00013117316,0.0028783171,0.0017867958,0.76974094,0.044071056,0.18095343],"study_design_scores_gemma":[0.0000060489456,0.000012893235,0.00022468827,0.000047880338,0.000011782292,0.00011465616,0.000036942012,0.00736003,0.0013487887,0.9036233,0.08720172,0.00001132002],"about_ca_topic_score_codex":0.00049253856,"about_ca_topic_score_gemma":0.0007301536,"teacher_disagreement_score":0.027529443,"about_ca_system_score_codex":0.0005479698,"about_ca_system_score_gemma":0.000426494,"threshold_uncertainty_score":0.092095196},"labels":[],"label_agreement":null},{"id":"W1509321318","doi":"10.1109/etfa.1994.402005","title":"Smart neural nets for fast learning","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial neural network; Backpropagation; Computer science; Artificial intelligence; Types of artificial neural networks; Construct (python library); Time delay neural network; Machine learning; Computer network","score_opus":0.030099181282071643,"score_gpt":0.23731044961651335,"score_spread":0.2072112683344417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509321318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010286568,0.001571449,0.9891083,0.00050668325,0.00030561598,0.000034186185,0.00014010092,0.0010087179,0.006296277],"genre_scores_gemma":[0.12937614,0.0053879563,0.83366275,0.0013361964,0.0008325197,0.00059505674,0.0011122823,0.0006931342,0.027004004],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905735,0.00023527458,0.00007321083,0.00016076634,0.0003981336,0.00007520064],"domain_scores_gemma":[0.9986519,0.0006583456,0.0000903474,0.00023820424,0.0003046423,0.000056554352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012124002,0.0012078966,0.0010433502,0.000844471,0.00048659477,0.0019155771,0.0016922324,0.0017378167,0.012391145],"category_scores_gemma":[0.0056625283,0.0005940908,0.0007563738,0.0011483409,0.0013360783,0.0034281765,0.0016649511,0.003092337,0.004872446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012619441,0.000041683415,0.00036397963,0.00037719472,0.00007761715,0.00014185296,0.00007639836,0.2806596,0.0028392272,0.45403278,0.020964598,0.24029891],"study_design_scores_gemma":[0.000018013154,0.000025305922,0.000081878046,0.000051322975,0.000012760357,0.00005658938,0.000009413026,0.72959065,0.0015048889,0.24362235,0.02500753,0.000019325182],"about_ca_topic_score_codex":0.002415003,"about_ca_topic_score_gemma":0.0025037406,"teacher_disagreement_score":0.012391145,"about_ca_system_score_codex":0.001038402,"about_ca_system_score_gemma":0.0010267376,"threshold_uncertainty_score":0.041452527},"labels":[],"label_agreement":null},{"id":"W1509786722","doi":"10.1109/icnn.1995.487324","title":"Learning with ease: smart neural nets","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial neural network; Backpropagation; Computer science; Sigmoid function; Feedforward neural network; Artificial intelligence; Types of artificial neural networks; Time delay neural network","score_opus":0.016057291941148893,"score_gpt":0.20297636973422503,"score_spread":0.18691907779307615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509786722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006641871,0.0018052469,0.97414726,0.00082068774,0.00035648947,0.00006652127,0.0000744319,0.0014074189,0.014680083],"genre_scores_gemma":[0.29811224,0.004275298,0.66654676,0.001488585,0.0006687028,0.00029858676,0.00034728984,0.00038770091,0.027874714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994287,0.00014197498,0.00004955633,0.000090695416,0.00023887906,0.000050229406],"domain_scores_gemma":[0.9990263,0.0005027859,0.000083344006,0.00013188302,0.0002056928,0.000049961094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000896194,0.0008737256,0.000617391,0.00063809246,0.00031368254,0.0013742357,0.00091284286,0.0013753689,0.00431143],"category_scores_gemma":[0.0044233818,0.00045806656,0.00060815824,0.0005964516,0.0009891692,0.0028748303,0.0012668238,0.0016514191,0.0018510469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017596214,0.00007746245,0.0008247089,0.0003504199,0.00011850312,0.00019531157,0.00014295528,0.26229426,0.0051840814,0.20454165,0.012417599,0.51367706],"study_design_scores_gemma":[0.000048402995,0.00010241267,0.00035492264,0.00010105787,0.00004777307,0.00015719526,0.000027993208,0.75766,0.0053740377,0.18693307,0.04914282,0.00005037821],"about_ca_topic_score_codex":0.0015201973,"about_ca_topic_score_gemma":0.0021565885,"teacher_disagreement_score":0.00431143,"about_ca_system_score_codex":0.00061404984,"about_ca_system_score_gemma":0.00055238756,"threshold_uncertainty_score":0.014423192},"labels":[],"label_agreement":null},{"id":"W1511069927","doi":"10.1090/conm/618","title":"Mathematics of Continuous and Discrete Dynamical Systems","year":2014,"lang":"en","type":"book","venue":"Contemporary mathematics - American Mathematical Society","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Dynamical systems theory; Mathematics; Computer science; Calculus (dental); Physics","score_opus":0.017282634708543316,"score_gpt":0.24713262719812826,"score_spread":0.22984999248958493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1511069927","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004066946,0.12890945,0.10500025,0.007603463,0.006002875,0.000053717584,0.00055016775,0.0003804083,0.74743265],"genre_scores_gemma":[0.19519864,0.18958543,0.09723571,0.003722047,0.016441828,0.00039828874,0.0014423309,0.00057400606,0.49540174],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99974304,0.000044977187,0.000014998181,0.00003985807,0.00014129402,0.00001574657],"domain_scores_gemma":[0.9996606,0.00016233149,0.000024460567,0.00005154986,0.000070372196,0.000030619623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032194136,0.0008540005,0.00067643565,0.001438579,0.0005701771,0.002384405,0.0006715712,0.0006957345,0.012318132],"category_scores_gemma":[0.0010161322,0.00030578475,0.00042077573,0.0016027129,0.0022014198,0.0030225252,0.0007799921,0.002725313,0.0039234925],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038897524,0.000009937522,0.00006667109,0.00017105194,0.000006537392,0.000026491036,0.000117107076,0.0008811364,0.00041371153,0.90933526,0.045637526,0.04333058],"study_design_scores_gemma":[0.0000039777383,0.0000077356035,0.0002599703,0.00007829734,0.0000037517618,0.00010866311,0.00003586464,0.0018781292,0.000108855515,0.7178298,0.27967745,0.0000075300145],"about_ca_topic_score_codex":0.0008351233,"about_ca_topic_score_gemma":0.0010992609,"teacher_disagreement_score":0.012318132,"about_ca_system_score_codex":0.0012637536,"about_ca_system_score_gemma":0.00102208,"threshold_uncertainty_score":0.041208267},"labels":[],"label_agreement":null},{"id":"W1513826108","doi":"10.1109/wescan.1991.160550","title":"A neural network mapper for stochastic code book parameter encoding in code-excited linear predictive speech processing","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Code-excited linear prediction; Computer science; Artificial neural network; Speech recognition; Vector sum excited linear prediction; Linear predictive coding; Code (set theory); Encoding (memory); Coding (social sciences); Speech coding; Source code; Artificial intelligence; Programming language; Mathematics","score_opus":0.03902769467448234,"score_gpt":0.273069572611193,"score_spread":0.2340418779367107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1513826108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008753608,0.00017462384,0.9866588,0.0001369636,0.000081830265,0.000060850307,0.00005685422,0.0020088358,0.0020675927],"genre_scores_gemma":[0.17946488,0.0001699676,0.8108544,0.00016385235,0.000060638617,0.00017326405,0.00017385147,0.00019979921,0.00873938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998542,0.000032121032,0.000007929975,0.000027187236,0.00006843884,0.000010041201],"domain_scores_gemma":[0.99982685,0.000067874345,0.000015245702,0.0000349904,0.00004418037,0.000010842271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028442463,0.0003556674,0.00025514432,0.00032377153,0.00031057524,0.00046243865,0.00083959755,0.00067514146,0.004739987],"category_scores_gemma":[0.0009390066,0.00020520715,0.00015851388,0.0003259789,0.0002783864,0.00078407547,0.0005124515,0.0006404912,0.0014663264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023815922,0.00010862623,0.0003094326,0.00013376887,0.000041119332,0.00023816581,0.00011082934,0.054459263,0.099760905,0.025781995,0.006571916,0.81224585],"study_design_scores_gemma":[0.000024434667,0.00006415625,0.00018762484,0.000014348189,0.000010663166,0.00019001348,0.00000998111,0.92738557,0.057795007,0.00485694,0.009445439,0.000015832622],"about_ca_topic_score_codex":0.0014706902,"about_ca_topic_score_gemma":0.0024443946,"teacher_disagreement_score":0.004739987,"about_ca_system_score_codex":0.00039126296,"about_ca_system_score_gemma":0.00036358627,"threshold_uncertainty_score":0.015856862},"labels":[],"label_agreement":null},{"id":"W1514794976","doi":"10.1007/978-3-642-04146-4_2","title":"Image Analysis and Machine Learning: How to Foster a Stronger Connection?","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Selection (genetic algorithm); Machine learning; Image (mathematics); Cognitive science; Psychology","score_opus":0.015201461122925834,"score_gpt":0.24404315248455546,"score_spread":0.22884169136162963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514794976","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016869145,0.07830062,0.68522656,0.12191739,0.0039346847,0.000083112696,0.00019313999,0.0011411714,0.09233423],"genre_scores_gemma":[0.37236786,0.0660677,0.48483497,0.01905388,0.0116792815,0.0003118991,0.0003126236,0.0009770957,0.044394743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976676,0.00097194326,0.00013996083,0.00046155456,0.00065269734,0.00010617805],"domain_scores_gemma":[0.9849606,0.010157817,0.0005535562,0.0022923513,0.0014858077,0.00055000425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045483457,0.0012518854,0.0016543895,0.0017058996,0.001329957,0.005997825,0.0024542825,0.004740871,0.015737236],"category_scores_gemma":[0.024067633,0.0008469509,0.0008782818,0.0018300645,0.008911707,0.0323555,0.0070237545,0.008442054,0.0055498593],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014532666,0.00012590078,0.0005023907,0.0006088289,0.00009570485,0.00006459689,0.0002393752,0.0014922342,0.0020380178,0.73523206,0.020862307,0.23859325],"study_design_scores_gemma":[0.000023879058,0.000036845762,0.00024931625,0.00015652124,0.000031499923,0.00014053092,0.000057306155,0.008312301,0.00093521224,0.9572535,0.032776482,0.0000264426],"about_ca_topic_score_codex":0.0010355623,"about_ca_topic_score_gemma":0.0010757877,"teacher_disagreement_score":0.015737236,"about_ca_system_score_codex":0.0011235516,"about_ca_system_score_gemma":0.0010520231,"threshold_uncertainty_score":0.05264628},"labels":[],"label_agreement":null},{"id":"W1515722553","doi":"10.1023/a:1013805129793","title":"Input Multiplexing in Artificial Neurons Employing Stochastic Arithmetic","year":2002,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Multiplexing; Stochastic computing; Computer science; Artificial neural network; Radix (gastropod); Variance (accounting); Computational intelligence; Artificial intelligence; Algorithm; Mathematics; Telecommunications","score_opus":0.04679930916523693,"score_gpt":0.2544074955774007,"score_spread":0.20760818641216378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515722553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29136223,0.0005210443,0.69193196,0.0003297108,0.00023996741,0.000044819568,0.000040872987,0.00043782053,0.015091556],"genre_scores_gemma":[0.9568026,0.00016368234,0.040610254,0.000049878272,0.000042056705,0.000022331762,0.000014608741,0.000041852574,0.0022526702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975973,0.00006933577,0.000017965636,0.00002568736,0.00009487657,0.000032329477],"domain_scores_gemma":[0.9992399,0.0004395783,0.00007178649,0.000085232954,0.00011560439,0.000047918445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080728816,0.00029172807,0.00032210466,0.00023855695,0.0003300901,0.0008643368,0.0007463842,0.00046109306,0.0019080119],"category_scores_gemma":[0.002749125,0.0002489556,0.00027346838,0.00041310812,0.0005030816,0.0011292633,0.00069862074,0.0005567502,0.00021083943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004715067,0.00014072302,0.00094239356,0.0001479251,0.00005807527,0.00023836724,0.00017361857,0.5061963,0.07706626,0.30652842,0.00095166615,0.107084826],"study_design_scores_gemma":[0.00000883884,0.000041071216,0.00013549814,0.000006366439,0.000009407031,0.000035928744,0.000009749726,0.96659845,0.007869864,0.024897221,0.00037868906,0.000009012639],"about_ca_topic_score_codex":0.0004620604,"about_ca_topic_score_gemma":0.0006657188,"teacher_disagreement_score":0.0019080119,"about_ca_system_score_codex":0.00046583146,"about_ca_system_score_gemma":0.00037678995,"threshold_uncertainty_score":0.0063828826},"labels":[],"label_agreement":null},{"id":"W1515958763","doi":"10.1109/iscas.1995.519931","title":"The impact of VLSI fabrication on neural learning","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Very-large-scale integration; Adder; Computer science; Artificial neural network; Competitive learning; Hebbian theory; Unsupervised learning; Artificial intelligence; Electronic circuit; Offset (computer science); Thresholding; Electronic engineering; CMOS; Electrical engineering; Engineering; Embedded system","score_opus":0.02527256498090361,"score_gpt":0.2712423987858565,"score_spread":0.24596983380495288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515958763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56065774,0.030319966,0.2973112,0.0078131035,0.0028690149,0.00023588783,0.0006600075,0.0035522524,0.09658077],"genre_scores_gemma":[0.9015995,0.0065257135,0.084665,0.0006351767,0.0001917348,0.00006675944,0.00020005851,0.00023555753,0.0058805514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99901116,0.0001838251,0.00006382752,0.00014200108,0.0005140514,0.000085099506],"domain_scores_gemma":[0.99558467,0.002609149,0.00049280043,0.0005965767,0.00066158536,0.000055331613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014147217,0.00031905176,0.00031009447,0.00031444096,0.0005305103,0.0008481075,0.0009146285,0.0008509078,0.0024887284],"category_scores_gemma":[0.010533916,0.00047425219,0.00024005942,0.00050182943,0.0008157732,0.0016381502,0.00059793086,0.0006974117,0.0010534142],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005092048,0.00025697713,0.008817568,0.0016250393,0.00011487035,0.0015545199,0.00036383345,0.31920552,0.28682014,0.116589464,0.005942132,0.25820073],"study_design_scores_gemma":[0.00009595954,0.0013650579,0.011486097,0.0003112479,0.00018020079,0.0020353722,0.000249708,0.33906952,0.5429503,0.048685502,0.053456135,0.0001148699],"about_ca_topic_score_codex":0.0010857913,"about_ca_topic_score_gemma":0.0011991234,"teacher_disagreement_score":0.0024887284,"about_ca_system_score_codex":0.0012650864,"about_ca_system_score_gemma":0.0007134788,"threshold_uncertainty_score":0.0091789365},"labels":[],"label_agreement":null},{"id":"W151672344","doi":"","title":"Deep Learning using Robust Interdependent Codes","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Autoencoder; Deep neural networks; Artificial neural network; Classifier (UML); Pattern recognition (psychology); Machine learning; Algorithm","score_opus":0.02962068412313832,"score_gpt":0.2674497748802302,"score_spread":0.23782909075709188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W151672344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009523622,0.00016887642,0.9881874,0.00013432065,0.0000293869,0.000022455906,0.000045125642,0.00058434386,0.001304514],"genre_scores_gemma":[0.46945012,0.00037057642,0.5232312,0.00032407037,0.00010100973,0.00017091141,0.00036475237,0.0002661168,0.0057212464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987804,0.00031589545,0.000057128793,0.00025870118,0.00048801673,0.00009976845],"domain_scores_gemma":[0.99778974,0.0007697716,0.00026208823,0.00060935714,0.000474834,0.00009427873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015313172,0.0009132061,0.0007398718,0.00070795463,0.00034246332,0.0010669576,0.002010014,0.0013826572,0.0024206915],"category_scores_gemma":[0.006999701,0.0005161894,0.00062001916,0.0007334058,0.0012876709,0.002647821,0.0026120462,0.002390655,0.0014247485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024140226,0.0001213598,0.00094106724,0.0001313274,0.000090269896,0.00013658544,0.000113277165,0.6078935,0.02532585,0.106326066,0.003971063,0.25470817],"study_design_scores_gemma":[0.000008503384,0.00002436215,0.000057113557,0.000006487983,0.0000045652623,0.00001905181,0.000003445985,0.9804962,0.0040746084,0.014566693,0.00073115464,0.000007869791],"about_ca_topic_score_codex":0.0016382891,"about_ca_topic_score_gemma":0.0017386872,"teacher_disagreement_score":0.0024206915,"about_ca_system_score_codex":0.00077380403,"about_ca_system_score_gemma":0.00086609606,"threshold_uncertainty_score":0.008098483},"labels":[],"label_agreement":null},{"id":"W1517808374","doi":"","title":"No Unbiased Estimator of the Variance of K-Fold Cross-Validation","year":2003,"lang":"en","type":"article","venue":"Érudit documents and data repository (Érudit Consortium, University of Montreal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":708,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Estimator; Generalization; Mathematics; Cross-validation; Variance (accounting); Covariance matrix; Generalization error; Variance decomposition of forecast errors; Minimum-variance unbiased estimator; Eigenvalues and eigenvectors; Covariance; Bias of an estimator; Applied mathematics; Degrees of freedom (physics and chemistry); Consistent estimator; Algorithm; Statistics; Computer science; Artificial intelligence","score_opus":0.012845284767984915,"score_gpt":0.2329795089063419,"score_spread":0.22013422413835698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1517808374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017542323,0.0018850656,0.9764454,0.00020464446,0.00034127504,0.00021010332,0.00030854484,0.0014520374,0.0016106139],"genre_scores_gemma":[0.42005715,0.0013528807,0.5648304,0.0008594759,0.00038967698,0.0015150532,0.0029415681,0.0011631658,0.0068905274],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97138906,0.016346965,0.0021134687,0.0041809846,0.0051491032,0.00082039257],"domain_scores_gemma":[0.9191212,0.04878993,0.004379653,0.016753627,0.010373323,0.00058225624],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03196534,0.0017788379,0.0031061426,0.0021047958,0.0013108711,0.0031504119,0.0029455675,0.0027414723,0.0029055055],"category_scores_gemma":[0.10865555,0.0010986133,0.0024870536,0.0019546035,0.0018016136,0.0029590167,0.002475783,0.0038014736,0.0022384876],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013240929,0.0004961595,0.040675383,0.0018629142,0.0037141158,0.0002101244,0.00057734444,0.20167741,0.011798833,0.030094737,0.013986288,0.69358265],"study_design_scores_gemma":[0.00019987058,0.0006696553,0.02746064,0.0010303169,0.0007422864,0.0006769419,0.00034200872,0.8879801,0.01754491,0.045076597,0.017994775,0.0002817619],"about_ca_topic_score_codex":0.0042865244,"about_ca_topic_score_gemma":0.0065455534,"teacher_disagreement_score":0.9680347,"about_ca_system_score_codex":0.0012676718,"about_ca_system_score_gemma":0.00405357,"threshold_uncertainty_score":0.16905093},"labels":[],"label_agreement":null},{"id":"W1519873353","doi":"10.1109/iscas.1994.409596","title":"A multilayer feedforward neural network model for digital hardware implementation","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Sigmoid function; Feed forward; Computer science; Artificial neural network; Feedforward neural network; Computer hardware; Algorithm; Layer (electronics); Artificial intelligence; Control engineering; Engineering","score_opus":0.048147208002783916,"score_gpt":0.28828222482567667,"score_spread":0.24013501682289276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519873353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021768487,0.00023171218,0.9931151,0.00010485484,0.00006729833,0.000038113558,0.00009871999,0.0008912185,0.003276075],"genre_scores_gemma":[0.30433726,0.00089931476,0.66926575,0.00019254998,0.00012310242,0.0006687785,0.0005537008,0.00014116212,0.02381848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998031,0.000035310713,0.000015544752,0.000039668124,0.00008435724,0.00002206041],"domain_scores_gemma":[0.99981874,0.000052649513,0.00001830761,0.000024665133,0.00007755032,0.000008067938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036396654,0.00071297074,0.0004446785,0.00031231972,0.00033716136,0.0008061454,0.001375631,0.0010062457,0.0080924705],"category_scores_gemma":[0.00083219283,0.00033115927,0.0005892296,0.0003720298,0.0002731182,0.0010576472,0.00042968028,0.0009958725,0.0021472648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081777835,0.000037765807,0.0003123513,0.0002125795,0.000044427336,0.00013784521,0.000048335693,0.80050635,0.009911921,0.028277129,0.004065019,0.15636447],"study_design_scores_gemma":[0.0000062760423,0.00002240539,0.000053291285,0.000009706166,0.000008217994,0.00002393415,0.0000025463742,0.99196273,0.0013942772,0.00323496,0.0032765325,0.000005023415],"about_ca_topic_score_codex":0.004203729,"about_ca_topic_score_gemma":0.0070889606,"teacher_disagreement_score":0.0080924705,"about_ca_system_score_codex":0.00071209914,"about_ca_system_score_gemma":0.0007981182,"threshold_uncertainty_score":0.027072012},"labels":[],"label_agreement":null},{"id":"W1519914509","doi":"10.1109/icsmc.2001.973006","title":"Scientific data visualization using three-dimensional self-organizing feature maps","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Goddard Space Flight Center","keywords":"Visualization; Computer science; Data visualization; Data mining; Scientific visualization; Exploratory data analysis; Feature (linguistics); Geovisualization; Similarity (geometry); Information visualization; Pattern recognition (psychology); Artificial intelligence; Image (mathematics)","score_opus":0.0761823261910711,"score_gpt":0.28346758003360256,"score_spread":0.20728525384253146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519914509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027844828,0.00040612984,0.96446747,0.0004072424,0.000051584448,0.00006345697,0.00044947703,0.004078436,0.0022315076],"genre_scores_gemma":[0.3929256,0.00059429236,0.60457265,0.00006667088,0.000056381865,0.00019940195,0.0004964151,0.00019840532,0.0008901905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996908,0.00009910382,0.000017391709,0.00003245427,0.00013812924,0.000022150387],"domain_scores_gemma":[0.9992471,0.00037173592,0.00007390729,0.0000774926,0.00019551061,0.00003443111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000644675,0.0004342158,0.00033299802,0.0020220773,0.00040533085,0.0013529853,0.00043398837,0.00046927165,0.0019657593],"category_scores_gemma":[0.0021396005,0.00020269549,0.00047682598,0.0014021365,0.00037878918,0.0012528796,0.00093822466,0.00044753723,0.0002983823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036995695,0.00013035059,0.0046805847,0.0005199043,0.00021765589,0.000781494,0.0016130992,0.22316049,0.049973264,0.056631975,0.020337844,0.6415834],"study_design_scores_gemma":[0.00003607649,0.000054933542,0.002652958,0.000047434543,0.00002698564,0.00032697048,0.00020512108,0.9246344,0.0152760185,0.04113032,0.015533749,0.000075006945],"about_ca_topic_score_codex":0.0021873927,"about_ca_topic_score_gemma":0.0015051047,"teacher_disagreement_score":0.0021873927,"about_ca_system_score_codex":0.00038994438,"about_ca_system_score_gemma":0.0005023578,"threshold_uncertainty_score":0.006576121},"labels":[],"label_agreement":null},{"id":"W1520142450","doi":"10.1109/icnn.1995.487372","title":"Recognition of traffic signs by artificial neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Subnetwork; Computer science; Artificial intelligence; Artificial neural network; Computer vision; Preprocessor; Disjoint sets; Traffic sign; Frame (networking); Feature extraction; Pattern recognition (psychology); Traffic sign recognition; Noise (video); Sign (mathematics); Image (mathematics); Mathematics","score_opus":0.045755914777133457,"score_gpt":0.23033686284910185,"score_spread":0.1845809480719684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1520142450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117651604,0.0007747989,0.873194,0.0002546419,0.00016951971,0.00007355189,0.00013037937,0.0031313142,0.0046201493],"genre_scores_gemma":[0.7716764,0.0005223012,0.22270891,0.00011156225,0.00007281001,0.00010524323,0.0004020117,0.00004784392,0.004352896],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997992,0.00004776618,0.000016012305,0.000043820466,0.00006617689,0.000027181073],"domain_scores_gemma":[0.9997247,0.0000764464,0.00003553569,0.000023683026,0.00012880642,0.000010873678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033848066,0.00037388492,0.0004040042,0.00053992664,0.0001929063,0.00054539536,0.0004900834,0.0006595989,0.0009808737],"category_scores_gemma":[0.0011784318,0.00019543956,0.0002544881,0.0005712272,0.00023877768,0.0006145946,0.00022719127,0.0003902819,0.0004379529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004218512,0.00015918638,0.004245871,0.00020617132,0.00008675225,0.00028680585,0.00008280358,0.32136863,0.09294516,0.0036407786,0.0035220294,0.5730339],"study_design_scores_gemma":[0.00000917972,0.00004343268,0.0010678209,0.000009815538,0.000017727512,0.000057744543,0.000008568092,0.98859555,0.008064645,0.00092389906,0.0011894881,0.000012133258],"about_ca_topic_score_codex":0.0029464837,"about_ca_topic_score_gemma":0.0029109698,"teacher_disagreement_score":0.0029464837,"about_ca_system_score_codex":0.00033401133,"about_ca_system_score_gemma":0.0002919738,"threshold_uncertainty_score":0.0058587193},"labels":[],"label_agreement":null},{"id":"W1521290850","doi":"10.1007/s10712-007-9025-8","title":"K. Bahr and F. Simpson, Practical Magnetotellurics","year":2007,"lang":"en","type":"article","venue":"Surveys in Geophysics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Magnetotellurics; Environmental science; Geology; Meteorology; Physics; Engineering; Electrical engineering; Electrical resistivity and conductivity","score_opus":0.017571378710871062,"score_gpt":0.2877163588834111,"score_spread":0.2701449801725401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521290850","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013896792,0.16149724,0.7739228,0.012699582,0.0059373043,0.000035415058,0.0006072984,0.0007048128,0.030698676],"genre_scores_gemma":[0.4514565,0.15709895,0.27577916,0.0027354239,0.008485916,0.00012803156,0.0013973748,0.00084047235,0.10207811],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995946,0.000111391324,0.000030454523,0.00010378576,0.00011603916,0.000043634496],"domain_scores_gemma":[0.9994773,0.00023016962,0.000052936186,0.00007978351,0.00012665385,0.000033242857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009044093,0.00073521334,0.0009315547,0.0018135692,0.00078389473,0.001473221,0.0013310658,0.0024330425,0.0055060196],"category_scores_gemma":[0.0030396117,0.00076209335,0.00061681547,0.0019885926,0.0019413197,0.003534104,0.0011877827,0.0013685211,0.0023504952],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037376894,0.00004856314,0.0010161237,0.00090437516,0.00011481721,0.0003214832,0.0003744563,0.033903044,0.008836849,0.6739228,0.10026201,0.17992167],"study_design_scores_gemma":[0.00004668249,0.000073565316,0.0019625996,0.00010232202,0.00008077527,0.0007010075,0.00014934766,0.11627634,0.008583572,0.7090311,0.1627963,0.00019634483],"about_ca_topic_score_codex":0.002771278,"about_ca_topic_score_gemma":0.0040024747,"teacher_disagreement_score":0.0055060196,"about_ca_system_score_codex":0.0006277095,"about_ca_system_score_gemma":0.0003163455,"threshold_uncertainty_score":0.018419445},"labels":[],"label_agreement":null},{"id":"W1521364546","doi":"10.1007/3-540-47922-8","title":"Advances in artificial intelligence : 15th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2002, Calgary, Canada, May 27-29, 2002 : proceedings","year":2002,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Parsing; Machine learning; Theoretical computer science; Natural language processing","score_opus":0.06126826163676912,"score_gpt":0.2907266393298533,"score_spread":0.2294583776930842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521364546","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061713667,0.27500513,0.036646925,0.04639247,0.06728702,0.00048545073,0.00931482,0.0033332945,0.5553635],"genre_scores_gemma":[0.013894108,0.12431829,0.01480643,0.0018402437,0.0032258835,0.00016594083,0.006023773,0.0011608658,0.83456457],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988631,0.00009887569,0.000052081472,0.00013529186,0.00070634263,0.0001443641],"domain_scores_gemma":[0.99582136,0.0003927074,0.00008746586,0.0002655409,0.0027483983,0.0006845507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002497109,0.0014726138,0.0022435992,0.002167002,0.001820424,0.005638574,0.001816529,0.0013244314,0.0927321],"category_scores_gemma":[0.0031908052,0.0006536785,0.00065133435,0.0040332726,0.0017108357,0.0024766605,0.0017468352,0.003360211,0.03784023],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050101768,0.000038624,0.0002163584,0.00019601366,0.000013833309,0.000022353302,0.0000846884,0.00035462633,0.0002580537,0.0050204936,0.88790286,0.10584203],"study_design_scores_gemma":[0.000012959394,0.000010853143,0.0008956726,0.00015702724,0.000016516555,0.000035787085,0.00010561752,0.0008422921,0.00029373728,0.001956533,0.99565804,0.000014992241],"about_ca_topic_score_codex":0.24240407,"about_ca_topic_score_gemma":0.40174276,"teacher_disagreement_score":0.24240407,"about_ca_system_score_codex":0.0063099614,"about_ca_system_score_gemma":0.018365992,"threshold_uncertainty_score":0.48198622},"labels":[],"label_agreement":null},{"id":"W1521600952","doi":"10.1109/acc.1994.752456","title":"Self-tuning neurocontrol of nonlinear systems using localized polynomial networks with CLI cells","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Nonlinear system; Control theory (sociology); Computer science; Polynomial; Controller (irrigation); Stability (learning theory); Scheme (mathematics); Equivalence (formal languages); Mathematics; Artificial intelligence; Control (management); Physics","score_opus":0.011201850918876783,"score_gpt":0.22524537132495748,"score_spread":0.21404352040608068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521600952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08560607,0.0002968625,0.9099597,0.00010111312,0.000032803964,0.000038009744,0.00001699489,0.0004882547,0.0034601663],"genre_scores_gemma":[0.9697538,0.00009197419,0.029050115,0.000026428284,0.000012855905,0.000034091325,0.000012631572,0.000010667204,0.0010074681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988115,0.000020289302,0.0000063090065,0.000031954394,0.000042508153,0.000017725171],"domain_scores_gemma":[0.9997707,0.0000634519,0.000060251998,0.00003233286,0.000057334513,0.000015883774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002733099,0.000340995,0.0002415656,0.00012687137,0.00026530516,0.0003237777,0.00067946204,0.0002701083,0.0006806669],"category_scores_gemma":[0.0005773236,0.00014804541,0.00026257834,0.00015419743,0.0004450141,0.00044267546,0.00044541134,0.00041458418,0.00014115337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017718734,0.00010167941,0.001110472,0.00015199289,0.000056920147,0.0001816752,0.00026273692,0.7319086,0.09721487,0.022531306,0.0008386664,0.14546387],"study_design_scores_gemma":[0.0000106539355,0.000094863804,0.00016974269,0.0000034750935,0.0000095085215,0.000028866536,0.000007910284,0.9921252,0.005357524,0.0016101974,0.00057500205,0.0000070392293],"about_ca_topic_score_codex":0.0020409848,"about_ca_topic_score_gemma":0.0021694996,"teacher_disagreement_score":0.0020409848,"about_ca_system_score_codex":0.00036712553,"about_ca_system_score_gemma":0.00028160578,"threshold_uncertainty_score":0.0040581822},"labels":[],"label_agreement":null},{"id":"W1521605157","doi":"10.1109/ccece.1995.528085","title":"NEUREX-an expert system for the autonomous design of artificial neural nets","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Artificial neural network; Computer science; Backpropagation; Artificial intelligence; Process (computing); Expert system; Machine learning; Function (biology)","score_opus":0.0861623998047861,"score_gpt":0.27054139372603797,"score_spread":0.18437899392125187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521605157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00467084,0.00014551509,0.97206694,0.00007058292,0.00006406785,0.00020897876,0.00018938536,0.014940353,0.007643426],"genre_scores_gemma":[0.15301703,0.0005112109,0.8197821,0.00019084272,0.000045557343,0.0009705089,0.00086210505,0.0013751255,0.023245515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995509,0.00009860729,0.000042571788,0.00008276078,0.000196898,0.000028307639],"domain_scores_gemma":[0.99945,0.00020258604,0.000050273902,0.00008303323,0.00017485107,0.00003916773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001034832,0.0007734855,0.0006081023,0.00037988418,0.0003035583,0.0010474155,0.0015291818,0.00095931493,0.011775651],"category_scores_gemma":[0.002284685,0.00045874628,0.0004466895,0.00016650475,0.0004437911,0.00081066886,0.00097427354,0.00109732,0.003799266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079593185,0.0002066509,0.0016898526,0.0010918034,0.00019773841,0.0009804218,0.00063084025,0.36207917,0.07790151,0.047172002,0.03468927,0.4725648],"study_design_scores_gemma":[0.00014212132,0.00013054554,0.00031199065,0.00009205256,0.000050186623,0.0002492669,0.000035109628,0.88953817,0.030488085,0.0077721747,0.07115252,0.00003772724],"about_ca_topic_score_codex":0.0009110217,"about_ca_topic_score_gemma":0.0011488545,"teacher_disagreement_score":0.011775651,"about_ca_system_score_codex":0.0003695488,"about_ca_system_score_gemma":0.0008830774,"threshold_uncertainty_score":0.039393425},"labels":[],"label_agreement":null},{"id":"W1521695033","doi":"10.1007/3-540-45486-1_23","title":"A Recognition-Based Alternative to Discrimination-Based Multi-layer Perceptrons","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Perceptron; Layer (electronics); Artificial intelligence; Pattern recognition (psychology); Speech recognition; Artificial neural network","score_opus":0.0446931283991654,"score_gpt":0.28524388890273394,"score_spread":0.24055076050356855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521695033","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029532106,0.00065157283,0.9886902,0.0002818757,0.0003044543,0.00004095238,0.00010130979,0.0013604793,0.0056159957],"genre_scores_gemma":[0.23491272,0.00089972804,0.7441321,0.00081503176,0.0003646337,0.00013211474,0.00039978893,0.00037630732,0.017967544],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993326,0.00012330108,0.00006767924,0.00017434983,0.00023344763,0.00006859539],"domain_scores_gemma":[0.99885345,0.00044130415,0.000057446905,0.0003042073,0.00028810723,0.000055523557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011323462,0.0007111765,0.0013233104,0.0005422312,0.00035645833,0.002189892,0.0029198618,0.0018248213,0.009982317],"category_scores_gemma":[0.0026353651,0.00042101325,0.0009254805,0.0011785299,0.0006982407,0.0028464415,0.0012420748,0.0024931156,0.0039089597],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047412026,0.00025233353,0.00042812817,0.00044439174,0.0001891172,0.00018451938,0.00006792285,0.05819369,0.042800635,0.14193802,0.007979581,0.7470475],"study_design_scores_gemma":[0.0000379913,0.00008709234,0.00023437604,0.00003458507,0.000062960586,0.00020076401,0.000013009526,0.9145464,0.01549298,0.062273283,0.006985091,0.000031466625],"about_ca_topic_score_codex":0.0015531065,"about_ca_topic_score_gemma":0.0025073614,"teacher_disagreement_score":0.009982317,"about_ca_system_score_codex":0.00046811273,"about_ca_system_score_gemma":0.000725789,"threshold_uncertainty_score":0.033394158},"labels":[],"label_agreement":null},{"id":"W1521939213","doi":"10.1109/ccece.1995.528087","title":"The effect of limited-precision weights on the perfect generalization requirements for threshold Adalines","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Generalization; Binary number; Artificial neural network; Ternary operation; Computer science; Set (abstract data type); Algorithm; Measure (data warehouse); Mathematics; Artificial intelligence; Arithmetic; Data mining","score_opus":0.03936129735253736,"score_gpt":0.27564092570146026,"score_spread":0.2362796283489229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521939213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9092664,0.00042166517,0.08633154,0.0002957807,0.000055438864,0.000072692266,0.000105299434,0.0010112926,0.0024400721],"genre_scores_gemma":[0.9639639,0.00012871806,0.034820303,0.000085017185,0.00001714906,0.0000626774,0.00010229078,0.00013565976,0.0006843122],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974726,0.00035489484,0.00039775955,0.00046013962,0.0010510976,0.00026345992],"domain_scores_gemma":[0.9631423,0.02489204,0.004087225,0.0039685876,0.0034040292,0.00050586386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002114713,0.00062613597,0.00060873525,0.00048199604,0.0005484123,0.0010168159,0.0017617816,0.0009270533,0.0022650396],"category_scores_gemma":[0.03420652,0.00057834305,0.00038436515,0.00044899387,0.00070507155,0.004359111,0.00087719364,0.0011932004,0.00040664928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022646515,0.0005022847,0.004862728,0.00068986206,0.00011183441,0.0006983981,0.0005325998,0.113491446,0.7061036,0.006788323,0.0009437448,0.16301045],"study_design_scores_gemma":[0.00007441222,0.0026424055,0.00829343,0.0000598251,0.0000867121,0.00076292024,0.00014371934,0.17862184,0.8023997,0.004405267,0.0024424507,0.00006725958],"about_ca_topic_score_codex":0.0009798398,"about_ca_topic_score_gemma":0.0014785716,"teacher_disagreement_score":0.0022650396,"about_ca_system_score_codex":0.00073870167,"about_ca_system_score_gemma":0.00058059616,"threshold_uncertainty_score":0.011183798},"labels":[],"label_agreement":null},{"id":"W1522023542","doi":"10.1109/ijcnn.2005.1556291","title":"Virtual reality visual data mining with nonlinear discriminant neural networks: application to leukemia and Alzheimer gene expression data","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Data mining; Generalization; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.08209635693344464,"score_gpt":0.3194906335331427,"score_spread":0.23739427659969808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1522023542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15920697,0.0007321669,0.8366855,0.0009356835,0.00007332713,0.000084422434,0.00017765697,0.0008366977,0.001267537],"genre_scores_gemma":[0.66439444,0.00029937248,0.33385447,0.000112090085,0.00004223103,0.00008469254,0.00018081842,0.000048648286,0.0009832169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994874,0.0002687182,0.000025062523,0.00007448257,0.000105397885,0.000038916452],"domain_scores_gemma":[0.9984295,0.001106722,0.0001066441,0.00012669434,0.00018082184,0.000049663777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001703616,0.00045116936,0.00065791636,0.0010812753,0.00032903883,0.00091098045,0.00067241595,0.0006478766,0.00067655236],"category_scores_gemma":[0.0052509923,0.00027508626,0.0006614925,0.0010177235,0.0004919673,0.0005486225,0.0009663298,0.00061093725,0.00014728324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004613085,0.00023051031,0.00355689,0.00013973053,0.000116567,0.00029331417,0.00026271632,0.56410825,0.008222552,0.0053435564,0.0020991336,0.4151655],"study_design_scores_gemma":[0.000009649242,0.000021442269,0.00035536435,0.0000031036332,0.0000042078345,0.000034535235,0.00001951961,0.99616086,0.0012047816,0.0019156898,0.00026497914,0.000005958893],"about_ca_topic_score_codex":0.0033783012,"about_ca_topic_score_gemma":0.0033330652,"teacher_disagreement_score":0.0033783012,"about_ca_system_score_codex":0.0005871488,"about_ca_system_score_gemma":0.00046048558,"threshold_uncertainty_score":0.009009719},"labels":[],"label_agreement":null},{"id":"W1524112065","doi":"10.1109/iscas.1995.523768","title":"The investigation of using limited precision on a TDNN for consonant recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"TIMIT; Speech recognition; Computer science; Time delay neural network; Artificial neural network; Backpropagation; Consonant; Speech processing; Set (abstract data type); Recurrent neural network; Artificial intelligence; Pattern recognition (psychology); Hidden Markov model","score_opus":0.1382744163063619,"score_gpt":0.2768611195221183,"score_spread":0.1385867032157564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1524112065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21903604,0.0012209662,0.7747103,0.00033053212,0.000094326424,0.000085527994,0.000043724354,0.00070780714,0.003770877],"genre_scores_gemma":[0.72178394,0.000445179,0.27566993,0.00010848492,0.000052533025,0.00007156295,0.00005312921,0.00005665162,0.001758554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988661,0.00026768283,0.00010702581,0.00020665996,0.00048766774,0.00006490156],"domain_scores_gemma":[0.9909225,0.006473841,0.00047145944,0.0009353901,0.0011233863,0.00007345462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019307212,0.0004800512,0.00047284292,0.0003726114,0.00036036427,0.0009026702,0.001245295,0.00088911416,0.0013654688],"category_scores_gemma":[0.013745423,0.00027253007,0.00025659142,0.00046838858,0.00052181334,0.0015726703,0.00047171273,0.0007592295,0.00041049093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017865298,0.00014804461,0.0073636295,0.0005825413,0.0001608458,0.0006249944,0.00030058462,0.25068432,0.1519512,0.013591011,0.0006219557,0.5721843],"study_design_scores_gemma":[0.000104647224,0.0014733094,0.0043679904,0.00012193463,0.00017900887,0.0014835708,0.00009325624,0.86834,0.11384887,0.0044505727,0.005478236,0.000058552436],"about_ca_topic_score_codex":0.0024756028,"about_ca_topic_score_gemma":0.0028858576,"teacher_disagreement_score":0.0024756028,"about_ca_system_score_codex":0.00062810455,"about_ca_system_score_gemma":0.00060746016,"threshold_uncertainty_score":0.010210812},"labels":[],"label_agreement":null},{"id":"W1526818844","doi":"10.1109/ccece.1996.548196","title":"Neural network-based recognition of mine environments","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Mobile robot; Computer science; Mobile robot navigation; Artificial intelligence; Computer vision; Robot; Artificial neural network; Robot control","score_opus":0.034418551699593486,"score_gpt":0.21490152752160097,"score_spread":0.18048297582200748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1526818844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18263091,0.0016077375,0.80221736,0.0004885997,0.00018488645,0.00004693947,0.00071553024,0.002683154,0.009424903],"genre_scores_gemma":[0.8651325,0.00062890066,0.12916659,0.00010267295,0.00004100799,0.000050978546,0.00072248816,0.00006869904,0.004086058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998524,0.000025825977,0.000007956581,0.000049526778,0.00003629045,0.000027993998],"domain_scores_gemma":[0.99979454,0.00005588594,0.000033892975,0.000022872766,0.00008049685,0.000012449876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020595222,0.00031542248,0.00026127326,0.00046161574,0.00014760239,0.00049829716,0.00043781204,0.00049624,0.0011977963],"category_scores_gemma":[0.001181435,0.00015651618,0.00021444664,0.0005259429,0.00026575377,0.0008166634,0.00044765606,0.0004105109,0.0004601526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003262778,0.00012654596,0.0066145016,0.00014955076,0.000073068884,0.00018639854,0.00011573184,0.28694642,0.07153586,0.0057299864,0.0057956968,0.62239987],"study_design_scores_gemma":[0.000005621357,0.000030839197,0.003946508,0.000016870998,0.00001285754,0.000075738084,0.000028355262,0.9771821,0.0121337585,0.0045216638,0.0020339736,0.0000116793235],"about_ca_topic_score_codex":0.0029963125,"about_ca_topic_score_gemma":0.003985721,"teacher_disagreement_score":0.0029963125,"about_ca_system_score_codex":0.00035471862,"about_ca_system_score_gemma":0.00021882965,"threshold_uncertainty_score":0.0059577823},"labels":[],"label_agreement":null},{"id":"W1527091362","doi":"10.1109/acc.1994.752457","title":"Performance comparison of dynamic neural networks as applied to robot inverse kinematic computations","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Computer science; Kinematics; Feedforward neural network; Recurrent neural network; Time delay neural network; Feed forward; Inverse kinematics; Control theory (sociology); Types of artificial neural networks; Physical neural network; Robot; Artificial intelligence; Control engineering; Engineering; Physics","score_opus":0.016701002881897624,"score_gpt":0.28209497671114625,"score_spread":0.2653939738292486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527091362","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57065296,0.0033561224,0.40654948,0.0004802493,0.0002716877,0.00012300255,0.0001396793,0.0028931308,0.015533648],"genre_scores_gemma":[0.96467775,0.0005772537,0.032883883,0.000044859018,0.0000228088,0.000059651236,0.00013395738,0.00006911777,0.0015309002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992675,0.00023583406,0.00005135151,0.00012569602,0.00023886286,0.000080799284],"domain_scores_gemma":[0.9982027,0.0010551433,0.000093756964,0.0001331332,0.00045647484,0.000058791535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011736153,0.00067445205,0.00061907055,0.0006835444,0.00025751564,0.00076106656,0.00087059836,0.00073609385,0.0013968473],"category_scores_gemma":[0.006149811,0.00031285762,0.00023090618,0.0005805505,0.00037673808,0.0009962062,0.00075159676,0.00040386058,0.0003208114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010548495,0.00013063788,0.002158831,0.00029303442,0.00012207625,0.00011580942,0.000096072574,0.7242858,0.016270483,0.0035380104,0.00069686724,0.2512376],"study_design_scores_gemma":[0.00001518567,0.00019324043,0.00051663077,0.00001035401,0.000016896147,0.000029763562,0.00002044426,0.989881,0.008202258,0.00065973453,0.0004437673,0.00001065208],"about_ca_topic_score_codex":0.0054249186,"about_ca_topic_score_gemma":0.0031664756,"teacher_disagreement_score":0.0054249186,"about_ca_system_score_codex":0.00073190086,"about_ca_system_score_gemma":0.0005074358,"threshold_uncertainty_score":0.010786653},"labels":[],"label_agreement":null},{"id":"W1527521201","doi":"10.1007/978-3-540-70829-2_12","title":"Two Frameworks for Improving Gradient-Based Learning Algorithms","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backpropagation; Benchmark (surveying); Computer science; Artificial neural network; Transfer of learning; Convergence (economics); Artificial intelligence; Generalization; Algorithm; Machine learning; Function (biology); Transfer function; Mathematics; Engineering","score_opus":0.08395700026629305,"score_gpt":0.35524153484463866,"score_spread":0.2712845345783456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527521201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006825258,0.0017698224,0.99153346,0.00050345046,0.00037602987,0.000053736265,0.000029769977,0.0004134605,0.004637733],"genre_scores_gemma":[0.034821935,0.0024191404,0.9503403,0.00074660627,0.0009271134,0.00029817707,0.00015835189,0.0004738552,0.009814502],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960737,0.0010639456,0.00020343592,0.0004876671,0.0018943814,0.00027685706],"domain_scores_gemma":[0.995468,0.0018555622,0.00019660102,0.000930852,0.001375415,0.000173538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00459685,0.0024766957,0.0024504275,0.0032810036,0.0011492781,0.0030343842,0.0060310834,0.0040460154,0.0070185154],"category_scores_gemma":[0.020307189,0.0010947078,0.0020048402,0.0037556654,0.0028082684,0.005673466,0.004869753,0.0063094296,0.0031480044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018513063,0.00022580191,0.00038442743,0.0003500547,0.00012223289,0.0000674084,0.00017611745,0.082116544,0.0017378987,0.47752017,0.020325836,0.41678855],"study_design_scores_gemma":[0.00011073093,0.00016002517,0.00031320474,0.00014988253,0.00009852182,0.00017302897,0.00003442344,0.72082734,0.002972457,0.24384265,0.03125402,0.00006375137],"about_ca_topic_score_codex":0.003993549,"about_ca_topic_score_gemma":0.0045390436,"teacher_disagreement_score":0.0070185154,"about_ca_system_score_codex":0.0014591619,"about_ca_system_score_gemma":0.0017743103,"threshold_uncertainty_score":0.024310708},"labels":[],"label_agreement":null},{"id":"W1531285217","doi":"10.1109/ccece.1995.528108","title":"A preliminary comparison of FIR-synapse neural networks with and without local output feedback","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Feed forward; Generality; Interconnection; Computer science; Artificial neural network; Feedforward neural network; Convergence (economics); Identification (biology); Control engineering; Artificial intelligence; Engineering; Telecommunications","score_opus":0.026374732675503464,"score_gpt":0.24805240636245485,"score_spread":0.22167767368695138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531285217","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47250643,0.0067619733,0.45260406,0.0008356482,0.000761442,0.0002176429,0.00095765525,0.0016405493,0.063714646],"genre_scores_gemma":[0.9105426,0.0020134575,0.07657239,0.00010658393,0.000081401675,0.000053566837,0.0006331161,0.00008524404,0.009911545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973613,0.00006309815,0.000020285683,0.000034000874,0.000112216534,0.0000341954],"domain_scores_gemma":[0.9984085,0.000879259,0.00006151531,0.000121801015,0.0005002109,0.000028711738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014921728,0.00033127074,0.00038929126,0.0003055056,0.00024911397,0.0004917069,0.000529691,0.0005113512,0.0051855636],"category_scores_gemma":[0.005791822,0.000105233215,0.00022149894,0.0005348399,0.00024795547,0.0012695065,0.00024953068,0.00026886887,0.00076111726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028296188,0.0002606481,0.007695021,0.0012242607,0.00017233242,0.00026326318,0.00013907517,0.41731063,0.046099823,0.04260759,0.0032970242,0.47810066],"study_design_scores_gemma":[0.000058657057,0.0012551119,0.011189987,0.000096831696,0.000105387444,0.00027005287,0.0000952623,0.93296206,0.02582441,0.02050711,0.0075963433,0.000038850118],"about_ca_topic_score_codex":0.0016967455,"about_ca_topic_score_gemma":0.00473365,"teacher_disagreement_score":0.0051855636,"about_ca_system_score_codex":0.00044293105,"about_ca_system_score_gemma":0.0002928042,"threshold_uncertainty_score":0.017347455},"labels":[],"label_agreement":null},{"id":"W1531902512","doi":"","title":"Combining Competitive And Cooperative Coevolution For Training Cascade Neural Networks","year":2002,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Coevolution; Artificial neural network; Computer science; Cascade; Crossover; Artificial intelligence; Retraining; Evolutionary algorithm; Quality (philosophy); Machine learning; Ecology; Biology; Engineering","score_opus":0.043327552540351366,"score_gpt":0.25077273915800685,"score_spread":0.20744518661765549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531902512","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2259856,0.0006101557,0.76509446,0.00026085234,0.00009304739,0.00023748612,0.000025649528,0.00069505157,0.006997811],"genre_scores_gemma":[0.8751143,0.00015155597,0.12263978,0.000103796185,0.000027783804,0.00018832147,0.000044639713,0.000041667638,0.0016881471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955696,0.00013474977,0.00003649028,0.00008454247,0.00012816362,0.000059062837],"domain_scores_gemma":[0.9987251,0.0007242582,0.00007881584,0.0001035918,0.00030946653,0.000058804133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021332726,0.00096316775,0.0008545724,0.00081516046,0.00039942077,0.0005700442,0.0015053636,0.0014673943,0.0016420136],"category_scores_gemma":[0.0056628184,0.00047407523,0.00046414376,0.0006502235,0.0005700728,0.0011617303,0.001026302,0.0007439347,0.0003184926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009447718,0.00015351725,0.0024880709,0.00008085695,0.000089535744,0.00014462187,0.00013828516,0.82645154,0.0097468225,0.004451743,0.0007091349,0.15545146],"study_design_scores_gemma":[0.0000063580906,0.000034304347,0.0001141591,0.000002950978,0.0000075812577,0.000014111314,0.0000047502645,0.9981523,0.000846505,0.00065072085,0.00016284824,0.000003370248],"about_ca_topic_score_codex":0.003616558,"about_ca_topic_score_gemma":0.0055164187,"teacher_disagreement_score":0.003616558,"about_ca_system_score_codex":0.00058647216,"about_ca_system_score_gemma":0.00050721917,"threshold_uncertainty_score":0.011281908},"labels":[],"label_agreement":null},{"id":"W1532452946","doi":"10.1007/978-3-540-87536-9_10","title":"A New Type of ART2 Architecture and Application to Color Image Segmentation","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Color space; Pattern recognition (psychology); Segmentation; Cluster analysis; Image segmentation; Feature (linguistics); Adaptive resonance theory; Similarity (geometry); Artificial neural network; Architecture; Feature vector; Computer vision; Color image; Image (mathematics); Image processing","score_opus":0.013044934826912002,"score_gpt":0.25908820514548403,"score_spread":0.24604327031857204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1532452946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011593592,0.0006296468,0.9666508,0.00015288532,0.00023521698,0.000052284126,0.00016869928,0.0059808604,0.014535979],"genre_scores_gemma":[0.16756305,0.0006020321,0.797435,0.00037747127,0.00018674468,0.00010249914,0.00051796355,0.0008817704,0.03233339],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997633,0.000023832621,0.000010848202,0.00008354098,0.00008658535,0.000031857373],"domain_scores_gemma":[0.9997558,0.000044743316,0.000009499421,0.00007063266,0.00009703779,0.000022326632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003352293,0.0007834858,0.0005862002,0.00080307206,0.0005950261,0.0016371306,0.002568536,0.0014192072,0.016064472],"category_scores_gemma":[0.00073216,0.0005402376,0.00087070593,0.0011609526,0.00051277305,0.0013812898,0.0010526779,0.000996367,0.0036199186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038880378,0.00010915435,0.0006623376,0.00031696958,0.0001417911,0.000245044,0.0001292544,0.056400675,0.11141586,0.03610453,0.01731806,0.77676755],"study_design_scores_gemma":[0.000032471507,0.00015147739,0.00070550933,0.00003426922,0.000084637904,0.000534513,0.000028062219,0.8729948,0.068569675,0.021068174,0.035754822,0.000041639563],"about_ca_topic_score_codex":0.0033339476,"about_ca_topic_score_gemma":0.0068173814,"teacher_disagreement_score":0.016064472,"about_ca_system_score_codex":0.00055802794,"about_ca_system_score_gemma":0.00049689057,"threshold_uncertainty_score":0.05374098},"labels":[],"label_agreement":null},{"id":"W1533861849","doi":"","title":"Understanding the difficulty of training deep feedforward neural networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12692,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Initialization; Computer science; Artificial neural network; Artificial intelligence; Deep neural networks; Deep learning; Gradient descent; Jacobian matrix and determinant; Sigmoid function; Machine learning; Mathematics","score_opus":0.0766112791778204,"score_gpt":0.2560151960910123,"score_spread":0.1794039169131919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533861849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07508201,0.0054257116,0.9025662,0.008326568,0.00026468025,0.00009511194,0.00023581457,0.0009108178,0.0070931073],"genre_scores_gemma":[0.7114234,0.0059503745,0.27485067,0.0010754456,0.00063598744,0.00025077528,0.0005459769,0.00044059346,0.0048268023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873227,0.00050570746,0.0001198882,0.0002780401,0.00027685866,0.00008723133],"domain_scores_gemma":[0.9844468,0.013089329,0.0006046043,0.0006283273,0.0010567068,0.0001742129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004251104,0.0012313061,0.0008083709,0.00055932056,0.00058789586,0.0016976933,0.0012579483,0.002456123,0.0023332038],"category_scores_gemma":[0.03511711,0.001132296,0.00044494346,0.0006012978,0.0021680922,0.0073523596,0.001444381,0.003728366,0.00068278785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030694623,0.000088401466,0.005512543,0.00096572115,0.000112890506,0.00032178426,0.0005437847,0.71784145,0.008650141,0.060506325,0.005986174,0.19916391],"study_design_scores_gemma":[0.000029611561,0.000074358664,0.0013896435,0.00012561478,0.00001885873,0.0001412341,0.000098343364,0.88303375,0.0044679944,0.10736001,0.0032308514,0.00002977859],"about_ca_topic_score_codex":0.0042147595,"about_ca_topic_score_gemma":0.0030140954,"teacher_disagreement_score":0.004251104,"about_ca_system_score_codex":0.0011528285,"about_ca_system_score_gemma":0.00095061224,"threshold_uncertainty_score":0.022482276},"labels":[],"label_agreement":null},{"id":"W1534704610","doi":"10.1007/3-540-44886-1","title":"Advances in artificial intelligence : 16th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2003, Halifax, Canada, June 11-13, 2003 : proceedings","year":2003,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Bayesian network","score_opus":0.04829966800434944,"score_gpt":0.2894619036975843,"score_spread":0.24116223569323486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1534704610","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068725087,0.23099439,0.03675235,0.04704222,0.057778895,0.0004167443,0.009559349,0.003501042,0.6070825],"genre_scores_gemma":[0.014514386,0.088774174,0.013504751,0.0014175639,0.0023269393,0.00014721022,0.0053430665,0.0010943642,0.87287754],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99902403,0.00007870497,0.00004471129,0.000114680515,0.0006078691,0.00013001959],"domain_scores_gemma":[0.9967175,0.0003074993,0.000069534326,0.00020918756,0.0021463432,0.0005498963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020388588,0.0013418217,0.0020267852,0.0019140263,0.0018530558,0.0058696773,0.0016480427,0.0011957097,0.101975754],"category_scores_gemma":[0.0026218,0.00056355284,0.00063656457,0.0034410395,0.0016461853,0.002379547,0.0016510001,0.0029385902,0.037560996],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051385054,0.000032180735,0.00023152433,0.00018408382,0.0000137786365,0.00002599812,0.00009851399,0.00035424845,0.0003088194,0.0059617204,0.8895366,0.103201106],"study_design_scores_gemma":[0.000011473171,0.0000093772305,0.0008414081,0.00013814385,0.000015012378,0.00003966351,0.000112121794,0.0008765959,0.0003201274,0.0021559806,0.9954657,0.000014414026],"about_ca_topic_score_codex":0.26273924,"about_ca_topic_score_gemma":0.4308991,"teacher_disagreement_score":0.9931256,"about_ca_system_score_codex":0.006874412,"about_ca_system_score_gemma":0.01829874,"threshold_uncertainty_score":0.52241987},"labels":[],"label_agreement":null},{"id":"W1534740900","doi":"10.1109/ijcnn.2005.1555976","title":"Optimizing resources in model selection for support vector machines","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Support vector machine; Machine learning; Kernel (algebra); Quadratic programming; Selection (genetic algorithm); Artificial intelligence; Inversion (geology); Relevance vector machine; Mathematical optimization; Algorithm; Data mining; Mathematics","score_opus":0.03870287643253006,"score_gpt":0.2762939522457917,"score_spread":0.2375910758132616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1534740900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028292552,0.0016826516,0.96328694,0.0005634624,0.00012241674,0.00018629669,0.00020599917,0.0036930519,0.001966705],"genre_scores_gemma":[0.48398116,0.0009409315,0.50875235,0.0004584761,0.00023682123,0.0008579146,0.0011231343,0.00095548877,0.0026936873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966366,0.00197266,0.00024582728,0.00036960893,0.00054200884,0.00023327724],"domain_scores_gemma":[0.9892419,0.008370306,0.00031609338,0.0008636128,0.0010344554,0.00017363686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004277851,0.0018694397,0.0025045292,0.0014430082,0.0008554808,0.001975495,0.002716003,0.0016247884,0.004824847],"category_scores_gemma":[0.024612045,0.00108269,0.00097550976,0.001988449,0.00074668333,0.0035872755,0.001967137,0.0021445,0.0024737867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009192356,0.0002615929,0.0022001467,0.00036499428,0.00020759148,0.00033060636,0.00012948361,0.49018705,0.004916104,0.011640391,0.009184643,0.47965816],"study_design_scores_gemma":[0.000053449636,0.0000636417,0.00029551872,0.00002455048,0.000027512073,0.000044568897,0.000027640937,0.98876673,0.0023276568,0.0072587696,0.0010938063,0.00001615774],"about_ca_topic_score_codex":0.004015656,"about_ca_topic_score_gemma":0.005007452,"teacher_disagreement_score":0.004824847,"about_ca_system_score_codex":0.0008992494,"about_ca_system_score_gemma":0.0018517043,"threshold_uncertainty_score":0.022623718},"labels":[],"label_agreement":null},{"id":"W1534745862","doi":"10.1109/icnn.1994.374513","title":"ADN-analysis and development of distributed neural networks for intelligent applications","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation, Science and Economic Development Canada","funders":"","keywords":"Subnetwork; Computer science; Artificial neural network; Context (archaeology); Artificial intelligence; Interface (matter); Computer network; Operating system","score_opus":0.03293577739323545,"score_gpt":0.2583436432802785,"score_spread":0.22540786588704304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1534745862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014076572,0.0004318878,0.9904721,0.00021139478,0.000079608515,0.00005803447,0.00012494472,0.0011462877,0.006068111],"genre_scores_gemma":[0.060653586,0.0012625251,0.92622066,0.000104001396,0.00007788691,0.00032468064,0.00055486674,0.00038828977,0.01041348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957293,0.000104756225,0.00002857996,0.00007680247,0.00019515496,0.000021681362],"domain_scores_gemma":[0.9993942,0.00022076858,0.00002446025,0.000085913234,0.00025397952,0.000020653933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011240655,0.0006138585,0.0004040389,0.0007459842,0.0004141777,0.0011741621,0.001162483,0.00057453586,0.0075409934],"category_scores_gemma":[0.0033337718,0.00043245932,0.00060551276,0.0006969602,0.0005279807,0.0015145808,0.0007719186,0.0013432142,0.0023256694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054957625,0.00006966004,0.0010483867,0.00038824693,0.00006639924,0.00017586432,0.00015778153,0.31694505,0.004934766,0.20606577,0.015895074,0.45419794],"study_design_scores_gemma":[0.000013416255,0.00001456772,0.0002119022,0.00005904982,0.000013101193,0.00007631084,0.000022074102,0.883105,0.004366538,0.07062407,0.04148478,0.000009197276],"about_ca_topic_score_codex":0.002913102,"about_ca_topic_score_gemma":0.0035664705,"teacher_disagreement_score":0.0075409934,"about_ca_system_score_codex":0.0009271721,"about_ca_system_score_gemma":0.0010847544,"threshold_uncertainty_score":0.025227189},"labels":[],"label_agreement":null},{"id":"W153524735","doi":"","title":"","year":2011,"lang":"en","type":"article","venue":"Edinburgh Research Explorer","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of California, Irvine; Syddansk Universitet; Universitetet i Bergen; Concordia University; Architectural League of New York; Murdoch University; Queensland University of Technology; University of Southampton; Central Queensland University; University of Technology Sydney; University of California, San Diego; University of Stirling; University of New South Wales; RMIT University; Lakehead University; National University of Singapore; Curtin University of Technology; Dartmouth College; Macquarie University; Universiteit van Amsterdam; Swinburne University of Technology; University of Western Sydney; York University; Manchester Metropolitan University; Chapman University; Johns Hopkins University; Strategiske Forskningsråd; Università degli Studi di Napoli Federico II","keywords":"Ubiquitous computing; Computer science; Human–computer interaction","score_opus":0.3473950956667528,"score_gpt":0.3826716499997692,"score_spread":0.03527655433301641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W153524735","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004495591,0.0258056,0.023618447,0.30739337,0.013259411,0.000047173722,0.00022080095,0.00042606427,0.6247335],"genre_scores_gemma":[0.22467582,0.018107213,0.014873293,0.19987462,0.008669682,0.00019255324,0.00041545156,0.0008883877,0.532303],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959792,0.0016008825,0.00014995682,0.00083688955,0.0010236291,0.00040947407],"domain_scores_gemma":[0.99709415,0.0010960955,0.00021303148,0.0005312292,0.00062898436,0.00043655105],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0032118065,0.0007574026,0.00041743452,0.0011521857,0.005796761,0.012036461,0.0013562087,0.007272176,0.03303158],"category_scores_gemma":[0.0098644085,0.0003281657,0.0004949796,0.001183402,0.016471613,0.015085631,0.005552028,0.007133671,0.012362085],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024307988,0.000009749244,0.00025659506,0.000069976064,0.0000050542926,0.000080034995,0.0046728724,0.000060272876,0.00014520854,0.80178636,0.1659416,0.02694805],"study_design_scores_gemma":[0.0000033235285,0.0000084477315,0.00019043367,0.00009955802,0.0000022961333,0.00011774321,0.001539437,0.00006988821,0.0001520517,0.091977656,0.9058295,0.000009662759],"about_ca_topic_score_codex":0.0056482204,"about_ca_topic_score_gemma":0.009256295,"teacher_disagreement_score":0.9669684,"about_ca_system_score_codex":0.0034675205,"about_ca_system_score_gemma":0.0023187029,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W1536018596","doi":"10.1109/ijcnn.2005.1556220","title":"Exploration of rank order coding with spiking neural networks for speech recognition","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Speech recognition; Linear predictive coding; Artificial neural network; Speech processing; Speech coding; Voice activity detection; Coding (social sciences); Noise (video); Time delay neural network; Context (archaeology); Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.0665136568064774,"score_gpt":0.2770767043616115,"score_spread":0.2105630475551341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1536018596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074842714,0.0018093429,0.91379184,0.0010584203,0.000097517914,0.00003504928,0.00011047299,0.0005713944,0.007683286],"genre_scores_gemma":[0.87036264,0.0012279504,0.12447777,0.00017250457,0.00008190869,0.00008461416,0.000093031915,0.00006521145,0.003434359],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998307,0.00007658096,0.000008454155,0.000022889568,0.000044702527,0.000016621876],"domain_scores_gemma":[0.99891984,0.0007842035,0.00006841122,0.00009917208,0.00009347575,0.000034990684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069405045,0.00033715556,0.00042716882,0.00024230721,0.00020137575,0.0008614599,0.0005950516,0.00064180227,0.0016010191],"category_scores_gemma":[0.002806737,0.00017746285,0.00034848216,0.0003488579,0.0005288263,0.00101711,0.00051413657,0.0007798917,0.0002512275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019635774,0.000079164594,0.0007085223,0.00016624363,0.00006366734,0.00011062528,0.000098156364,0.7233002,0.024279024,0.15472014,0.0014499254,0.094827846],"study_design_scores_gemma":[0.0000063946436,0.000020331634,0.00006712579,0.000004311209,0.0000033945503,0.000013306854,0.000005266856,0.95641947,0.0011562917,0.04193578,0.00036259682,0.0000056706754],"about_ca_topic_score_codex":0.0007777599,"about_ca_topic_score_gemma":0.0012138862,"teacher_disagreement_score":0.0016010191,"about_ca_system_score_codex":0.00045836752,"about_ca_system_score_gemma":0.00038153172,"threshold_uncertainty_score":0.0053558946},"labels":[],"label_agreement":null},{"id":"W1536571263","doi":"10.1007/3-540-70659-3_45","title":"Comparison of Two Classification Methodologies on a Real-World Biomedical Problem","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Computer science; Classifier (UML); Perceptron; Artificial intelligence; Pattern recognition (psychology); Linear discriminant analysis; Quadratic classifier; Exploit; Machine learning; Discriminant; Feature extraction; Linear classifier; Data mining; Artificial neural network","score_opus":0.13739427996515102,"score_gpt":0.3827225927617744,"score_spread":0.24532831279662337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1536571263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24640974,0.019382054,0.7127026,0.0023137399,0.0015634033,0.0006237275,0.0009471611,0.0023883656,0.01366932],"genre_scores_gemma":[0.50833493,0.0042596995,0.47653854,0.00069211406,0.00050987565,0.00033483902,0.002221763,0.00029656917,0.006811669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970938,0.0010740478,0.00028456285,0.00041349357,0.0009083321,0.0002258074],"domain_scores_gemma":[0.98438245,0.011181368,0.00038301994,0.0005818979,0.0031247837,0.00034650552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069477097,0.0014488195,0.0015138087,0.003875317,0.0007792361,0.002570483,0.0016942685,0.0026138506,0.002500827],"category_scores_gemma":[0.013272857,0.00023851827,0.0010982172,0.0024701403,0.00054991164,0.0018033809,0.0014194152,0.0009935251,0.0007883598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019691214,0.0008189034,0.007362717,0.00090955914,0.0005916427,0.00013956091,0.00030993874,0.045921583,0.0049523185,0.0036384931,0.008794781,0.9245914],"study_design_scores_gemma":[0.00037697947,0.0013049282,0.01355528,0.00021617493,0.00049328856,0.00049549894,0.0006953669,0.96031135,0.008331509,0.0067514903,0.0073851105,0.00008295692],"about_ca_topic_score_codex":0.0041778,"about_ca_topic_score_gemma":0.005328287,"teacher_disagreement_score":0.0069477097,"about_ca_system_score_codex":0.00094898115,"about_ca_system_score_gemma":0.0013468728,"threshold_uncertainty_score":0.036743462},"labels":[],"label_agreement":null},{"id":"W1538354650","doi":"10.1109/icnn.1994.374993","title":"Window structure and computation of neural networks for speech recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computation; Window (computing); Artificial neural network; Feed forward; Speech recognition; Implementation; Artificial intelligence; Pattern recognition (psychology); Parallel computing; Computer architecture; Algorithm; Programming language; Operating system","score_opus":0.03165906935218046,"score_gpt":0.2494964486060469,"score_spread":0.21783737925386643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1538354650","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021876253,0.0007724165,0.97526044,0.00010154941,0.00006235668,0.000015660366,0.00005392256,0.0004554346,0.0014019813],"genre_scores_gemma":[0.39099872,0.0016410986,0.6018608,0.00006741291,0.00011882128,0.00013842287,0.00023691867,0.00019863118,0.004739099],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998585,0.0000324848,0.000012883907,0.000034196783,0.000048648082,0.000013348364],"domain_scores_gemma":[0.99974424,0.00013321347,0.000022214299,0.000036963556,0.00005293623,0.000010503663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029628546,0.00032641835,0.00027442822,0.00033634558,0.00018501111,0.00060106925,0.00044604996,0.00029805335,0.0020423527],"category_scores_gemma":[0.0015034555,0.000255451,0.00024827247,0.00051976665,0.00035454214,0.0015957822,0.00036255756,0.0005459441,0.000396486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000413908,0.00003626289,0.0010483448,0.00022397228,0.000058081107,0.00015171208,0.00016831973,0.26736984,0.05961322,0.30245253,0.0038067896,0.36465704],"study_design_scores_gemma":[0.000013990049,0.000041281553,0.00027601267,0.000012447138,0.00001175248,0.000030418767,0.000010561594,0.9304949,0.0126716895,0.051342238,0.0050843037,0.000010305174],"about_ca_topic_score_codex":0.0012922562,"about_ca_topic_score_gemma":0.001736626,"teacher_disagreement_score":0.0020423527,"about_ca_system_score_codex":0.00044944993,"about_ca_system_score_gemma":0.00030011951,"threshold_uncertainty_score":0.006832361},"labels":[],"label_agreement":null},{"id":"W1539203662","doi":"10.1017/cbo9780511841583.005","title":"Sampling and quantization","year":2009,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of Saskatchewan","funders":"","keywords":"Keypad; Computer science; Quantization (signal processing); Transmission (telecommunications); Sampling (signal processing); Computer graphics (images); Computer hardware; Computer vision; Telecommunications","score_opus":0.03384646684392479,"score_gpt":0.2164031722795096,"score_spread":0.1825567054355848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539203662","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032203528,0.0350397,0.8017053,0.0017854313,0.0027878205,0.000088642126,0.00030907674,0.000923459,0.15414023],"genre_scores_gemma":[0.19012089,0.065002695,0.55443347,0.0020681585,0.0033462145,0.0002649559,0.0010246014,0.0008163209,0.18292275],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998987,0.00019301681,0.000062818115,0.00017175917,0.0005332878,0.00005212167],"domain_scores_gemma":[0.9994992,0.0002504441,0.000018166747,0.000075254204,0.00013951228,0.000017461627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008513859,0.0005337908,0.000616395,0.0010455004,0.0005141348,0.0019845879,0.0007326545,0.0007489956,0.017881444],"category_scores_gemma":[0.0032400263,0.00023512976,0.00035786422,0.0016954512,0.0013548053,0.0020473914,0.00085063255,0.0013371685,0.0045407135],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057868576,0.000011293992,0.00017467848,0.00039584446,0.000015266933,0.00007670934,0.00044496456,0.006261452,0.0027393259,0.52087814,0.023630265,0.44531423],"study_design_scores_gemma":[0.000017046239,0.000054351523,0.00041772693,0.00036344022,0.000016345033,0.00049921614,0.0002157421,0.040256664,0.005206116,0.41910365,0.5338086,0.000041131872],"about_ca_topic_score_codex":0.0014064193,"about_ca_topic_score_gemma":0.0016703106,"teacher_disagreement_score":0.017881444,"about_ca_system_score_codex":0.0008779566,"about_ca_system_score_gemma":0.00055262377,"threshold_uncertainty_score":0.0598194},"labels":[],"label_agreement":null},{"id":"W1541328469","doi":"10.1109/ijcnn.2003.1223303","title":"Automatic basis selection for RBF networks using Stein's unbiased risk estimator","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Basis (linear algebra); Radial basis function; Estimator; Basis function; Computer science; Generalization; Radial basis function network; Orthogonal basis; Artificial neural network; Selection (genetic algorithm); Mathematical optimization; Flexibility (engineering); Bias of an estimator; Minimum-variance unbiased estimator; Algorithm; Artificial intelligence; Mathematics; Statistics","score_opus":0.020365944903169314,"score_gpt":0.26781437551371956,"score_spread":0.24744843061055025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1541328469","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044782464,0.000046134483,0.99503976,0.000036720074,0.000004797948,0.000013214461,0.0000035288317,0.000112279115,0.00026540257],"genre_scores_gemma":[0.19937642,0.00014586045,0.7992114,0.00005323965,0.00002128239,0.00016483969,0.00005410261,0.00013271351,0.00084015133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809057,0.0009138206,0.00009316188,0.00018731692,0.00061770953,0.00009737728],"domain_scores_gemma":[0.99634534,0.001983821,0.0003161499,0.00029874523,0.0009809764,0.00007491887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005658776,0.0008916512,0.0012239726,0.00095558894,0.0006089584,0.0009382889,0.0010103919,0.0012306026,0.00080840825],"category_scores_gemma":[0.014460786,0.00053212454,0.00061602145,0.00056146167,0.0009654679,0.0012554241,0.0011580543,0.00137098,0.00056425587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001405638,0.000085525644,0.0011200308,0.00010915324,0.000052630152,0.000087315515,0.00012203779,0.6957963,0.016131317,0.046799295,0.0013348537,0.23822099],"study_design_scores_gemma":[0.000006747804,0.000019394729,0.000082565944,0.0000067485566,0.0000033240522,0.000014241411,0.0000052168702,0.9908398,0.002252124,0.0065207933,0.00024323809,0.0000058246533],"about_ca_topic_score_codex":0.0015611151,"about_ca_topic_score_gemma":0.002051701,"teacher_disagreement_score":0.005658776,"about_ca_system_score_codex":0.0008044369,"about_ca_system_score_gemma":0.0012624946,"threshold_uncertainty_score":0.029926836},"labels":[],"label_agreement":null},{"id":"W1542153889","doi":"","title":"Statistical analysis of adaptive neural network inversion of Hammerstein systems for Gaussian inputs","year":2002,"lang":"en","type":"article","venue":"European Signal Processing Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial neural network; Multilayer perceptron; Adaptive filter; Kernel adaptive filter; Computer science; Backpropagation; Adaptive system; Gaussian; Perceptron; Algorithm; Nonlinear system; Wiener filter; Filter (signal processing); Least mean squares filter; Activation function; Control theory (sociology); Deconvolution; Inversion (geology); Mathematics; Artificial intelligence; Filter design","score_opus":0.05691095865527767,"score_gpt":0.2564117848771848,"score_spread":0.19950082622190712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542153889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036504455,0.00053325744,0.96000046,0.00021634971,0.000025969084,0.000017535402,0.000037900892,0.00012327469,0.0025407085],"genre_scores_gemma":[0.94320154,0.00081066426,0.051603757,0.00010323585,0.00010616111,0.00008524577,0.00015231725,0.00011174568,0.0038254214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948347,0.0001541283,0.000023258313,0.00007374635,0.00022155186,0.00004395774],"domain_scores_gemma":[0.9961463,0.0026969223,0.00036829142,0.00018400273,0.00055758725,0.00004700205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016181136,0.00034243346,0.0003956636,0.0007041583,0.00020309343,0.0004739388,0.00055973546,0.0005458185,0.0012550224],"category_scores_gemma":[0.008866484,0.00024432712,0.00045240557,0.0004692003,0.0010857002,0.0011402229,0.00052330585,0.000875416,0.00015087395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050898685,0.000026607664,0.0017507924,0.00011320725,0.000063925574,0.0001315876,0.00010475682,0.8336116,0.005966955,0.12822168,0.00051373354,0.029444218],"study_design_scores_gemma":[0.0000010986817,0.000007962818,0.00055908,0.0000037136813,0.0000032535454,0.000019009687,0.0000043112336,0.9814197,0.00050107145,0.01731363,0.00016197129,0.0000053420536],"about_ca_topic_score_codex":0.0023950373,"about_ca_topic_score_gemma":0.001718793,"teacher_disagreement_score":0.0023950373,"about_ca_system_score_codex":0.0007660277,"about_ca_system_score_gemma":0.0007483313,"threshold_uncertainty_score":0.008557498},"labels":[],"label_agreement":null},{"id":"W1542275956","doi":"10.1007/978-3-540-44999-7_4","title":"Weather Forecasting Models Using Ensembles of Neural Networks","year":2003,"lang":"en","type":"book-chapter","venue":"Intelligent Systems Design and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Artificial neural network; Wind speed; Multilayer perceptron; Meteorology; Perceptron; Computer science; Radial basis function network; Weather forecasting; Radial basis function; Artificial intelligence; Environmental science; Machine learning; Geography","score_opus":0.1172280951312277,"score_gpt":0.2600673484098273,"score_spread":0.14283925327859961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542275956","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07979666,0.0028987445,0.90878075,0.00039273774,0.00046484478,0.000032493714,0.00023147246,0.00096900493,0.006433213],"genre_scores_gemma":[0.8233313,0.0018120162,0.16744034,0.0001093116,0.0003587007,0.000097261494,0.00048950786,0.00014709511,0.006214513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997019,0.000081711165,0.000025610123,0.00006577133,0.00008866695,0.00003630447],"domain_scores_gemma":[0.9991535,0.0004059028,0.0000804243,0.000104414205,0.00022375224,0.000031929496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094505027,0.00072454434,0.0011700997,0.0005415969,0.0003790108,0.0011292734,0.0011799425,0.00087130093,0.0012490347],"category_scores_gemma":[0.0031051666,0.00078193407,0.00082338066,0.0009388299,0.00027419784,0.002256477,0.00073773187,0.0017952073,0.00046022586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046086654,0.000029900115,0.0008194977,0.00002081149,0.000110443485,0.00002160751,0.000022966857,0.9312764,0.0006359212,0.002491522,0.0011472641,0.063377544],"study_design_scores_gemma":[0.0000013986993,0.0000038835233,0.000098823475,0.0000023516604,0.000007644402,0.0000034325292,0.0000013811705,0.9982004,0.000121068,0.0014313429,0.00012567465,0.0000024696621],"about_ca_topic_score_codex":0.007970353,"about_ca_topic_score_gemma":0.011322232,"teacher_disagreement_score":0.007970353,"about_ca_system_score_codex":0.0006171059,"about_ca_system_score_gemma":0.00040046277,"threshold_uncertainty_score":0.015847921},"labels":[],"label_agreement":null},{"id":"W1543614656","doi":"10.1016/s0079-6123(06)65034-6","title":"To recognize shapes, first learn to generate images","year":2007,"lang":"en","type":"review","venue":"Progress in brain research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":306,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Sensory system; Architecture; Cortex (anatomy); Machine learning; Pattern recognition (psychology); Neuroscience; Psychology; Geography","score_opus":0.27497692840460114,"score_gpt":0.504336092142962,"score_spread":0.2293591637383609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543614656","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024072304,0.9159406,0.059642095,0.0029302924,0.0009734702,0.00005921634,0.000119041724,0.0002448707,0.01768322],"genre_scores_gemma":[0.008590794,0.95884424,0.022789415,0.0008239769,0.00051558926,0.000058628553,0.00016022233,0.000029324863,0.0081877755],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99988484,0.00001272187,0.000009239562,0.000028383121,0.000057341498,0.000007397665],"domain_scores_gemma":[0.9996722,0.00015193092,0.000030060111,0.000022726555,0.00011006463,0.000013085609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035854927,0.0011616013,0.0008287422,0.0008291412,0.0001845986,0.0007437164,0.001297887,0.0011217395,0.002483812],"category_scores_gemma":[0.00088622444,0.0002752589,0.0002718968,0.0010964067,0.00081842707,0.001454628,0.00053794123,0.0011279254,0.003032538],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019352972,0.0000393482,0.00018668252,0.0020341075,0.000024406028,0.000032821143,0.000014793689,0.00074428687,0.0019570403,0.0053594094,0.008588489,0.98099923],"study_design_scores_gemma":[0.000020005358,0.0001333349,0.0012982644,0.0015256476,0.00009693921,0.0014301385,0.000072074356,0.004420097,0.0116926115,0.029976984,0.9492918,0.00004200293],"about_ca_topic_score_codex":0.0022226148,"about_ca_topic_score_gemma":0.0040225736,"teacher_disagreement_score":0.002483812,"about_ca_system_score_codex":0.00056910963,"about_ca_system_score_gemma":0.0010844391,"threshold_uncertainty_score":0.0083091855},"labels":[],"label_agreement":null},{"id":"W1543809054","doi":"10.1109/scs.2003.1227109","title":"Parametric mapping of neural networks to fine-grained FPGAs","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Field-programmable gate array; Artificial neural network; Computer science; Multiplier (economics); Parametric statistics; Realization (probability); Implementation; Computer architecture; Computer engineering; Computer hardware; Embedded system; Artificial intelligence; Mathematics","score_opus":0.023291004130975387,"score_gpt":0.2439525504294602,"score_spread":0.22066154629848483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543809054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15017733,0.0003005345,0.8356375,0.0001689335,0.00009166371,0.000080172234,0.00006342974,0.0013872873,0.012093149],"genre_scores_gemma":[0.76908976,0.00020171153,0.22802429,0.000041069034,0.000015876178,0.00009702057,0.00005793126,0.00010124311,0.0023710998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998665,0.00003485604,0.000008578461,0.000022337283,0.000049115853,0.00001862726],"domain_scores_gemma":[0.9997634,0.000094460986,0.000029833229,0.0000786937,0.000026328737,0.00000724465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018260968,0.00029900883,0.0001982424,0.00019440918,0.000158453,0.00041260553,0.0005467195,0.00022187667,0.0021269761],"category_scores_gemma":[0.0009512378,0.00018793214,0.00019040715,0.00015814521,0.00030982762,0.00046523812,0.00037005078,0.00048330863,0.00021539665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013690452,0.00006828193,0.00063148385,0.00022481565,0.00004102896,0.0002674048,0.00013607874,0.5542005,0.21124543,0.05855678,0.001310463,0.17318082],"study_design_scores_gemma":[0.000026111251,0.00018321791,0.0006675476,0.000018806833,0.000014563999,0.00017861031,0.000037626996,0.877893,0.08539589,0.022703694,0.012863072,0.0000179225],"about_ca_topic_score_codex":0.00047350035,"about_ca_topic_score_gemma":0.0008286479,"teacher_disagreement_score":0.0021269761,"about_ca_system_score_codex":0.00035284404,"about_ca_system_score_gemma":0.00028368286,"threshold_uncertainty_score":0.0071154237},"labels":[],"label_agreement":null},{"id":"W1544532352","doi":"10.48550/arxiv.1301.3583","title":"Big Neural Networks Waste Capacity","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Leverage (statistics); Artificial neural network; Deep neural networks; Computer science; Parametrization (atmospheric modeling); Generalization; Gradient descent; Artificial intelligence; Generalization error; Mathematical optimization; Mathematics","score_opus":0.08645081458260345,"score_gpt":0.17399409022816079,"score_spread":0.08754327564555733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1544532352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13981798,0.009434296,0.78186226,0.026416546,0.001802701,0.00013370211,0.0011904108,0.0051075183,0.034234617],"genre_scores_gemma":[0.9085359,0.0032002106,0.069383286,0.0044333087,0.0010781224,0.00022065979,0.00063927827,0.0011559353,0.011353351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977119,0.0008453096,0.00015938326,0.00047940106,0.000544114,0.00025989028],"domain_scores_gemma":[0.9845169,0.00928683,0.00060656865,0.0038859635,0.0011514805,0.00055228145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006450464,0.0017074643,0.0019261508,0.0012065431,0.00095649937,0.0026371202,0.0026108203,0.002023214,0.0059465575],"category_scores_gemma":[0.038605515,0.0010173343,0.0012380991,0.0011831783,0.003859484,0.008987527,0.0046469425,0.0051788283,0.001645047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067745155,0.00016427191,0.0036202176,0.00062658533,0.00038571577,0.00047424217,0.00035758846,0.52725303,0.005751305,0.23081699,0.02700778,0.20286487],"study_design_scores_gemma":[0.00005684269,0.00015755431,0.0007782526,0.0001527953,0.00007501418,0.00018744881,0.000050078797,0.5769337,0.0043499777,0.4067823,0.010433555,0.00004255502],"about_ca_topic_score_codex":0.002111416,"about_ca_topic_score_gemma":0.0024245128,"teacher_disagreement_score":0.006450464,"about_ca_system_score_codex":0.0016317896,"about_ca_system_score_gemma":0.0012036092,"threshold_uncertainty_score":0.034113705},"labels":[],"label_agreement":null},{"id":"W1544720864","doi":"10.1109/iembs.1990.691822","title":"Image Processing With Artificial Neural Nets","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Image processing; Computer vision; Image (mathematics)","score_opus":0.016245729353936984,"score_gpt":0.25250144459960694,"score_spread":0.23625571524566996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1544720864","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028735653,0.0022765202,0.9861275,0.00025337742,0.00027200233,0.00004194904,0.00009113057,0.0012557503,0.006808199],"genre_scores_gemma":[0.15225041,0.0044211154,0.82272327,0.00028861203,0.00038884912,0.00016583745,0.0003946027,0.0002770645,0.019090313],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997595,0.00005397691,0.000016298472,0.00004210386,0.00011163943,0.000016386233],"domain_scores_gemma":[0.9996654,0.0001338519,0.00003092424,0.000070041584,0.00008907408,0.000010837998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040918324,0.0005171455,0.00059484184,0.0006244096,0.00019937275,0.0011201458,0.0007069811,0.00073301594,0.0076515195],"category_scores_gemma":[0.0019055896,0.00036839186,0.00042620502,0.0009511518,0.00036688408,0.0011729386,0.0008793603,0.00094281905,0.0026950534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019080412,0.00006258151,0.0002802441,0.0005257374,0.00010203078,0.00011093593,0.000042617703,0.1388023,0.02084572,0.041872546,0.012198416,0.78496605],"study_design_scores_gemma":[0.000014811852,0.000028843368,0.00023916177,0.000050736526,0.000026731885,0.00008383791,0.000010245927,0.9339837,0.014276687,0.0324413,0.018826606,0.000017326869],"about_ca_topic_score_codex":0.0011376739,"about_ca_topic_score_gemma":0.0012954837,"teacher_disagreement_score":0.0076515195,"about_ca_system_score_codex":0.00027234774,"about_ca_system_score_gemma":0.00031764383,"threshold_uncertainty_score":0.025596857},"labels":[],"label_agreement":null},{"id":"W1546722179","doi":"10.1002/0471427950.ch6","title":"Radial Basis Function Neural Networks","year":2003,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Radial basis function; Artificial neural network; Basis (linear algebra); Radial basis function network; Gaussian; Artificial intelligence; Function (biology); Computer science; Gaussian function; Machine learning; Mathematics; Physics; Geometry","score_opus":0.010975018052496854,"score_gpt":0.2138770686172793,"score_spread":0.20290205056478244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546722179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020303568,0.011029541,0.92493904,0.0014431807,0.0010271934,0.00008327852,0.0003765936,0.0019960864,0.05707481],"genre_scores_gemma":[0.15221602,0.026910586,0.5953186,0.001081711,0.0018218283,0.00033620876,0.0024194417,0.0010917837,0.21880388],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998863,0.00025949156,0.00005761809,0.0002591965,0.00049225986,0.00006844236],"domain_scores_gemma":[0.99896705,0.00019129425,0.00007924085,0.00015755827,0.00056767114,0.00003718519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010401634,0.0013637292,0.0015760756,0.00093595195,0.00062750344,0.0026539492,0.001872339,0.002328581,0.019540126],"category_scores_gemma":[0.0043174974,0.0004436509,0.0007447869,0.0023270084,0.0007293761,0.0026970138,0.0013280357,0.001909409,0.020915562],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010746632,0.000056731966,0.00042367494,0.00055304344,0.00006198416,0.000117057905,0.000060020873,0.063509576,0.0035317966,0.1608574,0.0747669,0.6959544],"study_design_scores_gemma":[0.000029905856,0.000055257344,0.0007661527,0.00022761628,0.000049447885,0.00032448387,0.00005752045,0.573958,0.0067138798,0.16006549,0.25766644,0.00008584558],"about_ca_topic_score_codex":0.0034357228,"about_ca_topic_score_gemma":0.0022988871,"teacher_disagreement_score":0.019540126,"about_ca_system_score_codex":0.0008408112,"about_ca_system_score_gemma":0.0010505234,"threshold_uncertainty_score":0.065368235},"labels":[],"label_agreement":null},{"id":"W1548154052","doi":"10.1007/3-540-45486-1_24","title":"Accelerated Backpropagation Learning: Extended Dynamic Parallel Tangent Optimization Algorithm","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Backpropagation; Computer science; Algorithm; Artificial neural network; Convergence (economics); Gradient descent; Rate of convergence; Acceleration; Artificial intelligence","score_opus":0.015339068690620898,"score_gpt":0.24793504692690832,"score_spread":0.23259597823628742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548154052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068235546,0.0003163711,0.9850065,0.00013879348,0.00020820074,0.000057331865,0.000100131394,0.0032288753,0.004120203],"genre_scores_gemma":[0.11571598,0.00040969902,0.8640327,0.00012577859,0.00015513966,0.00023733525,0.00041563142,0.0009070678,0.018000795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994962,0.0000917862,0.000024284973,0.000088974455,0.00025936455,0.000039334962],"domain_scores_gemma":[0.99938726,0.00010606894,0.000034990502,0.00012694462,0.00031534728,0.00002930779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076853676,0.0008902403,0.0010485837,0.00063462305,0.0004938521,0.00093140075,0.0022064731,0.0010958372,0.008210676],"category_scores_gemma":[0.0017992886,0.0005217974,0.0005438661,0.0013185098,0.00037795503,0.0015378651,0.0012922179,0.0013889743,0.0035435706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023693833,0.0001559164,0.00042076074,0.00013582161,0.00009007525,0.00009905433,0.00005810722,0.34109542,0.0074634566,0.017402576,0.02161071,0.6112312],"study_design_scores_gemma":[0.000016831951,0.000015530652,0.00006946576,0.0000039699867,0.000008541691,0.000029609308,0.000002808208,0.9919803,0.0016263857,0.0029824653,0.0032578288,0.0000062127906],"about_ca_topic_score_codex":0.0059070946,"about_ca_topic_score_gemma":0.0070987977,"teacher_disagreement_score":0.008210676,"about_ca_system_score_codex":0.00057475275,"about_ca_system_score_gemma":0.0016400542,"threshold_uncertainty_score":0.02746749},"labels":[],"label_agreement":null},{"id":"W1548749311","doi":"","title":"On the correlation between genotype and classifier diversity","year":2012,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Classifier (UML); Artificial intelligence; Random subspace method; Correlation; Artificial neural network; Machine learning; Computer science; Pattern recognition (psychology); Hyperparameter; Ensemble learning; Cascading classifiers; Data mining; Mathematics","score_opus":0.030507257176840448,"score_gpt":0.2420673031375726,"score_spread":0.21156004596073216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548749311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70489806,0.0014499191,0.28830844,0.00051137956,0.00007419341,0.00005547093,0.00034004735,0.00022283354,0.0041397377],"genre_scores_gemma":[0.9863826,0.0002115272,0.012430538,0.00007230415,0.000083046994,0.00003451806,0.00032690595,0.000060331433,0.00039825658],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9872859,0.005770433,0.00053211633,0.0026210274,0.003247403,0.0005431817],"domain_scores_gemma":[0.7068919,0.26034468,0.011614333,0.010866776,0.008873329,0.00140902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014004476,0.00084042037,0.0014611308,0.0026927872,0.0007107672,0.0021743702,0.00082506175,0.0015347762,0.0008348906],"category_scores_gemma":[0.10840834,0.0004874188,0.000514491,0.0020661661,0.002393355,0.003725126,0.0021081765,0.0015948457,0.00027377074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095319276,0.00020701434,0.30049217,0.0001765421,0.0009478287,0.00069539394,0.00068722287,0.5438951,0.010380972,0.018477349,0.0012604543,0.121826716],"study_design_scores_gemma":[0.00003346158,0.0005538527,0.09358903,0.00005576273,0.00018097305,0.00091914105,0.00020605048,0.8613704,0.0070502544,0.03514503,0.0007518484,0.00014424288],"about_ca_topic_score_codex":0.0010442288,"about_ca_topic_score_gemma":0.0010146676,"teacher_disagreement_score":0.014004476,"about_ca_system_score_codex":0.0009950764,"about_ca_system_score_gemma":0.0005829358,"threshold_uncertainty_score":0.07406366},"labels":[],"label_agreement":null},{"id":"W1550780726","doi":"10.1109/ijcnn.2005.1556016","title":"Evaluation of cluster combination functions for mixture of experts","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education, India; Ministry of Earth Sciences","keywords":"Computer science; Modular design; Artificial neural network; Artificial intelligence; Cluster (spacecraft); Basis (linear algebra); Data mining; Machine learning; Architecture; Regression; Data modeling; Software engineering; Mathematics; Statistics; Computer network","score_opus":0.0579647939723289,"score_gpt":0.29768240147837716,"score_spread":0.23971760750604826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550780726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07733246,0.0017514687,0.91391885,0.00054339424,0.00017308134,0.00034960924,0.0002868398,0.0019280787,0.0037162246],"genre_scores_gemma":[0.66761667,0.00046765473,0.32611555,0.00022089528,0.00013123683,0.0003884994,0.0014510967,0.0006729788,0.002935467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99199355,0.0042810813,0.00035860782,0.0011292843,0.0016992893,0.0005382196],"domain_scores_gemma":[0.9772954,0.012665564,0.00093924947,0.001825611,0.006294926,0.0009793021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022889946,0.0027342478,0.0030205243,0.0045115957,0.0015359969,0.0032767775,0.002987582,0.0039514285,0.0033645253],"category_scores_gemma":[0.048146166,0.0009564684,0.0020171488,0.002350018,0.0013342001,0.0029122143,0.0034916757,0.002308758,0.0011590525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025139991,0.00022168588,0.004894666,0.00030023276,0.00071640563,0.00008165213,0.0002341347,0.70793146,0.0018159113,0.008343673,0.00711386,0.26583242],"study_design_scores_gemma":[0.00002433214,0.000071804054,0.00048755584,0.000019108189,0.000035606452,0.000019029863,0.00003730051,0.9959752,0.0009026264,0.0020844273,0.00032958688,0.000013392004],"about_ca_topic_score_codex":0.007206956,"about_ca_topic_score_gemma":0.004828952,"teacher_disagreement_score":0.022889946,"about_ca_system_score_codex":0.0034069181,"about_ca_system_score_gemma":0.0020220187,"threshold_uncertainty_score":0.12105501},"labels":[],"label_agreement":null},{"id":"W1551448255","doi":"10.1109/iscas.1995.520383","title":"Design of 2-D FIR filters by feedback neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Finite impulse response; Very-large-scale integration; Artificial neural network; Computer science; Filter (signal processing); Network synthesis filters; Hopfield network; Electronic engineering; Control theory (sociology); Algorithm; Artificial intelligence; Engineering; Embedded system","score_opus":0.027395097535676417,"score_gpt":0.21530416961953733,"score_spread":0.1879090720838609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551448255","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016566092,0.00031178864,0.9782016,0.00007487444,0.00006682266,0.000057209618,0.00004043953,0.00046203128,0.004219061],"genre_scores_gemma":[0.5858011,0.00056053133,0.4078228,0.00020220826,0.00005416425,0.00028540814,0.000093056915,0.000047118712,0.00513361],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998698,0.000020362966,0.000009649389,0.000029670126,0.000050916762,0.00001962387],"domain_scores_gemma":[0.9999101,0.000031154585,0.000016053073,0.000007162446,0.000029128854,0.0000064352953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000254091,0.00048406262,0.00028153975,0.00024980085,0.00025525258,0.00041508224,0.0005081747,0.00069605187,0.0015159594],"category_scores_gemma":[0.0005114524,0.00026537676,0.00029303125,0.00021336936,0.00020183266,0.00038096492,0.00025038698,0.00025674267,0.00037394068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017861901,0.00007364045,0.0006385287,0.00024841283,0.00006517106,0.00024030889,0.00014912168,0.56675845,0.13306428,0.01748735,0.0017331357,0.2793629],"study_design_scores_gemma":[0.000026608512,0.00012501757,0.00028917403,0.000020431691,0.000018557763,0.00008326663,0.00000876157,0.97864634,0.013757167,0.002959726,0.004048897,0.000016084101],"about_ca_topic_score_codex":0.0018397998,"about_ca_topic_score_gemma":0.004079835,"teacher_disagreement_score":0.0018397998,"about_ca_system_score_codex":0.0004327709,"about_ca_system_score_gemma":0.0004322821,"threshold_uncertainty_score":0.0050714016},"labels":[],"label_agreement":null},{"id":"W1551529421","doi":"","title":"Performing Complex Associations Using a Feature-Extracting Bidirectional Associative Memory","year":2010,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Hebbian theory; Computer science; Bidirectional associative memory; Content-addressable memory; Associative property; Artificial intelligence; Feature (linguistics); Machine learning; Cognition; Artificial neural network; Theoretical computer science; Mathematics","score_opus":0.12968765831148438,"score_gpt":0.4128740794142278,"score_spread":0.28318642110274345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551529421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10178454,0.00015412818,0.889501,0.00027643796,0.000089498746,0.000060296425,0.00007940803,0.0014299438,0.0066247117],"genre_scores_gemma":[0.80007935,0.00015599464,0.19292183,0.00011149305,0.000033634242,0.00010997302,0.00014259771,0.00006208234,0.006383117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981374,0.000045323988,0.000013123483,0.000058558362,0.000035421814,0.00003392985],"domain_scores_gemma":[0.9994466,0.00017575592,0.000059528917,0.00014907394,0.00011227908,0.000056736408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006684889,0.0004596011,0.00055813405,0.00025211438,0.00043449504,0.00065604167,0.001427103,0.0007908481,0.0027748183],"category_scores_gemma":[0.0014836052,0.00033723176,0.0004614501,0.00037322022,0.00066027977,0.0015172651,0.0012605422,0.0009637603,0.0009435914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062803784,0.0004015446,0.0035600115,0.00022890256,0.00018231342,0.00029917937,0.000263922,0.38859978,0.12907028,0.046510812,0.004508191,0.425747],"study_design_scores_gemma":[0.000016979124,0.00012531369,0.0004084816,0.000011128026,0.000021911515,0.00008996753,0.000021090756,0.9658582,0.019453185,0.012458609,0.0015162256,0.000018899034],"about_ca_topic_score_codex":0.0015047,"about_ca_topic_score_gemma":0.0019919397,"teacher_disagreement_score":0.0027748183,"about_ca_system_score_codex":0.00033315932,"about_ca_system_score_gemma":0.0005918051,"threshold_uncertainty_score":0.009282649},"labels":[],"label_agreement":null},{"id":"W1554612648","doi":"10.1007/3-540-45720-8_38","title":"Bistable Gradient Neural Networks: Their Computational Properties","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bistability; Spurious relationship; Computer science; Artificial neural network; Attractor; Hopfield network; Coupling (piping); Topology (electrical circuits); Artificial intelligence; Physics; Mathematics; Machine learning; Materials science","score_opus":0.024828242152620868,"score_gpt":0.22467505415401337,"score_spread":0.1998468120013925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1554612648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27107552,0.004883326,0.6741231,0.0018762472,0.00022599821,0.00006786874,0.00044856302,0.000835504,0.0464638],"genre_scores_gemma":[0.94391125,0.0014746649,0.04502339,0.00024469462,0.00018954773,0.00009425779,0.00016768563,0.0001477485,0.008746709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999224,0.000019185618,0.0000034730813,0.000017843391,0.000026369464,0.000010770505],"domain_scores_gemma":[0.9987525,0.00088411715,0.00012906069,0.000089728266,0.00008866616,0.000055897744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003030205,0.00027849575,0.00043476996,0.00041573687,0.00030867927,0.0008662414,0.0006133596,0.00082306453,0.0036385045],"category_scores_gemma":[0.003931725,0.00027233709,0.00019061331,0.0005273364,0.0007739423,0.0015578573,0.0005300289,0.0008296252,0.00032556182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009594515,0.00003069892,0.00083571236,0.00012952751,0.000020036136,0.00009397301,0.00009506423,0.073459126,0.00765579,0.8580742,0.003164524,0.056345332],"study_design_scores_gemma":[0.000016621425,0.0000222665,0.00059435325,0.00001533997,0.0000085763095,0.00012252464,0.000015215312,0.54483145,0.0015591023,0.4516189,0.0011833091,0.000012351735],"about_ca_topic_score_codex":0.000543905,"about_ca_topic_score_gemma":0.00071772636,"teacher_disagreement_score":0.0036385045,"about_ca_system_score_codex":0.00036788263,"about_ca_system_score_gemma":0.00023698862,"threshold_uncertainty_score":0.012172043},"labels":[],"label_agreement":null},{"id":"W1555411907","doi":"10.3233/kes-2007-11601","title":"Time series pattern recognition using s-transform and soft computing","year":2008,"lang":"en","type":"article","venue":"International Journal of Knowledge-based and Intelligent Engineering Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Series (stratigraphy); Soft computing; Computer science; Pattern recognition (psychology); Artificial intelligence; Speech recognition; Artificial neural network; Geology","score_opus":0.02231817609438435,"score_gpt":0.24012579762969302,"score_spread":0.21780762153530867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1555411907","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0094002355,0.00034959384,0.9875473,0.0001241179,0.00009683603,0.00002680613,0.000034208024,0.00046290673,0.001957911],"genre_scores_gemma":[0.2763488,0.0009711666,0.7178665,0.00018337424,0.00022928124,0.00009682802,0.00015260378,0.00011158323,0.00403988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995746,0.00006758906,0.000049862057,0.00008067086,0.0002075168,0.000019796302],"domain_scores_gemma":[0.9994997,0.00023991842,0.00006773686,0.00006426066,0.00011191931,0.00001643087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043557328,0.00044317349,0.00083204894,0.0010120802,0.00019000798,0.000956663,0.0004964569,0.0005493904,0.0018585975],"category_scores_gemma":[0.001202784,0.00020213425,0.00083904766,0.0017312403,0.00050866127,0.0012132499,0.00053441495,0.00056543574,0.000726562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014270423,0.00012561613,0.0013193077,0.00035120978,0.00015718739,0.00022911472,0.00008499596,0.056161884,0.06324792,0.035967745,0.001944101,0.84026825],"study_design_scores_gemma":[0.000015879714,0.00014263787,0.0013564503,0.00003998019,0.00004862794,0.0002824855,0.000038764618,0.9416091,0.02813363,0.022630744,0.005669946,0.000031762618],"about_ca_topic_score_codex":0.0006173636,"about_ca_topic_score_gemma":0.0006332805,"teacher_disagreement_score":0.0018585975,"about_ca_system_score_codex":0.0002426413,"about_ca_system_score_gemma":0.00028074367,"threshold_uncertainty_score":0.006217599},"labels":[],"label_agreement":null},{"id":"W1556380390","doi":"","title":"Advanced signal processing technologies for intelligent hearing aids","year":2005,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Digital signal processing; Computer science; Signal processing; Application-specific integrated circuit; Digital signal processor; Embedded system; Hearing aid; Software; Computer hardware; Adaptation (eye); Computer architecture; Engineering; Electrical engineering","score_opus":0.022545701950406984,"score_gpt":0.25950317543392304,"score_spread":0.23695747348351606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1556380390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007869618,0.08803621,0.75963646,0.004728823,0.004354755,0.00023987201,0.00034002794,0.002843992,0.13195018],"genre_scores_gemma":[0.10468719,0.080321945,0.640268,0.0033182467,0.002543135,0.00047085664,0.0011334915,0.00032615894,0.16693094],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962676,0.00006136828,0.000025563959,0.000042766038,0.00021527083,0.000028245848],"domain_scores_gemma":[0.99960417,0.00007401893,0.000023231725,0.000039590926,0.00023395086,0.000025113233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004738963,0.00074943394,0.00034472137,0.00075517956,0.0003249144,0.0012031399,0.00065797556,0.0012193559,0.018701179],"category_scores_gemma":[0.00085944415,0.00020218476,0.00030980754,0.0006327351,0.00030072528,0.0012248987,0.00055740227,0.0012650051,0.010506015],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016677794,0.00008070609,0.00035696564,0.0009565015,0.00003795012,0.0005946305,0.00016271345,0.0031287689,0.09806809,0.09249197,0.048450243,0.7555047],"study_design_scores_gemma":[0.000043275766,0.00025681636,0.0006451611,0.0004219615,0.000052121,0.0020231914,0.00009375242,0.018428707,0.031455666,0.043873608,0.9026425,0.00006323287],"about_ca_topic_score_codex":0.00039454532,"about_ca_topic_score_gemma":0.0005287492,"teacher_disagreement_score":0.018701179,"about_ca_system_score_codex":0.00036594202,"about_ca_system_score_gemma":0.000555643,"threshold_uncertainty_score":0.06256163},"labels":[],"label_agreement":null},{"id":"W1556738854","doi":"","title":"Processing of Speech and Non-Speech Tonal Information by Native and Nonnative Tone Language Speakers: an Event-Related Electrophysiological Study","year":2010,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tone (literature); Speech recognition; Computer science; Event (particle physics); Speech processing; Linguistics; Physics; Astrophysics","score_opus":0.004696560725624769,"score_gpt":0.2521741476048757,"score_spread":0.24747758687925095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1556738854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99899036,0.000060535327,0.00022862903,0.000014247878,0.0000061485775,0.000015377125,0.0000698334,0.0000020790562,0.0006127607],"genre_scores_gemma":[0.9982206,0.00012981471,0.00040150285,0.000057074885,0.000013844168,0.00002941176,0.00014181757,0.0000060085845,0.0009998992],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.999907,0.000020981543,0.0000072576,0.000026407712,0.00001664602,0.000021682385],"domain_scores_gemma":[0.9995591,0.00026112143,0.000037208258,0.000027576698,0.000056611992,0.000058397687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028055126,0.00017423007,0.00018900873,0.00014470652,0.00026208386,0.00051366433,0.00016559039,0.00028572,0.002693666],"category_scores_gemma":[0.0014536572,0.00012788875,0.0001781386,0.0001368748,0.0004470639,0.00047653995,0.00032682816,0.00033686496,0.00026569323],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007480879,0.0008831968,0.04732113,0.00024270036,0.00012965516,0.0024157683,0.0048043337,0.0001756221,0.90211,0.0007042949,0.00028438724,0.033447895],"study_design_scores_gemma":[0.000206107,0.0018728361,0.9598451,0.000012113706,0.00010469317,0.0034844284,0.0035768035,0.0008818038,0.028327826,0.00063807366,0.001026884,0.000023257686],"about_ca_topic_score_codex":0.0015194844,"about_ca_topic_score_gemma":0.0025921515,"teacher_disagreement_score":0.002693666,"about_ca_system_score_codex":0.00008183181,"about_ca_system_score_gemma":0.00021336287,"threshold_uncertainty_score":0.009011149},"labels":[],"label_agreement":null},{"id":"W1557693712","doi":"10.1007/978-3-540-30499-9_23","title":"An Efficient Two-Level SOMART Document Clustering Through Dimensionality Reduction","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Computer science; Dimensionality reduction; Artificial intelligence; Adaptive resonance theory; Pattern recognition (psychology); Clustering high-dimensional data; Artificial neural network; Data mining; Brown clustering; Hierarchical clustering; Reduction (mathematics); Entropy (arrow of time); Dimension (graph theory); Document clustering; Correlation clustering; Curse of dimensionality; Canopy clustering algorithm; Mathematics","score_opus":0.02858114946698372,"score_gpt":0.28656072384581666,"score_spread":0.25797957437883295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557693712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008584171,0.00056251977,0.982605,0.00014493096,0.00018857224,0.00014523396,0.0005092863,0.005442344,0.001817995],"genre_scores_gemma":[0.032197043,0.0002674308,0.95676136,0.000105550505,0.000078526784,0.00016501045,0.0021055916,0.00025675804,0.008062756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828535,0.00023009868,0.00015554267,0.0003382257,0.0008427085,0.00014810891],"domain_scores_gemma":[0.9991761,0.00013273614,0.000035592682,0.00024932524,0.00035565082,0.000050541596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006591622,0.001027154,0.0025341758,0.0021325569,0.0015041169,0.0024377913,0.0024760317,0.0012984718,0.006823619],"category_scores_gemma":[0.0017242851,0.0006321372,0.0023448775,0.0031918911,0.00047028522,0.0018909366,0.0028064516,0.0013613201,0.008940409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022897033,0.00017921135,0.00034965487,0.00018823722,0.000118218995,0.00008829513,0.00011951789,0.009724282,0.031738676,0.0046532876,0.016221061,0.93639064],"study_design_scores_gemma":[0.000114343464,0.00041968306,0.0020057773,0.000043849745,0.00018393176,0.000995976,0.00021672188,0.89171135,0.057244275,0.014544645,0.032387026,0.00013239973],"about_ca_topic_score_codex":0.0047104596,"about_ca_topic_score_gemma":0.008507082,"teacher_disagreement_score":0.006823619,"about_ca_system_score_codex":0.00064012874,"about_ca_system_score_gemma":0.0021084875,"threshold_uncertainty_score":0.022827327},"labels":[],"label_agreement":null},{"id":"W1558201422","doi":"10.1109/ijcnn.1999.833520","title":"Learning decision fusion in cooperative modular neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Modular design; Modular neural network; Computer science; Artificial neural network; Artificial intelligence; Fusion; Machine learning; Mechanism (biology); Time delay neural network","score_opus":0.01121965192554719,"score_gpt":0.2433157136925257,"score_spread":0.2320960617669785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558201422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050238453,0.00033376095,0.94736487,0.00019345804,0.000052427633,0.00004856473,0.000026926708,0.00025300984,0.0014885665],"genre_scores_gemma":[0.89682436,0.0001885209,0.101298094,0.0001421933,0.000065415,0.0001082694,0.000071323375,0.000026051028,0.0012757059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982755,0.00069706206,0.000111042405,0.00032049505,0.00039488354,0.00020108237],"domain_scores_gemma":[0.9963581,0.0021882865,0.00039968122,0.00030650324,0.00062299165,0.00012440835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005056591,0.00089157006,0.001161796,0.0007308658,0.0005648152,0.0010300733,0.0015623523,0.0013161823,0.0012538023],"category_scores_gemma":[0.010766935,0.00044875985,0.0006811629,0.0008425323,0.0011662568,0.0021667692,0.0022268943,0.001225599,0.0002984981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032198118,0.00010384451,0.0014963375,0.00012222496,0.00012785304,0.00015986052,0.00023450781,0.7605192,0.0052995994,0.03904889,0.0011609682,0.19140474],"study_design_scores_gemma":[0.000012324414,0.00003918054,0.00011625574,0.000005848859,0.000013135471,0.000015476511,0.000007468786,0.98053956,0.0012928767,0.017721431,0.0002290632,0.0000074171676],"about_ca_topic_score_codex":0.0016062481,"about_ca_topic_score_gemma":0.001211663,"teacher_disagreement_score":0.005056591,"about_ca_system_score_codex":0.0009377594,"about_ca_system_score_gemma":0.0007345396,"threshold_uncertainty_score":0.0267421},"labels":[],"label_agreement":null},{"id":"W1558908107","doi":"10.1109/mwscas.1989.101897","title":"VLSI implementations of neural-like networks for finite ring computations","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Very-large-scale integration; Computer science; Modulo; Artificial neural network; Adder; Latency (audio); Parallel computing; Throughput; Ring (chemistry); Implementation; Computer architecture; Theoretical computer science; Computer engineering; Embedded system; Artificial intelligence; Mathematics; Discrete mathematics; Programming language","score_opus":0.032096586963052176,"score_gpt":0.2986980344998913,"score_spread":0.2666014475368391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558908107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039547067,0.00056028966,0.94250864,0.00021951942,0.00016363456,0.00006936451,0.00010209409,0.002093416,0.014735842],"genre_scores_gemma":[0.4327377,0.00046741316,0.5593796,0.00012839516,0.00006396422,0.000110115216,0.00016373185,0.00005257024,0.0068965675],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998784,0.00002838337,0.000012602254,0.000019219771,0.0000445888,0.000016800594],"domain_scores_gemma":[0.99986637,0.000039401875,0.000015486576,0.000029624503,0.000042121057,0.0000070189703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024696076,0.00020813696,0.00015837127,0.00026293882,0.00023290259,0.0005594109,0.0007465663,0.00031906302,0.003925116],"category_scores_gemma":[0.0007127685,0.00012429088,0.00022583711,0.0002738582,0.0002256845,0.0006519321,0.00025234258,0.00031911838,0.00080454076],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002618859,0.000113013055,0.0006815885,0.0004074065,0.00007051158,0.00029496872,0.00016906495,0.17786248,0.11429573,0.21318983,0.0068406565,0.48581296],"study_design_scores_gemma":[0.0000486747,0.00027077124,0.00039191818,0.000045689558,0.000049987724,0.0003210486,0.000042158965,0.8696898,0.06785171,0.032402497,0.028853478,0.00003225179],"about_ca_topic_score_codex":0.0007519306,"about_ca_topic_score_gemma":0.0018865849,"teacher_disagreement_score":0.003925116,"about_ca_system_score_codex":0.00044283035,"about_ca_system_score_gemma":0.00042349802,"threshold_uncertainty_score":0.013130844},"labels":[],"label_agreement":null},{"id":"W1558917119","doi":"10.1109/icnn.1995.487332","title":"Voting schemes for cooperative neural network classifiers","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Voting; Computer science; Ballot; Modular design; Representation (politics); Artificial neural network; Artificial intelligence; Majority rule; Machine learning; Power (physics); Weighted voting; Modular neural network; Data mining; Time delay neural network","score_opus":0.04728089764715796,"score_gpt":0.26106578039626627,"score_spread":0.2137848827491083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558917119","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03442313,0.00031827195,0.9590761,0.0002466252,0.00006778172,0.00016069772,0.00004057348,0.00020013732,0.0054666777],"genre_scores_gemma":[0.8124645,0.00022683117,0.17870535,0.00015684424,0.00008944952,0.00046218804,0.00016154254,0.00004981033,0.0076834452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99661463,0.0015689888,0.00023604771,0.0005568782,0.00071360293,0.0003099411],"domain_scores_gemma":[0.9927861,0.0038442267,0.0005673967,0.0012680561,0.001312612,0.00022162963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007868011,0.0007337889,0.0009811294,0.0010436798,0.001022694,0.0015432503,0.0033566775,0.0022650342,0.004189125],"category_scores_gemma":[0.018956076,0.00048587268,0.00080373965,0.0009741933,0.0014750219,0.0030677083,0.0023283926,0.0012233328,0.00088858814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005115032,0.00012645815,0.0012438326,0.00018000543,0.00008039113,0.00013440054,0.00040171266,0.5038417,0.005792115,0.30835858,0.0029567787,0.1763725],"study_design_scores_gemma":[0.00004162876,0.00006232154,0.00012404239,0.000015441734,0.000017947781,0.000031451666,0.00002098508,0.93779045,0.001371068,0.05918392,0.0013294911,0.000011111811],"about_ca_topic_score_codex":0.0011073621,"about_ca_topic_score_gemma":0.0013278604,"teacher_disagreement_score":0.007868011,"about_ca_system_score_codex":0.0016578214,"about_ca_system_score_gemma":0.0007857423,"threshold_uncertainty_score":0.04161048},"labels":[],"label_agreement":null},{"id":"W1559132467","doi":"10.1109/iscas.2015.7168700","title":"Hyperbolic tangent passive resistive-type neuron","year":2015,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hyperbolic function; Tangent; Artificial neural network; Standby power; Voltage; Neuron; Biological neuron model; Resistive touchscreen; CMOS; Function (biology); Computer science; Topology (electrical circuits); Control theory (sociology); Mathematics; Electronic engineering; Mathematical analysis; Artificial intelligence; Electrical engineering; Engineering; Geometry","score_opus":0.049453308145629066,"score_gpt":0.2677537256034056,"score_spread":0.21830041745777654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1559132467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07166572,0.0010319164,0.8999038,0.0003605773,0.00033796034,0.000135817,0.00019058501,0.0022987362,0.024074933],"genre_scores_gemma":[0.86582243,0.00074134266,0.11170357,0.00028599548,0.0000462432,0.00007030739,0.00014029993,0.00006713002,0.021122726],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998685,0.000015277878,0.000011225082,0.000037409915,0.00005140195,0.00001618488],"domain_scores_gemma":[0.9998814,0.000014363774,0.000012345725,0.000018267248,0.00006555842,0.0000079749525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014554511,0.00028135834,0.00024573706,0.000154675,0.0002541455,0.00055020663,0.0014551745,0.00046989182,0.0023909996],"category_scores_gemma":[0.00024459144,0.00016552853,0.00028101038,0.00021108102,0.00024326137,0.00079374155,0.000266487,0.00030458107,0.00091228855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046473902,0.000133999,0.0018875966,0.000971079,0.00013775929,0.00085811014,0.00028353307,0.07110367,0.59791744,0.04127854,0.005177357,0.27978614],"study_design_scores_gemma":[0.00006190652,0.0009226943,0.0017510145,0.00009405855,0.0001860412,0.0017233482,0.00008948841,0.6648173,0.27937892,0.008926244,0.041980762,0.00006826096],"about_ca_topic_score_codex":0.00067570055,"about_ca_topic_score_gemma":0.0012158123,"teacher_disagreement_score":0.0023909996,"about_ca_system_score_codex":0.00040561962,"about_ca_system_score_gemma":0.00031270197,"threshold_uncertainty_score":0.007998705},"labels":[],"label_agreement":null},{"id":"W1562137704","doi":"10.1002/9783527631506.ch6","title":"Switched‐Capacitor Filters","year":2010,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Switched capacitor; Capacitor; Electrical engineering; Engineering; Voltage","score_opus":0.010370372814709417,"score_gpt":0.22879832616548965,"score_spread":0.21842795335078022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1562137704","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016303662,0.007001582,0.7695942,0.001120562,0.0013687662,0.00009772414,0.00034429226,0.0025930253,0.2015762],"genre_scores_gemma":[0.5796935,0.011081666,0.17650558,0.0009820524,0.0011101813,0.00018892369,0.00057243375,0.00032401626,0.22954166],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99973387,0.000021600643,0.000010969224,0.000050435912,0.00014741425,0.000035752008],"domain_scores_gemma":[0.99986374,0.000042772583,0.000010279645,0.000015426556,0.000058110214,0.000009640904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015520657,0.00043893253,0.00036809174,0.0005236167,0.0005496641,0.0015624363,0.0006000781,0.000751465,0.019342652],"category_scores_gemma":[0.00058263814,0.00017610892,0.00031392396,0.00072291045,0.00034376522,0.0011090514,0.00033466442,0.00070038764,0.0030874852],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015711554,0.000057785306,0.00025800226,0.00035696005,0.000043068383,0.00021610539,0.00024657662,0.019446133,0.07626055,0.39531833,0.020967593,0.4866717],"study_design_scores_gemma":[0.000044402925,0.0001869654,0.00079210236,0.00013021044,0.000047521316,0.001044564,0.00015922179,0.13914885,0.065965675,0.119349375,0.6730749,0.00005625488],"about_ca_topic_score_codex":0.0010439652,"about_ca_topic_score_gemma":0.001188249,"teacher_disagreement_score":0.019342652,"about_ca_system_score_codex":0.0009081487,"about_ca_system_score_gemma":0.00042600906,"threshold_uncertainty_score":0.06470764},"labels":[],"label_agreement":null},{"id":"W1566217458","doi":"10.1109/icnn.1995.488185","title":"Linear neural network learning algorithm analysis","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Robustness (evolution); Perceptron; Computer science; Artificial neural network; Wake-sleep algorithm; Unsupervised learning; Algorithm; Artificial intelligence; Convergence (economics); Principal component analysis; Machine learning; Generalization error","score_opus":0.019174082582928593,"score_gpt":0.23644093337451574,"score_spread":0.21726685079158714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1566217458","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008365104,0.0005641332,0.9963121,0.00010319211,0.000030505915,0.000026033214,0.00002405457,0.0001403292,0.001963188],"genre_scores_gemma":[0.193697,0.002793418,0.78355986,0.00040516493,0.00041100758,0.0009365112,0.00048760325,0.00029345838,0.01741593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822336,0.00074976095,0.00009198124,0.00032387435,0.00052032305,0.00009067454],"domain_scores_gemma":[0.9978998,0.0012976452,0.00015757662,0.00016385377,0.00045000593,0.000031113897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021638442,0.0009296342,0.00083840237,0.0009020998,0.00043062237,0.001382835,0.0011109652,0.0009827885,0.004138289],"category_scores_gemma":[0.00800772,0.0003592825,0.0006179744,0.0012761634,0.0007488214,0.0015058611,0.0011908839,0.0017073733,0.0014022727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049331622,0.00005343485,0.0006569431,0.00025090014,0.00012264345,0.00006798078,0.00008380516,0.5474734,0.0018294844,0.22226359,0.0045654806,0.222583],"study_design_scores_gemma":[0.0000039551696,0.000015184245,0.00008187401,0.000013118256,0.000008259697,0.000024855099,0.000004591465,0.94971454,0.00046256607,0.046914104,0.002750949,0.000006052682],"about_ca_topic_score_codex":0.0018531528,"about_ca_topic_score_gemma":0.0014550186,"teacher_disagreement_score":0.004138289,"about_ca_system_score_codex":0.001107981,"about_ca_system_score_gemma":0.0010845817,"threshold_uncertainty_score":0.013843954},"labels":[],"label_agreement":null},{"id":"W156682437","doi":"","title":"Phoneme-based speech recognition using self-organizing map.","year":2003,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Speech recognition; Computer science; Artificial intelligence; Natural language processing","score_opus":0.03329162217097556,"score_gpt":0.21970837726355796,"score_spread":0.1864167550925824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W156682437","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030191438,0.00201616,0.95881474,0.00017858158,0.0002942056,0.00012067749,0.00045993875,0.0030000326,0.0049241767],"genre_scores_gemma":[0.3657776,0.001119751,0.6238827,0.0001298979,0.00009381427,0.00022014002,0.0009930104,0.00012593644,0.0076570367],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976796,0.000051827366,0.0000122415795,0.000048687074,0.0001037998,0.000015469346],"domain_scores_gemma":[0.9997594,0.0000810168,0.000020383564,0.000033221368,0.00009649238,0.000009501376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034869983,0.00031998788,0.0003012016,0.00067471724,0.0001926677,0.000438109,0.00040637606,0.00044425836,0.0016715331],"category_scores_gemma":[0.00095075753,0.00018528616,0.00035340086,0.00051481795,0.0002142472,0.0007059577,0.00030499834,0.00033259284,0.001273081],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017706747,0.00009214761,0.0011686587,0.0003177212,0.00011776555,0.0001808045,0.00014164681,0.023615867,0.06870155,0.0037844006,0.007920866,0.89378154],"study_design_scores_gemma":[0.000029679528,0.00017930142,0.0065307273,0.000050503386,0.00007127276,0.00057047553,0.00013706254,0.86942655,0.09315243,0.009172623,0.020614984,0.0000643055],"about_ca_topic_score_codex":0.0012147473,"about_ca_topic_score_gemma":0.0022823894,"teacher_disagreement_score":0.0016715331,"about_ca_system_score_codex":0.00019354174,"about_ca_system_score_gemma":0.00020212925,"threshold_uncertainty_score":0.0055918097},"labels":[],"label_agreement":null},{"id":"W1567205655","doi":"10.1016/s0079-6123(06)65025-5","title":"Motor control in a meta-network with attractor dynamics","year":2007,"lang":"en","type":"review","venue":"Progress in brain research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Attractor; Control theory (sociology); Modular design; Noise (video); Computer science; Dynamics (music); Representation (politics); Torque; SIGNAL (programming language); Control (management); Mathematics; Artificial intelligence; Physics","score_opus":0.25149112833919757,"score_gpt":0.4803913138533712,"score_spread":0.22890018551417363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1567205655","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0421247,0.51192147,0.39558694,0.0012847107,0.0009359849,0.00006845986,0.00016121345,0.0005862251,0.047330286],"genre_scores_gemma":[0.42818093,0.46517402,0.08513705,0.00028271947,0.0010170945,0.00015310089,0.0003500793,0.00006941019,0.01963567],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999553,0.0000069848033,0.0000046910927,0.000014379942,0.000015221219,0.000003446706],"domain_scores_gemma":[0.9999145,0.000045010478,0.000011605908,0.0000069482444,0.000018265277,0.0000037303948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013883603,0.00040917937,0.00041935418,0.00031559065,0.000086778644,0.00044134152,0.00060927216,0.000513894,0.0013384102],"category_scores_gemma":[0.00028072626,0.00014275438,0.00022734057,0.0004187422,0.00034797323,0.0009075493,0.0002573917,0.0003093203,0.0003452494],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000741675,0.000050084414,0.0006016005,0.0033073926,0.00018307874,0.00026210168,0.000047117665,0.08818153,0.017983694,0.09062926,0.0036522353,0.7950278],"study_design_scores_gemma":[0.000078644145,0.00041377294,0.0034225292,0.0011630584,0.00039187304,0.0020694744,0.00007308647,0.46943468,0.026869608,0.20707005,0.2889052,0.00010801491],"about_ca_topic_score_codex":0.00042803152,"about_ca_topic_score_gemma":0.00049717247,"teacher_disagreement_score":0.0013384102,"about_ca_system_score_codex":0.00023964391,"about_ca_system_score_gemma":0.00024142445,"threshold_uncertainty_score":0.0044774413},"labels":[],"label_agreement":null},{"id":"W1567821529","doi":"10.1007/978-3-540-69731-2_11","title":"Nonlinear Function Learning Using Radial Basis Function Networks: Convergence and Rates","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Radial basis function; Empirical risk minimization; Computer science; Nonlinear system; Convergence (economics); Regularization (linguistics); Minification; Radial basis function network; Mathematical optimization; Function (biology); Rate of convergence; Applied mathematics; Artificial intelligence; Artificial neural network; Algorithm; Mathematics","score_opus":0.020294905977010565,"score_gpt":0.2370871132192182,"score_spread":0.21679220724220763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1567821529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018737618,0.009165187,0.96197623,0.00066997117,0.0002827297,0.00007231756,0.00007811842,0.0004467254,0.008571028],"genre_scores_gemma":[0.39488623,0.014751722,0.557443,0.00036842984,0.00064827927,0.00043697338,0.000445495,0.0015448595,0.029475043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830073,0.00076248054,0.00010082145,0.00025178085,0.0004885966,0.000095542535],"domain_scores_gemma":[0.98124254,0.013631759,0.00063234946,0.001009795,0.0032708156,0.00021270993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0083936,0.001724482,0.0018561514,0.0012806101,0.0006291395,0.002090796,0.0022809003,0.0032902528,0.0034687205],"category_scores_gemma":[0.03846896,0.0009268607,0.0012955465,0.0017293436,0.0018951163,0.005683608,0.003172594,0.0042032436,0.002035407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045417342,0.00014596604,0.0015858123,0.0010273447,0.00014864693,0.0001459325,0.00041431596,0.47492167,0.005525688,0.14872225,0.008999476,0.35790876],"study_design_scores_gemma":[0.00000795929,0.000034061726,0.00019313335,0.00007054059,0.000023484521,0.0000869639,0.000026319305,0.97549653,0.0018600601,0.020877972,0.0013055475,0.00001732598],"about_ca_topic_score_codex":0.0024154717,"about_ca_topic_score_gemma":0.0013085296,"teacher_disagreement_score":0.0083936,"about_ca_system_score_codex":0.0013068309,"about_ca_system_score_gemma":0.0008672027,"threshold_uncertainty_score":0.044390142},"labels":[],"label_agreement":null},{"id":"W1568012513","doi":"10.1109/icnn.1995.488164","title":"A sparse matrix approach to neural network training","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Backpropagation; Artificial neural network; Training (meteorology); Artificial intelligence; Sparse matrix; Computation; Pattern recognition (psychology); Training set; Exploit; Matrix (chemical analysis); Multilayer perceptron; Machine learning; Speech recognition; Algorithm","score_opus":0.07347713352903198,"score_gpt":0.2598918640087691,"score_spread":0.18641473047973714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1568012513","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006742178,0.0001303569,0.99836284,0.000059793285,0.000035834066,0.000017114211,0.000016043587,0.00013117486,0.00057253585],"genre_scores_gemma":[0.09701938,0.0010156555,0.89609385,0.00018604165,0.00033769783,0.0003244457,0.00025028625,0.00012106238,0.0046516093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952185,0.00015208927,0.000024487465,0.000062912084,0.00020451783,0.000034145418],"domain_scores_gemma":[0.9993325,0.00032036356,0.000039420214,0.000073885705,0.00020657376,0.000027210443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006382825,0.0005954272,0.0005427687,0.00062606676,0.000341095,0.00060788036,0.0010006092,0.0007957011,0.0031862843],"category_scores_gemma":[0.0028460543,0.00033622087,0.0004006735,0.00095160905,0.000496354,0.0009890208,0.0009130991,0.0016611053,0.0010352511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079603335,0.00008446315,0.00033205084,0.00025333633,0.000083880615,0.000107435866,0.00012693601,0.4966044,0.018211666,0.0959786,0.007511442,0.38062617],"study_design_scores_gemma":[0.0000054553043,0.000026422189,0.000050665254,0.000007060806,0.000004781428,0.000033543463,0.000004911453,0.983576,0.0016430917,0.012042614,0.0025993357,0.000006022518],"about_ca_topic_score_codex":0.0022124988,"about_ca_topic_score_gemma":0.0032954814,"teacher_disagreement_score":0.0031862843,"about_ca_system_score_codex":0.00032209425,"about_ca_system_score_gemma":0.0005729159,"threshold_uncertainty_score":0.010659158},"labels":[],"label_agreement":null},{"id":"W1570088283","doi":"10.20381/ruor-7736","title":"Uncertainty management in the activated sludge process: Innovative applications of computational learning theory.","year":2000,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Activated sludge; Process (computing); Computer science; Engineering; Engineering management; Waste management; Sewage treatment","score_opus":0.003352953792985771,"score_gpt":0.18302696196383483,"score_spread":0.17967400817084905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1570088283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009971612,0.0058843535,0.974853,0.0023771832,0.00025601048,0.000038391154,0.000055655622,0.00005050115,0.0065133083],"genre_scores_gemma":[0.7280578,0.0140030775,0.2481693,0.0008394574,0.00167567,0.00034473184,0.00021308954,0.000072349496,0.006624414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926597,0.0003542921,0.000035999823,0.00008959769,0.00021177427,0.000042328727],"domain_scores_gemma":[0.99714667,0.002344478,0.00021534934,0.0000837936,0.00015470183,0.000055069235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002120396,0.0006999963,0.0010096384,0.00074524235,0.000457967,0.0019235893,0.0010692547,0.0014429925,0.00096274714],"category_scores_gemma":[0.004774438,0.00026355372,0.0010800096,0.00077920005,0.0016767676,0.0017766072,0.0016696882,0.0018547914,0.00014239682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002085417,0.000034963723,0.00057082373,0.00022891488,0.00007553662,0.00009445473,0.00010651701,0.626416,0.00057878305,0.33447218,0.0013418626,0.036058974],"study_design_scores_gemma":[0.000003913008,0.000017621347,0.00009787784,0.00003824911,0.00000948836,0.00002109506,0.000019758041,0.81903803,0.0001916783,0.17851666,0.002034559,0.000011012412],"about_ca_topic_score_codex":0.0014182309,"about_ca_topic_score_gemma":0.0009880437,"teacher_disagreement_score":0.002120396,"about_ca_system_score_codex":0.0010776637,"about_ca_system_score_gemma":0.0010667797,"threshold_uncertainty_score":0.011213839},"labels":[],"label_agreement":null},{"id":"W1570683751","doi":"","title":"Conditioning and Concept Formation in Embodied Agents","year":2001,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Complement (music); Embodied cognition; Artificial intelligence; State (computer science); Control (management); Machine learning; Algorithm","score_opus":0.02507449090259674,"score_gpt":0.27000149317442707,"score_spread":0.24492700227183034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1570683751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46125263,0.0017721552,0.4848818,0.0015352669,0.00012682323,0.00008751633,0.00011121112,0.0004578794,0.049774844],"genre_scores_gemma":[0.961743,0.00040370008,0.0340067,0.00008295312,0.000019832947,0.00004788859,0.00005416158,0.000021954706,0.0036198176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995783,0.0001568024,0.000018492205,0.000107331056,0.00009713601,0.000041975964],"domain_scores_gemma":[0.99883026,0.0006117251,0.0002022169,0.00016667668,0.0000973495,0.00009172985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008289685,0.0002565687,0.00026445612,0.0002760763,0.0003937411,0.0014920485,0.0006747401,0.00087964226,0.0029957257],"category_scores_gemma":[0.0044460697,0.00021936592,0.00027939255,0.00028421025,0.0027045952,0.0022418268,0.0010182637,0.00085934636,0.00023364343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001805425,0.00012403334,0.0027143043,0.0002124322,0.000069149995,0.0002113987,0.0017912955,0.11034742,0.01865959,0.77321094,0.00076482055,0.09171416],"study_design_scores_gemma":[0.000052392654,0.0001405954,0.002528968,0.000039283048,0.00002298668,0.00011033563,0.00022039755,0.14120007,0.0067766234,0.8439165,0.004958846,0.000032963566],"about_ca_topic_score_codex":0.0023937684,"about_ca_topic_score_gemma":0.0012473506,"teacher_disagreement_score":0.0029957257,"about_ca_system_score_codex":0.0007461275,"about_ca_system_score_gemma":0.00061603263,"threshold_uncertainty_score":0.010021687},"labels":[],"label_agreement":null},{"id":"W1570737320","doi":"10.1109/icnn.1994.374491","title":"Structure adaptation in feed-forward neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pruning; Computer science; Artificial neural network; Adaptation (eye); Feedforward neural network; Feed forward; Artificial intelligence; Algorithm; Machine learning; Engineering; Control engineering; Neuroscience","score_opus":0.018723352880855578,"score_gpt":0.21675291512886996,"score_spread":0.19802956224801438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1570737320","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025765458,0.00058598997,0.97126806,0.00010791345,0.00007440523,0.00003887749,0.000016633081,0.00055067596,0.0015919247],"genre_scores_gemma":[0.6398914,0.00079252955,0.35426992,0.00018030699,0.00009685438,0.00016937936,0.000100462064,0.00010047854,0.004398632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971706,0.000071960836,0.000015655674,0.000049690567,0.00011977763,0.000025836054],"domain_scores_gemma":[0.99945253,0.0002498208,0.00006365763,0.00006419631,0.00015417073,0.000015700463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065100077,0.0005486601,0.0005054681,0.00038254345,0.00028776872,0.00058292446,0.0009336068,0.0012028534,0.001007654],"category_scores_gemma":[0.0028068987,0.00039632083,0.0003057795,0.00036215942,0.00048013084,0.0010466874,0.0005643581,0.0008683441,0.00037429217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008915314,0.00006336735,0.00078951946,0.00011481744,0.00005072971,0.0001298116,0.0001374848,0.64128137,0.015492711,0.014266652,0.0008540034,0.3267304],"study_design_scores_gemma":[0.000012462049,0.000054840293,0.00019916607,0.000012641723,0.000013446141,0.000049237107,0.000007002988,0.98808146,0.0045223488,0.005764279,0.0012745839,0.000008506358],"about_ca_topic_score_codex":0.0013843047,"about_ca_topic_score_gemma":0.0018915514,"teacher_disagreement_score":0.0013843047,"about_ca_system_score_codex":0.00039318594,"about_ca_system_score_gemma":0.00032745034,"threshold_uncertainty_score":0.0034428835},"labels":[],"label_agreement":null},{"id":"W1571346420","doi":"10.1002/9780470479216.corpsy0608","title":"Nonparametric Statistical Tests","year":2010,"lang":"en","type":"other","venue":"The Corsini Encyclopedia of Psychology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Nonparametric statistics; Mathematics; Statistics; Parametric statistics; Statistical hypothesis testing; Normal distribution; Standard deviation; Nonparametric regression; Population; Econometrics","score_opus":0.014715882541661117,"score_gpt":0.3168446983183398,"score_spread":0.30212881577667866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1571346420","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022262342,0.0054532285,0.71663064,0.005034457,0.0055081276,0.010891017,0.05605857,0.015237982,0.16292366],"genre_scores_gemma":[0.27180994,0.0064973207,0.55921096,0.004440744,0.002797436,0.045674182,0.044729467,0.008354795,0.056485094],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9566326,0.017007066,0.0045803944,0.004835757,0.015870837,0.0010733363],"domain_scores_gemma":[0.811902,0.111925215,0.011914341,0.028148182,0.03311135,0.002998842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0307671,0.0020537407,0.002761881,0.007022235,0.0014994394,0.0055584847,0.0043826425,0.0018697316,0.08958239],"category_scores_gemma":[0.23618351,0.000765804,0.0021594656,0.0070405295,0.0036010973,0.0043881554,0.004013576,0.0046946965,0.027163127],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016459271,0.0007405192,0.012503378,0.0043260353,0.000823647,0.0006278082,0.0018609692,0.0052401545,0.0023311127,0.08124104,0.28357482,0.6050846],"study_design_scores_gemma":[0.0005566503,0.0018419386,0.024570525,0.004669431,0.0004704064,0.0018653444,0.0022429584,0.018462108,0.005890464,0.22793558,0.7111927,0.00030188996],"about_ca_topic_score_codex":0.0010092412,"about_ca_topic_score_gemma":0.0012153247,"teacher_disagreement_score":0.08958239,"about_ca_system_score_codex":0.0022778907,"about_ca_system_score_gemma":0.00757008,"threshold_uncertainty_score":0.29968292},"labels":[],"label_agreement":null},{"id":"W1572105693","doi":"10.1109/icnn.1994.374616","title":"Control of nonlinear multivariable systems using a dynamic neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Multivariable calculus; Artificial neural network; Computer science; Nonlinear system; Artificial intelligence; Control system; Control (management); Nervous system network models; Control engineering; Neural system; Control theory (sociology); Machine learning; Time delay neural network; Types of artificial neural networks; Engineering","score_opus":0.02169455269198501,"score_gpt":0.24146917775114501,"score_spread":0.21977462505916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1572105693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014587604,0.000521235,0.977265,0.00022015483,0.000061889186,0.00002767945,0.000032678377,0.00037672243,0.0069069806],"genre_scores_gemma":[0.8917911,0.0011240599,0.09893207,0.00014981306,0.00009924879,0.00015100908,0.000090543384,0.000032211752,0.00763001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998728,0.00003125496,0.000008129026,0.000033322744,0.000041935633,0.000012555925],"domain_scores_gemma":[0.99990976,0.00004165517,0.000013280618,0.0000068302024,0.000023498324,0.000005042273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002468506,0.00028128727,0.00028137176,0.0001786179,0.00026525813,0.0005821258,0.00044433674,0.00040232085,0.0013970495],"category_scores_gemma":[0.00062841555,0.00015091755,0.0002028517,0.00022069072,0.0003236666,0.00050955464,0.00058936153,0.00047475312,0.00017858265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050917944,0.000029569603,0.000233071,0.000070475944,0.000025981848,0.00006919188,0.000044114622,0.8876735,0.008186401,0.01838695,0.0008483516,0.08438142],"study_design_scores_gemma":[0.000005267064,0.000016583299,0.0000628332,0.0000036528518,0.0000043036594,0.000010014977,0.0000025369663,0.9952962,0.0005426312,0.0027421066,0.0013107584,0.0000031787565],"about_ca_topic_score_codex":0.003374272,"about_ca_topic_score_gemma":0.0030568284,"teacher_disagreement_score":0.003374272,"about_ca_system_score_codex":0.00033104626,"about_ca_system_score_gemma":0.00034110705,"threshold_uncertainty_score":0.0067092776},"labels":[],"label_agreement":null},{"id":"W1574082713","doi":"10.1007/3-540-44634-6","title":"Algorithms and Data Structures","year":2001,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":626,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science","score_opus":0.030087856308124047,"score_gpt":0.28947257153670514,"score_spread":0.2593847152285811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574082713","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017101808,0.010076211,0.9336457,0.0020909498,0.0010340365,0.00011598637,0.0008927943,0.0029905248,0.04744351],"genre_scores_gemma":[0.045326456,0.0131050665,0.83149135,0.001457127,0.0019606946,0.00076355314,0.005023324,0.0022335253,0.09863888],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99807966,0.00036687663,0.00019673104,0.00049544027,0.00078319875,0.000078113364],"domain_scores_gemma":[0.99754745,0.0008912673,0.0000723185,0.0011448191,0.00029067998,0.000053464355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001398621,0.0014618608,0.0018539593,0.0020187448,0.0011087413,0.0051519508,0.0020917153,0.0015752699,0.030891253],"category_scores_gemma":[0.0066411253,0.0010660737,0.0011957899,0.0048497226,0.0020409334,0.0068693613,0.0028915836,0.0035266057,0.026168715],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003943349,0.000044187003,0.0001881236,0.0003807618,0.00004402254,0.00003943364,0.00011376232,0.004293122,0.0009982031,0.42656606,0.06745116,0.49984187],"study_design_scores_gemma":[0.000018158153,0.000022852315,0.00012491674,0.00011113483,0.000025753823,0.00019265275,0.000033293774,0.020270586,0.0015917766,0.8036885,0.17390424,0.000016128908],"about_ca_topic_score_codex":0.0005720347,"about_ca_topic_score_gemma":0.0005422638,"teacher_disagreement_score":0.030891253,"about_ca_system_score_codex":0.0009797567,"about_ca_system_score_gemma":0.0012739265,"threshold_uncertainty_score":0.10334152},"labels":[],"label_agreement":null},{"id":"W1574730574","doi":"10.1109/icsmc.2005.1571240","title":"A Fuzzy Evidential Reasoning Data Fusion Approach with Uncertainty Evaluation for Robust Pattern Classification","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Evidential reasoning approach; Artificial intelligence; Probabilistic logic; Fuzzy logic; Computer science; Fuzzy classification; Entropy (arrow of time); Machine learning; Data mining; Neuro-fuzzy; Robustness (evolution); Fuzzy set operations; Fuzzy set; Pattern recognition (psychology); Fuzzy control system; Decision support system","score_opus":0.10673309230861161,"score_gpt":0.30682205673144447,"score_spread":0.20008896442283286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574730574","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016528019,0.0002700891,0.997537,0.00007609072,0.000018882134,0.00002854673,0.000018336865,0.000062399675,0.00033580308],"genre_scores_gemma":[0.14824204,0.00047739918,0.8502965,0.0000886005,0.00010647118,0.00017616045,0.00011122259,0.000026642023,0.00047489876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99627715,0.0010510981,0.0004297512,0.00049726275,0.001637731,0.00010702986],"domain_scores_gemma":[0.9973348,0.0013114461,0.00030157162,0.00035920434,0.00064070954,0.00005221522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006637128,0.0010700164,0.002020393,0.0028691185,0.00082188536,0.0023323747,0.0018897383,0.0015716817,0.0014601266],"category_scores_gemma":[0.011379645,0.0005053607,0.0020771795,0.002049054,0.0010846303,0.0029540465,0.0020174494,0.0016735586,0.00039258087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017334099,0.00018278326,0.0009214894,0.0006094223,0.00048727097,0.0003944786,0.00042779266,0.25401232,0.015048823,0.13366546,0.0022188565,0.59185797],"study_design_scores_gemma":[0.000022113254,0.00007680732,0.00030112034,0.000048442926,0.00009685015,0.00012597955,0.000026529275,0.93432236,0.0048058126,0.05714688,0.0029805144,0.00004661426],"about_ca_topic_score_codex":0.00084595405,"about_ca_topic_score_gemma":0.00081582216,"teacher_disagreement_score":0.006637128,"about_ca_system_score_codex":0.000873381,"about_ca_system_score_gemma":0.001059206,"threshold_uncertainty_score":0.035100877},"labels":[],"label_agreement":null},{"id":"W157493903","doi":"","title":"Wide Diameters and Diameter of Networks.","year":2008,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Geometry","score_opus":0.013075799793172263,"score_gpt":0.21234871856420115,"score_spread":0.19927291877102887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W157493903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32108974,0.021235315,0.4196545,0.01184827,0.0013371772,0.00010526834,0.0022718888,0.00092758983,0.22153021],"genre_scores_gemma":[0.9226579,0.0077772075,0.039222155,0.0010612203,0.0020269882,0.00020559505,0.0009173115,0.00031123133,0.025820497],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985513,0.00048414874,0.000091292284,0.00035594267,0.0003068927,0.00021042861],"domain_scores_gemma":[0.9843106,0.010698389,0.001359861,0.0010231456,0.0010656537,0.0015423071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021208832,0.00072816975,0.0011429214,0.0026600056,0.0015678321,0.0044497587,0.0014409916,0.0013537904,0.008818175],"category_scores_gemma":[0.017774237,0.000691707,0.00063914084,0.0015420839,0.0037111316,0.009657918,0.0030489913,0.0025958822,0.0017416427],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006872396,0.000012012568,0.00095419,0.00008313142,0.000018792529,0.00005746243,0.00018416689,0.0030998287,0.00070869527,0.9778252,0.005988242,0.010999468],"study_design_scores_gemma":[0.000015473026,0.000021260787,0.0007895406,0.00003614313,0.000016607575,0.00025852374,0.000116427145,0.008981717,0.000499659,0.97922647,0.010026994,0.000011199672],"about_ca_topic_score_codex":0.0005409084,"about_ca_topic_score_gemma":0.0005787114,"teacher_disagreement_score":0.008818175,"about_ca_system_score_codex":0.0016044604,"about_ca_system_score_gemma":0.00041726566,"threshold_uncertainty_score":0.02949977},"labels":[],"label_agreement":null},{"id":"W1576405550","doi":"10.4271/2000-01-2616","title":"Application of Simulation and Knowledge Processing in Contamination Control","year":2000,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Monash University","keywords":"Contamination control; Contamination; Computer science; Control (management); Systems engineering; Engineering; Artificial intelligence","score_opus":0.010174042050904499,"score_gpt":0.2687439171466298,"score_spread":0.2585698750957253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576405550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015624706,0.00055234495,0.9655313,0.0006688641,0.000077583194,0.00007776408,0.000054831085,0.0004994432,0.016913163],"genre_scores_gemma":[0.5970441,0.002066865,0.39322472,0.00022699888,0.000085157255,0.00034845498,0.00025574237,0.000103505445,0.0066445074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991615,0.00045312286,0.00005842414,0.00010278829,0.00018634133,0.00003789919],"domain_scores_gemma":[0.9972357,0.0022634203,0.000092189286,0.00015553908,0.00020831362,0.00004496856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011347947,0.0006359939,0.0006254802,0.0006750414,0.00034895455,0.0017537193,0.0007435041,0.0012931345,0.0048847613],"category_scores_gemma":[0.004457048,0.0002982333,0.00068695855,0.00079356116,0.0009848304,0.0014547734,0.00083589676,0.0008969683,0.0005865932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011001697,0.000097262564,0.0006769683,0.00016842945,0.000068824535,0.00013173209,0.000191271,0.8286781,0.002291381,0.060014904,0.00081858976,0.106752545],"study_design_scores_gemma":[0.000012806725,0.000023077047,0.00012135615,0.000023307648,0.000010370905,0.000021194652,0.000023892866,0.97068834,0.0014095858,0.024291333,0.0033655518,0.000009253697],"about_ca_topic_score_codex":0.0072487434,"about_ca_topic_score_gemma":0.0033404534,"teacher_disagreement_score":0.0072487434,"about_ca_system_score_codex":0.0009396976,"about_ca_system_score_gemma":0.0012009145,"threshold_uncertainty_score":0.01634109},"labels":[],"label_agreement":null},{"id":"W1577979322","doi":"10.1109/ijcnn.2001.939537","title":"Input decay: simple and effective soft variable selection","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Overfitting; Regularization (linguistics); Computer science; Variable (mathematics); Simple (philosophy); Task (project management); Selection (genetic algorithm); Feature selection; Artificial intelligence; Machine learning; Mathematical optimization; Mathematics; Artificial neural network; Engineering","score_opus":0.009226466990641504,"score_gpt":0.2185175931543817,"score_spread":0.2092911261637402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1577979322","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007469582,0.00014677204,0.99028546,0.00012091022,0.00005446431,0.00008245147,0.00005114927,0.0010804074,0.0007087952],"genre_scores_gemma":[0.2820392,0.00033021104,0.7100908,0.0006409634,0.00026425562,0.0006076784,0.00066588086,0.00069079327,0.004670236],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99612206,0.001774172,0.00024377667,0.0004478641,0.0011443243,0.00026767296],"domain_scores_gemma":[0.9937118,0.0027522796,0.00039484902,0.001384594,0.0014968318,0.00025960183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071787406,0.0018937242,0.0020711925,0.0017188649,0.0006729173,0.0017386267,0.003069955,0.0025046864,0.0032073595],"category_scores_gemma":[0.017546715,0.0007271029,0.0014401788,0.0015964197,0.0014374513,0.0022678573,0.00395217,0.002857122,0.0015912601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071017514,0.0003963633,0.0032398875,0.00034108583,0.00033037268,0.00030009684,0.0002174866,0.15616278,0.019421497,0.03366749,0.008385743,0.77682704],"study_design_scores_gemma":[0.00009346989,0.00014976297,0.0005456472,0.00004009362,0.00004834009,0.000116864,0.000021856225,0.9657023,0.0077026924,0.023134507,0.002410163,0.00003423814],"about_ca_topic_score_codex":0.00072917284,"about_ca_topic_score_gemma":0.001032653,"teacher_disagreement_score":0.0071787406,"about_ca_system_score_codex":0.00052296976,"about_ca_system_score_gemma":0.0014895388,"threshold_uncertainty_score":0.037965238},"labels":[],"label_agreement":null},{"id":"W1578246419","doi":"10.1109/isvlsi.2005.18","title":"Analysis of Incremental Communication for Multilayer Neural Networks on a Field Programmable Gate Array","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Artificial neural network; Field-programmable gate array; Massively parallel; Telecommunications network; Network architecture; Distributed computing; Computer architecture; Computer hardware; Computer network; Parallel computing; Artificial intelligence","score_opus":0.02193062937349195,"score_gpt":0.2894317272328995,"score_spread":0.26750109785940757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1578246419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15918626,0.00055144465,0.8238807,0.0004038755,0.000056876954,0.0000776554,0.000060509185,0.0005691388,0.015213676],"genre_scores_gemma":[0.9634684,0.00025756832,0.032529734,0.000066323104,0.000029286368,0.000071725444,0.000049753224,0.000052802563,0.0034744216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966085,0.00006458745,0.000011785538,0.000035312558,0.0001653275,0.00006211321],"domain_scores_gemma":[0.99799335,0.0013755733,0.00015234524,0.000088992114,0.0003484787,0.000041297768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064155273,0.0003689273,0.0002680639,0.0003961218,0.00033920232,0.00044290486,0.0007823044,0.00042803847,0.0033640265],"category_scores_gemma":[0.004087701,0.00021215038,0.00034650092,0.00029080696,0.0004478951,0.0010002756,0.00036654834,0.0006239842,0.0001928504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012669325,0.000032538945,0.00083435565,0.000101222744,0.000020900708,0.00015445446,0.00007788079,0.9220376,0.005706293,0.03484883,0.00080686144,0.03525233],"study_design_scores_gemma":[0.0000019983288,0.000019487947,0.00011731672,0.00000235973,0.0000042205047,0.000018252194,0.000004048583,0.99655485,0.00067463936,0.0024478822,0.0001528914,0.0000019857487],"about_ca_topic_score_codex":0.0045791776,"about_ca_topic_score_gemma":0.004514301,"teacher_disagreement_score":0.0045791776,"about_ca_system_score_codex":0.001263362,"about_ca_system_score_gemma":0.000888388,"threshold_uncertainty_score":0.011253774},"labels":[],"label_agreement":null},{"id":"W1580513831","doi":"10.71781/10523","title":"Étude de techniques d'apprentissage non-supervisé pour l'amélioration de l'entraînement supervisé de modèles connexionnistes","year":2008,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Artificial intelligence; Artificial neural network; Computer science; Field (mathematics); Machine learning; Kernel (algebra); Mathematics","score_opus":0.01124228761749813,"score_gpt":0.1963150616199673,"score_spread":0.1850727740024692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580513831","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0439624,0.0010500485,0.95172715,0.00020894918,0.000096035845,0.000104056904,0.000060779035,0.0018750047,0.00091549515],"genre_scores_gemma":[0.39685512,0.0006571299,0.59162915,0.00024547658,0.00016163453,0.00024344133,0.00043296497,0.0007122087,0.0090628555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862826,0.00046760097,0.00009160238,0.00034030457,0.00034274242,0.00012960951],"domain_scores_gemma":[0.99441504,0.0032075916,0.0002823363,0.0006008404,0.0013333259,0.00016075565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003452362,0.0018712557,0.001972426,0.00064120063,0.0005492171,0.0015267556,0.0024187877,0.0016372716,0.0025509633],"category_scores_gemma":[0.010710519,0.0009244547,0.0012798511,0.00044810175,0.0008759867,0.0017193077,0.0015571237,0.0029014288,0.0008203236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080520747,0.0004434071,0.0017018561,0.00026987732,0.00039566736,0.00020332975,0.0005144103,0.34487417,0.03837694,0.005701091,0.003079823,0.6036342],"study_design_scores_gemma":[0.000020091364,0.000100583944,0.0003517241,0.000008279314,0.000030084382,0.000035335535,0.000019467776,0.9921077,0.0061010327,0.00061148964,0.0006053188,0.000008853975],"about_ca_topic_score_codex":0.018657241,"about_ca_topic_score_gemma":0.021848273,"teacher_disagreement_score":0.018657241,"about_ca_system_score_codex":0.0008327843,"about_ca_system_score_gemma":0.0016868213,"threshold_uncertainty_score":0.037097275},"labels":[],"label_agreement":null},{"id":"W1580918357","doi":"10.1109/mwscas.1992.271072","title":"Weight storage elements for analog implementation of artificial neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"CMOS; Artificial neural network; Capacitor; Computer science; Electronic circuit; Electronic engineering; Chip; Realization (probability); Analogue electronics; Very-large-scale integration; Electrical engineering; Voltage; Computer hardware; Artificial intelligence; Engineering; Embedded system; Mathematics","score_opus":0.02253052664324727,"score_gpt":0.30047318783579247,"score_spread":0.2779426611925452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580918357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07447479,0.0017569565,0.8911912,0.00053063215,0.0005891514,0.00008703142,0.00022187445,0.0030963663,0.028052142],"genre_scores_gemma":[0.6966835,0.00088874926,0.28333774,0.00033274884,0.0001585979,0.00012999483,0.00027280432,0.0001326851,0.018063089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999101,0.000012402393,0.000009528062,0.000014502716,0.000043340115,0.0000101047135],"domain_scores_gemma":[0.9998462,0.000038107926,0.000017710445,0.000024823043,0.00006564921,0.0000074482273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001297748,0.00024800535,0.00011693462,0.0002362865,0.00020942514,0.0006943799,0.0008163362,0.00035627317,0.0071207816],"category_scores_gemma":[0.0006772733,0.00012203999,0.00011165756,0.00042746798,0.00020565708,0.0009708174,0.0003028807,0.0004223677,0.0008468993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032503557,0.00010725325,0.00042938138,0.00036109707,0.000047592617,0.00032615373,0.00017887863,0.026495282,0.25638026,0.13999048,0.009871074,0.5654875],"study_design_scores_gemma":[0.00010095938,0.00038053503,0.00067518826,0.00011078125,0.000078312645,0.0005957613,0.000084635874,0.5052534,0.33219263,0.062906064,0.09756692,0.000054801585],"about_ca_topic_score_codex":0.0004821527,"about_ca_topic_score_gemma":0.0012672426,"teacher_disagreement_score":0.0071207816,"about_ca_system_score_codex":0.0003095879,"about_ca_system_score_gemma":0.00019604502,"threshold_uncertainty_score":0.023821414},"labels":[],"label_agreement":null},{"id":"W1581656238","doi":"10.1109/icci-cc.2015.7259408","title":"Big Data tool integration in physical design process find hidden patterns, predictive analysis and classifying Big Data","year":2015,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Big data; Process (computing); Data mining; Data cleansing; Data extraction; External Data Representation; Unstructured data; Representation (politics); Data validation; Data modeling; Database; Data science; Data quality; Artificial intelligence; Engineering","score_opus":0.24403766028606277,"score_gpt":0.33984132436257736,"score_spread":0.09580366407651458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581656238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027376259,0.0005511869,0.8937499,0.0009147014,0.00019938686,0.0004476354,0.0070811277,0.06082547,0.008854385],"genre_scores_gemma":[0.20024164,0.00057760737,0.7772687,0.00039916084,0.000069652575,0.0008602803,0.012157541,0.0032256404,0.005199807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99635786,0.0007667166,0.00047297793,0.00055471767,0.0017187655,0.00012893703],"domain_scores_gemma":[0.9902024,0.004773257,0.00049215934,0.0025463696,0.0016992367,0.00028658408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004436635,0.0013535898,0.0012403534,0.005527178,0.0007207436,0.0045225974,0.002197853,0.0008674973,0.0061923265],"category_scores_gemma":[0.0130533455,0.0010248169,0.0013758196,0.004460399,0.00079205056,0.0037604943,0.0021523007,0.0014991116,0.0018578758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010919116,0.0007685093,0.020140423,0.0022032738,0.00063852535,0.0016083458,0.0022905695,0.098952845,0.025402438,0.033267643,0.05812038,0.7555151],"study_design_scores_gemma":[0.00017608215,0.00052733824,0.01159476,0.00044556454,0.00022796569,0.000851377,0.00092839805,0.7254689,0.076132126,0.0704699,0.11294157,0.00023609182],"about_ca_topic_score_codex":0.001701137,"about_ca_topic_score_gemma":0.00255162,"teacher_disagreement_score":0.0061923265,"about_ca_system_score_codex":0.000808379,"about_ca_system_score_gemma":0.0020514892,"threshold_uncertainty_score":0.023463488},"labels":[],"label_agreement":null},{"id":"W1582390718","doi":"10.5555/763438.763440","title":"Extended - input neural network applications, implementation and learning","year":2002,"lang":"en","type":"article","venue":"Neural, Parallel & Scientific Computations archive","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saskatchewan Polytechnic","funders":"","keywords":"Artificial neural network; Computer science; Nonlinear system; Binary number; Inverse; Encoder; Electronic circuit; XOR gate; Simple (philosophy); Input/output; Function (biology); Matrix (chemical analysis); Algorithm; Arithmetic; Topology (electrical circuits); Logic gate; Mathematics; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.02142315147233419,"score_gpt":0.2754415440694061,"score_spread":0.2540183925970719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1582390718","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007503557,0.0019465121,0.96866155,0.00041231583,0.00023631105,0.000042230276,0.00007142447,0.0012166728,0.019909324],"genre_scores_gemma":[0.33010542,0.0046281763,0.61521053,0.00034799107,0.00021073529,0.00023536263,0.0003136279,0.00024261743,0.048705515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999777,0.000051955663,0.00001763123,0.000040915333,0.000084558174,0.00002799269],"domain_scores_gemma":[0.999701,0.00008661927,0.000015003215,0.000075241405,0.000107762826,0.000014379603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003750957,0.00043971612,0.00034119323,0.00025281194,0.00022960828,0.0009449503,0.0011594384,0.00068988337,0.0073338193],"category_scores_gemma":[0.001305598,0.0002275125,0.0003319884,0.0004942243,0.00055177615,0.001251141,0.000799835,0.0009195138,0.0016331241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016733099,0.00008604537,0.00075877184,0.00056525046,0.00008096924,0.0006746578,0.00023326292,0.14817394,0.022199623,0.21616265,0.008133377,0.60276407],"study_design_scores_gemma":[0.000022430268,0.00008954524,0.00038466405,0.00009921307,0.000033189866,0.0006133744,0.000047214486,0.7945961,0.018001962,0.1301968,0.05588293,0.000032505763],"about_ca_topic_score_codex":0.0011722858,"about_ca_topic_score_gemma":0.0017526136,"teacher_disagreement_score":0.0073338193,"about_ca_system_score_codex":0.0004248581,"about_ca_system_score_gemma":0.0003405603,"threshold_uncertainty_score":0.024534047},"labels":[],"label_agreement":null},{"id":"W1582517295","doi":"10.1029/gm094p0329","title":"Observations of the Density Perturbations which Drive Thermohaline Intrusions","year":2011,"lang":"en","type":"book-chapter","venue":"Geophysical monograph","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Thermohaline circulation; Geology; Geophysics; Physics; Oceanography","score_opus":0.028899383065345778,"score_gpt":0.21004441592643744,"score_spread":0.18114503286109165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1582517295","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93611866,0.002633043,0.011434702,0.00082871894,0.00017323132,0.000044306275,0.010837248,0.0006542235,0.037275847],"genre_scores_gemma":[0.9795574,0.0030487229,0.008070815,0.00012423038,0.00015799145,0.000019910061,0.0033195436,0.00009771826,0.005603803],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99996436,0.0000014612634,0.000001559205,0.000008658604,0.00001906204,0.00000486194],"domain_scores_gemma":[0.9999577,0.000010645604,0.000012940257,0.000005918371,0.000008126361,0.000004654651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006994263,0.00018664975,0.00015026779,0.0002633829,0.00023039401,0.00039740076,0.00018045025,0.00025308857,0.0019025238],"category_scores_gemma":[0.00023238617,0.00022867885,0.00013170221,0.0005922867,0.00018883833,0.00040616264,0.0002716149,0.00048392543,0.00042253427],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029798932,0.00010468587,0.12515076,0.00051889365,0.000093685296,0.0004749744,0.000699369,0.0076777884,0.71000123,0.00480076,0.006376572,0.14380321],"study_design_scores_gemma":[0.000016776494,0.000053405856,0.829155,0.00007193744,0.000041058476,0.0003995612,0.00023852586,0.014334411,0.12714171,0.0021202692,0.026386272,0.000041115643],"about_ca_topic_score_codex":0.003948275,"about_ca_topic_score_gemma":0.0091048265,"teacher_disagreement_score":0.003948275,"about_ca_system_score_codex":0.00026035318,"about_ca_system_score_gemma":0.00011626839,"threshold_uncertainty_score":0.007850587},"labels":[],"label_agreement":null},{"id":"W1584270863","doi":"10.1109/ijcnn.2005.1556178","title":"Pattern classification by assembling small neural networks","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Northern British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Jilin University","keywords":"Artificial neural network; Time delay neural network; Computer science; Stochastic neural network; Nervous system network models; Types of artificial neural networks; Physical neural network; Artificial intelligence; Probabilistic neural network; Pattern recognition (psychology); Recurrent neural network","score_opus":0.054924385775895875,"score_gpt":0.27005353585366326,"score_spread":0.2151291500777674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1584270863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030937664,0.0004329133,0.96412426,0.00015007812,0.000100004385,0.00007283971,0.00004225174,0.00095786416,0.0031820694],"genre_scores_gemma":[0.5989143,0.00069344696,0.39113817,0.00023866516,0.00021093152,0.00039495557,0.00029475836,0.0002014422,0.007913241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992488,0.00018956495,0.00006236094,0.00023516105,0.0002014526,0.000062545696],"domain_scores_gemma":[0.99766636,0.0011010097,0.0002107691,0.00048331343,0.00044764858,0.00009089972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011147498,0.0007459444,0.0011348466,0.001032901,0.0005671714,0.00094419747,0.0015561086,0.0009664016,0.0033369642],"category_scores_gemma":[0.006133436,0.00067812356,0.0006245301,0.0009655134,0.0010128808,0.0021735148,0.0016815732,0.0011333838,0.001017947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024079395,0.00008036384,0.0015635743,0.00019554082,0.00011533686,0.00024536447,0.0001916074,0.7112914,0.018361181,0.021513212,0.0014941815,0.24470748],"study_design_scores_gemma":[0.0000061346386,0.000053365267,0.00017048571,0.0000065519475,0.000015249781,0.000032111835,0.000013198468,0.9862753,0.0016823265,0.010351415,0.0013878571,0.0000060699253],"about_ca_topic_score_codex":0.0025100997,"about_ca_topic_score_gemma":0.0026211308,"teacher_disagreement_score":0.0033369642,"about_ca_system_score_codex":0.0006075259,"about_ca_system_score_gemma":0.0003835758,"threshold_uncertainty_score":0.011163235},"labels":[],"label_agreement":null},{"id":"W1587364178","doi":"10.1007/b97823","title":"Advances in artificial intelligence : 17th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2004, London, Ontario, Canada, May 17-19, 2004 : proceedings","year":2004,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Reinforcement learning","score_opus":0.045987377656716705,"score_gpt":0.2814521722277933,"score_spread":0.23546479457107658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587364178","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051361825,0.2500289,0.040061455,0.04240967,0.054969825,0.0004676902,0.010927781,0.0032778687,0.5927207],"genre_scores_gemma":[0.012050592,0.10174272,0.015342381,0.0012872599,0.0025156103,0.00014239723,0.005669583,0.0010681514,0.8601813],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988412,0.00009318261,0.000052371746,0.00012283915,0.00074707816,0.00014329777],"domain_scores_gemma":[0.9963295,0.0003756946,0.00007974428,0.00022848611,0.002431567,0.0005550357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022654783,0.0014608531,0.0021607503,0.0024486713,0.0020905114,0.0061689974,0.0019302058,0.0012655021,0.10789803],"category_scores_gemma":[0.0031646206,0.00071845896,0.0007226549,0.0047943345,0.0018926106,0.0029168357,0.0016893212,0.003007115,0.035435528],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041954256,0.000027461085,0.00019890165,0.0002209229,0.000014111618,0.000022326745,0.000098263925,0.000353465,0.00024513897,0.0064662285,0.89880586,0.09350546],"study_design_scores_gemma":[0.00001047335,0.000009064931,0.0007547482,0.00015127656,0.000018068751,0.000039133753,0.000104173225,0.00075057853,0.0002856943,0.0024357238,0.99542636,0.000014801757],"about_ca_topic_score_codex":0.36012477,"about_ca_topic_score_gemma":0.545756,"teacher_disagreement_score":0.63987523,"about_ca_system_score_codex":0.008622507,"about_ca_system_score_gemma":0.022155104,"threshold_uncertainty_score":0.7160572},"labels":[],"label_agreement":null},{"id":"W1591760165","doi":"10.1023/a:1013943418833","title":"Model Selection for Small Sample Regression","year":2002,"lang":"en","type":"article","venue":"Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":125,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Overfitting; Estimator; Generalization; Model selection; Generalization error; Mathematics; Covariance matrix; Artificial intelligence; Selection (genetic algorithm); Linear regression; Applied mathematics; Regression; Sample size determination; Computer science; Statistics; Artificial neural network","score_opus":0.05444036630139729,"score_gpt":0.265644620019226,"score_spread":0.21120425371782872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1591760165","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028220206,0.0006377593,0.9950647,0.00027109912,0.00007703193,0.000045088917,0.000121072255,0.0005798171,0.00038140462],"genre_scores_gemma":[0.34190723,0.002696171,0.6346595,0.00061664305,0.00094957853,0.0015066261,0.0031542284,0.001440692,0.0130693475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956636,0.003182198,0.00013765848,0.00044553308,0.00043728962,0.00013377392],"domain_scores_gemma":[0.97492653,0.021880837,0.00051858375,0.0014858404,0.0009662104,0.00022196067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008449601,0.0014935462,0.0030420115,0.0014545323,0.00086238584,0.0014058602,0.0025297825,0.0016980747,0.005845841],"category_scores_gemma":[0.035715073,0.0013706987,0.001781841,0.0015259258,0.0009505574,0.0021008945,0.0014863188,0.0035545724,0.0023279907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007737982,0.00030050104,0.0031670404,0.000709627,0.00094271294,0.0004681339,0.00012381669,0.5831216,0.0034227937,0.07582723,0.022616088,0.3085266],"study_design_scores_gemma":[0.00004056881,0.00003322147,0.0002507144,0.000012537632,0.00003964151,0.000027512371,0.000006297491,0.97188765,0.00046019157,0.026058247,0.0011739059,0.000009518382],"about_ca_topic_score_codex":0.0029534989,"about_ca_topic_score_gemma":0.0031369436,"teacher_disagreement_score":0.008449601,"about_ca_system_score_codex":0.0007939163,"about_ca_system_score_gemma":0.0012823982,"threshold_uncertainty_score":0.044686258},"labels":[],"label_agreement":null},{"id":"W1594439527","doi":"10.1108/ijicc-11-2014-0046","title":"Auto-regressive multiple-valued logic neurons with sequential Chua’s oscillator back-propagation learning for online prediction and synchronization of chaotic trajectories","year":2015,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Chaotic; Artificial neural network; Nonlinear system; Context (archaeology); Sequence (biology); Trigonometric functions; Attractor; Class (philosophy); Synchronization (alternating current); Function (biology); Artificial intelligence; Algorithm; Mathematics","score_opus":0.03263782837959502,"score_gpt":0.28598459608890614,"score_spread":0.25334676770931114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594439527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072960466,0.00077732105,0.9209419,0.00021524035,0.000100977755,0.00006131781,0.000038746643,0.0005025436,0.004401504],"genre_scores_gemma":[0.9104682,0.00027296008,0.086509205,0.000053228923,0.000023312503,0.000071259834,0.00004460032,0.000019414789,0.0025377576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998275,0.000039958417,0.00001176512,0.00003836502,0.000060098922,0.000022254804],"domain_scores_gemma":[0.9996685,0.00017114129,0.000040752715,0.000022114898,0.00008433495,0.000013199894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006342355,0.00043806146,0.0004724129,0.0002636686,0.00022608036,0.0006247986,0.0008527137,0.0005688691,0.001484533],"category_scores_gemma":[0.0013809017,0.00027765852,0.00049176713,0.00031556864,0.00032295028,0.00067084614,0.0004843442,0.0008641314,0.00021021756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084623585,0.00007911969,0.0009029834,0.00009566766,0.00005913681,0.00007106,0.00006425273,0.9078995,0.0049600145,0.0078340685,0.00043852616,0.07751108],"study_design_scores_gemma":[0.0000013370643,0.000013498606,0.00003163383,0.0000020232553,0.000002631685,0.000003080163,0.0000013566766,0.9992859,0.00026339875,0.0003237142,0.00007037709,0.0000011041134],"about_ca_topic_score_codex":0.004548471,"about_ca_topic_score_gemma":0.0044754767,"teacher_disagreement_score":0.004548471,"about_ca_system_score_codex":0.0004910619,"about_ca_system_score_gemma":0.0006065068,"threshold_uncertainty_score":0.009043992},"labels":[],"label_agreement":null},{"id":"W1594640477","doi":"10.1023/a:1012703414861","title":"Combining RBF Networks Trained by Different Clustering Techniques","year":2001,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Cluster analysis; Computer science; Radial basis function; Artificial intelligence; Hierarchical RBF; Computational intelligence; Pattern recognition (psychology); Data mining; Artificial neural network; Machine learning","score_opus":0.01438073587780904,"score_gpt":0.24029075672174716,"score_spread":0.22591002084393813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594640477","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013973013,0.00054103637,0.9815594,0.00012329817,0.00024242954,0.00004938292,0.000058945556,0.0014539142,0.0019985766],"genre_scores_gemma":[0.24137601,0.0006934276,0.7508424,0.00019182482,0.00018756322,0.00016618319,0.00045222178,0.0005005823,0.0055898046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893385,0.0002996531,0.000082463564,0.0002130796,0.00036578838,0.00010521622],"domain_scores_gemma":[0.9978124,0.00044942208,0.00008499569,0.00037596645,0.0012149774,0.000062177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023252224,0.0014755841,0.0018881247,0.0018521266,0.00064356264,0.0014192099,0.0017554164,0.0021382328,0.002517987],"category_scores_gemma":[0.005425513,0.00084632175,0.0015437161,0.0018334263,0.00038390706,0.0024758454,0.0010992676,0.0013191773,0.002325665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077149307,0.00020932722,0.001450116,0.00041008205,0.00061616674,0.00010619513,0.00012684501,0.22343507,0.04207486,0.0047360794,0.005034693,0.7210291],"study_design_scores_gemma":[0.000026941216,0.00006265388,0.00089312956,0.00003134794,0.00015896282,0.00005417714,0.000028955685,0.9744153,0.019774176,0.0025175954,0.0019914098,0.000045489698],"about_ca_topic_score_codex":0.0032152692,"about_ca_topic_score_gemma":0.0043617194,"teacher_disagreement_score":0.0032152692,"about_ca_system_score_codex":0.00058247487,"about_ca_system_score_gemma":0.0004981314,"threshold_uncertainty_score":0.012297094},"labels":[],"label_agreement":null},{"id":"W1597315667","doi":"10.6000/2371-1647.2015.01.05","title":"A Softcomputing Knowledge Areas Model","year":2015,"lang":"en","type":"article","venue":"Journal of Advances in Management Sciences & Information Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Scope (computer science); Computer science; Project manager; Project management; Procurement; Knowledge management; Quality (philosophy); Process management; Systems engineering; Engineering; Business","score_opus":0.03223708350990058,"score_gpt":0.3095213657592276,"score_spread":0.27728428224932705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597315667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058547217,0.001523834,0.8573914,0.005052404,0.0003051703,0.00041018619,0.003220697,0.0011317052,0.07241729],"genre_scores_gemma":[0.75649005,0.002251858,0.18347442,0.00095766364,0.00038772842,0.0009567704,0.002857247,0.00011892895,0.05250531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989054,0.00029621855,0.00009082723,0.00028204249,0.00024618936,0.00017932396],"domain_scores_gemma":[0.9981622,0.00095794175,0.00020104325,0.00016633059,0.00037619116,0.00013624209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010172403,0.0008127952,0.00074435555,0.0026207543,0.0010432602,0.004037119,0.0018451464,0.0018830039,0.012726259],"category_scores_gemma":[0.004184024,0.00039259548,0.0014235834,0.0031854815,0.0011184734,0.004551615,0.0017771154,0.0016735563,0.0032177214],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015322992,0.00025405214,0.0026680422,0.00026481235,0.00011979169,0.00043881542,0.00047857672,0.5223918,0.0009676592,0.3412167,0.010069937,0.12097658],"study_design_scores_gemma":[0.000017027867,0.00003324297,0.0004286312,0.00003942185,0.0000303574,0.00008486972,0.000090039546,0.8590382,0.00026932496,0.13164505,0.008309888,0.000013910855],"about_ca_topic_score_codex":0.012399447,"about_ca_topic_score_gemma":0.008746683,"teacher_disagreement_score":0.012726259,"about_ca_system_score_codex":0.002073409,"about_ca_system_score_gemma":0.0021107371,"threshold_uncertainty_score":0.04257351},"labels":[],"label_agreement":null},{"id":"W1597430260","doi":"10.1109/ccece.1996.548067","title":"On-chip learning in neurocomputers","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Very-large-scale integration; Analogue electronics; Implementation; Chip; Artificial neural network; Computer architecture; Computer engineering; Electronic circuit; Digital electronics; System on a chip; Computation; Electronic engineering; Artificial intelligence; Embedded system; Electrical engineering; Engineering; Telecommunications; Algorithm","score_opus":0.021375304805545503,"score_gpt":0.22135561021552483,"score_spread":0.19998030540997933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597430260","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045218024,0.02267654,0.85198826,0.0029852772,0.000940445,0.0001202195,0.00009522887,0.0016398415,0.07433609],"genre_scores_gemma":[0.76903987,0.014256305,0.1794045,0.0009509604,0.0005064773,0.00021973228,0.00014607386,0.00018149632,0.035294507],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962926,0.00012204884,0.000014891552,0.000052599946,0.00014447745,0.000036749057],"domain_scores_gemma":[0.999408,0.00033388496,0.00002509837,0.00008852658,0.00012126511,0.000023111097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004899365,0.00033570023,0.0004901448,0.00027764315,0.00036419457,0.0015459594,0.0009731552,0.0010926786,0.0039735436],"category_scores_gemma":[0.0019000011,0.00030338415,0.00018645496,0.0006158254,0.0011464041,0.0017073157,0.0006761421,0.0011319967,0.0009863653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011344847,0.00009969128,0.0007714334,0.00045484022,0.00003820535,0.00014212918,0.00018119455,0.25004244,0.0066059716,0.4543502,0.008427004,0.27877343],"study_design_scores_gemma":[0.00004262243,0.00011497274,0.0004591801,0.00013346231,0.000025932975,0.00017232742,0.0000797107,0.5755459,0.010347631,0.34294835,0.07009476,0.000035165973],"about_ca_topic_score_codex":0.0014793187,"about_ca_topic_score_gemma":0.0016501859,"teacher_disagreement_score":0.0039735436,"about_ca_system_score_codex":0.0007255891,"about_ca_system_score_gemma":0.00063306506,"threshold_uncertainty_score":0.013292849},"labels":[],"label_agreement":null},{"id":"W1598512943","doi":"10.1109/pacrim.2005.1517362","title":"Incremental communication for multilayer neural networks in a field programmable gate array","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Artificial neural network; Massively parallel; Telecommunications network; Layer (electronics); Field (mathematics); Field-programmable gate array; Distributed computing; Computer architecture; Computer hardware; Computer network; Parallel computing; Artificial intelligence","score_opus":0.01949877682949239,"score_gpt":0.2789224088678945,"score_spread":0.2594236320384021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1598512943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10512151,0.000349971,0.88593906,0.00027879258,0.00009431562,0.000082254366,0.0000550094,0.0015088728,0.0065702414],"genre_scores_gemma":[0.8692605,0.00019735155,0.12734173,0.00010503145,0.00003440515,0.00011090199,0.00006059729,0.000040639665,0.0028487775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977535,0.000057648394,0.000016650623,0.000031989064,0.0000857478,0.000032638727],"domain_scores_gemma":[0.9993789,0.0003312176,0.000074437274,0.00007350364,0.00012255568,0.000019284325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035990542,0.00026676894,0.00018765441,0.00023448165,0.00034497224,0.0004477945,0.0008024429,0.0003452802,0.0022102136],"category_scores_gemma":[0.0017256406,0.00012582069,0.00023125518,0.00026849564,0.00034364333,0.0008398656,0.00039881657,0.00059740624,0.00021404913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047155164,0.00017811682,0.0013045848,0.00032875562,0.000058168713,0.00044118238,0.00039777058,0.5413293,0.058255434,0.054891825,0.0032642998,0.33907896],"study_design_scores_gemma":[0.000017934803,0.00017783792,0.0003317882,0.0000123269865,0.000026113557,0.000094419636,0.000020762003,0.96739453,0.017958056,0.0111728925,0.002779025,0.000014324286],"about_ca_topic_score_codex":0.0019259186,"about_ca_topic_score_gemma":0.0032657601,"teacher_disagreement_score":0.0022102136,"about_ca_system_score_codex":0.0005354071,"about_ca_system_score_gemma":0.00051575556,"threshold_uncertainty_score":0.007393837},"labels":[],"label_agreement":null},{"id":"W1601371087","doi":"10.1109/ijcnn.1992.226977","title":"Dynamic neural units in the control of linear and nonlinear systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Nonlinear system; Artificial neural network; Computer science; Dynamical systems theory; Linear dynamical system; Feed forward; Control theory (sociology); Dynamical system (definition); Feedforward neural network; Scale (ratio); Nonlinear dynamical systems; Artificial intelligence; Control (management); Control engineering; Physics; Engineering","score_opus":0.014067095509820564,"score_gpt":0.23949089437361643,"score_spread":0.22542379886379585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601371087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017837388,0.023072325,0.8994584,0.0011110937,0.00052334653,0.00006408,0.0000674666,0.0004266272,0.057439294],"genre_scores_gemma":[0.82802,0.013869673,0.13129748,0.00039396595,0.0004757714,0.0001592688,0.00012155652,0.000116049785,0.025546325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995479,0.00013334058,0.00002759391,0.000073876196,0.00019011147,0.000027186268],"domain_scores_gemma":[0.999752,0.00012523586,0.000022615464,0.000019083891,0.00006996319,0.0000110324545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008039717,0.0004889668,0.0004510758,0.00038680664,0.00026999068,0.0014159038,0.0005529246,0.0009542665,0.00274955],"category_scores_gemma":[0.0018860974,0.00018875192,0.0002065349,0.0006359939,0.0011235982,0.0012626492,0.0005922734,0.000966043,0.000483459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006681762,0.000025692578,0.0003142625,0.0002957653,0.000031070682,0.00010313151,0.00011388955,0.52727157,0.0055434504,0.35463333,0.0027843069,0.10881673],"study_design_scores_gemma":[0.000010365416,0.000059812905,0.00017947763,0.000048787617,0.000011089681,0.00004677786,0.000022789642,0.8982108,0.002545736,0.08605568,0.012788629,0.000020027459],"about_ca_topic_score_codex":0.004573061,"about_ca_topic_score_gemma":0.002282411,"teacher_disagreement_score":0.004573061,"about_ca_system_score_codex":0.00096319674,"about_ca_system_score_gemma":0.00044915572,"threshold_uncertainty_score":0.009198189},"labels":[],"label_agreement":null},{"id":"W1602548444","doi":"10.1109/mwscas.2002.1187300","title":"A neural network based data association technique for tracking","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Data association; Artificial neural network; Computer science; Travelling salesman problem; Hopfield network; Kalman filter; Tracking (education); Association (psychology); Artificial intelligence; Function (biology); Filter (signal processing); Algorithm; Computer vision","score_opus":0.05510103801095167,"score_gpt":0.29630967178270445,"score_spread":0.24120863377175278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1602548444","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002114441,0.00018199385,0.9968278,0.000041061845,0.00003863762,0.000017896285,0.000016189855,0.0002367058,0.0005252274],"genre_scores_gemma":[0.16371532,0.0006453863,0.8306212,0.00015234131,0.00011662704,0.00012752383,0.00016289794,0.00005156735,0.004407131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995377,0.00005107612,0.000030470568,0.00011944168,0.00022418928,0.00003701934],"domain_scores_gemma":[0.9994178,0.00019002837,0.000075381315,0.00008094858,0.0002167377,0.000019094694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080651196,0.0004656565,0.0005043227,0.000751864,0.0005576839,0.00064143626,0.0013023999,0.0009244978,0.001629886],"category_scores_gemma":[0.0019900817,0.000313792,0.0004014749,0.0013772997,0.00039416697,0.0014235976,0.000802332,0.0010556293,0.00076594786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014199669,0.0000988657,0.0010507054,0.00017277297,0.00012295766,0.0001739624,0.000090063324,0.14596105,0.04063802,0.014666807,0.0021987194,0.7946842],"study_design_scores_gemma":[0.000016270957,0.00006362414,0.000598098,0.00002008781,0.00004302402,0.00025576664,0.000011532378,0.9673605,0.01897479,0.0059847403,0.0066420836,0.000029472703],"about_ca_topic_score_codex":0.0036728266,"about_ca_topic_score_gemma":0.0045861667,"teacher_disagreement_score":0.0036728266,"about_ca_system_score_codex":0.00057817827,"about_ca_system_score_gemma":0.000795493,"threshold_uncertainty_score":0.00730294},"labels":[],"label_agreement":null},{"id":"W1602982398","doi":"10.1007/3-540-45129-3_42","title":"On the Learning of Complex Movement Sequences","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Movement (music); Heuristic; Artificial intelligence; Construct (python library); Event (particle physics); Machine learning","score_opus":0.03650490253193118,"score_gpt":0.2655433063203154,"score_spread":0.22903840378838425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1602982398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01694036,0.00083113404,0.97865343,0.00025784422,0.0000667542,0.000029082124,0.00009579604,0.00044118066,0.00268446],"genre_scores_gemma":[0.5421119,0.0034369943,0.4296547,0.00039313614,0.00030205728,0.000273273,0.0013839382,0.00036308388,0.022080949],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966466,0.00008808699,0.000029878769,0.000110049536,0.00007388855,0.000033413304],"domain_scores_gemma":[0.9970112,0.00227281,0.0001661911,0.00025687306,0.00020595422,0.00008700705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001088163,0.0009457741,0.0012684755,0.00073870516,0.00035521758,0.0012203354,0.0017696981,0.001301902,0.0066402447],"category_scores_gemma":[0.006554375,0.0006523743,0.0006418408,0.0013747398,0.0011498212,0.0026718418,0.00219957,0.0015206583,0.0008170923],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001430625,0.000050369123,0.0008355143,0.00016860051,0.00006145102,0.00005783358,0.00013488965,0.4128123,0.001884879,0.049219612,0.0041645644,0.530467],"study_design_scores_gemma":[0.0000117224445,0.000031228,0.00028002492,0.0000218587,0.0000068124195,0.000026469465,0.000012387817,0.9419759,0.0005162812,0.055990472,0.0011198904,0.000006895221],"about_ca_topic_score_codex":0.006695581,"about_ca_topic_score_gemma":0.0057640574,"teacher_disagreement_score":0.006695581,"about_ca_system_score_codex":0.0005980121,"about_ca_system_score_gemma":0.000595179,"threshold_uncertainty_score":0.022213876},"labels":[],"label_agreement":null},{"id":"W1603364293","doi":"","title":"Learning coordination classifiers","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Boosting (machine learning); Classifier (UML); Computer science; Artificial intelligence; Independent and identically distributed random variables; Machine learning; Random subspace method; Ensemble learning; Training set; Pattern recognition (psychology); Mathematics; Random variable; Statistics","score_opus":0.012951165834694892,"score_gpt":0.24158344419725755,"score_spread":0.22863227836256267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603364293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007417941,0.00021808299,0.9885699,0.00023559443,0.000091643196,0.00006597716,0.00013271437,0.00075353525,0.002514598],"genre_scores_gemma":[0.36642674,0.0005373319,0.6229898,0.00065319706,0.00046556827,0.0005826075,0.0015004174,0.0003501359,0.006494262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99659985,0.0010501068,0.00016284015,0.0010617396,0.00086502515,0.00026040646],"domain_scores_gemma":[0.9942743,0.0019058274,0.0005501087,0.0017038226,0.0012029341,0.00036296324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004740959,0.0014271472,0.0023406658,0.002058585,0.0011695191,0.002220767,0.003417802,0.0017595266,0.0035110197],"category_scores_gemma":[0.012446576,0.0008131399,0.0013939795,0.002022731,0.0012264496,0.0038635067,0.003414393,0.0028717278,0.0025702615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043753773,0.00029790538,0.00678219,0.00023374545,0.0004361055,0.0002141439,0.00032790384,0.2066921,0.007694098,0.10658287,0.02087803,0.6494234],"study_design_scores_gemma":[0.000040868017,0.00012283865,0.0004920442,0.000026132342,0.00008080709,0.00008222503,0.000042580643,0.8944617,0.0031551188,0.09455592,0.006918745,0.00002100065],"about_ca_topic_score_codex":0.0013305246,"about_ca_topic_score_gemma":0.0014936407,"teacher_disagreement_score":0.004740959,"about_ca_system_score_codex":0.0010019725,"about_ca_system_score_gemma":0.0015588062,"threshold_uncertainty_score":0.025072873},"labels":[],"label_agreement":null},{"id":"W1604609413","doi":"10.1109/acssc.1996.600935","title":"Nonlinear state estimation using neural filters","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Nonlinear system; Radial basis function; Nonlinear dynamical systems; Artificial neural network; Computer science; State (computer science); Control theory (sociology); Function (biology); Basis (linear algebra); Algorithm; Applied mathematics; Artificial intelligence; Mathematics; Physics","score_opus":0.04293610855209108,"score_gpt":0.2641662744025062,"score_spread":0.2212301658504151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1604609413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00816587,0.0003367848,0.9903053,0.000050507628,0.000035401925,0.000010298452,0.000013295767,0.000403104,0.0006794662],"genre_scores_gemma":[0.4463234,0.00095571537,0.5478729,0.00014204574,0.00014357778,0.0000757497,0.00012709278,0.00009539375,0.00426421],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956983,0.00010262956,0.000024058267,0.00011984305,0.00014281312,0.00004072422],"domain_scores_gemma":[0.9993544,0.00032500658,0.000097316464,0.00006996392,0.00013704595,0.000016248083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080213975,0.0005747859,0.00061233656,0.00080554996,0.00033922482,0.00069761777,0.0005787097,0.0010212074,0.0011775622],"category_scores_gemma":[0.002782404,0.00029186133,0.0003888677,0.0005889528,0.00038148003,0.0013020552,0.0006408338,0.0007026326,0.0007996351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025083113,0.000091129456,0.0013890157,0.00016587687,0.00010541221,0.00008875498,0.00011577663,0.19449945,0.04520941,0.010181901,0.0011145823,0.74678785],"study_design_scores_gemma":[0.000008379175,0.000039391252,0.00058675674,0.000014795773,0.000017814882,0.000039498387,0.000008429211,0.98334426,0.010529398,0.0039582537,0.0014317407,0.000021190064],"about_ca_topic_score_codex":0.0034098886,"about_ca_topic_score_gemma":0.0031533616,"teacher_disagreement_score":0.0034098886,"about_ca_system_score_codex":0.00039258276,"about_ca_system_score_gemma":0.00035665513,"threshold_uncertainty_score":0.006780088},"labels":[],"label_agreement":null},{"id":"W1605189022","doi":"10.1016/s0079-6123(06)65033-4","title":"On the challenge of learning complex functions","year":2007,"lang":"en","type":"review","venue":"Progress in brain research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Boosting (machine learning); Curse of dimensionality; Deep learning; Computational learning theory; Artificial neural network; Algorithmic learning theory; Unsupervised learning","score_opus":0.43375663983188134,"score_gpt":0.5119422594268859,"score_spread":0.07818561959500453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1605189022","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042484075,0.9767086,0.013371275,0.0045940992,0.0008646603,0.000007347373,0.000023010334,0.000042307103,0.003963992],"genre_scores_gemma":[0.0038393696,0.98637563,0.0045995237,0.0012271784,0.0017965768,0.000012703022,0.000043658,0.000014441572,0.0020909768],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99963295,0.000075208816,0.00002553103,0.000059704973,0.00018651997,0.000020171106],"domain_scores_gemma":[0.99804854,0.001308589,0.000079733145,0.00008158031,0.00042051455,0.00006107997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014023246,0.0011046934,0.0014851523,0.0011921347,0.00038968577,0.0016061951,0.0020099862,0.0023396572,0.0023945873],"category_scores_gemma":[0.0024486925,0.00034403606,0.00034626597,0.0023894873,0.0023467222,0.004916761,0.0012238247,0.0033400706,0.0018703119],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047688223,0.00003985325,0.00016792868,0.0047766515,0.00007126039,0.00009705193,0.00007083989,0.0037238721,0.0012011238,0.07065977,0.05996456,0.8591794],"study_design_scores_gemma":[0.000019843454,0.000056199347,0.00042950592,0.0016210615,0.000053069056,0.00064334477,0.000077593635,0.003254148,0.0012494854,0.11978343,0.8727701,0.00004221518],"about_ca_topic_score_codex":0.0016341521,"about_ca_topic_score_gemma":0.002566796,"teacher_disagreement_score":0.0023945873,"about_ca_system_score_codex":0.0009403389,"about_ca_system_score_gemma":0.0014474791,"threshold_uncertainty_score":0.008010685},"labels":[],"label_agreement":null},{"id":"W1606621089","doi":"10.1016/b978-0-08-097086-8.43065-5","title":"Memory Models: Quantitative","year":2015,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Cognitive science; Field (mathematics); Psychology; Mathematics","score_opus":0.07758391566170338,"score_gpt":0.28440670681934177,"score_spread":0.2068227911576384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1606621089","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005215717,0.041268446,0.6727293,0.008645456,0.0014871125,0.000049034265,0.0017975896,0.0024029715,0.26640442],"genre_scores_gemma":[0.33822092,0.04714726,0.12206179,0.0019008559,0.003385336,0.00041324037,0.0034569488,0.0017984802,0.48161516],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99977785,0.000060214403,0.000011254055,0.000045970013,0.000086053165,0.000018685902],"domain_scores_gemma":[0.99934226,0.00029584463,0.000044176904,0.00016115318,0.00012594793,0.000030614076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007214603,0.0013886238,0.00096930994,0.0009514182,0.00036417146,0.0025908328,0.0014145419,0.0014426551,0.038694527],"category_scores_gemma":[0.0029999928,0.00054655847,0.0006147265,0.0013294809,0.0010734955,0.0044417745,0.0010793319,0.0018114892,0.010040849],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013007465,0.000031973726,0.00020142253,0.00027477086,0.000034035074,0.000032454205,0.00006873059,0.025576144,0.00042339065,0.7412538,0.09284331,0.13924697],"study_design_scores_gemma":[0.000004299891,0.000008131017,0.00016128452,0.00008547591,0.00001674277,0.000057894275,0.000028861454,0.06735426,0.00033363965,0.8762519,0.055683993,0.000013558721],"about_ca_topic_score_codex":0.002430795,"about_ca_topic_score_gemma":0.002110284,"teacher_disagreement_score":0.038694527,"about_ca_system_score_codex":0.001164499,"about_ca_system_score_gemma":0.00087148434,"threshold_uncertainty_score":0.12944609},"labels":[],"label_agreement":null},{"id":"W1607067797","doi":"10.1002/0470021187.ch4","title":"Advanced Topics in Random Processes","year":2004,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Transformation (genetics); Random element; Random compact set; Random function; Stochastic process; Statistical physics; Mathematics; Linear map; Random field; Computer science; Pure mathematics; Physics; Statistics","score_opus":0.00887258469175431,"score_gpt":0.2471180942082534,"score_spread":0.2382455095164991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1607067797","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068478915,0.46263608,0.21689118,0.022231124,0.017669024,0.0001395969,0.00062611786,0.0005088608,0.2724501],"genre_scores_gemma":[0.19976363,0.40370184,0.112752564,0.007421541,0.05935157,0.00079871004,0.0012796306,0.0006385318,0.21429186],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99896526,0.00035913952,0.000073592215,0.00021950723,0.00032956278,0.000052932028],"domain_scores_gemma":[0.99791735,0.0013890177,0.00008482517,0.00019938035,0.00031389252,0.00009552507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019271766,0.0016240143,0.0020950513,0.0021179686,0.0006925285,0.0035827241,0.0010041858,0.0023035894,0.014818743],"category_scores_gemma":[0.004492725,0.00046839457,0.0011066329,0.0046534687,0.00296223,0.0034225867,0.0011939394,0.0058288067,0.0044915583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008763224,0.000019071858,0.000119785764,0.00028330355,0.0000205224,0.000042055617,0.00014704354,0.0010074307,0.00025970882,0.956515,0.016958844,0.024618525],"study_design_scores_gemma":[0.000014896729,0.000032592012,0.00044062122,0.00019460921,0.000017436181,0.00016023865,0.000054657503,0.0058850823,0.00014005911,0.80895674,0.18409055,0.000012550957],"about_ca_topic_score_codex":0.0010259149,"about_ca_topic_score_gemma":0.0007027495,"teacher_disagreement_score":0.014818743,"about_ca_system_score_codex":0.0020906308,"about_ca_system_score_gemma":0.001256104,"threshold_uncertainty_score":0.04957366},"labels":[],"label_agreement":null},{"id":"W1608087182","doi":"","title":"What kind of a graphical model is the brain","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Computer science; Graphical model; Discriminative model; Generative model; Artificial intelligence; Series (stratigraphy); Latent variable; Machine learning; Deep learning; Layer (electronics); Generative grammar; Pattern recognition (psychology)","score_opus":0.020661369499850393,"score_gpt":0.26526276794012926,"score_spread":0.24460139844027887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1608087182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040695485,0.006727732,0.7359053,0.09866732,0.0022096448,0.00011187661,0.004796506,0.0046856184,0.10620049],"genre_scores_gemma":[0.7694941,0.0068668714,0.18411669,0.009804945,0.001390298,0.00013849082,0.00265308,0.0011623319,0.024373166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99920815,0.00030060217,0.00002278737,0.00027818818,0.00011717819,0.00007310752],"domain_scores_gemma":[0.9985341,0.0005890148,0.00013425446,0.0004000366,0.00019523104,0.0001473431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000742844,0.0005070961,0.00056321186,0.00092551135,0.0006975221,0.004915688,0.0011755163,0.0021786338,0.012599599],"category_scores_gemma":[0.006583881,0.00059215346,0.0011009083,0.00073873514,0.0046026353,0.010962105,0.00093440997,0.0020819302,0.0042418167],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092942224,0.000039092596,0.0034650275,0.00035034792,0.00019016785,0.00019843923,0.0010717431,0.008917052,0.004197791,0.8475047,0.06007262,0.07390016],"study_design_scores_gemma":[0.000016008133,0.00002066354,0.0010661007,0.00011048183,0.000034966546,0.00032482666,0.00023284912,0.018588701,0.00070012175,0.9435632,0.035297364,0.000044613116],"about_ca_topic_score_codex":0.0034921083,"about_ca_topic_score_gemma":0.002443803,"teacher_disagreement_score":0.012599599,"about_ca_system_score_codex":0.0008779906,"about_ca_system_score_gemma":0.00061216677,"threshold_uncertainty_score":0.04214984},"labels":[],"label_agreement":null},{"id":"W1608136203","doi":"10.1109/icnn.1994.374339","title":"A new learning algorithm for bidirectional associative memory neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Bidirectional associative memory; Artificial neural network; Content-addressable storage; Associative property; Generalization; Content-addressable memory; Noise (video); Algorithm; Associative learning; Function (biology); Artificial intelligence; Recall; Mathematics; Image (mathematics)","score_opus":0.021651150635277422,"score_gpt":0.2434468317403018,"score_spread":0.22179568110502437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1608136203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021303694,0.00018092361,0.9957527,0.00007192205,0.00007848032,0.000036302583,0.000027441072,0.00033068057,0.0013911534],"genre_scores_gemma":[0.06000459,0.0003769285,0.93149054,0.0001570662,0.0001075982,0.00037711728,0.0001961701,0.00020939733,0.0070804874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952984,0.00008785859,0.0000390423,0.00009982198,0.00019186677,0.00005157085],"domain_scores_gemma":[0.99941874,0.00017589993,0.000044267035,0.000069045585,0.0002623109,0.00002968487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009105977,0.0008137488,0.0008540876,0.0010235821,0.00066277763,0.0010439423,0.0017128534,0.0013257546,0.0063251983],"category_scores_gemma":[0.002601037,0.00041578893,0.0005910179,0.0010850783,0.00052763306,0.001984961,0.0016129283,0.0012502,0.0018813789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114566225,0.00007214357,0.00054177496,0.00019305736,0.00006877308,0.00009454324,0.00009164633,0.14653078,0.010664414,0.058879435,0.0067137745,0.7760351],"study_design_scores_gemma":[0.000039418166,0.000050367245,0.000113109716,0.00003007184,0.00002087279,0.00012947431,0.000016129334,0.9595261,0.0048846113,0.023419904,0.011750641,0.000019254094],"about_ca_topic_score_codex":0.0016458451,"about_ca_topic_score_gemma":0.0017629822,"teacher_disagreement_score":0.0063251983,"about_ca_system_score_codex":0.00051876117,"about_ca_system_score_gemma":0.00081458205,"threshold_uncertainty_score":0.021159828},"labels":[],"label_agreement":null},{"id":"W1611222858","doi":"10.1007/978-3-642-02457-3_56","title":"On-Demand Chaotic Neural Network for Broadcast Scheduling Problem","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Time division multiple access; Chaotic; Scheduling (production processes); Noise (video); Artificial neural network; Network packet; Mathematical optimization; Real-time computing; Computer network; Algorithm; Artificial intelligence; Mathematics","score_opus":0.019035469822240147,"score_gpt":0.25105701064078095,"score_spread":0.23202154081854082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1611222858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21071298,0.0014621442,0.76630217,0.001961029,0.0002947835,0.00014659487,0.0004193616,0.0003296811,0.01837123],"genre_scores_gemma":[0.9762165,0.00035469298,0.01674377,0.00009864857,0.00010449627,0.00010601599,0.00017869593,0.000038768147,0.006158396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999775,0.0000626737,0.000010281536,0.00004986844,0.000034934943,0.00006736115],"domain_scores_gemma":[0.9989039,0.00077308825,0.00008572602,0.00003512895,0.00013650685,0.00006567954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066401745,0.00069410005,0.0013829073,0.00038957954,0.00044074046,0.00074298563,0.0013576567,0.0016614302,0.004739245],"category_scores_gemma":[0.0024978777,0.00047796685,0.0003901454,0.0005915485,0.00056231313,0.0010531523,0.00086782756,0.0011718286,0.00021634388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015948746,0.000036630583,0.00023667216,0.00009101493,0.000017903369,0.000038957038,0.000022194385,0.9804585,0.00070325425,0.0048847143,0.0015227831,0.011827899],"study_design_scores_gemma":[0.000008187898,0.000008971294,0.000031512765,0.0000017733362,0.0000024325936,0.0000029950818,0.0000031745524,0.9984156,0.000053061915,0.0014206688,0.00005019477,0.000001441165],"about_ca_topic_score_codex":0.008868152,"about_ca_topic_score_gemma":0.0064181183,"teacher_disagreement_score":0.008868152,"about_ca_system_score_codex":0.0011178954,"about_ca_system_score_gemma":0.00086616375,"threshold_uncertainty_score":0.01763308},"labels":[],"label_agreement":null},{"id":"W1615026946","doi":"","title":"Convex combination initialization method for kohonen neural network implemented in the CMOS technology","year":2012,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Initialization; Computer science; Self-organizing map; Artificial neural network; CMOS; Realization (probability); Process (computing); Block (permutation group theory); Artificial intelligence; Computer engineering; Computer architecture; Electronic engineering; Engineering; Mathematics","score_opus":0.022142540625883975,"score_gpt":0.32314336963257034,"score_spread":0.30100082900668634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1615026946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003773294,0.00008688968,0.99371886,0.000035739973,0.000027534741,0.000017226594,0.000012531813,0.00023788675,0.0020899675],"genre_scores_gemma":[0.23751909,0.0002796765,0.7576958,0.000091095324,0.00003898654,0.00011804676,0.0000764012,0.000109844645,0.0040709707],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982065,0.000036507983,0.000008819031,0.000028032677,0.00009024118,0.00001579826],"domain_scores_gemma":[0.9998684,0.000046563775,0.000013482268,0.000017545546,0.000046294917,0.0000077638115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021153917,0.0003361598,0.00028481134,0.00020484609,0.0002048246,0.00036599752,0.0006486752,0.0003673387,0.0021074733],"category_scores_gemma":[0.00048182125,0.00017592985,0.00031685684,0.00029334068,0.00024377357,0.00046084044,0.00038308706,0.000630762,0.0005371635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017462872,0.000060304734,0.00061556546,0.00029842864,0.00008385093,0.00023822127,0.00017125526,0.40148184,0.091268815,0.05284278,0.0041890177,0.4485753],"study_design_scores_gemma":[0.000007805787,0.000035871915,0.00014996719,0.000009127596,0.000008435169,0.00007290634,0.0000075829216,0.97717595,0.014815899,0.0037283842,0.0039757043,0.000012367056],"about_ca_topic_score_codex":0.0010715531,"about_ca_topic_score_gemma":0.0015306291,"teacher_disagreement_score":0.0021074733,"about_ca_system_score_codex":0.0003482329,"about_ca_system_score_gemma":0.00043712082,"threshold_uncertainty_score":0.007050216},"labels":[],"label_agreement":null},{"id":"W1618089052","doi":"10.1109/ijcnn.1992.287073","title":"Decoupled recursive estimation training and trainable degree of feedforward neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Extended Kalman filter; Backpropagation; Artificial neural network; Convergence (economics); Computer science; Kalman filter; Feed forward; Feedforward neural network; Filter (signal processing); Nonlinear system; Artificial intelligence; Control theory (sociology); Algorithm; Control engineering; Engineering; Control (management)","score_opus":0.03644424867913133,"score_gpt":0.255813838423464,"score_spread":0.21936958974433268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1618089052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008496689,0.00011184461,0.98992234,0.000040997587,0.000017928089,0.000014145558,0.000017097234,0.00028138337,0.0010975673],"genre_scores_gemma":[0.50532436,0.00032631555,0.48783177,0.00007789607,0.000046078796,0.00015708631,0.0001940395,0.00009088111,0.0059515466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994978,0.00011465045,0.00003060938,0.00012727991,0.00017936957,0.000050292463],"domain_scores_gemma":[0.9992499,0.00024214278,0.00009770856,0.00013872363,0.00024430995,0.000027240958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065844477,0.0005529741,0.0005803868,0.00033634462,0.00036590453,0.0005858076,0.0010889993,0.00068502047,0.0015794294],"category_scores_gemma":[0.0033637357,0.0004514681,0.00049001287,0.00039294144,0.0005144216,0.0012735558,0.000997417,0.0010662773,0.00041246947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075947304,0.00003922675,0.00068617414,0.00007727289,0.00003949322,0.000072051604,0.0001179358,0.77696174,0.012424161,0.026945837,0.001000659,0.1815595],"study_design_scores_gemma":[0.0000029997705,0.000012914957,0.00010245712,0.0000036012455,0.00000397344,0.000011089487,0.0000020779237,0.99500716,0.0014304637,0.0029170103,0.0005024904,0.0000037645684],"about_ca_topic_score_codex":0.004297039,"about_ca_topic_score_gemma":0.0062015746,"teacher_disagreement_score":0.004297039,"about_ca_system_score_codex":0.00067723746,"about_ca_system_score_gemma":0.0007219906,"threshold_uncertainty_score":0.008544087},"labels":[],"label_agreement":null},{"id":"W1621349616","doi":"10.1063/1.2737007","title":"Biological and Physical Principles in Self-Organization of Brain","year":2007,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Self-organization; Cognitive science; Pruning; Phenomenon; Self-organizing map; Self organisation; Artificial intelligence; Feature (linguistics); Scale (ratio); Theoretical computer science; Artificial neural network; Psychology; Epistemology; Biology; Geography","score_opus":0.02825268864900549,"score_gpt":0.25881191958005856,"score_spread":0.23055923093105307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1621349616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1915889,0.10719913,0.41366112,0.04267945,0.0021380782,0.00017695953,0.00042013178,0.0007838204,0.2413524],"genre_scores_gemma":[0.90498567,0.018995645,0.05525794,0.0016494935,0.0010390142,0.00015458067,0.00011982286,0.00010523927,0.017692512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99977285,0.000044807064,0.0000135315095,0.000048776867,0.00009614838,0.00002383794],"domain_scores_gemma":[0.9996698,0.000121920704,0.000046233046,0.00005302677,0.00007488257,0.00003414184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047534634,0.00025766718,0.00030916167,0.00068245153,0.00064243533,0.0017918616,0.00053118425,0.0010401956,0.0021201698],"category_scores_gemma":[0.000989115,0.00021929397,0.00035276267,0.00037032887,0.0043652165,0.0022929115,0.00084558094,0.0011202168,0.00045446426],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066614816,0.00001605672,0.00037908013,0.000120172444,0.000014287409,0.00011250528,0.00037236846,0.0082098255,0.0029820872,0.9690871,0.0024008462,0.016298903],"study_design_scores_gemma":[0.0000034239044,0.000008239725,0.0006702002,0.000022498038,0.0000026302607,0.00006221358,0.000061257575,0.0041223657,0.0002916427,0.9860951,0.008651639,0.0000089676405],"about_ca_topic_score_codex":0.00064188085,"about_ca_topic_score_gemma":0.0005148646,"teacher_disagreement_score":0.0021201698,"about_ca_system_score_codex":0.0005839276,"about_ca_system_score_gemma":0.00047444392,"threshold_uncertainty_score":0.0070926547},"labels":[],"label_agreement":null},{"id":"W162907590","doi":"10.82308/44454","title":"On characteristics of Markov decision processes and reinforcement learning in large domains","year":2005,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Computer science; Markov decision process; Artificial intelligence; Machine learning; Task (project management); Instance-based learning; Process (computing); Temporal difference learning; Stability (learning theory); Markov process; Mathematics; Engineering","score_opus":0.010570715767718578,"score_gpt":0.23555058796916845,"score_spread":0.22497987220144988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W162907590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036001343,0.0016051215,0.94862455,0.0022274123,0.000100944235,0.00012600917,0.00016383492,0.00018468036,0.010966091],"genre_scores_gemma":[0.80960286,0.0031716626,0.17922169,0.0005840547,0.00040191118,0.00086998416,0.0003272807,0.00016308353,0.005657525],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99651754,0.0016771747,0.00019730045,0.0005676433,0.00078960607,0.0002507417],"domain_scores_gemma":[0.9379128,0.05280216,0.0037691852,0.0021180043,0.002195525,0.0012022324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006155832,0.0012182879,0.0017041034,0.0013443731,0.00085810304,0.0028729516,0.0016171825,0.0021609396,0.003749066],"category_scores_gemma":[0.04646743,0.00074897846,0.0015688669,0.001576257,0.004442608,0.006317776,0.002322171,0.004825896,0.0004316743],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007941167,0.00006858783,0.0017067977,0.00019420627,0.0000615384,0.00012243228,0.00026020335,0.29032183,0.00085306406,0.6919293,0.00084619503,0.013556469],"study_design_scores_gemma":[0.000025687277,0.000058882095,0.0004256583,0.000038209524,0.00001236868,0.000049490438,0.000029685245,0.6176061,0.00032423227,0.38030526,0.0011058239,0.000018524592],"about_ca_topic_score_codex":0.0034284093,"about_ca_topic_score_gemma":0.0018276594,"teacher_disagreement_score":0.006155832,"about_ca_system_score_codex":0.0028908837,"about_ca_system_score_gemma":0.0019786288,"threshold_uncertainty_score":0.03255552},"labels":[],"label_agreement":null},{"id":"W1630927467","doi":"10.1109/ijcnn.2015.7280655","title":"Learning valid categorical syllogisms using an associative memory","year":2015,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Syllogism; Categorical variable; Computer science; Artificial intelligence; Artificial neural network; Associative property; Unsupervised learning; Content-addressable memory; Machine learning; Mathematics","score_opus":0.12864680778832335,"score_gpt":0.33350550301807264,"score_spread":0.20485869522974928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1630927467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18021357,0.0002596538,0.8063756,0.0005116738,0.00012893562,0.00011972489,0.00016139411,0.0013379953,0.010891459],"genre_scores_gemma":[0.7656882,0.00030299148,0.22776748,0.0002943259,0.000053403346,0.000117856296,0.00035207643,0.000059090307,0.005364515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964654,0.000062253705,0.000030207166,0.0001259679,0.00010355307,0.000031492786],"domain_scores_gemma":[0.9989679,0.00040936135,0.00011250466,0.00024020368,0.000193278,0.00007677326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005851681,0.0004208987,0.0003486164,0.0003927439,0.0004082612,0.0009703578,0.0009452709,0.0005479243,0.0030550866],"category_scores_gemma":[0.0029454413,0.000257429,0.0005986788,0.00033407693,0.00075509713,0.0026770183,0.0010562174,0.0012092585,0.000636911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039577126,0.00054345635,0.009044408,0.0002849443,0.00019593685,0.0004041364,0.00045159287,0.046794012,0.066109344,0.08005663,0.005049954,0.7906698],"study_design_scores_gemma":[0.000044408673,0.00030195335,0.0038286047,0.000049807408,0.00013132734,0.00048457907,0.00017980934,0.75360316,0.037416708,0.19655186,0.0073350663,0.00007277429],"about_ca_topic_score_codex":0.0011783824,"about_ca_topic_score_gemma":0.0022368662,"teacher_disagreement_score":0.0030550866,"about_ca_system_score_codex":0.00033939158,"about_ca_system_score_gemma":0.00075855944,"threshold_uncertainty_score":0.010220289},"labels":[],"label_agreement":null},{"id":"W163273042","doi":"","title":"A mixed-signal feed-forward neural network architecture with on-chip learning in CMOS 0.18 microns.","year":2003,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor","funders":"","keywords":"CMOS; Artificial neural network; Computer science; Chip; SIGNAL (programming language); Computer architecture; Artificial intelligence; Electronic engineering; Engineering; Telecommunications","score_opus":0.017550198469259738,"score_gpt":0.2023843228346863,"score_spread":0.18483412436542657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W163273042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08402047,0.003199735,0.8771984,0.00066783896,0.0005219811,0.0002802527,0.0003132913,0.006476829,0.02732116],"genre_scores_gemma":[0.4950714,0.0006623172,0.4841614,0.00046055307,0.000053863187,0.00020314015,0.00032593383,0.00007820302,0.01898315],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984694,0.00002633189,0.000008037837,0.000031059644,0.000071492905,0.000016202659],"domain_scores_gemma":[0.9999124,0.000014269899,0.000010412132,0.000010309864,0.000044102184,0.000008586385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002019033,0.00031890793,0.00021383086,0.00022511286,0.00023143936,0.0004396876,0.0012046559,0.00049170613,0.0033082378],"category_scores_gemma":[0.00027115512,0.00016584068,0.0001844174,0.00018755262,0.00014727679,0.0005867796,0.00030442604,0.00039502577,0.00077410747],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035550824,0.0002532966,0.0015674126,0.0006082873,0.0001412878,0.0004015564,0.0001960736,0.073619485,0.30582005,0.01805919,0.01384188,0.5851358],"study_design_scores_gemma":[0.000069008165,0.0008279325,0.0013229586,0.000054010998,0.00007065435,0.00037370768,0.000045528344,0.8386989,0.108914495,0.0023882007,0.047184348,0.000050273648],"about_ca_topic_score_codex":0.0032716729,"about_ca_topic_score_gemma":0.008406488,"teacher_disagreement_score":0.0033082378,"about_ca_system_score_codex":0.000554486,"about_ca_system_score_gemma":0.0006216179,"threshold_uncertainty_score":0.011067152},"labels":[],"label_agreement":null},{"id":"W1635598920","doi":"","title":"The art of representation: memory reduction and noise tolerance in a neural network vision system","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Feature (linguistics); Computer science; Set (abstract data type); Representation (politics); Artificial neural network; Reduction (mathematics); Noise (video); Object (grammar); Generalization; Computer vision; Constraint (computer-aided design); Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.01107222238191022,"score_gpt":0.25407115314860557,"score_spread":0.24299893076669535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1635598920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058859564,0.00063719944,0.93312645,0.00042221355,0.0000915235,0.000037537502,0.000056306573,0.0015653452,0.005203898],"genre_scores_gemma":[0.79380006,0.0002786691,0.201025,0.0002764537,0.00007963567,0.000090732115,0.00010338539,0.000101806625,0.0042443145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967873,0.000059082424,0.000019904495,0.0001020082,0.000090887756,0.000049383394],"domain_scores_gemma":[0.9994067,0.00025979057,0.00006418027,0.00012895274,0.000116096155,0.000024355577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007769454,0.0004648024,0.00052152394,0.00039011007,0.0004049294,0.0009997893,0.0018500965,0.0010144071,0.0017617146],"category_scores_gemma":[0.00279546,0.00032648776,0.00044448374,0.0004204784,0.00076850713,0.0020013151,0.001012918,0.0011311183,0.00033344372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031339325,0.00010636541,0.00070578913,0.00010971251,0.0000720477,0.00013434581,0.00017967602,0.60051155,0.028542913,0.02374298,0.0028619457,0.34271935],"study_design_scores_gemma":[0.0000070556025,0.00003757636,0.00011214152,0.000005433279,0.0000094232955,0.000024654622,0.0000062213653,0.9911862,0.002971364,0.0050048656,0.0006283917,0.0000065877744],"about_ca_topic_score_codex":0.0043245587,"about_ca_topic_score_gemma":0.0037152926,"teacher_disagreement_score":0.0043245587,"about_ca_system_score_codex":0.0008682408,"about_ca_system_score_gemma":0.0005613902,"threshold_uncertainty_score":0.008598745},"labels":[],"label_agreement":null},{"id":"W1637112887","doi":"10.1109/iscas.1989.100466","title":"A neural network for temporal pattern recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial neural network; Node (physics); Fading; Artificial intelligence; Process (computing); Detector; Sequence (biology); Motion (physics); Pattern recognition (psychology); Computer network; Engineering; Programming language; Telecommunications","score_opus":0.03895575353832752,"score_gpt":0.2572915249298162,"score_spread":0.2183357713914887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1637112887","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035146624,0.0015681724,0.98765844,0.00023319761,0.00019794397,0.000045405228,0.00029138892,0.0017651154,0.00472569],"genre_scores_gemma":[0.21797255,0.00303187,0.7514794,0.0003893449,0.00025936458,0.0004258417,0.0014748666,0.0002405564,0.024726182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997584,0.000036152116,0.000019947985,0.00007891244,0.00008099015,0.000025553529],"domain_scores_gemma":[0.99979836,0.000072079885,0.000018686946,0.000033302123,0.00006512701,0.000012545296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037024854,0.0006047696,0.000485687,0.0005256946,0.00030699116,0.00094067585,0.001196986,0.0010367049,0.006242673],"category_scores_gemma":[0.0012086798,0.0003160823,0.0005867016,0.0009094948,0.00040444292,0.0012153855,0.00053097366,0.0010969517,0.0018411864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002108811,0.00008126942,0.00073596317,0.0003466247,0.00016419757,0.0002503826,0.00006626973,0.28715184,0.030579135,0.07176289,0.015984705,0.5926659],"study_design_scores_gemma":[0.000009878564,0.000023484792,0.00022065488,0.000019491703,0.00001810743,0.00007464085,0.0000047887615,0.9725381,0.0033262507,0.014559688,0.009191319,0.000013556911],"about_ca_topic_score_codex":0.005229811,"about_ca_topic_score_gemma":0.0048513524,"teacher_disagreement_score":0.006242673,"about_ca_system_score_codex":0.00064221036,"about_ca_system_score_gemma":0.0005599233,"threshold_uncertainty_score":0.020883799},"labels":[],"label_agreement":null},{"id":"W1638423567","doi":"10.1109/hpcs.2005.24","title":"Distributed Artificial Neural Network Architectures","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Artificial neural network; Perceptron; Artificial intelligence; Computation; Matrix multiplication; Simple (philosophy); Multiplication (music); Construct (python library); Matrix (chemical analysis); Machine learning; Algorithm; Mathematics","score_opus":0.016004300282953476,"score_gpt":0.2462004559817902,"score_spread":0.23019615569883672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1638423567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027902951,0.0018866432,0.9518313,0.0005848303,0.00034827823,0.00009903152,0.00013135557,0.0017040266,0.015511574],"genre_scores_gemma":[0.6726898,0.0020825088,0.299556,0.00036924914,0.0002069506,0.0003432219,0.0003857739,0.000109623186,0.024256846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995857,0.00009181848,0.000026280059,0.00010645769,0.00014837385,0.00004136473],"domain_scores_gemma":[0.999405,0.0001599242,0.000048249494,0.0001040352,0.0002563989,0.000026325171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073070044,0.00062534906,0.0006391457,0.00042820844,0.00039473682,0.0010820677,0.001527461,0.0012324854,0.0035047242],"category_scores_gemma":[0.0017552477,0.00023268306,0.00034135283,0.0005973274,0.00043807543,0.0012990513,0.00076395343,0.00090724404,0.001395695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002011447,0.00013982363,0.0010317714,0.0002341373,0.00009697001,0.00015526946,0.00007782862,0.6891757,0.009915979,0.047172863,0.0052000573,0.24659848],"study_design_scores_gemma":[0.00002341753,0.00007028793,0.00022339249,0.00002169027,0.00002160441,0.00006834438,0.000014816836,0.9742206,0.0029910943,0.01640884,0.00592187,0.000014042875],"about_ca_topic_score_codex":0.0013740819,"about_ca_topic_score_gemma":0.0018967295,"teacher_disagreement_score":0.0035047242,"about_ca_system_score_codex":0.00062295503,"about_ca_system_score_gemma":0.00061187916,"threshold_uncertainty_score":0.011724472},"labels":[],"label_agreement":null},{"id":"W1641425490","doi":"10.1109/mwscas.1993.343395","title":"On multiple-valued logic design of neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Artificial neural network; Massively parallel; Realization (probability); Computer science; Interconnection; Binary number; Logic synthesis; Theoretical computer science; Logic gate; Artificial intelligence; Computer architecture; Algorithm; Mathematics; Parallel computing; Arithmetic; Telecommunications","score_opus":0.06058017895093048,"score_gpt":0.25026417998668155,"score_spread":0.18968400103575106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1641425490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006597018,0.0017330939,0.9748316,0.00038949493,0.00011810746,0.000042574113,0.00004371048,0.00026411418,0.015980314],"genre_scores_gemma":[0.51460314,0.0039052877,0.47012183,0.0004781672,0.00020288091,0.00022525669,0.00016118711,0.00008095116,0.010221234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966204,0.00009256291,0.000023622943,0.00006683754,0.00012143783,0.000033552245],"domain_scores_gemma":[0.99978954,0.00009009081,0.00002107284,0.00002274796,0.000066687906,0.000009829228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004831612,0.00038476806,0.00029521875,0.0003076354,0.00029946526,0.0012208319,0.0007807085,0.00047303815,0.0034700292],"category_scores_gemma":[0.00094629097,0.00025732885,0.00036003822,0.0004362643,0.0005994473,0.0010784854,0.0004962263,0.00073552487,0.00061907084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010714781,0.000040677798,0.00022727689,0.00033851404,0.00003571878,0.00020486383,0.00020822136,0.1993648,0.020179652,0.5922149,0.0027323305,0.18434598],"study_design_scores_gemma":[0.000035487483,0.00009981921,0.00008144994,0.00009310449,0.000020754855,0.00015044649,0.0000253264,0.71308136,0.010128444,0.25135362,0.024906926,0.000023195926],"about_ca_topic_score_codex":0.00073777797,"about_ca_topic_score_gemma":0.0010992027,"teacher_disagreement_score":0.0034700292,"about_ca_system_score_codex":0.00077438896,"about_ca_system_score_gemma":0.00044962863,"threshold_uncertainty_score":0.011608422},"labels":[],"label_agreement":null},{"id":"W1643759488","doi":"10.1109/mwscas.1994.519371","title":"An efficient algorithm for identifying the structure of artificial neural networks for forecasting problems","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Computer science; Time series; Artificial intelligence; Backpropagation; Series (stratigraphy); Principal component analysis; Data mining; Machine learning; Algorithm","score_opus":0.06412861334239142,"score_gpt":0.2780171240682451,"score_spread":0.21388851072585366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1643759488","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010638444,0.00012626991,0.9981651,0.000048912763,0.000024318035,0.00003115537,0.000012132765,0.00016599892,0.00036230916],"genre_scores_gemma":[0.033837996,0.00031639522,0.9641554,0.000055196906,0.00005975031,0.0002922025,0.000113609356,0.000070547234,0.0010989424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994879,0.0001685326,0.000034958262,0.00008126785,0.0001957376,0.00003166208],"domain_scores_gemma":[0.9989767,0.0006580072,0.00007262602,0.00006769696,0.00020308963,0.000021900989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016627375,0.0013846904,0.0009710704,0.0010947192,0.0008554474,0.0006604128,0.0011990017,0.0015830263,0.0024021843],"category_scores_gemma":[0.00607525,0.0007163368,0.00066149,0.001186063,0.00074002834,0.0017038535,0.0010220429,0.0019268795,0.0010171051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074769814,0.000083323655,0.00052081014,0.00022342907,0.00008485945,0.0000902488,0.00012492768,0.51375073,0.004655101,0.036650464,0.0039070523,0.43983436],"study_design_scores_gemma":[0.00002104538,0.00002722763,0.000090533744,0.000021026583,0.000010734944,0.000031191485,0.000008759543,0.98111,0.00096799043,0.015378558,0.0023244016,0.0000085487245],"about_ca_topic_score_codex":0.0020608902,"about_ca_topic_score_gemma":0.0022792318,"teacher_disagreement_score":0.0024021843,"about_ca_system_score_codex":0.00058387365,"about_ca_system_score_gemma":0.0010615294,"threshold_uncertainty_score":0.008793473},"labels":[],"label_agreement":null},{"id":"W1645164775","doi":"10.1109/ccece.1993.332183","title":"A fast and efficient correlation calculation method","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Extension (predicate logic); Computer science; Correlation; Algorithm; Variable (mathematics); Artificial intelligence; Robot; Mathematics; Geometry","score_opus":0.01853381493310069,"score_gpt":0.25679756196457015,"score_spread":0.23826374703146946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1645164775","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010498221,0.00016884571,0.9943135,0.00006620112,0.00009247165,0.00004098688,0.000058021233,0.0013481114,0.0028620057],"genre_scores_gemma":[0.045968026,0.0003103608,0.9432032,0.00015672903,0.00011942437,0.00016591123,0.00038153978,0.00061578804,0.009079089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859625,0.00018579669,0.00006301286,0.00019587057,0.0008455539,0.00011352553],"domain_scores_gemma":[0.99882,0.00023462699,0.000062486724,0.00023728835,0.0005782956,0.00006727717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082649663,0.00087230274,0.0010717768,0.0018695234,0.001022394,0.0015434045,0.0016577281,0.00083055976,0.014127408],"category_scores_gemma":[0.0032625704,0.0005088474,0.0008998558,0.0021611413,0.00061087694,0.0017599168,0.0017460587,0.0013350472,0.006819881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017070434,0.00008569673,0.0009477581,0.00028407088,0.00009454813,0.00027404522,0.00015640305,0.052250765,0.026311321,0.12208242,0.038125597,0.75921667],"study_design_scores_gemma":[0.000069280766,0.00006669468,0.0005751427,0.00004554007,0.000048111553,0.00064323517,0.000046177443,0.8859491,0.02385834,0.031873655,0.05674037,0.00008438335],"about_ca_topic_score_codex":0.002827063,"about_ca_topic_score_gemma":0.0021322495,"teacher_disagreement_score":0.014127408,"about_ca_system_score_codex":0.0007204232,"about_ca_system_score_gemma":0.00193187,"threshold_uncertainty_score":0.04726088},"labels":[],"label_agreement":null},{"id":"W1665018864","doi":"10.1109/tnnls.2015.2426182","title":"Global Nonlinear Kernel Prediction for Large Data Set With a Particle Swarm-Optimized Interval Support Vector Regression","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Particle swarm optimization; Support vector machine; Kernel (algebra); Computer science; Generalization; Mathematical optimization; Interval (graph theory); Nonlinear system; Regression; Algorithm; Mathematics; Artificial intelligence; Data mining; Statistics","score_opus":0.04546472639087722,"score_gpt":0.2897368285218907,"score_spread":0.24427210213101347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1665018864","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016377991,0.00015201261,0.9825143,0.00006291593,0.000030425606,0.000013328139,0.000018287348,0.00028521614,0.00054553885],"genre_scores_gemma":[0.7740307,0.0003143059,0.22213814,0.00007103926,0.00006656696,0.00010701238,0.00023224094,0.00012373998,0.002916302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995283,0.00012451684,0.000030558458,0.00011132287,0.00016523732,0.000040136052],"domain_scores_gemma":[0.99906987,0.00040466335,0.00012897445,0.000111274465,0.00025189892,0.000033328644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011392713,0.00064928253,0.0010352333,0.00044718818,0.00020561159,0.00076414994,0.0009702723,0.00054646266,0.00095832604],"category_scores_gemma":[0.0031547123,0.00030495386,0.0006283829,0.00060865487,0.0003426539,0.0011245013,0.00076464826,0.0012644461,0.00027778244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011514669,0.00006877323,0.0018818121,0.000079635436,0.00006755336,0.00007902776,0.00006446719,0.8671197,0.0038847062,0.0046136645,0.0013239868,0.120701544],"study_design_scores_gemma":[0.000001343747,0.000010390969,0.000060261034,0.0000010886356,0.0000017952999,0.0000031321367,0.000001478487,0.9994105,0.00027053288,0.00016960995,0.000068366666,0.000001532231],"about_ca_topic_score_codex":0.0031000578,"about_ca_topic_score_gemma":0.001847114,"teacher_disagreement_score":0.0031000578,"about_ca_system_score_codex":0.00027636232,"about_ca_system_score_gemma":0.0006351701,"threshold_uncertainty_score":0.006164074},"labels":[],"label_agreement":null},{"id":"W1666587748","doi":"10.1109/mwscas.1992.271156","title":"A VLSI neural network design approach using a priori knowledge","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"A priori and a posteriori; Computer science; Artificial neural network; Very-large-scale integration; Table (database); Implementation; Artificial intelligence; Content-addressable memory; Lookup table; Value (mathematics); Machine learning; Computer engineering; Theoretical computer science; Data mining; Programming language; Embedded system","score_opus":0.07650081984100482,"score_gpt":0.281699450044968,"score_spread":0.20519863020396317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1666587748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022446858,0.00036972752,0.98965704,0.00015180912,0.000050126302,0.000032830147,0.000046136254,0.00043643184,0.007011266],"genre_scores_gemma":[0.19558951,0.0013037965,0.7868047,0.0003721352,0.00012913065,0.00022164719,0.00025798424,0.000088967055,0.015232025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978906,0.000036421978,0.00001560143,0.000053518208,0.000087091685,0.000018303192],"domain_scores_gemma":[0.9998115,0.000057199326,0.000015404756,0.00002791285,0.00008219283,0.000005831533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028008252,0.00044273687,0.00029459404,0.00037669193,0.00024563045,0.0010502753,0.0010695583,0.00072458026,0.0055850605],"category_scores_gemma":[0.000655622,0.00027807627,0.00034021505,0.0003384397,0.00032150306,0.0011194331,0.0003149252,0.00062243396,0.0014982868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001181775,0.000069797046,0.00066408917,0.0004570717,0.00012752834,0.00023454509,0.000120019635,0.3403984,0.052462965,0.11650114,0.0067593413,0.48208702],"study_design_scores_gemma":[0.000027638816,0.00016102733,0.00026831633,0.000060805312,0.00006403374,0.00022874598,0.000021732409,0.9137611,0.01992363,0.032635447,0.0328235,0.000024055607],"about_ca_topic_score_codex":0.0018582314,"about_ca_topic_score_gemma":0.0039239693,"teacher_disagreement_score":0.0055850605,"about_ca_system_score_codex":0.00064495584,"about_ca_system_score_gemma":0.0007408494,"threshold_uncertainty_score":0.01868391},"labels":[],"label_agreement":null},{"id":"W1668864538","doi":"10.1109/nafips.2001.944308","title":"A truncated normalized max product set of equations and its solution for a recurrent fuzzy neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Artificial neural network; Normalization (sociology); Bounded function; Finite set; Binary number; Cardinality (data modeling); Recurrent neural network; Algorithm; Applied mathematics; Computer science; Discrete mathematics; Artificial intelligence; Arithmetic; Mathematical analysis","score_opus":0.06794248512529702,"score_gpt":0.27722298069190016,"score_spread":0.20928049556660314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1668864538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00374646,0.00018163449,0.9902609,0.00015180586,0.000051047027,0.000039078615,0.00007539478,0.00007341503,0.0054201973],"genre_scores_gemma":[0.207573,0.00080720417,0.7725044,0.00024461743,0.00011949192,0.0007740179,0.00045912425,0.00021169854,0.017306471],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990188,0.0002964613,0.00007614686,0.00022882575,0.00031066866,0.000069068985],"domain_scores_gemma":[0.99888164,0.00060927164,0.00010689486,0.00006521105,0.0002950145,0.00004192955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002223498,0.0011312616,0.0010528318,0.0010114778,0.0006299038,0.0018531962,0.0015231131,0.0019144517,0.0070512416],"category_scores_gemma":[0.0058001694,0.00058550003,0.0016035945,0.0011973982,0.0017274134,0.002428161,0.0017729359,0.0026428767,0.0013669195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060940743,0.000040418825,0.00039718728,0.00023158186,0.00006471086,0.00034784368,0.00034743093,0.35914734,0.0031697925,0.57315624,0.0036148522,0.05942165],"study_design_scores_gemma":[0.0000096339445,0.000029821598,0.00006596062,0.000022987515,0.000011126949,0.00008072898,0.000025772555,0.868641,0.0007997599,0.12718186,0.0031102523,0.000020978672],"about_ca_topic_score_codex":0.003617379,"about_ca_topic_score_gemma":0.0037334226,"teacher_disagreement_score":0.0070512416,"about_ca_system_score_codex":0.001153647,"about_ca_system_score_gemma":0.0019062936,"threshold_uncertainty_score":0.023588717},"labels":[],"label_agreement":null},{"id":"W1673923490","doi":"10.48550/arxiv.1312.6199","title":"Intriguing properties of neural networks","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5745,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial neural network; Artifact (error); Perturbation (astronomy); Artificial intelligence; Deep neural networks; Machine learning","score_opus":0.07728990108899962,"score_gpt":0.17018709919439542,"score_spread":0.0928971981053958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1673923490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16522975,0.0100678755,0.705673,0.02419975,0.00094769266,0.000115326366,0.0011494926,0.0010156942,0.09160152],"genre_scores_gemma":[0.93976855,0.0035178624,0.046200175,0.0013278052,0.0010267952,0.00018790035,0.0003802492,0.00017883163,0.007412015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986798,0.00039580863,0.00007366498,0.0003382894,0.000405142,0.00010729258],"domain_scores_gemma":[0.9950073,0.0031714656,0.0003764799,0.0007370085,0.00051918416,0.00018859169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002174663,0.0005454027,0.0006522624,0.001131281,0.0010113202,0.0025691018,0.0011237116,0.002028613,0.0041438285],"category_scores_gemma":[0.017581932,0.00051583647,0.00062248524,0.0006068708,0.0037836079,0.0054553924,0.0019480065,0.0033701789,0.0005411797],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006678484,0.000028305087,0.0020175797,0.00015657327,0.000074635296,0.00014763999,0.00020062881,0.041741766,0.002813379,0.92014766,0.0032739732,0.029331073],"study_design_scores_gemma":[0.00001636185,0.000025609019,0.0007467676,0.000035049812,0.000015124589,0.00013857512,0.00003863982,0.09786592,0.00070217287,0.89588076,0.0045200456,0.000014985265],"about_ca_topic_score_codex":0.0011296765,"about_ca_topic_score_gemma":0.00080921943,"teacher_disagreement_score":0.0041438285,"about_ca_system_score_codex":0.0010085737,"about_ca_system_score_gemma":0.0004997265,"threshold_uncertainty_score":0.013862491},"labels":[],"label_agreement":null},{"id":"W1678358232","doi":"10.1109/ijcnn.2015.7280448","title":"A fully integrated analog neuron for dynamic multi-layer perceptron networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Wideband; Activation function; Predistortion; Linearity; Baseband; Perceptron; Neuron; Multilayer perceptron; Artificial neural network; Synaptic weight; Bandwidth (computing); Amplifier; Electronic engineering; Artificial intelligence; Telecommunications; Neuroscience; Engineering","score_opus":0.05650268950013642,"score_gpt":0.30402418317647384,"score_spread":0.2475214936763374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1678358232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037786048,0.000851287,0.9478395,0.00020192628,0.0003493568,0.00012644989,0.00027484243,0.002999206,0.009571436],"genre_scores_gemma":[0.56999207,0.00036755798,0.41531786,0.00030122453,0.000055587658,0.00013761608,0.000268214,0.00011777259,0.01344218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999705,0.000033828335,0.000021092452,0.00006862888,0.00013160011,0.000039888495],"domain_scores_gemma":[0.9998178,0.000038846058,0.00001305605,0.000035998066,0.00007674528,0.000017510727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000319868,0.00034226978,0.00029320086,0.00018367262,0.00029091735,0.000731111,0.0018642605,0.000756399,0.0065326826],"category_scores_gemma":[0.0006508965,0.00028363746,0.00032888856,0.00027949485,0.0002373355,0.00085013505,0.00054035475,0.000752815,0.0012984533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000664893,0.0002496609,0.0017301373,0.0008766032,0.0002674264,0.00061044627,0.0001770989,0.0983516,0.3945273,0.026409123,0.007337714,0.468798],"study_design_scores_gemma":[0.00008784893,0.00074856833,0.002436904,0.00012017974,0.00017572242,0.00091682567,0.00003887699,0.8268133,0.12873739,0.007669803,0.03217373,0.00008092745],"about_ca_topic_score_codex":0.001312745,"about_ca_topic_score_gemma":0.0036391837,"teacher_disagreement_score":0.0065326826,"about_ca_system_score_codex":0.0005358602,"about_ca_system_score_gemma":0.0005061614,"threshold_uncertainty_score":0.021853983},"labels":[],"label_agreement":null},{"id":"W1680549972","doi":"10.48550/arxiv.1206.5240","title":"Analysis of Semi-Supervised Learning with the Yarowsky Algorithm","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Algorithm","score_opus":0.045030865225252625,"score_gpt":0.18008915249776986,"score_spread":0.13505828727251723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1680549972","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074524228,0.0004440106,0.99040437,0.00020522767,0.000019337938,0.000034775378,0.00004204808,0.00012966416,0.0012681744],"genre_scores_gemma":[0.52356344,0.0014774206,0.46552065,0.00037082678,0.00034626634,0.000558238,0.0007332958,0.0004942035,0.0069356444],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9925472,0.0031099569,0.00031047087,0.0010920179,0.0026413845,0.00029899637],"domain_scores_gemma":[0.9685398,0.023259463,0.002247514,0.0016320151,0.0037455766,0.00057561987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010035912,0.0010670247,0.0019768379,0.0027882897,0.0010976745,0.002793124,0.0032810268,0.002019998,0.0039891433],"category_scores_gemma":[0.03775846,0.0010347777,0.0013507997,0.0022556763,0.0038284587,0.006028223,0.003059102,0.0033997756,0.00075834367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012928263,0.00007190779,0.0018536863,0.0002746978,0.00016802433,0.00014835648,0.00034644615,0.43123007,0.0015321006,0.5038871,0.0023913335,0.057966992],"study_design_scores_gemma":[0.000007353422,0.000019742989,0.0002547457,0.00002395865,0.000008780128,0.000026551263,0.000016514112,0.8530246,0.000306991,0.14562283,0.00067676546,0.000011274724],"about_ca_topic_score_codex":0.0023764374,"about_ca_topic_score_gemma":0.0018440086,"teacher_disagreement_score":0.010035912,"about_ca_system_score_codex":0.0024408891,"about_ca_system_score_gemma":0.0017898738,"threshold_uncertainty_score":0.05307561},"labels":[],"label_agreement":null},{"id":"W1680987942","doi":"10.1109/imtc.1999.776100","title":"A VLSI parallel architecture of a piecewise linear neural network for nonlinear channel equalization","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Very-large-scale integration; Computer science; Pipeline (software); Artificial neural network; Activation function; Piecewise linear function; CMOS; Nonlinear system; Piecewise; Channel (broadcasting); Sigmoid function; Algorithm; Parallel computing; Electronic engineering; Mathematics; Artificial intelligence; Embedded system; Engineering; Telecommunications","score_opus":0.025366661898651534,"score_gpt":0.2702442782087884,"score_spread":0.24487761631013685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1680987942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031204777,0.0004993068,0.9606424,0.0001470344,0.0001196467,0.000032584794,0.00006669562,0.0016145167,0.0056730756],"genre_scores_gemma":[0.4214342,0.0004425746,0.569038,0.000150253,0.00007450948,0.000075112424,0.000215286,0.00005362839,0.008516464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999256,0.000012744137,0.0000046873492,0.000018194407,0.000026965601,0.000011892194],"domain_scores_gemma":[0.99988246,0.000025558698,0.0000104315295,0.000015485584,0.00005699206,0.000008969192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017473067,0.00023847031,0.00019519821,0.00021675724,0.00026054017,0.00028836305,0.00077668595,0.0003223609,0.003223774],"category_scores_gemma":[0.00030506667,0.00015159918,0.00021780012,0.00031505822,0.00018581781,0.0005727065,0.0002296838,0.00035057156,0.0009225036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031074203,0.00011647748,0.0014045605,0.00025370417,0.00009820031,0.0002635971,0.000107973894,0.09156001,0.18857984,0.015322968,0.0055516153,0.69643027],"study_design_scores_gemma":[0.000063386,0.0006319187,0.0013320685,0.000031680236,0.00012270075,0.00051813124,0.000043224965,0.8942315,0.07428231,0.005946022,0.022762425,0.00003460884],"about_ca_topic_score_codex":0.0015468819,"about_ca_topic_score_gemma":0.0034186367,"teacher_disagreement_score":0.003223774,"about_ca_system_score_codex":0.00033977957,"about_ca_system_score_gemma":0.0005203057,"threshold_uncertainty_score":0.010784626},"labels":[],"label_agreement":null},{"id":"W168462225","doi":"","title":"Data mining tasks and methods: Classification: neural network approaches","year":2002,"lang":"en","type":"book","venue":"Oxford University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Perceptron; Process (computing); Machine learning; Time delay neural network; Data mining","score_opus":0.18524695103245623,"score_gpt":0.27915194895361245,"score_spread":0.09390499792115622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W168462225","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014566515,0.47951037,0.42290667,0.011543936,0.005050424,0.00040568467,0.0009214309,0.001307557,0.07689739],"genre_scores_gemma":[0.032731015,0.38631558,0.49578953,0.006354141,0.008071234,0.0014151075,0.0028519249,0.00058757875,0.06588393],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973967,0.0007796777,0.00023978768,0.00034667214,0.001155641,0.00008143332],"domain_scores_gemma":[0.9975745,0.0015658279,0.00012433129,0.00020991423,0.0004413488,0.000084005056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032400908,0.002150897,0.0021905776,0.0048670894,0.00079822354,0.005462859,0.0027255258,0.0024619328,0.007966335],"category_scores_gemma":[0.007173621,0.0006795822,0.00081339915,0.009540649,0.0018086169,0.006588567,0.0023747697,0.0029552237,0.00808897],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043796037,0.00011348122,0.00053938647,0.003737645,0.00013211227,0.00012310344,0.00035155783,0.0059031523,0.0007696695,0.13925263,0.15271367,0.6963197],"study_design_scores_gemma":[0.000019448911,0.000041388368,0.001078524,0.0021266197,0.000059993592,0.0006414126,0.00033321843,0.022324223,0.00077371346,0.41216993,0.5603742,0.000057385336],"about_ca_topic_score_codex":0.0012653617,"about_ca_topic_score_gemma":0.0016423411,"teacher_disagreement_score":0.007966335,"about_ca_system_score_codex":0.001434803,"about_ca_system_score_gemma":0.0013283734,"threshold_uncertainty_score":0.026650071},"labels":[],"label_agreement":null},{"id":"W1689346920","doi":"10.1109/iscas.1993.394248","title":"A new algorithm for training multilayer feedforward neural networks","year":2002,"lang":"en","type":"article","venue":"1993 IEEE International Symposium on Circuits and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Backpropagation; Artificial neural network; Computer science; Algorithm; Feedforward neural network; Feed forward; Sigmoid function; Rprop; Limiting; Process (computing); Artificial intelligence; Training (meteorology); Function (biology); Probabilistic neural network; Separable space; Time delay neural network; Types of artificial neural networks; Mathematics; Engineering","score_opus":0.05615242375691548,"score_gpt":0.2712902085578993,"score_spread":0.21513778480098383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1689346920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066212815,0.00018801463,0.9976199,0.000048307524,0.00008093538,0.000035479006,0.00002504028,0.0004455088,0.00089456554],"genre_scores_gemma":[0.024372805,0.0003403904,0.96961886,0.00011682167,0.00008752392,0.00028499166,0.00015245691,0.00015498925,0.0048711593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994916,0.00008579682,0.000053050197,0.0000904979,0.00023922283,0.000039894967],"domain_scores_gemma":[0.9995202,0.00019871537,0.000037424492,0.00004944973,0.00017393744,0.00002027285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093971466,0.0010947061,0.00072052865,0.00094594073,0.00053293386,0.0008565701,0.001386812,0.0014591384,0.0061741597],"category_scores_gemma":[0.0023946795,0.0006700436,0.00083995005,0.00080273504,0.0004995374,0.0016580785,0.0009158105,0.0017027946,0.0022915853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001007585,0.000048547234,0.0004221231,0.00030810919,0.000106525855,0.00012197489,0.00012426276,0.27432242,0.013508082,0.031514365,0.007813446,0.6716094],"study_design_scores_gemma":[0.000033229062,0.000063035244,0.00012271297,0.000048047208,0.000025632848,0.0001023531,0.000008901242,0.96004343,0.0060918997,0.011838457,0.021600207,0.00002211583],"about_ca_topic_score_codex":0.002213064,"about_ca_topic_score_gemma":0.0027760114,"teacher_disagreement_score":0.0061741597,"about_ca_system_score_codex":0.0005397129,"about_ca_system_score_gemma":0.00078719965,"threshold_uncertainty_score":0.02065456},"labels":[],"label_agreement":null},{"id":"W1702497178","doi":"10.1109/icm.2000.916414","title":"Data-driven dynamic logic versus NP-CMOS logic, a comparison","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"CMOS; Pass transistor logic; Domino logic; Logic gate; Logic family; Logic synthesis; AND-OR-Invert; Computer science; Dynamic logic (digital electronics); Logic optimization; Logic level; Dissipation; Pull-up resistor; Electronic engineering; Electrical engineering; Algorithm; Engineering; Transistor; Electronic circuit; Physics; Digital electronics; Voltage","score_opus":0.1476456003630096,"score_gpt":0.33711035613533674,"score_spread":0.18946475577232713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1702497178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42518407,0.013601354,0.39308813,0.0017696783,0.0006035794,0.0001741252,0.0004879761,0.0010604206,0.1640306],"genre_scores_gemma":[0.9351395,0.0029878702,0.051089387,0.0004647471,0.00007460716,0.000041056624,0.00015921854,0.000059472364,0.009984193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984515,0.000028921802,0.000010685413,0.000023474273,0.000079723024,0.000011934523],"domain_scores_gemma":[0.9997906,0.00008878829,0.000026411388,0.000028489732,0.00005445697,0.00001115576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015956434,0.00014895688,0.00011532102,0.00021125618,0.000136823,0.00060020166,0.00042263622,0.00026090187,0.004057545],"category_scores_gemma":[0.0007407329,0.000052451567,0.00015766942,0.00028246894,0.00014805078,0.00082904473,0.00020995176,0.00019967754,0.0005388758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001214963,0.00015238303,0.0026243178,0.0009576633,0.00008139582,0.00044453348,0.00015315549,0.019433724,0.28163156,0.12845013,0.0040211016,0.5608351],"study_design_scores_gemma":[0.00016818172,0.0036838301,0.00532971,0.00016193556,0.00020269453,0.0046519153,0.00027171007,0.1888433,0.55503315,0.057201203,0.18438666,0.0000657975],"about_ca_topic_score_codex":0.0001655492,"about_ca_topic_score_gemma":0.00049700524,"teacher_disagreement_score":0.004057545,"about_ca_system_score_codex":0.0003837413,"about_ca_system_score_gemma":0.00021120603,"threshold_uncertainty_score":0.013573885},"labels":[],"label_agreement":null},{"id":"W1707522557","doi":"10.1016/j.conb.2015.07.006","title":"Learning with hidden variables","year":2015,"lang":"en","type":"review","venue":"Current Opinion in Neurobiology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Artificial neural network; Deep learning; Machine learning; Neuroscience; Psychology","score_opus":0.12373622166333308,"score_gpt":0.37970021314417357,"score_spread":0.2559639914808405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1707522557","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006457838,0.5281203,0.4271142,0.007185894,0.003409872,0.000053396292,0.00036491588,0.0008053174,0.026488286],"genre_scores_gemma":[0.1460643,0.7010103,0.109252326,0.0024861135,0.005053566,0.00013506088,0.0010557793,0.00017774459,0.03476483],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997702,0.00005062019,0.000017777103,0.000065377084,0.00008052668,0.000015427482],"domain_scores_gemma":[0.9994399,0.00037550775,0.000042834647,0.000055043965,0.00007074515,0.00001584577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075231266,0.0007298809,0.0007588002,0.00054212887,0.00014434477,0.0009355261,0.00068724767,0.0008236035,0.0031083154],"category_scores_gemma":[0.0017887648,0.00022553136,0.00038058736,0.0008795103,0.00084670854,0.0016661437,0.00087731617,0.0016424013,0.0012027893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031527183,0.000036115565,0.00028821823,0.0012017891,0.00007708247,0.00002889576,0.00002228375,0.013718667,0.000687835,0.034396984,0.012599286,0.9369114],"study_design_scores_gemma":[0.00004954301,0.00019552211,0.0026137712,0.002639745,0.00026301015,0.00058769627,0.00005541521,0.19799985,0.007878462,0.3599797,0.42763284,0.000104564155],"about_ca_topic_score_codex":0.0008292347,"about_ca_topic_score_gemma":0.0008214684,"teacher_disagreement_score":0.0031083154,"about_ca_system_score_codex":0.0004728817,"about_ca_system_score_gemma":0.00074591313,"threshold_uncertainty_score":0.010398388},"labels":[],"label_agreement":null},{"id":"W1716430306","doi":"10.15200/winn.144243.37003","title":"How becoming an open scientist made me fall back in love with neuroscience","year":2015,"lang":"en","type":"dataset","venue":"The Winnower","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Neuroscience; Cognitive science; Psychology","score_opus":0.05621442058306263,"score_gpt":0.3030722678800218,"score_spread":0.24685784729695914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1716430306","genre_codex":"commentary","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031132337,0.014815731,0.0053159883,0.9140191,0.043267988,0.000029600815,0.00012119863,0.00022521184,0.019091934],"genre_scores_gemma":[0.111921854,0.026182683,0.015374475,0.72378397,0.047231175,0.00028299447,0.00031399226,0.0015481262,0.07336072],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97482103,0.012206639,0.0006520655,0.0036933678,0.006061462,0.0025654014],"domain_scores_gemma":[0.9207297,0.016044261,0.0034496535,0.0042161914,0.015573686,0.039986517],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.023134304,0.001317454,0.0018515986,0.0019225477,0.017081203,0.035273854,0.002836963,0.012785914,0.015888667],"category_scores_gemma":[0.08129703,0.00092084997,0.0014426578,0.0011497434,0.027393293,0.038120866,0.014330832,0.042478863,0.016807437],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015937112,0.0001669773,0.002076538,0.0003636256,0.00010074923,0.0006278521,0.022027967,0.00023889876,0.0011653255,0.17849296,0.74340576,0.051173903],"study_design_scores_gemma":[0.000032917116,0.00010084988,0.00044351272,0.00075874274,0.000023104269,0.00055725494,0.009381611,0.00017139672,0.00034453985,0.12216249,0.86590356,0.00011992883],"about_ca_topic_score_codex":0.0014603671,"about_ca_topic_score_gemma":0.0024865249,"teacher_disagreement_score":0.99716306,"about_ca_system_score_codex":0.0048451084,"about_ca_system_score_gemma":0.011284626,"threshold_uncertainty_score":0.122347355},"labels":[],"label_agreement":null},{"id":"W1719049820","doi":"10.1109/dcc.2006.48","title":"MST for Lossy Compression of Image Sets","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Lossy compression; Computer science; Image compression; Data compression; Compression (physics); JPEG 2000; Image (mathematics); Artificial intelligence; Image retrieval; Texture compression; Minimum spanning tree; Computer vision; Image processing; Algorithm","score_opus":0.010649809784988815,"score_gpt":0.2633224547261871,"score_spread":0.2526726449411983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1719049820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027130548,0.002830506,0.9520019,0.00058827025,0.00051082583,0.00010349701,0.0009057563,0.0027519015,0.013176748],"genre_scores_gemma":[0.30930102,0.0037631402,0.64885277,0.0002572963,0.0006304647,0.00032210155,0.0033752378,0.0006250351,0.032872934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976116,0.000030293804,0.000015006034,0.000029342504,0.00014945857,0.000014704765],"domain_scores_gemma":[0.999469,0.00023397256,0.000047434598,0.00011667513,0.00011155463,0.000021397378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020663081,0.00045111708,0.00038284625,0.0009395795,0.00021631789,0.00048910524,0.0006576471,0.0003301713,0.018452957],"category_scores_gemma":[0.0016564204,0.00012626141,0.00027351663,0.0013669468,0.00031244004,0.00078680547,0.0005855257,0.00053909334,0.004460282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030310475,0.000042747906,0.00032927265,0.0005071066,0.000060080638,0.00029866694,0.00009353667,0.12371989,0.034301084,0.028853018,0.040040083,0.7714515],"study_design_scores_gemma":[0.000029114046,0.00018866631,0.0008130398,0.0001278993,0.00003669251,0.000788765,0.000033901833,0.8931366,0.022508383,0.027992591,0.054318376,0.000025931093],"about_ca_topic_score_codex":0.0006045751,"about_ca_topic_score_gemma":0.00083292986,"teacher_disagreement_score":0.018452957,"about_ca_system_score_codex":0.00035662777,"about_ca_system_score_gemma":0.00018013858,"threshold_uncertainty_score":0.06173134},"labels":[],"label_agreement":null},{"id":"W1730644401","doi":"10.1109/ijcnn.2005.1556002","title":"Factors of overtraining with fuzzy ARTMAP neural networks","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Overtraining; Artificial neural network; Artificial intelligence; Computer science; Fuzzy logic; Training set; Set (abstract data type); Training (meteorology); Fuzzy set; Machine learning; Class (philosophy); Pattern recognition (psychology); Data set; Convergence (economics); Athletes","score_opus":0.04633023031705269,"score_gpt":0.2606226859177297,"score_spread":0.214292455600677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1730644401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77621096,0.0025781044,0.21603169,0.0006512783,0.00009729293,0.00016513823,0.0000539983,0.0012371108,0.0029744236],"genre_scores_gemma":[0.9655997,0.00025269543,0.03292627,0.00012897696,0.000019264604,0.000051532836,0.000050815626,0.000113668604,0.00085708697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972351,0.0010244732,0.00036630654,0.00043485628,0.000636062,0.00030328459],"domain_scores_gemma":[0.9541971,0.034557935,0.0032608428,0.0043366696,0.003152095,0.0004954094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005050475,0.0010949554,0.0010649352,0.00044053674,0.00080467615,0.0016472639,0.0015165312,0.0013637027,0.0015130824],"category_scores_gemma":[0.055677675,0.00088038563,0.000496748,0.00058971834,0.0014078483,0.0045925076,0.001416487,0.0020455623,0.00037911953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021678554,0.00036287162,0.013314941,0.000605189,0.00017046179,0.0009845087,0.0014068148,0.71934193,0.045396093,0.0039255857,0.0006929018,0.21163082],"study_design_scores_gemma":[0.00009233529,0.0015500053,0.014334598,0.00014145453,0.00015330936,0.0013025346,0.00037509788,0.90709287,0.06679313,0.0051971166,0.0028662882,0.00010119997],"about_ca_topic_score_codex":0.0040467666,"about_ca_topic_score_gemma":0.0046598343,"teacher_disagreement_score":0.005050475,"about_ca_system_score_codex":0.001097552,"about_ca_system_score_gemma":0.0010952955,"threshold_uncertainty_score":0.026709795},"labels":[],"label_agreement":null},{"id":"W1734738426","doi":"10.1109/isit.2001.935956","title":"Nonlinear function learning using optimal radial basis function networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Radial basis function; Basis (linear algebra); Computer science; Radial basis function network; Kernel (algebra); Nonlinear system; Function (biology); Basis function; Artificial intelligence; Mathematical optimization; Radial basis function kernel; Kernel method; Algorithm; Machine learning; Mathematics; Artificial neural network; Support vector machine; Mathematical analysis","score_opus":0.027826458410820883,"score_gpt":0.22612234036243145,"score_spread":0.19829588195161058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1734738426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035445795,0.000488641,0.99436617,0.00022559206,0.000040892304,0.000012372434,0.000015504871,0.000079543584,0.001226696],"genre_scores_gemma":[0.43591303,0.0026557094,0.5511861,0.00030595216,0.0003194024,0.00017591417,0.00021048609,0.0002899895,0.008943394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907684,0.0003502515,0.00005336179,0.00014848837,0.00029632731,0.00007478159],"domain_scores_gemma":[0.9986733,0.00062151556,0.00014027717,0.00010740644,0.00041679534,0.00004072286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021110906,0.0010373105,0.0013124445,0.000697427,0.00033853878,0.0010429671,0.0010937132,0.0016935121,0.0015838419],"category_scores_gemma":[0.009059792,0.0005971498,0.0006558357,0.00080532004,0.0009485503,0.0020874736,0.0013899071,0.0015754626,0.00087489566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006562435,0.000053691136,0.0004789829,0.0001922321,0.000061317674,0.00010641608,0.00007924653,0.7487594,0.0033267892,0.13805082,0.0027981917,0.10602743],"study_design_scores_gemma":[0.0000028023435,0.0000074575373,0.00005103325,0.0000068044264,0.00000476114,0.0000139902195,0.0000028657007,0.9875551,0.00045160684,0.011376318,0.0005219145,0.000005399325],"about_ca_topic_score_codex":0.0031910464,"about_ca_topic_score_gemma":0.002693535,"teacher_disagreement_score":0.0031910464,"about_ca_system_score_codex":0.00084450596,"about_ca_system_score_gemma":0.0009594845,"threshold_uncertainty_score":0.011164665},"labels":[],"label_agreement":null},{"id":"W173759896","doi":"","title":"Simplified neural architectures for symmetric boolean functions","year":2000,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial neural network; Boolean function; Network topology; Computer science; Computation; Topology (electrical circuits); Simple (philosophy); Boolean network; Stochastic neural network; Theoretical computer science; Time delay neural network; Algorithm; Artificial intelligence; Mathematics","score_opus":0.012417057846504861,"score_gpt":0.22112446254052148,"score_spread":0.2087074046940166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W173759896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09529688,0.0019066783,0.86449116,0.0007901541,0.00042270796,0.00008217678,0.000705317,0.0010387618,0.035266187],"genre_scores_gemma":[0.82811296,0.0015609867,0.13980763,0.00020557338,0.00018177116,0.00013689269,0.0008699707,0.00014139744,0.028982846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998541,0.00003891874,0.00001170962,0.000027403783,0.000037931575,0.000029951796],"domain_scores_gemma":[0.99978274,0.00008326334,0.000017028484,0.000047240526,0.00005103804,0.00001869086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030245766,0.00046670702,0.00057509466,0.00037538132,0.00027107552,0.0009392017,0.0009943576,0.0007169354,0.011479847],"category_scores_gemma":[0.001117296,0.0003074891,0.00056225905,0.0003945997,0.0004687492,0.0013661556,0.00057680573,0.0008852016,0.0016841319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023254151,0.000057297202,0.00026720233,0.00018772772,0.00007438948,0.00013247858,0.000056617682,0.5775905,0.010845144,0.2562552,0.0065779593,0.14772291],"study_design_scores_gemma":[0.000018185792,0.000028782268,0.00013205783,0.00001165857,0.000017168428,0.000035465273,0.00000723149,0.90522844,0.0015433913,0.09016639,0.0028016015,0.000009589583],"about_ca_topic_score_codex":0.0028940206,"about_ca_topic_score_gemma":0.0073359758,"teacher_disagreement_score":0.011479847,"about_ca_system_score_codex":0.0007670266,"about_ca_system_score_gemma":0.00060691306,"threshold_uncertainty_score":0.03840393},"labels":[],"label_agreement":null},{"id":"W1744468441","doi":"10.1109/iscas.1999.777648","title":"An in-the-loop training method for VLSI neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial neural network; Computer science; Physical neural network; Activation function; Time delay neural network; Realization (probability); Quantization (signal processing); Very-large-scale integration; Artificial intelligence; Types of artificial neural networks; Algorithm; Embedded system; Mathematics","score_opus":0.054475051949899725,"score_gpt":0.3331176544449864,"score_spread":0.2786426024950867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1744468441","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002607556,0.00008097908,0.99506783,0.0000458701,0.00003731525,0.000017424212,0.000007881995,0.00036781727,0.001767402],"genre_scores_gemma":[0.20508467,0.0002458471,0.78633845,0.00013550243,0.00007617543,0.00016188914,0.000047963033,0.00013847282,0.0077710757],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998412,0.000036203797,0.000007685215,0.000029372937,0.00007440228,0.000011138549],"domain_scores_gemma":[0.99980944,0.00008829582,0.000015784739,0.00003476243,0.000044116492,0.000007705981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024758672,0.0003170738,0.00023808613,0.00017677496,0.00024056762,0.00027277728,0.00071852334,0.0004040905,0.0034179678],"category_scores_gemma":[0.0009181521,0.0001518703,0.00016344771,0.0002135224,0.0003354902,0.00052265235,0.00029604262,0.0006636151,0.00061864115],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014894469,0.00010494529,0.00034485533,0.00018315793,0.00004255307,0.00008805336,0.00015690654,0.18384604,0.061614826,0.05347855,0.0033320265,0.69665927],"study_design_scores_gemma":[0.000019154479,0.00009716858,0.00013893373,0.000019089284,0.000013502819,0.00011283497,0.000008589325,0.95352274,0.022228014,0.0101799555,0.013649537,0.000010342885],"about_ca_topic_score_codex":0.00067191053,"about_ca_topic_score_gemma":0.0010168459,"teacher_disagreement_score":0.0034179678,"about_ca_system_score_codex":0.00026196567,"about_ca_system_score_gemma":0.00022684387,"threshold_uncertainty_score":0.011434257},"labels":[],"label_agreement":null},{"id":"W175572396","doi":"","title":"Learning the structure of abstract groups","year":2009,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Group (periodic table); Computer science; Artificial intelligence; Task (project management); Artificial neural network; Cognitive science; Mathematics education; Psychology; Engineering","score_opus":0.009536281777235188,"score_gpt":0.20933164957061653,"score_spread":0.19979536779338133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W175572396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26217678,0.0002750043,0.7275017,0.0010943471,0.0000635252,0.00006092629,0.0002731131,0.0006588199,0.00789578],"genre_scores_gemma":[0.87731135,0.0001929756,0.118302,0.00018909466,0.00004744986,0.00007103327,0.000682993,0.00007500863,0.0031279498],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993001,0.00024073174,0.000039704166,0.0002388572,0.0001156473,0.000064924316],"domain_scores_gemma":[0.9973833,0.0011619988,0.00029399857,0.00072734896,0.00026774418,0.00016555031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010025015,0.0003933931,0.00046123154,0.0008258924,0.0005291896,0.0014603232,0.0010055116,0.00089640025,0.0029361758],"category_scores_gemma":[0.008527104,0.00032380424,0.00058287085,0.0005372436,0.0018468571,0.004850443,0.001990725,0.0016596265,0.00063396926],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040766914,0.0001405705,0.0084770825,0.0002986549,0.00009571039,0.00017160305,0.0020107874,0.12773076,0.017395468,0.47305864,0.0061144223,0.36409867],"study_design_scores_gemma":[0.000022101409,0.000061512306,0.0010062123,0.000029465498,0.000016693079,0.000045594163,0.00016337025,0.27208197,0.00448227,0.71904767,0.0030247567,0.000018330647],"about_ca_topic_score_codex":0.00093065604,"about_ca_topic_score_gemma":0.0009685617,"teacher_disagreement_score":0.0029361758,"about_ca_system_score_codex":0.0007356356,"about_ca_system_score_gemma":0.0004967142,"threshold_uncertainty_score":0.009822488},"labels":[],"label_agreement":null},{"id":"W1768442763","doi":"10.1109/ijcnn.1999.831579","title":"The optimal value of self-connection","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Connection (principal bundle); Spurious relationship; Value (mathematics); Mathematics; Reduction (mathematics); Attractor; RADIUS; Range (aeronautics); Computer science; Inverse; Mathematical optimization; Mathematical analysis; Statistics; Engineering; Geometry","score_opus":0.00856690910155991,"score_gpt":0.22670852989344312,"score_spread":0.2181416207918832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1768442763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78539336,0.0026248586,0.18967733,0.0008211328,0.000108616245,0.00009732259,0.00010789264,0.0005572728,0.020612264],"genre_scores_gemma":[0.98196363,0.00028627843,0.016981496,0.000039416496,0.000011800018,0.000031656942,0.00002058464,0.00003410241,0.00063109474],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99968326,0.000091803195,0.000021098067,0.000073446834,0.0000754186,0.000054919972],"domain_scores_gemma":[0.99842215,0.00087287155,0.00015272439,0.00015853978,0.00024973656,0.00014402582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006170327,0.0003329901,0.0004717096,0.000460087,0.00024826938,0.0006102328,0.0004929458,0.00073078816,0.0013547248],"category_scores_gemma":[0.004657499,0.00022367299,0.00014435728,0.00017445764,0.0005453073,0.0010690276,0.0006243629,0.00037555426,0.00035269372],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009991701,0.00042720183,0.008836362,0.0009916157,0.0002894799,0.00056992006,0.00043479752,0.2977665,0.3614835,0.091877826,0.004078895,0.23224469],"study_design_scores_gemma":[0.00015559496,0.00096235686,0.009050617,0.0001973891,0.00020698433,0.0010097877,0.00034433216,0.7820741,0.1377939,0.059050426,0.00899452,0.0001600104],"about_ca_topic_score_codex":0.00021175205,"about_ca_topic_score_gemma":0.00026926008,"teacher_disagreement_score":0.0013547248,"about_ca_system_score_codex":0.00021970249,"about_ca_system_score_gemma":0.0004005376,"threshold_uncertainty_score":0.0045319796},"labels":[],"label_agreement":null},{"id":"W1770194575","doi":"10.1109/mwscas.1993.342949","title":"Coherent grouping of power systems for use in training artificial neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Artificial neural network; Computer science; Artificial intelligence; Set (abstract data type); Electric power system; Power (physics); Training (meteorology); Machine learning","score_opus":0.10779623679384857,"score_gpt":0.26454150293701945,"score_spread":0.15674526614317086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1770194575","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032258075,0.00018024069,0.9637512,0.00006766017,0.000058261776,0.00013854977,0.00015828223,0.0017969259,0.0015908688],"genre_scores_gemma":[0.30005205,0.00019101588,0.6951291,0.00007995271,0.00005696421,0.0005509681,0.0011606019,0.00037843548,0.002400877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942774,0.00015886073,0.000046331657,0.00015415058,0.00014757374,0.00006524023],"domain_scores_gemma":[0.99904674,0.00034933025,0.00012326064,0.00024082774,0.00021105769,0.000028806144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096118683,0.0010406709,0.000972109,0.0014454343,0.00070065336,0.00073186005,0.0010210366,0.00073953206,0.0046891873],"category_scores_gemma":[0.0058121406,0.0007580294,0.00067718426,0.0028622348,0.00043719375,0.0012576069,0.0010448745,0.0012429027,0.0012722726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015305237,0.00016005538,0.0018446163,0.00013666585,0.00009815025,0.00012341893,0.00025482906,0.6659992,0.009424748,0.0077671316,0.0024447916,0.31159332],"study_design_scores_gemma":[0.000019057745,0.00008001518,0.00063361845,0.0000129062355,0.000018274945,0.0000129866,0.000034336277,0.98933065,0.0032132217,0.004630522,0.0020040977,0.000010287443],"about_ca_topic_score_codex":0.0035536538,"about_ca_topic_score_gemma":0.005098304,"teacher_disagreement_score":0.0046891873,"about_ca_system_score_codex":0.00046849754,"about_ca_system_score_gemma":0.0006421033,"threshold_uncertainty_score":0.01568687},"labels":[],"label_agreement":null},{"id":"W1789857285","doi":"10.1109/ijcnn.1999.832689","title":"Choosing a choice function: granting new capabilities to ART","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of National Defence","funders":"","keywords":"Vigilance (psychology); Computer science; Adaptive resonance theory; Function (biology); Artificial intelligence; Artificial neural network; Psychology; Cognitive psychology","score_opus":0.019519282392503345,"score_gpt":0.2415487811881383,"score_spread":0.22202949879563497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1789857285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31407714,0.00035022254,0.6488976,0.0015420805,0.00010825261,0.00008008414,0.000070506176,0.0007226721,0.034151584],"genre_scores_gemma":[0.9248858,0.0001144576,0.07042288,0.00015994588,0.000037602484,0.00008147706,0.0000355307,0.000100884194,0.004161331],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989507,0.00034288186,0.000066865956,0.00024987594,0.00023585981,0.0001536569],"domain_scores_gemma":[0.9964947,0.0013444577,0.0002532371,0.0011020878,0.00032711306,0.0004783023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020501902,0.00044113645,0.000522675,0.0005840464,0.00064764696,0.0022459198,0.0012385537,0.0013322016,0.00343997],"category_scores_gemma":[0.008878171,0.00041052324,0.00074813666,0.00028982852,0.0042164787,0.006377107,0.0031682854,0.0019368605,0.0005786179],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006786113,0.00016346056,0.0034858922,0.00016736612,0.00010046389,0.00057147746,0.0017770004,0.06794369,0.044623446,0.74880266,0.0013223568,0.13036364],"study_design_scores_gemma":[0.00012046504,0.00026015562,0.0015132552,0.00004558844,0.00004830175,0.00040094214,0.000313222,0.31090152,0.011937448,0.66766685,0.006683746,0.00010852318],"about_ca_topic_score_codex":0.0004803636,"about_ca_topic_score_gemma":0.0004274252,"teacher_disagreement_score":0.00343997,"about_ca_system_score_codex":0.00053547474,"about_ca_system_score_gemma":0.00062358973,"threshold_uncertainty_score":0.011507809},"labels":[],"label_agreement":null},{"id":"W1796501187","doi":"10.1109/iscas.2001.921382","title":"Indirect inverse adaptive control based on neural networks using dynamic back propagation for nonlinear dynamic systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Backpropagation; Control theory (sociology); Artificial neural network; Nonlinear system; Trajectory; Controller (irrigation); Context (archaeology); Computer science; Inverse; Inverse dynamics; Adaptive control; Tracking error; Control (management); Mathematics; Artificial intelligence","score_opus":0.02746706102245941,"score_gpt":0.24428043723747406,"score_spread":0.21681337621501465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1796501187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0099161565,0.00055803655,0.98706204,0.00009858726,0.000062235165,0.000018579482,0.0000061849473,0.00021792464,0.0020601968],"genre_scores_gemma":[0.6917926,0.0018657851,0.2982595,0.00014504846,0.00010862302,0.00015240895,0.000067400484,0.00007529235,0.0075333873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998048,0.00004443593,0.000011744696,0.000032201762,0.00009170978,0.000015108733],"domain_scores_gemma":[0.99976104,0.000125859,0.000022995988,0.00002503245,0.00005935431,0.0000056871263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004568523,0.0006139638,0.00046571068,0.00022744767,0.00025215885,0.00054463436,0.0006106828,0.00061251025,0.0009478241],"category_scores_gemma":[0.0011415939,0.00022031355,0.0003698344,0.00029069636,0.00049130747,0.0006946721,0.00055501604,0.0008858095,0.00019514759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011662022,0.000090409114,0.00071664265,0.0002585236,0.000108237735,0.00014898268,0.00016779511,0.6487156,0.026593847,0.02174769,0.0008520091,0.3004836],"study_design_scores_gemma":[0.0000060621683,0.000042760308,0.000102144775,0.000010117968,0.000015260359,0.000029330491,0.0000038568705,0.9937749,0.0028602127,0.0021123553,0.00103556,0.0000073909227],"about_ca_topic_score_codex":0.0025204266,"about_ca_topic_score_gemma":0.0028603422,"teacher_disagreement_score":0.0025204266,"about_ca_system_score_codex":0.00027986921,"about_ca_system_score_gemma":0.0003098368,"threshold_uncertainty_score":0.0050115585},"labels":[],"label_agreement":null},{"id":"W1815076433","doi":"10.48550/arxiv.1211.5063","title":"On the difficulty of training Recurrent Neural Networks","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3804,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Constraint (computer-aided design); Perspective (graphical); Computer science; Artificial neural network; Simple (philosophy); Norm (philosophy); Artificial intelligence; Gradient descent; Algorithm; Mathematical optimization; Mathematics; Geometry; Epistemology","score_opus":0.1297134932191586,"score_gpt":0.20029158105233916,"score_spread":0.07057808783318056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1815076433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03060964,0.0022857103,0.9544731,0.005884202,0.00023330159,0.00005771481,0.00013571928,0.0008616491,0.0054589924],"genre_scores_gemma":[0.6736971,0.0034461715,0.30887297,0.0020163348,0.0012165107,0.00041027254,0.0005607983,0.0008760756,0.008903671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980514,0.0010093759,0.00016944877,0.00035239014,0.00032134974,0.00009601689],"domain_scores_gemma":[0.97610474,0.02093835,0.00058371725,0.0009843021,0.0011408814,0.00024797692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006543901,0.0012425729,0.001429484,0.00069961784,0.00064319745,0.0017029236,0.0017910285,0.002737481,0.003570139],"category_scores_gemma":[0.05403323,0.00088871946,0.0006588045,0.0006967178,0.0020646781,0.0058111376,0.002586878,0.0044359337,0.0008949247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033498436,0.00010611424,0.0018845494,0.0005794047,0.00013799222,0.00061692134,0.00046906946,0.6808471,0.0036560493,0.15684906,0.013024179,0.14149468],"study_design_scores_gemma":[0.000027612565,0.00004401989,0.0002302273,0.00004832887,0.000011974744,0.000088111796,0.000038832004,0.9066316,0.0011030558,0.090439096,0.0013224796,0.000014647088],"about_ca_topic_score_codex":0.0028374551,"about_ca_topic_score_gemma":0.0021530648,"teacher_disagreement_score":0.006543901,"about_ca_system_score_codex":0.00082686765,"about_ca_system_score_gemma":0.0007541947,"threshold_uncertainty_score":0.034607887},"labels":[],"label_agreement":null},{"id":"W1822512427","doi":"10.1109/icsmc.1992.271654","title":"Artificial neural networks with nonlinear synapses and nonlinear synaptic contacts","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Polynomial; Generalization; Artificial neural network; Nonlinear system; Product (mathematics); Computer science; Gradient descent; Feedforward neural network; Mathematics; Artificial intelligence; Mathematical analysis","score_opus":0.014437939561126647,"score_gpt":0.23043852921031804,"score_spread":0.2160005896491914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1822512427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0820621,0.0018368183,0.8941398,0.0008634517,0.00019681854,0.000041396812,0.00018704981,0.00079817127,0.019874375],"genre_scores_gemma":[0.84844345,0.0016999228,0.12064113,0.00016662198,0.0001499114,0.00011119325,0.00027880445,0.00008334577,0.028425628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998115,0.000052273554,0.000011514316,0.000045981644,0.0000618769,0.000016904316],"domain_scores_gemma":[0.999689,0.00013959337,0.000043995136,0.000045893477,0.00006436195,0.000017210119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028984758,0.00042438915,0.00050686876,0.00025117985,0.0002949577,0.0009780145,0.0010066058,0.0010519375,0.0024335135],"category_scores_gemma":[0.0013513654,0.00028818083,0.00039677141,0.0005701143,0.0005650217,0.0016006685,0.00060678,0.0010003905,0.00077862816],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008985488,0.00004835746,0.000479746,0.00008671921,0.00003932742,0.000219465,0.000050215942,0.87268114,0.006907756,0.06831629,0.0018458982,0.0492352],"study_design_scores_gemma":[0.0000031412087,0.000013067814,0.000053450905,0.0000028881593,0.0000046906507,0.00002615746,0.0000021416095,0.98777044,0.0005858602,0.01061858,0.000915835,0.000003662679],"about_ca_topic_score_codex":0.0018457833,"about_ca_topic_score_gemma":0.0019384034,"teacher_disagreement_score":0.0024335135,"about_ca_system_score_codex":0.0004706846,"about_ca_system_score_gemma":0.00033026733,"threshold_uncertainty_score":0.008140922},"labels":[],"label_agreement":null},{"id":"W1825965305","doi":"10.1017/cbo9780511810954.005","title":"Three-dimensional networks","year":2001,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science","score_opus":0.022229162290813703,"score_gpt":0.1940105712296444,"score_spread":0.1717814089388307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1825965305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02914004,0.023924595,0.6664168,0.0057698586,0.0007812707,0.00012900592,0.0021192569,0.0010459016,0.27067327],"genre_scores_gemma":[0.59684986,0.04435871,0.21824852,0.0009218461,0.00070534204,0.000553998,0.0034590915,0.00023335475,0.13466932],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998134,0.000039731985,0.000011303762,0.0000523741,0.00006562278,0.00001754557],"domain_scores_gemma":[0.9997818,0.000083944,0.000022809172,0.000038747497,0.000047880734,0.000024914742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001915909,0.00049851235,0.00034363728,0.0007873744,0.00062869233,0.002237755,0.00072067935,0.0009942354,0.009889673],"category_scores_gemma":[0.00097389676,0.00023801236,0.00034765125,0.0010073315,0.0011757537,0.002277079,0.0010105107,0.00078885845,0.0018363729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011632887,0.000007960959,0.00021922892,0.00014217297,0.000017729226,0.00007596809,0.00013053569,0.022856575,0.0011303399,0.9231684,0.008867259,0.04337224],"study_design_scores_gemma":[0.000006521204,0.000006586839,0.00027697877,0.00004927129,0.0000074120208,0.00014747659,0.000052145086,0.04028292,0.0004757151,0.8221636,0.13651955,0.000011850883],"about_ca_topic_score_codex":0.0014229029,"about_ca_topic_score_gemma":0.0017753452,"teacher_disagreement_score":0.009889673,"about_ca_system_score_codex":0.0008536365,"about_ca_system_score_gemma":0.00034985426,"threshold_uncertainty_score":0.033084214},"labels":[],"label_agreement":null},{"id":"W1830660145","doi":"10.1109/pes.2004.1372930","title":"On-line trained neurofuzzy distance relay with directional element","year":2004,"lang":"en","type":"article","venue":"IEEE Power Engineering Society General Meeting, 2004.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Relay; Fault (geology); Line (geometry); Transmission line; Fuzzy logic; Computer science; Electric power transmission; Protective relay; Control theory (sociology); Algorithm; Artificial intelligence; Engineering; Mathematics; Telecommunications; Electrical engineering; Geometry; Physics; Geology","score_opus":0.007683881583317229,"score_gpt":0.21580475514552916,"score_spread":0.20812087356221193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1830660145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033947058,0.00013268263,0.9586861,0.00011103486,0.00009717328,0.00006788765,0.000042160722,0.0006898814,0.00622603],"genre_scores_gemma":[0.8427373,0.00009076307,0.15041013,0.000078213416,0.000032218697,0.00009391125,0.00006401926,0.000026099382,0.0064672264],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996978,0.0000577694,0.00002062308,0.00006512686,0.00013812941,0.000020541658],"domain_scores_gemma":[0.9997367,0.000056985762,0.000033437515,0.000040713854,0.00011689676,0.000015122349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035078335,0.00041070738,0.00048022604,0.00017710363,0.0002930726,0.0005556364,0.0012790432,0.0009036004,0.0022383356],"category_scores_gemma":[0.0007495272,0.00018882626,0.00026910944,0.00013722137,0.00028657963,0.0006661904,0.00049920566,0.00057858176,0.00061053067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003540607,0.00011793556,0.00089405815,0.00018272594,0.00006629392,0.00023162451,0.00013517975,0.7285576,0.04585193,0.0106255645,0.0022344708,0.21074852],"study_design_scores_gemma":[0.000013744377,0.00009114892,0.00011964652,0.0000071803033,0.000013978419,0.000052816624,0.000005140456,0.99143934,0.0059280773,0.00046472228,0.0018558548,0.00000830581],"about_ca_topic_score_codex":0.0027664038,"about_ca_topic_score_gemma":0.004091212,"teacher_disagreement_score":0.0027664038,"about_ca_system_score_codex":0.0005897737,"about_ca_system_score_gemma":0.00053134555,"threshold_uncertainty_score":0.0074879527},"labels":[],"label_agreement":null},{"id":"W183162252","doi":"10.1007/978-3-642-27509-8_17","title":"Neural Control of the Self-Excited Induction Generator for Variable-Speed Wind Turbine Generation","year":2012,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Induction generator; Control theory (sociology); Turbine; Wind power; Voltage; Controller (irrigation); Engineering; Variable speed wind turbine; Artificial neural network; Wind speed; Transient (computer programming); Generator (circuit theory); Control engineering; Computer science; Power (physics); Control (management); Physics; Permanent magnet synchronous generator; Electrical engineering; Artificial intelligence","score_opus":0.030351276856424265,"score_gpt":0.22102753403015732,"score_spread":0.19067625717373304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W183162252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06336689,0.008805591,0.8278378,0.0007714901,0.0008567883,0.000056620767,0.00010165849,0.0006341652,0.09756899],"genre_scores_gemma":[0.89620644,0.0032640842,0.04926029,0.00014520597,0.000288984,0.000056806835,0.000110957524,0.000062992396,0.050604198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99995863,0.0000074360755,0.0000023227788,0.0000062593913,0.000021361871,0.0000040702985],"domain_scores_gemma":[0.99996376,0.0000126041705,0.000004512761,0.0000034283767,0.0000138495225,0.0000018476511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010918212,0.00026486212,0.00018592113,0.00012745011,0.00010761112,0.00035780846,0.00045505146,0.00041330708,0.0017560985],"category_scores_gemma":[0.00021900644,0.00011024578,0.00016284616,0.00018506899,0.00022478706,0.0002666306,0.00016303554,0.00039091584,0.00023967179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001003548,0.000070752736,0.00021572821,0.00019163803,0.000042746684,0.00012047447,0.000058686575,0.6709023,0.018968113,0.03570843,0.00620658,0.2674143],"study_design_scores_gemma":[0.00000761942,0.000031147607,0.00019672138,0.000013838444,0.0000068980626,0.000022466318,0.0000038487383,0.98845786,0.0016157194,0.005664052,0.0039740195,0.0000058196024],"about_ca_topic_score_codex":0.001407839,"about_ca_topic_score_gemma":0.0028539018,"teacher_disagreement_score":0.0017560985,"about_ca_system_score_codex":0.00020247627,"about_ca_system_score_gemma":0.00013887283,"threshold_uncertainty_score":0.005874753},"labels":[],"label_agreement":null},{"id":"W1834156195","doi":"10.1109/ijcnn.1992.227334","title":"A comparison between Kalman filters and recurrent neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Kalman filter; Extended Kalman filter; Artificial neural network; Computer science; Estimator; Fast Kalman filter; Invariant extended Kalman filter; Alpha beta filter; Artificial intelligence; SIGNAL (programming language); Control theory (sociology); Mathematics; Moving horizon estimation; Statistics","score_opus":0.03804759846307454,"score_gpt":0.2969830682251434,"score_spread":0.2589354697620689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1834156195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09109967,0.0033152718,0.89599985,0.0003467634,0.00022541828,0.000042718875,0.00015873562,0.0018664497,0.006945073],"genre_scores_gemma":[0.847809,0.0019058553,0.14617816,0.00009321016,0.000111346126,0.000053833606,0.00024715587,0.0001767073,0.0034246051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863964,0.00047621437,0.000084383086,0.00022157813,0.00047629612,0.00010186661],"domain_scores_gemma":[0.99217904,0.005788874,0.00040146397,0.0004576248,0.0011049735,0.000068114045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035929761,0.00058272865,0.0008712544,0.00080099254,0.00029700212,0.0011657237,0.00066984666,0.0010285308,0.0019646802],"category_scores_gemma":[0.015545392,0.00029545615,0.0004504171,0.00084935536,0.00035304597,0.0019845134,0.00043839688,0.0005080691,0.0003854735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082870503,0.00007070484,0.0035678602,0.00026968928,0.0003279063,0.00007000009,0.00012974963,0.7162759,0.0038432241,0.024279283,0.0011326364,0.24920435],"study_design_scores_gemma":[0.000024190631,0.00014067523,0.0012097174,0.000028693818,0.000057061858,0.000024334577,0.000020337091,0.99095094,0.0026406574,0.0037219354,0.0011593126,0.000022052387],"about_ca_topic_score_codex":0.010637284,"about_ca_topic_score_gemma":0.007455921,"teacher_disagreement_score":0.010637284,"about_ca_system_score_codex":0.0009227312,"about_ca_system_score_gemma":0.000776155,"threshold_uncertainty_score":0.021150708},"labels":[],"label_agreement":null},{"id":"W1843038572","doi":"10.1080/10798587.2009.10643050","title":"The Combined Statistical Stepwise and Iterative Neural Network Pruning Algorithm","year":2009,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Pruning; Computer science; Algorithm; Artificial neural network; Layer (electronics); Artificial intelligence; Pattern recognition (psychology)","score_opus":0.009896342511281164,"score_gpt":0.2534941095013542,"score_spread":0.24359776699007304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1843038572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008524651,0.00025659613,0.9893863,0.00006574316,0.000039687187,0.000060079277,0.000032259177,0.0004892707,0.001145243],"genre_scores_gemma":[0.11230317,0.00017493484,0.8847295,0.0001048747,0.00007099122,0.00021330305,0.00019680012,0.00011601477,0.0020904478],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833506,0.00047555138,0.00013930848,0.00017278409,0.0007920729,0.00008530045],"domain_scores_gemma":[0.9982377,0.00067254173,0.00013008698,0.00022987954,0.00067535986,0.000054450953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019399372,0.00089611666,0.0010401565,0.0017425074,0.00037600723,0.0006488736,0.0024468014,0.000867573,0.001897851],"category_scores_gemma":[0.003908555,0.00045694594,0.0009060325,0.0012249559,0.0004352431,0.0009631009,0.0010790064,0.0007661767,0.0007313601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019706319,0.00012496675,0.0022261094,0.00019376191,0.00026442975,0.00025267148,0.000084007595,0.13812627,0.019198336,0.010863457,0.004158105,0.8243109],"study_design_scores_gemma":[0.00003251867,0.000097961325,0.00074840954,0.000017815972,0.000053278225,0.0002558714,0.000008140801,0.9866698,0.006980446,0.0025998268,0.002521465,0.000014559347],"about_ca_topic_score_codex":0.002013062,"about_ca_topic_score_gemma":0.0034295928,"teacher_disagreement_score":0.0024468014,"about_ca_system_score_codex":0.00041431867,"about_ca_system_score_gemma":0.0016050664,"threshold_uncertainty_score":0.010259509},"labels":[],"label_agreement":null},{"id":"W1843531989","doi":"10.5430/air.v4n2p83","title":"About the sensitivity of ordinal classifiers to non-monotone noise","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Noise (video); Monotone polygon; Mathematics; Statistics; Artificial intelligence; Ordinal Scale; Pattern recognition (psychology); Sensitivity (control systems); Econometrics; Machine learning; Computer science","score_opus":0.3022652006054436,"score_gpt":0.44374539109109423,"score_spread":0.14148019048565064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1843531989","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82042617,0.0070780604,0.1497902,0.005187945,0.0009296073,0.0009902567,0.0015484149,0.0007186477,0.01333073],"genre_scores_gemma":[0.9745635,0.0006111765,0.020955104,0.0012293316,0.00024750415,0.00027769,0.0010600236,0.00018446789,0.0008713829],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.86427015,0.078055575,0.008220366,0.01948229,0.026535014,0.0034365945],"domain_scores_gemma":[0.21141641,0.71661913,0.027660627,0.031244833,0.010765465,0.0022935777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.13158156,0.0019946974,0.0019773233,0.0030202672,0.0018835093,0.007061144,0.0022818637,0.0045375894,0.0029855487],"category_scores_gemma":[0.4992045,0.0011482167,0.002665135,0.0024378977,0.005757803,0.009730253,0.0041150735,0.007412018,0.0007800006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.02061048,0.0021083353,0.27593276,0.004094131,0.008688896,0.0015990998,0.008559424,0.29338267,0.04191847,0.027492644,0.009387826,0.30622524],"study_design_scores_gemma":[0.0008518189,0.014970676,0.34156218,0.001504939,0.002837998,0.0035007002,0.006525354,0.4002229,0.083703324,0.13376057,0.009008977,0.0015505372],"about_ca_topic_score_codex":0.002667731,"about_ca_topic_score_gemma":0.0013961736,"teacher_disagreement_score":0.13158156,"about_ca_system_score_codex":0.002705987,"about_ca_system_score_gemma":0.0014306771,"threshold_uncertainty_score":0.69587815},"labels":[],"label_agreement":null},{"id":"W184366100","doi":"","title":"Fast Generation of a Sequence of Trained and Validated Feed-Forward Networks.","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Initialization; Pruning; Artificial neural network; Computer science; Maxima and minima; Sequence (biology); Algorithm; Set (abstract data type); Minification; Artificial intelligence; Function (biology); Machine learning; Mathematics; Mathematical optimization","score_opus":0.03034388564012433,"score_gpt":0.2442601187926614,"score_spread":0.21391623315253708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W184366100","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027579417,0.00018572342,0.9657543,0.00008447753,0.00015953311,0.00025748578,0.0002856369,0.0036366174,0.002056912],"genre_scores_gemma":[0.26177782,0.00014377297,0.73203284,0.00009666777,0.000029669569,0.00059319846,0.0015255082,0.00053747714,0.0032630942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901044,0.00023744589,0.00006703981,0.00018241775,0.00044123863,0.00006143394],"domain_scores_gemma":[0.99342936,0.0030477322,0.00049676315,0.0010535489,0.0018652884,0.000107262575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027328988,0.0010639967,0.0006288295,0.00077865564,0.00038674212,0.0007167858,0.0013315444,0.0009487655,0.004531199],"category_scores_gemma":[0.014097991,0.0006471404,0.00040021466,0.0004415776,0.00035973664,0.001153864,0.00084063184,0.0009993679,0.0021541019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004548933,0.00016220138,0.0034474551,0.0003768196,0.00017734413,0.00056293345,0.00024682484,0.28510395,0.07040485,0.01055514,0.0065163476,0.6219912],"study_design_scores_gemma":[0.000030504265,0.00014877647,0.0009854137,0.00005108357,0.000034487755,0.00020014259,0.000027985772,0.9245755,0.065351985,0.003969305,0.0046002674,0.000024537201],"about_ca_topic_score_codex":0.001685509,"about_ca_topic_score_gemma":0.0028547812,"teacher_disagreement_score":0.004531199,"about_ca_system_score_codex":0.00063380855,"about_ca_system_score_gemma":0.0011898584,"threshold_uncertainty_score":0.015158415},"labels":[],"label_agreement":null},{"id":"W1854169473","doi":"10.1109/ijcnn.1999.831569","title":"New single neuron structure for solving nonlinear problems","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; University of Pittsburgh","keywords":"Computer science; Feed forward; Separable space; Nonlinear system; Perceptron; Artificial intelligence; Artificial neural network; Feedforward neural network; Pattern recognition (psychology); Algorithm; Mathematics; Control engineering; Engineering","score_opus":0.021565418521779966,"score_gpt":0.235323599715327,"score_spread":0.21375818119354703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1854169473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03413532,0.0010641504,0.9486814,0.00036618178,0.00029050634,0.000049827013,0.0000704054,0.00044093904,0.014901293],"genre_scores_gemma":[0.4203773,0.0012227654,0.5606217,0.00034810932,0.0002195833,0.00025005112,0.00024194669,0.000120490746,0.016598048],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998666,0.000017524764,0.000009036895,0.000029229435,0.000061341685,0.000016143536],"domain_scores_gemma":[0.999859,0.000035316592,0.000013600653,0.000021568061,0.000049303624,0.000021141539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002247592,0.00032138996,0.0005624399,0.00029205478,0.00029929884,0.0005408105,0.0008864681,0.0008082603,0.0032403744],"category_scores_gemma":[0.00069326675,0.00022957937,0.0004275909,0.00026826715,0.00045244137,0.0009895401,0.0007545491,0.00092637556,0.0010200576],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017659609,0.00011842179,0.00069077715,0.0005013955,0.00009515694,0.0002790629,0.00023972924,0.31614673,0.07951165,0.3342025,0.006374124,0.26166385],"study_design_scores_gemma":[0.000021322954,0.00006938747,0.00013318639,0.000022079246,0.000013329383,0.0000828601,0.000010613963,0.91333556,0.0052105193,0.07550057,0.005586652,0.000013847643],"about_ca_topic_score_codex":0.0005086966,"about_ca_topic_score_gemma":0.00084539806,"teacher_disagreement_score":0.0032403744,"about_ca_system_score_codex":0.0003435808,"about_ca_system_score_gemma":0.0005773594,"threshold_uncertainty_score":0.010840118},"labels":[],"label_agreement":null},{"id":"W1857774513","doi":"10.1109/icecs.1999.813409","title":"Sensitivity study and improvements on a nonlinear resistive-type neuron circuit","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"NMOS logic; PMOS logic; CMOS; Realization (probability); Resistive touchscreen; Sensitivity (control systems); Chip; Nonlinear system; Electronic engineering; Transistor; Electronic circuit; Logic gate; Topology (electrical circuits); Electrical engineering; Computer science; Physics; Voltage; Engineering; Mathematics","score_opus":0.027676880972199412,"score_gpt":0.2640329468408105,"score_spread":0.2363560658686111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1857774513","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7517665,0.0007940814,0.23672272,0.00045489694,0.00007525927,0.000108569264,0.00018165502,0.0011179227,0.008778359],"genre_scores_gemma":[0.9816243,0.00011302498,0.017373547,0.000080719554,0.000013014598,0.000016192651,0.0000479874,0.000030883195,0.000700339],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995308,0.0001418636,0.000018649618,0.00011591739,0.00014665173,0.000046004217],"domain_scores_gemma":[0.99840975,0.0011197429,0.00010738925,0.00016441694,0.0001741204,0.000024599269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004110446,0.0004920109,0.00025482342,0.00019942809,0.0001707254,0.00028235934,0.0006792552,0.00053073256,0.0015931618],"category_scores_gemma":[0.0031655214,0.00019089087,0.00028252605,0.00016869875,0.0002396101,0.00062569574,0.00038834236,0.00040984087,0.0001694875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006449083,0.00009250275,0.0047161197,0.0004394329,0.00016280646,0.0007569569,0.00016307544,0.3126778,0.60542905,0.0053555355,0.0005040206,0.06905778],"study_design_scores_gemma":[0.000035181805,0.0010306379,0.0049230824,0.00003269839,0.00017576748,0.00083142327,0.00004645898,0.7001444,0.28657028,0.0023679906,0.0038011388,0.000041041065],"about_ca_topic_score_codex":0.0011346905,"about_ca_topic_score_gemma":0.0015250448,"teacher_disagreement_score":0.0015931618,"about_ca_system_score_codex":0.00079992233,"about_ca_system_score_gemma":0.00016423367,"threshold_uncertainty_score":0.005803883},"labels":[],"label_agreement":null},{"id":"W1864708098","doi":"10.1109/mwsym.1999.779444","title":"Robust training of microwave neural models","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial neural network; Computer science; Nonlinear system; Training (meteorology); Artificial intelligence; Algorithm; Process (computing); Robustness (evolution)","score_opus":0.10297114205165406,"score_gpt":0.2402922326684728,"score_spread":0.13732109061681874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1864708098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02323623,0.0003213753,0.9727893,0.00016082064,0.00004058469,0.000016826947,0.0000491391,0.00057153543,0.002814131],"genre_scores_gemma":[0.8498823,0.00046292832,0.14413406,0.000100845085,0.00006155595,0.00013607887,0.00021623602,0.00015343333,0.004852563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975306,0.00008419333,0.000013562713,0.000050177376,0.00007278735,0.000026216967],"domain_scores_gemma":[0.9994407,0.0003069772,0.00007407885,0.000065800756,0.000100371784,0.000012091725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007644938,0.000497032,0.0005424253,0.00031837876,0.0001986036,0.00050768495,0.0007962055,0.00076386495,0.0012705426],"category_scores_gemma":[0.0032376356,0.00040916374,0.00032790427,0.00033699442,0.00046668656,0.00080513867,0.0006438019,0.00085242203,0.00035963327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030255407,0.000007809784,0.00014497217,0.000021354395,0.000013536295,0.000015814141,0.000011692408,0.96991986,0.001577082,0.00420578,0.0003037074,0.023748223],"study_design_scores_gemma":[0.0000010911582,0.000003284239,0.000028672743,0.0000014577183,0.000001108072,0.0000019083786,5.680447e-7,0.998555,0.00038515363,0.00088496716,0.00013574635,0.000001060196],"about_ca_topic_score_codex":0.0033177007,"about_ca_topic_score_gemma":0.0030255269,"teacher_disagreement_score":0.0033177007,"about_ca_system_score_codex":0.00052094983,"about_ca_system_score_gemma":0.00043731797,"threshold_uncertainty_score":0.0065968037},"labels":[],"label_agreement":null},{"id":"W1866015479","doi":"","title":"Feedback: Sorting Signal from Noise","year":2013,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Noise (video); SIGNAL (programming language); Sorting; Computer science; Background noise; Artificial intelligence; Telecommunications; Algorithm","score_opus":0.01286270047670045,"score_gpt":0.21725566934548024,"score_spread":0.20439296886877978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1866015479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04644126,0.0009309548,0.94064015,0.0014000018,0.00096224115,0.00011013095,0.00026573156,0.003953853,0.0052956077],"genre_scores_gemma":[0.73559123,0.0007477841,0.23760624,0.0014286212,0.0008375687,0.0002057809,0.0006019454,0.0010006462,0.021980168],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99893194,0.00024197942,0.000058454625,0.00022051857,0.00041424122,0.00013284349],"domain_scores_gemma":[0.995076,0.0027589127,0.00027266363,0.00045892026,0.0010773262,0.00035613767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017248556,0.0013202287,0.0012684345,0.0012885273,0.0009364028,0.0017749385,0.0018232082,0.0021299508,0.010440524],"category_scores_gemma":[0.015477897,0.00063754444,0.0004669976,0.0009380727,0.0012744756,0.0028350574,0.0023375144,0.0014826681,0.0020226922],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002695059,0.00032185082,0.0017361746,0.0004339371,0.000083760344,0.00034886168,0.00037600624,0.09112406,0.044591904,0.06975631,0.022277951,0.766254],"study_design_scores_gemma":[0.00018155629,0.00045103117,0.0008171284,0.00006745451,0.000065453394,0.00014130438,0.000106017054,0.8583249,0.022182884,0.111782305,0.005818393,0.000061637016],"about_ca_topic_score_codex":0.004106172,"about_ca_topic_score_gemma":0.003951532,"teacher_disagreement_score":0.010440524,"about_ca_system_score_codex":0.00092922203,"about_ca_system_score_gemma":0.0015205605,"threshold_uncertainty_score":0.03492707},"labels":[],"label_agreement":null},{"id":"W1867104150","doi":"10.1109/ccece.2000.849556","title":"A summary of applications of Hopfield neural network to economic load dispatch","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Economic dispatch; Computer science; Artificial neural network; Mathematical optimization; Hopfield network; Electric power system; Scheduling (production processes); Operating cost; Power Balance; Process (computing); Optimization problem; Power (physics); Engineering; Artificial intelligence; Mathematics; Algorithm","score_opus":0.014626514544184746,"score_gpt":0.2300528988280921,"score_spread":0.21542638428390737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1867104150","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010846035,0.25646794,0.56482774,0.0030117077,0.0025714196,0.0002999535,0.0005918106,0.00079844706,0.16058502],"genre_scores_gemma":[0.09936589,0.43801996,0.36647674,0.0014750746,0.002927586,0.0002856333,0.00119695,0.00028610314,0.08996605],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997123,0.000048850394,0.00004722143,0.000052696047,0.00011265155,0.000026359172],"domain_scores_gemma":[0.9996061,0.00015391204,0.000014436384,0.000027207045,0.00017931631,0.00001905799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006204173,0.00085898495,0.0005350676,0.0015464931,0.0004175975,0.00095821504,0.00073334744,0.0008610553,0.0046922644],"category_scores_gemma":[0.0012347879,0.0003114684,0.00059159443,0.002838721,0.00033850808,0.0012197281,0.000464551,0.00084475667,0.0019426588],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114657865,0.00010838085,0.00072308123,0.0019452665,0.0001276733,0.00030437805,0.0001367774,0.058353875,0.0045127976,0.0559872,0.019487293,0.8581986],"study_design_scores_gemma":[0.00003113286,0.00025427632,0.0022056184,0.0010366857,0.00013157507,0.00079092215,0.00010307471,0.120856576,0.012127966,0.13844146,0.7238801,0.0001406138],"about_ca_topic_score_codex":0.003096545,"about_ca_topic_score_gemma":0.004088792,"teacher_disagreement_score":0.0046922644,"about_ca_system_score_codex":0.00087908586,"about_ca_system_score_gemma":0.00047573092,"threshold_uncertainty_score":0.015697181},"labels":[],"label_agreement":null},{"id":"W1867217292","doi":"10.1109/ccece.2001.933698","title":"A study of microscopic images of human breast disease using competitive neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Automation; Thresholding; Computer science; Artificial intelligence; Artificial neural network; Medical diagnosis; Edge detection; Computer vision; Competitive advantage; Image processing; Pattern recognition (psychology); Enhanced Data Rates for GSM Evolution; Machine learning; Image (mathematics); Pathology; Medicine; Engineering","score_opus":0.031359205593986646,"score_gpt":0.2813000520037836,"score_spread":0.24994084640979697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1867217292","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94622564,0.0008456367,0.05003125,0.00018756521,0.00002316564,0.00004931852,0.00011826261,0.00008059451,0.0024386572],"genre_scores_gemma":[0.98442554,0.00024223547,0.014526855,0.000021352947,0.000015043533,0.00001275286,0.00009635691,0.000010007668,0.0006499432],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986017,0.000031086965,0.0000050867466,0.000023675397,0.000058564492,0.000021380292],"domain_scores_gemma":[0.9995326,0.0002683398,0.000043333548,0.00003044427,0.00009875173,0.000026504376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044402003,0.00019323749,0.00020783555,0.0005207133,0.00015721725,0.00024398796,0.00022476907,0.00032419004,0.0004938975],"category_scores_gemma":[0.002085994,0.00014493824,0.0002155633,0.00043326893,0.00034193663,0.00033164487,0.00013139252,0.00019468258,0.00007310417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013570643,0.00031305518,0.030778568,0.0007046134,0.00020657654,0.0017246968,0.0010742093,0.28754362,0.4112564,0.00921672,0.002510639,0.25331378],"study_design_scores_gemma":[0.000016284617,0.00036909242,0.08471454,0.000013022215,0.000028024255,0.00097329606,0.00020261538,0.8848118,0.026070843,0.001656272,0.0011198937,0.000024329398],"about_ca_topic_score_codex":0.005438249,"about_ca_topic_score_gemma":0.0046551563,"teacher_disagreement_score":0.005438249,"about_ca_system_score_codex":0.00047936075,"about_ca_system_score_gemma":0.00016335548,"threshold_uncertainty_score":0.010813177},"labels":[],"label_agreement":null},{"id":"W1870064773","doi":"10.1109/icsmc.1992.271661","title":"A neural network-based robot controller","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Controller (irrigation); Artificial neural network; Robot; Control theory (sociology); Computer science; Stability (learning theory); Control engineering; Artificial intelligence; Closed loop; Robot control; Robot manipulator; Control (management); Mobile robot; Engineering; Machine learning","score_opus":0.01550220654088915,"score_gpt":0.23013314523984807,"score_spread":0.2146309386989589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1870064773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007645723,0.00084973744,0.97318935,0.00030391125,0.00044086616,0.00017684589,0.00010246257,0.0026025546,0.014688589],"genre_scores_gemma":[0.5756864,0.0011403314,0.3825618,0.00073696446,0.00029056743,0.00079402473,0.00031853368,0.00014265094,0.038328685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996475,0.000032941454,0.000020352943,0.000098202094,0.00017061883,0.000030372425],"domain_scores_gemma":[0.9997626,0.000040365325,0.000022618247,0.000017890286,0.00013997706,0.000016535936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037034368,0.0005596048,0.0005985675,0.00036436907,0.0005479115,0.0008679703,0.0014663816,0.00126808,0.004029721],"category_scores_gemma":[0.00084814016,0.0002248874,0.00032642367,0.00035010165,0.00037115838,0.0006489732,0.0006470636,0.0009782894,0.0014920294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002644508,0.00020361405,0.00049707195,0.00044889413,0.00010644187,0.00044691958,0.00010482442,0.4360087,0.065278746,0.019429494,0.012270696,0.46494013],"study_design_scores_gemma":[0.00005170267,0.00009418662,0.00027434202,0.000025371364,0.00002828389,0.00012163258,0.0000060707466,0.98059356,0.008150466,0.001811009,0.00882224,0.000021207788],"about_ca_topic_score_codex":0.0051871017,"about_ca_topic_score_gemma":0.0058327042,"teacher_disagreement_score":0.0051871017,"about_ca_system_score_codex":0.000513075,"about_ca_system_score_gemma":0.000844714,"threshold_uncertainty_score":0.0134807825},"labels":[],"label_agreement":null},{"id":"W1870216299","doi":"10.3968/j.pam.1925252820130502.2994","title":"On Some Properties of a Hetereogeneous Transfer Function Involving Symmetric Saturated Linear (SATLINS) with Hyperbolic Tangent Sigmoid (TANSIG) Transfer Functions","year":2013,"lang":"en","type":"article","venue":"Progress in applied mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sigmoid function; Hyperbolic function; Transfer function; Mathematics; Tangent; Function (biology); Property (philosophy); Transfer (computing); Probability density function; Probability distribution; Layer (electronics); Pure mathematics; Mathematical analysis; Artificial neural network; Computer science; Geometry; Statistics; Artificial intelligence","score_opus":0.02137491394886415,"score_gpt":0.20849086583494436,"score_spread":0.1871159518860802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1870216299","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08484165,0.0007151735,0.9033407,0.0006145891,0.0000586782,0.000031885997,0.00012457078,0.00016652094,0.010106216],"genre_scores_gemma":[0.9483217,0.0012503044,0.04537002,0.00021127559,0.00020505635,0.00010055953,0.00020163807,0.0001421125,0.0041974215],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991748,0.00022514063,0.000063554784,0.00027799752,0.00016891127,0.0000895926],"domain_scores_gemma":[0.99291855,0.0050361017,0.0005881245,0.00051577186,0.0007577292,0.00018368517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027004802,0.0007104672,0.0005912515,0.0010892235,0.0006137479,0.0015406279,0.00074357743,0.0010183399,0.002533431],"category_scores_gemma":[0.011864302,0.00042071397,0.000992868,0.0011050717,0.0026143803,0.004003256,0.0011438243,0.001699774,0.0003711975],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026017954,0.00010032528,0.003426079,0.00018389661,0.00008231231,0.00062354095,0.0004056228,0.13568108,0.011486246,0.79311556,0.0022621688,0.052373033],"study_design_scores_gemma":[0.000017595714,0.00018327146,0.0015426656,0.00003802211,0.00003447683,0.00040488556,0.00007684084,0.66008645,0.004350371,0.33162865,0.0016111579,0.00002561365],"about_ca_topic_score_codex":0.0016083576,"about_ca_topic_score_gemma":0.0010009687,"teacher_disagreement_score":0.0027004802,"about_ca_system_score_codex":0.0010670852,"about_ca_system_score_gemma":0.00077914406,"threshold_uncertainty_score":0.01428169},"labels":[],"label_agreement":null},{"id":"W1874420577","doi":"10.1109/iembs.1993.979099","title":"Supervised classification of motor unit action potentials based on estimates of classification certainty","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Certainty; Artificial intelligence; Computer science; Action (physics); Unit (ring theory); Motor unit; Statistical classification; Machine learning; Pattern recognition (psychology); Mathematics; Neuroscience; Psychology; Physics","score_opus":0.08019750610333004,"score_gpt":0.3106538496828579,"score_spread":0.23045634357952788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1874420577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3178692,0.0007044851,0.6773457,0.00041303097,0.00010414148,0.00011556051,0.00029077672,0.0008372864,0.0023198954],"genre_scores_gemma":[0.9627907,0.00011357326,0.03620116,0.00003860272,0.0000761635,0.00004202664,0.00025848107,0.00003207189,0.0004471872],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886477,0.00028373438,0.00012281323,0.0003355002,0.0003074373,0.00008582139],"domain_scores_gemma":[0.9920975,0.004223996,0.0011360181,0.00072278664,0.0016073256,0.00021223417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020204603,0.0005382608,0.00084925134,0.0007806036,0.00028829376,0.0012275144,0.00070949394,0.00090341707,0.000975898],"category_scores_gemma":[0.014141267,0.00021223557,0.0005384979,0.00038571467,0.00051002763,0.0013127939,0.0008101294,0.0012480951,0.0003274179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017916961,0.00031723653,0.029710338,0.000302069,0.00033923946,0.00016129902,0.00028667066,0.13116437,0.038811464,0.0113010015,0.003340917,0.7824737],"study_design_scores_gemma":[0.000037413924,0.00014764354,0.009512605,0.000032358737,0.000042773172,0.00009407734,0.000034833123,0.9715992,0.007436341,0.0106744645,0.0003672873,0.000020979385],"about_ca_topic_score_codex":0.0007309975,"about_ca_topic_score_gemma":0.0010511315,"teacher_disagreement_score":0.0020204603,"about_ca_system_score_codex":0.00047318538,"about_ca_system_score_gemma":0.000605196,"threshold_uncertainty_score":0.010685384},"labels":[],"label_agreement":null},{"id":"W1876557134","doi":"10.1007/3-540-45813-1_36","title":"Time Series Model Mining with Similarity-Based Neuro-fuzzy Networks and Genetic Algorithms: A Parallel Implementation","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Granularity; Multivariate statistics; Algorithm; Dependency (UML); Data mining; Theoretical computer science; Artificial intelligence; Machine learning","score_opus":0.01758930483008633,"score_gpt":0.24106116471400515,"score_spread":0.22347185988391882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1876557134","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015837777,0.00016712895,0.9805162,0.00009999408,0.00004637154,0.00008143365,0.000042154337,0.0015949822,0.0016139342],"genre_scores_gemma":[0.15177456,0.00013891344,0.8459223,0.0000588728,0.000042556574,0.0001550889,0.00010031032,0.00008349712,0.0017239407],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969625,0.0000670566,0.000029764498,0.0000785106,0.00010159601,0.000026736685],"domain_scores_gemma":[0.99942786,0.00028137089,0.00003154547,0.00008453349,0.00015261587,0.000022026361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000957902,0.00057358353,0.001073767,0.00074866053,0.00061192876,0.0010546804,0.0014646511,0.0010410675,0.0031662108],"category_scores_gemma":[0.0022468518,0.0004934006,0.00093894894,0.001255145,0.00040120864,0.0010854552,0.0007655049,0.00089148973,0.0007888159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003043152,0.00022506874,0.001147264,0.00010295061,0.00020057442,0.000114237766,0.00008716582,0.4299397,0.0048747608,0.011973503,0.0020703727,0.5489601],"study_design_scores_gemma":[0.000028439248,0.000018253057,0.00010359016,0.0000027454482,0.00002200618,0.000020556645,0.000007960046,0.99516207,0.0013459141,0.0029281431,0.00035637213,0.0000039121333],"about_ca_topic_score_codex":0.012344794,"about_ca_topic_score_gemma":0.012031432,"teacher_disagreement_score":0.012344794,"about_ca_system_score_codex":0.0007217808,"about_ca_system_score_gemma":0.001048287,"threshold_uncertainty_score":0.024545908},"labels":[],"label_agreement":null},{"id":"W1877811508","doi":"","title":"Temporal Robustness of an Automatic Aural Classifier","year":2010,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research","keywords":"Robustness (evolution); Computer science; Classifier (UML); Artificial intelligence; Speech recognition","score_opus":0.012651506576140363,"score_gpt":0.23328152553377426,"score_spread":0.2206300189576339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1877811508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48659733,0.001761317,0.4900178,0.0013012247,0.0011804398,0.000077619254,0.001078259,0.0045531765,0.013432743],"genre_scores_gemma":[0.9451377,0.00023842014,0.045743376,0.0002207538,0.00034761458,0.000030100364,0.0012053939,0.00036164335,0.0067151217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862456,0.00028301438,0.000077581826,0.0003527272,0.0004710431,0.00019099264],"domain_scores_gemma":[0.9958087,0.002279587,0.0001714078,0.0004439802,0.001143539,0.00015282299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025982587,0.0005520702,0.0007551699,0.000977302,0.0006977332,0.001643422,0.000732131,0.0011515077,0.0025138059],"category_scores_gemma":[0.00922897,0.00028444498,0.00064214424,0.00040459508,0.0004102028,0.0010519881,0.0010980896,0.0011138009,0.0016745628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035917542,0.00025384926,0.0073495307,0.00018740374,0.00027421408,0.00026290806,0.000111217654,0.10037421,0.17440617,0.0032497153,0.008050198,0.70188874],"study_design_scores_gemma":[0.000021932126,0.000115681425,0.00741624,0.000011104108,0.00007719431,0.000149994,0.000033392305,0.9603069,0.02951659,0.00096272986,0.0013632716,0.000024996973],"about_ca_topic_score_codex":0.0052650464,"about_ca_topic_score_gemma":0.0047431104,"teacher_disagreement_score":0.0052650464,"about_ca_system_score_codex":0.00064003846,"about_ca_system_score_gemma":0.0008857057,"threshold_uncertainty_score":0.013741076},"labels":[],"label_agreement":null},{"id":"W1878432473","doi":"10.1109/iembs.1993.978532","title":"Classification of muscle contraction levels with an artificial neural network","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial neural network; Contraction (grammar); Artificial muscle; Artificial intelligence; Muscle contraction; Anatomy; Medicine; Internal medicine","score_opus":0.051515529318152266,"score_gpt":0.2799814075033577,"score_spread":0.22846587818520545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1878432473","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45701373,0.000980361,0.53380394,0.0003673459,0.00032513865,0.00033237648,0.0009004114,0.0014442913,0.004832375],"genre_scores_gemma":[0.8664656,0.00036876777,0.12933722,0.00008319115,0.00007391648,0.00018851958,0.00047996468,0.00005285763,0.002949997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997377,0.00005071054,0.000030778592,0.000074322386,0.000067075875,0.000039421535],"domain_scores_gemma":[0.99915004,0.00043526103,0.00006182211,0.00005713696,0.00024642513,0.000049323018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008074324,0.0005279862,0.00045909535,0.0012046776,0.0002841899,0.00060474453,0.0003486189,0.0008078233,0.0019386736],"category_scores_gemma":[0.0020932904,0.0002428498,0.00060419086,0.0008692469,0.00029681387,0.00040968714,0.00038275978,0.00051614165,0.00050000765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024632574,0.00036359715,0.034898233,0.0003072983,0.00021402402,0.00022775092,0.00017562811,0.06425579,0.0942625,0.0010721842,0.0025256523,0.79923415],"study_design_scores_gemma":[0.00003803105,0.00038753173,0.03730383,0.00004373533,0.0001078649,0.00017798405,0.000083735635,0.94582725,0.014108994,0.0010731488,0.0008112404,0.000036767215],"about_ca_topic_score_codex":0.0022552782,"about_ca_topic_score_gemma":0.0022382075,"teacher_disagreement_score":0.0022552782,"about_ca_system_score_codex":0.00033035487,"about_ca_system_score_gemma":0.00030337373,"threshold_uncertainty_score":0.006485522},"labels":[],"label_agreement":null},{"id":"W1879911731","doi":"10.1109/ijcnn.1999.832604","title":"Components for a sequence processing neural network","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Artificial neural network; Sequence (biology); Extension (predicate logic); Artificial intelligence; Set (abstract data type); Range (aeronautics); Time delay neural network; Pattern recognition (psychology); Engineering; Programming language","score_opus":0.06513749440386349,"score_gpt":0.29350294356914786,"score_spread":0.22836544916528437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1879911731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031763283,0.00053863047,0.96161455,0.00041411442,0.0003537401,0.00062146917,0.001043016,0.013448753,0.018789433],"genre_scores_gemma":[0.038657214,0.0007178937,0.92224634,0.00056759256,0.00014017237,0.0009980473,0.002637285,0.0011140258,0.03292145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994728,0.00004564047,0.000035350655,0.00011640284,0.00027809953,0.00005175858],"domain_scores_gemma":[0.9995346,0.000084967185,0.000025368052,0.00010134571,0.00021546317,0.00003833001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064101093,0.0012033856,0.0006762581,0.0009879256,0.0007233449,0.00162963,0.0024481309,0.001467425,0.025723433],"category_scores_gemma":[0.0019413854,0.0008429732,0.0009918795,0.0008969846,0.00046342757,0.002733758,0.0013824847,0.001964841,0.019495958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006340756,0.00033265338,0.0020052786,0.00073785987,0.00026129742,0.0005786697,0.00023113798,0.057492718,0.0471467,0.11147522,0.05639505,0.7227095],"study_design_scores_gemma":[0.00011575368,0.0002969811,0.0016603951,0.0001751072,0.0001973144,0.00097062887,0.00007762987,0.4352043,0.057768945,0.069227934,0.43418002,0.00012489856],"about_ca_topic_score_codex":0.005469931,"about_ca_topic_score_gemma":0.0069413693,"teacher_disagreement_score":0.025723433,"about_ca_system_score_codex":0.00084567646,"about_ca_system_score_gemma":0.0013495474,"threshold_uncertainty_score":0.08605343},"labels":[],"label_agreement":null},{"id":"W1880557952","doi":"10.1109/isit.1991.695142","title":"Harmonic Analysis And The Complexity Of Computing With Threshold (Neural) Elements","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Boolean function; Artificial neural network; Computer science; Key (lock); Focus (optics); Node (physics); Function (biology); Circuit complexity; Theoretical computer science; Field (mathematics); Boolean circuit; Harmonic analysis; Boolean network; Nonlinear system; Network analysis; Algorithm; Mathematics; Electronic circuit; Artificial intelligence; Pure mathematics; Physics","score_opus":0.03469607814071121,"score_gpt":0.2652734176941344,"score_spread":0.23057733955342316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1880557952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14509684,0.004162872,0.7752603,0.004973952,0.0004582914,0.00007793684,0.0006781775,0.0003208024,0.06897091],"genre_scores_gemma":[0.8767017,0.0036244632,0.098342516,0.00055495603,0.0011735038,0.00019765292,0.000596529,0.00017529758,0.018633222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991328,0.00019924091,0.000038561673,0.00019389353,0.00032566543,0.00010989998],"domain_scores_gemma":[0.99601173,0.003131962,0.00021911408,0.00028586556,0.0002237959,0.00012751712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009559336,0.0004465479,0.00048286354,0.0012165688,0.00060061825,0.002457335,0.0010592135,0.0008008788,0.0054740673],"category_scores_gemma":[0.005894457,0.00025101966,0.0006619276,0.00094995176,0.002736134,0.0049222326,0.0015064642,0.002119954,0.0005322691],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018762079,0.000008022433,0.00015191018,0.00004688791,0.000006249634,0.000019150637,0.000084893,0.010804829,0.0005550226,0.97919285,0.0011839202,0.007927548],"study_design_scores_gemma":[0.0000030174292,0.0000078784315,0.000115777104,0.0000069383173,0.0000032749792,0.000017489367,0.000025149862,0.051227886,0.00042868653,0.9460888,0.0020680889,0.000007043676],"about_ca_topic_score_codex":0.001059593,"about_ca_topic_score_gemma":0.0006742021,"teacher_disagreement_score":0.0054740673,"about_ca_system_score_codex":0.0017704817,"about_ca_system_score_gemma":0.000664327,"threshold_uncertainty_score":0.018312573},"labels":[],"label_agreement":null},{"id":"W1881775820","doi":"10.1109/icca.2002.1229534","title":"Symbolic data mining using self-organizing map","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Self-organizing map; Data mining; Information retrieval; Artificial intelligence; Cluster analysis","score_opus":0.06415944298090538,"score_gpt":0.2944755036433811,"score_spread":0.23031606066247573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1881775820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03306817,0.0008323724,0.9617469,0.00021606348,0.00008097004,0.00010322573,0.0002707656,0.0019375122,0.001744047],"genre_scores_gemma":[0.4904403,0.00066051714,0.50549686,0.00008405593,0.0000871666,0.00021805693,0.0010778882,0.00009869519,0.0018364695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994579,0.00010842577,0.00006297468,0.000121663856,0.00020752918,0.000041459312],"domain_scores_gemma":[0.99870944,0.00065896113,0.00009859299,0.0001710673,0.00031567182,0.000046298843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059731543,0.0004953664,0.0010164226,0.0027645864,0.00076843315,0.001237206,0.0008112627,0.00049356854,0.0013844208],"category_scores_gemma":[0.0032697828,0.0003324819,0.00095395057,0.0022732087,0.00058950426,0.0012191741,0.001051148,0.00064842484,0.0006604119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021219626,0.00018534936,0.00580796,0.00036207162,0.00024844363,0.00029978942,0.00027073515,0.11871319,0.006089471,0.010690046,0.0048512653,0.8522694],"study_design_scores_gemma":[0.000020035124,0.000048031157,0.0011092355,0.000025325273,0.000050837756,0.00012628747,0.00007904738,0.96461904,0.0038014383,0.02810153,0.0020013144,0.000017931201],"about_ca_topic_score_codex":0.002451655,"about_ca_topic_score_gemma":0.0026109023,"teacher_disagreement_score":0.0027645864,"about_ca_system_score_codex":0.00037275546,"about_ca_system_score_gemma":0.0011204323,"threshold_uncertainty_score":0.004874766},"labels":[],"label_agreement":null},{"id":"W1882577836","doi":"10.1109/nafips.2001.943749","title":"A compensatory wavelet neuron model","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wavelet; Biological neuron model; Computer science; Neuron; Artificial neural network; Wavelet transform; Function (biology); Pattern recognition (psychology); Artificial intelligence; Algorithm; Mathematics; Neuroscience; Biology","score_opus":0.0368437957190277,"score_gpt":0.228431688256406,"score_spread":0.1915878925373783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1882577836","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03403068,0.0005916552,0.9474175,0.00044601675,0.0002647304,0.00007140025,0.00012914337,0.00031057745,0.016738307],"genre_scores_gemma":[0.8155782,0.0015078608,0.1566364,0.00029419726,0.00013568989,0.00024321857,0.00020927099,0.00007803511,0.02531706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974185,0.000052019874,0.000017296958,0.000053778655,0.0000995467,0.00003549897],"domain_scores_gemma":[0.9997874,0.00004881969,0.000022337605,0.000030432522,0.00008454002,0.000026572134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050688034,0.0004650612,0.00052373676,0.0004798396,0.00028827562,0.0007159727,0.0016876431,0.0010548664,0.0033347842],"category_scores_gemma":[0.0010546659,0.00020563572,0.00070773944,0.00052673655,0.0004594447,0.001377267,0.0007592808,0.00074215664,0.0008592309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002572414,0.000116320916,0.0018852456,0.0003050392,0.00014187035,0.0009955649,0.00023239493,0.44659507,0.03938621,0.38520238,0.004646691,0.12023594],"study_design_scores_gemma":[0.000015521651,0.00006608044,0.0002156457,0.000013542433,0.000023124896,0.00021416924,0.000017104552,0.9627954,0.002429143,0.030498581,0.0036952018,0.00001639072],"about_ca_topic_score_codex":0.0011980451,"about_ca_topic_score_gemma":0.00074314076,"teacher_disagreement_score":0.0033347842,"about_ca_system_score_codex":0.00040713875,"about_ca_system_score_gemma":0.00062090496,"threshold_uncertainty_score":0.011155963},"labels":[],"label_agreement":null},{"id":"W1887553753","doi":"10.1007/11499145_83","title":"Modeling Inaccurate Perception: Desynchronization Issues of a Chaotic Pattern Recognition Neural Network","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Chaotic; Computer science; Lyapunov exponent; Artificial intelligence; Perception; Numeral system; Image (mathematics); Object (grammar); Point (geometry); Artificial neural network; Algorithm; Pattern recognition (psychology); Computer vision; Mathematics; Psychology","score_opus":0.03173570963163696,"score_gpt":0.26230069798816197,"score_spread":0.230564988356525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1887553753","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19771658,0.0012402205,0.7857409,0.0024571687,0.00024358588,0.00004401591,0.000126755,0.0002607803,0.012169942],"genre_scores_gemma":[0.98473454,0.0004963448,0.0119970525,0.000094851464,0.000084508305,0.00002452713,0.000036630456,0.000067046436,0.0024645175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974257,0.00008113984,0.0000143317075,0.00006438431,0.000062530664,0.00003504035],"domain_scores_gemma":[0.9985852,0.00081691967,0.00018651914,0.00021456125,0.00014096092,0.00005583639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083241845,0.0004964127,0.00071222096,0.0002760547,0.00047552353,0.0012633138,0.0011916806,0.0013081714,0.0017794458],"category_scores_gemma":[0.0066979597,0.00055487297,0.00054263993,0.00047186608,0.00096703565,0.0023947672,0.0011780462,0.0015238676,0.00019617104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016519403,0.000042023057,0.0013200665,0.00009281687,0.000054821314,0.00022754853,0.00023509903,0.88858455,0.0072508175,0.07394281,0.0014890294,0.026595205],"study_design_scores_gemma":[0.000003714569,0.000007987837,0.0001690742,0.0000028016266,0.0000052718797,0.000018020313,0.00000718267,0.98931944,0.0002891558,0.01006085,0.000112078254,0.00000438815],"about_ca_topic_score_codex":0.004265858,"about_ca_topic_score_gemma":0.0018350746,"teacher_disagreement_score":0.004265858,"about_ca_system_score_codex":0.0008117484,"about_ca_system_score_gemma":0.0004553797,"threshold_uncertainty_score":0.008482039},"labels":[],"label_agreement":null},{"id":"W1890310797","doi":"10.1109/icsmc.1998.725130","title":"Subsystem modelling using neural networks with application to manufacturing systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial neural network; Compatibility (geochemistry); Empirical modelling; Physical system; Control engineering; Artificial intelligence; Engineering; Simulation","score_opus":0.0323664970714857,"score_gpt":0.21795471648230114,"score_spread":0.18558821941081544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1890310797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0114299925,0.00064428657,0.9843813,0.00019326954,0.000025027199,0.000028350283,0.000036787213,0.00044086535,0.0028201472],"genre_scores_gemma":[0.73367584,0.0018942337,0.25559953,0.00009832892,0.00010376426,0.0003505402,0.00019343353,0.00014389338,0.007940593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996697,0.00014829748,0.000019538402,0.000051679242,0.00008272413,0.000028057138],"domain_scores_gemma":[0.99936193,0.00041003575,0.000060918483,0.000038469712,0.00011284084,0.000015925823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006453364,0.0009343022,0.0008428399,0.0006289674,0.00055179006,0.00083826686,0.00085925503,0.0014155073,0.0018634514],"category_scores_gemma":[0.002103281,0.00052493624,0.00065327366,0.000973933,0.00057426817,0.0011337613,0.000643256,0.0009710625,0.00037345957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008264922,0.000004567929,0.00007085856,0.000017056891,0.0000087304215,0.00001391267,0.00001137138,0.9906183,0.0002261798,0.0020924697,0.00007385127,0.0068544727],"study_design_scores_gemma":[5.619516e-7,0.0000019559698,0.0000145235,0.0000015607428,0.0000010087634,0.0000018820732,0.0000011000024,0.99875665,0.00007028892,0.001062016,0.000086921056,0.0000015104808],"about_ca_topic_score_codex":0.016000167,"about_ca_topic_score_gemma":0.011651858,"teacher_disagreement_score":0.016000167,"about_ca_system_score_codex":0.0010616053,"about_ca_system_score_gemma":0.00089803303,"threshold_uncertainty_score":0.03181404},"labels":[],"label_agreement":null},{"id":"W1892688224","doi":"10.1109/iembs.1990.691321","title":"Orthogonal Expansions Of Electrophysiological Data Leading To Significant Loss Of Information","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Electrophysiology; Computer science; Neuroscience; Biology","score_opus":0.034312903492127,"score_gpt":0.28406886266323755,"score_spread":0.24975595917111054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1892688224","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19813772,0.00041367198,0.7969952,0.0004940669,0.00013487884,0.00004964078,0.0003617978,0.0006029662,0.002810082],"genre_scores_gemma":[0.69221455,0.0013928827,0.2995615,0.00029352735,0.00016387863,0.000121085,0.0013021788,0.0002810959,0.0046692938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936134,0.00020614656,0.000046748075,0.00006549778,0.00024747144,0.000072886396],"domain_scores_gemma":[0.9961569,0.0026445703,0.00023249481,0.0005892486,0.0002776658,0.000099208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013950771,0.0009803426,0.0004518862,0.00046105307,0.0002466504,0.0008033503,0.00037976395,0.0008998656,0.0019714723],"category_scores_gemma":[0.007849061,0.0003807977,0.00040832913,0.00093750434,0.0008374071,0.0016588144,0.0011226739,0.0016256471,0.00083120546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013321972,0.00043403098,0.0018521965,0.00038570742,0.000140796,0.0013835807,0.00026169998,0.084070385,0.4005027,0.04174965,0.004548734,0.46333835],"study_design_scores_gemma":[0.000052165124,0.00027293447,0.005715343,0.000058873527,0.00009186803,0.002236954,0.00015410125,0.75581425,0.18640284,0.045891535,0.003236641,0.00007247108],"about_ca_topic_score_codex":0.0003488564,"about_ca_topic_score_gemma":0.0006449174,"teacher_disagreement_score":0.0019714723,"about_ca_system_score_codex":0.00017611294,"about_ca_system_score_gemma":0.00044188285,"threshold_uncertainty_score":0.007377982},"labels":[],"label_agreement":null},{"id":"W1893008450","doi":"10.1109/pacrim.1997.620380","title":"A CORDIC implementation of a digital artificial neuron","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"CORDIC; Computer science; Activation function; Artificial neural network; Perceptron; Artificial neuron; Transcendental function; Implementation; Sigmoid function; Computer engineering; Computer hardware; Field-programmable gate array; Artificial intelligence; Mathematics; Programming language","score_opus":0.03718936788679452,"score_gpt":0.27739896081447585,"score_spread":0.24020959292768132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1893008450","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07505993,0.0010265448,0.8149348,0.0010878394,0.0013176282,0.0002627154,0.0003689022,0.0062274043,0.0997143],"genre_scores_gemma":[0.61500126,0.0005631275,0.3538437,0.0006188926,0.00015279061,0.00014835535,0.00021947465,0.0001190669,0.029333364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987006,0.000018410592,0.00001340686,0.000027075732,0.00005621812,0.000014772672],"domain_scores_gemma":[0.99978524,0.000025089172,0.000014890397,0.00003869261,0.00011728948,0.000018835131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001388882,0.0002836791,0.00018491519,0.00023904465,0.00034676364,0.0007027649,0.00068426295,0.000549047,0.009314408],"category_scores_gemma":[0.0006690603,0.00009402308,0.00012896357,0.00031210927,0.0002544864,0.00046844885,0.00030081274,0.00033698426,0.0019438417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007607059,0.00015365634,0.0015467898,0.00071571383,0.0000613887,0.0011534073,0.00027505972,0.0393058,0.25766045,0.18807033,0.019393086,0.49090362],"study_design_scores_gemma":[0.00022858135,0.0013506399,0.0015744865,0.0002714059,0.00013488563,0.002949122,0.00011083661,0.41456655,0.29293355,0.028931146,0.25686866,0.00008013268],"about_ca_topic_score_codex":0.0010779569,"about_ca_topic_score_gemma":0.0011084335,"teacher_disagreement_score":0.009314408,"about_ca_system_score_codex":0.00040910154,"about_ca_system_score_gemma":0.00051451556,"threshold_uncertainty_score":0.031159818},"labels":[],"label_agreement":null},{"id":"W1896462936","doi":"10.1109/mwscas.1998.759551","title":"Model validation and determination for neural network activation function modeling","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial neural network; Activation function; Unavailability; Computer science; Obstacle; Artificial intelligence; Perspective (graphical); Function (biology); Machine learning; Engineering","score_opus":0.07144757570013831,"score_gpt":0.25825678395187074,"score_spread":0.18680920825173242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1896462936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032944646,0.000086640524,0.96552616,0.00012325957,0.00001823644,0.000049346796,0.000048684567,0.00031876293,0.00088427536],"genre_scores_gemma":[0.8542049,0.00011844583,0.14417122,0.000060123853,0.0000111506615,0.00035170815,0.00017838726,0.00013613739,0.0007678706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989667,0.00051074213,0.00005740741,0.00013665117,0.00026311332,0.00006547528],"domain_scores_gemma":[0.9958676,0.0028324558,0.00021038487,0.00039681327,0.00064708554,0.000045616533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037893418,0.0006221184,0.0005563744,0.0006050798,0.00046233003,0.00072788494,0.00088956545,0.00090052566,0.001037602],"category_scores_gemma":[0.015871825,0.0004128379,0.00052110234,0.0003244133,0.0006771023,0.00089366996,0.0007248738,0.0017100818,0.0002602636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047095044,0.000029046363,0.0007126014,0.00005046036,0.000023836968,0.00003057591,0.000032910328,0.96889496,0.0040199272,0.011352759,0.0001622759,0.014643576],"study_design_scores_gemma":[0.000002982245,0.00000961121,0.0000844867,0.0000047891135,0.0000018545828,0.000004941683,0.0000033164652,0.99564123,0.0015560549,0.0025206432,0.00016718448,0.0000029206667],"about_ca_topic_score_codex":0.0045815385,"about_ca_topic_score_gemma":0.0033299257,"teacher_disagreement_score":0.0045815385,"about_ca_system_score_codex":0.00085422304,"about_ca_system_score_gemma":0.0016704528,"threshold_uncertainty_score":0.020040154},"labels":[],"label_agreement":null},{"id":"W1897739593","doi":"10.1109/cdc.1989.70456","title":"A neural network based control strategy for flexible-joint manipulators","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Computer science; Inverse dynamics; Backpropagation; Scheme (mathematics); Feed forward; Convergence (economics); Control theory (sociology); Path (computing); Joint (building); Control (management); Nonlinear system; Control engineering; Artificial intelligence; Engineering; Mathematics; Kinematics","score_opus":0.050762969381146654,"score_gpt":0.2681881579098879,"score_spread":0.21742518852874126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1897739593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006034624,0.00030461547,0.98967034,0.000098130564,0.000057755416,0.000036326288,0.000013432052,0.00026052562,0.0035242103],"genre_scores_gemma":[0.7081378,0.0006348538,0.27633524,0.00017722657,0.00010936861,0.00031985043,0.000074818425,0.00005134115,0.014159473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997942,0.00003514075,0.000012065057,0.000046162488,0.000087034765,0.000025533589],"domain_scores_gemma":[0.9999044,0.000022483184,0.000022557495,0.000009038013,0.000032437933,0.000009142771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039165572,0.0007057899,0.00045310642,0.00032805998,0.00041511905,0.0004906008,0.0009949489,0.0008582229,0.0022433002],"category_scores_gemma":[0.00046488634,0.00023676705,0.00033127013,0.00031422699,0.00064336305,0.0006511401,0.0007099149,0.00055395375,0.0004045995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009208004,0.000047278387,0.00017586467,0.00018133823,0.000043334883,0.00021095811,0.00010858975,0.7661902,0.027589815,0.03572946,0.001758208,0.16787285],"study_design_scores_gemma":[0.000018967674,0.00008097285,0.00008567099,0.000013506523,0.000010777134,0.000038432314,0.000004879224,0.9915707,0.002214964,0.0038918937,0.002058041,0.000011243013],"about_ca_topic_score_codex":0.003003242,"about_ca_topic_score_gemma":0.0030087468,"teacher_disagreement_score":0.003003242,"about_ca_system_score_codex":0.00048891857,"about_ca_system_score_gemma":0.00048006652,"threshold_uncertainty_score":0.007504642},"labels":[],"label_agreement":null},{"id":"W1898301487","doi":"10.1109/mwscas.1993.343273","title":"Multiplierless multilayer feedforward neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Feed forward; Sigmoid function; Artificial neural network; Layer (electronics); Feedforward neural network; Activation function; Function (biology); Computation; Algorithm; Arithmetic; Artificial intelligence; Mathematics; Control engineering; Engineering","score_opus":0.024433702019031955,"score_gpt":0.2264159067898202,"score_spread":0.20198220477078827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1898301487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017393519,0.00013685504,0.996781,0.000029566174,0.000035580248,0.00003126577,0.000021751492,0.00030443224,0.0009201715],"genre_scores_gemma":[0.13329689,0.00038261988,0.8593024,0.000125992,0.00007205695,0.00033389076,0.00014103761,0.000072841496,0.0062722075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996902,0.00008190588,0.000026095438,0.00006479115,0.0001120482,0.000025049541],"domain_scores_gemma":[0.99964905,0.00013680113,0.00004816086,0.000039582243,0.000113911716,0.000012457778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008045522,0.0007916137,0.00049572706,0.00042387669,0.00033374428,0.0007592913,0.0013074945,0.0010841639,0.0040134066],"category_scores_gemma":[0.0017113009,0.00040745965,0.00046543946,0.0003812979,0.00032856638,0.00084598875,0.0005664385,0.0007988658,0.0012441368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016967121,0.000052615957,0.00048819312,0.00037896354,0.00009350818,0.00019341464,0.00011040149,0.394279,0.033384513,0.038827263,0.0038743594,0.528148],"study_design_scores_gemma":[0.000020355244,0.000081803286,0.0001488715,0.000027537044,0.000019066858,0.000096271506,0.000005106409,0.9789844,0.006883313,0.007468816,0.006252552,0.000011855106],"about_ca_topic_score_codex":0.00069719274,"about_ca_topic_score_gemma":0.0013898271,"teacher_disagreement_score":0.0040134066,"about_ca_system_score_codex":0.0005086006,"about_ca_system_score_gemma":0.0004889687,"threshold_uncertainty_score":0.013426244},"labels":[],"label_agreement":null},{"id":"W1899138907","doi":"10.1109/mdsp.1991.639437","title":"Properties Of Adaptive Morphological Operators, Fast Algorithms And Their Applications","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Algorithm","score_opus":0.028719269578545387,"score_gpt":0.22804577782301874,"score_spread":0.19932650824447334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1899138907","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053093277,0.002668676,0.93322724,0.00037362098,0.00015087017,0.00005114989,0.00012585014,0.000380416,0.009928956],"genre_scores_gemma":[0.5563388,0.005239943,0.4214981,0.00032962745,0.0006336744,0.00033063968,0.00038575634,0.00061773154,0.014625684],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923587,0.00016175419,0.000063457526,0.00012554791,0.00035486103,0.000058528367],"domain_scores_gemma":[0.9898225,0.006433325,0.0011232496,0.0009795653,0.0013846314,0.00025675626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019365388,0.0007931424,0.00069893704,0.0022519052,0.0008502079,0.0022031183,0.0011342819,0.0013465418,0.0047212136],"category_scores_gemma":[0.01572292,0.00059123116,0.0006135932,0.0022059744,0.001796852,0.004682207,0.0010838158,0.0015149313,0.00089504954],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028919912,0.0000758981,0.0010834965,0.00030002167,0.00004604554,0.00019069792,0.00025940666,0.05591406,0.033015843,0.69135815,0.003021616,0.21444553],"study_design_scores_gemma":[0.000048879177,0.00016260943,0.0013786611,0.000059030997,0.000044923792,0.0008040832,0.00009601298,0.42304316,0.01571896,0.55082595,0.0077497945,0.000068019464],"about_ca_topic_score_codex":0.00064480555,"about_ca_topic_score_gemma":0.00048536187,"teacher_disagreement_score":0.0047212136,"about_ca_system_score_codex":0.00057456654,"about_ca_system_score_gemma":0.0008087954,"threshold_uncertainty_score":0.015794098},"labels":[],"label_agreement":null},{"id":"W1901640163","doi":"10.1109/ijcnn.2001.938812","title":"Adaptive resonance theory networks using incremental communication","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Convergence (economics); Adaptive resonance theory; Node (physics); Implementation; Communications system; Artificial neural network; Floating point; Telecommunications network; Bounded function; Algorithm; Artificial intelligence; Mathematics; Computer network; Engineering","score_opus":0.0485318242145249,"score_gpt":0.25022331031604234,"score_spread":0.20169148610151744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1901640163","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030289367,0.0002057419,0.9613988,0.00015324767,0.00007275866,0.000042719494,0.000016146516,0.00038461995,0.0074366443],"genre_scores_gemma":[0.7584608,0.00020912492,0.23655517,0.00012235642,0.00005575837,0.00020120459,0.00004916869,0.000051148516,0.004295225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997558,0.00008891739,0.0000084715675,0.000037421818,0.00008673619,0.00002258296],"domain_scores_gemma":[0.99932075,0.0004036442,0.000049357513,0.00008798223,0.000115404,0.000022871696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004333563,0.00035781288,0.0003204959,0.00030177904,0.00036712727,0.00044918506,0.0009059052,0.00059230166,0.0018466638],"category_scores_gemma":[0.002165632,0.00018793195,0.00031396945,0.0003316082,0.0005710454,0.001082772,0.00070496165,0.00080796925,0.00031852114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014905463,0.00004869357,0.00045377295,0.000090347254,0.00004009196,0.00018092996,0.00017320413,0.7982157,0.019041358,0.07880358,0.001655482,0.10114782],"study_design_scores_gemma":[0.000009991398,0.000056188455,0.000060373695,0.0000032710875,0.0000058976684,0.000033938286,0.0000071103514,0.98646754,0.0018036036,0.010507503,0.0010370675,0.000007566519],"about_ca_topic_score_codex":0.0013537155,"about_ca_topic_score_gemma":0.0015524529,"teacher_disagreement_score":0.0018466638,"about_ca_system_score_codex":0.00048408477,"about_ca_system_score_gemma":0.00038711727,"threshold_uncertainty_score":0.0061777234},"labels":[],"label_agreement":null},{"id":"W1905411689","doi":"10.1109/icassp.1988.196522","title":"Use of neural networks for the recognition of place of articulation","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Boltzmann machine; Place of articulation; Artificial neural network; Coding (social sciences); Computer science; Markov chain; Articulation (sociology); Artificial intelligence; Set (abstract data type); Speech recognition; Test set; Algorithm; Word error rate; Pattern recognition (psychology); Theoretical computer science; Mathematics; Machine learning; Statistics; Programming language","score_opus":0.06509446945183091,"score_gpt":0.25907755617427186,"score_spread":0.19398308672244097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1905411689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14210346,0.0014093689,0.84777385,0.00043419443,0.00029793716,0.00006286104,0.00032882352,0.0026766697,0.004912789],"genre_scores_gemma":[0.72299284,0.0005846682,0.27029794,0.00009603231,0.000059001064,0.000058655976,0.00036994318,0.00010256168,0.0054384405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960285,0.00010200922,0.000027176226,0.00011664292,0.000115171904,0.000036153906],"domain_scores_gemma":[0.9991478,0.00047147015,0.000078150144,0.00010028667,0.00017644574,0.000025946865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091395807,0.0007381366,0.00043030598,0.0009045997,0.00036670666,0.00097606855,0.0007383241,0.00072855677,0.001295536],"category_scores_gemma":[0.0033373954,0.00044910132,0.0005001778,0.00080403575,0.0006047411,0.0011589449,0.0005914149,0.00094272237,0.0005191749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004304908,0.0001279458,0.0031835535,0.00015105848,0.00016042824,0.00022677235,0.00015799799,0.37595296,0.034279864,0.010057628,0.0018329984,0.5734382],"study_design_scores_gemma":[0.000009666702,0.000026159256,0.00078900973,0.000011343232,0.000017904067,0.000058264326,0.0000144404785,0.98013675,0.013479526,0.0045636306,0.0008716684,0.000021620557],"about_ca_topic_score_codex":0.0057717906,"about_ca_topic_score_gemma":0.0048316633,"teacher_disagreement_score":0.0057717906,"about_ca_system_score_codex":0.00077070104,"about_ca_system_score_gemma":0.00041164062,"threshold_uncertainty_score":0.0114763975},"labels":[],"label_agreement":null},{"id":"W1905656136","doi":"10.1007/11871637_28","title":"Efficient Spatial Classification Using Decoupled Conditional Random Fields","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"CRFS; Conditional random field; Discriminative model; Computer science; Random field; Artificial intelligence; Generative grammar; Markov random field; Machine learning; Boosting (machine learning); Pattern recognition (psychology); Markov process; Mathematics; Image (mathematics); Statistics","score_opus":0.022754883563641963,"score_gpt":0.25812011596751083,"score_spread":0.23536523240386886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1905656136","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008795534,0.00021947162,0.98719597,0.0001500827,0.000055230674,0.000029875464,0.00018712971,0.0018311617,0.0015355666],"genre_scores_gemma":[0.2759022,0.00037634914,0.7105063,0.0002861066,0.00014803885,0.0001703254,0.00226157,0.00045238886,0.009896694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915445,0.00020437148,0.000039815422,0.00020239118,0.000271532,0.00012741871],"domain_scores_gemma":[0.99799466,0.0009982433,0.000105788786,0.00048952404,0.00032642562,0.00008539523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012889649,0.00093013473,0.0020087815,0.0014745059,0.00069933315,0.0017401328,0.003458194,0.0017830466,0.007819839],"category_scores_gemma":[0.0033276347,0.00081424403,0.0015367694,0.0022102061,0.0007106917,0.0028929876,0.00245918,0.001777108,0.0028953063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038318287,0.00020908068,0.00096385553,0.00013329694,0.00011537837,0.00007505687,0.00006580434,0.2812341,0.009998783,0.047355287,0.01620506,0.64326113],"study_design_scores_gemma":[0.0000106397065,0.000011114549,0.00011764762,0.000004243337,0.000007885666,0.000023077106,0.000006884898,0.9818829,0.0011955485,0.01603359,0.00069918256,0.0000072412295],"about_ca_topic_score_codex":0.0074628326,"about_ca_topic_score_gemma":0.010783189,"teacher_disagreement_score":0.007819839,"about_ca_system_score_codex":0.0010876558,"about_ca_system_score_gemma":0.001408448,"threshold_uncertainty_score":0.026159942},"labels":[],"label_agreement":null},{"id":"W1906820698","doi":"10.1109/cacsd.1992.274435","title":"Learning and adaptation in control systems with interactive computer graphics","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Controller (irrigation); A priori and a posteriori; Adaptation (eye); Control (management); Computer graphics; Graphics; Artificial neural network; Scheme (mathematics); Adaptive control; Control system; Control engineering; Artificial intelligence; Inverse dynamics; Computer graphics (images); Engineering; Mathematics","score_opus":0.010684537369883203,"score_gpt":0.22112385213290936,"score_spread":0.21043931476302616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1906820698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000890775,0.00073075836,0.98568326,0.00023987405,0.00009930103,0.000041532043,0.000048883718,0.0036434282,0.00862223],"genre_scores_gemma":[0.09444404,0.0024063769,0.8613514,0.0004796828,0.00031232435,0.0007105253,0.0003627593,0.0009113655,0.039021593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995604,0.00016629868,0.00002461555,0.000049919774,0.00017509515,0.000023646257],"domain_scores_gemma":[0.9995159,0.0003254009,0.00002370931,0.000059067224,0.000059655067,0.000016207094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005331102,0.0008264407,0.00056208274,0.000596778,0.00029895664,0.00127606,0.0009863183,0.0012211226,0.025171587],"category_scores_gemma":[0.0019604203,0.00034533205,0.00049443,0.0006505099,0.000970736,0.0011109377,0.0009036891,0.0013609993,0.003832771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019829752,0.0000701916,0.00026472253,0.00056406163,0.00007253515,0.000489199,0.00038807004,0.19498862,0.015210341,0.1943073,0.052424666,0.541022],"study_design_scores_gemma":[0.00005115259,0.00008656076,0.0002668065,0.00008496994,0.000023029184,0.00030959098,0.000030625695,0.77106315,0.007340399,0.06781913,0.15289167,0.000032862314],"about_ca_topic_score_codex":0.0014817204,"about_ca_topic_score_gemma":0.0018463044,"teacher_disagreement_score":0.025171587,"about_ca_system_score_codex":0.0004723555,"about_ca_system_score_gemma":0.00031072306,"threshold_uncertainty_score":0.0842073},"labels":[],"label_agreement":null},{"id":"W1910105393","doi":"10.1109/iscas.1993.394341","title":"Pipelined analog multi-layer feedforward neural networks","year":2002,"lang":"en","type":"article","venue":"1993 IEEE International Symposium on Circuits and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial neural network; Layer (electronics); Feed forward; Feedforward neural network; Multiplexing; Computer architecture; Artificial intelligence; Control engineering; Engineering; Telecommunications","score_opus":0.055142627031310586,"score_gpt":0.2715600008468343,"score_spread":0.2164173738155237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1910105393","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013820877,0.0008228154,0.97753006,0.00012812958,0.00011929274,0.000039003822,0.00013820606,0.0015387029,0.0058628893],"genre_scores_gemma":[0.37276724,0.0009212463,0.6081549,0.00017870998,0.00008755074,0.00014039982,0.0003134407,0.000088528206,0.017347971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986744,0.000016750462,0.000010416818,0.000034059736,0.0000538242,0.000017487062],"domain_scores_gemma":[0.9998547,0.0000457703,0.000015653739,0.000034442513,0.00003909882,0.000010362547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023669485,0.00043453233,0.00019760209,0.00024846595,0.00025238542,0.00049788365,0.0009048993,0.0004660155,0.006041646],"category_scores_gemma":[0.0005618588,0.00026101008,0.00023767016,0.00031661283,0.00019542541,0.0010696587,0.0004671365,0.0005174401,0.00071112666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036016182,0.000094246345,0.0004968587,0.00043491132,0.000060570594,0.0002189714,0.00011004515,0.14105505,0.12427807,0.04322472,0.0058940183,0.6837723],"study_design_scores_gemma":[0.000044826975,0.000202828,0.0004891771,0.00004573012,0.000041988416,0.00016919445,0.000014377,0.90792876,0.054863553,0.015113062,0.021061614,0.000024839146],"about_ca_topic_score_codex":0.001289849,"about_ca_topic_score_gemma":0.004475417,"teacher_disagreement_score":0.006041646,"about_ca_system_score_codex":0.0004960565,"about_ca_system_score_gemma":0.00044583657,"threshold_uncertainty_score":0.020211339},"labels":[],"label_agreement":null},{"id":"W1910253334","doi":"10.1109/ijcnn.2001.939061","title":"A new online unsupervised learning rule for the BSB model","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Unsupervised learning; Artificial intelligence; Learning rule; Task (project management); Projection (relational algebra); Machine learning; Noise (video); Data modeling; Artificial neural network; Algorithm; Engineering","score_opus":0.056137570513823606,"score_gpt":0.2613927358237724,"score_spread":0.2052551653099488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1910253334","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011975138,0.000087570676,0.98157054,0.00024127234,0.00006450366,0.0000463265,0.00006971317,0.0004489707,0.0054959413],"genre_scores_gemma":[0.59492993,0.0002825469,0.3914154,0.0003657339,0.0001238795,0.0003764611,0.00020716016,0.00036452297,0.01193434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944395,0.00011673075,0.000033402866,0.00012801123,0.00024418597,0.000033722175],"domain_scores_gemma":[0.9988557,0.00043756719,0.00012002761,0.00024562204,0.00026165062,0.00007950685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090803363,0.00040824426,0.0005829839,0.00042911927,0.00045398582,0.0007892091,0.0012716813,0.0010113823,0.0024481264],"category_scores_gemma":[0.0039076675,0.00026249746,0.0004965433,0.00026421694,0.0011871628,0.0015914418,0.0008890112,0.0017512719,0.00094690785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014383634,0.00016765365,0.0014265026,0.00018208542,0.00009827073,0.0003375902,0.00030190937,0.41469184,0.049318433,0.36160156,0.0060698614,0.16566049],"study_design_scores_gemma":[0.00001390869,0.00002842063,0.00018331662,0.000010996574,0.000008347653,0.00014488724,0.000007227751,0.9382653,0.003915873,0.05413735,0.0032655,0.000018854062],"about_ca_topic_score_codex":0.0016605732,"about_ca_topic_score_gemma":0.0020944043,"teacher_disagreement_score":0.0024481264,"about_ca_system_score_codex":0.0005172123,"about_ca_system_score_gemma":0.00068749563,"threshold_uncertainty_score":0.008189738},"labels":[],"label_agreement":null},{"id":"W1910801719","doi":"10.1109/ijcnn.1999.831542","title":"A learning-theory-based training algorithm for variable-structure dynamic neural modeling","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Machine learning; Algorithm; Variable (mathematics); Evolutionary algorithm; Quadratic equation; Mathematics","score_opus":0.016843756513812674,"score_gpt":0.2470888978274016,"score_spread":0.23024514131358892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1910801719","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012700323,0.00006849243,0.99791247,0.000043019,0.000009794629,0.000018419953,0.000005713576,0.000103824466,0.0005682583],"genre_scores_gemma":[0.11319505,0.00023044778,0.88440204,0.00009106606,0.000031619602,0.000342732,0.00006443924,0.00007037751,0.001572327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999708,0.0000904872,0.0000176899,0.0000505442,0.00011075301,0.00002263518],"domain_scores_gemma":[0.99949753,0.00027901123,0.000047966194,0.000039921084,0.00011919303,0.000016379174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011026469,0.0006919965,0.000844712,0.00059680786,0.0004430723,0.0005081667,0.0014677095,0.0013731119,0.002331759],"category_scores_gemma":[0.0031545893,0.0004352356,0.0005798322,0.00084497285,0.0006983025,0.0009964216,0.0009506286,0.0015343816,0.0005465637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024625666,0.000041326493,0.00022309656,0.00008429679,0.00004039578,0.000042112497,0.000069473965,0.785123,0.0023348057,0.033522032,0.001142828,0.17735206],"study_design_scores_gemma":[0.0000036184927,0.000011273029,0.000023362843,0.000005345216,0.0000029117027,0.000014764358,0.0000018543845,0.99622715,0.00026800722,0.002896704,0.0005422597,0.0000026734047],"about_ca_topic_score_codex":0.0028119937,"about_ca_topic_score_gemma":0.0024220035,"teacher_disagreement_score":0.0028119937,"about_ca_system_score_codex":0.0007979452,"about_ca_system_score_gemma":0.00091881887,"threshold_uncertainty_score":0.0078005195},"labels":[],"label_agreement":null},{"id":"W1911414707","doi":"10.1109/ijcnn.2001.938838","title":"Image reconstruction using bistable gradient neural-like networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Attractor; Bistability; Artificial neural network; Computer science; Generalization; Image restoration; White noise; Noise (video); Artificial intelligence; Multiplicative function; Convergence (economics); Gaussian; Gaussian noise; Image (mathematics); Image processing; Algorithm; Mathematics; Physics; Mathematical analysis","score_opus":0.028979363255184864,"score_gpt":0.22925065726315247,"score_spread":0.20027129400796762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1911414707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2106542,0.000459468,0.78384477,0.00035204066,0.000055307046,0.00002240693,0.000049074668,0.0006698954,0.0038928068],"genre_scores_gemma":[0.8967155,0.00015772461,0.10158578,0.00009965152,0.00001307439,0.000019180316,0.000045928064,0.000032898963,0.0013302098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999496,0.00001315642,0.0000022685053,0.000011501396,0.000016108579,0.0000074511922],"domain_scores_gemma":[0.99989665,0.000037447444,0.000019798601,0.00001763228,0.000019516689,0.000008941929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016323527,0.00022590108,0.0001968133,0.00015276295,0.00010648734,0.00021700641,0.00037288608,0.0003993701,0.00043104187],"category_scores_gemma":[0.00072501093,0.00012432715,0.00015783895,0.00017893665,0.00025845598,0.000557942,0.00034198017,0.000294228,0.00009283034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017904473,0.000054730164,0.0011041915,0.00007661224,0.000051469968,0.00023632146,0.000082419785,0.72899383,0.10658967,0.026600404,0.0010577829,0.13497351],"study_design_scores_gemma":[0.000004017441,0.000014139423,0.00012544965,0.0000013741783,0.0000023315627,0.000024367211,0.0000024301683,0.9921455,0.0039195768,0.0035318253,0.00022565137,0.0000033016736],"about_ca_topic_score_codex":0.00100088,"about_ca_topic_score_gemma":0.0012840867,"teacher_disagreement_score":0.00100088,"about_ca_system_score_codex":0.00025000935,"about_ca_system_score_gemma":0.00012819642,"threshold_uncertainty_score":0.0019901395},"labels":[],"label_agreement":null},{"id":"W1912997277","doi":"10.1109/ccece.1995.528128","title":"Induction motor design using neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Atlantic Hydrogen (Canada); University of New Brunswick","funders":"","keywords":"Artificial neural network; Computer science; Backpropagation; Induction motor; Set (abstract data type); Artificial intelligence; Task (project management); Control engineering; Time delay neural network; Machine learning; Engineering","score_opus":0.10359832948197131,"score_gpt":0.25741081821182404,"score_spread":0.15381248872985273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1912997277","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006261115,0.00077206874,0.9868674,0.00010901416,0.000046407917,0.000048481423,0.000021957892,0.0004936236,0.005379874],"genre_scores_gemma":[0.49894768,0.0022667488,0.48384854,0.00016995137,0.00008062715,0.00035303936,0.00016889434,0.00014950616,0.014015026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972147,0.000061396044,0.000020636377,0.000053328124,0.00012500295,0.000018142751],"domain_scores_gemma":[0.9998016,0.00007899183,0.000024918561,0.000015651787,0.00007304943,0.0000058089067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045650318,0.00050566666,0.0005074989,0.00037955947,0.00023863108,0.000503319,0.00046298144,0.00083285285,0.0016205189],"category_scores_gemma":[0.00086778915,0.00027488993,0.0003431791,0.00042657167,0.00027439528,0.00055287825,0.0002633046,0.0004817831,0.0007579474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010162392,0.000034604793,0.00042906252,0.00027863294,0.00005240023,0.00008151356,0.000042813408,0.76008576,0.02625285,0.0075950706,0.001265664,0.20378003],"study_design_scores_gemma":[0.000009871055,0.000055918434,0.0002172778,0.000021256876,0.000019306306,0.000036579673,0.0000047391436,0.98492,0.0061576017,0.002905892,0.0056417994,0.000009844498],"about_ca_topic_score_codex":0.00174693,"about_ca_topic_score_gemma":0.0020994742,"teacher_disagreement_score":0.00174693,"about_ca_system_score_codex":0.00044229248,"about_ca_system_score_gemma":0.00041838508,"threshold_uncertainty_score":0.0054212213},"labels":[],"label_agreement":null},{"id":"W1917753824","doi":"10.1109/aps.1999.789335","title":"Neural network structures for EM/microwave modeling","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial neural network; Microwave; Computer science; Microwave engineering; Time delay neural network; Artificial intelligence; Electronic engineering; Engineering; Telecommunications","score_opus":0.028895066480702374,"score_gpt":0.25836720349940584,"score_spread":0.22947213701870348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1917753824","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031174454,0.0032558981,0.9810986,0.0005647471,0.00013082777,0.000024036797,0.00007484316,0.00024508694,0.011488495],"genre_scores_gemma":[0.47522312,0.015559237,0.4607069,0.00051388005,0.00061604823,0.0005963797,0.00049225095,0.00023568845,0.046056464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998299,0.00006258899,0.000009584263,0.000021371572,0.000065632245,0.000010907996],"domain_scores_gemma":[0.9997801,0.00011506826,0.000022483993,0.000015202189,0.00006053831,0.0000065827144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004540713,0.0006023906,0.00050042383,0.0005238648,0.00028104094,0.0007100976,0.0009607826,0.0012617662,0.0039051033],"category_scores_gemma":[0.0014570554,0.00028924772,0.00039680928,0.0008145052,0.00048837194,0.0011111398,0.0004941289,0.0014081374,0.0009978865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027060181,0.00002119225,0.00022656133,0.00015325082,0.000042706884,0.000069401016,0.000053861186,0.72529334,0.0017514966,0.19673686,0.0025403046,0.073083945],"study_design_scores_gemma":[0.0000031481552,0.000007204138,0.000043263513,0.000016780501,0.00000543576,0.000014472513,0.000004297269,0.9583651,0.00040191767,0.037560396,0.003572465,0.0000055650103],"about_ca_topic_score_codex":0.0027667538,"about_ca_topic_score_gemma":0.0027546408,"teacher_disagreement_score":0.0039051033,"about_ca_system_score_codex":0.0006547509,"about_ca_system_score_gemma":0.00037007072,"threshold_uncertainty_score":0.013063908},"labels":[],"label_agreement":null},{"id":"W1920065104","doi":"10.1109/iscas.1993.394238","title":"A design method for multilayer feedforward neural networks for simple hardware implementation","year":2002,"lang":"en","type":"article","venue":"1993 IEEE International Symposium on Circuits and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial neural network; Feed forward; Feedforward neural network; Quantization (signal processing); Sigmoid function; Simple (philosophy); Computer hardware; Layer (electronics); Algorithm; Artificial intelligence; Computer engineering; Control engineering; Engineering","score_opus":0.07475573353588333,"score_gpt":0.3348145455271668,"score_spread":0.26005881199128345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1920065104","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032243243,0.00005597716,0.99840266,0.000020455813,0.000028392256,0.000025709263,0.00001307959,0.00031445458,0.00081682333],"genre_scores_gemma":[0.031583615,0.00018989589,0.9624971,0.00006419529,0.00004597922,0.00034523377,0.000060052113,0.00012697147,0.0050869687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965847,0.00007276116,0.00002618111,0.000053642376,0.00016944394,0.000019442012],"domain_scores_gemma":[0.99973255,0.000082036386,0.00002841262,0.000035340152,0.00011108587,0.000010632656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005465819,0.0008846228,0.00029722587,0.00048389495,0.00041891448,0.00053110044,0.00093629526,0.0006547172,0.008664396],"category_scores_gemma":[0.0012333803,0.00046395115,0.00048779824,0.00032692912,0.00030591423,0.00075852906,0.00045376128,0.00092652737,0.0025003608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112467875,0.000057438694,0.00030163018,0.00072062056,0.00010251045,0.00024795183,0.00016127773,0.13567908,0.099356316,0.06767641,0.008496061,0.6870882],"study_design_scores_gemma":[0.000066941975,0.00022835682,0.00027260295,0.000120745884,0.000059858314,0.0004537539,0.000022154549,0.85311204,0.04497642,0.024631888,0.07600947,0.000045841723],"about_ca_topic_score_codex":0.0009104091,"about_ca_topic_score_gemma":0.0019143998,"teacher_disagreement_score":0.008664396,"about_ca_system_score_codex":0.00044949967,"about_ca_system_score_gemma":0.0005251732,"threshold_uncertainty_score":0.028985322},"labels":[],"label_agreement":null},{"id":"W1924740559","doi":"10.1109/cca.1993.348214","title":"Adaptive tracking in nonlinear systems using neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Computer science; Nonlinear system; Physical neural network; Artificial intelligence; Nervous system network models; Stochastic neural network; Time delay neural network; Models of neural computation; Types of artificial neural networks","score_opus":0.06665022560678177,"score_gpt":0.2688207160896911,"score_spread":0.20217049048290933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1924740559","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010596211,0.0013435427,0.98203653,0.00026692072,0.0000658073,0.000023893792,0.000021753574,0.00034121666,0.005304119],"genre_scores_gemma":[0.81606215,0.0030970853,0.16937286,0.00015072073,0.00020838733,0.0002723911,0.00011673351,0.00007666324,0.010643015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998124,0.00006271839,0.000012187235,0.000033790115,0.0000659575,0.00001304397],"domain_scores_gemma":[0.99973804,0.00015568912,0.00003353285,0.000020868174,0.000044278982,0.000007553714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044572266,0.0004580065,0.00045758518,0.00037354894,0.0002677908,0.0007193095,0.0004200201,0.00071817625,0.0013862851],"category_scores_gemma":[0.0015083345,0.00022674288,0.00033459105,0.00050247344,0.0005539577,0.0008172348,0.00067840767,0.00066547556,0.00028452094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016825075,0.000008815084,0.00017738761,0.000048644735,0.000019521227,0.00003131794,0.00003724777,0.9463689,0.0016261494,0.019390421,0.00034368475,0.03193123],"study_design_scores_gemma":[0.0000026601701,0.0000052526852,0.000038329857,0.0000040706755,0.0000021218154,0.0000044754124,0.0000019811475,0.9933822,0.0001791187,0.0058340863,0.00054322125,0.0000024640003],"about_ca_topic_score_codex":0.005037067,"about_ca_topic_score_gemma":0.004027167,"teacher_disagreement_score":0.005037067,"about_ca_system_score_codex":0.0005173632,"about_ca_system_score_gemma":0.0003787854,"threshold_uncertainty_score":0.010015488},"labels":[],"label_agreement":null},{"id":"W1928273700","doi":"10.1109/ijcnn.1992.226861","title":"A neural model for adaptive Karhunen Loeve transformation (KLT)","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Dimension (graph theory); Covariance matrix; Gradient descent; Eigenvalues and eigenvectors; Artificial intelligence; Learning rule; Artificial neural network; Rate of convergence; Computer science; Convergence (economics); Transformation (genetics); Stochastic gradient descent; Covariance; Hebbian theory; Mathematics; Algorithm; Matrix (chemical analysis); Sequence (biology); Principal component analysis; Key (lock); Statistics; Combinatorics","score_opus":0.04101221170945962,"score_gpt":0.2571821347134992,"score_spread":0.2161699230040396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1928273700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010137028,0.00013393395,0.9967006,0.00009027653,0.00005281515,0.000018429086,0.000042490978,0.00035492197,0.0015927834],"genre_scores_gemma":[0.2895598,0.0009889533,0.6851136,0.0003436237,0.00020931587,0.0004791798,0.0005849034,0.00041012283,0.022310399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969876,0.000047566013,0.000016408647,0.00008601223,0.00011999861,0.000031264513],"domain_scores_gemma":[0.99979347,0.000056586505,0.000024280176,0.000028597417,0.00008370029,0.00001338598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043067613,0.00048258904,0.0008265573,0.00037598732,0.0003546241,0.0011036042,0.0017369442,0.0011504972,0.004045898],"category_scores_gemma":[0.0012919503,0.0003218849,0.00073086825,0.00064631016,0.00058573723,0.0015240823,0.0008469857,0.0014879593,0.002332417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007634833,0.000080705,0.00066700537,0.0001982033,0.00007328021,0.00023359492,0.00011639432,0.5917109,0.014654798,0.121755905,0.006739568,0.2636933],"study_design_scores_gemma":[0.0000063670154,0.000015785288,0.00009867584,0.000007996033,0.0000063095495,0.00005894148,0.000004162153,0.9819756,0.00097610627,0.013312731,0.00352575,0.000011652169],"about_ca_topic_score_codex":0.004583257,"about_ca_topic_score_gemma":0.004944747,"teacher_disagreement_score":0.004583257,"about_ca_system_score_codex":0.0006720777,"about_ca_system_score_gemma":0.0011774931,"threshold_uncertainty_score":0.0135349035},"labels":[],"label_agreement":null},{"id":"W1930720625","doi":"10.1007/978-3-642-33266-1_36","title":"Improving Neural Networks Classification through Chaining","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Chaining; Computer science; Artificial neural network; Artificial intelligence; Time delay neural network; Machine learning; Backward chaining; Nervous system network models; Types of artificial neural networks; Data mining; Pattern recognition (psychology); Expert system","score_opus":0.03358219382364943,"score_gpt":0.258982748861842,"score_spread":0.2254005550381926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1930720625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045824584,0.0020469595,0.94083047,0.00036675294,0.00044073796,0.000111699825,0.00020085693,0.0030683933,0.00710958],"genre_scores_gemma":[0.41434208,0.0020360611,0.55307055,0.0005082387,0.0006545604,0.00025893401,0.0018355474,0.00096210476,0.026331943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991672,0.00016173413,0.000072539835,0.00023852248,0.00026657467,0.00009341565],"domain_scores_gemma":[0.9968213,0.0014702565,0.00018551563,0.00073869096,0.0006687303,0.00011547061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016759689,0.0012554821,0.001502249,0.0014457027,0.0007053081,0.0012793673,0.0020403594,0.0015341458,0.011803389],"category_scores_gemma":[0.005137848,0.0006646262,0.001009157,0.0019272034,0.0008322515,0.0030452772,0.0023601905,0.0026419056,0.004484234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002564877,0.00018542407,0.0011799176,0.00016644989,0.00010894646,0.000089340276,0.00005904435,0.16331075,0.010799612,0.010587023,0.0067550223,0.8065021],"study_design_scores_gemma":[0.000014231888,0.000086401604,0.00023877095,0.000032520995,0.000040304516,0.00003941354,0.000014028598,0.9748576,0.0069892793,0.014909396,0.0027670816,0.000010992476],"about_ca_topic_score_codex":0.0032329164,"about_ca_topic_score_gemma":0.004199587,"teacher_disagreement_score":0.011803389,"about_ca_system_score_codex":0.0006864098,"about_ca_system_score_gemma":0.00075839786,"threshold_uncertainty_score":0.03948629},"labels":[],"label_agreement":null},{"id":"W1932803353","doi":"10.1109/ssst.2003.1194591","title":"Adaptive fuzzy associative memory for online quality control","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Learning vector quantization; Computer science; Fuzzy logic; Artificial intelligence; Associative property; Quantization (signal processing); Content-addressable memory; Process (computing); Quality (philosophy); Vector quantization; Component (thermodynamics); Fuzzy control system; Computer vision; Pattern recognition (psychology); Machine learning; Artificial neural network; Mathematics","score_opus":0.04898841360920194,"score_gpt":0.3176300917539091,"score_spread":0.2686416781447072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1932803353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041788306,0.0016882457,0.94744426,0.00025564161,0.00030468588,0.000060548697,0.00004290525,0.0009168867,0.007498468],"genre_scores_gemma":[0.9369333,0.0005841353,0.058037795,0.00016356245,0.00009724814,0.00008843978,0.000057663787,0.000018068635,0.00401971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998086,0.000027454513,0.0000163739,0.000050374114,0.00006846033,0.000028836965],"domain_scores_gemma":[0.99967813,0.00010579668,0.00003481914,0.000040127565,0.00012720785,0.000014015055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043181094,0.00033009358,0.00041701115,0.00023009654,0.00027106414,0.0005880105,0.0009032652,0.00053959846,0.0023818146],"category_scores_gemma":[0.00091565656,0.00011245875,0.00029199242,0.00027470704,0.00043129877,0.00079097843,0.00038328007,0.00062618137,0.00032627338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005034294,0.0002927094,0.0015887639,0.00038826914,0.00019549986,0.00033145995,0.00022959342,0.31408358,0.05752725,0.07190058,0.004343416,0.54861546],"study_design_scores_gemma":[0.00003766799,0.00019610737,0.00044925377,0.000017300054,0.00005111514,0.0001068848,0.00001975973,0.970604,0.009534095,0.014973543,0.0039884476,0.000021819382],"about_ca_topic_score_codex":0.0018665565,"about_ca_topic_score_gemma":0.0018998944,"teacher_disagreement_score":0.0023818146,"about_ca_system_score_codex":0.00035569386,"about_ca_system_score_gemma":0.00039043484,"threshold_uncertainty_score":0.007967949},"labels":[],"label_agreement":null},{"id":"W1936135814","doi":"10.1109/ijcnn.2003.1223758","title":"Document clustering using hierarchical SOMART neural network","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Computer science; Brown clustering; Adaptive resonance theory; Document clustering; Hierarchical clustering; Artificial neural network; Artificial intelligence; Entropy (arrow of time); Data mining; Self-organizing map; Measure (data warehouse); Pattern recognition (psychology); Correlation clustering; CURE data clustering algorithm","score_opus":0.022312892704221796,"score_gpt":0.2688289975415405,"score_spread":0.2465161048373187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1936135814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07843063,0.0016744186,0.909259,0.0003550768,0.0001650083,0.00016324477,0.0003414854,0.003935441,0.005675784],"genre_scores_gemma":[0.54948556,0.000745361,0.43893102,0.00022189811,0.00014211333,0.00018363143,0.0009641301,0.00012456588,0.009201743],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994912,0.00009714914,0.0000377653,0.00015052565,0.00016907905,0.000054225322],"domain_scores_gemma":[0.9994742,0.0001532361,0.00005432419,0.000058197867,0.0002401119,0.000019982694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006417001,0.0004519004,0.0006715287,0.0015305352,0.0006651956,0.0008437314,0.0009904928,0.0008324335,0.0014255064],"category_scores_gemma":[0.0016626614,0.00029624082,0.0006933497,0.0015246768,0.00034546835,0.0009722183,0.000550477,0.00061724125,0.0007473411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032296666,0.00015676717,0.0016670114,0.0001339735,0.00015773084,0.000105423795,0.00013937432,0.40008557,0.011340814,0.004958755,0.0057645855,0.57516706],"study_design_scores_gemma":[0.000006083596,0.000020666292,0.00025956103,0.0000046730256,0.000009976719,0.000017364468,0.000007813659,0.9964898,0.0014972918,0.001237475,0.00044253407,0.000006789493],"about_ca_topic_score_codex":0.019302528,"about_ca_topic_score_gemma":0.019611102,"teacher_disagreement_score":0.019302528,"about_ca_system_score_codex":0.0013991612,"about_ca_system_score_gemma":0.0007732556,"threshold_uncertainty_score":0.038380325},"labels":[],"label_agreement":null},{"id":"W1939201499","doi":"10.1109/ijcnn.1999.831161","title":"Iterative fast orthogonal search algorithm for sparse self-structuring generalized single-layer networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Algorithm; Computer science; Pruning; Basis (linear algebra); Basis function; Iterative method; Selection (genetic algorithm); Construct (python library); Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.036457707486794735,"score_gpt":0.26924309280993275,"score_spread":0.23278538532313803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1939201499","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009312299,0.00006746891,0.9896315,0.00004240598,0.0000075226567,0.00002216339,0.000022167154,0.0002814292,0.00061305426],"genre_scores_gemma":[0.13832326,0.00008040047,0.8595428,0.000051351926,0.000012951584,0.0001941918,0.00019918806,0.00010942014,0.0014864927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997547,0.00009298026,0.000014795061,0.000030880525,0.00007934531,0.000027239339],"domain_scores_gemma":[0.9993507,0.00033049518,0.0000690534,0.00008070989,0.000145899,0.000023007233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087820133,0.00055126334,0.0007165102,0.0005797015,0.00031167213,0.00044012122,0.0009623547,0.0005993011,0.0019020023],"category_scores_gemma":[0.00254299,0.00041301144,0.00038844123,0.0006508683,0.00049086276,0.0010152376,0.00094616786,0.00066719047,0.00036768647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107812426,0.000036633995,0.00043407944,0.000091835536,0.000045889898,0.00007124526,0.00010318426,0.70202285,0.0056368266,0.03798374,0.0022072447,0.25125864],"study_design_scores_gemma":[0.0000060193524,0.000007252434,0.000019760408,0.0000016617531,0.000001613426,0.0000065659833,0.000003204754,0.9961212,0.00044040498,0.0031459816,0.00024444182,0.0000017555261],"about_ca_topic_score_codex":0.002816656,"about_ca_topic_score_gemma":0.004963133,"teacher_disagreement_score":0.002816656,"about_ca_system_score_codex":0.00052353885,"about_ca_system_score_gemma":0.0010118902,"threshold_uncertainty_score":0.0063628554},"labels":[],"label_agreement":null},{"id":"W1939232281","doi":"10.1109/vims.2001.924912","title":"Achieving optimal Bayesian classification performance using a novel approach: the 'race to the attractor' neural network model","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Attractor; Artificial neural network; Computer science; Classifier (UML); Iterated function; Nonlinear system; Class (philosophy); Bayesian probability; Set (abstract data type); Artificial intelligence; Algorithm; Mathematics","score_opus":0.11306835286838196,"score_gpt":0.26360828898424954,"score_spread":0.15053993611586758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1939232281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025714181,0.00020990345,0.97052693,0.00054334896,0.000023276296,0.00003146047,0.000028760296,0.00019410526,0.0027280126],"genre_scores_gemma":[0.69419605,0.0004401518,0.30086598,0.00035992384,0.00013469973,0.0002497203,0.00014512907,0.000100546094,0.0035078176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974963,0.0011069758,0.0001258464,0.00040593662,0.0006729908,0.00019194922],"domain_scores_gemma":[0.99634296,0.0021776755,0.00037503632,0.00038366122,0.00061255996,0.00010811115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005144268,0.0008112944,0.0018630018,0.0008210666,0.0006229675,0.0016453587,0.0017688636,0.0021606486,0.0014270834],"category_scores_gemma":[0.014328164,0.00057529507,0.00068678474,0.0006280617,0.0013299057,0.003558899,0.001652395,0.0013155475,0.00051925116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031944073,0.0001810235,0.0022393083,0.00018236985,0.00017235354,0.00017876935,0.0003341884,0.6731265,0.00822552,0.15228705,0.004205065,0.15854837],"study_design_scores_gemma":[0.00002006009,0.000036434627,0.00012012052,0.000008079934,0.000008177352,0.000038332717,0.000009192415,0.97128177,0.00092958513,0.027051076,0.00048570585,0.000011462161],"about_ca_topic_score_codex":0.0025595997,"about_ca_topic_score_gemma":0.0031532415,"teacher_disagreement_score":0.005144268,"about_ca_system_score_codex":0.0012449523,"about_ca_system_score_gemma":0.0021028216,"threshold_uncertainty_score":0.027205825},"labels":[],"label_agreement":null},{"id":"W1940245494","doi":"10.1109/iscas.2004.1329238","title":"A neural network approach for human emotion recognition in speech","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Speech recognition; Artificial neural network; Selection (genetic algorithm); Artificial intelligence; Consistency (knowledge bases); Feature selection; Feature extraction; Feature (linguistics); Modular design; Identification (biology); Natural language processing; Independence (probability theory); Voice activity detection; Speech processing","score_opus":0.046761698220564675,"score_gpt":0.27135267646544736,"score_spread":0.2245909782448827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1940245494","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008519191,0.0008647224,0.9877424,0.00015377968,0.00008830015,0.00004594225,0.000060571983,0.00035352056,0.002171641],"genre_scores_gemma":[0.4029835,0.0019663272,0.5845913,0.00022562547,0.00025609566,0.00032102328,0.00025968344,0.00007487224,0.00932161],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998179,0.000051820713,0.000010321019,0.00006166046,0.00004467642,0.000013516126],"domain_scores_gemma":[0.9998987,0.00004681436,0.000009109229,0.000009751279,0.00003209713,0.0000036854979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043390028,0.0005669627,0.0003600638,0.00041025403,0.00018856215,0.00050465023,0.000543167,0.0005609537,0.0018192218],"category_scores_gemma":[0.00072893285,0.00015725174,0.00038617972,0.00048099845,0.00024915696,0.0005883345,0.00026839122,0.0006456979,0.000519976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023300083,0.00016309402,0.0013691698,0.00038994386,0.0002394491,0.0002876131,0.00015640444,0.22541814,0.091216385,0.032335848,0.0026304221,0.64556056],"study_design_scores_gemma":[0.0000080980535,0.00006281598,0.0008185319,0.0000141807795,0.000033938628,0.000073995136,0.00001884165,0.97975504,0.007543744,0.009077849,0.002579867,0.000013101213],"about_ca_topic_score_codex":0.0014419298,"about_ca_topic_score_gemma":0.0017144099,"teacher_disagreement_score":0.0018192218,"about_ca_system_score_codex":0.00032455093,"about_ca_system_score_gemma":0.0002302236,"threshold_uncertainty_score":0.0060859323},"labels":[],"label_agreement":null},{"id":"W1943050723","doi":"10.1109/ijcnn.1992.287071","title":"The effects of segmentation on back-propagation networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Backpropagation; Segmentation; Artificial neural network; Artificial intelligence; Computer science; Variation (astronomy); Process (computing); Network topology; Recall rate; Learning rule; Pattern recognition (psychology); Machine learning; Computer network","score_opus":0.006846743950647078,"score_gpt":0.2260519309499571,"score_spread":0.21920518699931002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1943050723","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16766077,0.0029229587,0.81894296,0.0010164287,0.0002301733,0.00014963528,0.00019106657,0.0022949066,0.0065911496],"genre_scores_gemma":[0.6694461,0.0017845449,0.32297948,0.00041884047,0.00010287619,0.00014387172,0.000394502,0.00038089446,0.0043489165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798006,0.0009394163,0.00015192971,0.00027278948,0.0005091821,0.00014670654],"domain_scores_gemma":[0.9784178,0.016883766,0.0008814732,0.0012471124,0.0023631305,0.00020667895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004080234,0.001277778,0.00090772565,0.00095290196,0.0007435502,0.001595748,0.0012265951,0.0015151146,0.0029164625],"category_scores_gemma":[0.029932003,0.0007761876,0.0006792879,0.0011677375,0.0015191841,0.0042882813,0.0011171667,0.0012377588,0.0006622356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009574872,0.00010778235,0.002342134,0.00026218966,0.00014407585,0.0002481855,0.00038298554,0.7972714,0.019401057,0.010962371,0.001338489,0.16658184],"study_design_scores_gemma":[0.000024653495,0.00018442098,0.00082587724,0.000045312598,0.000053904754,0.00006697425,0.00004417897,0.97259074,0.013552404,0.011213715,0.0013769965,0.000020751613],"about_ca_topic_score_codex":0.009125689,"about_ca_topic_score_gemma":0.0078035695,"teacher_disagreement_score":0.009125689,"about_ca_system_score_codex":0.0019077192,"about_ca_system_score_gemma":0.0009782867,"threshold_uncertainty_score":0.02157861},"labels":[],"label_agreement":null},{"id":"W1946098947","doi":"10.1109/pacrim.1993.407168","title":"A connectionist approach to direct dynamic programming control","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dynamic programming; Computer science; Generalization; Controller (irrigation); Point (geometry); State (computer science); Artificial neural network; Connectionism; Control (management); State space; Artificial intelligence; Programming language; Algorithm; Mathematics","score_opus":0.014855096842526463,"score_gpt":0.22309366456688287,"score_spread":0.2082385677243564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1946098947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003578093,0.00036115784,0.9635485,0.0004075796,0.000101822625,0.000055256143,0.00006160114,0.0005553967,0.031330578],"genre_scores_gemma":[0.5569403,0.0017093945,0.37661713,0.0004527065,0.00022605975,0.00052183954,0.00019189157,0.00019688721,0.06314382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997545,0.000036700843,0.0000123647405,0.000066466586,0.00011073155,0.000019279894],"domain_scores_gemma":[0.9998553,0.00005487345,0.000012563467,0.000025413827,0.000040796185,0.000011140044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021842046,0.00043695793,0.0002674999,0.0002630908,0.0003284449,0.0010949819,0.0010797632,0.0007053588,0.00817257],"category_scores_gemma":[0.0007099978,0.00027527256,0.00035742234,0.00039951692,0.00079479965,0.00087553693,0.0007226821,0.0013780574,0.0009212558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006879124,0.000086355336,0.00022992297,0.00018645318,0.00006933475,0.00016859877,0.00013311859,0.3984204,0.011931367,0.3862121,0.0055765216,0.1969171],"study_design_scores_gemma":[0.00002094485,0.00004772141,0.00013899681,0.000020003927,0.000013921855,0.00008690072,0.000008621701,0.8477863,0.002920152,0.1296155,0.019324496,0.000016496764],"about_ca_topic_score_codex":0.003431642,"about_ca_topic_score_gemma":0.0053675612,"teacher_disagreement_score":0.00817257,"about_ca_system_score_codex":0.0007689862,"about_ca_system_score_gemma":0.0007699185,"threshold_uncertainty_score":0.027339995},"labels":[],"label_agreement":null},{"id":"W1947790329","doi":"10.1109/iscas.1990.112633","title":"A hierarchical neural network for temporal pattern recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Hierarchy; Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Very-large-scale integration; Sequence (biology); Hierarchical database model; Data mining; Embedded system","score_opus":0.051296595139372306,"score_gpt":0.254348755937364,"score_spread":0.2030521607979917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1947790329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008433179,0.00074108783,0.9831064,0.00016084813,0.0000910851,0.000048496968,0.00024335404,0.0018175,0.00535807],"genre_scores_gemma":[0.2755946,0.0008589241,0.7089843,0.00023607037,0.00007908656,0.00021499145,0.00082823227,0.00014247955,0.013061318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998492,0.000024986166,0.000010682544,0.00004249135,0.000050142018,0.000022384707],"domain_scores_gemma":[0.9998647,0.000036897003,0.000013125728,0.000025401125,0.000047919293,0.000011907811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028773962,0.00031989333,0.00022389147,0.00035484764,0.00023960847,0.00040596578,0.0007474636,0.00049608917,0.005344285],"category_scores_gemma":[0.0006979636,0.00021454974,0.0003787565,0.0004994491,0.00027612396,0.0007882242,0.000412058,0.00058007514,0.0011148958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017975579,0.00008161734,0.00083346764,0.00024909206,0.00008120113,0.00015993774,0.00007332245,0.3404269,0.060082838,0.055823136,0.01410028,0.52790844],"study_design_scores_gemma":[0.000009329852,0.000036102527,0.00031067416,0.0000120906525,0.000015923417,0.000040932315,0.0000048401807,0.9809429,0.004659209,0.008366768,0.005591392,0.000009819681],"about_ca_topic_score_codex":0.008808411,"about_ca_topic_score_gemma":0.012471628,"teacher_disagreement_score":0.008808411,"about_ca_system_score_codex":0.00078447605,"about_ca_system_score_gemma":0.0007360903,"threshold_uncertainty_score":0.017878354},"labels":[],"label_agreement":null},{"id":"W1948266555","doi":"10.1109/ccece.2000.849557","title":"Inductive learning inability of artificial neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Simple (philosophy); Machine learning; Physical neural network; Inductive bias; Types of artificial neural networks; Time delay neural network; Engineering; Multi-task learning","score_opus":0.035338670223563226,"score_gpt":0.24608135382364438,"score_spread":0.21074268360008114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1948266555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03296845,0.0017700447,0.8911077,0.0076483255,0.00049608084,0.000111195586,0.0004010722,0.0008119172,0.0646852],"genre_scores_gemma":[0.78936905,0.0032386612,0.19141532,0.0030549315,0.0010456976,0.00054929615,0.00071466045,0.00018820608,0.010424238],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9924075,0.0021887294,0.00073155644,0.0010845518,0.003142094,0.0004455534],"domain_scores_gemma":[0.9778047,0.014500431,0.0010204933,0.0037663728,0.0025627692,0.00034526057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068613472,0.0007067638,0.0010818148,0.0015226043,0.0012981521,0.0031826622,0.0027479737,0.002789482,0.0032147495],"category_scores_gemma":[0.039704703,0.00065175566,0.0012777421,0.0010447119,0.008323789,0.00742185,0.0051027783,0.0051573697,0.0012015828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004624513,0.000029055946,0.0006322232,0.00025834257,0.000039966482,0.00022736183,0.00028422425,0.017873675,0.0023143713,0.95107573,0.002918138,0.024300674],"study_design_scores_gemma":[0.000010470952,0.000023787372,0.00021692376,0.0000377686,0.0000107929145,0.00023782373,0.0000444097,0.037747357,0.0028785663,0.95298994,0.00577888,0.000023434357],"about_ca_topic_score_codex":0.0007935782,"about_ca_topic_score_gemma":0.000421221,"teacher_disagreement_score":0.0068613472,"about_ca_system_score_codex":0.0012635587,"about_ca_system_score_gemma":0.0012530384,"threshold_uncertainty_score":0.03628671},"labels":[],"label_agreement":null},{"id":"W1953259232","doi":"10.1109/ccece.2001.933632","title":"Investigation of a robust adaptive nonlinear controller for induction motors","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Control theory (sociology); Induction motor; Control engineering; Electronic speed control; Robust control; Controller (irrigation); Servomotor; Servo; Torque; PID controller; Vector control; Nonlinear system; DC motor; Computer science; Adaptive control; Servomechanism; Machine control; Direct torque control; Servo drive; Engineering; Control system; Control (management); Temperature control; Physics","score_opus":0.07876988828693511,"score_gpt":0.231568828442814,"score_spread":0.15279894015587892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953259232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040683396,0.0010079136,0.94720644,0.00018387499,0.000072928466,0.00010655771,0.000011974693,0.0004653976,0.010261486],"genre_scores_gemma":[0.933586,0.00049383345,0.060343612,0.00010085857,0.00006289168,0.000114260496,0.000030854582,0.00003174252,0.005236063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997863,0.000029078734,0.000010022122,0.00004486526,0.00011297254,0.000016765258],"domain_scores_gemma":[0.99958354,0.00019294687,0.00005234458,0.000020465366,0.00014002126,0.000010724121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052569533,0.00040722842,0.00033996717,0.00016198694,0.00018321155,0.0005089309,0.0006270706,0.0005281166,0.0012794794],"category_scores_gemma":[0.0015763929,0.00014447696,0.00020406845,0.000091285976,0.00031945627,0.00032503522,0.00023949292,0.00048534406,0.00021679816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025928608,0.0001319317,0.00067080103,0.0004485543,0.00009030614,0.00045459284,0.00016632145,0.65439373,0.08310647,0.025917603,0.0011623577,0.233198],"study_design_scores_gemma":[0.000012576102,0.00010398256,0.00013203891,0.0000067905894,0.000008176732,0.000027230079,0.0000039977344,0.9948826,0.0034609342,0.0004028596,0.00095480063,0.0000041140447],"about_ca_topic_score_codex":0.0019427098,"about_ca_topic_score_gemma":0.0012155019,"teacher_disagreement_score":0.0019427098,"about_ca_system_score_codex":0.00035275173,"about_ca_system_score_gemma":0.00042033472,"threshold_uncertainty_score":0.004280269},"labels":[],"label_agreement":null},{"id":"W1953783695","doi":"10.1109/cdc.1993.325708","title":"Feedback stabilization: control Lyapunov functions modelled by neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Lyapunov function; Control theory (sociology); Lyapunov redesign; Affine transformation; Control-Lyapunov function; Artificial neural network; Controller (irrigation); Nonlinear system; Computer science; State (computer science); Function (biology); Control (management); Control engineering; Mathematics; Artificial intelligence; Engineering; Algorithm; Pure mathematics","score_opus":0.01390818498937408,"score_gpt":0.1959419609367951,"score_spread":0.18203377594742104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953783695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021204052,0.0015260952,0.96619576,0.00041089224,0.00013199126,0.000026963848,0.00006088224,0.00037933068,0.01006391],"genre_scores_gemma":[0.92428356,0.0017462465,0.058140673,0.00016735954,0.00013698025,0.000184514,0.00012356641,0.00009152514,0.01512564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985397,0.00005248563,0.000008326248,0.00002847684,0.00004344714,0.00001325413],"domain_scores_gemma":[0.99987113,0.00005590584,0.000023836965,0.000008785822,0.00003440749,0.0000058119485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032326445,0.0005883171,0.0003020145,0.00033529513,0.00021421124,0.0007317136,0.0005546348,0.00085104845,0.0014711525],"category_scores_gemma":[0.00091509224,0.00020824495,0.00027894485,0.00031314619,0.00049996236,0.0010023763,0.00045687216,0.0006574428,0.00041826622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004948971,0.000016926433,0.00016620327,0.000090824855,0.000023118464,0.00007973653,0.00006092031,0.9122454,0.0056162686,0.059929747,0.0010374567,0.020683937],"study_design_scores_gemma":[0.000003870147,0.000011762801,0.000036303572,0.0000068874897,0.0000031638895,0.0000067702535,0.000003527982,0.9858187,0.00061528134,0.012360419,0.0011296624,0.0000037207485],"about_ca_topic_score_codex":0.0031615938,"about_ca_topic_score_gemma":0.0021972677,"teacher_disagreement_score":0.0031615938,"about_ca_system_score_codex":0.00059395947,"about_ca_system_score_gemma":0.00033236557,"threshold_uncertainty_score":0.0062863827},"labels":[],"label_agreement":null},{"id":"W1955580075","doi":"10.1007/978-3-319-08970-6_31","title":"On the Formalization of Z-Transform in HOL","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Correctness; Infinite impulse response; Z-transform; HOL; Transcendental number; Computer science; Algorithm; Signal processing; Mathematics; Algebra over a field; Digital filter; Digital signal processing; Calculus (dental); Theoretical computer science; Discrete mathematics; Filter (signal processing); Pure mathematics; Mathematical analysis; Fractional Fourier transform; Programming language","score_opus":0.013977977191459733,"score_gpt":0.2323210986302469,"score_spread":0.21834312143878717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1955580075","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044818074,0.0070883804,0.69043845,0.005742623,0.0018390814,0.00011038561,0.0006683434,0.0012202708,0.24807444],"genre_scores_gemma":[0.76906985,0.005099973,0.1670724,0.0027279283,0.0017380121,0.00026593183,0.0007561813,0.0011895697,0.05208025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992506,0.00021515002,0.00006946893,0.00014347768,0.00020467301,0.00011666522],"domain_scores_gemma":[0.99944144,0.0002563464,0.000051030693,0.00010852412,0.000099769466,0.000042913263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012933076,0.0008281971,0.00082986197,0.0016234176,0.0014534025,0.003271362,0.0012348426,0.0012002238,0.010529603],"category_scores_gemma":[0.001837953,0.00050210813,0.0013640183,0.0013965798,0.0067129945,0.0071152346,0.0025312013,0.004531984,0.0019930378],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008678474,0.0000052018004,0.000026696065,0.00002001553,0.0000020520506,0.000023409455,0.00009028588,0.00020752933,0.00019958052,0.99537414,0.0006976922,0.0033448115],"study_design_scores_gemma":[0.000010152699,0.000009699274,0.000057754056,0.00001926644,0.0000035423548,0.00005146274,0.000042779822,0.0010559798,0.00027786687,0.99062526,0.007838844,0.000007290697],"about_ca_topic_score_codex":0.0014075809,"about_ca_topic_score_gemma":0.0014201507,"teacher_disagreement_score":0.010529603,"about_ca_system_score_codex":0.0017117652,"about_ca_system_score_gemma":0.0006269735,"threshold_uncertainty_score":0.035225034},"labels":[],"label_agreement":null},{"id":"W1956724972","doi":"10.3233/idt-2012-0120","title":"The entire range of Chaotic pattern recognition properties possessed by the Adachi neural network1","year":2011,"lang":"en","type":"article","venue":"Intelligent Decision Technologies","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Chaotic; Range (aeronautics); Artificial neural network; Computer science; Materials science; Pattern recognition (psychology); Artificial intelligence; Composite material","score_opus":0.07228037437211568,"score_gpt":0.24950118709701632,"score_spread":0.17722081272490064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1956724972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11319107,0.00870075,0.80939585,0.0030253967,0.00064993365,0.000099705016,0.0005151234,0.0006422331,0.06377988],"genre_scores_gemma":[0.907402,0.004185283,0.081328236,0.0002849384,0.00045435558,0.00010942516,0.0002599528,0.00009075696,0.0058851563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999624,0.00009186528,0.000026158865,0.000067934976,0.000160224,0.000029656157],"domain_scores_gemma":[0.9983151,0.0009467611,0.00014648118,0.00030439554,0.0002420635,0.000045216017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080915156,0.0004776908,0.00076784095,0.00057263445,0.00076309516,0.0013345547,0.0005464593,0.0008280985,0.002070005],"category_scores_gemma":[0.0047416743,0.00035836696,0.00040019464,0.00077656645,0.0015242144,0.0020360355,0.00075324374,0.0010072178,0.00090656395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057673466,0.00007703612,0.007840802,0.0010015409,0.00012708755,0.0008196599,0.00064761733,0.20381162,0.044833284,0.3095701,0.007779375,0.42291504],"study_design_scores_gemma":[0.000035226025,0.00027455427,0.005269097,0.00013285532,0.000058681355,0.0012114105,0.00011743644,0.6116614,0.0113543095,0.35238624,0.017382216,0.000116569776],"about_ca_topic_score_codex":0.0012528748,"about_ca_topic_score_gemma":0.0010919211,"teacher_disagreement_score":0.002070005,"about_ca_system_score_codex":0.00044749436,"about_ca_system_score_gemma":0.0003763583,"threshold_uncertainty_score":0.0069248676},"labels":[],"label_agreement":null},{"id":"W1958708463","doi":"10.1109/iscas.1992.230013","title":"Convergence and generalization properties of multilayer feedforward neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Backpropagation; Generalization; Artificial neural network; Convergence (economics); Rprop; Computer science; Feed forward; Feedforward neural network; Artificial intelligence; Algorithm; Function (biology); Process (computing); Activation function; Mathematics; Types of artificial neural networks; Time delay neural network; Engineering; Control engineering","score_opus":0.02084182134114359,"score_gpt":0.2180529085774212,"score_spread":0.19721108723627762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1958708463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3232783,0.002731514,0.66248405,0.00048052004,0.000083181396,0.00009492911,0.0002098478,0.000762562,0.009875158],"genre_scores_gemma":[0.94174707,0.0010647516,0.055050805,0.000057481426,0.00004837869,0.00009484734,0.00021154234,0.00014693986,0.0015781892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897707,0.00026716522,0.0000875239,0.0001678059,0.0004137711,0.00008668524],"domain_scores_gemma":[0.98878795,0.007378551,0.000972218,0.0007280396,0.0020063296,0.00012697969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004881112,0.00056570285,0.00063246017,0.0012304216,0.00042226491,0.0007732618,0.0005796672,0.00085586275,0.0012528319],"category_scores_gemma":[0.026315311,0.00028543084,0.000650213,0.0006642773,0.0007897622,0.0017732868,0.0006299975,0.00073815166,0.00028384358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029242362,0.000042828673,0.004357488,0.00022767221,0.000099827346,0.00015508672,0.00026329563,0.8651508,0.011835757,0.013030208,0.0008129346,0.10373176],"study_design_scores_gemma":[0.000007903093,0.00006031484,0.0018588435,0.000030981195,0.00002286182,0.00010163372,0.000021312648,0.98796195,0.0035111888,0.00605758,0.00034739688,0.000017968972],"about_ca_topic_score_codex":0.0033317807,"about_ca_topic_score_gemma":0.001442848,"teacher_disagreement_score":0.004881112,"about_ca_system_score_codex":0.00068435294,"about_ca_system_score_gemma":0.00054234674,"threshold_uncertainty_score":0.025814116},"labels":[],"label_agreement":null},{"id":"W1960232818","doi":"10.1109/apccas.2000.913501","title":"An iterative multi-stage algorithm for robust training of RF/microwave neural models","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial neural network; Computer science; Microwave; Microwave imaging; Key (lock); Microwave engineering; Iterative method; Algorithm; Artificial intelligence; Telecommunications","score_opus":0.18094961859739306,"score_gpt":0.30944579017154866,"score_spread":0.1284961715741556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1960232818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026743184,0.000057744397,0.99648345,0.00004135702,0.000008095693,0.000025164472,0.000009007177,0.00025433695,0.00044646676],"genre_scores_gemma":[0.1853377,0.00011955947,0.8110131,0.000107285305,0.000033279874,0.0004130651,0.00013532018,0.00012400435,0.0027166528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950385,0.00018190019,0.00003443476,0.00008510682,0.00014458805,0.000050226576],"domain_scores_gemma":[0.9987908,0.00072124915,0.000112414906,0.00009680315,0.0002466762,0.000032057596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019312026,0.0010776755,0.0008469929,0.00056655676,0.0004429355,0.0005815551,0.001790039,0.0015810024,0.0028459125],"category_scores_gemma":[0.0044278945,0.0008410881,0.0008081764,0.00060950796,0.00066420547,0.0012610549,0.0012995537,0.0017962449,0.000758145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009403045,0.0000447213,0.00038208204,0.000069511145,0.00005765161,0.00004264468,0.00007841979,0.8291183,0.0038424511,0.008143384,0.0010857165,0.15704092],"study_design_scores_gemma":[0.0000038843373,0.000014346242,0.000026408075,0.000002449477,0.0000027138103,0.000005893579,0.0000015161013,0.9983229,0.0005165386,0.0008598217,0.00024115204,0.000002368328],"about_ca_topic_score_codex":0.0045735366,"about_ca_topic_score_gemma":0.0073838024,"teacher_disagreement_score":0.0045735366,"about_ca_system_score_codex":0.00072997896,"about_ca_system_score_gemma":0.0013385173,"threshold_uncertainty_score":0.0102133155},"labels":[],"label_agreement":null},{"id":"W1964052581","doi":"10.1109/icsmc.2010.5641813","title":"Adaptive control of dynamic nonlinear systems using Sigmoid Diagonal Recurrent Neural Network","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Sigmoid function; Diagonal; Computer science; Artificial neural network; Nonlinear system; Control theory (sociology); Adaptive control; Backpropagation; Artificial intelligence; Mathematics; Control (management); Physics","score_opus":0.015691321135249403,"score_gpt":0.25543514807188333,"score_spread":0.23974382693663393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964052581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035679583,0.00052234344,0.95935017,0.00011757206,0.00007565027,0.00003123918,0.000026606724,0.00050703075,0.0036897985],"genre_scores_gemma":[0.9320712,0.00047366988,0.06448547,0.00006632711,0.000036903642,0.000065631444,0.00005794457,0.000024413928,0.0027185425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997693,0.00005780285,0.000023093362,0.000051583054,0.00007395755,0.000024255867],"domain_scores_gemma":[0.99976367,0.000076310396,0.000035067027,0.000024044028,0.000092460185,0.000008452443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060548447,0.0003400291,0.00036846581,0.00014628614,0.00018082505,0.00042347057,0.00065743306,0.0003905901,0.00062545965],"category_scores_gemma":[0.0009573819,0.00016609483,0.00033796317,0.00022311728,0.0002851855,0.00042788507,0.00040769123,0.00040534072,0.0001764996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015603281,0.00006608313,0.0008635041,0.0001899063,0.00009303657,0.00022631347,0.00012960815,0.7836043,0.044600036,0.012567829,0.00091369654,0.1565897],"study_design_scores_gemma":[0.00000586688,0.000037974358,0.00011390661,0.000004404949,0.000008779104,0.000022349066,0.0000030516087,0.99688536,0.001821749,0.0006900897,0.00040147392,0.000005018209],"about_ca_topic_score_codex":0.002517168,"about_ca_topic_score_gemma":0.0024887922,"teacher_disagreement_score":0.002517168,"about_ca_system_score_codex":0.00024494686,"about_ca_system_score_gemma":0.0002643616,"threshold_uncertainty_score":0.005005002},"labels":[],"label_agreement":null},{"id":"W1964746899","doi":"10.1162/neco.2008.07-07-572","title":"The Problem of Rapid Variable Creation","year":2008,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Connectionism; Generalization; Variable (mathematics); Cognitive science; Computer science; Representation (politics); Artificial intelligence; Psychology; Artificial neural network; Philosophy; Epistemology; Mathematics","score_opus":0.01923181225308657,"score_gpt":0.24500126132322464,"score_spread":0.22576944907013807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964746899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06302049,0.0019667621,0.85878766,0.013099854,0.00067144237,0.00010649314,0.00028566306,0.0024259926,0.05963566],"genre_scores_gemma":[0.78932196,0.0011199838,0.18954279,0.0019796756,0.00046036078,0.00035944497,0.00028026648,0.0008393833,0.016096126],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99666256,0.0011049194,0.00016267995,0.0010141429,0.0007286125,0.00032702964],"domain_scores_gemma":[0.9844096,0.008893737,0.000726599,0.004958357,0.0006954162,0.00031629155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043009412,0.00051800295,0.0007004521,0.0007555942,0.0015227745,0.0023404707,0.002553644,0.0022833648,0.006948423],"category_scores_gemma":[0.025066845,0.00073100167,0.0010518483,0.0007568528,0.0063293986,0.011514053,0.0042888694,0.0060212067,0.001626264],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008944005,0.000013806446,0.0006449782,0.000088867506,0.000019574285,0.00018843795,0.00057757576,0.0052642445,0.0024843349,0.94408643,0.0037968294,0.04274556],"study_design_scores_gemma":[0.000033132554,0.000029169245,0.0003352901,0.00003032202,0.0000132638,0.0004314602,0.000106678555,0.021353913,0.0033548968,0.9480049,0.026275886,0.00003111592],"about_ca_topic_score_codex":0.00080800854,"about_ca_topic_score_gemma":0.00061259285,"teacher_disagreement_score":0.006948423,"about_ca_system_score_codex":0.0011882891,"about_ca_system_score_gemma":0.000963227,"threshold_uncertainty_score":0.023244739},"labels":[],"label_agreement":null},{"id":"W1967150189","doi":"10.1080/00207721.2014.955552","title":"Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series","year":2014,"lang":"en","type":"article","venue":"International Journal of Systems Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Universidade de Macau; Beijing Normal University; University of Texas at San Antonio","keywords":"Interpretability; Autoregressive model; Series (stratigraphy); Radial basis function; Parametric model; Nonlinear system; Time series; Parametric statistics; Computer science; Artificial neural network; Nonlinear autoregressive exogenous model; Artificial intelligence; Machine learning; Econometrics; Mathematics; Statistics","score_opus":0.026087828673478294,"score_gpt":0.2643327062341815,"score_spread":0.23824487756070317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967150189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04207783,0.0007128605,0.95408803,0.0002581694,0.00006302082,0.000018338675,0.00007300533,0.00017817128,0.0025306093],"genre_scores_gemma":[0.8870119,0.0013005226,0.10819369,0.0000874741,0.00011266452,0.000048601636,0.00028355155,0.0000619644,0.0028997404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961686,0.00015132778,0.000023658444,0.00009796188,0.00007911375,0.000031038642],"domain_scores_gemma":[0.99868375,0.0008651334,0.00012648934,0.00010497702,0.00019797616,0.000021743168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001530963,0.0007650057,0.0005577613,0.00044790233,0.00021966876,0.0008040188,0.0007168801,0.0009384576,0.0009949304],"category_scores_gemma":[0.0055603404,0.00021095364,0.00081066845,0.00045744935,0.00043814708,0.0011928236,0.00043521423,0.0011893944,0.00036135502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010237795,0.00007564616,0.004811638,0.00022232156,0.0001353339,0.00027885693,0.00020602289,0.8416388,0.009142878,0.04229711,0.0009062057,0.100182876],"study_design_scores_gemma":[0.0000020818218,0.000017881224,0.00051142677,0.000005875658,0.000011043305,0.000031203912,0.000009549275,0.99457943,0.00033531184,0.0041129505,0.00037630508,0.0000069478883],"about_ca_topic_score_codex":0.007829516,"about_ca_topic_score_gemma":0.005073746,"teacher_disagreement_score":0.007829516,"about_ca_system_score_codex":0.00031475408,"about_ca_system_score_gemma":0.00045972594,"threshold_uncertainty_score":0.015567899},"labels":[],"label_agreement":null},{"id":"W1967377033","doi":"10.1142/s0218127404011090","title":"MULTIPLE PERIODIC PATTERNS VIA DISCRETE NEURAL NETS WITH DELAYED FEEDBACK LOOPS","year":2004,"lang":"en","type":"article","venue":"International Journal of Bifurcation and Chaos","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; York University","funders":"","keywords":"Discrete time and continuous time; Artificial neural network; Periodic orbits; Loop (graph theory); Control theory (sociology); Computer science; Mathematics; Mathematical analysis; Artificial intelligence; Control (management); Combinatorics","score_opus":0.008092667092318222,"score_gpt":0.2355667205173138,"score_spread":0.22747405342499558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967377033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4541475,0.0007975592,0.5351489,0.00037777537,0.00014578558,0.00003876141,0.000059670678,0.00043463934,0.008849405],"genre_scores_gemma":[0.979002,0.00015454034,0.018651785,0.00002715817,0.000015719877,0.000022354363,0.000014281831,0.000010869657,0.0021012083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999137,0.000025215219,0.0000057095485,0.000015613572,0.000028171487,0.000011633992],"domain_scores_gemma":[0.99974173,0.00013322078,0.00004833609,0.00002606609,0.000027542412,0.000023085788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002659023,0.00025554796,0.00024584017,0.00021959443,0.00018357037,0.00045811682,0.00048640626,0.00036930226,0.0009268823],"category_scores_gemma":[0.0008501071,0.0002010172,0.00019912027,0.00015764567,0.00052896497,0.0006879462,0.00056803774,0.00032047453,0.00007128837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024806536,0.000044535347,0.000987813,0.00011521994,0.000045780387,0.0004313008,0.00013165583,0.8151111,0.031596612,0.1143847,0.00045069144,0.03645263],"study_design_scores_gemma":[0.000014178803,0.000025555897,0.00006945395,0.000003385766,0.0000058620076,0.00003159912,0.000004976967,0.9844098,0.0015694948,0.01354944,0.0003117731,0.000004441482],"about_ca_topic_score_codex":0.0009582621,"about_ca_topic_score_gemma":0.00088747806,"teacher_disagreement_score":0.0009582621,"about_ca_system_score_codex":0.00041469326,"about_ca_system_score_gemma":0.00018671808,"threshold_uncertainty_score":0.0031007528},"labels":[],"label_agreement":null},{"id":"W1967519701","doi":"10.1145/772862.772885","title":"KDD-2002 workshop report fractals and self-similarity in data mining","year":2002,"lang":"en","type":"article","venue":"ACM SIGKDD Explorations Newsletter","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Carnegie Mellon University; University of Southern California; National Science Foundation","keywords":"Computer science; Similarity (geometry); Data mining; Conjunction (astronomy); Fractal; Data science; Self-similarity; Information retrieval; Artificial intelligence; Mathematics","score_opus":0.11892294112044009,"score_gpt":0.29976370573021005,"score_spread":0.18084076460976994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967519701","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016856601,0.08913877,0.69750744,0.055875923,0.050355807,0.0013037137,0.014668021,0.007713255,0.06658049],"genre_scores_gemma":[0.12121659,0.08215702,0.5237071,0.010171836,0.025831264,0.0019908429,0.0760776,0.00411619,0.15473153],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99158937,0.0022769845,0.000800215,0.001079015,0.0038880587,0.00036634732],"domain_scores_gemma":[0.9846577,0.0046572783,0.00036120467,0.0022230598,0.006263889,0.0018369063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012742248,0.0012202007,0.0023062946,0.0042950152,0.0011616439,0.0067951726,0.0025146322,0.002629062,0.009911507],"category_scores_gemma":[0.023557842,0.00080808974,0.0012766983,0.0042725815,0.0008576661,0.004709425,0.0030331525,0.003625528,0.0090189325],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027283392,0.00025176382,0.0015000573,0.0008629645,0.0001589015,0.00024946773,0.00020425298,0.0057716356,0.0013645027,0.0208231,0.68995935,0.2785811],"study_design_scores_gemma":[0.00019414927,0.00022845154,0.0021313988,0.00047622068,0.00013743658,0.00060725916,0.00026906878,0.04328719,0.0063844896,0.050715994,0.8954658,0.00010253462],"about_ca_topic_score_codex":0.003146335,"about_ca_topic_score_gemma":0.0023301002,"teacher_disagreement_score":0.012742248,"about_ca_system_score_codex":0.001703676,"about_ca_system_score_gemma":0.0038935777,"threshold_uncertainty_score":0.067388296},"labels":[],"label_agreement":null},{"id":"W1969518951","doi":"10.3182/20020721-6-es-1901.00724","title":"HYBRID NEURAL MODELS FOR TIME-SERIES FORECASTING","year":2002,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Series (stratigraphy); Computer science; Nonlinear system; Time series; Simple (philosophy); Chaotic; Artificial intelligence; Machine learning","score_opus":0.037007006652312095,"score_gpt":0.22210111479944109,"score_spread":0.18509410814712898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969518951","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036794543,0.006641429,0.94990313,0.00040804083,0.00052271725,0.0000295044,0.00025469644,0.0007851676,0.004660764],"genre_scores_gemma":[0.8058287,0.0043708477,0.17062385,0.00015396714,0.0006274395,0.00015512959,0.00064849603,0.00016834757,0.017423166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997584,0.000085467706,0.000021059086,0.000041362764,0.000073086696,0.000020649935],"domain_scores_gemma":[0.9991273,0.0005527334,0.000056064233,0.00008614189,0.00015542551,0.000022260567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091744453,0.00064781366,0.0010999842,0.0005933645,0.00029600394,0.0012195847,0.0012467639,0.001189174,0.0032561847],"category_scores_gemma":[0.002381767,0.0004820206,0.00052156247,0.0012713616,0.0003597267,0.0015382101,0.0006680033,0.0011458473,0.0005888467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118322096,0.000043890504,0.00034602522,0.000079895224,0.000100551864,0.000032000375,0.000020141735,0.92149365,0.0010467478,0.008925491,0.0015517515,0.066241555],"study_design_scores_gemma":[0.0000034724744,0.0000052709197,0.000049261955,0.0000025654306,0.000006012786,0.000003075767,0.0000011364147,0.9960305,0.00011894995,0.003544543,0.0002325864,0.0000025635156],"about_ca_topic_score_codex":0.0071807834,"about_ca_topic_score_gemma":0.006489917,"teacher_disagreement_score":0.0071807834,"about_ca_system_score_codex":0.0005204912,"about_ca_system_score_gemma":0.00034691393,"threshold_uncertainty_score":0.014277995},"labels":[],"label_agreement":null},{"id":"W1969764706","doi":"10.1515/jisys.2011.005","title":"Cascading SOFM and RBF Networks for Categorization and Indexing of Fly Ashes","year":2011,"lang":"en","type":"article","venue":"Journal of Intelligent Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Centroid; Categorization; Search engine indexing; Artificial intelligence; Radial basis function; Computer science; Artificial neural network; Feature (linguistics); Pattern recognition (psychology); Self-organizing map; Function (biology); Basis (linear algebra); Cluster (spacecraft); Data mining; Machine learning; Mathematics","score_opus":0.046324157596466776,"score_gpt":0.24689902445396952,"score_spread":0.20057486685750275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969764706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17896637,0.0006287532,0.81556946,0.00017457065,0.000086296444,0.00011786656,0.00009744587,0.0018280207,0.0025312817],"genre_scores_gemma":[0.83862215,0.0001773158,0.15811336,0.000041901418,0.000030369516,0.000085053114,0.00016118081,0.000036983587,0.0027316383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958116,0.000080636324,0.000030475265,0.00010597069,0.0001401024,0.0000616016],"domain_scores_gemma":[0.99934536,0.0001844001,0.00005399223,0.00005590016,0.00032952282,0.000030874537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001539729,0.0007500627,0.00077726226,0.0013153896,0.00040973382,0.0005629859,0.0010965953,0.0008252387,0.0016133976],"category_scores_gemma":[0.0021840462,0.00031490508,0.00088539143,0.00081673614,0.00036138535,0.00091687177,0.00064221036,0.0006900129,0.0007433149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005374153,0.00023227656,0.0040669916,0.00015224676,0.0001337846,0.0001237605,0.00017952573,0.33893952,0.021604903,0.002000165,0.001392845,0.6306366],"study_design_scores_gemma":[0.000004274098,0.000044983368,0.00055679533,0.000004633348,0.000013647029,0.000009794907,0.000016023703,0.9965172,0.0021981492,0.00043874606,0.00019085623,0.000004974708],"about_ca_topic_score_codex":0.0077194255,"about_ca_topic_score_gemma":0.006203035,"teacher_disagreement_score":0.0077194255,"about_ca_system_score_codex":0.0006761592,"about_ca_system_score_gemma":0.0004666761,"threshold_uncertainty_score":0.015348971},"labels":[],"label_agreement":null},{"id":"W1969898186","doi":"10.1016/j.jfranklin.2005.09.005","title":"Multi-layer self-organizing polynomial neural networks and their development with the use of genetic algorithms","year":2005,"lang":"en","type":"article","venue":"Journal of the Franklin Institute","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Economy","keywords":"Artificial neural network; Genetic algorithm; Computer science; Algorithm; Layer (electronics); Polynomial; Development (topology); Artificial intelligence; Mathematics; Machine learning; Materials science; Nanotechnology","score_opus":0.034706062788815994,"score_gpt":0.2295474382526359,"score_spread":0.1948413754638199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969898186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029646939,0.0021514245,0.96227163,0.00038370516,0.000094459945,0.000045714445,0.000019751787,0.00016496761,0.0052213967],"genre_scores_gemma":[0.4431806,0.0019810644,0.5512423,0.00006677566,0.00007911632,0.000111055866,0.000039279093,0.00007833536,0.003221466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984527,0.00006022748,0.000007685194,0.000030268571,0.000042928146,0.0000137168645],"domain_scores_gemma":[0.99929476,0.000421939,0.000059494225,0.000057224424,0.00014391955,0.000022698572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010396284,0.00028230145,0.00031919265,0.00047615173,0.00028236688,0.00072359486,0.0009706606,0.0008280389,0.00083173113],"category_scores_gemma":[0.0034479515,0.00039550543,0.00041368348,0.0005548111,0.0007816126,0.0011765895,0.0005374331,0.0009295142,0.00019298357],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049028502,0.000048793227,0.0011032302,0.000108271706,0.000045286903,0.00007736715,0.00021706826,0.6998152,0.003876719,0.15735112,0.0009097419,0.1363981],"study_design_scores_gemma":[0.000008277984,0.000018206469,0.00013757947,0.0000130395265,0.000008500889,0.00003603465,0.000010628584,0.96874624,0.0012189186,0.028093092,0.0017022336,0.000007120773],"about_ca_topic_score_codex":0.0018907819,"about_ca_topic_score_gemma":0.002265749,"teacher_disagreement_score":0.0018907819,"about_ca_system_score_codex":0.00064581906,"about_ca_system_score_gemma":0.00067672005,"threshold_uncertainty_score":0.0054981112},"labels":[],"label_agreement":null},{"id":"W1970384291","doi":"10.1007/s00521-014-1600-6","title":"Improved robust stability criteria for bidirectional associative memory neural networks under parameter uncertainties","year":2014,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China; Chongqing Postdoctoral Science Foundation","keywords":"Bidirectional associative memory; Artificial neural network; Computer science; Stability (learning theory); Content-addressable memory; Upper and lower bounds; Associative property; Equilibrium point; Lyapunov function; Exponential stability; Interconnection; Applied mathematics; Mathematics; Control theory (sociology); Artificial intelligence; Nonlinear system; Differential equation; Machine learning; Control (management)","score_opus":0.036754329822103284,"score_gpt":0.2874988226150134,"score_spread":0.2507444927929101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970384291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03434295,0.00054406194,0.955064,0.00020284907,0.00007364805,0.00006624164,0.000093527015,0.00019139796,0.009421379],"genre_scores_gemma":[0.9580791,0.0004033628,0.034990486,0.000069020476,0.00005828286,0.00020604134,0.00013344885,0.00008963725,0.00597069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995043,0.00013630278,0.00003695722,0.00009981506,0.00015951222,0.00006302087],"domain_scores_gemma":[0.9987447,0.00053780276,0.00015927033,0.000069289956,0.00045294556,0.000035948524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018272601,0.0013511975,0.0009724846,0.0006723618,0.00041646214,0.0014198872,0.0012162258,0.0012730161,0.0041033924],"category_scores_gemma":[0.0035628374,0.00032724,0.00060715515,0.0004718913,0.00087888574,0.0011183183,0.0016871161,0.0011123625,0.00057452085],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014103916,0.000027060645,0.00021708668,0.00023779679,0.00006266829,0.00009485389,0.00009564434,0.9221479,0.006306687,0.0443801,0.00080917083,0.025479864],"study_design_scores_gemma":[0.0000039804863,0.000025546555,0.00004941209,0.0000083196,0.0000065974896,0.000008386987,0.000007831912,0.99279,0.00062702276,0.006290946,0.00017567881,0.0000062585777],"about_ca_topic_score_codex":0.0034080094,"about_ca_topic_score_gemma":0.0022813536,"teacher_disagreement_score":0.0041033924,"about_ca_system_score_codex":0.00086365925,"about_ca_system_score_gemma":0.0007270049,"threshold_uncertainty_score":0.013727188},"labels":[],"label_agreement":null},{"id":"W1970396088","doi":"10.1109/isscs.2007.4292702","title":"Architecture and Algorithms for Syntetizable Neural Networks with On-Chip Learning","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"Computer science; Backpropagation; Scalability; Computer architecture; Artificial neural network; Architecture; Chip; Deep learning; Algorithm; Parallel computing; Artificial intelligence","score_opus":0.013132206787753077,"score_gpt":0.2444458510290518,"score_spread":0.23131364424129874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970396088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005081418,0.00018031913,0.99113667,0.000085440646,0.000038008766,0.0000420815,0.000032096945,0.0008736587,0.0025302232],"genre_scores_gemma":[0.20600927,0.00044984394,0.7874409,0.00012507294,0.00003532002,0.00025823002,0.00024029939,0.00013900062,0.005302093],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980134,0.00004026531,0.000018092554,0.0000367605,0.000079602156,0.000023876617],"domain_scores_gemma":[0.99979335,0.000066209715,0.000021294376,0.000046707217,0.000064507316,0.000007884866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035379897,0.0005301309,0.0002839708,0.0004734922,0.00037954605,0.0008975271,0.0013203928,0.00073025835,0.0034061926],"category_scores_gemma":[0.001107302,0.00029362718,0.00034468737,0.00040173638,0.00041302622,0.0012098178,0.00065828767,0.0010333149,0.0009780956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001592381,0.00008425635,0.00053872,0.00023959133,0.000058132366,0.000108888475,0.00011018683,0.31862038,0.027505523,0.14640947,0.0032971988,0.50286835],"study_design_scores_gemma":[0.000034545956,0.00006956747,0.00015794398,0.000031297568,0.000023398,0.000080671125,0.000018544752,0.9340344,0.017526288,0.037931275,0.010078574,0.000013473412],"about_ca_topic_score_codex":0.002039047,"about_ca_topic_score_gemma":0.0039357063,"teacher_disagreement_score":0.0034061926,"about_ca_system_score_codex":0.0007198708,"about_ca_system_score_gemma":0.0008834781,"threshold_uncertainty_score":0.011394858},"labels":[],"label_agreement":null},{"id":"W1970570705","doi":"10.4018/jcini.2007010104","title":"Representing an Intrinsically Nonmetric Space of Compass Directions in an Artificial Neural Network","year":2007,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Heading (navigation); Artificial neural network; Artificial intelligence; Compass; Representation (politics); Space (punctuation); Metric (unit); Geography; Cartography","score_opus":0.027112970698773328,"score_gpt":0.3304836083178635,"score_spread":0.30337063761909017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970570705","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68231,0.00012328952,0.31225333,0.00048999744,0.000051079503,0.00003600647,0.00010216051,0.0002716422,0.0043625874],"genre_scores_gemma":[0.9693331,0.000054468797,0.02952549,0.000053057243,0.000008209993,0.000020452502,0.00006676438,0.000008533942,0.000930014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997918,0.000060841485,0.000012342805,0.000066354936,0.000037281465,0.00003127324],"domain_scores_gemma":[0.9994173,0.00028150214,0.00009472891,0.00007863584,0.000103096114,0.000024840901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005156715,0.00034473994,0.00026922981,0.00029563054,0.00030097595,0.000969147,0.0005514141,0.000605447,0.0008938897],"category_scores_gemma":[0.0026473424,0.0003130429,0.0003510756,0.00037041714,0.0010145941,0.0012543704,0.00051605457,0.0008650388,0.000113231465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028948867,0.00009209081,0.008031324,0.00008590031,0.000085694366,0.00013128111,0.00044328062,0.86929727,0.02826214,0.022147551,0.0005256041,0.07060838],"study_design_scores_gemma":[0.000005235963,0.000028767165,0.0012197791,0.000005885655,0.000008926767,0.000013954807,0.000026908448,0.991277,0.0014893427,0.005786477,0.00012878036,0.000008980767],"about_ca_topic_score_codex":0.008551071,"about_ca_topic_score_gemma":0.008171759,"teacher_disagreement_score":0.008551071,"about_ca_system_score_codex":0.00068549917,"about_ca_system_score_gemma":0.0005755756,"threshold_uncertainty_score":0.017002583},"labels":[],"label_agreement":null},{"id":"W1970657189","doi":"10.1145/2345396.2345554","title":"Neuro-fuzzy expert system for breast cancer diagnosis","year":2012,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial neural network; Adaptive neuro fuzzy inference system; Artificial intelligence; Support vector machine; Machine learning; Neuro-fuzzy; Probabilistic neural network; Fuzzy logic; Hybrid system; Multilayer perceptron; Breast cancer; MATLAB; Data mining; Fuzzy control system; Pattern recognition (psychology); Time delay neural network; Cancer; Medicine","score_opus":0.026798602639526013,"score_gpt":0.2789423732392769,"score_spread":0.2521437705997509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970657189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053739727,0.0017278243,0.9135329,0.0006672916,0.00035744256,0.0005777687,0.0013281325,0.008322759,0.019746223],"genre_scores_gemma":[0.66378605,0.0006415755,0.3217048,0.0003658815,0.000071926406,0.00055564067,0.0010381349,0.000068490044,0.011767531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996692,0.000057178862,0.00003697245,0.000082790946,0.00012603709,0.00002784433],"domain_scores_gemma":[0.99955255,0.00013177567,0.000024572411,0.000028343777,0.00024692345,0.000015834077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006268313,0.0004172893,0.00056829903,0.0006225556,0.00047258046,0.0007593488,0.0009080379,0.0010907869,0.0073806364],"category_scores_gemma":[0.0016649029,0.0001879325,0.00040365505,0.0003188736,0.00013382659,0.00039033304,0.00037676646,0.0005302921,0.0023546135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010368918,0.0003443578,0.0029758362,0.000766494,0.00014417821,0.0007880482,0.00025918585,0.20542075,0.047217194,0.0045241676,0.01769921,0.71882373],"study_design_scores_gemma":[0.00007034982,0.00014033091,0.0017450466,0.00007027345,0.000055632132,0.00026048892,0.00006477123,0.97108793,0.013943062,0.0023211646,0.010207171,0.000033747358],"about_ca_topic_score_codex":0.0076364838,"about_ca_topic_score_gemma":0.008759085,"teacher_disagreement_score":0.0076364838,"about_ca_system_score_codex":0.00062699267,"about_ca_system_score_gemma":0.00078124175,"threshold_uncertainty_score":0.024690688},"labels":[],"label_agreement":null},{"id":"W1971159983","doi":"10.1007/s10115-012-0610-x","title":"Fuzzy set-oriented neural networks based on fuzzy polynomial inference and dynamic genetic optimization","year":2013,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematical optimization; Fuzzy logic; Computer science; Polynomial; Mathematics; Adaptive neuro fuzzy inference system; Neuro-fuzzy; Algorithm; Fuzzy control system; Artificial intelligence","score_opus":0.007798783949087225,"score_gpt":0.22675202680502993,"score_spread":0.2189532428559427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971159983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009897048,0.00036173058,0.98638004,0.00013595371,0.00006427047,0.000026402544,0.000023238184,0.00013243248,0.0029788096],"genre_scores_gemma":[0.6379192,0.00073379517,0.35676557,0.00010332786,0.000106272346,0.00016583248,0.00009718555,0.000062936604,0.004045922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972934,0.0000794734,0.000014286185,0.00005283447,0.000097803655,0.000026244563],"domain_scores_gemma":[0.9994184,0.0003148129,0.000058950827,0.000034126875,0.00015373279,0.000020059591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007482593,0.0005039724,0.0010439482,0.0006174254,0.0005895737,0.0010259594,0.0013504943,0.0011205625,0.0020991105],"category_scores_gemma":[0.0029065707,0.0003329199,0.00062661956,0.001102346,0.00068288646,0.0012666242,0.00060143165,0.0011041746,0.00039857827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005085978,0.000027912669,0.00014259653,0.000056239227,0.000031271113,0.00002589365,0.000045851113,0.91611993,0.0013711368,0.029270127,0.0005777501,0.05228034],"study_design_scores_gemma":[0.0000031184848,0.00000446906,0.000019084808,0.000002966125,0.0000036843437,0.0000036847653,0.0000015547485,0.9960437,0.00017928371,0.003617097,0.00011859065,0.0000027112399],"about_ca_topic_score_codex":0.009594932,"about_ca_topic_score_gemma":0.008683268,"teacher_disagreement_score":0.009594932,"about_ca_system_score_codex":0.0011479739,"about_ca_system_score_gemma":0.0009389539,"threshold_uncertainty_score":0.019078135},"labels":[],"label_agreement":null},{"id":"W1971944463","doi":"10.1109/tnn.2011.2169809","title":"Parallel Programmable Asynchronous Neighborhood Mechanism for Kohonen SOM Implemented in CMOS Technology","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Self-organizing map; Computer science; Asynchronous communication; Artificial neural network; Chip; CMOS; Realization (probability); Topology (electrical circuits); Artificial intelligence; Electronic engineering; Electrical engineering; Mathematics; Engineering","score_opus":0.025429751763777934,"score_gpt":0.24847249950286876,"score_spread":0.22304274773909083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971944463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17107445,0.0006598402,0.815811,0.000115393355,0.00019877788,0.000074444666,0.000106018706,0.0019313225,0.010028814],"genre_scores_gemma":[0.8095333,0.0001870277,0.18712662,0.00004723487,0.000025620242,0.00006794482,0.000056827714,0.000038742604,0.0029167454],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99991465,0.000010027176,0.0000062331624,0.000020621495,0.00003493519,0.000013605885],"domain_scores_gemma":[0.99992096,0.000019142697,0.000012017252,0.000015318437,0.000024305074,0.0000082598945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001223861,0.00018662767,0.00019247252,0.00019640986,0.0002301683,0.00034637444,0.0010511732,0.0002845342,0.0014905113],"category_scores_gemma":[0.00021210022,0.00011097218,0.00022166163,0.00017490386,0.00017810192,0.00047786982,0.0002955136,0.00020331371,0.00025518762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004894761,0.0001673427,0.0015003199,0.000541532,0.00011407928,0.0005578632,0.0002497877,0.08354507,0.51368207,0.047431268,0.0027559325,0.34896523],"study_design_scores_gemma":[0.000108378415,0.0006314027,0.0017128524,0.000034478464,0.00008018616,0.00067731034,0.00006793393,0.71199185,0.24965796,0.015331304,0.019648362,0.00005794936],"about_ca_topic_score_codex":0.00045826007,"about_ca_topic_score_gemma":0.00086088834,"teacher_disagreement_score":0.0014905113,"about_ca_system_score_codex":0.00019359545,"about_ca_system_score_gemma":0.00019735562,"threshold_uncertainty_score":0.0049862266},"labels":[],"label_agreement":null},{"id":"W197205934","doi":"","title":"A Measure of Relatedness for Selecting Consolidated Task Knowledge.","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Task (project management); Computer science; Artificial intelligence; Generalization; Measure (data warehouse); Artificial neural network; Transfer of learning; Context (archaeology); Task analysis; Cosine similarity; Similarity measure; Representation (politics); Machine learning; Pattern recognition (psychology); Mathematics; Data mining","score_opus":0.020752772653632828,"score_gpt":0.27964890957693167,"score_spread":0.2588961369232988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W197205934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14967063,0.0015390796,0.8306558,0.00032969637,0.00006673309,0.00051657436,0.0008966394,0.00078385184,0.01554095],"genre_scores_gemma":[0.8091895,0.00042358096,0.18510562,0.00015368871,0.00009557914,0.00047954978,0.0013500451,0.0002574074,0.0029449842],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99584144,0.001329045,0.00035131726,0.0011081583,0.0012218822,0.00014808742],"domain_scores_gemma":[0.9793754,0.010866092,0.0030923616,0.0034381747,0.0024733243,0.0007545767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060874014,0.0008036926,0.00097587425,0.0041415407,0.0007381985,0.0017761413,0.0013621495,0.0013521796,0.003159988],"category_scores_gemma":[0.05448894,0.0004196911,0.0005540655,0.0028319897,0.0013391931,0.0055009094,0.0025778215,0.0014920798,0.0011987101],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010792838,0.0007396151,0.067026995,0.0010189066,0.0008856848,0.00032798853,0.0018570938,0.056189314,0.04400496,0.04627983,0.007657405,0.77293295],"study_design_scores_gemma":[0.00016611014,0.0015399605,0.09123612,0.00017555538,0.00048443512,0.0014727823,0.00079397624,0.6769536,0.03206782,0.17804198,0.01682439,0.0002432921],"about_ca_topic_score_codex":0.00088311237,"about_ca_topic_score_gemma":0.0012232076,"teacher_disagreement_score":0.0060874014,"about_ca_system_score_codex":0.0009620082,"about_ca_system_score_gemma":0.00069458474,"threshold_uncertainty_score":0.03219366},"labels":[],"label_agreement":null},{"id":"W1972168640","doi":"10.1007/s11004-007-9115-7","title":"A Comparison between a Modified Counter Propagation Network and an Extended Self-Organizing Map in Remotely Sensed Data Classification","year":2007,"lang":"en","type":"article","venue":"Mathematical Geology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Jilin University; National Natural Science Foundation of China","keywords":"Self-organizing map; Computer science; Layer (electronics); Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Data mining","score_opus":0.09274232628576105,"score_gpt":0.350159695735264,"score_spread":0.25741736944950294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972168640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43188787,0.00223694,0.5616153,0.00070437725,0.0005160347,0.00011151521,0.00015857839,0.00064340915,0.0021259398],"genre_scores_gemma":[0.8795251,0.00045776187,0.11814369,0.00013659202,0.00014624337,0.000058885762,0.00014966303,0.000054526954,0.0013276131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923325,0.0003181931,0.000059399346,0.000111844034,0.00022906245,0.000048232556],"domain_scores_gemma":[0.99291235,0.004798299,0.00019883779,0.0003307678,0.0016504185,0.0001092666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031030905,0.00040966412,0.0005659223,0.0010993388,0.00040050436,0.0011978454,0.0011329105,0.0013263607,0.0009142246],"category_scores_gemma":[0.008703453,0.0001947955,0.00040570658,0.0010635965,0.0005623828,0.0018578847,0.00062486826,0.00053493347,0.00015558455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040040473,0.0005455575,0.010613573,0.00024171294,0.00044914824,0.0002136764,0.00015836525,0.38496307,0.009966106,0.007398049,0.002645754,0.5788009],"study_design_scores_gemma":[0.000019644005,0.00007470539,0.0012407746,0.0000041612125,0.00003692941,0.000025450292,0.00001256394,0.99684995,0.00090497255,0.00068339094,0.00014023195,0.000007149234],"about_ca_topic_score_codex":0.0077175545,"about_ca_topic_score_gemma":0.0064378097,"teacher_disagreement_score":0.0077175545,"about_ca_system_score_codex":0.00083985436,"about_ca_system_score_gemma":0.0007432332,"threshold_uncertainty_score":0.016410887},"labels":[],"label_agreement":null},{"id":"W1973107936","doi":"10.3758/bf03195528","title":"Testing curvatures of learning functions on individual trial and block average data","year":2003,"lang":"en","type":"review","venue":"Behavior Research Methods, Instruments, & Computers","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Block (permutation group theory); Distortion (music); Noise (video); Mathematics; Curvature; Function (biology); Statistical hypothesis testing; Algorithm; Computer science; Statistics; Test data; Artificial intelligence; Image (mathematics)","score_opus":0.5900270898296182,"score_gpt":0.5561764514506264,"score_spread":0.033850638378991715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973107936","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8208231,0.0013939551,0.17222825,0.00021367699,0.000053909687,0.00018591208,0.0011128475,0.0013171447,0.002671271],"genre_scores_gemma":[0.9709056,0.0004085291,0.025796903,0.00003066763,0.0000143511925,0.00007649514,0.0013073348,0.00023925249,0.0012208051],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966355,0.001584466,0.00022980428,0.0006722991,0.00070970826,0.00016817586],"domain_scores_gemma":[0.94378847,0.042161193,0.0031190969,0.005735666,0.0043474897,0.00084812404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0101563465,0.000672282,0.0010322699,0.0017638702,0.0002856143,0.0012244058,0.0008844913,0.001044386,0.0021813503],"category_scores_gemma":[0.06408373,0.00052658375,0.0013700161,0.0012060174,0.000788326,0.0016745452,0.0009684229,0.0014298046,0.0012552154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042615524,0.00043022772,0.33958593,0.0006397781,0.0018145908,0.0001944722,0.001117646,0.10219016,0.037888173,0.0058182892,0.0032389944,0.50282013],"study_design_scores_gemma":[0.00007039904,0.0012511697,0.49042895,0.00009093326,0.00027017354,0.00063922786,0.0002957481,0.48153004,0.014700843,0.008962667,0.0016051945,0.00015462472],"about_ca_topic_score_codex":0.004346239,"about_ca_topic_score_gemma":0.004069985,"teacher_disagreement_score":0.0101563465,"about_ca_system_score_codex":0.0009110234,"about_ca_system_score_gemma":0.0009953717,"threshold_uncertainty_score":0.053712547},"labels":[],"label_agreement":null},{"id":"W1974230797","doi":"10.1007/s10044-007-0060-3","title":"Periodicity and stability issues of a chaotic pattern recognition neural network","year":2007,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Chaotic; Artificial neural network; Eigenvalues and eigenvectors; Computer science; Stability (learning theory); Lyapunov exponent; Control theory (sociology); Artificial intelligence; Pattern recognition (psychology); Topology (electrical circuits); Mathematics; Control (management); Machine learning; Physics","score_opus":0.02468098660401145,"score_gpt":0.2704438658288962,"score_spread":0.24576287922488474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974230797","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7315382,0.0015884918,0.24901636,0.0012901201,0.00021211887,0.00005310685,0.00019342142,0.00012595537,0.015982242],"genre_scores_gemma":[0.98822415,0.00030113457,0.008710373,0.0000384917,0.00010487795,0.00003567428,0.00006101774,0.000034831075,0.0024894997],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997961,0.000055654506,0.00001950948,0.00004390964,0.00006263392,0.00002213819],"domain_scores_gemma":[0.9984182,0.0008579043,0.00025234537,0.00013199821,0.00023595609,0.000103621016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006472594,0.00021491807,0.0004772034,0.000683057,0.00060250686,0.0008970868,0.0005177529,0.0006616987,0.0016748458],"category_scores_gemma":[0.004594361,0.00031865385,0.0003441771,0.00034246515,0.0010342879,0.0010646076,0.00053701963,0.00050508406,0.00019344137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068836496,0.00010927904,0.0086129205,0.00038004701,0.00013932765,0.0013782544,0.00082601624,0.16613239,0.07216261,0.68012935,0.0031862338,0.06625517],"study_design_scores_gemma":[0.000029826044,0.000086650885,0.002793895,0.000016708314,0.000018566909,0.0002956182,0.000059826325,0.85150963,0.0025611757,0.14201476,0.0005883422,0.00002496181],"about_ca_topic_score_codex":0.00044418196,"about_ca_topic_score_gemma":0.00027728896,"teacher_disagreement_score":0.0016748458,"about_ca_system_score_codex":0.0003211535,"about_ca_system_score_gemma":0.00030056966,"threshold_uncertainty_score":0.005602956},"labels":[],"label_agreement":null},{"id":"W1974274543","doi":"10.1142/s0129065700000284","title":"INSTABILITIES AND OSCILLATION IN THE DETERMINISTIC BOLTZMANN MACHINE","year":2000,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Boltzmann machine; Computer science; Nonlinear system; Schedule; Oscillation (cell signaling); Robustness (evolution); Control theory (sociology); Mathematics; Applied mathematics; Mathematical optimization; Artificial intelligence; Artificial neural network; Physics","score_opus":0.015783617856012462,"score_gpt":0.2615887436573754,"score_spread":0.24580512580136293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974274543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.400172,0.0007707144,0.58296525,0.0015295158,0.00010808054,0.00004866167,0.000080363076,0.00080926355,0.013516244],"genre_scores_gemma":[0.98218566,0.0001268364,0.016322127,0.000059049067,0.000026077101,0.000049907543,0.000023037333,0.00006092163,0.0011464277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99923503,0.00028268874,0.00004673138,0.00010309215,0.00024431382,0.00008821026],"domain_scores_gemma":[0.99663615,0.0023484523,0.00037346504,0.00030110756,0.00024669518,0.00009423603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016747477,0.00031962994,0.00055956293,0.0005436266,0.00059744297,0.0010458506,0.0006753374,0.0008958943,0.0008736374],"category_scores_gemma":[0.0112537155,0.0004469787,0.00043889135,0.00035030782,0.002438776,0.0014929766,0.0011245683,0.0010199317,0.00024396657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109836146,0.00001827468,0.0018770959,0.000043663767,0.000026101561,0.00011286853,0.00025456503,0.8135683,0.007920314,0.1661109,0.00046725795,0.0094909435],"study_design_scores_gemma":[0.000012351417,0.000017531513,0.00034529093,0.000005340165,0.0000037470913,0.000029469136,0.0000130992785,0.91611356,0.0010954022,0.081975944,0.0003745005,0.000013758859],"about_ca_topic_score_codex":0.0016098223,"about_ca_topic_score_gemma":0.000627502,"teacher_disagreement_score":0.0016747477,"about_ca_system_score_codex":0.000880602,"about_ca_system_score_gemma":0.00060395716,"threshold_uncertainty_score":0.008857012},"labels":[],"label_agreement":null},{"id":"W1974581996","doi":"10.1111/1467-9876.00400","title":"Regression Models for Cyclic Data","year":2003,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Clockwise; Orientation (vector space); Logistic regression; Sine; Event (particle physics); Statistics; Mathematics; Probability distribution; Statistical physics; Geometry; Physics; Rotation (mathematics)","score_opus":0.033993234583456364,"score_gpt":0.28209683278284714,"score_spread":0.24810359819939076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974581996","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034300577,0.002638349,0.95168537,0.00209082,0.00020998594,0.00016711517,0.003754858,0.0019109712,0.003242043],"genre_scores_gemma":[0.70632404,0.005041108,0.25040868,0.0007362184,0.00085657975,0.0017934513,0.01231092,0.0010604862,0.021468477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99482614,0.0030380958,0.00029074724,0.0011131114,0.00040071417,0.00033107743],"domain_scores_gemma":[0.96384585,0.029796308,0.0026239564,0.0016956276,0.0017387277,0.0002994531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012049782,0.0017737072,0.0017975423,0.0026331185,0.00060548366,0.0021748615,0.0036134871,0.002572288,0.0085303495],"category_scores_gemma":[0.051364742,0.0009466959,0.001938596,0.0043737376,0.0012802321,0.0029922796,0.0015842641,0.0031989887,0.0026052475],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018436245,0.000092793125,0.007052849,0.0003836753,0.00039757756,0.00024144434,0.00030568553,0.8034515,0.00053882686,0.11524588,0.007936657,0.06416878],"study_design_scores_gemma":[0.00001441556,0.000021940976,0.0006721873,0.000025388292,0.000023219285,0.000038982154,0.000023661827,0.95340455,0.00007078866,0.04356546,0.0021190047,0.000020422081],"about_ca_topic_score_codex":0.017937737,"about_ca_topic_score_gemma":0.011973541,"teacher_disagreement_score":0.017937737,"about_ca_system_score_codex":0.001960827,"about_ca_system_score_gemma":0.0012539213,"threshold_uncertainty_score":0.06372607},"labels":[],"label_agreement":null},{"id":"W1974709327","doi":"10.1155/2012/902139","title":"Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression","year":2012,"lang":"en","type":"article","venue":"Journal of Applied Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China; City University of Hong Kong; National Science Foundation","keywords":"Quantile regression; Regression analysis; Regression; Support vector machine; Computer science; Mathematics; Algorithm; Artificial intelligence; Machine learning; Statistics","score_opus":0.035637246743410574,"score_gpt":0.30799490547514957,"score_spread":0.272357658731739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974709327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009913036,0.0012568311,0.9863788,0.00048813687,0.00006520261,0.000043137945,0.00003513394,0.00011216974,0.0017075257],"genre_scores_gemma":[0.56410605,0.0045830626,0.41517857,0.0008067743,0.0006546293,0.0005939739,0.0006385641,0.0004933331,0.012945093],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950631,0.0027280499,0.00020194512,0.00050172856,0.0011952256,0.00030995155],"domain_scores_gemma":[0.97358334,0.020533241,0.0017830115,0.0012439245,0.0023178412,0.0005386103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012137029,0.0018337737,0.0017051721,0.0016715028,0.0006525667,0.002275522,0.0027052732,0.0026747342,0.002579856],"category_scores_gemma":[0.057546787,0.00084680936,0.0017305855,0.0021668463,0.0027265146,0.004969842,0.0030636336,0.004906011,0.00075346197],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001964878,0.00010191048,0.0014466827,0.0003527504,0.0001448759,0.00008806169,0.00022550255,0.43722874,0.0019278482,0.51129544,0.0020706092,0.044921182],"study_design_scores_gemma":[0.000010705506,0.00004615207,0.00013861428,0.00002259898,0.00001351077,0.000021906755,0.000012232463,0.9256036,0.00046834533,0.073145226,0.00050690427,0.000010136746],"about_ca_topic_score_codex":0.0022629974,"about_ca_topic_score_gemma":0.0010054527,"teacher_disagreement_score":0.012137029,"about_ca_system_score_codex":0.0026497748,"about_ca_system_score_gemma":0.001635153,"threshold_uncertainty_score":0.06418747},"labels":[],"label_agreement":null},{"id":"W1974884708","doi":"10.1007/s10928-006-9004-6","title":"A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection","year":2006,"lang":"en","type":"article","venue":"Journal of Pharmacokinetics and Pharmacodynamics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"","keywords":"Computer science; Covariate; Residual; Genetic algorithm; Identification (biology); Model selection; Selection (genetic algorithm); Machine learning; Algorithm; Artificial intelligence; Linear model; Mathematical optimization; Mathematics","score_opus":0.010015236009317292,"score_gpt":0.25086351424430353,"score_spread":0.24084827823498625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974884708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010190834,0.00026308637,0.9870918,0.00017566394,0.00006111884,0.00008397278,0.000044753247,0.00047600735,0.0016127609],"genre_scores_gemma":[0.3657971,0.0003057749,0.6291045,0.00036460062,0.00013147171,0.0005627589,0.00025218396,0.00020935335,0.0032721623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989812,0.0005322594,0.00005246838,0.00012177062,0.00025470782,0.000057553512],"domain_scores_gemma":[0.99748343,0.0018464691,0.00010251639,0.00009102461,0.00041767387,0.000058902326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021965338,0.0012678071,0.0022090687,0.0015688862,0.00079652766,0.0013270172,0.0025691593,0.002288475,0.0020452978],"category_scores_gemma":[0.005465383,0.00076935126,0.0012661556,0.0015232799,0.00068966276,0.000984104,0.0012317618,0.0018889129,0.00052937097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005259287,0.000066777895,0.00041037277,0.00003457325,0.00015510355,0.00006069651,0.00002866644,0.94632256,0.000659325,0.00281943,0.00060947187,0.048780378],"study_design_scores_gemma":[0.0000096883905,0.00001704104,0.00004401937,0.0000021858675,0.000011641278,0.000009567138,0.0000021398046,0.9989231,0.000093110655,0.000780579,0.000103382656,0.0000035715875],"about_ca_topic_score_codex":0.009691291,"about_ca_topic_score_gemma":0.009295871,"teacher_disagreement_score":0.009691291,"about_ca_system_score_codex":0.000930278,"about_ca_system_score_gemma":0.0013687811,"threshold_uncertainty_score":0.019269764},"labels":[],"label_agreement":null},{"id":"W1975758309","doi":"10.1142/s0218127406016732","title":"SPHERICAL SELF-ORGANIZING FEATURE MAP: AN INTRODUCTORY REVIEW","year":2006,"lang":"en","type":"article","venue":"International Journal of Bifurcation and Chaos","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation","funders":"National Institute on Disability and Rehabilitation Research","keywords":"Fractal; Feature (linguistics); Chaotic; Computer science; Fractal dimension; Lattice (music); Variety (cybernetics); Coupled map lattice; Dimension (graph theory); Nonlinear system; Topology (electrical circuits); Theoretical computer science; Artificial intelligence; Mathematics; Pure mathematics; Physics; Mathematical analysis; Combinatorics","score_opus":0.0065605037634943976,"score_gpt":0.2498062536896019,"score_spread":0.2432457499261075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975758309","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00097052625,0.98374337,0.008333758,0.0008570927,0.00073835993,0.000013642475,0.000040621377,0.000046329882,0.005256354],"genre_scores_gemma":[0.017480593,0.96965194,0.0065088696,0.0006130878,0.0025939157,0.000042623902,0.00010649835,0.000018359031,0.0029841114],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998648,0.000022353697,0.000015604606,0.000031997115,0.000054864347,0.000010354946],"domain_scores_gemma":[0.9996247,0.00019191866,0.00003253313,0.0000113575325,0.00011759532,0.00002183517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004580456,0.0005696787,0.0007473906,0.0018928528,0.00023183158,0.00092579983,0.00077478244,0.0011189657,0.0016305161],"category_scores_gemma":[0.0010081572,0.00026188028,0.0003351136,0.0019372561,0.00058799365,0.0016598911,0.00038052048,0.0006826548,0.0013335567],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051531704,0.000075122836,0.00092566415,0.0045557315,0.00006510386,0.0005943782,0.00014447489,0.00411822,0.0014909728,0.028180176,0.03329046,0.9265082],"study_design_scores_gemma":[0.00001286535,0.00019119849,0.0026233953,0.001444601,0.000056475703,0.003487277,0.00016454808,0.0058191307,0.0015748309,0.038318656,0.94623595,0.00007107677],"about_ca_topic_score_codex":0.0014484415,"about_ca_topic_score_gemma":0.0010253091,"teacher_disagreement_score":0.0018928528,"about_ca_system_score_codex":0.00053247105,"about_ca_system_score_gemma":0.00046152447,"threshold_uncertainty_score":0.0054546},"labels":[],"label_agreement":null},{"id":"W1975973957","doi":"10.1007/s10107-007-0145-1","title":"How does a stochastic optimization/approximation algorithm adapt to a randomly evolving optimum/root with jump Markov sample paths","year":2007,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Jump; Sample (material); Markov chain; Algorithm; Root (linguistics); Mathematical optimization; Stochastic approximation; Jump process; Computer science; Statistics","score_opus":0.011375396128301001,"score_gpt":0.23885479756186767,"score_spread":0.22747940143356668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975973957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06966328,0.000087281485,0.9258429,0.00089157524,0.00011092242,0.000037378682,0.000020360847,0.00037473932,0.0029715507],"genre_scores_gemma":[0.5963571,0.00013325857,0.3995314,0.00027110107,0.00007768035,0.000073965784,0.00007248135,0.00032127983,0.0031616753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99947125,0.00016687177,0.000026905735,0.0001561081,0.00010991781,0.00006886068],"domain_scores_gemma":[0.9977276,0.0014186294,0.00018327053,0.00024880594,0.00031615226,0.00010554696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020247533,0.00043719207,0.0008067264,0.00043956295,0.0004501497,0.0011559784,0.0011529833,0.0025604118,0.0028962465],"category_scores_gemma":[0.01178516,0.000477809,0.00064013025,0.00037328925,0.00081512437,0.002880407,0.0010128248,0.0012943649,0.0005487517],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008016517,0.00005865971,0.0016277483,0.000049079048,0.000060327748,0.00007318108,0.000081031576,0.9152201,0.0021792888,0.02459347,0.0010365485,0.054940425],"study_design_scores_gemma":[0.0000066076104,0.000009401496,0.00007813208,0.000003302884,0.000004832784,0.000012076252,0.000006961558,0.9955407,0.00028979382,0.003892355,0.00015228624,0.00000362396],"about_ca_topic_score_codex":0.003177293,"about_ca_topic_score_gemma":0.0020229754,"teacher_disagreement_score":0.003177293,"about_ca_system_score_codex":0.0005768888,"about_ca_system_score_gemma":0.00087805325,"threshold_uncertainty_score":0.010708034},"labels":[],"label_agreement":null},{"id":"W1976168061","doi":"10.1117/12.448231","title":"<title>Constructive Hermite polynomial feedforward neural networks with application to facial expression recognition</title>","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Hermite polynomials; Activation function; Constructive; Feedforward neural network; Computer science; Artificial neural network; Polynomial; Artificial intelligence; Expression (computer science); Pattern recognition (psychology); Facial expression; Feed forward; Speech recognition; Mathematics; Engineering; Pure mathematics; Control engineering","score_opus":0.008805619008991001,"score_gpt":0.21548437814037805,"score_spread":0.20667875913138706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976168061","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015840905,0.008628547,0.9103154,0.002436073,0.0049563977,0.000118385455,0.00032188118,0.0026648853,0.054717544],"genre_scores_gemma":[0.3488136,0.011710454,0.35511932,0.0009140091,0.0023059873,0.00017043293,0.0011795348,0.00079464726,0.278992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998729,0.000024747264,0.0000061586475,0.000029498455,0.000052986386,0.0000136167655],"domain_scores_gemma":[0.99973744,0.000085022206,0.00001879248,0.000025627869,0.00011825246,0.000014843479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027037552,0.00041482295,0.00035816696,0.00025228315,0.0001682135,0.00046484117,0.00048679704,0.0004717991,0.015082138],"category_scores_gemma":[0.000740985,0.000106900916,0.00017649657,0.00049553195,0.00032183333,0.00055061636,0.0003158308,0.0006324831,0.0047778543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028356593,0.000067986926,0.00031792241,0.0003655426,0.000031465766,0.00039345847,0.00005933823,0.050711595,0.033722863,0.043451052,0.06375002,0.80684537],"study_design_scores_gemma":[0.000028671426,0.00018244536,0.0007141574,0.000068065645,0.000026664678,0.0003109277,0.000021148264,0.7991853,0.035655636,0.020712927,0.14305359,0.000040557163],"about_ca_topic_score_codex":0.0012657883,"about_ca_topic_score_gemma":0.002063906,"teacher_disagreement_score":0.015082138,"about_ca_system_score_codex":0.00040112121,"about_ca_system_score_gemma":0.0002084772,"threshold_uncertainty_score":0.050454795},"labels":[],"label_agreement":null},{"id":"W1977302802","doi":"10.1167/12.9.944","title":"Attention is drawn spontaneously to regularities during statistical learning","year":2012,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Prioritization; Artificial intelligence; Computer science; Pattern recognition (psychology); Fixation (population genetics); Feature (linguistics); Chemistry","score_opus":0.00947384936762481,"score_gpt":0.2785560346030969,"score_spread":0.2690821852354721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977302802","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70910513,0.00033715073,0.27955803,0.00073741475,0.00010440806,0.00010042119,0.000108069726,0.0009196463,0.00902971],"genre_scores_gemma":[0.9858521,0.00012867525,0.012671478,0.00020080828,0.00005742778,0.000042442553,0.000095541836,0.00010628428,0.0008452454],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986292,0.00028534536,0.000057910416,0.00057787064,0.00028955514,0.00016020796],"domain_scores_gemma":[0.99047476,0.005527309,0.00091356155,0.0021863817,0.0005611431,0.00033698764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018425961,0.00034779977,0.00054398767,0.00047348655,0.0003047501,0.0011545072,0.00076530065,0.00055224675,0.0013655229],"category_scores_gemma":[0.020464774,0.00053048023,0.00054939353,0.00050024356,0.0009664954,0.0014254629,0.0013241374,0.0015367856,0.0003414472],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077335275,0.00031386435,0.073066145,0.00038349733,0.00054215227,0.0006823031,0.0030884324,0.027612638,0.37444884,0.037611574,0.0036739407,0.4778033],"study_design_scores_gemma":[0.00017355396,0.0010217249,0.28805938,0.00010030763,0.0002609948,0.0010334916,0.0007932244,0.4057436,0.0765809,0.21549973,0.010514706,0.00021838231],"about_ca_topic_score_codex":0.0015722682,"about_ca_topic_score_gemma":0.0014046879,"teacher_disagreement_score":0.0018425961,"about_ca_system_score_codex":0.0005932319,"about_ca_system_score_gemma":0.00044825985,"threshold_uncertainty_score":0.009744704},"labels":[],"label_agreement":null},{"id":"W1977587097","doi":"10.1080/09658210344000260","title":"Storage and retrieval of serial‐order information","year":2004,"lang":"en","type":"review","venue":"Memory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Chaining; Chunking (psychology); Computer science; Set (abstract data type); Serial learning; Associative property; Order (exchange); Argument (complex analysis); Power set; Cognitive science; Serialization; Psychology; Information retrieval; Artificial intelligence; Cognitive psychology; Recall; Programming language; Mathematics","score_opus":0.02345746758940665,"score_gpt":0.28109608658221463,"score_spread":0.257638618992808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977587097","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013127504,0.97838634,0.0035024527,0.00071698386,0.00030592698,0.000016686192,0.000048036138,0.00003567602,0.015675213],"genre_scores_gemma":[0.009993697,0.98311067,0.0013013694,0.00033845755,0.0003829883,0.000031228392,0.00007517969,0.0000069320995,0.004759594],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99980587,0.000032141477,0.00001939728,0.00004694019,0.00007669965,0.000018830633],"domain_scores_gemma":[0.9996309,0.00015138907,0.00005959671,0.000040101073,0.000103035774,0.000014859587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046168198,0.00064146775,0.00093084626,0.0015576725,0.0003147928,0.0013397761,0.0012362942,0.0012403487,0.0038021419],"category_scores_gemma":[0.0011250564,0.00028444445,0.00032496243,0.0021505828,0.00104177,0.0034104686,0.0006430191,0.00079829583,0.0033691605],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005690313,0.000047643287,0.00027715223,0.0074357037,0.000054698365,0.00026254714,0.00024347383,0.0008252074,0.0035322423,0.06797575,0.015632931,0.9036558],"study_design_scores_gemma":[0.000015135764,0.0001139684,0.0014176498,0.002370291,0.00007503791,0.0022272929,0.00018886816,0.0004925969,0.005977181,0.04842116,0.9386623,0.00003844261],"about_ca_topic_score_codex":0.0013127852,"about_ca_topic_score_gemma":0.001303913,"teacher_disagreement_score":0.0038021419,"about_ca_system_score_codex":0.0006179685,"about_ca_system_score_gemma":0.0012232461,"threshold_uncertainty_score":0.012719452},"labels":[],"label_agreement":null},{"id":"W1977602574","doi":"10.1109/iscas.2012.6271922","title":"Architecture and implementation of an associative memory using sparse clustered networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Content-addressable memory; Associative property; Artificial neural network; Parallel computing; Content-addressable storage; Memory management; Theoretical computer science; Computer engineering; Computer hardware; Artificial intelligence; Semiconductor memory; Mathematics","score_opus":0.025272693075371076,"score_gpt":0.30842947458454945,"score_spread":0.2831567815091784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977602574","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21323225,0.00050817133,0.7569674,0.00062049174,0.00027122805,0.0002475051,0.00027023634,0.0069006677,0.020981954],"genre_scores_gemma":[0.738087,0.0002341239,0.25444967,0.00019076117,0.000044212058,0.00014170531,0.00023487778,0.00004404847,0.0065736957],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987936,0.000016273492,0.0000095078185,0.000023890574,0.00004955079,0.000021343665],"domain_scores_gemma":[0.99973863,0.000033981185,0.000019230669,0.000049050737,0.00014395849,0.000015121195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016550516,0.00021017547,0.00021750793,0.0003367521,0.00034027005,0.00052373926,0.0016732106,0.00049604423,0.0031141483],"category_scores_gemma":[0.0003918979,0.0001569807,0.0001472765,0.00040034327,0.00020394415,0.00080518157,0.0002952962,0.00033913588,0.0008549523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007392792,0.0005007958,0.0022366333,0.00051605655,0.00015029963,0.0005220122,0.00022113898,0.12365812,0.32506868,0.041498788,0.01116123,0.49372697],"study_design_scores_gemma":[0.00009995198,0.0006513379,0.0009302962,0.000036513684,0.00008865251,0.0005055174,0.000080678634,0.76172155,0.21373399,0.0073759,0.014730106,0.00004549301],"about_ca_topic_score_codex":0.0018214145,"about_ca_topic_score_gemma":0.0032078964,"teacher_disagreement_score":0.0031141483,"about_ca_system_score_codex":0.00039768065,"about_ca_system_score_gemma":0.0005961518,"threshold_uncertainty_score":0.010417819},"labels":[],"label_agreement":null},{"id":"W1977885105","doi":"10.1061/(asce)0733-947x(2001)127:3(237)","title":"Statistical and Genetic Algorithms Classification of Highways","year":2001,"lang":"en","type":"article","venue":"Journal of Transportation Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Genetic algorithm; Computer science; Traffic volume; Volume (thermodynamics); Algorithm; Data mining; Statistics; Machine learning; Mathematics; Engineering; Transport engineering","score_opus":0.014405677885644364,"score_gpt":0.22961678552117,"score_spread":0.21521110763552564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977885105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51052547,0.0009794303,0.46818376,0.0012875277,0.00022605814,0.0005580094,0.0007059296,0.0013864059,0.016147537],"genre_scores_gemma":[0.6993786,0.0003256588,0.29534128,0.00029392244,0.00009994468,0.0005151663,0.00097024423,0.00007051085,0.0030046192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99719304,0.0010623392,0.0001553144,0.0003860391,0.0010278728,0.00017537868],"domain_scores_gemma":[0.9903251,0.006462011,0.0005924434,0.0004846497,0.0020133837,0.00012248429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037698436,0.00064078707,0.00073078397,0.0028990584,0.00047293378,0.0014110887,0.0012850753,0.0012482051,0.0011580851],"category_scores_gemma":[0.014406129,0.00020772933,0.00057850406,0.0025032584,0.00076274405,0.0009694333,0.0004625485,0.00079986744,0.00038323735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025626805,0.00029425006,0.009134653,0.00011236387,0.00016242795,0.000053758853,0.0000986853,0.73121464,0.0022240824,0.006816118,0.0014799619,0.2481528],"study_design_scores_gemma":[0.000024172114,0.00008216861,0.0025676382,0.0000075824823,0.000017903194,0.00002207046,0.000039386257,0.992308,0.0010071086,0.003232881,0.00067908457,0.0000119897095],"about_ca_topic_score_codex":0.012535566,"about_ca_topic_score_gemma":0.00929396,"teacher_disagreement_score":0.012535566,"about_ca_system_score_codex":0.002241174,"about_ca_system_score_gemma":0.0018195569,"threshold_uncertainty_score":0.024925232},"labels":[],"label_agreement":null},{"id":"W1978387158","doi":"10.4018/jitr.2008100105","title":"A Unified Approach To Fractal Dimensions","year":2008,"lang":"en","type":"article","venue":"Journal of Information Technology Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Division of Mathematical Sciences; McMaster University","keywords":"Fractal; Fractal dimension; Computer science; Fractal analysis; Fractal transform; Fractal dimension on networks; Theoretical computer science; Statistical physics; Algorithm; Mathematics; Pattern recognition (psychology); Artificial intelligence; Image processing; Image compression; Mathematical analysis; Image (mathematics); Physics","score_opus":0.05474452108578076,"score_gpt":0.3292712759326648,"score_spread":0.27452675484688405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978387158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016352663,0.02526255,0.9000776,0.005355988,0.0009943367,0.00009317529,0.0002759608,0.00025569,0.051332086],"genre_scores_gemma":[0.36560786,0.025599414,0.59175545,0.0015161242,0.0032440906,0.0005258126,0.00040545102,0.00016501905,0.011180783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998329,0.0004991299,0.00016378329,0.0003393774,0.0005524359,0.00011633751],"domain_scores_gemma":[0.9980338,0.00089425023,0.00015950661,0.0002949837,0.00053319806,0.00008411173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023302322,0.0008782215,0.001046444,0.0051469165,0.0012639415,0.0042317384,0.001197457,0.0013894181,0.0015964326],"category_scores_gemma":[0.004877583,0.00037849264,0.0010540787,0.0025042705,0.0056247446,0.0065908143,0.0025519258,0.003150894,0.0005002481],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031923585,0.0000038022363,0.0000968508,0.000046111407,0.000006153931,0.00002222039,0.00016425819,0.0020860254,0.00036785754,0.9836427,0.0007941374,0.012766872],"study_design_scores_gemma":[0.000003444656,0.000012467743,0.00019945383,0.00004553301,0.000004747305,0.00007772494,0.000064966516,0.0080700535,0.0001882529,0.96980214,0.021513946,0.000017321257],"about_ca_topic_score_codex":0.00074467866,"about_ca_topic_score_gemma":0.0005545235,"teacher_disagreement_score":0.0051469165,"about_ca_system_score_codex":0.001879216,"about_ca_system_score_gemma":0.0008884015,"threshold_uncertainty_score":0.013634741},"labels":[],"label_agreement":null},{"id":"W1978499618","doi":"","title":"Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference","year":2006,"lang":"en","type":"article","venue":"Max Planck Institute for Plasma Physics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.01523037321458407,"score_gpt":0.22567421401888774,"score_spread":0.21044384080430367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978499618","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023395961,0.32451305,0.3605966,0.04122529,0.063699886,0.00029573383,0.0019163617,0.002534804,0.18182226],"genre_scores_gemma":[0.21018937,0.22494155,0.16382052,0.0059442734,0.027024478,0.00036204947,0.005327866,0.0009591851,0.3614308],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99946004,0.00011647853,0.000047470647,0.00009642119,0.00023703362,0.00004263402],"domain_scores_gemma":[0.9988022,0.00028533218,0.000042233012,0.00012403718,0.00062525406,0.00012091223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015813246,0.00087672117,0.00095186563,0.0009605802,0.0004440535,0.0034657104,0.0010759409,0.0013797804,0.024076022],"category_scores_gemma":[0.0028324784,0.00029862195,0.0004481844,0.0011915932,0.0007533004,0.0023819923,0.00085142726,0.00201584,0.008991941],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000157495,0.00011358585,0.0006711687,0.0006787545,0.00014976699,0.00019244918,0.00015555268,0.0056548566,0.0037379542,0.032886103,0.39030147,0.56530076],"study_design_scores_gemma":[0.000029631718,0.00011062732,0.0019493119,0.000359569,0.000105827225,0.00052652106,0.00012126071,0.048886824,0.003373352,0.045584034,0.8989017,0.000051326024],"about_ca_topic_score_codex":0.0015489822,"about_ca_topic_score_gemma":0.0021755064,"teacher_disagreement_score":0.024076022,"about_ca_system_score_codex":0.00076827867,"about_ca_system_score_gemma":0.001328841,"threshold_uncertainty_score":0.08054227},"labels":[],"label_agreement":null},{"id":"W1978505713","doi":"","title":"Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Data science; Artificial intelligence","score_opus":0.010511516714457896,"score_gpt":0.2431796181183747,"score_spread":0.2326681014039168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978505713","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021377465,0.37752503,0.28757644,0.04220787,0.06702009,0.00026716318,0.0015997355,0.0020632069,0.20036294],"genre_scores_gemma":[0.18300784,0.2649684,0.13907383,0.005958126,0.033542916,0.0002698864,0.0047418475,0.00092765543,0.36750948],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999426,0.00011045557,0.000045980752,0.000098366654,0.0002747314,0.000044460285],"domain_scores_gemma":[0.99880016,0.00027567387,0.000038465252,0.000114980685,0.0006372796,0.00013346632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015781664,0.0008213925,0.0009829159,0.00090918504,0.00043337155,0.0033650387,0.0010332302,0.001292985,0.02132868],"category_scores_gemma":[0.0029049818,0.0002918989,0.00043511987,0.0012418726,0.00078670424,0.0022645677,0.0008752162,0.0021797097,0.00830547],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014084464,0.000110650006,0.00070328295,0.0006212798,0.00013235724,0.00016532918,0.00015613619,0.005059723,0.0032638407,0.03609369,0.42471394,0.5288389],"study_design_scores_gemma":[0.000023555369,0.00009154293,0.0018383487,0.00028792175,0.00008529176,0.0004351525,0.000104912855,0.03322556,0.0028040295,0.03908262,0.92197955,0.00004140456],"about_ca_topic_score_codex":0.0016325541,"about_ca_topic_score_gemma":0.0025247904,"teacher_disagreement_score":0.02132868,"about_ca_system_score_codex":0.00080331013,"about_ca_system_score_gemma":0.0013421087,"threshold_uncertainty_score":0.07135159},"labels":[],"label_agreement":null},{"id":"W1978677100","doi":"10.1117/12.811707","title":"A model of memory for incidental learning","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Traverse; Memorization; TRACE (psycholinguistics); Task (project management); Artificial intelligence; Perception; Tree traversal; Machine learning; Event (particle physics); Cognitive psychology; Algorithm; Psychology","score_opus":0.01547390414894246,"score_gpt":0.23756358390689591,"score_spread":0.22208967975795346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978677100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07277094,0.00084572216,0.8942644,0.0015218876,0.0001747353,0.000092180955,0.00041691685,0.0009510859,0.028962078],"genre_scores_gemma":[0.8533539,0.0009474477,0.100358844,0.00035980425,0.00022099024,0.00039255826,0.00039498284,0.00017350985,0.04379805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945396,0.00013393289,0.000024810417,0.00012631412,0.00013853404,0.00012239655],"domain_scores_gemma":[0.99880886,0.00040591316,0.00012151924,0.00023497056,0.0003024526,0.00012629558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010815025,0.00065851415,0.00096673134,0.0008276646,0.00078302383,0.0015860987,0.0037365789,0.0019596366,0.0077524404],"category_scores_gemma":[0.0034382085,0.0003466498,0.0012668481,0.00062834163,0.0013595615,0.004018936,0.0011436712,0.0015515597,0.0024645433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020025464,0.0001477271,0.0017785209,0.00011266,0.00007115032,0.00037144875,0.00029174553,0.38446647,0.003229803,0.5712605,0.0037236349,0.034346174],"study_design_scores_gemma":[0.000029812561,0.00006783292,0.00027652626,0.000011969101,0.00001704374,0.00015559218,0.000021890419,0.86276555,0.0004514553,0.13319394,0.0029901816,0.00001826503],"about_ca_topic_score_codex":0.007643078,"about_ca_topic_score_gemma":0.0037999805,"teacher_disagreement_score":0.0077524404,"about_ca_system_score_codex":0.0011895008,"about_ca_system_score_gemma":0.0014575635,"threshold_uncertainty_score":0.025934517},"labels":[],"label_agreement":null},{"id":"W1978862856","doi":"10.1142/s0218127407017550","title":"SYNCHRONIZATION AND DESYNCHRONIZATION IN A DELAYED DISCRETE NEURAL NETWORK","year":2007,"lang":"en","type":"article","venue":"International Journal of Bifurcation and Chaos","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Synchronization (alternating current); Asynchronous communication; Artificial neural network; Computer science; Control theory (sociology); Stability theory; Stability (learning theory); Symmetry (geometry); Scheme (mathematics); Process (computing); Mathematics; Topology (electrical circuits); Applied mathematics; Nonlinear system; Artificial intelligence; Physics; Mathematical analysis; Machine learning","score_opus":0.0069000160360972165,"score_gpt":0.26032141618465393,"score_spread":0.25342140014855674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978862856","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6514155,0.00026158447,0.3433717,0.00022263898,0.00005285001,0.000022152155,0.000046598623,0.00006941016,0.0045375493],"genre_scores_gemma":[0.9930026,0.000057256828,0.005846424,0.000010662015,0.0000069138564,0.000010834462,0.000008095368,0.000002921813,0.0010544187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988854,0.0000304188,0.0000070869332,0.000033597506,0.00002657224,0.00001376256],"domain_scores_gemma":[0.99987197,0.000043599674,0.000039936,0.00001392639,0.000017295053,0.000013326297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023204542,0.00014637754,0.00021248583,0.0001407464,0.00017957215,0.00032612254,0.00033754247,0.00028936245,0.000619533],"category_scores_gemma":[0.0006709788,0.00010903761,0.00017018385,0.00010958818,0.0004213212,0.00043495523,0.00036177505,0.00025562206,0.00004757754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003213207,0.000043888995,0.0037477256,0.0001068794,0.0000662433,0.00086041435,0.00031145103,0.70806617,0.10578769,0.16484113,0.00027280173,0.015574297],"study_design_scores_gemma":[0.000019947905,0.000039511437,0.00036420178,0.0000024740186,0.000008372387,0.00004768958,0.0000148616355,0.9872695,0.002085645,0.009776414,0.0003660953,0.000005306306],"about_ca_topic_score_codex":0.0012599546,"about_ca_topic_score_gemma":0.0008431592,"teacher_disagreement_score":0.0012599546,"about_ca_system_score_codex":0.00038208714,"about_ca_system_score_gemma":0.00019429522,"threshold_uncertainty_score":0.002772212},"labels":[],"label_agreement":null},{"id":"W1979039110","doi":"10.1142/s0129065712500104","title":"ON THE LEARNING POTENTIAL OF THE APPROXIMATED QUANTRON","year":2012,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Perceptron; Computer science; Heuristic; Artificial intelligence; Machine learning; Pattern recognition (psychology); Algorithm; Artificial neural network; Mathematical optimization; Mathematics","score_opus":0.022102827910809503,"score_gpt":0.26489023571489995,"score_spread":0.24278740780409044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979039110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1277002,0.0017753353,0.8494689,0.002088327,0.00020929327,0.000043007454,0.00009265649,0.00027339664,0.018348854],"genre_scores_gemma":[0.9243343,0.00073362625,0.06693313,0.00046452053,0.00010623366,0.000100814905,0.000087765264,0.000094790514,0.0071447855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994856,0.00029645168,0.00001736928,0.00004744136,0.00009063383,0.00006235122],"domain_scores_gemma":[0.99786997,0.0015372491,0.00010556606,0.00011938006,0.0002493032,0.00011849917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020029978,0.00043372266,0.00087757147,0.0005892506,0.0005666577,0.0011484371,0.001220486,0.0014869445,0.002762572],"category_scores_gemma":[0.010141602,0.00043645568,0.0005686735,0.0005104097,0.0020315882,0.002108321,0.0012605169,0.0014575357,0.00039420585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018803134,0.000029232175,0.0007343273,0.00008864979,0.000031756474,0.00010731916,0.000064372674,0.70494354,0.001447171,0.2759648,0.0014974454,0.014903378],"study_design_scores_gemma":[0.0000067524916,0.000013131144,0.000054419397,0.000009156042,0.0000026822822,0.000015899235,0.0000053892777,0.96482563,0.00009847654,0.034746755,0.00021662042,0.0000050066396],"about_ca_topic_score_codex":0.0020691394,"about_ca_topic_score_gemma":0.0013840707,"teacher_disagreement_score":0.002762572,"about_ca_system_score_codex":0.0010393491,"about_ca_system_score_gemma":0.0007725721,"threshold_uncertainty_score":0.010592997},"labels":[],"label_agreement":null},{"id":"W1979913355","doi":"10.1109/icsc.2014.38","title":"Enabling 'Question Answering' in the MBAT Vector Symbolic Architecture by Exploiting Orthogonal Random Matrices","year":2014,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Computer science; Representation (politics); Multiplication (music); Theoretical computer science; Exploit; Feature (linguistics); Artificial intelligence; Matrix (chemical analysis); Architecture; Question answering; Matrix multiplication; Mathematics","score_opus":0.007881745473146155,"score_gpt":0.2299175229195642,"score_spread":0.22203577744641803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979913355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038717892,0.00024908053,0.9471116,0.00063611224,0.00018103549,0.00009722085,0.00024023849,0.0043483176,0.008418475],"genre_scores_gemma":[0.50851125,0.0002949093,0.47877857,0.00042970534,0.00013122224,0.00029375058,0.0005893639,0.0003919957,0.01057925],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922335,0.00028529164,0.000053036365,0.00016352467,0.00020544694,0.00006930234],"domain_scores_gemma":[0.99877805,0.0005262535,0.00007952887,0.00033073797,0.00019894027,0.00008644443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007714108,0.00052616704,0.00065718876,0.00048476036,0.000600845,0.0015949939,0.0017228674,0.0009321967,0.007919713],"category_scores_gemma":[0.0035118356,0.00028603317,0.0007398366,0.00070693425,0.0012686265,0.00371016,0.0019325534,0.0014381093,0.0020609293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005227245,0.0002467035,0.0008699883,0.0004960889,0.00011390985,0.00027852264,0.0008009928,0.08487172,0.059360918,0.4377926,0.01109421,0.40355158],"study_design_scores_gemma":[0.000043602387,0.0001346037,0.00016237266,0.00003057635,0.000047168898,0.00011754012,0.00007885072,0.7495306,0.026639555,0.20771243,0.015462244,0.00004043406],"about_ca_topic_score_codex":0.0022074978,"about_ca_topic_score_gemma":0.0033906808,"teacher_disagreement_score":0.007919713,"about_ca_system_score_codex":0.00060662156,"about_ca_system_score_gemma":0.0010632171,"threshold_uncertainty_score":0.026494026},"labels":[],"label_agreement":null},{"id":"W1980092256","doi":"10.1155/2012/920920","title":"Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network","year":2012,"lang":"en","type":"article","venue":"Advances in Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminant; Pattern recognition (psychology); Artificial intelligence; Fuzzy logic; Preprocessor; Curse of dimensionality; Benchmark (surveying); Computer science; Linear discriminant analysis; Feature (linguistics); Class (philosophy); Machine learning; Data mining","score_opus":0.039076066781132374,"score_gpt":0.29575550077504165,"score_spread":0.25667943399390925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980092256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24972169,0.00026541948,0.74700636,0.00039318632,0.00008297121,0.00012647979,0.0001544935,0.0004917567,0.0017576735],"genre_scores_gemma":[0.6984562,0.00013524374,0.29936546,0.000089787594,0.000039272458,0.000114079485,0.0002068013,0.000013591239,0.0015794858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995704,0.00008858455,0.000027917675,0.000109273875,0.00016090675,0.000043001477],"domain_scores_gemma":[0.9991799,0.00036534134,0.00006544287,0.0000595496,0.0002902729,0.00003952539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001505719,0.0005616069,0.00064931717,0.0014044436,0.00054064946,0.0010091907,0.00084626325,0.000838626,0.0009999212],"category_scores_gemma":[0.0032095981,0.00019614861,0.00043977064,0.0010409486,0.0004295775,0.001005411,0.00056967273,0.00075685896,0.00024488475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008474502,0.00040288657,0.0098450435,0.00011103986,0.00012345065,0.00020793722,0.00017656274,0.3814433,0.042482648,0.0070518255,0.0018471185,0.55546075],"study_design_scores_gemma":[0.000008230798,0.00003209814,0.0005360975,0.0000030216297,0.000007846845,0.000014626347,0.000009444462,0.9960983,0.0023305619,0.00080531853,0.00014900845,0.0000054856655],"about_ca_topic_score_codex":0.004040291,"about_ca_topic_score_gemma":0.0038916995,"teacher_disagreement_score":0.004040291,"about_ca_system_score_codex":0.00079913385,"about_ca_system_score_gemma":0.0006736151,"threshold_uncertainty_score":0.008033574},"labels":[],"label_agreement":null},{"id":"W1980442328","doi":"10.1103/physreve.67.016119","title":"Bistable gradient networks. II. Storage capacity and behavior near saturation","year":2003,"lang":"en","type":"article","venue":"Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bistability; Saturation (graph theory); Attractor; Weighting; Blackout; Coupling (piping); Coupling strength; Biological system; Artificial neural network; Statistical physics; Control theory (sociology); Phase transition; Topology (electrical circuits); Computer science; Materials science; Condensed matter physics; Chemical physics; Physics; Engineering; Mathematics; Optoelectronics; Artificial intelligence; Thermodynamics; Mathematical analysis; Electrical engineering; Power (physics); Biology","score_opus":0.013841367986472933,"score_gpt":0.2836741922865617,"score_spread":0.26983282430008876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980442328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9215595,0.0017245457,0.057599112,0.00081433146,0.000041224666,0.000048733727,0.00026977216,0.00031544708,0.017627366],"genre_scores_gemma":[0.99688584,0.00017612302,0.0017880404,0.000035196263,0.000007168211,0.00002138123,0.000041786552,0.000015938476,0.0010285719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999317,0.000013931527,0.0000031264296,0.00000862649,0.00001578679,0.00002693595],"domain_scores_gemma":[0.9993005,0.00038980055,0.00009811428,0.00005382201,0.00008595533,0.000071669696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032443678,0.00023513449,0.00029085492,0.0004447249,0.00024746492,0.0005375138,0.0005243934,0.0007152839,0.0026086585],"category_scores_gemma":[0.0027061058,0.00015780568,0.00022754507,0.0002610758,0.0009503265,0.0017531986,0.00059908745,0.00042655884,0.00019657324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033895383,0.000061697974,0.0041260356,0.00028464512,0.000090879585,0.00043126056,0.00026642013,0.71294814,0.05797805,0.20415778,0.0014908914,0.017825184],"study_design_scores_gemma":[0.00002254069,0.000044360855,0.0012792893,0.000024089682,0.000011828765,0.00011642018,0.000042657102,0.92878234,0.006166571,0.06306704,0.00043093922,0.000011871393],"about_ca_topic_score_codex":0.0013005541,"about_ca_topic_score_gemma":0.0007915142,"teacher_disagreement_score":0.0026086585,"about_ca_system_score_codex":0.0006628661,"about_ca_system_score_gemma":0.00026427512,"threshold_uncertainty_score":0.008726835},"labels":[],"label_agreement":null},{"id":"W1981118911","doi":"10.1109/icnnsp.2003.1279329","title":"Phoneme sequence pattern recognition using fuzzy neural network","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Speech recognition; Vector quantization; Artificial neural network; Pattern recognition (psychology); Sequence (biology); Time delay neural network; Artificial intelligence; Learning vector quantization; Feature vector; Neural gas; Viterbi algorithm; Feature (linguistics); Hidden Markov model","score_opus":0.07821578035256349,"score_gpt":0.27830923409879643,"score_spread":0.20009345374623294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981118911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059051804,0.00057744194,0.9350316,0.0001509547,0.00014265142,0.00006587733,0.00013187251,0.0014116366,0.0034361572],"genre_scores_gemma":[0.64049774,0.00032480503,0.3546444,0.0001074949,0.00004540972,0.00010933765,0.00022729322,0.000037915128,0.0040056384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997868,0.0000353073,0.000016354754,0.00005711768,0.00008446141,0.000019989324],"domain_scores_gemma":[0.9997619,0.00008817088,0.000021361724,0.00002317111,0.00009677822,0.000008701081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027170012,0.0003150414,0.000351577,0.0005040762,0.00021162834,0.0004722754,0.00043519185,0.00061097305,0.0015788383],"category_scores_gemma":[0.0010889745,0.00016439788,0.00028470112,0.0003932039,0.00020414473,0.00061787956,0.00020117461,0.00028660422,0.0005059171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037641602,0.000070086695,0.0020374802,0.00019191626,0.0000764184,0.00025738802,0.00010271733,0.15613061,0.063905746,0.0039679958,0.0022984082,0.7705848],"study_design_scores_gemma":[0.000015701085,0.000076721524,0.0016256545,0.00001677064,0.000018753428,0.00016311427,0.00001833204,0.9779408,0.014779601,0.0029312673,0.0023924522,0.000020896074],"about_ca_topic_score_codex":0.0038662308,"about_ca_topic_score_gemma":0.003881085,"teacher_disagreement_score":0.0038662308,"about_ca_system_score_codex":0.00031076602,"about_ca_system_score_gemma":0.0002815204,"threshold_uncertainty_score":0.0076874495},"labels":[],"label_agreement":null},{"id":"W1981245562","doi":"10.2333/bhmk.37.15","title":"Total Information Analysis: Comprehensive Dual Scaling","year":2010,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Multidimensional scaling; Scaling; Dual (grammatical number); Set (abstract data type); Computer science; Space (punctuation); Data mining; Algorithm; Mathematics; Theoretical computer science; Statistics; Geometry","score_opus":0.013352634997579686,"score_gpt":0.2633182908765978,"score_spread":0.24996565587901812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981245562","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024175806,0.0013261067,0.9527677,0.00080117874,0.00016240914,0.00009137527,0.00042947484,0.00047570097,0.019770376],"genre_scores_gemma":[0.72853464,0.0019345849,0.24985498,0.00055924716,0.0007448065,0.00042248346,0.001056588,0.0009209034,0.015971713],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99825674,0.0006371084,0.000089402274,0.00036008543,0.00050149247,0.0001552516],"domain_scores_gemma":[0.9954987,0.0017295567,0.00030130768,0.0014244941,0.0008109041,0.00023494773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030982888,0.0012759452,0.0016955853,0.0032088843,0.001169928,0.003338909,0.0024082507,0.001030085,0.00880881],"category_scores_gemma":[0.015582865,0.00059934705,0.0017767957,0.0025499212,0.0018610486,0.0065833665,0.0034456046,0.002599533,0.0009854649],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008034563,0.00010297159,0.00069808454,0.00019205858,0.000118616255,0.00005801451,0.00015444221,0.023680711,0.0013537413,0.8832712,0.006048766,0.08424101],"study_design_scores_gemma":[0.000007817401,0.000026159714,0.0005184133,0.000025292074,0.00003150812,0.000045931523,0.000034102046,0.30576038,0.00050366955,0.6908699,0.002156361,0.000020418664],"about_ca_topic_score_codex":0.001998926,"about_ca_topic_score_gemma":0.0014126234,"teacher_disagreement_score":0.00880881,"about_ca_system_score_codex":0.0013190153,"about_ca_system_score_gemma":0.0012411139,"threshold_uncertainty_score":0.029468417},"labels":[],"label_agreement":null},{"id":"W1981395566","doi":"10.1016/j.cogsys.2007.06.006","title":"Deconstructing and reconstructing ACT-R: Exploring the architectural space","year":2007,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Python (programming language); Computer science; Cognitive architecture; Architecture; Theoretical computer science; Cognitive science; Programming language; Space (punctuation); Syntax; Cognition; Computational model; Artificial intelligence; Psychology","score_opus":0.17915555726908625,"score_gpt":0.37995518361572184,"score_spread":0.2007996263466356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981395566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09669458,0.0005284765,0.8756631,0.0022539643,0.000076510376,0.000049684088,0.00036536204,0.0012093516,0.023158988],"genre_scores_gemma":[0.72017574,0.00040429583,0.2720565,0.0001924812,0.00002314472,0.000070582595,0.000679087,0.0005321636,0.005866022],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998464,0.0010145714,0.000051869814,0.00026952304,0.00012359626,0.00007655254],"domain_scores_gemma":[0.9957675,0.002036815,0.0003055982,0.0013531687,0.0003448004,0.00019217581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021870858,0.0009632366,0.00047523604,0.0011546419,0.001143887,0.0045903022,0.0018380116,0.0011270661,0.0061641433],"category_scores_gemma":[0.014067416,0.0007524338,0.0011534393,0.00096921856,0.005554872,0.0075305407,0.0025707055,0.0026144336,0.0014975029],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016049786,0.000046424615,0.008063867,0.00020846885,0.000069486596,0.00040573254,0.0040805927,0.068097904,0.0023882615,0.8024233,0.003000613,0.111054815],"study_design_scores_gemma":[0.000013595672,0.00002863376,0.0011111782,0.000052380034,0.000025920403,0.00018610881,0.0008443501,0.18055983,0.0014050152,0.8089371,0.006807808,0.00002803372],"about_ca_topic_score_codex":0.0070193727,"about_ca_topic_score_gemma":0.011776893,"teacher_disagreement_score":0.0070193727,"about_ca_system_score_codex":0.0010711816,"about_ca_system_score_gemma":0.0017856567,"threshold_uncertainty_score":0.020621061},"labels":[],"label_agreement":null},{"id":"W1981483255","doi":"10.1016/s0925-2312(02)00459-9","title":"Linearly decodable functions from neural population codes","year":2002,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Eye Institute","keywords":"Population; Set (abstract data type); Nonlinear system; Singular value decomposition; Mathematics; Decoding methods; Value (mathematics); Computer science; Algorithm; Statistics; Physics","score_opus":0.023936609096803087,"score_gpt":0.2255288762730228,"score_spread":0.2015922671762197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981483255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05978495,0.00062343036,0.92972803,0.00048442403,0.000117529395,0.000050102706,0.00025786224,0.00064252067,0.008311085],"genre_scores_gemma":[0.72929364,0.001049166,0.24182448,0.0004100788,0.0002452677,0.00028746435,0.00094370474,0.00054369785,0.02540255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968135,0.00009758049,0.000018737739,0.000053264386,0.00010817607,0.00004091883],"domain_scores_gemma":[0.9983058,0.0010570164,0.00013218666,0.00026026703,0.00018204568,0.000062650885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061142014,0.0006553243,0.0005854403,0.0006368582,0.000378412,0.0008755127,0.0009717705,0.0013964568,0.0034822044],"category_scores_gemma":[0.0058220294,0.00038406483,0.00049456366,0.00068524905,0.0011292972,0.0016257152,0.0014199655,0.0017881247,0.0009706787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029025858,0.000048368674,0.0007744416,0.00032671334,0.000063442545,0.0002915362,0.0003422924,0.22537203,0.028468909,0.5738854,0.0041863876,0.16595033],"study_design_scores_gemma":[0.000025918493,0.000040563864,0.0003711115,0.00004550534,0.000014025128,0.0002115211,0.000028298971,0.7448524,0.0086893905,0.24372,0.001958582,0.000042704956],"about_ca_topic_score_codex":0.00080761634,"about_ca_topic_score_gemma":0.0011253549,"teacher_disagreement_score":0.0034822044,"about_ca_system_score_codex":0.00058714324,"about_ca_system_score_gemma":0.0005631354,"threshold_uncertainty_score":0.011649072},"labels":[],"label_agreement":null},{"id":"W1983334819","doi":"10.1109/cvpr.2010.5539962","title":"Modeling pixel means and covariances using factorized third-order boltzmann machines","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":225,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Pixel; Probabilistic logic; Covariance; Covariance matrix; Image (mathematics); Boltzmann machine; Set (abstract data type); Machine learning; Generative model; Gaussian; Binary number; Mixture model; Algorithm; Generative grammar; Mathematics; Deep learning; Statistics","score_opus":0.02851994556779858,"score_gpt":0.2757607040053723,"score_spread":0.2472407584375737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983334819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019244103,0.00011224962,0.97904474,0.00014644592,0.000026373606,0.000026178597,0.000119793505,0.00051984587,0.00076031935],"genre_scores_gemma":[0.75098693,0.00032777348,0.24391161,0.00023480841,0.00008805425,0.00020754024,0.00045301442,0.00026865661,0.003521594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994523,0.00016005263,0.000023726212,0.00017452695,0.00011459824,0.00007484494],"domain_scores_gemma":[0.9989981,0.0005158834,0.00015047293,0.00016783104,0.00011602799,0.000051661125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012166122,0.00065713114,0.0010930378,0.00082666636,0.00040679894,0.0014002349,0.0028722521,0.0016573722,0.0021850127],"category_scores_gemma":[0.0044482867,0.00095215323,0.0016444292,0.001054995,0.0014218247,0.0025915438,0.0009344189,0.0022842719,0.00066017814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006799721,0.000039868002,0.0013522776,0.000030126308,0.000063667765,0.000054938875,0.000074202544,0.92683434,0.0029030035,0.040697116,0.0010624701,0.026820065],"study_design_scores_gemma":[0.0000042695337,0.000004199578,0.00012725273,0.0000020379032,0.000003511748,0.000017018807,0.0000035392843,0.9835263,0.00027314437,0.015862918,0.00016909128,0.0000066652706],"about_ca_topic_score_codex":0.009349705,"about_ca_topic_score_gemma":0.012710992,"teacher_disagreement_score":0.009349705,"about_ca_system_score_codex":0.0014955663,"about_ca_system_score_gemma":0.0009407005,"threshold_uncertainty_score":0.01859057},"labels":[],"label_agreement":null},{"id":"W1983873206","doi":"10.1016/j.physleta.2004.12.078","title":"Fastest learning in small-world neural networks","year":2005,"lang":"en","type":"article","venue":"Physics Letters A","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Time delay neural network; Machine learning; Domain (mathematical analysis); Deep learning; Backpropagation; Pattern recognition (psychology); Mathematics","score_opus":0.016488479521724463,"score_gpt":0.22570037982799981,"score_spread":0.20921190030627534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983873206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1521134,0.007876234,0.82172626,0.0038951242,0.000814188,0.00008494413,0.00031218384,0.001298397,0.011879259],"genre_scores_gemma":[0.86887556,0.0028921554,0.11416636,0.00049730117,0.0004452005,0.00019037168,0.0004176656,0.0004015153,0.012113883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990828,0.0003728684,0.000053991327,0.00020745704,0.00016390087,0.00011902432],"domain_scores_gemma":[0.9796491,0.016401974,0.0007540149,0.0012128013,0.001410745,0.00057129096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030109133,0.00066347123,0.0016079695,0.001228102,0.0013318337,0.002237178,0.0026919497,0.0022018095,0.0039469306],"category_scores_gemma":[0.033330984,0.00087520666,0.00063494884,0.0011458336,0.0020861865,0.007024939,0.001878193,0.0028126263,0.0006401089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042176168,0.00010260681,0.002166907,0.0006487709,0.00014470266,0.000117492076,0.0002394316,0.4815809,0.0014666474,0.38539258,0.009184161,0.11853398],"study_design_scores_gemma":[0.000032393928,0.0000271249,0.00024437055,0.000022661281,0.000018044591,0.00003497932,0.000017857546,0.7101661,0.0007488479,0.28770807,0.0009659113,0.000013748143],"about_ca_topic_score_codex":0.002793695,"about_ca_topic_score_gemma":0.0025966614,"teacher_disagreement_score":0.0039469306,"about_ca_system_score_codex":0.0012024547,"about_ca_system_score_gemma":0.0011802713,"threshold_uncertainty_score":0.01592344},"labels":[],"label_agreement":null},{"id":"W1983981859","doi":"10.1007/s11571-008-9036-2","title":"Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications","year":2008,"lang":"en","type":"article","venue":"Cognitive Neurodynamics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Recurrent neural network; Variety (cybernetics); Artificial neural network; Modular design; Convergence (economics); Optimization problem; Mathematical optimization; Constrained optimization; Artificial intelligence; Algorithm; Mathematics","score_opus":0.04275163150933067,"score_gpt":0.268968233077894,"score_spread":0.22621660156856335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983981859","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009815374,0.03090177,0.95278025,0.0005650615,0.00009604726,0.000029052744,0.0000846962,0.00023116649,0.0054965825],"genre_scores_gemma":[0.5959744,0.07853718,0.31056947,0.0004394336,0.0009594141,0.0003310165,0.00040670548,0.00025764253,0.0125247715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970514,0.000095123425,0.000023165661,0.00007347568,0.00008049511,0.000022693303],"domain_scores_gemma":[0.9994955,0.00027484482,0.00006380958,0.000054174176,0.0000919241,0.000019660782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010737287,0.001035911,0.0019187105,0.0006517265,0.00029911232,0.0012140486,0.0020101,0.0014701112,0.0016435792],"category_scores_gemma":[0.0021338165,0.0006197368,0.000816041,0.0018916593,0.0008303077,0.0021463241,0.0011339199,0.001290032,0.00049367535],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006987838,0.0000741645,0.0006275017,0.0006523311,0.00016648624,0.00006464376,0.000112872716,0.6574134,0.00187717,0.17479321,0.0033688897,0.16077939],"study_design_scores_gemma":[0.0000067852457,0.000027505337,0.00009962177,0.000029584491,0.000021034239,0.000025338646,0.000008943977,0.9511346,0.00027450218,0.045479815,0.0028792792,0.000012904014],"about_ca_topic_score_codex":0.0020445785,"about_ca_topic_score_gemma":0.002489641,"teacher_disagreement_score":0.0020445785,"about_ca_system_score_codex":0.0007091776,"about_ca_system_score_gemma":0.0006459964,"threshold_uncertainty_score":0.0056785345},"labels":[],"label_agreement":null},{"id":"W1984083252","doi":"10.1109/smc.2014.6974106","title":"Modular deep Recurrent Neural Network: Application to quadrotors","year":2014,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Recurrent neural network; Modular design; Feed forward; Modularity (biology); Computer science; Feedforward neural network; Layer (electronics); Artificial intelligence; Artificial neural network; Process (computing); Set (abstract data type); Control engineering; Engineering","score_opus":0.00957349713561455,"score_gpt":0.24154965847209103,"score_spread":0.23197616133647647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984083252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15023895,0.0011558662,0.8350126,0.00037411012,0.00016372591,0.00005149674,0.0003041241,0.00222941,0.010469763],"genre_scores_gemma":[0.92519534,0.0002455118,0.07134595,0.00004309957,0.000021228996,0.000018791317,0.00014567318,0.00006970371,0.002914816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999126,0.000021002683,0.000004941819,0.000028097405,0.000021861215,0.000011603762],"domain_scores_gemma":[0.9998184,0.00006541278,0.0000303594,0.000025595355,0.000044034245,0.000016296372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003064183,0.00054414093,0.00044125586,0.00023132596,0.00020663496,0.0003177059,0.00052420294,0.00053680234,0.0016973474],"category_scores_gemma":[0.0007467413,0.00021972973,0.00038768467,0.00034735672,0.00027043401,0.00047106075,0.00047365317,0.00047070504,0.000290235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055877055,0.000024020941,0.0008112509,0.00006558977,0.000027758422,0.000283822,0.00003786842,0.94741213,0.0079576215,0.0041500493,0.0007889195,0.03838503],"study_design_scores_gemma":[0.0000018734077,0.000017733084,0.00015060822,0.0000017508503,0.0000025157146,0.000023249004,0.0000038410662,0.9979482,0.0008034384,0.00072721386,0.0003169386,0.0000026127777],"about_ca_topic_score_codex":0.0073062596,"about_ca_topic_score_gemma":0.0073234923,"teacher_disagreement_score":0.0073062596,"about_ca_system_score_codex":0.0004548281,"about_ca_system_score_gemma":0.000266279,"threshold_uncertainty_score":0.01452744},"labels":[],"label_agreement":null},{"id":"W1984179161","doi":"10.3390/e14122478","title":"Maximum Entropy Gibbs Density Modeling for Pattern Classification","year":2012,"lang":"en","type":"article","venue":"Entropy","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; École de Technologie Supérieure","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Entropy (arrow of time); Self-organizing map; Mathematics; Representation (politics); Gibbs free energy; Feature (linguistics); Computer science; Artificial neural network; Physics; Thermodynamics","score_opus":0.044209870399210066,"score_gpt":0.27322263995255514,"score_spread":0.22901276955334507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984179161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013824706,0.00045082884,0.98306215,0.00028599528,0.000031799136,0.00003652269,0.00009716108,0.00020481054,0.0020059533],"genre_scores_gemma":[0.82798505,0.00065807515,0.16357532,0.00015734254,0.00010914797,0.00031286734,0.00032767883,0.0001467131,0.006727783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958926,0.00018350116,0.000017074814,0.00006230453,0.00011554823,0.000032292854],"domain_scores_gemma":[0.9989643,0.0007579817,0.00006176665,0.00008425377,0.000100766076,0.00003094233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011682427,0.0005260873,0.0008295868,0.0007518754,0.00038712609,0.0008895239,0.001415229,0.0010095008,0.0018254747],"category_scores_gemma":[0.004219337,0.00044557996,0.0007455472,0.0010276155,0.0011337277,0.0016024406,0.0007670402,0.0010326816,0.00042351053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033598848,0.000018071232,0.00042515417,0.000048382848,0.000023973367,0.00003740463,0.00005203678,0.9222664,0.0007914404,0.057706203,0.00068144785,0.017915817],"study_design_scores_gemma":[0.0000010755284,0.0000017405383,0.00003739522,0.0000019725064,9.0979955e-7,0.0000033919405,0.0000015043292,0.98200995,0.000070403235,0.017745122,0.00012445275,0.0000019814886],"about_ca_topic_score_codex":0.0048417505,"about_ca_topic_score_gemma":0.003950948,"teacher_disagreement_score":0.0048417505,"about_ca_system_score_codex":0.0012208697,"about_ca_system_score_gemma":0.0006086819,"threshold_uncertainty_score":0.009627104},"labels":[],"label_agreement":null},{"id":"W1985190707","doi":"10.1016/j.compchemeng.2009.02.003","title":"Developing real-time wave-net models for non-linear time-varying experimental processes","year":2009,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial neural network; MATLAB; Process (computing); Toolbox; Wavelet; Line (geometry); Artificial intelligence; Time domain; Algorithm; Machine learning; Computer vision; Mathematics","score_opus":0.02092776598400916,"score_gpt":0.24524681043914986,"score_spread":0.2243190444551407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985190707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013340195,0.00014835158,0.98378986,0.00017409913,0.00004262843,0.000046534347,0.0000926884,0.00030484825,0.0020607659],"genre_scores_gemma":[0.7696105,0.0007216859,0.20518807,0.00020164551,0.00007795709,0.0005757696,0.00058367656,0.00038922144,0.022651404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982053,0.000045240176,0.000010335377,0.000045712204,0.00004991296,0.000028216024],"domain_scores_gemma":[0.9990388,0.00061231514,0.000119089935,0.00005495632,0.00012924304,0.000045594512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001084961,0.0010584303,0.0008025871,0.00041662794,0.00035855814,0.0011076803,0.002094522,0.0021609182,0.0027462612],"category_scores_gemma":[0.0027529371,0.0009970109,0.0009300241,0.000518078,0.0007345711,0.001604222,0.0008881861,0.0015842143,0.0007778891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010845885,0.000012752744,0.0000975044,0.000018925322,0.000009522032,0.000017267235,0.000012792429,0.99204564,0.00057969725,0.0039892443,0.00012928079,0.0030765608],"study_design_scores_gemma":[0.000001223895,0.00000167772,0.0000094326715,7.509821e-7,0.0000011831215,0.000001568826,7.1784035e-7,0.9991001,0.00010448531,0.00071176427,0.00006601777,0.0000011304982],"about_ca_topic_score_codex":0.008742962,"about_ca_topic_score_gemma":0.009416133,"teacher_disagreement_score":0.008742962,"about_ca_system_score_codex":0.001240608,"about_ca_system_score_gemma":0.0013211067,"threshold_uncertainty_score":0.017384112},"labels":[],"label_agreement":null},{"id":"W1985257459","doi":"10.1016/j.cam.2004.12.039","title":"On the construction of a nonlinear recursive predictor","year":2005,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Initialization; Nonlinear system; Mathematics; Series (stratigraphy); Artificial neural network; Set (abstract data type); Scaling; Time series; Algorithm; Term (time); Applied mathematics; Mathematical optimization; Computer science; Machine learning; Statistics","score_opus":0.012447213790597888,"score_gpt":0.23254851878006583,"score_spread":0.22010130498946795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985257459","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045347987,0.00006761162,0.9931757,0.00006803748,0.00004594804,0.000016204749,0.000018977586,0.00024837995,0.0018244834],"genre_scores_gemma":[0.20810011,0.0003829082,0.7805048,0.00012571174,0.00007995413,0.000112399306,0.0001615657,0.00021870241,0.010313934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99961805,0.00012414489,0.000022406697,0.00007399738,0.00012142646,0.000039939034],"domain_scores_gemma":[0.9992005,0.00046058212,0.000037237925,0.0001126291,0.00014945655,0.000039516253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006824665,0.00037386903,0.0006580543,0.00036744887,0.00061109447,0.0007596778,0.0007497271,0.00087324996,0.0044701113],"category_scores_gemma":[0.0033066159,0.0004848186,0.00046541134,0.00043068867,0.00081180444,0.00084407604,0.0017389719,0.0012986585,0.0016279139],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024961607,0.00011492934,0.001331482,0.00021458781,0.000057420926,0.00047986791,0.00044078898,0.17001596,0.042592227,0.40307003,0.0046493346,0.37678367],"study_design_scores_gemma":[0.000029312963,0.0001226018,0.00026443528,0.000041061176,0.000031070187,0.00018563769,0.000033970213,0.90232366,0.009003714,0.07807714,0.0098601915,0.000027295106],"about_ca_topic_score_codex":0.0013442156,"about_ca_topic_score_gemma":0.0022726334,"teacher_disagreement_score":0.0044701113,"about_ca_system_score_codex":0.00028274348,"about_ca_system_score_gemma":0.0007489206,"threshold_uncertainty_score":0.0149540305},"labels":[],"label_agreement":null},{"id":"W1985565672","doi":"10.1142/s0129183101002462","title":"STORING PATTERNS IN HAMILTONIAN NEURAL NETWORKS","year":2001,"lang":"en","type":"article","venue":"International Journal of Modern Physics C","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Hamiltonian (control theory); Artificial neural network; Computer science; Covariant Hamiltonian field theory; Quantum; Quantization (signal processing); Excited state; Hamiltonian system; Artificial intelligence; Mathematics; Physics; Algorithm; Quantum mechanics; Classical mechanics; Mathematical optimization","score_opus":0.021060052091206462,"score_gpt":0.27153982819068234,"score_spread":0.25047977609947586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985565672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28468844,0.0010324082,0.6993963,0.0011898213,0.00017818842,0.00004732396,0.00018585596,0.00058936054,0.01269239],"genre_scores_gemma":[0.9319607,0.0004805896,0.062052093,0.00017191833,0.000061696366,0.000057378595,0.00009005854,0.000053836186,0.0050717294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987686,0.000036138408,0.000008292102,0.000024150839,0.000033745597,0.000020778642],"domain_scores_gemma":[0.9995627,0.00023003852,0.000054861524,0.00006949122,0.000049729726,0.00003326761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033538035,0.00015955872,0.0003090267,0.0003381478,0.0003401755,0.0006458222,0.0006390355,0.0006092501,0.002777308],"category_scores_gemma":[0.00203602,0.0002402293,0.00021163176,0.00043931033,0.0009392138,0.0019855525,0.00068035786,0.0005111298,0.00024961637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015289089,0.000064939166,0.00091325934,0.00015035986,0.000050141964,0.00021506609,0.00025974552,0.29485506,0.0167501,0.58813196,0.002648318,0.09580819],"study_design_scores_gemma":[0.000016769593,0.000020624431,0.0001598327,0.000008983395,0.0000059964677,0.000028141585,0.000021143087,0.6906401,0.001593459,0.30665722,0.00083481136,0.000012887966],"about_ca_topic_score_codex":0.00079211255,"about_ca_topic_score_gemma":0.0009998336,"teacher_disagreement_score":0.002777308,"about_ca_system_score_codex":0.00030749643,"about_ca_system_score_gemma":0.00018226057,"threshold_uncertainty_score":0.009290993},"labels":[],"label_agreement":null},{"id":"W1985730965","doi":"10.1139/l00-053","title":"Drinking water quality and treatment: the use of artificial neural networks","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Water quality; Water treatment; Process (computing); Automation; Computer science; Engineering; Artificial intelligence; Environmental engineering; Ecology","score_opus":0.048147720758612046,"score_gpt":0.23123539429062773,"score_spread":0.1830876735320157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985730965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05398882,0.040516485,0.8546213,0.0072395136,0.000574426,0.00010998571,0.00029236585,0.00073525374,0.041921794],"genre_scores_gemma":[0.8418819,0.032236967,0.11213256,0.0008130621,0.00044576332,0.00011331826,0.00023402537,0.00007057602,0.01207174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959403,0.0001346737,0.000022177539,0.00006655772,0.00015904057,0.000023491935],"domain_scores_gemma":[0.9997371,0.00011864212,0.00004967858,0.00001697969,0.000068050496,0.000009497141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006789257,0.00070622447,0.00044189297,0.0005491716,0.00025766547,0.0014529811,0.0007314131,0.0018201129,0.0011342679],"category_scores_gemma":[0.0016922478,0.00023594114,0.00032057535,0.0011647502,0.00075409986,0.0012021855,0.00064304576,0.0008551356,0.0002018036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013019069,0.00007663522,0.0029537114,0.0005838165,0.00015229298,0.00012958582,0.00010520735,0.66172093,0.005069435,0.028291155,0.003483391,0.29730356],"study_design_scores_gemma":[0.000012351674,0.000058315272,0.0016432274,0.00013011754,0.000051560317,0.00005634243,0.000051411396,0.93989766,0.0034437054,0.041586135,0.013028891,0.000040341376],"about_ca_topic_score_codex":0.015407094,"about_ca_topic_score_gemma":0.012557295,"teacher_disagreement_score":0.015407094,"about_ca_system_score_codex":0.0012347609,"about_ca_system_score_gemma":0.0008125342,"threshold_uncertainty_score":0.03063482},"labels":[],"label_agreement":null},{"id":"W1986355477","doi":"10.1063/1.4776513","title":"Short-term synaptic plasticity and heterogeneity in neural systems","year":2013,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Junta de Andalucía; Ministerio de Ciencia y Tecnología","keywords":"Icon; Citation; Computer science; Information retrieval; Download; Term (time); World Wide Web; Publishing; Art","score_opus":0.029795815593646545,"score_gpt":0.2485202576574118,"score_spread":0.21872444206376526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986355477","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47289044,0.037284814,0.24032968,0.0140684545,0.0009379939,0.00011800805,0.002379556,0.0020347917,0.22995634],"genre_scores_gemma":[0.97976625,0.0034681868,0.0054296544,0.00021921423,0.0002237014,0.000037039772,0.00028987433,0.000085385676,0.0104806805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998265,0.000030276118,0.000010657756,0.000036533136,0.00007389115,0.000022231025],"domain_scores_gemma":[0.99947935,0.0001960709,0.00007656639,0.0000853095,0.00009012644,0.000072638315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003611114,0.00011254522,0.00028615783,0.0006463881,0.0002806449,0.0012142863,0.000404052,0.00043086725,0.009474568],"category_scores_gemma":[0.0018243846,0.00013053099,0.00020036928,0.00035744125,0.00073830254,0.0020493877,0.0006751759,0.0005659453,0.0010032377],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017254229,0.00006415944,0.009634009,0.0005574367,0.00021047184,0.0008787149,0.0004106854,0.037794735,0.039011095,0.6729049,0.03441828,0.203943],"study_design_scores_gemma":[0.000023135823,0.00009686539,0.01871246,0.0001131519,0.000048586728,0.000828813,0.0002767788,0.099320315,0.006461137,0.84534746,0.028704992,0.00006618554],"about_ca_topic_score_codex":0.00065689004,"about_ca_topic_score_gemma":0.0006897972,"teacher_disagreement_score":0.009474568,"about_ca_system_score_codex":0.00049870624,"about_ca_system_score_gemma":0.00025928332,"threshold_uncertainty_score":0.031695604},"labels":[],"label_agreement":null},{"id":"W1986503901","doi":"10.4103/0976-8580.99293","title":"Wavelet Entropy Measure to Quantify Information Transmission in Human Cerebral Cortex","year":2012,"lang":"en","type":"article","venue":"Journal of Engineering & Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Wavelet; Information transmission; Measure (data warehouse); Entropy (arrow of time); Transfer entropy; Artificial intelligence; Neuroscience; Econometrics; Biological system; Pattern recognition (psychology); Statistical physics; Statistics; Computer science; Mathematics; Psychology; Data mining; Physics; Biology; Principle of maximum entropy; Thermodynamics","score_opus":0.008629982201796964,"score_gpt":0.2320741288861207,"score_spread":0.22344414668432372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986503901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28940234,0.0029787114,0.6994665,0.00035921985,0.0002056148,0.00009004414,0.0006933057,0.00022346666,0.006580822],"genre_scores_gemma":[0.9463706,0.0008540963,0.050736096,0.000055618755,0.00017314858,0.000067929315,0.0003700968,0.000054530352,0.0013179411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972934,0.000069420275,0.000024153147,0.000039240265,0.00011649024,0.000021378411],"domain_scores_gemma":[0.9992586,0.00047923432,0.00008338946,0.000061987404,0.00008381042,0.00003293665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009223967,0.00035016457,0.00029074043,0.0019006352,0.00017188511,0.00060676987,0.00028781922,0.00032353643,0.0012496975],"category_scores_gemma":[0.0024383178,0.00009210423,0.00036651985,0.0011344759,0.0005252989,0.0010907053,0.0005015877,0.00040448536,0.00013864684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009382745,0.00036401275,0.019984914,0.00097183615,0.0006175013,0.001170481,0.0009359468,0.23169933,0.19735463,0.20424417,0.0067806263,0.33493823],"study_design_scores_gemma":[0.000035064575,0.00043784454,0.061645754,0.000084479725,0.00015062366,0.0010775506,0.0002650338,0.8141898,0.024800481,0.09257954,0.004633566,0.00010033575],"about_ca_topic_score_codex":0.00029420893,"about_ca_topic_score_gemma":0.00016892861,"teacher_disagreement_score":0.0019006352,"about_ca_system_score_codex":0.00035031734,"about_ca_system_score_gemma":0.00019881729,"threshold_uncertainty_score":0.0048781633},"labels":[],"label_agreement":null},{"id":"W1987745123","doi":"10.3166/ria.15.373-391","title":"De l'utilisation d'OBD pour la sélection de variables dans les perceptrons multicouches","year":2001,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.07620762452562653,"score_gpt":0.30058081514385954,"score_spread":0.22437319061823302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987745123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03301407,0.0023446688,0.9567559,0.001008239,0.0007716244,0.00007345963,0.00030999654,0.0010282071,0.0046937917],"genre_scores_gemma":[0.3265044,0.0022798995,0.6369042,0.00088886713,0.0006840739,0.0003399255,0.0008121181,0.0004903569,0.031096175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979691,0.00089426775,0.000117996125,0.00034248238,0.0005198228,0.00015640148],"domain_scores_gemma":[0.9942181,0.0040946165,0.00010635745,0.00054581434,0.0009001515,0.00013494657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041205958,0.0010183206,0.001482215,0.0010065775,0.00093371543,0.0030472323,0.0010473551,0.002116814,0.008912268],"category_scores_gemma":[0.011032648,0.00046550052,0.0016496675,0.00097450893,0.0011391508,0.0013887509,0.0011741342,0.0025595801,0.0024128694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016034752,0.00023168832,0.0056131096,0.0006331406,0.00032660828,0.00041689663,0.00023491622,0.30517557,0.03102994,0.05283174,0.0072409995,0.5946619],"study_design_scores_gemma":[0.00005594956,0.00012473889,0.0014060213,0.00007472747,0.000045100376,0.00016158755,0.000047912454,0.966285,0.01557389,0.008550864,0.00764951,0.000024790557],"about_ca_topic_score_codex":0.013124304,"about_ca_topic_score_gemma":0.011172851,"teacher_disagreement_score":0.013124304,"about_ca_system_score_codex":0.0011140953,"about_ca_system_score_gemma":0.0012685233,"threshold_uncertainty_score":0.029814482},"labels":[],"label_agreement":null},{"id":"W1987935667","doi":"10.1109/adprl.2014.7010623","title":"Cognitive control in cognitive dynamic systems: A new way of thinking inspired by the brain","year":2014,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Perception; Cognition; Computer science; Coding (social sciences); Set (abstract data type); Bayesian probability; Control (management); Controller (irrigation); Artificial intelligence; State (computer science); Information processing; Cognitive psychology; Machine learning; Psychology; Algorithm; Mathematics; Neuroscience","score_opus":0.011747531200571638,"score_gpt":0.25318075312354715,"score_spread":0.2414332219229755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987935667","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044205305,0.012799967,0.95173645,0.0090502165,0.00066425523,0.00004994678,0.00006744458,0.00021805227,0.020993194],"genre_scores_gemma":[0.40243784,0.021461194,0.5512673,0.006077939,0.00502747,0.00054687815,0.00010443042,0.00020356207,0.012873349],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999343,0.0002818734,0.000039101848,0.00015913261,0.00012320728,0.00005377576],"domain_scores_gemma":[0.9991478,0.00054991787,0.00005713122,0.00011315888,0.00008088443,0.000051064955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013705356,0.00098229,0.00082827045,0.0015668441,0.0006365612,0.0046144277,0.0016791088,0.0021992633,0.0024441436],"category_scores_gemma":[0.0015117265,0.00031411793,0.0011501713,0.0009551584,0.008735312,0.006033119,0.0016097326,0.003362489,0.0005427967],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023153938,0.000019713521,0.00030494737,0.00025658775,0.000049462335,0.000097407006,0.0005896702,0.0070797354,0.0014832157,0.9657916,0.0014457749,0.022858722],"study_design_scores_gemma":[0.000014976138,0.000046696798,0.00021370823,0.000079524514,0.000019127154,0.00011586197,0.00012135631,0.027794154,0.0006067578,0.9516515,0.019304866,0.00003141282],"about_ca_topic_score_codex":0.0012647755,"about_ca_topic_score_gemma":0.0009238631,"teacher_disagreement_score":0.0046144277,"about_ca_system_score_codex":0.0011286133,"about_ca_system_score_gemma":0.0008540404,"threshold_uncertainty_score":0.0081887245},"labels":[],"label_agreement":null},{"id":"W1988678830","doi":"10.1167/11.11.982","title":"A cascade-correlation model of bistable perception","year":2011,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Stimulus (psychology); Bistability; Psychology; Correlation; Positive feedback; Cognitive psychology; Perception; Cascade; Binocular rivalry; Neuroscience; Developmental psychology; Mathematics; Visual perception; Physics; Chemistry","score_opus":0.037762479868780265,"score_gpt":0.2742616730698138,"score_spread":0.23649919320103355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988678830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5126769,0.0011429677,0.4506781,0.001298461,0.0001573865,0.0001085988,0.00051515456,0.000657083,0.032765303],"genre_scores_gemma":[0.9854629,0.0002527314,0.008809982,0.000057259287,0.000021126558,0.000053277847,0.0000598947,0.000018883062,0.0052639325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985886,0.000021969425,0.0000051721295,0.000049737824,0.000030300553,0.000034051813],"domain_scores_gemma":[0.999749,0.000085336746,0.00004377395,0.000018925553,0.000050923187,0.000052054686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038213743,0.00048774225,0.0006281982,0.00048456818,0.00037325683,0.00056811085,0.0014198242,0.0009602019,0.0037297264],"category_scores_gemma":[0.0009003875,0.00037880742,0.00064522785,0.00034460422,0.0006673323,0.0012587785,0.0005059545,0.0007748112,0.00033132444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018904387,0.000109615314,0.0030150195,0.00012607168,0.00009029687,0.00061922707,0.00026657726,0.82180345,0.028732454,0.12704855,0.002118324,0.01588125],"study_design_scores_gemma":[0.000008613411,0.00001896792,0.00043834993,0.0000028178165,0.0000075676608,0.00004418771,0.000004173659,0.9908501,0.00026607153,0.008230117,0.00012209882,0.000007026737],"about_ca_topic_score_codex":0.009072927,"about_ca_topic_score_gemma":0.0057331244,"teacher_disagreement_score":0.009072927,"about_ca_system_score_codex":0.0009336278,"about_ca_system_score_gemma":0.00055216695,"threshold_uncertainty_score":0.01804024},"labels":[],"label_agreement":null},{"id":"W1989100100","doi":"10.1016/j.ejc.2010.11.004","title":"Uniform approximation and Bernstein polynomials with coefficients in the unit interval","year":2011,"lang":"en","type":"article","venue":"European Journal of Combinatorics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematics; Bernstein polynomial; Interval (graph theory); Unit interval; Polynomial; Degree (music); Interval arithmetic; Discrete mathematics; Combinatorics; Applied mathematics; Mathematical analysis","score_opus":0.034299187845906305,"score_gpt":0.22523143031778817,"score_spread":0.19093224247188187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989100100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15828441,0.003370543,0.79184514,0.0017891098,0.00032898056,0.00004275404,0.00026931497,0.00020730191,0.043862425],"genre_scores_gemma":[0.896226,0.0030479233,0.080448166,0.00030020764,0.00050559215,0.00008570053,0.00034619754,0.00017473122,0.018865513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997951,0.00083438226,0.000057816585,0.00026242647,0.0005470596,0.0003473673],"domain_scores_gemma":[0.99214995,0.005228259,0.00063679693,0.0004873334,0.0009666591,0.00053096906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029431107,0.00080514204,0.0011904235,0.0017019316,0.0007394359,0.0025431714,0.0020145483,0.0011709426,0.006930711],"category_scores_gemma":[0.02339988,0.00048451917,0.00077684683,0.0019489743,0.002745283,0.004034518,0.0024818752,0.0035246138,0.0011010388],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100493766,0.0000186236,0.00031046916,0.00005820305,0.0000121143785,0.000048215457,0.0001210899,0.0114268875,0.00078974356,0.9752935,0.0011087208,0.01071189],"study_design_scores_gemma":[0.00003271937,0.000037665224,0.00032783052,0.000038243918,0.000016086793,0.000121998426,0.00009787091,0.135889,0.0005689522,0.86023235,0.002608357,0.000028930972],"about_ca_topic_score_codex":0.0029919767,"about_ca_topic_score_gemma":0.0018700449,"teacher_disagreement_score":0.006930711,"about_ca_system_score_codex":0.0017269817,"about_ca_system_score_gemma":0.0009405526,"threshold_uncertainty_score":0.023185551},"labels":[],"label_agreement":null},{"id":"W1989130143","doi":"10.1016/s0925-2312(00)00342-8","title":"Granular neural networks","year":2001,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":141,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; University of Alberta","funders":"","keywords":"Granular computing; Computer science; Artificial neural network; Generalization; Rough set; Class (philosophy); Probabilistic logic; Artificial intelligence; Set (abstract data type); Information processing; Types of artificial neural networks; Machine learning; Theoretical computer science; Time delay neural network; Mathematics","score_opus":0.015082755026883811,"score_gpt":0.23787360438950614,"score_spread":0.22279084936262233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989130143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08623676,0.008212171,0.81829876,0.0024897992,0.0021331322,0.00014081229,0.0008023308,0.0025491652,0.07913701],"genre_scores_gemma":[0.86914414,0.0029511775,0.09049342,0.00034964725,0.0005674837,0.00008345692,0.00066040416,0.00020521639,0.03554513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973005,0.000038869443,0.000020101856,0.0000742674,0.000099157536,0.000037534195],"domain_scores_gemma":[0.9993278,0.00017921443,0.00008035108,0.0002097434,0.0001279844,0.0000750642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046567316,0.00047135202,0.0009314473,0.0008477653,0.0005800983,0.002350701,0.00068986067,0.0010015692,0.00912187],"category_scores_gemma":[0.0029754413,0.0003642923,0.000386999,0.0010290704,0.0009440644,0.0023345372,0.0012530736,0.001287419,0.0015188616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043076515,0.000078018944,0.0016049714,0.00036614636,0.0001381965,0.00026111273,0.00007761848,0.15437312,0.008988724,0.54440147,0.015749287,0.2735305],"study_design_scores_gemma":[0.000031255106,0.00003662476,0.0010026508,0.00004472316,0.00004706068,0.00012483897,0.000030774503,0.6488296,0.003451778,0.33501393,0.011359015,0.000027739548],"about_ca_topic_score_codex":0.001597707,"about_ca_topic_score_gemma":0.0013610836,"teacher_disagreement_score":0.00912187,"about_ca_system_score_codex":0.0007467851,"about_ca_system_score_gemma":0.00045170335,"threshold_uncertainty_score":0.03051567},"labels":[],"label_agreement":null},{"id":"W1989144556","doi":"10.1016/j.neucom.2012.07.051","title":"Controlling deterministic output variability in a feature extracting chaotic BAM","year":2013,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Chaotic; Feature (linguistics); Attractor; Computer science; Noise (video); Property (philosophy); Trajectory; Salient; Pattern recognition (psychology); Fixed point; Artificial intelligence; Control theory (sociology); Simple (philosophy); Point (geometry); Algorithm; Control (management); Mathematics; Image (mathematics); Physics","score_opus":0.015109885731958,"score_gpt":0.2404814259736598,"score_spread":0.22537154024170178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989144556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45008522,0.000109432614,0.54588497,0.00016015586,0.000043306933,0.000023238392,0.000037413003,0.0004258142,0.003230497],"genre_scores_gemma":[0.99017566,0.000016173639,0.0093736,0.000008174918,0.0000035114376,0.000007395177,0.0000049816194,0.000014372516,0.00039604108],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999944,0.00001207665,0.00000361075,0.000014965243,0.000015225837,0.000010090899],"domain_scores_gemma":[0.9996718,0.00019889566,0.000044592896,0.000033469354,0.000032454398,0.000018848159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022367555,0.00017680397,0.00024285862,0.00010354124,0.0002322515,0.00036620133,0.0003155488,0.00025335944,0.00062947115],"category_scores_gemma":[0.00096511893,0.0001439308,0.00013789255,0.00014280692,0.00023651886,0.00030866367,0.00032523545,0.00026425222,0.00011334025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007705368,0.0000911602,0.0033840777,0.00016506822,0.000059634272,0.0004733622,0.00030251124,0.43356067,0.47678864,0.01495213,0.0004563769,0.068995915],"study_design_scores_gemma":[0.000006925523,0.00004337692,0.00045739615,0.0000034922027,0.000006947718,0.0000372127,0.000007288642,0.98235047,0.015488047,0.0014296152,0.00016464626,0.0000046284426],"about_ca_topic_score_codex":0.00045811696,"about_ca_topic_score_gemma":0.0004824994,"teacher_disagreement_score":0.00062947115,"about_ca_system_score_codex":0.00026549416,"about_ca_system_score_gemma":0.00014579763,"threshold_uncertainty_score":0.002105832},"labels":[],"label_agreement":null},{"id":"W1989880408","doi":"10.1145/1068009.1068347","title":"GAMM","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Computer science; Operator (biology); Image (mathematics); Process (computing); Artificial intelligence; Domain (mathematical analysis); Interpretation (philosophy); Machine learning; Principal (computer security); Contextual image classification; Mathematics; Programming language; Computer security","score_opus":0.009604047564218033,"score_gpt":0.23045507278837848,"score_spread":0.22085102522416045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989880408","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015438467,0.0063004773,0.3320423,0.005074834,0.0028078225,0.00061698235,0.0053231963,0.015095025,0.61730087],"genre_scores_gemma":[0.18609,0.0068516806,0.21979417,0.0038337363,0.00091858994,0.00059480424,0.014484065,0.0029263932,0.5645066],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989477,0.00019001217,0.00006949464,0.00035454676,0.0003426798,0.000095554686],"domain_scores_gemma":[0.9988226,0.00018697085,0.00007434137,0.0003855741,0.0004355373,0.0000949965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011691154,0.0010362624,0.0005951143,0.0014930725,0.0012006355,0.003034915,0.0020940944,0.0017549729,0.1366615],"category_scores_gemma":[0.003434637,0.00042036577,0.000615853,0.0012292553,0.000681819,0.0026370035,0.0024602602,0.0011525364,0.07501228],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026090327,0.00010781943,0.0020554783,0.0006415084,0.000055292065,0.00033154996,0.00029798274,0.0058057103,0.010025434,0.10755761,0.15100881,0.72185194],"study_design_scores_gemma":[0.000027448074,0.0000696239,0.0012552176,0.00012232363,0.000027924623,0.00051666127,0.000111110545,0.011207896,0.0063828854,0.027085254,0.9531593,0.00003441371],"about_ca_topic_score_codex":0.002054069,"about_ca_topic_score_gemma":0.0031016704,"teacher_disagreement_score":0.1366615,"about_ca_system_score_codex":0.0009159475,"about_ca_system_score_gemma":0.0011207915,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W1990202118","doi":"10.1109/itng.2010.246","title":"Tuning Neural Networks by Both Connectivity and Size","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University; Queen's University","funders":"","keywords":"Computer science; Artificial neural network; Simple (philosophy); Weighting; Process (computing); Artificial intelligence; Function (biology)","score_opus":0.0077861529643203815,"score_gpt":0.22545968975231476,"score_spread":0.21767353678799437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990202118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12299573,0.000464493,0.8705621,0.00029817043,0.00008426469,0.00007804984,0.000022903101,0.0010187082,0.0044756248],"genre_scores_gemma":[0.81313837,0.00023350304,0.18474124,0.00011842222,0.0000703035,0.0001272641,0.000051184503,0.00023209571,0.0012875523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995401,0.00010276479,0.00003139049,0.0001167176,0.00017355067,0.00003548044],"domain_scores_gemma":[0.99815196,0.0011776371,0.00022297482,0.00019192084,0.00020530768,0.00005025129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092723523,0.00068362156,0.00045816638,0.00058233197,0.00032357004,0.0007114385,0.00138088,0.0008079304,0.0012851427],"category_scores_gemma":[0.0071111796,0.00033416707,0.00032842642,0.00029053123,0.00045891813,0.0017783713,0.0007704279,0.00064042595,0.00024530632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017754336,0.00016399201,0.0050783614,0.00015819099,0.0001481266,0.00014468572,0.00011080688,0.5640346,0.077401266,0.010624973,0.00096619234,0.34099132],"study_design_scores_gemma":[0.000036802787,0.00016954262,0.001702617,0.000018178054,0.00004996976,0.0001629064,0.000013961811,0.97679895,0.014081968,0.00511652,0.0018295059,0.000019101013],"about_ca_topic_score_codex":0.00089742534,"about_ca_topic_score_gemma":0.0016864103,"teacher_disagreement_score":0.00138088,"about_ca_system_score_codex":0.0005107348,"about_ca_system_score_gemma":0.00028694107,"threshold_uncertainty_score":0.0049037933},"labels":[],"label_agreement":null},{"id":"W1990634878","doi":"10.1239/jap/996986649","title":"Convergence in distribution of the multi-dimensional Kohonen algorithm","year":2001,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Mathematics; Probability measure; Invariant measure; Measure (data warehouse); Markov chain; Rate of convergence; Probability distribution; Self-organizing map; Invariant (physics); Algorithm; Markov process; Applied mathematics; Cluster analysis; Discrete mathematics; Mathematical analysis; Statistics; Ergodic theory; Computer science; Data mining","score_opus":0.0191232212571621,"score_gpt":0.24076750282997614,"score_spread":0.22164428157281404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990634878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108673304,0.0006531993,0.8849975,0.0005934495,0.000062340536,0.000044093977,0.00003626885,0.00015198364,0.004787881],"genre_scores_gemma":[0.8978664,0.00054497435,0.0966733,0.00012349278,0.000040579373,0.000099099474,0.00005606221,0.00007392911,0.0045221825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995478,0.00013700007,0.000022070892,0.00008374023,0.00014475817,0.000064653184],"domain_scores_gemma":[0.9973937,0.0017396975,0.00016916278,0.00016227041,0.00041001124,0.00012518113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025585443,0.000325618,0.0005412231,0.0007532699,0.00045131365,0.0013311462,0.0011863856,0.0011347223,0.0014591049],"category_scores_gemma":[0.010476746,0.00033018328,0.0005152502,0.00046226324,0.0014453207,0.0022428338,0.001311335,0.0009700805,0.00022247978],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011209504,0.000042847794,0.002444896,0.00010544665,0.000038005022,0.0001591242,0.00022069759,0.6631552,0.004205485,0.3051414,0.0005218049,0.023853045],"study_design_scores_gemma":[0.0000034984598,0.000008835134,0.00012369186,0.0000062813215,0.0000019621716,0.000014957577,0.000013341279,0.9767586,0.00036898302,0.022549924,0.00014405353,0.0000058677474],"about_ca_topic_score_codex":0.0031834412,"about_ca_topic_score_gemma":0.0016415383,"teacher_disagreement_score":0.0031834412,"about_ca_system_score_codex":0.0011967529,"about_ca_system_score_gemma":0.0008883744,"threshold_uncertainty_score":0.013531029},"labels":[],"label_agreement":null},{"id":"W1990838225","doi":"10.1117/12.421169","title":"<title>Moving image compression and generalization capability of constructive neural networks</title>","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Generalization; Constructive; Artificial intelligence; Artificial neural network; Similarity (geometry); Computer vision; Data compression; Image compression; Pattern recognition (psychology); Feed forward; Image (mathematics); Backpropagation; Image processing; Mathematics; Engineering","score_opus":0.008988659837501913,"score_gpt":0.22355547845957918,"score_spread":0.21456681862207727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990838225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3105111,0.0031180268,0.66281015,0.00068615004,0.00029587722,0.000077496254,0.00019555936,0.0010861079,0.02121954],"genre_scores_gemma":[0.9134812,0.0014988704,0.079342715,0.00012522687,0.00012173075,0.000050919734,0.00031019826,0.000102794016,0.004966344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997224,0.00006553888,0.000016968934,0.00005410427,0.000113740636,0.0000273417],"domain_scores_gemma":[0.9982305,0.0009314127,0.00020080426,0.00017919802,0.0004250155,0.000033100663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005982663,0.0004809636,0.0003195108,0.00041100543,0.00015251245,0.00040749967,0.00048113446,0.00035686142,0.00254871],"category_scores_gemma":[0.0030848254,0.00009764973,0.000233427,0.00039431016,0.00058652775,0.0007861471,0.0003390921,0.00046893468,0.00047612135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046165806,0.000113354814,0.0024322032,0.00028875167,0.00006465017,0.00041724357,0.00012837937,0.28035644,0.1243106,0.017157787,0.004497562,0.5697714],"study_design_scores_gemma":[0.000004368812,0.00016696726,0.001942813,0.000020643076,0.000015612019,0.0001580787,0.000018362853,0.9644269,0.02874339,0.0031725897,0.0013131327,0.000017218814],"about_ca_topic_score_codex":0.0016802584,"about_ca_topic_score_gemma":0.0012882511,"teacher_disagreement_score":0.00254871,"about_ca_system_score_codex":0.00032623668,"about_ca_system_score_gemma":0.00013821824,"threshold_uncertainty_score":0.008526325},"labels":[],"label_agreement":null},{"id":"W1990989764","doi":"10.1007/s11634-012-0108-0","title":"Adaptation of interval PCA to symbolic histogram variables","year":2012,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hôpital Saint-François d'Assise","funders":"","keywords":"Principal component analysis; Histogram; Symbolic data analysis; Mathematics; Projection (relational algebra); Representation (politics); Pattern recognition (psychology); Interval (graph theory); Artificial intelligence; Algorithm; Computer science; Statistics; Combinatorics","score_opus":0.05461420121741699,"score_gpt":0.32804995560749833,"score_spread":0.27343575439008133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990989764","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013534733,0.00019596035,0.98303425,0.00005738418,0.00011581271,0.000034080615,0.000108799395,0.0009170456,0.002002024],"genre_scores_gemma":[0.42144266,0.00080925366,0.5707063,0.00010829057,0.00035971153,0.00019736965,0.00089929684,0.0006372371,0.004839857],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994085,0.0001629049,0.000027716856,0.0001427126,0.00020585668,0.000052230112],"domain_scores_gemma":[0.9989171,0.0002937554,0.0000479238,0.00033337137,0.0003584502,0.00004949838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006543301,0.00049110566,0.0005849643,0.0008448911,0.00027057383,0.00089582347,0.0010159827,0.00030658575,0.0038784165],"category_scores_gemma":[0.0043693944,0.00022610494,0.0006408197,0.0017222671,0.00044854856,0.0010445461,0.0011893638,0.0013098664,0.0013008825],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021361512,0.00014992882,0.0014141718,0.00011492261,0.0000789679,0.00007266406,0.0000942049,0.07943669,0.019132,0.018688194,0.0039664265,0.8766381],"study_design_scores_gemma":[0.0000147521005,0.00006766618,0.0024634246,0.0000119639,0.000027217518,0.00009473828,0.000026180775,0.97251046,0.004984131,0.015806723,0.003967138,0.000025638401],"about_ca_topic_score_codex":0.0018155728,"about_ca_topic_score_gemma":0.0016554925,"teacher_disagreement_score":0.0038784165,"about_ca_system_score_codex":0.00022858163,"about_ca_system_score_gemma":0.00057505164,"threshold_uncertainty_score":0.01297456},"labels":[],"label_agreement":null},{"id":"W1991109541","doi":"10.1142/s1469026802000476","title":"STABILITY OF INFORMATION GRANULATION AND INFORMATION GRANULES","year":2002,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; University of Alberta","funders":"","keywords":"Computer science; Stability (learning theory); Granulation; Inference; Artificial intelligence; Machine learning","score_opus":0.022794193225933925,"score_gpt":0.26571925445287364,"score_spread":0.24292506122693971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991109541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08273781,0.0047235996,0.88885075,0.0022559275,0.00026878028,0.000307128,0.0006256139,0.0005653044,0.01966508],"genre_scores_gemma":[0.7835366,0.0018671592,0.20693493,0.00051984977,0.0005403721,0.00074301416,0.0006645955,0.00023991859,0.0049535837],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.993292,0.0018180367,0.000784932,0.0014928058,0.0021513067,0.00046098797],"domain_scores_gemma":[0.95836955,0.026578793,0.005180599,0.0048768655,0.0041170553,0.00087698764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062658363,0.0008018712,0.0015241106,0.0041291537,0.0015928615,0.006809889,0.0015553478,0.0018187116,0.003973155],"category_scores_gemma":[0.047884006,0.000616881,0.0016290722,0.0031897193,0.0068390667,0.011298253,0.003925112,0.001970088,0.00063725776],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018664378,0.000031240637,0.0016463192,0.00028722326,0.00008923237,0.0002769305,0.0009076405,0.017471965,0.0029824981,0.94630426,0.0011862608,0.028629744],"study_design_scores_gemma":[0.000030285853,0.00007418651,0.0012889882,0.000055699587,0.000034474604,0.00022037284,0.00020072256,0.046628855,0.0021346938,0.9428076,0.0064723943,0.00005176372],"about_ca_topic_score_codex":0.0008038071,"about_ca_topic_score_gemma":0.0003222266,"teacher_disagreement_score":0.006809889,"about_ca_system_score_codex":0.0023985761,"about_ca_system_score_gemma":0.001023211,"threshold_uncertainty_score":0.03313732},"labels":[],"label_agreement":null},{"id":"W1991481685","doi":"10.3166/jesa.37.49-81","title":"Réseaux de neurones récurrents à fonctions de base radiales. Application à la surveillance dynamique","year":2003,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Radial basis function; Computer science; Function (biology); Artificial neural network; Base (topology); Basis (linear algebra); Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.021881534861242204,"score_gpt":0.2713258871019421,"score_spread":0.2494443522406999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991481685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6119988,0.0017991372,0.37587324,0.00056304445,0.00024270387,0.000059834816,0.00018301283,0.0016113273,0.007668935],"genre_scores_gemma":[0.9522613,0.0002959129,0.042624693,0.00003766627,0.00003646557,0.000027478947,0.000073936615,0.00008768241,0.004554944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998877,0.000026902575,0.0000058300093,0.000021842246,0.00003707895,0.000020631207],"domain_scores_gemma":[0.9989372,0.00056903413,0.000070860704,0.00006336781,0.00028840054,0.000071169605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078178575,0.000590445,0.00041402908,0.00038937456,0.00034213666,0.0005811647,0.00053414167,0.00080958195,0.0024127525],"category_scores_gemma":[0.0035537381,0.00020902355,0.00041774384,0.0002654357,0.00038774955,0.0005430736,0.0002509376,0.0005697789,0.0003429814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011585354,0.00019888407,0.002328138,0.00028828686,0.00015551671,0.0007845609,0.0002478648,0.60024446,0.18692587,0.013607235,0.0029854681,0.19107522],"study_design_scores_gemma":[0.000013546244,0.000085392945,0.0007604122,0.0000080704385,0.000016121301,0.0001173037,0.000024322273,0.9822166,0.014857647,0.0014289372,0.00046122237,0.000010444731],"about_ca_topic_score_codex":0.006010013,"about_ca_topic_score_gemma":0.0055927276,"teacher_disagreement_score":0.006010013,"about_ca_system_score_codex":0.00067904574,"about_ca_system_score_gemma":0.0002574604,"threshold_uncertainty_score":0.011950076},"labels":[],"label_agreement":null},{"id":"W1991744168","doi":"10.1016/j.memsci.2007.03.011","title":"Neural networks modeling of hollow fiber membrane processes","year":2007,"lang":"en","type":"article","venue":"Journal of Membrane Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Ottawa","keywords":"Artificial neural network; Radial basis function; Isotropy; Computation; Computer science; Regularization (linguistics); Algorithm; Software; Artificial intelligence; Generalization; Early stopping; Data mining; Mathematics","score_opus":0.019960984597846013,"score_gpt":0.26971964819914057,"score_spread":0.24975866360129456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991744168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38076594,0.0013822997,0.5968629,0.0013701555,0.00024751746,0.00006440886,0.00021689276,0.0003623159,0.018727524],"genre_scores_gemma":[0.9753852,0.00042770422,0.014262185,0.00006772597,0.000036857557,0.000049882252,0.00007047351,0.000034739503,0.009665258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987745,0.000031128086,0.0000054775455,0.000029076728,0.00003508835,0.000021744921],"domain_scores_gemma":[0.99956197,0.00022273151,0.000063603366,0.000020257696,0.000102022066,0.000029426577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003815277,0.00037999154,0.000602742,0.0003180344,0.00039393877,0.00065219967,0.0008368053,0.0015591097,0.0017240486],"category_scores_gemma":[0.0014510681,0.00033736115,0.0005016307,0.0003602466,0.0006335329,0.0010842222,0.00047881753,0.00071710115,0.00021840858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020409601,0.000008505822,0.000115981566,0.000010003857,0.0000066421035,0.000020560243,0.0000106757025,0.99241215,0.0012430795,0.004158398,0.000082036364,0.0019114543],"study_design_scores_gemma":[7.8933004e-7,0.0000014164499,0.000023603243,5.835227e-7,7.007089e-7,0.0000013428185,8.948991e-7,0.999303,0.000116915035,0.0005213013,0.000028379185,0.0000010985864],"about_ca_topic_score_codex":0.013570005,"about_ca_topic_score_gemma":0.006624169,"teacher_disagreement_score":0.013570005,"about_ca_system_score_codex":0.0012273279,"about_ca_system_score_gemma":0.0007622946,"threshold_uncertainty_score":0.026982069},"labels":[],"label_agreement":null},{"id":"W1991942570","doi":"10.1162/neco_a_00124","title":"Projective Clustering Using Neural Networks with Adaptive Delay and Signal Transmission Loss","year":2011,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Institute of Mental Health","keywords":"Cluster analysis; Artificial neural network; Computer science; SIGNAL (programming language); Transmission (telecommunications); Similarity (geometry); Feature (linguistics); Algorithm; Nonlinear system; Topology (electrical circuits); Pattern recognition (psychology); Mathematics; Artificial intelligence; Physics; Telecommunications","score_opus":0.04516566340210802,"score_gpt":0.2540578133883002,"score_spread":0.20889214998619215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991942570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022677142,0.0001499862,0.9759101,0.000090644055,0.000020017143,0.000032102154,0.000029136996,0.00044809945,0.00064287597],"genre_scores_gemma":[0.4816828,0.00025226778,0.51447004,0.00012473603,0.000059399852,0.00015368583,0.00020822287,0.000119124226,0.0029298803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992854,0.00016890405,0.000052731397,0.00021404235,0.00020905993,0.000069812064],"domain_scores_gemma":[0.99859744,0.0004893704,0.00025102386,0.00028473823,0.0003182634,0.000059012553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014337943,0.00082607067,0.0009275772,0.0010065334,0.00076909835,0.0012586843,0.0023966944,0.0011437743,0.00095376564],"category_scores_gemma":[0.004534846,0.00061813404,0.0008732532,0.001554946,0.0013207651,0.0028581915,0.0025083607,0.0012499087,0.000498644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010902874,0.000037315218,0.00083501183,0.000069376685,0.00009023056,0.00008272934,0.00012437397,0.8613739,0.008433687,0.022734722,0.00060621253,0.10550338],"study_design_scores_gemma":[0.0000032927983,0.000021354932,0.0000843963,0.000002519012,0.000005706181,0.000013621339,0.000005328397,0.9934203,0.0019159798,0.0042794202,0.00023988837,0.000008227337],"about_ca_topic_score_codex":0.004686175,"about_ca_topic_score_gemma":0.0050289263,"teacher_disagreement_score":0.004686175,"about_ca_system_score_codex":0.001554797,"about_ca_system_score_gemma":0.0010530795,"threshold_uncertainty_score":0.011280954},"labels":[],"label_agreement":null},{"id":"W1992240699","doi":"10.1109/neurel.2006.341189","title":"Dynamic-Order-Extended Time-Delay Dynamic Neural Units","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial neural network; Identification (biology); Backpropagation; Recurrent neural network; Dynamic equation; Control theory (sociology); Differential equation; Artificial intelligence; Mathematics; Nonlinear system; Physics","score_opus":0.006422450616229056,"score_gpt":0.22468330895177674,"score_spread":0.21826085833554768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992240699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016456943,0.00054087886,0.9774276,0.00009511446,0.00014105914,0.00002628674,0.000049648625,0.0003343699,0.0049281036],"genre_scores_gemma":[0.7438465,0.0009194759,0.24401142,0.00017941977,0.00006646952,0.0000875219,0.00015234538,0.000037096866,0.010699743],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998758,0.000019072624,0.000010964819,0.000037029673,0.000043890657,0.000013260564],"domain_scores_gemma":[0.999892,0.00003142167,0.000018317438,0.000020172485,0.000030311094,0.000007726582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017712811,0.00028695367,0.00025949551,0.00012174732,0.00013485344,0.00042207816,0.0007842854,0.000502101,0.0014534759],"category_scores_gemma":[0.0005224725,0.00012317215,0.0002417557,0.000237407,0.0002221936,0.0005567839,0.00029659376,0.0004649393,0.00030828104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023170932,0.00007763933,0.0012105092,0.00028743342,0.00006641908,0.00046785374,0.000106353196,0.38993764,0.11375531,0.06537776,0.0019014964,0.4265799],"study_design_scores_gemma":[0.0000073062383,0.000088207205,0.00028714805,0.000013343307,0.000014305562,0.0002138791,0.000008936804,0.9708756,0.014200065,0.0058744885,0.0084022675,0.000014454768],"about_ca_topic_score_codex":0.00087419903,"about_ca_topic_score_gemma":0.00096867105,"teacher_disagreement_score":0.0014534759,"about_ca_system_score_codex":0.00027458003,"about_ca_system_score_gemma":0.000239624,"threshold_uncertainty_score":0.0048624277},"labels":[],"label_agreement":null},{"id":"W1993183401","doi":"10.1016/j.neunet.2010.05.005","title":"Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks","year":2010,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Toronto Metropolitan University","funders":"","keywords":"Content-addressable memory; Computer science; Artificial neural network; Bidirectional associative memory; Attractor; Content-addressable storage; Associative property; Recurrent neural network; Hybrid neural network; Artificial intelligence; Representation (politics); Algorithm; Mathematics","score_opus":0.015476364333940692,"score_gpt":0.24329110752638142,"score_spread":0.22781474319244072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993183401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069237664,0.0009384061,0.9230408,0.00033716252,0.00032303826,0.000042382653,0.00011629754,0.0008390514,0.0051251114],"genre_scores_gemma":[0.9140961,0.0006553638,0.07438568,0.0001800038,0.00015960675,0.000113057846,0.00014685598,0.000088054076,0.010175129],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997042,0.000060438015,0.000017875105,0.00009934427,0.00007979401,0.00003831926],"domain_scores_gemma":[0.99958986,0.00013050206,0.000054327364,0.000054851822,0.0001415143,0.000028904935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049994735,0.0005278187,0.0014215065,0.0003533212,0.0004001397,0.0009302332,0.0023120795,0.0010078263,0.002485729],"category_scores_gemma":[0.0010177973,0.00030673077,0.0007596217,0.0006290789,0.00064428465,0.0016466288,0.0007316756,0.0007481492,0.0004445494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025201423,0.00015601801,0.0012576808,0.0002112158,0.00035098847,0.00031103342,0.00015936868,0.8240986,0.017294314,0.07499538,0.0023567274,0.07855668],"study_design_scores_gemma":[0.0000063422176,0.000027032642,0.0001079828,0.0000023071636,0.000022517708,0.000031628995,0.0000030191868,0.99318236,0.00056057615,0.005715437,0.00033298275,0.000007911632],"about_ca_topic_score_codex":0.0030055544,"about_ca_topic_score_gemma":0.0032062544,"teacher_disagreement_score":0.0030055544,"about_ca_system_score_codex":0.00043854883,"about_ca_system_score_gemma":0.00060268975,"threshold_uncertainty_score":0.008315563},"labels":[],"label_agreement":null},{"id":"W1993619022","doi":"10.1007/s00521-006-0064-8","title":"Modified multi-layered perceptron applied to packing and covering problems","year":2006,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Convergence (economics); Computational Science and Engineering; Limit (mathematics); Multilayer perceptron; Mathematical optimization; Perceptron; Artificial intelligence; Algorithm; Artificial neural network; Machine learning; Mathematics","score_opus":0.020964747757151913,"score_gpt":0.254700122397957,"score_spread":0.23373537464080507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993619022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035542663,0.0006130586,0.9609122,0.00024522963,0.00020931839,0.0000551698,0.000093036244,0.00083607185,0.0014933235],"genre_scores_gemma":[0.45445332,0.00046410927,0.5379985,0.0002442699,0.00024376829,0.00024673692,0.00043128725,0.0002659728,0.005652016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888605,0.0004377036,0.00009454411,0.00014726269,0.0002785147,0.00015597444],"domain_scores_gemma":[0.99736917,0.001436512,0.00014731754,0.0003703806,0.0004950274,0.00018148296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021231337,0.0008393619,0.0022224085,0.0011914121,0.0005772804,0.0014974168,0.002478084,0.002502143,0.003751687],"category_scores_gemma":[0.0068237092,0.0008693513,0.0013559245,0.0020246615,0.00070030993,0.0032028942,0.0019199308,0.0019860258,0.0007424881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029869526,0.00019220733,0.0008213874,0.0001676598,0.00011073544,0.00011906583,0.0000568056,0.8287994,0.0028737397,0.015069407,0.0029834071,0.14850745],"study_design_scores_gemma":[0.000004399086,0.000012410008,0.00003965114,0.0000018021816,0.0000036879155,0.000006519408,0.0000013859205,0.9970788,0.00020735271,0.0025551454,0.0000863811,0.0000025298737],"about_ca_topic_score_codex":0.004221373,"about_ca_topic_score_gemma":0.004441248,"teacher_disagreement_score":0.004221373,"about_ca_system_score_codex":0.0009793022,"about_ca_system_score_gemma":0.0011611665,"threshold_uncertainty_score":0.012550652},"labels":[],"label_agreement":null},{"id":"W1993837825","doi":"10.1016/j.procs.2014.05.383","title":"Supervised Discretization for Optimal Prediction","year":2014,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Discretization; Artificial intelligence; Machine learning; Mathematical optimization; Mathematics","score_opus":0.010548499401469631,"score_gpt":0.22992879712260564,"score_spread":0.21938029772113601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993837825","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005236958,0.00018126625,0.9934714,0.00012820709,0.000035456294,0.000031622094,0.00008378745,0.00032558732,0.00050579716],"genre_scores_gemma":[0.33917087,0.0003196331,0.65609056,0.0002579317,0.00015108028,0.00039717616,0.0011205255,0.00019667186,0.0022955213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99691975,0.0015104308,0.00024725063,0.0005662477,0.00060395023,0.00015245676],"domain_scores_gemma":[0.99046445,0.00683147,0.0003711334,0.001077683,0.0010813223,0.00017399243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004571093,0.00068501564,0.001733466,0.0007686591,0.0006242614,0.0012548927,0.0014092791,0.0011870147,0.0021230935],"category_scores_gemma":[0.01688639,0.0005571656,0.001007354,0.00096077524,0.0010623301,0.0015646011,0.0014749096,0.0026435002,0.00059372996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002882751,0.00012652711,0.002177221,0.0001928528,0.00011176015,0.00008842523,0.00019213375,0.74504715,0.0018568668,0.045385014,0.0041361884,0.20039761],"study_design_scores_gemma":[0.0000075225257,0.000012709836,0.00008318927,0.000006513818,0.0000025400911,0.000009285836,0.000005769486,0.98471224,0.00028576094,0.0145433135,0.00032775095,0.0000033262568],"about_ca_topic_score_codex":0.0037864575,"about_ca_topic_score_gemma":0.0033099568,"teacher_disagreement_score":0.004571093,"about_ca_system_score_codex":0.0012389336,"about_ca_system_score_gemma":0.001698323,"threshold_uncertainty_score":0.024174571},"labels":[],"label_agreement":null},{"id":"W1994324208","doi":"10.1007/s13198-013-0204-3","title":"A new method of mapping relations from data based on artificial neural network","year":2013,"lang":"en","type":"article","venue":"International Journal of Systems Assurance Engineering and Management","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Artificial neural network; Key (lock); Relation (database); Computer science; Field (mathematics); Reliability (semiconductor); Data mining; Artificial intelligence; Graph; Machine learning; Mathematics; Theoretical computer science","score_opus":0.02344631557999055,"score_gpt":0.26076552936304515,"score_spread":0.2373192137830546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994324208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020983722,0.00032090003,0.99582434,0.0000737261,0.000092201764,0.00004842893,0.00009563732,0.000720306,0.0007260828],"genre_scores_gemma":[0.060957253,0.00066623406,0.93279254,0.00013293965,0.00013779862,0.00020801535,0.0004838398,0.00015621615,0.0044651544],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981236,0.00031769613,0.00014903855,0.0005958493,0.0007427574,0.000071095645],"domain_scores_gemma":[0.9987123,0.0005365264,0.00008956958,0.00020517055,0.00041544804,0.000040920353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001540506,0.00084615074,0.0010845183,0.0026137177,0.00063992257,0.0018458967,0.0017012446,0.0010085318,0.0029284314],"category_scores_gemma":[0.00405512,0.00051604497,0.001088006,0.0029399,0.00053903094,0.002703677,0.0016965955,0.0014499223,0.0011982494],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013142805,0.00011634911,0.001928075,0.0002865874,0.00027632402,0.00020302428,0.0002127652,0.03059652,0.012985465,0.020260207,0.0050596227,0.92794365],"study_design_scores_gemma":[0.000039696344,0.00009706772,0.002516934,0.00007094465,0.00014558993,0.00050111656,0.00010949271,0.938585,0.011198045,0.028326068,0.018340291,0.00006966992],"about_ca_topic_score_codex":0.0046786596,"about_ca_topic_score_gemma":0.005219517,"teacher_disagreement_score":0.0046786596,"about_ca_system_score_codex":0.00046055694,"about_ca_system_score_gemma":0.0011857095,"threshold_uncertainty_score":0.009796619},"labels":[],"label_agreement":null},{"id":"W1994538256","doi":"10.1080/03610920701666328","title":"A Useful Matrix Decomposition and Its Statistical Applications in Linear Regression","year":2008,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decomposition; Linear regression; Mathematics; Design matrix; Matrix (chemical analysis); Statistics; Applied mathematics; Chemistry; Chromatography","score_opus":0.04426919459250996,"score_gpt":0.43957266796990024,"score_spread":0.3953034733773903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994538256","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018864794,0.008366117,0.9834743,0.0013328688,0.00069281127,0.000026859598,0.00022350565,0.00025101632,0.0037461205],"genre_scores_gemma":[0.10140482,0.016500713,0.8643523,0.002167764,0.0048701228,0.0002820953,0.00074996985,0.0005610218,0.009111062],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973679,0.0014848247,0.00014610737,0.0003290835,0.0005903882,0.00008168181],"domain_scores_gemma":[0.9942346,0.0035923105,0.0004584981,0.00053894654,0.0010281085,0.00014759066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031696365,0.001527354,0.00095869455,0.002331129,0.00071460084,0.0013491901,0.0010206306,0.0013470935,0.0043088393],"category_scores_gemma":[0.016805474,0.000481751,0.0009504628,0.004683358,0.002158313,0.0024136375,0.0013997003,0.003488264,0.0031726994],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005395878,0.00005351023,0.00066616765,0.00044683195,0.00006180501,0.00029958997,0.0002923618,0.0129441,0.0039158673,0.757046,0.027677186,0.19654259],"study_design_scores_gemma":[0.000017112567,0.00007110775,0.0005768639,0.00013705554,0.0000278201,0.0004976201,0.000052480143,0.093403935,0.0014118833,0.8658846,0.037864752,0.00005466364],"about_ca_topic_score_codex":0.0013839199,"about_ca_topic_score_gemma":0.0013819279,"teacher_disagreement_score":0.0043088393,"about_ca_system_score_codex":0.0005085323,"about_ca_system_score_gemma":0.00086719607,"threshold_uncertainty_score":0.016762853},"labels":[],"label_agreement":null},{"id":"W1994771526","doi":"10.1139/s04-005","title":"Advanced process control techniques for water treatment using artificial neural networks","year":2004,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Process (computing); Artificial neural network; PID controller; Process control; Advanced process control; Computer science; Control engineering; Control (management); Automation; Engineering; Artificial intelligence; Temperature control","score_opus":0.008026612070018372,"score_gpt":0.22626836736967876,"score_spread":0.2182417552996604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994771526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034839942,0.0022533564,0.989465,0.00021592696,0.00010483142,0.00004187836,0.000020601921,0.0005263198,0.003888024],"genre_scores_gemma":[0.38004074,0.006740111,0.597982,0.00032716634,0.00021335116,0.00043347335,0.00016850317,0.00008505328,0.014009663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979216,0.00004313153,0.000017306771,0.000030958112,0.00010482857,0.000011558022],"domain_scores_gemma":[0.9998229,0.000066124594,0.000024021365,0.0000147207575,0.0000681066,0.0000040867267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043150954,0.0006279833,0.00040042933,0.00039956337,0.00022005237,0.0006067424,0.0006340723,0.00080420275,0.0023685768],"category_scores_gemma":[0.0008283941,0.00023293965,0.000436809,0.0005756982,0.0003037297,0.0006555282,0.00040648668,0.0010773588,0.0006103852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060193586,0.00007453743,0.00031335885,0.0003239213,0.0000677973,0.00009390102,0.00008083758,0.59627736,0.01676593,0.018715758,0.002386017,0.3648404],"study_design_scores_gemma":[0.000009767003,0.000035800356,0.0001322955,0.000033599266,0.000014037869,0.000034013545,0.0000066556336,0.98628193,0.0024371236,0.0051504476,0.005853708,0.00001066227],"about_ca_topic_score_codex":0.003686627,"about_ca_topic_score_gemma":0.0047093644,"teacher_disagreement_score":0.003686627,"about_ca_system_score_codex":0.00048498478,"about_ca_system_score_gemma":0.0004165918,"threshold_uncertainty_score":0.007923663},"labels":[],"label_agreement":null},{"id":"W1995110138","doi":"10.1142/s0219622012500198","title":"A NEW RBF NEURAL NETWORK FOR PREDICTION IN INDUSTRIAL CONTROL","year":2012,"lang":"en","type":"article","venue":"International Journal of Information Technology & Decision Making","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Initialization; Sigmoid function; Computer science; Radial basis function; Artificial neural network; Hierarchical RBF; Activation function; Simple (philosophy); Radial basis function network; Artificial intelligence; Backpropagation; Function (biology); Algorithm; Control theory (sociology); Control (management)","score_opus":0.018261251539668198,"score_gpt":0.2934220487833543,"score_spread":0.27516079724368614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995110138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030286731,0.0011271618,0.99335986,0.000109236535,0.00012070954,0.000019242763,0.00003594932,0.00047488487,0.0017242581],"genre_scores_gemma":[0.39990854,0.00283858,0.58454007,0.0002668544,0.00037532664,0.00023111355,0.0003946928,0.0001551074,0.01128975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994344,0.00012813568,0.00003424904,0.00012568814,0.00023777943,0.000039725488],"domain_scores_gemma":[0.9997422,0.000062389525,0.000023448452,0.000022772061,0.00013683109,0.000012334344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007998038,0.00071541034,0.0009727891,0.00047447695,0.00030043157,0.00077707495,0.0012660138,0.0013259407,0.0016755969],"category_scores_gemma":[0.0012249431,0.0003166035,0.0006097166,0.0006825927,0.00033981827,0.001217604,0.00056844094,0.0011361473,0.0009306153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026638576,0.000099927296,0.0007977303,0.000377094,0.00011267264,0.0002797837,0.000091578535,0.46797845,0.036691688,0.0261756,0.005704649,0.46142456],"study_design_scores_gemma":[0.00000766181,0.000028110377,0.0001108876,0.000009774814,0.000009454217,0.000046343743,0.0000024355254,0.99398744,0.0018408252,0.0011546036,0.0027919794,0.00001054109],"about_ca_topic_score_codex":0.0029949055,"about_ca_topic_score_gemma":0.0020972816,"teacher_disagreement_score":0.0029949055,"about_ca_system_score_codex":0.00045652868,"about_ca_system_score_gemma":0.000505432,"threshold_uncertainty_score":0.0059549212},"labels":[],"label_agreement":null},{"id":"W1995384700","doi":"10.1016/s0303-2647(02)00092-8","title":"Reconstruction of noisy patterns by bistable gradient neural-like network","year":2003,"lang":"en","type":"article","venue":"Biosystems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Attractor; Bistability; Artificial neural network; Generalization; Multiplicative function; Gaussian; Convergence (economics); Computer science; White noise; Noise (video); Mathematics; Topology (electrical circuits); Statistical physics; Algorithm; Artificial intelligence; Mathematical analysis; Physics; Combinatorics; Telecommunications","score_opus":0.010866958132415876,"score_gpt":0.20413577832743043,"score_spread":0.19326882019501457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995384700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.262776,0.00037275598,0.73271686,0.00039172365,0.00009809368,0.000019280438,0.00012429769,0.0005489064,0.0029521743],"genre_scores_gemma":[0.93838936,0.00017963123,0.059328884,0.000077240154,0.000022481448,0.000016963138,0.00012132036,0.000060541446,0.0018036035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994147,0.000013831634,0.000002685392,0.000015361587,0.000016197828,0.00001042232],"domain_scores_gemma":[0.999828,0.00005466371,0.00003392089,0.000031063384,0.00003098705,0.000021337277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020631662,0.0003482487,0.00035619215,0.00029227286,0.00015600631,0.00041832527,0.00055053213,0.00058911025,0.00087450026],"category_scores_gemma":[0.0011325998,0.00030642445,0.00025958187,0.00033809076,0.0003605894,0.00082943984,0.00038773275,0.0004492285,0.00017544655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006131331,0.000077518445,0.0029402978,0.00018353156,0.000105513995,0.00042039066,0.00015041394,0.7331738,0.10232888,0.042567573,0.0023510777,0.115087934],"study_design_scores_gemma":[0.000006005592,0.000011295225,0.00028301042,0.0000022568593,0.0000032564858,0.000029161052,0.0000049059,0.9903679,0.0029229685,0.0062190075,0.00014446779,0.000005769185],"about_ca_topic_score_codex":0.0009233759,"about_ca_topic_score_gemma":0.0008779212,"teacher_disagreement_score":0.0009233759,"about_ca_system_score_codex":0.00027879808,"about_ca_system_score_gemma":0.00019483209,"threshold_uncertainty_score":0.0029254556},"labels":[],"label_agreement":null},{"id":"W1996717640","doi":"10.1109/ccece.1995.528107","title":"A study of various desired response and error scaling sequences for temporal pattern classification using a FIR neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Scaling; Computer science; Artificial neural network; Finite impulse response; Classifier (UML); Impulse response; Word error rate; Pattern recognition (psychology); Artificial intelligence; Error analysis; Algorithm; Impulse (physics); Machine learning; Mathematics","score_opus":0.17340511585575177,"score_gpt":0.32443438924049794,"score_spread":0.15102927338474617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996717640","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5819092,0.00095080835,0.41152516,0.00023807472,0.000033386284,0.000106388645,0.000056546243,0.00038987707,0.0047905813],"genre_scores_gemma":[0.9378435,0.00030218426,0.060899746,0.000023730128,0.000013051589,0.00006316229,0.00005484752,0.00003989751,0.0007598085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939847,0.00028484222,0.000042111784,0.00007406527,0.000172049,0.000028514483],"domain_scores_gemma":[0.9842577,0.01322082,0.0007203224,0.00045061004,0.0011649949,0.00018560307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002394977,0.00038746913,0.00028698484,0.0003375515,0.00019200786,0.00047498354,0.0002781723,0.0004517779,0.0008460955],"category_scores_gemma":[0.01890801,0.00017568174,0.00023160137,0.00031667834,0.00044379532,0.0007869634,0.00017544371,0.00050791365,0.00013663435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016895646,0.00043408433,0.0061925487,0.000690108,0.0000972013,0.00054292916,0.0004408453,0.5519888,0.101539,0.032015532,0.0008379847,0.3035314],"study_design_scores_gemma":[0.000018008046,0.00034607828,0.0013670292,0.000023626655,0.000014946048,0.00014089572,0.000026846628,0.9802145,0.015962496,0.0016569031,0.00021638331,0.0000123801965],"about_ca_topic_score_codex":0.00058977207,"about_ca_topic_score_gemma":0.0006886524,"teacher_disagreement_score":0.002394977,"about_ca_system_score_codex":0.00041068761,"about_ca_system_score_gemma":0.0003242519,"threshold_uncertainty_score":0.012665987},"labels":[],"label_agreement":null},{"id":"W1997168082","doi":"10.1049/el:20020465","title":"Multiplierless multilayer feedforward neural network design using quantised neurons","year":2002,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Feed forward; Artificial neural network; Computer science; Activation function; Very-large-scale integration; Realisation; Feedforward neural network; Electronic engineering; Control theory (sociology); Control engineering; Artificial intelligence; Engineering; Embedded system; Control (management)","score_opus":0.04616709177346102,"score_gpt":0.24662984421040438,"score_spread":0.20046275243694336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997168082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015880242,0.0004717435,0.98030967,0.00010266768,0.0001020302,0.0000613812,0.000039783736,0.00044021112,0.0025923843],"genre_scores_gemma":[0.52609223,0.0005836038,0.46683,0.00020982184,0.000068183326,0.00023275359,0.00008917674,0.00003836443,0.005855964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998623,0.000025369738,0.000013681535,0.000027510005,0.0000558723,0.000015215594],"domain_scores_gemma":[0.99982494,0.00004724135,0.000027959666,0.000017492906,0.00007384975,0.000008485074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037685194,0.00036660844,0.00028086975,0.00019093456,0.00022871941,0.00046672902,0.0008880012,0.0005795465,0.00185487],"category_scores_gemma":[0.00060471543,0.0002289959,0.00024288964,0.00020560081,0.00024760677,0.0006989431,0.00028468616,0.0005380351,0.00038791244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026759796,0.00008993001,0.00068078033,0.0006069781,0.00015759104,0.00028373842,0.00018243324,0.2957037,0.28895947,0.03131348,0.0032150554,0.3785392],"study_design_scores_gemma":[0.00005889348,0.00036030143,0.000393064,0.00006450036,0.00007160753,0.0001957433,0.000015114888,0.921486,0.06009165,0.0077939946,0.00943615,0.00003290491],"about_ca_topic_score_codex":0.0008087969,"about_ca_topic_score_gemma":0.0019442795,"teacher_disagreement_score":0.00185487,"about_ca_system_score_codex":0.0004025042,"about_ca_system_score_gemma":0.0003514331,"threshold_uncertainty_score":0.0062051415},"labels":[],"label_agreement":null},{"id":"W1997374367","doi":"10.1007/s11425-013-4640-y","title":"Chaotic invariant sets of a delayed discrete neural network of two non-identical neurons","year":2013,"lang":"en","type":"article","venue":"Science China Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Chaotic; Topological conjugacy; Mathematics; Invariant (physics); Set (abstract data type); Artificial neural network; Pure mathematics; Topology (electrical circuits); Artificial intelligence; Computer science; Combinatorics","score_opus":0.01478344088188871,"score_gpt":0.27257002341309217,"score_spread":0.25778658253120346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997374367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83548063,0.00018974426,0.15478122,0.00022302353,0.000077124285,0.000028887149,0.000117407064,0.000056995792,0.009044969],"genre_scores_gemma":[0.9957123,0.000034237008,0.002575759,0.000010565384,0.0000075286225,0.000012779411,0.000032215554,0.0000051528514,0.0016095508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975675,0.000047488636,0.000015615171,0.0000641692,0.00007537395,0.000040636616],"domain_scores_gemma":[0.9994615,0.00019006802,0.00013570562,0.0000416889,0.000097459706,0.00007363861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036166675,0.00037309676,0.0005203434,0.00063549035,0.0005811764,0.0009106872,0.0011147998,0.000707378,0.0012856679],"category_scores_gemma":[0.0011310348,0.0002822202,0.0005477489,0.00037173295,0.0010507075,0.00094513805,0.0008341995,0.0005258268,0.00008400517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035839458,0.00011640446,0.0050224904,0.00011404549,0.00017758264,0.0009496231,0.00035500881,0.79036206,0.020867452,0.17005964,0.00058212277,0.011035168],"study_design_scores_gemma":[0.000009931059,0.000035428846,0.00072174694,0.0000037122613,0.000013843261,0.000054481094,0.00002869118,0.9813681,0.00076356996,0.016851328,0.00013411918,0.000015009374],"about_ca_topic_score_codex":0.0030089256,"about_ca_topic_score_gemma":0.0016954989,"teacher_disagreement_score":0.0030089256,"about_ca_system_score_codex":0.0010409569,"about_ca_system_score_gemma":0.0004960012,"threshold_uncertainty_score":0.0075526834},"labels":[],"label_agreement":null},{"id":"W1998779324","doi":"10.1007/s00034-005-2500-x","title":"Computationally Efficient Digital Filters: Design Techniques and Applications","year":2006,"lang":"en","type":"article","venue":"Circuits Systems and Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Digital filter; Computer architecture; Electronic engineering; Engineering; Telecommunications; Bandwidth (computing)","score_opus":0.016707130237817325,"score_gpt":0.22593212553460273,"score_spread":0.2092249952967854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998779324","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033220386,0.0016105239,0.9912212,0.00015812469,0.00004980708,0.000019841915,0.000026091033,0.00024600665,0.0033463142],"genre_scores_gemma":[0.12747025,0.005014597,0.85054845,0.00025369495,0.00026823496,0.00013399034,0.00014330032,0.0001857871,0.01598167],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997178,0.000051504452,0.00001778392,0.00004054314,0.00015325005,0.000019181545],"domain_scores_gemma":[0.99954695,0.00019443191,0.00006120797,0.00008693991,0.00009672403,0.000013613534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043386678,0.0007092394,0.000496297,0.0006281048,0.00034554332,0.0012868219,0.00073433976,0.0008174579,0.00551718],"category_scores_gemma":[0.0015199988,0.0003972396,0.0003506767,0.0009814654,0.00043674864,0.001212011,0.0004952958,0.00086755434,0.0019465069],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023539807,0.00012670334,0.00032722673,0.0005600574,0.0000795327,0.00010998868,0.00012299555,0.075370155,0.06578723,0.17785208,0.008678998,0.6707496],"study_design_scores_gemma":[0.000088552195,0.00019025106,0.0006538864,0.0001705383,0.000104558334,0.0006580782,0.000044627846,0.8018969,0.061126463,0.06653775,0.068476014,0.000052326126],"about_ca_topic_score_codex":0.00061359996,"about_ca_topic_score_gemma":0.0017408486,"teacher_disagreement_score":0.00551718,"about_ca_system_score_codex":0.00051958807,"about_ca_system_score_gemma":0.00045031792,"threshold_uncertainty_score":0.018456817},"labels":[],"label_agreement":null},{"id":"W1999023354","doi":"10.1007/s00226-006-0096-3","title":"Wood dielectric loss factor prediction with artificial neural networks","year":2006,"lang":"en","type":"article","venue":"Wood Science and Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial neural network; Dielectric; Biological system; Loss factor; Function (biology); Moisture; Materials science; Composite material; Process engineering; Solid wood; Pulp and paper industry; Computer science; Artificial intelligence; Engineering","score_opus":0.007057681500456246,"score_gpt":0.20707091096084765,"score_spread":0.2000132294603914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999023354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29793432,0.0010423635,0.69602305,0.00023824137,0.00014368663,0.000043735814,0.0002151927,0.0012733112,0.0030860628],"genre_scores_gemma":[0.9252438,0.00029956488,0.07149061,0.000053470405,0.000044178236,0.000034254885,0.00018615296,0.000039512426,0.002608351],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986243,0.000033171284,0.000008468335,0.00004746228,0.00003118764,0.00001719647],"domain_scores_gemma":[0.9990404,0.0006395842,0.000068756795,0.000054146254,0.00017815288,0.000018946026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060556585,0.00060887804,0.0005005744,0.0005903654,0.00017020803,0.00053593045,0.0004350359,0.00062103814,0.00097035593],"category_scores_gemma":[0.002383059,0.00035116985,0.00039310855,0.0005618795,0.00016415368,0.00089225837,0.00022659311,0.0007606372,0.00037965318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027652926,0.00013062169,0.004165588,0.000058786543,0.00006573679,0.000058115736,0.000016577711,0.8148981,0.0056536733,0.00048270513,0.0010773453,0.17311624],"study_design_scores_gemma":[0.0000020177326,0.0000048807833,0.00022796499,0.0000011092678,0.00000304137,0.0000024972526,9.0159705e-7,0.99886656,0.0006446636,0.00021113195,0.00003368158,0.0000015807246],"about_ca_topic_score_codex":0.004384451,"about_ca_topic_score_gemma":0.004248182,"teacher_disagreement_score":0.004384451,"about_ca_system_score_codex":0.00035707752,"about_ca_system_score_gemma":0.00023543417,"threshold_uncertainty_score":0.008717895},"labels":[],"label_agreement":null},{"id":"W1999249392","doi":"10.1016/j.neucom.2011.08.009","title":"From local neural networks to granular neural networks: A study in information granulation","year":2011,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Granularity; Granular computing; Granulation; Computer science; Artificial intelligence; Rough set; Exploit; Data mining; Machine learning; Engineering","score_opus":0.021549993455240245,"score_gpt":0.24065175409037026,"score_spread":0.21910176063513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999249392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14903252,0.017553879,0.80828774,0.0055436944,0.0004009429,0.00010108963,0.00020604355,0.0002137049,0.01866054],"genre_scores_gemma":[0.920324,0.0058170366,0.067549214,0.00045673116,0.0005729815,0.00009870658,0.00013410274,0.000117084026,0.004930072],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892443,0.00042855306,0.00008107221,0.00017585585,0.00028074952,0.00010928233],"domain_scores_gemma":[0.98746526,0.009261243,0.0010471903,0.0010420156,0.00080332236,0.00038101568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031758226,0.00044878572,0.0013404308,0.001454207,0.0010488179,0.0034326236,0.0015096097,0.0014938263,0.0023743121],"category_scores_gemma":[0.021521838,0.00056624337,0.0009914945,0.002048314,0.0032263664,0.006668069,0.0021329892,0.0020884105,0.00016588687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000685533,0.000032079308,0.00092448207,0.00021342371,0.00005067947,0.00010928856,0.0002702587,0.06612503,0.0007494982,0.91185933,0.001017984,0.018579427],"study_design_scores_gemma":[0.000014260737,0.000028090984,0.00055440754,0.00004428301,0.0000187357,0.000057646706,0.000076743796,0.26185998,0.0003876563,0.7355857,0.0013576245,0.000014930708],"about_ca_topic_score_codex":0.002810105,"about_ca_topic_score_gemma":0.0014946965,"teacher_disagreement_score":0.0034326236,"about_ca_system_score_codex":0.0017174785,"about_ca_system_score_gemma":0.0008431849,"threshold_uncertainty_score":0.016795576},"labels":[],"label_agreement":null},{"id":"W2000386272","doi":"10.1142/s0129065708001592","title":"EXPERIENCE-CONSISTENT MODELING FOR RADIAL BASIS FUNCTION NEURAL NETWORKS","year":2008,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Construct (python library); Basis (linear algebra); Artificial intelligence; Exploit; Process (computing); Machine learning; Function (biology); Radial basis function; Data mining","score_opus":0.05514804054007986,"score_gpt":0.2769193095317039,"score_spread":0.22177126899162405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000386272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007364776,0.0001713971,0.9909484,0.000158978,0.00001727884,0.000025230564,0.000018584487,0.00006658948,0.00122886],"genre_scores_gemma":[0.7167667,0.0006291607,0.2765257,0.0001611896,0.000077326935,0.00044572228,0.00011166182,0.00007483591,0.0052076946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990319,0.00047663273,0.000041265746,0.0001537745,0.00021680491,0.00007972879],"domain_scores_gemma":[0.9981097,0.0010056274,0.00025308863,0.00015929648,0.0003952084,0.000077060875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028210871,0.00083367305,0.0010314387,0.00043891688,0.00032644637,0.0012446898,0.0024702135,0.001615375,0.0013751149],"category_scores_gemma":[0.007669517,0.0006292702,0.0009017221,0.00047048833,0.0011699443,0.0017977674,0.0013302377,0.0016410928,0.00034918453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023049315,0.00001948826,0.00024587903,0.000029674364,0.000015770369,0.000035116722,0.000050308427,0.96616846,0.0004755198,0.024864154,0.00023763896,0.007834955],"study_design_scores_gemma":[0.0000024307215,0.000007818476,0.000019333926,0.0000029826672,0.0000017668148,0.000003861338,0.0000026088032,0.99406296,0.00009739957,0.005664602,0.0001315477,0.0000027216272],"about_ca_topic_score_codex":0.0038404672,"about_ca_topic_score_gemma":0.002989692,"teacher_disagreement_score":0.0038404672,"about_ca_system_score_codex":0.0010956561,"about_ca_system_score_gemma":0.00093080767,"threshold_uncertainty_score":0.014919519},"labels":[],"label_agreement":null},{"id":"W2000956074","doi":"10.1109/tvlsi.2012.2232321","title":"Efficient VLSI Implementation of Neural Networks With Hyperbolic Tangent Activation Function","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":160,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hyperbolic function; Activation function; Sigmoid function; Artificial neural network; Very-large-scale integration; Tangent; Nonlinear system; Computer science; Adder; Function (biology); Transfer function; Function approximation; Inverse trigonometric functions; Algorithm; Mathematics; Topology (electrical circuits); Mathematical analysis; Artificial intelligence; Engineering; Telecommunications","score_opus":0.009279577385021536,"score_gpt":0.23136076624549212,"score_spread":0.2220811888604706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000956074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0489722,0.00068966916,0.9388473,0.00020787759,0.00012020571,0.000068133166,0.00008683065,0.0018977057,0.00911009],"genre_scores_gemma":[0.7148418,0.0004744929,0.27708486,0.0001213982,0.00003406228,0.000099699195,0.00021824082,0.000049411286,0.0070760786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998512,0.000026003345,0.000014720445,0.000021277787,0.00006396685,0.00002263415],"domain_scores_gemma":[0.99987054,0.000030193483,0.000012379899,0.000022067874,0.000060072285,0.000004760294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018186234,0.00035301043,0.00021357981,0.0002799845,0.00023205142,0.0005078442,0.0008396359,0.00033765173,0.0027086523],"category_scores_gemma":[0.00043448864,0.00016137645,0.00019051158,0.0003478661,0.00016435352,0.0005927396,0.00024629154,0.00034029566,0.0006777905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003429908,0.00012906666,0.001177606,0.0004365328,0.00010855626,0.0003810133,0.00015033447,0.19347861,0.15700053,0.035657436,0.0058301734,0.60530716],"study_design_scores_gemma":[0.000039144474,0.00022806777,0.00056548044,0.00002811743,0.000037049762,0.0002337895,0.000029579915,0.90342164,0.0799214,0.004407802,0.011070989,0.00001690901],"about_ca_topic_score_codex":0.0014514745,"about_ca_topic_score_gemma":0.0026084275,"teacher_disagreement_score":0.0027086523,"about_ca_system_score_codex":0.00041696464,"about_ca_system_score_gemma":0.0005156693,"threshold_uncertainty_score":0.0090613365},"labels":[],"label_agreement":null},{"id":"W2001515828","doi":"10.1021/ci050367t","title":"Collaborative Filtering on a Family of Biological Targets","year":2006,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"AstraZeneca (Canada); Université de Montréal","funders":"AstraZeneca","keywords":"Computer science; Collaborative filtering; Machine learning; Artificial intelligence; Generalization; Prioritization; Artificial neural network; Task (project management); Representation (politics); Kernel (algebra); Data mining; Recommender system","score_opus":0.023757399116110894,"score_gpt":0.2549755280878101,"score_spread":0.23121812897169922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001515828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09465615,0.0005043988,0.90043294,0.0004669956,0.00004116777,0.00007438684,0.00044446904,0.00091733056,0.0024621275],"genre_scores_gemma":[0.8282709,0.0007137992,0.16268504,0.00027032424,0.00011300452,0.00024479374,0.0015894353,0.000104214705,0.0060084853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980269,0.0004783062,0.00010462183,0.0006130101,0.00060006086,0.0001770789],"domain_scores_gemma":[0.9936719,0.0037211874,0.0006032752,0.0009814842,0.0008913814,0.0001307403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004026869,0.0009925299,0.0020417531,0.0017320202,0.00092133443,0.0012867673,0.0019447902,0.001920567,0.001706334],"category_scores_gemma":[0.0109276185,0.0005141255,0.0018812768,0.0015705043,0.0010534388,0.0024846995,0.0010770829,0.0017129538,0.00052985083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003880211,0.00013853656,0.0045764945,0.00013559789,0.0001673261,0.00036469288,0.0001504942,0.82701576,0.007496147,0.026322033,0.002205478,0.13103932],"study_design_scores_gemma":[0.000007348905,0.0000449996,0.0004694713,0.000003932895,0.00001693513,0.00006302133,0.000007694261,0.98976445,0.0014479744,0.0075841416,0.0005812634,0.000008800422],"about_ca_topic_score_codex":0.008654865,"about_ca_topic_score_gemma":0.0048088715,"teacher_disagreement_score":0.008654865,"about_ca_system_score_codex":0.0015749802,"about_ca_system_score_gemma":0.0011376414,"threshold_uncertainty_score":0.021296382},"labels":[],"label_agreement":null},{"id":"W2002409155","doi":"10.1121/1.4777041","title":"Learning to perceive Mandarin tones: The role of acoustic information","year":2003,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mandarin Chinese; Tone (literature); Confusion; Contrast (vision); Perception; Speech recognition; Computer science; Acoustics; Psychology; Linguistics; Artificial intelligence","score_opus":0.005712903314698033,"score_gpt":0.22716481952004847,"score_spread":0.22145191620535043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002409155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973054,0.00029060253,0.0013786325,0.000054097392,0.000008714787,0.0000150364785,0.000009425781,0.000010826424,0.0009273332],"genre_scores_gemma":[0.99416876,0.00046293464,0.0040689954,0.000060818107,0.0000103879565,0.000027417262,0.00004798663,0.000007770825,0.0011449578],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997638,0.00007075741,0.000017632678,0.0000768477,0.000049519647,0.000021496358],"domain_scores_gemma":[0.9993554,0.00034928691,0.00011639382,0.00004642379,0.00005892491,0.00007351655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000639147,0.00041033598,0.00025579368,0.000119612836,0.00018358718,0.00046172866,0.0002634211,0.0003668617,0.0016156903],"category_scores_gemma":[0.0028766817,0.00014548976,0.000093722745,0.00008061863,0.00037686917,0.0004219826,0.0003044034,0.000363369,0.0002418945],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022712285,0.0005641498,0.011362319,0.00033333217,0.00004020345,0.00025346244,0.0009310882,0.0006411389,0.8677432,0.00022807215,0.00011037045,0.115521446],"study_design_scores_gemma":[0.00028164408,0.013005395,0.46737155,0.00013563332,0.00029443935,0.0013971976,0.0014530012,0.014807434,0.4947496,0.002811168,0.0036176322,0.00007529869],"about_ca_topic_score_codex":0.001762633,"about_ca_topic_score_gemma":0.0020919482,"teacher_disagreement_score":0.001762633,"about_ca_system_score_codex":0.00016471183,"about_ca_system_score_gemma":0.00026919207,"threshold_uncertainty_score":0.0054050684},"labels":[],"label_agreement":null},{"id":"W2003442330","doi":"10.1016/s0893-6080(00)00024-1","title":"A new algorithm for learning in piecewise-linear neural networks","year":2000,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Hyperplane; Algorithm; Piecewise linear function; Intersection (aeronautics); Artificial neural network; Computer science; Mathematics; Ramer–Douglas–Peucker algorithm; Dimension (graph theory); Conjugate gradient method; Function (biology); Artificial intelligence; Computation","score_opus":0.014269793953698361,"score_gpt":0.25204314593036464,"score_spread":0.23777335197666627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003442330","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010992932,0.00012650056,0.99765646,0.000058341917,0.0000699822,0.000024146115,0.000019966144,0.00037320727,0.000572104],"genre_scores_gemma":[0.04655363,0.0002723346,0.9485053,0.00012417574,0.00011100278,0.00020439623,0.0001340324,0.00022758011,0.003867661],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951136,0.000115533396,0.00003723186,0.000119672266,0.00017464848,0.00004153645],"domain_scores_gemma":[0.9988919,0.0005704427,0.000067975205,0.000123331,0.00029274597,0.000053705015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001029312,0.0009419355,0.0010881657,0.00087232474,0.00072326226,0.0011442401,0.0024673971,0.0016432046,0.007193253],"category_scores_gemma":[0.005125995,0.00069511257,0.0007227475,0.0013183515,0.0007437982,0.0022965053,0.0020636625,0.0024111678,0.0018433383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015934731,0.000067803514,0.0004258982,0.00025934423,0.00007947168,0.000094102106,0.000099099176,0.2882544,0.006078158,0.047582872,0.0065719397,0.6503275],"study_design_scores_gemma":[0.000020402224,0.000035128378,0.00006194544,0.000015347054,0.000017153316,0.000049134884,0.0000084583535,0.983272,0.0011975967,0.01230746,0.0030057903,0.00000956304],"about_ca_topic_score_codex":0.0029329166,"about_ca_topic_score_gemma":0.0032336616,"teacher_disagreement_score":0.007193253,"about_ca_system_score_codex":0.00071236154,"about_ca_system_score_gemma":0.0009209803,"threshold_uncertainty_score":0.024063885},"labels":[],"label_agreement":null},{"id":"W2003938687","doi":"10.1016/s0893-6080(01)00035-1","title":"Pattern classification by a condensed neural network","year":2001,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pattern recognition (psychology); Computer science; Artificial neural network; Classifier (UML); Artificial intelligence; Perceptron; k-nearest neighbors algorithm; Time delay neural network; Computation; Activation function; Multilayer perceptron; Algorithm","score_opus":0.022219203384802776,"score_gpt":0.24737011860407926,"score_spread":0.2251509152192765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003938687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0409861,0.0007736425,0.95477974,0.0004407397,0.00012630982,0.000090311914,0.00017079322,0.0003330405,0.0022993076],"genre_scores_gemma":[0.5927461,0.0013215397,0.39516884,0.00042268686,0.0005096807,0.00031498773,0.0007130073,0.00011239141,0.008690686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997247,0.0000943942,0.000019946496,0.000063180894,0.0000799085,0.000017837989],"domain_scores_gemma":[0.9990409,0.0004750752,0.00008932868,0.00018841717,0.00016870754,0.00003749062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005820573,0.00026446997,0.0007459049,0.00091056863,0.00033733898,0.00075552793,0.00071166054,0.0006941709,0.0038973247],"category_scores_gemma":[0.0031194019,0.00028233055,0.0005532061,0.0010808994,0.00084978837,0.0017999866,0.001275924,0.0007952618,0.0006897739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047596285,0.00013713779,0.0022348892,0.0005173934,0.00020247769,0.00023595408,0.0003088958,0.3746351,0.038525976,0.16742203,0.0074591283,0.40784502],"study_design_scores_gemma":[0.00001609027,0.00005730363,0.0002882852,0.000011076066,0.000016172145,0.000032648542,0.0000116747715,0.9578003,0.0016138058,0.03817951,0.0019669628,0.000006089457],"about_ca_topic_score_codex":0.0008834751,"about_ca_topic_score_gemma":0.0008552652,"teacher_disagreement_score":0.0038973247,"about_ca_system_score_codex":0.00051411084,"about_ca_system_score_gemma":0.00037442046,"threshold_uncertainty_score":0.01303792},"labels":[],"label_agreement":null},{"id":"W2005030534","doi":"10.1080/0308107031000090756","title":"Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks","year":2003,"lang":"en","type":"article","venue":"International Journal of General Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Wonkwang University","keywords":"Artificial neural network; Computer science; Fuzzy logic; Process (computing); Neuro-fuzzy; Polynomial; Artificial intelligence; Network topology; Layer (electronics); Topology (electrical circuits); Fuzzy control system; Mathematics","score_opus":0.011366941524011088,"score_gpt":0.2386665941891769,"score_spread":0.22729965266516583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005030534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040985916,0.000592732,0.9544278,0.0001542712,0.000070798495,0.000032131287,0.000049967977,0.00024192,0.003444527],"genre_scores_gemma":[0.8526409,0.0005189746,0.14270106,0.00008295757,0.000052037343,0.000079877726,0.000070156384,0.000016865573,0.0038370793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998374,0.000040836054,0.0000083766045,0.000031306656,0.0000629221,0.000019171845],"domain_scores_gemma":[0.99982196,0.00006882069,0.000024654839,0.000017383678,0.000056649133,0.000010623338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033819216,0.00028962357,0.0003764304,0.00025679433,0.00022171446,0.0004188937,0.0007906375,0.0004957764,0.00086128875],"category_scores_gemma":[0.0009907589,0.00014730376,0.00033359602,0.0003703188,0.00038682672,0.0007109068,0.000353365,0.00036788595,0.00019734753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000653269,0.000036187932,0.0008820949,0.000107379194,0.00004548222,0.0001209304,0.000099266625,0.83425486,0.0082100835,0.040067647,0.0011729845,0.114937715],"study_design_scores_gemma":[0.0000030346046,0.000012840915,0.000087131964,0.000002351679,0.0000038071325,0.000016734268,0.0000045161046,0.9941328,0.00061344006,0.0045497874,0.000569716,0.0000038245053],"about_ca_topic_score_codex":0.0015538431,"about_ca_topic_score_gemma":0.001989596,"teacher_disagreement_score":0.0015538431,"about_ca_system_score_codex":0.00033774914,"about_ca_system_score_gemma":0.00027124796,"threshold_uncertainty_score":0.0030896068},"labels":[],"label_agreement":null},{"id":"W2005249392","doi":"10.3389/fpsyg.2014.01515","title":"CFA with binary variables in small samples: a comparison of two methods","year":2015,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of California, Santa Cruz","keywords":"Statistics; Correlation; Polychoric correlation; Mathematics; Binary number; Goodness of fit; Confidence interval; Binary data; Standard error; Least-squares function approximation; Type I and type II errors; Correlation coefficient; Mean squared error; Estimator","score_opus":0.10376688273642405,"score_gpt":0.4066910691193412,"score_spread":0.3029241863829172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005249392","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02243632,0.0022356417,0.9730525,0.00028454702,0.00012186097,0.00028064035,0.0000603385,0.0002615352,0.001266619],"genre_scores_gemma":[0.21890427,0.0017648586,0.77748615,0.00016433829,0.000102708625,0.00087020046,0.00012820898,0.00014761242,0.00043173338],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9325589,0.057410423,0.0013547218,0.002565701,0.0056077726,0.0005025783],"domain_scores_gemma":[0.7024504,0.26949584,0.004999627,0.012396153,0.009565357,0.0010926251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.072849855,0.0015587126,0.0024560238,0.005280312,0.0008455797,0.0021203686,0.0026868365,0.0019484403,0.002465349],"category_scores_gemma":[0.27579555,0.0009147831,0.0017091216,0.0042961454,0.0026685365,0.004206454,0.002909459,0.001912217,0.00067977974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026418143,0.00058810227,0.024011483,0.0019148219,0.002848774,0.00020115895,0.0024433008,0.029624406,0.0025058452,0.112471186,0.0029277378,0.8178214],"study_design_scores_gemma":[0.0015918037,0.002475813,0.040684223,0.001588951,0.001523957,0.0010672647,0.0015041131,0.67927694,0.0044722846,0.2548591,0.010464279,0.00049128296],"about_ca_topic_score_codex":0.0034787203,"about_ca_topic_score_gemma":0.0041084774,"teacher_disagreement_score":0.072849855,"about_ca_system_score_codex":0.0012715304,"about_ca_system_score_gemma":0.0021371604,"threshold_uncertainty_score":0.3852715},"labels":[],"label_agreement":null},{"id":"W2005305392","doi":"10.4018/jcini.2008100103","title":"Foundations of Nonconventional Neural Units and their Classification","year":2008,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Computer science; Nonlinear system; Notation; Representation (politics); Artificial intelligence; Function (biology); Algorithm; Mathematics; Arithmetic","score_opus":0.05133391043283852,"score_gpt":0.3058905169632957,"score_spread":0.2545566065304572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005305392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035895374,0.0069406517,0.895531,0.0012212198,0.0005573607,0.00010074563,0.00032676721,0.0002488333,0.059178106],"genre_scores_gemma":[0.64100224,0.006820774,0.33196574,0.00057625526,0.0011069209,0.00047574693,0.00047165807,0.0001269528,0.017453743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990783,0.0001837053,0.00013326167,0.0002185365,0.00030823468,0.00007801022],"domain_scores_gemma":[0.9985487,0.0006085776,0.00020436778,0.00022886622,0.00035095465,0.000058600563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009952456,0.0006366516,0.0005642543,0.0014209625,0.0006279405,0.0025675688,0.0014278493,0.0011926033,0.003701537],"category_scores_gemma":[0.0040460476,0.00027457427,0.0006759598,0.0011308872,0.0031679373,0.0045344434,0.001077999,0.0016808776,0.00094376487],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065780014,0.0000047186168,0.0001668328,0.00006673945,0.000007054487,0.00005507714,0.00011228056,0.0029828039,0.0007336675,0.97891873,0.00039216908,0.01655345],"study_design_scores_gemma":[0.0000045405022,0.000026505146,0.00051916024,0.00004570525,0.000009139045,0.00017949792,0.000063186184,0.05806133,0.0007720102,0.9257244,0.014574678,0.000019762694],"about_ca_topic_score_codex":0.0011445791,"about_ca_topic_score_gemma":0.0006707509,"teacher_disagreement_score":0.003701537,"about_ca_system_score_codex":0.0010134904,"about_ca_system_score_gemma":0.0006146881,"threshold_uncertainty_score":0.012382865},"labels":[],"label_agreement":null},{"id":"W2005526465","doi":"10.1109/iscas.2010.5537102","title":"System-level design of low complexity CVNS feed forward neural network","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial neural network; Robustness (evolution); Redundancy (engineering); Sensitivity (control systems); Modular design; Multiplexer; Algorithm; Electronic engineering; Multiplexing; Artificial intelligence; Engineering","score_opus":0.05797396146599015,"score_gpt":0.2587032871959107,"score_spread":0.20072932572992053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005526465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034653246,0.00038732903,0.94831127,0.00025170925,0.00011693608,0.00015697697,0.00010798699,0.0009662797,0.015048327],"genre_scores_gemma":[0.8056259,0.00023182614,0.18733884,0.0001835129,0.00003461954,0.00025650926,0.00015027779,0.000058286165,0.006120195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996669,0.000057878566,0.000018244911,0.00007384561,0.00013933053,0.000043818472],"domain_scores_gemma":[0.99976486,0.000047587127,0.000030117795,0.000020685804,0.00012499717,0.000011749448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034169338,0.00038571874,0.00035191939,0.00027912736,0.00042704016,0.0010084698,0.0013308174,0.00063626305,0.002479926],"category_scores_gemma":[0.0006416998,0.00025127974,0.000300433,0.00023577923,0.0003337045,0.00058950257,0.0003580869,0.0005795993,0.0004975601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002544892,0.00011235536,0.0015571789,0.00045319862,0.00013034456,0.00024948624,0.00017114902,0.748541,0.069501676,0.0346707,0.0037477298,0.14061072],"study_design_scores_gemma":[0.000014455148,0.00011019032,0.00027155734,0.000017196342,0.000024211859,0.00005267126,0.000015218734,0.9820901,0.01184952,0.0017886371,0.00375646,0.000009699648],"about_ca_topic_score_codex":0.003891827,"about_ca_topic_score_gemma":0.0065822564,"teacher_disagreement_score":0.003891827,"about_ca_system_score_codex":0.0011672671,"about_ca_system_score_gemma":0.0009868903,"threshold_uncertainty_score":0.008469164},"labels":[],"label_agreement":null},{"id":"W2006242014","doi":"10.1007/s00500-002-0245-z","title":"Set oriented mappings on neural networks","year":2003,"lang":"en","type":"article","venue":"Soft Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Cargill (Canada)","funders":"","keywords":"Computer science; Artificial neural network; Set (abstract data type); Perceptron; Range (aeronautics); Domain (mathematical analysis); Feedforward neural network; Gradient descent; Function (biology); Real number; Activation function; Inverse function; Multilayer perceptron; Algorithm; Inverse; Pattern recognition (psychology); Artificial intelligence; Mathematics; Discrete mathematics","score_opus":0.017403366010051175,"score_gpt":0.24839885914764936,"score_spread":0.23099549313759818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006242014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052036688,0.0035188468,0.90725785,0.0011896444,0.0006220585,0.000054697917,0.00014464006,0.0002787632,0.034896836],"genre_scores_gemma":[0.63654864,0.00630886,0.30042136,0.0006215151,0.00071018684,0.00032132716,0.00031953928,0.00031327055,0.05443538],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99951935,0.00018422476,0.00003075289,0.00007833197,0.00015532733,0.000031977976],"domain_scores_gemma":[0.99916065,0.0004902624,0.000061022776,0.00009881888,0.00013225545,0.000057068515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082457485,0.00065708323,0.00064236735,0.0013985757,0.0005334456,0.001559138,0.00073037704,0.00079112395,0.0044317115],"category_scores_gemma":[0.003063545,0.0003685213,0.0005831673,0.0014785735,0.0012317754,0.002602106,0.0019076421,0.0014480643,0.00057183625],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029115745,0.00001633887,0.00013101606,0.00007218026,0.000018094592,0.000053889875,0.00009722525,0.017379731,0.001017803,0.92706144,0.0019479843,0.05217507],"study_design_scores_gemma":[0.0000044655376,0.000016840937,0.00008976973,0.00001865011,0.0000056770205,0.000038659444,0.000017270473,0.04705856,0.00031707255,0.9487093,0.0037184188,0.000005400323],"about_ca_topic_score_codex":0.0005966164,"about_ca_topic_score_gemma":0.0005597275,"teacher_disagreement_score":0.0044317115,"about_ca_system_score_codex":0.00087854493,"about_ca_system_score_gemma":0.00031805047,"threshold_uncertainty_score":0.0148255825},"labels":[],"label_agreement":null},{"id":"W2006278327","doi":"10.1109/tsmcb.2012.2221695","title":"Efficient Shortest-Path-Tree Computation in Network Routing Based on Pulse-Coupled Neural Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Dijkstra's algorithm; Computer science; Shortest path problem; Computation; Router; Path (computing); Tree (set theory); K shortest path routing; State (computer science); Routing (electronic design automation); Link-state routing protocol; Algorithm; Routing protocol; Theoretical computer science; Mathematics; Computer network; Graph","score_opus":0.016192480701716763,"score_gpt":0.2442163678952547,"score_spread":0.22802388719353794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006278327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023928296,0.00033974048,0.97336745,0.0001321035,0.00003796991,0.000023005832,0.0000237167,0.00020753288,0.0019402613],"genre_scores_gemma":[0.77487695,0.00070572883,0.22116409,0.000108920365,0.000044681812,0.00017358114,0.00011595352,0.000055345427,0.0027546918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998294,0.000040412513,0.000010380603,0.00003735622,0.0000631701,0.00001921707],"domain_scores_gemma":[0.9997008,0.00016257598,0.000025100839,0.000023647255,0.00007364023,0.00001421092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038319585,0.00041709925,0.0005977602,0.00036305562,0.0004780117,0.0005480162,0.0012109603,0.0008766777,0.00088826445],"category_scores_gemma":[0.0013691572,0.00039804418,0.00039092964,0.00074251846,0.0005812493,0.0014056481,0.0006211163,0.0008269267,0.000114576644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000346335,0.000019919951,0.00027592925,0.00004863168,0.000019738562,0.000047355676,0.00003327043,0.95755315,0.0023664173,0.013037259,0.00044511879,0.026118562],"study_design_scores_gemma":[0.0000014632544,0.0000033683157,0.000016277372,7.256454e-7,0.0000014935141,0.000004164967,0.0000010733819,0.9984824,0.00012783294,0.0013076058,0.000052212676,0.0000013999326],"about_ca_topic_score_codex":0.0084028,"about_ca_topic_score_gemma":0.007531892,"teacher_disagreement_score":0.0084028,"about_ca_system_score_codex":0.00091123005,"about_ca_system_score_gemma":0.0009877707,"threshold_uncertainty_score":0.016707778},"labels":[],"label_agreement":null},{"id":"W2006498932","doi":"10.1016/j.neucom.2007.10.027","title":"The HCM for perceptual image segmentation","year":2008,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Australian Research Council; Canada Research Chairs","keywords":"Gestalt psychology; Merge (version control); Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Artificial neural network; Image segmentation; Pixel; Computer vision; Similarity (geometry); Scale-space segmentation; Image (mathematics); Perception","score_opus":0.025840233439204635,"score_gpt":0.26534571232013693,"score_spread":0.2395054788809323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006498932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002347712,0.0006882545,0.99224323,0.00019890988,0.000121000296,0.000058403766,0.00013929984,0.0010452217,0.003157871],"genre_scores_gemma":[0.09517829,0.0012291233,0.88923204,0.00040125014,0.00042406213,0.00016519384,0.0004845746,0.00056924135,0.012316146],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946946,0.00010777362,0.000023805722,0.00015226401,0.00019012902,0.00005649075],"domain_scores_gemma":[0.9993382,0.00020921938,0.00003454496,0.0002223243,0.00013840706,0.000057214795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007650601,0.0005786281,0.001076833,0.0013701578,0.00060261367,0.0015015389,0.0017542421,0.0016233495,0.011428425],"category_scores_gemma":[0.0025389967,0.00043219762,0.0009642439,0.0014492363,0.0010213888,0.0015090842,0.0015873542,0.0013355,0.0033461128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016989752,0.000050069542,0.0002704125,0.00021679739,0.00007984008,0.000118529795,0.00006115582,0.06059205,0.015888624,0.111898065,0.020192629,0.79046184],"study_design_scores_gemma":[0.0000139041595,0.000033676115,0.00043402918,0.00003187715,0.000016692504,0.00011244409,0.000024028084,0.9065477,0.0059833084,0.07278401,0.014000129,0.000018208093],"about_ca_topic_score_codex":0.008665296,"about_ca_topic_score_gemma":0.007468751,"teacher_disagreement_score":0.011428425,"about_ca_system_score_codex":0.0009386878,"about_ca_system_score_gemma":0.0014363826,"threshold_uncertainty_score":0.03823185},"labels":[],"label_agreement":null},{"id":"W2007023969","doi":"10.1103/physreve.65.046212","title":"Neural networks for estimating intrinsic dimension","year":2002,"lang":"en","type":"article","venue":"Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Artificial neural network; Intrinsic dimension; Bottleneck; Computer science; Dimension (graph theory); Curse of dimensionality; Projection (relational algebra); Artificial intelligence; Dimensionality reduction; Nonlinear system; Pattern recognition (psychology); Function (biology); Series (stratigraphy); Algorithm; Mathematics; Physics","score_opus":0.01880945385761868,"score_gpt":0.30597272863014774,"score_spread":0.28716327477252906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007023969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075528207,0.0014721879,0.9894673,0.00029050704,0.00006004215,0.0000180698,0.000121655474,0.00019982734,0.00081760023],"genre_scores_gemma":[0.3657357,0.0046713147,0.62338513,0.00027700132,0.00044826188,0.0003626424,0.0009228382,0.00017841902,0.0040187472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922764,0.00023036881,0.000049544564,0.00018156886,0.0002606269,0.000050189283],"domain_scores_gemma":[0.9981958,0.00102816,0.00023825877,0.00019238448,0.00030232055,0.00004304152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017262537,0.0009357076,0.0010955392,0.0013851066,0.0004106015,0.001197603,0.0012101423,0.0012865671,0.0011536711],"category_scores_gemma":[0.008839264,0.00051579124,0.000709854,0.0014476444,0.000892858,0.0025110934,0.0014124278,0.002036274,0.00046737664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009316675,0.000041784977,0.002730478,0.00028819867,0.00021871975,0.000121506804,0.0001293242,0.68590236,0.006094598,0.13655448,0.0029767153,0.16484867],"study_design_scores_gemma":[0.0000026434864,0.00000827069,0.00027400677,0.000012771764,0.000007660161,0.000019783936,0.00000611146,0.95626336,0.0005164141,0.04202126,0.00085695996,0.000010819612],"about_ca_topic_score_codex":0.0027625416,"about_ca_topic_score_gemma":0.0018028411,"teacher_disagreement_score":0.0027625416,"about_ca_system_score_codex":0.0008643867,"about_ca_system_score_gemma":0.0006466919,"threshold_uncertainty_score":0.009129405},"labels":[],"label_agreement":null},{"id":"W2007567623","doi":"10.1007/s10489-012-0342-3","title":"A new selective neural network ensemble with negative correlation","year":2012,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Generalization; Artificial intelligence; Time delay neural network; Correlation; Neural ensemble; Set (abstract data type); Probabilistic neural network; Genetic algorithm; Stochastic neural network; Pattern recognition (psychology); Algorithm; Machine learning; Mathematics","score_opus":0.017007099485925877,"score_gpt":0.24105453929210874,"score_spread":0.22404743980618286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007567623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04108961,0.0009065709,0.9521237,0.0001532209,0.00032178668,0.00007254663,0.0001835653,0.0008603073,0.0042887465],"genre_scores_gemma":[0.47758982,0.0009234492,0.5075292,0.0004455047,0.0004946977,0.0002620476,0.0011574561,0.00023751367,0.011360289],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995066,0.00007741092,0.00003020593,0.00012539499,0.00020369522,0.00005656541],"domain_scores_gemma":[0.9994184,0.00010088891,0.00003191504,0.00010759262,0.0002835161,0.000057708326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009361093,0.00088971626,0.0013648688,0.00070045673,0.00067603803,0.0005957116,0.0016854567,0.00094119,0.001858525],"category_scores_gemma":[0.0011923084,0.0004178462,0.00078228844,0.00090697914,0.00027956307,0.0014315248,0.001506094,0.0008710668,0.0007916036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003203729,0.000324677,0.002764226,0.00016289696,0.000492392,0.00020904456,0.00007763878,0.23290253,0.036126625,0.008285587,0.011212888,0.7071211],"study_design_scores_gemma":[0.000015211105,0.000069357746,0.0004102327,0.000006255289,0.000080491154,0.00007808698,0.000007814624,0.9925532,0.003918501,0.0014208794,0.0014272699,0.000012645169],"about_ca_topic_score_codex":0.002051539,"about_ca_topic_score_gemma":0.0051231217,"teacher_disagreement_score":0.002051539,"about_ca_system_score_codex":0.00024323817,"about_ca_system_score_gemma":0.00079722475,"threshold_uncertainty_score":0.00621742},"labels":[],"label_agreement":null},{"id":"W2007835394","doi":"10.1016/j.neucom.2014.05.014","title":"An evolving fuzzy neural predictor for multi-dimensional system state forecasting","year":2014,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Data mining; Autoregressive model; Nonlinear system; Fuzzy logic; Artificial intelligence; Filter (signal processing); Machine learning; Mathematics; Econometrics","score_opus":0.038260335382732276,"score_gpt":0.2656833207302733,"score_spread":0.22742298534754102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007835394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05238484,0.00068153365,0.9435059,0.00019374404,0.000252513,0.000043636475,0.000080854305,0.00045228063,0.0024047568],"genre_scores_gemma":[0.7853038,0.00052157947,0.20876306,0.00012851047,0.00009239611,0.00008813993,0.00016253025,0.000055175027,0.004884772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997677,0.00005352661,0.000017229311,0.000053595773,0.00008234545,0.000025547139],"domain_scores_gemma":[0.99929464,0.00031014867,0.00003748595,0.000046101773,0.00028406043,0.00002741121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012936747,0.00041139664,0.000844572,0.0004539488,0.0004989861,0.0006004365,0.0009117129,0.0010794885,0.0017299752],"category_scores_gemma":[0.002696683,0.0002676586,0.00039476523,0.0005905975,0.00026934867,0.0008657529,0.00062411476,0.0012536388,0.00033343895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018120244,0.00009411593,0.0013299761,0.000075100485,0.00006398055,0.0001014869,0.00006048148,0.74195826,0.005280979,0.0043510217,0.00168158,0.2448218],"study_design_scores_gemma":[0.0000017288868,0.000008129381,0.000077284254,0.0000022970353,0.000003709573,0.0000043376504,0.0000011892361,0.99932814,0.00027188106,0.00020573512,0.00009392428,0.0000016108328],"about_ca_topic_score_codex":0.009880527,"about_ca_topic_score_gemma":0.010193012,"teacher_disagreement_score":0.009880527,"about_ca_system_score_codex":0.0004551204,"about_ca_system_score_gemma":0.0007379094,"threshold_uncertainty_score":0.019645989},"labels":[],"label_agreement":null},{"id":"W2007836289","doi":"10.1016/j.neunet.2014.08.007","title":"Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs)","year":2014,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Education, Science and Technology; Ministry of Education; National Natural Science Foundation of China","keywords":"Curse of dimensionality; Artificial neural network; Cluster analysis; Computer science; Fuzzy logic; Neuro-fuzzy; Radial basis function; Artificial intelligence; Polynomial; Mathematics; Pattern recognition (psychology); Fuzzy control system","score_opus":0.01458770127598802,"score_gpt":0.23210961373566685,"score_spread":0.21752191245967883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007836289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031455953,0.0004440275,0.96244997,0.00008464511,0.0000883694,0.00008456579,0.000063153435,0.0006917598,0.004637674],"genre_scores_gemma":[0.627072,0.00027835168,0.36944377,0.00006633161,0.000031026797,0.00020136153,0.00012451489,0.00004630366,0.0027363854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971026,0.000047282712,0.000018051958,0.00008613487,0.00009891747,0.000039364928],"domain_scores_gemma":[0.999705,0.000033936514,0.00004980568,0.000023837096,0.00016694328,0.000020376354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005457677,0.00061378593,0.0006048071,0.00039351816,0.0003323922,0.00052405585,0.0014449457,0.0009848938,0.0012758207],"category_scores_gemma":[0.0006397509,0.00031073374,0.00045920632,0.00038939703,0.00023785743,0.00057488645,0.0002859192,0.0004593451,0.0005384491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003103339,0.00015090393,0.0011250654,0.00039513365,0.00010202144,0.00014724245,0.000101344,0.63114107,0.078012586,0.010719676,0.0026585562,0.2751361],"study_design_scores_gemma":[0.000012684347,0.000107639826,0.0002986826,0.000010979449,0.000022284554,0.000045755693,0.000009788644,0.9867165,0.010332753,0.0008120645,0.0016179222,0.000012950136],"about_ca_topic_score_codex":0.0033576952,"about_ca_topic_score_gemma":0.0042712023,"teacher_disagreement_score":0.0033576952,"about_ca_system_score_codex":0.0005364336,"about_ca_system_score_gemma":0.0007471514,"threshold_uncertainty_score":0.0066763163},"labels":[],"label_agreement":null},{"id":"W2007970363","doi":"10.1109/ijcnn.2013.6706971","title":"FEBAMSOM-BAM*: Neural network model of human categorization of the N-bits parity problem","year":2013,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Categorization; Cognition; Computer science; Artificial neural network; Associative property; Parity (physics); Artificial intelligence; Feedforward neural network; Separable space; Task (project management); Bidirectional associative memory; Content-addressable memory; Machine learning; Pattern recognition (psychology); Mathematics; Psychology; Neuroscience; Engineering","score_opus":0.021615912698947855,"score_gpt":0.23108003077015118,"score_spread":0.20946411807120333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007970363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1756296,0.0011656728,0.7822145,0.0029068459,0.000349004,0.000074563926,0.00055426353,0.00062097097,0.03648458],"genre_scores_gemma":[0.9228151,0.0005363117,0.05779487,0.0002022194,0.00007550602,0.00013371622,0.00018486146,0.00004360907,0.018213853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999497,0.0000126502955,0.0000019339968,0.000013497722,0.000010300607,0.000011951764],"domain_scores_gemma":[0.99990773,0.000035920402,0.000010861457,0.000008993222,0.000020727744,0.00001581542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018052374,0.00033454862,0.0004042712,0.00023187978,0.00032982713,0.00059126806,0.0009283136,0.0011575277,0.0035122794],"category_scores_gemma":[0.00059142587,0.00020062315,0.00043030622,0.0003178098,0.00044749564,0.0010997856,0.0004168635,0.0007379759,0.00056068966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021900695,0.0000729439,0.0012737963,0.00013266985,0.00006789581,0.00031802995,0.00017607171,0.76376355,0.0085665705,0.17911534,0.004996164,0.041298084],"study_design_scores_gemma":[0.000009979383,0.000019469078,0.00017699762,0.0000049099003,0.000005307369,0.000033610693,0.000007816512,0.9536314,0.0004121986,0.045149747,0.0005433774,0.000005247878],"about_ca_topic_score_codex":0.0034718502,"about_ca_topic_score_gemma":0.0028762973,"teacher_disagreement_score":0.0035122794,"about_ca_system_score_codex":0.00041099274,"about_ca_system_score_gemma":0.0004576587,"threshold_uncertainty_score":0.011749804},"labels":[],"label_agreement":null},{"id":"W2009240858","doi":"10.1109/icsmc.2011.6083672","title":"Internal topographical structure in training autonomous robot","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Robot; Mobile robot; Computer science; Usability; Controller (irrigation); Artificial intelligence; Focus (optics); Human–computer interaction; Artificial neural network; Robot control; Layer (electronics)","score_opus":0.04529433821046721,"score_gpt":0.23967224512268648,"score_spread":0.19437790691221926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009240858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1957873,0.00023232038,0.8009118,0.00021916209,0.000020707004,0.000045525503,0.00002938644,0.0005397715,0.0022140637],"genre_scores_gemma":[0.9453195,0.000060393897,0.053942353,0.00004071984,0.000007733618,0.000033553766,0.000027828271,0.000022879627,0.00054511253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998272,0.000051829003,0.000009855411,0.000046469362,0.000045206703,0.00001940961],"domain_scores_gemma":[0.99918205,0.00042128772,0.00010390818,0.00013310049,0.00013174581,0.000027970562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046677838,0.0003110888,0.00031760143,0.00020821081,0.00025671648,0.00038113256,0.000566102,0.0005482135,0.0010052028],"category_scores_gemma":[0.0037170441,0.00035170492,0.00025807973,0.00020009739,0.0007477996,0.0012109072,0.0006360363,0.0006627903,0.00013535263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049955062,0.000031190324,0.0014416231,0.000055138084,0.000024814386,0.00007860692,0.00013201457,0.93035644,0.01022946,0.004717825,0.00014998995,0.052732933],"study_design_scores_gemma":[0.0000052735186,0.00005549011,0.0005469639,0.0000040719788,0.000005400487,0.00002501109,0.00001143306,0.9932928,0.001994952,0.003870548,0.00018286284,0.00000516361],"about_ca_topic_score_codex":0.0019266447,"about_ca_topic_score_gemma":0.0024194538,"teacher_disagreement_score":0.0019266447,"about_ca_system_score_codex":0.00035914194,"about_ca_system_score_gemma":0.0003537555,"threshold_uncertainty_score":0.0038309097},"labels":[],"label_agreement":null},{"id":"W2010084981","doi":"10.1109/nano.2011.6144315","title":"SET based Boltzmann machine and Hopfield neural networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Boltzmann machine; Restricted Boltzmann machine; Artificial neural network; Hopfield network; Computer science; Boltzmann constant; Probabilistic logic; Set (abstract data type); Stochastic neural network; Artificial intelligence; Statistical physics; Recurrent neural network; Physics; Quantum mechanics","score_opus":0.029999938575862917,"score_gpt":0.22974214187458383,"score_spread":0.19974220329872092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010084981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013328434,0.00052745925,0.9812624,0.00017689606,0.000072968374,0.000026085181,0.00003206167,0.00023291567,0.004340775],"genre_scores_gemma":[0.6323582,0.000835955,0.35765633,0.0001392314,0.00007430718,0.00014822716,0.000080504906,0.000069920134,0.00863739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996232,0.00012656052,0.000016629063,0.00004445142,0.00016487773,0.000024154868],"domain_scores_gemma":[0.9996426,0.00021256627,0.000025049352,0.000038156963,0.00006426219,0.000017450748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005847823,0.00032246817,0.0005761165,0.0003675424,0.0003369308,0.000787765,0.0011975671,0.0007908092,0.0016274204],"category_scores_gemma":[0.0019478616,0.00025492886,0.00043446085,0.00046743115,0.0007672496,0.0012773053,0.00060289947,0.0008052514,0.0003602959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006731237,0.000028828967,0.00037251442,0.00008762275,0.000055240107,0.00006943174,0.00008807919,0.7474829,0.004439015,0.18144822,0.00066995964,0.06519084],"study_design_scores_gemma":[0.0000071341597,0.000013522231,0.00006227343,0.0000072166586,0.0000059429112,0.000025334122,0.0000054929724,0.9541962,0.0010740856,0.043516792,0.0010757267,0.000010295827],"about_ca_topic_score_codex":0.0023552577,"about_ca_topic_score_gemma":0.0018825684,"teacher_disagreement_score":0.0023552577,"about_ca_system_score_codex":0.00062572083,"about_ca_system_score_gemma":0.00050652947,"threshold_uncertainty_score":0.0054442286},"labels":[],"label_agreement":null},{"id":"W2010144434","doi":"10.1142/s0129065700000211","title":"FPNA: INTERACTION BETWEEN FPGA AND NEURAL COMPUTATION","year":2000,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Field-programmable gate array; Computer science; Artificial neural network; Computation; Implementation; Network topology; Computer architecture; Computer hardware; Topology (electrical circuits); Artificial intelligence; Algorithm; Engineering","score_opus":0.027615065623175367,"score_gpt":0.30244353852389744,"score_spread":0.2748284729007221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010144434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028434549,0.0027561043,0.92154026,0.0010025394,0.00046814885,0.00005642983,0.000077603814,0.001851277,0.043813102],"genre_scores_gemma":[0.61580676,0.0027791543,0.36363396,0.00041388333,0.0002488988,0.00010906219,0.00011824766,0.00016369924,0.016726196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996587,0.00010483387,0.000019981351,0.000059652953,0.00012091105,0.00003585017],"domain_scores_gemma":[0.99964046,0.000169927,0.00002001169,0.000090005684,0.000060143804,0.000019435916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030757237,0.00033754116,0.00028617392,0.00033308915,0.00026770862,0.0013533481,0.00078678003,0.00053194095,0.0042136246],"category_scores_gemma":[0.0012079047,0.00019642971,0.00018755226,0.00035219864,0.00068846304,0.0019195189,0.00061699364,0.00071128766,0.0007684306],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022251479,0.000044477754,0.0012631635,0.00031953663,0.00006134682,0.0005438903,0.00020199026,0.06478286,0.028075568,0.45098805,0.006156411,0.44734025],"study_design_scores_gemma":[0.000058593738,0.00029101555,0.0011439011,0.00017266058,0.000056178473,0.001355586,0.00015627543,0.5278542,0.03926045,0.27919635,0.15038446,0.00007034823],"about_ca_topic_score_codex":0.0009268907,"about_ca_topic_score_gemma":0.0009846517,"teacher_disagreement_score":0.0042136246,"about_ca_system_score_codex":0.00046341546,"about_ca_system_score_gemma":0.00028656487,"threshold_uncertainty_score":0.014096022},"labels":[],"label_agreement":null},{"id":"W2010787909","doi":"10.1086/589448","title":"Fitting Probability Distributions to Animal Movement Trajectories: Using Artificial Neural Networks to Link Distance, Resources, and Memory","year":2008,"lang":"en","type":"article","venue":"The American Naturalist","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Link (geometry); Movement (music); Artificial neural network; Computer science; Artificial intelligence; Computer network; Physics","score_opus":0.02542370480373767,"score_gpt":0.2647566220710368,"score_spread":0.2393329172672991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010787909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73090667,0.00016468311,0.26757976,0.00021779693,0.000017591881,0.000047600286,0.00015653923,0.0003453192,0.0005640491],"genre_scores_gemma":[0.9600834,0.0000630781,0.039171405,0.00002978326,0.000008443549,0.0000496709,0.0002311909,0.000026239604,0.00033669855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994917,0.00022992743,0.000037157206,0.0001515257,0.00004458835,0.00004515048],"domain_scores_gemma":[0.9959629,0.0029468832,0.0004907605,0.00032704728,0.00019503871,0.00007737297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00232222,0.0007877688,0.00048345124,0.0012430388,0.00042631838,0.00081129867,0.0008139299,0.0008505953,0.00045267],"category_scores_gemma":[0.01395512,0.00059408066,0.00063837867,0.0009963613,0.0007656836,0.0013902023,0.0007258222,0.001075639,0.00012840355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008229456,0.0000642565,0.020600978,0.000015370388,0.00009661343,0.000030422894,0.0000845773,0.95310557,0.0006181479,0.0008559594,0.000116239135,0.024329582],"study_design_scores_gemma":[0.0000037698774,0.000007100065,0.0019821292,0.0000022883003,0.0000035895212,0.0000051351462,0.000011894182,0.99642557,0.00012371078,0.001398744,0.0000306835,0.0000053461376],"about_ca_topic_score_codex":0.034425553,"about_ca_topic_score_gemma":0.028957417,"teacher_disagreement_score":0.034425553,"about_ca_system_score_codex":0.0013912136,"about_ca_system_score_gemma":0.0008162631,"threshold_uncertainty_score":0.06845033},"labels":[],"label_agreement":null},{"id":"W2011056915","doi":"10.1103/physreve.63.046215","title":"Chaotic neural control","year":2001,"lang":"en","type":"article","venue":"Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Attractor; Chaotic; Lyapunov exponent; Artificial neural network; Reinforcement learning; Control theory (sociology); Controller (irrigation); Computer science; Lyapunov function; Statistical physics; Control of chaos; Control (management); Mathematics; Applied mathematics; Synchronization of chaos; Physics; Mathematical analysis; Artificial intelligence; Nonlinear system; Quantum mechanics","score_opus":0.012014703840000088,"score_gpt":0.304072056885321,"score_spread":0.29205735304532093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011056915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07364618,0.004924199,0.863273,0.0027182747,0.000593207,0.00006337028,0.00014196795,0.00020293212,0.05443682],"genre_scores_gemma":[0.954469,0.0020225,0.029423863,0.00030075957,0.00021715066,0.000111185574,0.000080058235,0.000023604402,0.013351963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998684,0.000027964214,0.0000070872707,0.00003353198,0.000043019958,0.000019972664],"domain_scores_gemma":[0.99979514,0.00009275738,0.000038170503,0.000015512716,0.000037968857,0.000020476842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002491154,0.00039268663,0.0004129392,0.00019556709,0.00032192204,0.00068389106,0.0006546879,0.00091844733,0.002021423],"category_scores_gemma":[0.0010550543,0.00012125214,0.00020178455,0.0002942395,0.0010057243,0.0006954697,0.0007142962,0.00070554204,0.00019807518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007008028,0.000029719395,0.0005473758,0.00016807985,0.00004396638,0.0002515711,0.000091726055,0.6350129,0.005794432,0.32292247,0.0024054423,0.032662176],"study_design_scores_gemma":[0.000017614117,0.000028854642,0.00012502441,0.0000101761525,0.000006536089,0.000043533742,0.000016750972,0.9124078,0.00067995046,0.08284338,0.0038123582,0.000008078693],"about_ca_topic_score_codex":0.0020349796,"about_ca_topic_score_gemma":0.0011216642,"teacher_disagreement_score":0.0020349796,"about_ca_system_score_codex":0.0005399614,"about_ca_system_score_gemma":0.00029635438,"threshold_uncertainty_score":0.0067623854},"labels":[],"label_agreement":null},{"id":"W2013199358","doi":"10.1016/j.neucom.2008.06.031","title":"A granular computing framework for self-organizing maps","year":2009,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Granular computing; Computer science; Self-organizing map; Theoretical computer science; Data mining; Artificial intelligence; Cluster analysis; Rough set","score_opus":0.015270087897530937,"score_gpt":0.2655429165834622,"score_spread":0.25027282868593126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013199358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011303168,0.00090955343,0.9788382,0.0006299752,0.00016843944,0.00003927927,0.00012444238,0.0002895009,0.0076975566],"genre_scores_gemma":[0.60439783,0.0013380095,0.3858073,0.0002215546,0.00038882188,0.0001995933,0.00023588458,0.00016587469,0.0072451476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995852,0.000106852094,0.0000333717,0.00006136397,0.00014378932,0.00006940232],"domain_scores_gemma":[0.9994235,0.00021727764,0.000052408574,0.000111525405,0.000107130174,0.000088234294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077881635,0.00041589062,0.0010530836,0.00110592,0.00088346365,0.0026783694,0.001555733,0.0010220815,0.0027275041],"category_scores_gemma":[0.002601809,0.0003347672,0.0008432002,0.0014980913,0.0011241377,0.0023277213,0.0020245942,0.0013189581,0.00046012033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041451975,0.00002738552,0.0002576699,0.000088305394,0.000033098007,0.00012979917,0.00011210044,0.112398855,0.0017646935,0.85511523,0.0023421855,0.027689135],"study_design_scores_gemma":[0.000009880975,0.000013809419,0.00013644795,0.000014831093,0.000011149197,0.000038708542,0.000027882763,0.5010811,0.00034859154,0.49512002,0.00318575,0.0000117579775],"about_ca_topic_score_codex":0.002487307,"about_ca_topic_score_gemma":0.0021278497,"teacher_disagreement_score":0.0027275041,"about_ca_system_score_codex":0.0007169317,"about_ca_system_score_gemma":0.00063012633,"threshold_uncertainty_score":0.009124398},"labels":[],"label_agreement":null},{"id":"W2013450515","doi":"10.1162/neco.2006.18.10.2509","title":"Nonlocal Estimation of Manifold Structure","year":2006,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Nonlinear dimensionality reduction; Curse of dimensionality; Tangent space; Manifold alignment; Manifold (fluid mechanics); Intrinsic dimension; Mathematics; Artificial intelligence; Kernel (algebra); Tangent; Nonparametric statistics; Dimension (graph theory); Pattern recognition (psychology); Algorithm; Dimensionality reduction; Computer science; Pure mathematics; Geometry","score_opus":0.007355929609288945,"score_gpt":0.23599824807772032,"score_spread":0.2286423184684314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013450515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021884268,0.00022812597,0.97653323,0.00021686046,0.000021628162,0.0000130339195,0.00001961765,0.0001943921,0.0008888713],"genre_scores_gemma":[0.71864647,0.00054678635,0.27715728,0.00013875209,0.00015386225,0.0000711693,0.00015973483,0.00013880251,0.0029871308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992218,0.00027651584,0.000026185797,0.00021013338,0.00021554162,0.00004970528],"domain_scores_gemma":[0.9961302,0.0016316391,0.0005351939,0.0011958275,0.00039813787,0.00010895368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015413602,0.00058024074,0.00071135577,0.00061125116,0.0004443501,0.0008270429,0.0009904226,0.0009586406,0.0014734245],"category_scores_gemma":[0.011713494,0.00031165642,0.0003916154,0.000587232,0.0014561359,0.0031860117,0.0024297268,0.0015587653,0.00057956117],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018858873,0.00010826415,0.0075970218,0.00035555876,0.00017805578,0.000350623,0.00058697024,0.4530557,0.03149065,0.20726106,0.0037414844,0.29508594],"study_design_scores_gemma":[0.000004576616,0.00008637252,0.0011733541,0.00001650925,0.000012358431,0.00011855346,0.000051864034,0.9333561,0.005694062,0.057708256,0.0017587033,0.000019312058],"about_ca_topic_score_codex":0.00062632107,"about_ca_topic_score_gemma":0.0009411953,"teacher_disagreement_score":0.0015413602,"about_ca_system_score_codex":0.00044703303,"about_ca_system_score_gemma":0.00036237203,"threshold_uncertainty_score":0.008151591},"labels":[],"label_agreement":null},{"id":"W2013864106","doi":"10.1142/s0218488505003357","title":"BALANCED FUZZY COMPUTING UNIT","year":2005,"lang":"en","type":"article","venue":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fuzzy logic; Fuzzy set operations; Defuzzification; Fuzzy number; Fuzzy classification; Neuro-fuzzy; Computer science; Artificial intelligence; Type-2 fuzzy sets and systems; Fuzzy associative matrix; Fuzzy Control Language; Mathematics; Theoretical computer science; Fuzzy set; Fuzzy control system","score_opus":0.020768127188699605,"score_gpt":0.2901990328543228,"score_spread":0.2694309056656232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013864106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076971166,0.00074763404,0.88147587,0.00044148625,0.00022714844,0.0000900093,0.00015169998,0.00022696763,0.039668016],"genre_scores_gemma":[0.75486493,0.0006432283,0.23060152,0.00019321016,0.00012596321,0.00018354335,0.00017247384,0.0000701818,0.0131449085],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995272,0.00006865674,0.000035580855,0.00013191454,0.00016737985,0.0000692057],"domain_scores_gemma":[0.9994795,0.00012950378,0.000069646594,0.00007766631,0.00017602473,0.00006775868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004450345,0.00038427356,0.0004634519,0.00071703724,0.0007725058,0.0016400769,0.00079456874,0.0006650482,0.0055890847],"category_scores_gemma":[0.0018939775,0.0001792348,0.00032987964,0.0007663114,0.0012901372,0.0030960934,0.0011452646,0.00076090806,0.00094931974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008651698,0.000017750328,0.00033279246,0.0001014472,0.000015768659,0.00016494529,0.000203529,0.014752847,0.014800656,0.9239826,0.0009404052,0.044600777],"study_design_scores_gemma":[0.00001771004,0.00013793244,0.0004564342,0.000053448843,0.000024452185,0.000402676,0.0001792609,0.19191587,0.014306394,0.7621969,0.030273974,0.00003492021],"about_ca_topic_score_codex":0.00053969666,"about_ca_topic_score_gemma":0.00036759514,"teacher_disagreement_score":0.0055890847,"about_ca_system_score_codex":0.0006980208,"about_ca_system_score_gemma":0.00041639764,"threshold_uncertainty_score":0.018697381},"labels":[],"label_agreement":null},{"id":"W2013930007","doi":"10.1109/icecs.2011.6122304","title":"FPGA-implementation of high-speed MLP neural network","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Field-programmable gate array; Backpropagation; Artificial neural network; Multilayer perceptron; Parallel computing; Activation function; Critical path method; Path (computing); Nonlinear system; Perceptron; Embedded system; Artificial intelligence; Engineering","score_opus":0.03860625694814743,"score_gpt":0.2740014172305932,"score_spread":0.23539516028244575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013930007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18621936,0.0019365145,0.7661756,0.00034802084,0.0004913281,0.00016125312,0.00046819355,0.010567566,0.03363212],"genre_scores_gemma":[0.7810622,0.00055293844,0.20772679,0.00011729357,0.00006310134,0.00008357338,0.0003508823,0.00011023756,0.009932993],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986327,0.000020228466,0.000009242755,0.000024778763,0.000054549033,0.000027760376],"domain_scores_gemma":[0.9998846,0.000030312574,0.000011467815,0.000022182378,0.00004261062,0.000008887488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014174182,0.0003501933,0.00021035028,0.00027697664,0.00014303,0.00034787803,0.00070107146,0.00029791216,0.0056647733],"category_scores_gemma":[0.0002788058,0.00018146913,0.0001387074,0.00021100917,0.000096466,0.0004420025,0.00017953584,0.00030833867,0.001149582],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010385846,0.00021128496,0.0027212875,0.0009202657,0.0001844031,0.0010894003,0.00013012644,0.070461184,0.2755484,0.010353478,0.011821118,0.6255206],"study_design_scores_gemma":[0.00029760302,0.0011769243,0.006504573,0.00012875578,0.00015901083,0.0017629268,0.000064434804,0.6023193,0.32689315,0.0030786083,0.057544813,0.00006989837],"about_ca_topic_score_codex":0.0011375292,"about_ca_topic_score_gemma":0.0016059881,"teacher_disagreement_score":0.0056647733,"about_ca_system_score_codex":0.00028933986,"about_ca_system_score_gemma":0.00024530868,"threshold_uncertainty_score":0.018950522},"labels":[],"label_agreement":null},{"id":"W2015745559","doi":"10.1109/icnc.2012.6234540","title":"Improving classification through ensemble neural networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Machine learning; Neural ensemble; Ensemble learning; Time delay neural network; Types of artificial neural networks; Ensemble forecasting; Deep neural networks; Data mining","score_opus":0.039150392049835586,"score_gpt":0.2682080769141744,"score_spread":0.22905768486433883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015745559","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049791135,0.0028817682,0.94136614,0.00076094497,0.0004715591,0.00007549508,0.00018439771,0.0017409269,0.0027276578],"genre_scores_gemma":[0.6326649,0.0026282903,0.35967794,0.0005431465,0.00075231865,0.00014720787,0.00092155,0.00017668077,0.0024879193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99708277,0.0009239288,0.00022139985,0.0006343696,0.00092548045,0.00021197362],"domain_scores_gemma":[0.99488884,0.0022435947,0.00050956855,0.0009465716,0.0013018514,0.00010947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004598833,0.0018766071,0.0023558103,0.0030728474,0.0006736362,0.0018474769,0.0015061328,0.0018481611,0.0011562477],"category_scores_gemma":[0.012037091,0.0005256041,0.0013618636,0.0031054886,0.00050967,0.0041457,0.0016450391,0.002425349,0.0010647938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014617885,0.00022243916,0.008842228,0.00018348177,0.0007542705,0.00012811425,0.0001278266,0.3402035,0.011963234,0.0039117867,0.0049058506,0.628611],"study_design_scores_gemma":[0.000006859162,0.000084955675,0.0014261298,0.000026057158,0.00010904832,0.000059056892,0.000022190647,0.984855,0.0050638574,0.006483942,0.0018384223,0.000024498426],"about_ca_topic_score_codex":0.002237477,"about_ca_topic_score_gemma":0.0030257443,"teacher_disagreement_score":0.004598833,"about_ca_system_score_codex":0.0005561032,"about_ca_system_score_gemma":0.0004841466,"threshold_uncertainty_score":0.024321198},"labels":[],"label_agreement":null},{"id":"W2015765178","doi":"10.1016/j.neunet.2010.10.001","title":"Quasi-objective nonlinear principal component analysis","year":2010,"lang":"en","type":"review","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Attractor; Feedforward neural network; Artificial neural network; Bottleneck; Feed forward; Principal component analysis; Nonlinear system; Computer science; Uniqueness; Control theory (sociology); Algorithm; Recurrent neural network; Chaotic; Pattern recognition (psychology); Artificial intelligence; Mathematics; Mathematical analysis; Engineering; Control (management); Control engineering; Physics","score_opus":0.030294728040366925,"score_gpt":0.31260981759813306,"score_spread":0.28231508955776613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015765178","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002447506,0.46840414,0.5113393,0.0019755901,0.0015427382,0.00006443696,0.00020322112,0.00027123306,0.0137518225],"genre_scores_gemma":[0.05225407,0.7464044,0.18205164,0.0005461016,0.002573887,0.00020023448,0.0006991377,0.00019235603,0.015078212],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99949133,0.00011784456,0.00004605055,0.00007418115,0.00025025997,0.000020447129],"domain_scores_gemma":[0.99897146,0.00044876026,0.00008941118,0.00007213135,0.00039873619,0.00001946567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013201206,0.0011110107,0.0015525802,0.0012018982,0.00021995317,0.0010676739,0.00096351886,0.00077857473,0.0022781065],"category_scores_gemma":[0.0024830927,0.0004032387,0.00051825756,0.0031069417,0.00083997246,0.0018085737,0.0008597501,0.0009062423,0.001255005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004064306,0.000036027264,0.00029783556,0.0029231093,0.00012176085,0.000034433036,0.000018528044,0.014725269,0.0010247594,0.028981261,0.014707109,0.9370893],"study_design_scores_gemma":[0.000060298076,0.0002563378,0.0069524753,0.0022216742,0.00047371018,0.0012036733,0.00013437528,0.32480693,0.007623191,0.16243754,0.4936565,0.00017337309],"about_ca_topic_score_codex":0.0014123081,"about_ca_topic_score_gemma":0.0015370966,"teacher_disagreement_score":0.0022781065,"about_ca_system_score_codex":0.00055258186,"about_ca_system_score_gemma":0.0010722817,"threshold_uncertainty_score":0.00762105},"labels":[],"label_agreement":null},{"id":"W2016189525","doi":"10.1142/s1469026801000068","title":"LEARNING OF FUZZY AUTOMATA","year":2001,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fuzzy logic; Generalization; Artificial intelligence; Fuzzy electronics; State (computer science); Theoretical computer science; Finite-state machine; Automaton; Neuro-fuzzy; Fuzzy control system; Algorithm; Mathematics","score_opus":0.024432313043924996,"score_gpt":0.3143575538523547,"score_spread":0.2899252408084297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016189525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02440937,0.0008245203,0.9598542,0.00054687465,0.00014168092,0.00005943936,0.00009053753,0.00031421147,0.013759198],"genre_scores_gemma":[0.76795506,0.0013603158,0.2196628,0.00033898174,0.0001930237,0.00020910527,0.00024668762,0.00005656456,0.009977471],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992762,0.00022069558,0.000052366184,0.00020382061,0.00018570472,0.00006113884],"domain_scores_gemma":[0.9980318,0.0013197444,0.00012822216,0.00026071796,0.00020480329,0.000054718083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009045541,0.0004028132,0.00058194826,0.00046577462,0.00050390826,0.0014108616,0.0008123186,0.0009878041,0.0037759282],"category_scores_gemma":[0.006956618,0.0002597985,0.0007707319,0.0003889107,0.0016387718,0.0019289762,0.0011252664,0.0012433408,0.0005795656],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007418155,0.000048638125,0.0010449156,0.00019649549,0.000047675003,0.00013552803,0.00036135968,0.20552468,0.003200213,0.6906205,0.0014754374,0.09727035],"study_design_scores_gemma":[0.000017900338,0.000046927453,0.0001459214,0.00003360234,0.0000133691465,0.00007322052,0.00005192702,0.5440308,0.0016994379,0.44792956,0.005940163,0.000017125116],"about_ca_topic_score_codex":0.00125454,"about_ca_topic_score_gemma":0.001128924,"teacher_disagreement_score":0.0037759282,"about_ca_system_score_codex":0.0009456051,"about_ca_system_score_gemma":0.0005981093,"threshold_uncertainty_score":0.012631714},"labels":[],"label_agreement":null},{"id":"W2017088518","doi":"10.1016/s0378-4371(02)00611-8","title":"Using principal component analysis to enhance the generalized multifractal analysis approach to textural segmentation: Theory and application to microresistivity well logs","year":2002,"lang":"en","type":"article","venue":"Physica A Statistical Mechanics and its Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Compute Canada","funders":"","keywords":"Principal component analysis; Multifractal system; Segmentation; Pattern recognition (psychology); Measure (data warehouse); Artificial intelligence; Mathematics; Basis (linear algebra); Similarity (geometry); Algorithm; Computer science; Function (biology); Texture (cosmology); Sliding window protocol; Uncorrelated; Fractal; Data mining; Image (mathematics); Window (computing); Statistics; Mathematical analysis; Geometry","score_opus":0.022723238725869715,"score_gpt":0.3018295518848186,"score_spread":0.27910631315894885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017088518","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019388273,0.00018342517,0.97841334,0.00014946504,0.000040321225,0.000028963545,0.00006757695,0.0006542818,0.0010743324],"genre_scores_gemma":[0.2756862,0.0006160383,0.7200897,0.000081183585,0.00013128527,0.00008506709,0.0002515736,0.0004274007,0.002631565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997447,0.0000838827,0.000014974681,0.000054316097,0.0000752282,0.000026915099],"domain_scores_gemma":[0.9989279,0.00045108065,0.00008797021,0.00013561921,0.0003539845,0.000043347492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007461509,0.0007343146,0.00061139156,0.0016120856,0.0005363942,0.0014123673,0.0006570181,0.00070884207,0.0021133802],"category_scores_gemma":[0.0028417732,0.00035523655,0.000749959,0.0016969822,0.00064906356,0.0017445784,0.00068038254,0.000981243,0.0007719212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021649302,0.00017322267,0.001843173,0.0003527735,0.00012178245,0.00021494128,0.00046089696,0.13238136,0.13556208,0.056687947,0.004214726,0.6677706],"study_design_scores_gemma":[0.000008326893,0.00003553819,0.0019191814,0.000011067716,0.000034770277,0.000063683685,0.000049549442,0.96224195,0.0098869065,0.02306777,0.0026414176,0.000039699287],"about_ca_topic_score_codex":0.0033869334,"about_ca_topic_score_gemma":0.005307778,"teacher_disagreement_score":0.0033869334,"about_ca_system_score_codex":0.00036320518,"about_ca_system_score_gemma":0.0006734398,"threshold_uncertainty_score":0.0070699453},"labels":[],"label_agreement":null},{"id":"W2017815570","doi":"10.5539/cis.v7n4p55","title":"Decision-Making in Physical Intelligent Systems Regulated by Growth Rate Factors","year":2014,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Computer science; Blueprint; Physical system; Field (mathematics); Computation; Process (computing); Intelligent decision support system; Component (thermodynamics); Cyber-physical system; Control (management); Artificial intelligence; Algorithm; Thermodynamics; Mathematics; Mechanical engineering","score_opus":0.008635382084829127,"score_gpt":0.24686746139437146,"score_spread":0.23823207930954232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017815570","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5146331,0.0018382524,0.44886997,0.002354302,0.0001397748,0.00010154054,0.00014081788,0.00027439123,0.031647902],"genre_scores_gemma":[0.98301506,0.00043695487,0.014253586,0.00007787524,0.000028495833,0.000059195107,0.000031163923,0.000017912038,0.0020798459],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990721,0.0003681489,0.00005099121,0.00019318204,0.00018205967,0.00013349717],"domain_scores_gemma":[0.9977684,0.0013510204,0.00038740248,0.00011610088,0.00021653876,0.00016052737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001573041,0.00044583293,0.00072768214,0.0004889747,0.0006955916,0.002286626,0.00080554764,0.0009731988,0.0016271741],"category_scores_gemma":[0.005095753,0.00026532085,0.0006755759,0.0004216109,0.0020202296,0.00188821,0.00080014,0.0009715368,0.00026377293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019322736,0.00009936527,0.0029476513,0.00013633975,0.000096464755,0.00042677842,0.00062898843,0.46651036,0.010171027,0.5024009,0.00079986174,0.015589001],"study_design_scores_gemma":[0.000030223488,0.00005696228,0.0008625638,0.000017231107,0.000020935497,0.000046073586,0.000109334775,0.75342834,0.0012865247,0.24340093,0.0007085607,0.000032290587],"about_ca_topic_score_codex":0.0019687815,"about_ca_topic_score_gemma":0.00091561204,"teacher_disagreement_score":0.002286626,"about_ca_system_score_codex":0.0010711557,"about_ca_system_score_gemma":0.001040885,"threshold_uncertainty_score":0.0083191395},"labels":[],"label_agreement":null},{"id":"W2019286445","doi":"10.1109/jstsp.2010.2051258","title":"Introduction to the Special Issue on Signal and Information Processing for Social Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Signal processing; SIGNAL (programming language); Data science; Information processing; Telecommunications; Radar; Psychology","score_opus":0.010818985491800359,"score_gpt":0.2596171325853093,"score_spread":0.24879814709350892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019286445","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00081642624,0.08092071,0.0396248,0.039599422,0.7961867,0.00029674315,0.001476302,0.0009468082,0.04013213],"genre_scores_gemma":[0.0036468816,0.05633554,0.009316875,0.013602627,0.8187568,0.00024795547,0.0017528306,0.00076469214,0.09557573],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99833065,0.00023462165,0.00019048901,0.00040446443,0.00071405625,0.00012562441],"domain_scores_gemma":[0.9943803,0.0022257017,0.00028951155,0.0004652002,0.0018238059,0.0008155055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002048862,0.0020254317,0.0024112645,0.004012865,0.0011777349,0.005352958,0.0015804569,0.0035495176,0.06233158],"category_scores_gemma":[0.0054951427,0.0007397569,0.0018804086,0.003037958,0.0010685852,0.0044004177,0.001848096,0.0073565817,0.038664278],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002971999,0.000043168166,0.00012631713,0.00039296967,0.000038851365,0.00010358952,0.00002895795,0.0002761776,0.0006731567,0.003852185,0.9362963,0.058138724],"study_design_scores_gemma":[0.000010985056,0.00006507596,0.0005130208,0.00018709473,0.000025600693,0.00043988274,0.000025654219,0.00084802555,0.00023168334,0.007922678,0.9897043,0.000026049442],"about_ca_topic_score_codex":0.00046529112,"about_ca_topic_score_gemma":0.0007924897,"teacher_disagreement_score":0.06233158,"about_ca_system_score_codex":0.0009794954,"about_ca_system_score_gemma":0.0011876395,"threshold_uncertainty_score":0.20851994},"labels":[],"label_agreement":null},{"id":"W2019619214","doi":"10.1007/s10115-012-0551-4","title":"A new approach to radial basis function-based polynomial neural networks: analysis and design","year":2012,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Particle swarm optimization; Radial basis function; Polynomial; Computer science; Artificial neural network; Perceptron; Radial basis function network; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.020905094039850285,"score_gpt":0.23030253814077742,"score_spread":0.20939744410092714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019619214","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003964149,0.00032530748,0.9985707,0.00007059059,0.000036054593,0.000010284401,0.00000816319,0.00003749871,0.0005450001],"genre_scores_gemma":[0.1547897,0.0034750053,0.83021224,0.0003331481,0.00055206113,0.00030619663,0.00014402432,0.00017065952,0.010017045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989814,0.00035475442,0.000052078205,0.00016981593,0.00039708035,0.000044862416],"domain_scores_gemma":[0.99888295,0.0004203078,0.000073839015,0.00008716765,0.00049821736,0.000037488302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018267618,0.0011082935,0.001170495,0.0007614949,0.00039034456,0.001341197,0.0021080321,0.0016772059,0.0022544242],"category_scores_gemma":[0.0045174137,0.0005596757,0.0009039208,0.0011234076,0.0008266636,0.0019468562,0.0011860743,0.0021968517,0.0011906315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007994013,0.00009244097,0.0003886204,0.00035804068,0.00015194324,0.00009635101,0.00009498386,0.6218399,0.005836134,0.16031595,0.005056097,0.20568962],"study_design_scores_gemma":[0.0000035374585,0.0000143620155,0.000031850246,0.0000063996113,0.00001014987,0.000019622668,0.0000030267124,0.98915416,0.00034095914,0.008957814,0.001452392,0.0000056420927],"about_ca_topic_score_codex":0.0030727056,"about_ca_topic_score_gemma":0.0033863941,"teacher_disagreement_score":0.0030727056,"about_ca_system_score_codex":0.0008607121,"about_ca_system_score_gemma":0.0010230386,"threshold_uncertainty_score":0.009660959},"labels":[],"label_agreement":null},{"id":"W2019653449","doi":"10.1007/s00500-006-0064-8","title":"Neural Units with Higher-Order Synaptic Operations for Robotic Image Processing Applications","year":2006,"lang":"en","type":"article","venue":"Soft Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial neural network; Image processing; Routing (electronic design automation); Mobile robot; Enhanced Data Rates for GSM Evolution; Hough transform; Artificial intelligence; Process (computing); Edge detection; Robot; Computer vision; Image (mathematics); Embedded system","score_opus":0.015790788394121173,"score_gpt":0.25123960017507835,"score_spread":0.23544881178095717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019653449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1777751,0.0019808868,0.799838,0.00064628676,0.00043027883,0.0000612725,0.000063735984,0.0007288773,0.018475706],"genre_scores_gemma":[0.8478406,0.0007387848,0.14264454,0.00011975615,0.0000900611,0.000052051586,0.00004704119,0.00006356708,0.008403565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99995077,0.000008190422,0.000005004251,0.0000067429396,0.000021330152,0.000008001248],"domain_scores_gemma":[0.9998714,0.00004353865,0.00001216532,0.000029497958,0.000031666674,0.000011700335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016682687,0.00018503422,0.00023284041,0.00013804687,0.00018790113,0.00047138167,0.00048808972,0.00052824744,0.0032646938],"category_scores_gemma":[0.0005280851,0.00011633903,0.00018129393,0.00031230244,0.0002824447,0.00076195254,0.00026925188,0.0006241603,0.00038578085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021547763,0.00022298584,0.000767351,0.0004345787,0.000059944377,0.00032258424,0.00013918438,0.17209437,0.22999981,0.22073376,0.003933863,0.37107614],"study_design_scores_gemma":[0.000011582888,0.00008269329,0.0006395337,0.000017179107,0.000020625763,0.00013228548,0.00002219754,0.9140143,0.034091827,0.04603542,0.004916711,0.000015694035],"about_ca_topic_score_codex":0.0004546014,"about_ca_topic_score_gemma":0.0015354194,"teacher_disagreement_score":0.0032646938,"about_ca_system_score_codex":0.0001864907,"about_ca_system_score_gemma":0.00027328462,"threshold_uncertainty_score":0.010921478},"labels":[],"label_agreement":null},{"id":"W2020488014","doi":"10.5402/2012/324194","title":"Using Radial Basis Function Networks for Function Approximation and Classification","year":2012,"lang":"en","type":"article","venue":"ISRN Applied Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":248,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University; City University of Hong Kong","keywords":"Radial basis function; Hierarchical RBF; Radial basis function network; Computer science; Function approximation; Artificial intelligence; Basis (linear algebra); Perceptron; Nonlinear system; Artificial neural network; Function (biology); Multilayer perceptron; Mathematical optimization; Machine learning; Algorithm; Mathematics; Physics","score_opus":0.06864731073519341,"score_gpt":0.2725820986777176,"score_spread":0.20393478794252418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020488014","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013470231,0.0046271435,0.9909355,0.00019860794,0.00012727939,0.00002989406,0.000059502603,0.00048255405,0.0021924225],"genre_scores_gemma":[0.12184669,0.016317183,0.8525787,0.00029631838,0.0005974609,0.00022496218,0.00062604254,0.00020157038,0.0073109614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983352,0.00047477972,0.00012979333,0.00029655697,0.00068713113,0.000076530545],"domain_scores_gemma":[0.99901843,0.00040370124,0.000100326215,0.00016472298,0.00028983378,0.000022997345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018109179,0.001454686,0.0016172588,0.0018885972,0.00044118482,0.0021324696,0.0016329786,0.0021217312,0.00224806],"category_scores_gemma":[0.004452888,0.00053396076,0.0010878869,0.0033695127,0.00084756536,0.0023983708,0.0012641912,0.0020113268,0.003090616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011578018,0.00007275659,0.0010258,0.0006913178,0.00018089481,0.00020968002,0.00012361168,0.1754995,0.010407818,0.06956185,0.009276864,0.73283416],"study_design_scores_gemma":[0.000010171916,0.000046983983,0.00041295958,0.000097346296,0.00003948585,0.00022741484,0.00002055144,0.931216,0.0059753037,0.03458351,0.02731578,0.000054474105],"about_ca_topic_score_codex":0.0030943486,"about_ca_topic_score_gemma":0.0017214286,"teacher_disagreement_score":0.0030943486,"about_ca_system_score_codex":0.00072175084,"about_ca_system_score_gemma":0.00068543875,"threshold_uncertainty_score":0.009577215},"labels":[],"label_agreement":null},{"id":"W2021076714","doi":"10.1115/gt2007-27542","title":"Management of an Industrial Facility Using an Artificial Neural Network Monitoring System","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Artificial neural network; Component (thermodynamics); Modular design; Computer science; Computation; Operating cost; Engineering; Artificial intelligence","score_opus":0.11917410536777735,"score_gpt":0.3112625668925244,"score_spread":0.19208846152474707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021076714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52945334,0.0003318059,0.4561156,0.0003417291,0.0000675984,0.00021363179,0.00040629483,0.003982956,0.009086996],"genre_scores_gemma":[0.9803294,0.00008177602,0.018203901,0.000020234596,0.000012499173,0.000049282484,0.000115060015,0.000011051836,0.0011768098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997402,0.0000663722,0.000017390346,0.00005954709,0.000094133524,0.000022312703],"domain_scores_gemma":[0.99958426,0.00012931829,0.00010408512,0.000037115664,0.00011804584,0.000027150516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003708505,0.0005057636,0.00035349707,0.00054210075,0.00035048588,0.00053810433,0.00061413355,0.0005219046,0.0013218542],"category_scores_gemma":[0.0008768423,0.0001726175,0.0001529883,0.00037063385,0.00013889177,0.0007021032,0.0003069528,0.00027087994,0.00024868612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085926463,0.0006457758,0.029598236,0.00021407907,0.00008724726,0.0003895877,0.000118462376,0.65604645,0.035368863,0.0008148826,0.002116335,0.2737409],"study_design_scores_gemma":[0.000016486989,0.00014832488,0.0030823848,0.000008285452,0.000014980683,0.000029185352,0.000016105498,0.9912764,0.004798411,0.00019974473,0.00039704397,0.000012734254],"about_ca_topic_score_codex":0.0026480553,"about_ca_topic_score_gemma":0.0030170456,"teacher_disagreement_score":0.0026480553,"about_ca_system_score_codex":0.0006053994,"about_ca_system_score_gemma":0.000334555,"threshold_uncertainty_score":0.0052652955},"labels":[],"label_agreement":null},{"id":"W2021528888","doi":"10.1109/tnb.2003.820275","title":"Adaptive learning algorithms for nernst potential and I-V curves in nerve cell membrane ion channels modeled as hidden markov models","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on NanoBioscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nernst equation; Markov chain; Computer science; Markov process; Hidden Markov model; Statistical physics; Markov model; Ion; Algorithm; Physics; Biophysics; Biological system; Artificial intelligence; Mathematics; Machine learning; Electrode; Quantum mechanics; Biology","score_opus":0.023128306865190136,"score_gpt":0.24097689530311464,"score_spread":0.2178485884379245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021528888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018268803,0.00014582567,0.9805664,0.00021068864,0.000015612235,0.000022169494,0.000016782038,0.00021960447,0.00053415104],"genre_scores_gemma":[0.5585938,0.00028140942,0.43773898,0.00014536276,0.000048315753,0.00027246305,0.00011458723,0.00016356204,0.0026414362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999785,0.00006673239,0.000013455544,0.000057169895,0.00004898794,0.000028705159],"domain_scores_gemma":[0.9984819,0.0010809756,0.00016725951,0.00005373168,0.00017040038,0.000045743218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001394002,0.0006011225,0.00064823794,0.00050860544,0.00043221802,0.00072499126,0.0014524534,0.0013026003,0.0014159449],"category_scores_gemma":[0.005392388,0.0005227682,0.00054097804,0.00050025695,0.000997007,0.0013305208,0.0009144681,0.0013563734,0.00028696953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002109622,0.000010137698,0.00018433094,0.00001456148,0.000008782688,0.000010384265,0.000025674919,0.97407305,0.0004404231,0.007548416,0.0001643661,0.017498832],"study_design_scores_gemma":[0.0000028770185,0.000002487341,0.000016163554,0.0000010807768,7.3204865e-7,0.000001634608,0.000001188384,0.99745566,0.00011574548,0.0023641526,0.000036618898,0.0000016582129],"about_ca_topic_score_codex":0.006757169,"about_ca_topic_score_gemma":0.0047459,"teacher_disagreement_score":0.006757169,"about_ca_system_score_codex":0.0013581946,"about_ca_system_score_gemma":0.0010701877,"threshold_uncertainty_score":0.013435662},"labels":[],"label_agreement":null},{"id":"W2021671116","doi":"10.1080/03610920600920503","title":"Estimation of Periodic Bilinear Time Series Models","year":2006,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Bilinear interpolation; Series (stratigraphy); Applied mathematics; Cumulant; Monte Carlo method; Nonlinear system; State-space representation; Representation (politics); Class (philosophy); Mathematics; Set (abstract data type); State space; Estimation theory; Algorithm; Computer science; Statistics; Artificial intelligence; Physics","score_opus":0.022150640650161212,"score_gpt":0.35283102235496505,"score_spread":0.33068038170480385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021671116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06117061,0.00023604282,0.9362563,0.00009833144,0.000053113054,0.00003608336,0.00017381716,0.00028359873,0.0016922014],"genre_scores_gemma":[0.8994828,0.0006103062,0.096383095,0.000043191078,0.000078494224,0.00009271648,0.00052863307,0.00007347765,0.0027073743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994272,0.00018733735,0.000030309313,0.00010722123,0.00018774137,0.000060294977],"domain_scores_gemma":[0.99899966,0.00048203784,0.0002146806,0.00013492457,0.00012409248,0.000044641773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012879224,0.00058667763,0.0007656917,0.00064191635,0.0002635854,0.000591895,0.0007089174,0.000537606,0.0013635397],"category_scores_gemma":[0.0047767907,0.00038984147,0.0005155745,0.0005784952,0.0003232993,0.0011465449,0.0009908406,0.0007959252,0.000321903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030116606,0.00012276434,0.0053111687,0.00023401772,0.00010386365,0.0002726215,0.00017580988,0.7970258,0.013254965,0.040239178,0.0013374739,0.14162125],"study_design_scores_gemma":[0.000002976743,0.000023201577,0.0004412615,0.0000049086902,0.0000052099576,0.00003946729,0.000009780817,0.9908744,0.0012198357,0.006782076,0.0005890909,0.000007712768],"about_ca_topic_score_codex":0.0015954624,"about_ca_topic_score_gemma":0.0012291676,"teacher_disagreement_score":0.0015954624,"about_ca_system_score_codex":0.00027197253,"about_ca_system_score_gemma":0.0006355661,"threshold_uncertainty_score":0.006811261},"labels":[],"label_agreement":null},{"id":"W2021856897","doi":"10.1080/01621459.2000.10473894","title":"Functional Components of Variation in Handwriting","year":2000,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Handwriting; Scripting language; Smoothing; Computer science; Replication (statistics); Variation (astronomy); Sample (material); Function (biology); Forcing (mathematics); Differential equation; Process (computing); Pattern recognition (psychology); Mathematics; Artificial intelligence; Statistics; Mathematical analysis","score_opus":0.012201554428516902,"score_gpt":0.2457886473898067,"score_spread":0.23358709296128982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021856897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9668297,0.00015387221,0.029994333,0.00010512806,0.000018486084,0.00006639835,0.0008336678,0.00017965517,0.0018188735],"genre_scores_gemma":[0.9973526,0.000022972446,0.0021060968,0.0000043302025,0.0000050495055,0.000019548519,0.00024627475,0.000014762386,0.00022842495],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993339,0.00018739849,0.00006361596,0.00021212928,0.00014499371,0.000057953665],"domain_scores_gemma":[0.9967822,0.001993467,0.00030532174,0.0003349209,0.0004659487,0.00011822378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001470742,0.00040093678,0.0003441182,0.0022486805,0.0002470147,0.0008584936,0.00025734823,0.00033629348,0.0024451052],"category_scores_gemma":[0.009284692,0.00017994923,0.00043116254,0.001632712,0.0006925955,0.00060217356,0.00047349854,0.00030673837,0.00025537235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077843183,0.00022069452,0.5869604,0.00049807806,0.000982489,0.0010104012,0.002331836,0.08395581,0.06109018,0.014273083,0.0017059174,0.24619277],"study_design_scores_gemma":[0.000008571787,0.00009478385,0.8571032,0.000019478184,0.000053830765,0.00033006677,0.0002459824,0.13358113,0.0020272485,0.0057423096,0.00074460334,0.000048755195],"about_ca_topic_score_codex":0.0043381127,"about_ca_topic_score_gemma":0.002179689,"teacher_disagreement_score":0.0043381127,"about_ca_system_score_codex":0.0005088955,"about_ca_system_score_gemma":0.0003376197,"threshold_uncertainty_score":0.008625746},"labels":[],"label_agreement":null},{"id":"W2022595735","doi":"10.5539/cis.v2n1p48","title":"Complexity Analysis in Heterogeneous System","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.017881341618916868,"score_gpt":0.2518921241736564,"score_spread":0.23401078255473956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022595735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30828798,0.00038480374,0.67265815,0.0010017224,0.000053656702,0.00010037976,0.00037410148,0.00057750795,0.016561776],"genre_scores_gemma":[0.9762381,0.00010551642,0.02014336,0.000050686223,0.000053392403,0.0000656553,0.00023089256,0.00008379865,0.003028543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843174,0.00046949985,0.000071615446,0.00028440036,0.00048127933,0.00026137673],"domain_scores_gemma":[0.992316,0.005118772,0.0007250248,0.000637038,0.0008106124,0.00039242924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013015065,0.0005322853,0.0011173945,0.0018685814,0.0011455968,0.0024659177,0.00085851725,0.000712934,0.005268484],"category_scores_gemma":[0.010512204,0.00036039707,0.00112721,0.0014122247,0.0010680223,0.0038739373,0.0020445522,0.0011604865,0.00026525988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020774358,0.00008153939,0.0070663607,0.00012562113,0.00017887152,0.0005185607,0.00044900458,0.513912,0.002163116,0.4357729,0.0035538431,0.03597034],"study_design_scores_gemma":[0.000004733016,0.000011165349,0.0007559531,0.0000036169085,0.00001744283,0.000032841515,0.000041989242,0.91264486,0.00021954761,0.085890934,0.00036813304,0.000008799425],"about_ca_topic_score_codex":0.005505122,"about_ca_topic_score_gemma":0.0026135473,"teacher_disagreement_score":0.005505122,"about_ca_system_score_codex":0.002373964,"about_ca_system_score_gemma":0.00103665,"threshold_uncertainty_score":0.017624855},"labels":[],"label_agreement":null},{"id":"W2023822800","doi":"10.3182/20020721-6-es-1901.00694","title":"INCREMENTAL NEURAL LEARNING BY DYNAMIC AND SPATIAL CHANGING WEIGHTS","year":2002,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Forgetting; Computer science; Artificial neural network; Artificial intelligence; Task (project management); Adaptation (eye); Identification (biology); Core (optical fiber); Machine learning; Engineering","score_opus":0.00726110274252106,"score_gpt":0.2042705121739903,"score_spread":0.19700940943146925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023822800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069924966,0.0004979975,0.92192274,0.0003476283,0.00032858324,0.000051772313,0.00008108247,0.0011505211,0.005694732],"genre_scores_gemma":[0.7801063,0.0004074597,0.21156588,0.00022855088,0.00016779621,0.00011812876,0.0001891336,0.00019345747,0.007023333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999747,0.000040767965,0.000018952573,0.0000730343,0.000088604844,0.000031698088],"domain_scores_gemma":[0.9986681,0.000588077,0.00007141447,0.00029647967,0.00031425842,0.000061667124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007472603,0.0005555059,0.00076098164,0.0004648447,0.00035797188,0.0007984609,0.0019596347,0.0009018201,0.0041438304],"category_scores_gemma":[0.005132122,0.00060182245,0.00048378584,0.0007356591,0.00077273813,0.002595887,0.0016349301,0.0011176165,0.00056054856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029601937,0.00019198579,0.0015046413,0.00015463213,0.000100053614,0.00015459732,0.00013199345,0.34828785,0.013586381,0.02827391,0.0062012644,0.60111666],"study_design_scores_gemma":[0.000013332364,0.00004434447,0.00022174203,0.0000062741733,0.000023241304,0.00004761135,0.000009850836,0.98477626,0.0025749954,0.011307733,0.00096727913,0.0000073402757],"about_ca_topic_score_codex":0.0030744444,"about_ca_topic_score_gemma":0.0048551406,"teacher_disagreement_score":0.0041438304,"about_ca_system_score_codex":0.0004192976,"about_ca_system_score_gemma":0.0006115183,"threshold_uncertainty_score":0.013862491},"labels":[],"label_agreement":null},{"id":"W2024313619","doi":"10.1007/s10489-008-0133-z","title":"Polynomial-based radial basis function neural networks (P-RBF NNs) and their application to pattern classification","year":2008,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Radial basis function; Artificial neural network; Artificial intelligence; Classifier (UML); Cluster analysis; Pattern recognition (psychology); Fuzzy logic; Machine learning; Radial basis function network; Data mining","score_opus":0.02426089099776379,"score_gpt":0.22781583521811835,"score_spread":0.20355494422035456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024313619","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00999681,0.0073600477,0.9800749,0.00033071337,0.00013663269,0.000029017709,0.00006918254,0.00036145118,0.00164136],"genre_scores_gemma":[0.31299904,0.013750652,0.66775745,0.00016069709,0.00040378058,0.00010072503,0.000242168,0.00010612611,0.004479328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999363,0.00018309084,0.00004659211,0.00011688516,0.0002556008,0.00003484553],"domain_scores_gemma":[0.9978301,0.0011616328,0.00023689002,0.00013675577,0.00059874076,0.0000358404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016499779,0.0004850395,0.0008585492,0.0010210235,0.00031616943,0.00096073264,0.001024917,0.0011545676,0.0011343878],"category_scores_gemma":[0.0064241476,0.00030525375,0.00065318024,0.0030163815,0.0008022273,0.0013652808,0.0005531605,0.0011309998,0.0007437432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021529186,0.00007191862,0.0018480273,0.0005656854,0.00009434791,0.00018019884,0.00014814486,0.2205137,0.010314471,0.044218116,0.0039005112,0.7179296],"study_design_scores_gemma":[0.000008200958,0.000044269567,0.0009319697,0.000027665672,0.00002401777,0.00016459642,0.000023677205,0.9730196,0.0022672084,0.019527769,0.0039357245,0.00002529139],"about_ca_topic_score_codex":0.0029745123,"about_ca_topic_score_gemma":0.0021002695,"teacher_disagreement_score":0.0029745123,"about_ca_system_score_codex":0.00037675406,"about_ca_system_score_gemma":0.0005375466,"threshold_uncertainty_score":0.008726001},"labels":[],"label_agreement":null},{"id":"W2025438594","doi":"10.1016/j.patrec.2010.02.014","title":"Adaptive algorithms and networks for optimal feature extraction from Gaussian data","year":2010,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Gaussian; Computer science; Algorithm; Feature (linguistics); Feature extraction; Covariance matrix; Pattern recognition (psychology); Artificial intelligence","score_opus":0.04132082418550615,"score_gpt":0.2766982532645318,"score_spread":0.23537742907902565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025438594","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017900342,0.00031495903,0.9975374,0.000051831168,0.000016197364,0.000009761568,0.000016029893,0.00011892071,0.00014475941],"genre_scores_gemma":[0.10022062,0.00086883554,0.89629716,0.00008341525,0.00012411542,0.00019626426,0.00016439268,0.000108906315,0.0019363551],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935156,0.00015520635,0.00006306666,0.00017309781,0.00020463865,0.00005248768],"domain_scores_gemma":[0.99829537,0.0010994581,0.00014170786,0.00018817639,0.00025150232,0.000023810026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014025484,0.0007684621,0.0010228388,0.0011740642,0.00041258804,0.00084783277,0.0013473378,0.0010580346,0.0012128431],"category_scores_gemma":[0.0068617566,0.0008042039,0.0008128784,0.0018445547,0.0010847006,0.001888715,0.0013559117,0.0016481283,0.0005265448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016069718,0.00004285738,0.0004812429,0.00012755547,0.00006483472,0.0000489603,0.00008864424,0.3564668,0.0069731716,0.037173368,0.0022392862,0.5961326],"study_design_scores_gemma":[0.000009394695,0.000013327135,0.00024311035,0.000009205397,0.000009137507,0.00002344101,0.0000064824417,0.9723808,0.0014253051,0.024988156,0.0008800269,0.000011487098],"about_ca_topic_score_codex":0.0042043896,"about_ca_topic_score_gemma":0.0039298823,"teacher_disagreement_score":0.0042043896,"about_ca_system_score_codex":0.00074349385,"about_ca_system_score_gemma":0.0006819363,"threshold_uncertainty_score":0.008359849},"labels":[],"label_agreement":null},{"id":"W2025842646","doi":"10.1109/ccece.2007.115","title":"Current Mode Euclidean Distance Calculation Circuit for Kohonen's Neural Network Implemented in CMOS 0.18�m Technology","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Euclidean distance; CMOS; Artificial neural network; Self-organizing map; Computer science; Algorithm; Power (physics); Voltage; Electronic engineering; Artificial intelligence; Electrical engineering; Engineering; Physics","score_opus":0.032457182615819376,"score_gpt":0.32942597970271204,"score_spread":0.29696879708689267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025842646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03158379,0.00077827566,0.94559205,0.00031532868,0.0003269978,0.00019198199,0.0003642091,0.006275394,0.014571941],"genre_scores_gemma":[0.45348075,0.0005007501,0.52501595,0.00041730507,0.000072520066,0.0002523912,0.00049203855,0.00020798956,0.019560374],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967635,0.00003303534,0.000027538485,0.000072088274,0.00016516363,0.00002585309],"domain_scores_gemma":[0.99973434,0.000057158435,0.000022596545,0.000033531032,0.00014194648,0.000010345975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022119464,0.000355402,0.00022984043,0.0003699268,0.00033844262,0.00078587106,0.0013625064,0.00040502805,0.009342853],"category_scores_gemma":[0.0007074535,0.0001620948,0.00016026178,0.00042896345,0.00017768973,0.0009250226,0.0003070204,0.00032153862,0.0013837868],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005195268,0.000118189506,0.0017574584,0.00088795705,0.000087732114,0.0003838364,0.0002957014,0.012286859,0.1918829,0.019465948,0.014305741,0.7580082],"study_design_scores_gemma":[0.00018925783,0.0011521529,0.0038409983,0.00013599903,0.0001578155,0.0027493462,0.00022219267,0.3465704,0.5199432,0.0100394925,0.114849426,0.00014978953],"about_ca_topic_score_codex":0.0011477596,"about_ca_topic_score_gemma":0.0024260506,"teacher_disagreement_score":0.009342853,"about_ca_system_score_codex":0.00058768137,"about_ca_system_score_gemma":0.0005088067,"threshold_uncertainty_score":0.031254947},"labels":[],"label_agreement":null},{"id":"W2026042300","doi":"10.1016/j.neucom.2005.12.003","title":"Rapid learning and robust recall of long sequences in modular associator networks","year":2006,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Associative property; Modular design; Artificial intelligence; Recall; Sequence (biology); Content-addressable memory; Function (biology); Artificial neural network; Architecture; Recurrent neural network; Machine learning; Mathematics; Programming language","score_opus":0.011095496081571865,"score_gpt":0.21616769889602747,"score_spread":0.2050722028144556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026042300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24068356,0.0010667772,0.7536861,0.00045726026,0.00018753186,0.0000510812,0.00028908727,0.0014014373,0.0021772352],"genre_scores_gemma":[0.92333394,0.00042163927,0.07071733,0.0002050065,0.00016948795,0.000110062945,0.00062755804,0.00013649893,0.004278511],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991304,0.00022164643,0.00007047124,0.00031550354,0.00016558335,0.000096398944],"domain_scores_gemma":[0.98947704,0.006263054,0.0014057755,0.0016645258,0.0008160017,0.00037358262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018421033,0.0008657092,0.0010914572,0.00090299157,0.00050259056,0.0011560423,0.0018772744,0.0014245326,0.0025649164],"category_scores_gemma":[0.017583942,0.0007120832,0.0005773104,0.0008621739,0.0010730615,0.003641088,0.0019197747,0.0019372237,0.00071473466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015940618,0.00033070293,0.0072080046,0.00056890707,0.00037718363,0.000539876,0.00061880145,0.32318482,0.060882803,0.11076706,0.006457564,0.48747024],"study_design_scores_gemma":[0.000048824786,0.00024118065,0.0014283276,0.000028942439,0.00005303688,0.00023850818,0.000040405317,0.85921854,0.013651859,0.12394235,0.0010692467,0.000038738497],"about_ca_topic_score_codex":0.0007186261,"about_ca_topic_score_gemma":0.0010948698,"teacher_disagreement_score":0.0025649164,"about_ca_system_score_codex":0.00056897925,"about_ca_system_score_gemma":0.0005654179,"threshold_uncertainty_score":0.009742081},"labels":[],"label_agreement":null},{"id":"W2026238615","doi":"10.1504/ijams.2011.040230","title":"Synergy of chaos theory and artificial neural networks in chaotic time series forecasting","year":2011,"lang":"en","type":"article","venue":"International Journal of Applied Management Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chaotic; Artificial neural network; Series (stratigraphy); Phase space; Embedding; Time series; Chaos theory; Computer science; Hénon map; Feedforward neural network; Logistic map; Applied mathematics; Mathematics; Algorithm; Artificial intelligence; Machine learning; Physics","score_opus":0.019653022950047366,"score_gpt":0.2294424575705151,"score_spread":0.20978943462046773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026238615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053115766,0.005425302,0.9339999,0.0008483723,0.0002624668,0.000052138377,0.00004714845,0.00017874426,0.00607016],"genre_scores_gemma":[0.8242297,0.0055538435,0.16698818,0.00011376526,0.00034968523,0.00009102792,0.000060955514,0.000031017924,0.0025817244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964607,0.00015858948,0.000024249315,0.000030259915,0.00012351068,0.000017474324],"domain_scores_gemma":[0.99938345,0.00046085013,0.000050514835,0.00002336926,0.000068095236,0.000013649629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085681333,0.00038546912,0.000545425,0.0007381243,0.00027732571,0.0005547416,0.00026473208,0.00051681366,0.0005222679],"category_scores_gemma":[0.0021114894,0.00019080861,0.0004376854,0.0008248405,0.000412048,0.001100603,0.0004947065,0.0005773011,0.00010306723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012692068,0.00007429946,0.0041205585,0.0003875237,0.00019838696,0.0004174658,0.00023285119,0.66900116,0.008231189,0.06738881,0.0013481829,0.24847269],"study_design_scores_gemma":[0.0000075649,0.000043705964,0.00080454873,0.000027124506,0.000020897702,0.000069880705,0.000021772468,0.9814507,0.001282274,0.01479829,0.0014532125,0.000020039515],"about_ca_topic_score_codex":0.001923451,"about_ca_topic_score_gemma":0.0021910416,"teacher_disagreement_score":0.001923451,"about_ca_system_score_codex":0.00036190526,"about_ca_system_score_gemma":0.00050452614,"threshold_uncertainty_score":0.004531324},"labels":[],"label_agreement":null},{"id":"W2026264938","doi":"10.1109/iscas.2014.6865279","title":"Incremental GMMSD2 with applications to feature extraction","year":2014,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Discriminative model; Feature extraction; Computation; Process (computing); Centroid; Feature (linguistics); Artificial intelligence; Data mining; Pattern recognition (psychology); Machine learning; Algorithm","score_opus":0.010016855509356966,"score_gpt":0.2572993232247362,"score_spread":0.24728246771537923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026264938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004145558,0.0003000799,0.99181306,0.00006639503,0.00006327711,0.000037593523,0.00009889689,0.0029228977,0.0005522761],"genre_scores_gemma":[0.075256914,0.00028428857,0.9207856,0.00012233725,0.00008390422,0.000104344224,0.0006288806,0.00031274935,0.0024211148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993986,0.00010680117,0.000040533134,0.00016351,0.00022720997,0.00006340445],"domain_scores_gemma":[0.99928635,0.00020502787,0.000038306458,0.00017437102,0.0002653964,0.00003048543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008534758,0.00091390626,0.0008203072,0.0013121401,0.00040144048,0.00069694297,0.0014530122,0.00074786256,0.0038498882],"category_scores_gemma":[0.0024792121,0.00052497926,0.00083835516,0.0014618636,0.0003850952,0.0008366542,0.001530619,0.00081968214,0.0018162888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015284981,0.00005060971,0.00074108393,0.00010909462,0.00006756104,0.000094349256,0.00008409895,0.028537063,0.030196391,0.0035428768,0.005641664,0.93078244],"study_design_scores_gemma":[0.000036918227,0.00014025153,0.0018966877,0.000016869577,0.000052186922,0.0005096482,0.0000573346,0.9002229,0.06377446,0.0076827323,0.025547361,0.00006263076],"about_ca_topic_score_codex":0.0043136743,"about_ca_topic_score_gemma":0.005632839,"teacher_disagreement_score":0.0043136743,"about_ca_system_score_codex":0.0003860577,"about_ca_system_score_gemma":0.0007666591,"threshold_uncertainty_score":0.012879193},"labels":[],"label_agreement":null},{"id":"W2027078133","doi":"10.1139/s04-004","title":"A neural network approach to selecting indicators for a sustainable ecosystem","year":2004,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sustainability; Ecological indicator; Artificial neural network; Performance indicator; Computer science; Ecosystem; Environmental resource management; Key (lock); Curse of dimensionality; Natural resource; Environmental science; Ecology; Machine learning; Business","score_opus":0.004553329679104115,"score_gpt":0.184990266884343,"score_spread":0.1804369372052389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027078133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030170526,0.0004759589,0.96408826,0.0007921147,0.000056710738,0.00015451845,0.00017745895,0.00026658375,0.0038178056],"genre_scores_gemma":[0.5147373,0.0006548816,0.47988182,0.00025749087,0.000104425875,0.00073412753,0.00024296928,0.000036169447,0.0033507978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998887,0.0005640071,0.00008678272,0.0002013954,0.0001906139,0.00007017598],"domain_scores_gemma":[0.99785304,0.0014496191,0.00020132802,0.000055252116,0.0003869959,0.000053859083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002254122,0.0008907663,0.00061009906,0.0021411006,0.00044156535,0.0014443769,0.000995856,0.0012664739,0.002197758],"category_scores_gemma":[0.007897414,0.00038295632,0.0004693175,0.0018262289,0.00066320004,0.0015766196,0.00083656015,0.0011141669,0.0002718989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013169635,0.00015061654,0.004730676,0.00019703463,0.00020374899,0.00011959306,0.00015575787,0.77486414,0.0018027393,0.017292246,0.0014461456,0.19890556],"study_design_scores_gemma":[0.000010994044,0.000036571433,0.000595671,0.00002456693,0.00001932664,0.000018900158,0.000029509205,0.98908967,0.00044349078,0.009228226,0.0004915394,0.000011510511],"about_ca_topic_score_codex":0.005924859,"about_ca_topic_score_gemma":0.008149559,"teacher_disagreement_score":0.005924859,"about_ca_system_score_codex":0.001263936,"about_ca_system_score_gemma":0.0011166614,"threshold_uncertainty_score":0.011921108},"labels":[],"label_agreement":null},{"id":"W2027316195","doi":"10.1016/s0925-2312(01)00679-8","title":"Dynamic range and error tolerance of stochastic neural rate codes","year":2002,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Unary operation; Range (aeronautics); Artificial neural network; Algorithm; Computer science; Noise (video); Stochastic computing; Integer (computer science); Mathematics; Discrete mathematics; Artificial intelligence","score_opus":0.02089619510910429,"score_gpt":0.24520476721076379,"score_spread":0.2243085721016595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027316195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2268082,0.0014616504,0.758362,0.0010742409,0.00008795297,0.000049085786,0.00017740372,0.00052863057,0.01145095],"genre_scores_gemma":[0.97064227,0.0005021672,0.025577588,0.00012278458,0.00008408607,0.000089492074,0.00012018634,0.00017640035,0.0026848703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972,0.00084817613,0.00014397716,0.00040532986,0.0009583102,0.00044428688],"domain_scores_gemma":[0.95668596,0.035185445,0.002706988,0.0022189117,0.0024404293,0.0007622627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039459895,0.00072252547,0.0009679801,0.001161671,0.0006761022,0.0018654091,0.0018424707,0.0016824347,0.0024501756],"category_scores_gemma":[0.046283584,0.0007630301,0.00043150646,0.00090607913,0.002224632,0.0032764878,0.0029011406,0.0022571986,0.0004370502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042328401,0.00005864447,0.0011005218,0.00010550107,0.00003385841,0.00014021639,0.00030140125,0.6834085,0.009293556,0.2782478,0.00071201037,0.026174696],"study_design_scores_gemma":[0.000022231929,0.00007170534,0.0003918716,0.00004008554,0.000010080669,0.00013165959,0.000048616497,0.869322,0.004319765,0.12524092,0.00036062708,0.000040449857],"about_ca_topic_score_codex":0.0010654558,"about_ca_topic_score_gemma":0.0005601849,"teacher_disagreement_score":0.0039459895,"about_ca_system_score_codex":0.0010558768,"about_ca_system_score_gemma":0.00092194695,"threshold_uncertainty_score":0.020868659},"labels":[],"label_agreement":null},{"id":"W2027857109","doi":"10.3115/1620853.1620921","title":"Quadratic features and deep architectures for chunking","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Quadratic equation; Generalization; Computer science; Logistic regression; Chunking (psychology); Artificial intelligence; Machine learning; Feature engineering; Quadratic programming; Feature (linguistics); Pattern recognition (psychology); Algorithm; Deep learning; Mathematics; Mathematical optimization","score_opus":0.009635036367745604,"score_gpt":0.2526397629431815,"score_spread":0.24300472657543593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027857109","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5319329,0.0025274688,0.42996585,0.003852726,0.00078064215,0.00045658127,0.0028544413,0.011893853,0.015735537],"genre_scores_gemma":[0.8525279,0.00038680222,0.13965334,0.00039611736,0.0000882094,0.00022689972,0.0021296495,0.00035740586,0.0042336253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913824,0.00022973021,0.000070546565,0.00025449655,0.00017336778,0.00013354779],"domain_scores_gemma":[0.99446255,0.0033425468,0.00023687673,0.0010860745,0.0006235332,0.00024842136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028555088,0.0014167731,0.00091586023,0.0006196806,0.00055315916,0.0012148058,0.0017121125,0.0017165901,0.006167448],"category_scores_gemma":[0.012443029,0.0005445277,0.00072134554,0.0008976891,0.0007369547,0.005566672,0.0014407487,0.002995495,0.0013169096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030130118,0.0010503143,0.005831781,0.0007611264,0.00030288007,0.00026478013,0.00033591295,0.60140395,0.01765694,0.023207279,0.02134538,0.32482666],"study_design_scores_gemma":[0.00013122734,0.0004001799,0.00065556314,0.000029113431,0.00003763658,0.000039055798,0.000034360713,0.97256315,0.007478988,0.017060354,0.0015433154,0.0000271454],"about_ca_topic_score_codex":0.006785599,"about_ca_topic_score_gemma":0.008077859,"teacher_disagreement_score":0.006785599,"about_ca_system_score_codex":0.00089736626,"about_ca_system_score_gemma":0.0009537639,"threshold_uncertainty_score":0.020632148},"labels":[],"label_agreement":null},{"id":"W2027933562","doi":"10.1109/jproc.2013.2270898","title":"Perception-Based Media Processing [Scanning the Issue]","year":2013,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Perception; Computer science; Audio visual; Haptic perception; Haptic technology; Human–computer interaction; Multimedia; Psychology; Artificial intelligence; Neuroscience","score_opus":0.013883340921644196,"score_gpt":0.22979891542255104,"score_spread":0.21591557450090684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027933562","genre_codex":"editorial","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001820066,0.18884951,0.014135351,0.03544019,0.69594896,0.00020047522,0.00079411623,0.0014847064,0.061326627],"genre_scores_gemma":[0.021708807,0.24012923,0.011513426,0.018710334,0.39771956,0.0001528885,0.001968734,0.00093356206,0.3071634],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995435,0.000031988173,0.000041146708,0.00008821174,0.00023749036,0.0000576849],"domain_scores_gemma":[0.9982691,0.00027889162,0.000101874844,0.00012783003,0.0010091696,0.00021316233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075791095,0.0012283437,0.0009943041,0.0018562011,0.0010258723,0.0051401113,0.001743348,0.0027410826,0.0732691],"category_scores_gemma":[0.0023211045,0.0004924128,0.00082282454,0.0017117352,0.0009321026,0.0032190306,0.001089114,0.0032479372,0.01906509],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008885588,0.000031609474,0.00012517993,0.0010055723,0.000049025046,0.00014144744,0.000027406591,0.00009191319,0.0049422793,0.0047351546,0.79816216,0.19059938],"study_design_scores_gemma":[0.000016464104,0.000085910986,0.00089798425,0.00037220857,0.000053289292,0.00040408366,0.000047609356,0.00059999217,0.0026278207,0.0044503883,0.99041134,0.00003291406],"about_ca_topic_score_codex":0.001431916,"about_ca_topic_score_gemma":0.003600276,"teacher_disagreement_score":0.0732691,"about_ca_system_score_codex":0.00070448325,"about_ca_system_score_gemma":0.0014699566,"threshold_uncertainty_score":0.24510956},"labels":[],"label_agreement":null},{"id":"W2028749934","doi":"10.1007/s10450-010-9287-1","title":"Neural network modelling of adsorption isotherms","year":2010,"lang":"en","type":"article","venue":"Adsorption","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Freundlich equation; Artificial neural network; Adsorption; Sorption isotherm; Langmuir; Dependency (UML); Langmuir adsorption model; Computer science; Biological system; Materials science; Artificial intelligence; Chemistry; Physical chemistry","score_opus":0.023120892807046805,"score_gpt":0.2351499554976216,"score_spread":0.2120290626905748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028749934","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5369066,0.0030829685,0.43682998,0.001704654,0.0003465613,0.00009708742,0.00056038436,0.0006247269,0.019847104],"genre_scores_gemma":[0.9812867,0.00080882595,0.009009082,0.00008055524,0.000052787214,0.000069830065,0.00015250368,0.000059676153,0.008479953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998118,0.000050748647,0.000009194052,0.00003928956,0.000056630517,0.000032364886],"domain_scores_gemma":[0.9992292,0.0005191974,0.000049342118,0.000031344178,0.00014688779,0.000024069226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000533804,0.00048840523,0.0009961292,0.0005063499,0.0004572096,0.0008205735,0.0012969256,0.0014453902,0.0024421026],"category_scores_gemma":[0.002288464,0.000478024,0.00058761775,0.00085433014,0.0005181516,0.001514529,0.0003995371,0.0010383016,0.00037890312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002493813,0.00001825658,0.00018200456,0.000033426517,0.000013005213,0.000015139119,0.00001117441,0.99364024,0.0012348645,0.0021511856,0.00017059925,0.0025050498],"study_design_scores_gemma":[7.430636e-7,0.0000012419242,0.000036067137,7.4501986e-7,8.6887104e-7,0.0000011083595,7.033206e-7,0.9993611,0.00012114984,0.00044925135,0.000025639314,0.0000013683359],"about_ca_topic_score_codex":0.020946914,"about_ca_topic_score_gemma":0.015946172,"teacher_disagreement_score":0.020946914,"about_ca_system_score_codex":0.0015233749,"about_ca_system_score_gemma":0.000841387,"threshold_uncertainty_score":0.041649997},"labels":[],"label_agreement":null},{"id":"W2029126611","doi":"10.1243/095440502320193076","title":"Development of a real-time monitoring system","year":2002,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Learning vector quantization; Computer science; Artificial neural network; Fault (geology); Artificial intelligence; Identification (biology); Fault detection and isolation; Machine learning; Data mining; Task (project management); Vector quantization; Pattern recognition (psychology); Real-time computing; Engineering","score_opus":0.013888689135198334,"score_gpt":0.19864916251198983,"score_spread":0.1847604733767915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029126611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027958468,0.00017278535,0.9538949,0.00046882321,0.00020726334,0.00036575817,0.0001660303,0.009320454,0.0074456255],"genre_scores_gemma":[0.3939376,0.00025286252,0.5881412,0.00033090275,0.00012056667,0.00052185386,0.000657621,0.00020654881,0.015830899],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991085,0.00013167117,0.00005542165,0.00023829748,0.0004013131,0.0000648417],"domain_scores_gemma":[0.9991811,0.00013434528,0.000054270047,0.00016207514,0.00037922477,0.00008887721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011215728,0.0003934685,0.00049932365,0.000518599,0.00034519774,0.000964153,0.0016835508,0.00082353357,0.0069574574],"category_scores_gemma":[0.0013339034,0.00027967745,0.00027722734,0.0003402673,0.00027465256,0.0014632,0.0009123664,0.0008233901,0.0029162199],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068427774,0.00048462232,0.0037163363,0.0003438877,0.00007286767,0.0005794044,0.00037054767,0.046675358,0.19651338,0.017402971,0.011405239,0.72175103],"study_design_scores_gemma":[0.00023374557,0.0014889865,0.0032272625,0.00008462474,0.00007495634,0.000716845,0.00009420112,0.7789473,0.13819338,0.0040645585,0.07279538,0.00007881772],"about_ca_topic_score_codex":0.001177943,"about_ca_topic_score_gemma":0.00051556167,"teacher_disagreement_score":0.0069574574,"about_ca_system_score_codex":0.00037651634,"about_ca_system_score_gemma":0.0007408603,"threshold_uncertainty_score":0.023275077},"labels":[],"label_agreement":null},{"id":"W2030632567","doi":"10.1007/bf02471197","title":"Superimposing neural learning by dynamic and spatial changing weights","year":2004,"lang":"en","type":"article","venue":"Artificial Life and Robotics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Forgetting; Computer science; Artificial intelligence; Artificial neural network; Task (project management); Adaptation (eye); Core (optical fiber); Identification (biology); Connection (principal bundle); Machine learning; Mathematics","score_opus":0.011040847010787638,"score_gpt":0.2305338971273467,"score_spread":0.21949305011655906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030632567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06358865,0.00034592237,0.922199,0.00032858455,0.0003448502,0.000026442713,0.000040421422,0.0006619063,0.012464172],"genre_scores_gemma":[0.832155,0.0005432379,0.15418938,0.00017281286,0.0001361382,0.000048024885,0.000060097613,0.00022511354,0.012470252],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974674,0.00004663063,0.000015051884,0.00006738315,0.000089867375,0.000034418703],"domain_scores_gemma":[0.99937075,0.00021252953,0.000046063094,0.00019247837,0.000110683766,0.000067472116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051960367,0.00052516744,0.00048234922,0.00039647502,0.00032419994,0.00097540615,0.0011355836,0.00072910194,0.0048620133],"category_scores_gemma":[0.0030989603,0.0004477915,0.0005501303,0.0008838477,0.00089120783,0.0021035112,0.0016179743,0.00085656723,0.00065128406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030145785,0.000116091695,0.0009147448,0.00012325012,0.00013213127,0.00027594806,0.00021202452,0.5242014,0.051583964,0.07963475,0.0018768529,0.3406275],"study_design_scores_gemma":[0.0000133573485,0.00006348632,0.00035814277,0.00000842701,0.000029980523,0.00008431902,0.00003199376,0.95399576,0.008784083,0.034208328,0.002402527,0.00001957736],"about_ca_topic_score_codex":0.002148887,"about_ca_topic_score_gemma":0.002914856,"teacher_disagreement_score":0.0048620133,"about_ca_system_score_codex":0.00042536992,"about_ca_system_score_gemma":0.00049927825,"threshold_uncertainty_score":0.016265035},"labels":[],"label_agreement":null},{"id":"W2032467567","doi":"10.1117/12.380608","title":"&lt;title&gt;New strategy for adaptively constructing multilayer feed-forward neural networks&lt;/title&gt;","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Constructive; Computer science; Artificial neural network; Layer (electronics); Function (biology); Artificial intelligence; Algorithm; Nonlinear system; Process (computing)","score_opus":0.017010791178247803,"score_gpt":0.23648368713175516,"score_spread":0.21947289595350736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032467567","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035805772,0.000091182665,0.9949356,0.000059963408,0.000041699077,0.000028853849,0.000017015382,0.00041186318,0.00083336706],"genre_scores_gemma":[0.1246785,0.0001573563,0.8714558,0.0001405094,0.00005986406,0.0001422088,0.00012019381,0.00012976654,0.0031158347],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99972206,0.000075154,0.000024791572,0.000063743086,0.00009098405,0.00002330628],"domain_scores_gemma":[0.9995346,0.00017688428,0.000054037897,0.000079943,0.00012563948,0.00002893453],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00067838375,0.0007433001,0.00050417613,0.0004989171,0.00026255855,0.0005131109,0.0011339092,0.00084018894,0.0025005],"category_scores_gemma":[0.001788557,0.00029699306,0.00042334324,0.00039143173,0.0005099416,0.00087213604,0.0006592859,0.0008114274,0.0008342683],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016210631,0.00008748926,0.00086185196,0.00017238395,0.000072403,0.00023190535,0.000112963135,0.31800905,0.040281426,0.02723144,0.004990079,0.6077869],"study_design_scores_gemma":[0.000010013422,0.00003365639,0.00010079345,0.000010366379,0.000010574185,0.00005086007,0.0000044033377,0.98781186,0.0066513754,0.0033346263,0.0019726683,0.000008705572],"about_ca_topic_score_codex":0.0017384291,"about_ca_topic_score_gemma":0.002723941,"teacher_disagreement_score":0.9974995,"about_ca_system_score_codex":0.0004686775,"about_ca_system_score_gemma":0.00046992206,"threshold_uncertainty_score":0.008365035},"labels":[],"label_agreement":null},{"id":"W2032530952","doi":"10.1190/1.1816525","title":"Playing with fire: Noise alignment in trim and residual statics","year":2001,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Statics; Spurious relationship; Trim; Residual; Noise (video); SIGNAL (programming language); Context (archaeology); Wavelet; Function (biology); Smoothing; Control theory (sociology); Mathematics; Computer science; Algorithm; Physics; Structural engineering; Statistics; Engineering; Geology; Classical mechanics","score_opus":0.01697446742357733,"score_gpt":0.24175978943312096,"score_spread":0.22478532200954363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032530952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036404826,0.00064366206,0.8376971,0.002857594,0.004906626,0.00034540385,0.0052087377,0.06617994,0.04575606],"genre_scores_gemma":[0.16567023,0.0005574804,0.7095117,0.00094303204,0.0010149254,0.00028316333,0.011849329,0.030511755,0.07965846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981877,0.00019432628,0.00008816219,0.00027653886,0.001082678,0.0001705607],"domain_scores_gemma":[0.9970276,0.00066447,0.0001972746,0.0007349744,0.0011933087,0.00018239977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00224611,0.001416715,0.00076449924,0.0026462928,0.0013545209,0.0027116167,0.0019459133,0.0014660828,0.094906576],"category_scores_gemma":[0.010446372,0.0007336438,0.001028523,0.002131715,0.0007397721,0.002436903,0.0028657618,0.0020921493,0.030708537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087813596,0.0001297716,0.0034900254,0.00026281373,0.000073254276,0.00046984362,0.00058565906,0.011149846,0.023243846,0.00847076,0.21902852,0.7322175],"study_design_scores_gemma":[0.0003339085,0.0004856999,0.017964246,0.00036148084,0.00011783107,0.001139371,0.0011942902,0.28798953,0.12969832,0.02935117,0.53095293,0.00041125008],"about_ca_topic_score_codex":0.0072665084,"about_ca_topic_score_gemma":0.015655717,"teacher_disagreement_score":0.094906576,"about_ca_system_score_codex":0.0008065822,"about_ca_system_score_gemma":0.0014617543,"threshold_uncertainty_score":0.3174941},"labels":[],"label_agreement":null},{"id":"W2032676284","doi":"10.1016/j.neunet.2009.10.009","title":"Temporal-Kernel Recurrent Neural Networks","year":2009,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Recurrent neural network; Computer science; Connectionism; Artificial intelligence; Task (project management); String (physics); Kernel (algebra); Term (time); Sequence (biology); Recall; Machine learning; Artificial neural network; Mathematics","score_opus":0.017705250299200365,"score_gpt":0.2562555405495667,"score_spread":0.2385502902503663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032676284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050278593,0.0015138767,0.94330674,0.00027329262,0.00024214163,0.00002583031,0.00020960727,0.0009882398,0.0031616888],"genre_scores_gemma":[0.88098216,0.0009985315,0.10416861,0.00012317095,0.00013222672,0.00003772528,0.00060388085,0.00018580937,0.012767977],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999713,0.00006065762,0.000026264715,0.00008169307,0.00007829665,0.000040098934],"domain_scores_gemma":[0.99896777,0.0003290338,0.00012595826,0.00020610272,0.0003317843,0.000039283204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008140416,0.0004135496,0.0005893953,0.0002807945,0.0002209992,0.000783435,0.0007963462,0.0007569098,0.003083308],"category_scores_gemma":[0.0035353252,0.0002177532,0.0003789454,0.00052333454,0.0002951428,0.0017581175,0.0006008046,0.000783575,0.0010202306],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005069401,0.00017308029,0.002696606,0.00023589571,0.00017937213,0.00020798505,0.00009373204,0.42021912,0.022865253,0.06361149,0.00811687,0.48109356],"study_design_scores_gemma":[0.0000033673634,0.000013688758,0.00017745132,0.0000031672937,0.00001406373,0.00002101684,0.0000041697185,0.99282277,0.0013655744,0.005012955,0.0005568842,0.000004874946],"about_ca_topic_score_codex":0.0023469008,"about_ca_topic_score_gemma":0.0032102223,"teacher_disagreement_score":0.003083308,"about_ca_system_score_codex":0.0004137961,"about_ca_system_score_gemma":0.00042360867,"threshold_uncertainty_score":0.010314703},"labels":[],"label_agreement":null},{"id":"W2033272835","doi":"10.1162/neco.2008.12-07-661","title":"Deep, Narrow Sigmoid Belief Networks Are Universal Approximators","year":2008,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":134,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sigmoid function; Curse of dimensionality; Binary number; Computer science; Deep neural networks; Artificial intelligence; Deep belief network; Artificial neural network; Algorithm; Theoretical computer science; Mathematics; Arithmetic","score_opus":0.016770676330829223,"score_gpt":0.22105101769491226,"score_spread":0.20428034136408302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033272835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030365746,0.00092750485,0.95763415,0.0009547975,0.00009990368,0.000022009264,0.00019489619,0.0006041064,0.009196957],"genre_scores_gemma":[0.8594321,0.0016261683,0.12661865,0.00062058517,0.00010728904,0.000095496034,0.00035729125,0.00022266855,0.01091978],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992524,0.00015886663,0.00004357685,0.00021220888,0.00021320856,0.00011972366],"domain_scores_gemma":[0.9972566,0.0015986656,0.00023402597,0.00040611922,0.00038391142,0.00012065468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015403873,0.00083449844,0.0008107913,0.0006063256,0.0004893897,0.0018542134,0.0010290615,0.0012663925,0.002801424],"category_scores_gemma":[0.012912703,0.0007133085,0.00055585516,0.00066944683,0.00214957,0.004992743,0.0025276141,0.0033306077,0.00094799825],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030277917,0.000060492315,0.00212624,0.00030806844,0.00008491336,0.00016832002,0.00032236162,0.2757176,0.005570436,0.58028674,0.006251298,0.12880078],"study_design_scores_gemma":[0.000011141981,0.000026605589,0.0003083346,0.000043950084,0.000019854015,0.00006723326,0.000040356794,0.6757088,0.002498331,0.31879675,0.0024652623,0.000013476014],"about_ca_topic_score_codex":0.0020029615,"about_ca_topic_score_gemma":0.0020638804,"teacher_disagreement_score":0.002801424,"about_ca_system_score_codex":0.0010991613,"about_ca_system_score_gemma":0.0006835366,"threshold_uncertainty_score":0.009371698},"labels":[],"label_agreement":null},{"id":"W2033829372","doi":"10.1016/j.cognition.2006.12.004","title":"Rule following and rule use in the balance-scale task","year":2007,"lang":"en","type":"letter","venue":"Cognition","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Connectionism; Psychology; Task (project management); Cognition; Balance (ability); Scale (ratio); Focus (optics); Cognitive psychology; Simple (philosophy); Cognitive science; Epistemology","score_opus":0.025376766820651587,"score_gpt":0.25053697003816394,"score_spread":0.22516020321751234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033829372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9511715,0.0002484436,0.016406234,0.0005501255,0.00038570966,0.00024341694,0.0014768274,0.00028865127,0.029229047],"genre_scores_gemma":[0.9728598,0.0001841022,0.01057958,0.0006134921,0.00016520137,0.00030576804,0.0015578184,0.00019118241,0.013543147],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998863,0.0001586508,0.00010430731,0.00021992227,0.0005991531,0.00005493381],"domain_scores_gemma":[0.99127495,0.0053224536,0.001344895,0.0009590453,0.0005506604,0.00054799306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015961332,0.0006521462,0.00071061886,0.00052918005,0.00033836468,0.0019407581,0.0005981825,0.0018015973,0.011790365],"category_scores_gemma":[0.02941831,0.00039080795,0.00019638777,0.00048201438,0.00042103432,0.0021621976,0.00082404306,0.0013559627,0.00213883],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.012458284,0.003061857,0.14027262,0.0004822564,0.00032167233,0.0010040137,0.0018068594,0.00727276,0.31643313,0.01055502,0.01908565,0.4872458],"study_design_scores_gemma":[0.00081837457,0.0023070034,0.85053897,0.00010071071,0.00013784425,0.0018176633,0.00031989487,0.056907147,0.028902259,0.050241735,0.0077458397,0.00016261319],"about_ca_topic_score_codex":0.0014983738,"about_ca_topic_score_gemma":0.001527304,"teacher_disagreement_score":0.011790365,"about_ca_system_score_codex":0.00027475652,"about_ca_system_score_gemma":0.00047957554,"threshold_uncertainty_score":0.039442718},"labels":[],"label_agreement":null},{"id":"W2034870679","doi":"10.1016/s0893-6080(01)00108-3","title":"Projective ART for clustering data sets in high dimensional spaces","year":2002,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Mitacs; Canada Research Chairs","keywords":"Cluster analysis; Computer science; Node (physics); Layer (electronics); SIGNAL (programming language); Artificial neural network; Clustering high-dimensional data; Topology (electrical circuits); Data mining; Algorithm; Pattern recognition (psychology); Artificial intelligence; Mathematics","score_opus":0.05417052076373584,"score_gpt":0.27959832995250977,"score_spread":0.22542780918877392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034870679","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052715503,0.000755029,0.99218786,0.00015382802,0.000039174793,0.000026125994,0.00010333405,0.0005308663,0.00093219714],"genre_scores_gemma":[0.227443,0.0016176093,0.7642534,0.00023374776,0.00030838393,0.00024877267,0.0010948398,0.00032044234,0.0044798343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687177,0.0012609265,0.0001563161,0.000681052,0.00086248555,0.00016747654],"domain_scores_gemma":[0.99662375,0.0015566245,0.0002040176,0.0010224791,0.00047514905,0.00011802477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034102271,0.0012120145,0.0023068178,0.0028460284,0.0014800275,0.0037148672,0.0026589278,0.002282043,0.0035561621],"category_scores_gemma":[0.010231706,0.0011822224,0.0022083246,0.00438686,0.0029090869,0.0040004053,0.0041875793,0.0034243928,0.001266842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002190871,0.00008985912,0.0016317,0.0005130814,0.00033048465,0.00014200264,0.00036920072,0.2871376,0.0051242416,0.22588535,0.008987201,0.46957016],"study_design_scores_gemma":[0.000015101448,0.000042227388,0.00055259856,0.000024431229,0.000037477457,0.00007126507,0.00003714019,0.88926715,0.0013217642,0.10658938,0.0020167262,0.000024785313],"about_ca_topic_score_codex":0.003945182,"about_ca_topic_score_gemma":0.0040273983,"teacher_disagreement_score":0.003945182,"about_ca_system_score_codex":0.0012454707,"about_ca_system_score_gemma":0.0010436067,"threshold_uncertainty_score":0.018035173},"labels":[],"label_agreement":null},{"id":"W2034877773","doi":"10.1016/j.neunet.2012.12.009","title":"A class of finite-time dual neural networks for solving quadratic programming problems and its -winners-take-all application","year":2013,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":159,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Artificial neural network; Quadratic programming; Computer science; Convergence (economics); Mathematical optimization; Class (philosophy); Quadratic equation; Activation function; Function (biology); Recurrent neural network; Stability (learning theory); Limiting; Property (philosophy); Dual (grammatical number); Mathematics; Artificial intelligence; Machine learning","score_opus":0.020726949841964645,"score_gpt":0.23871005013158808,"score_spread":0.21798310028962342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034877773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024265153,0.002129737,0.9585652,0.000585859,0.0003079155,0.000070986156,0.00010799557,0.00022524744,0.013741965],"genre_scores_gemma":[0.675988,0.0026373614,0.2991209,0.0005435646,0.0005075807,0.00034460408,0.00037174832,0.00025339553,0.020232817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940884,0.00023179223,0.000031519907,0.00010907163,0.00014644349,0.000072272785],"domain_scores_gemma":[0.99910504,0.00041313897,0.00009542175,0.00008059776,0.00020447072,0.00010131355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00194988,0.0011834538,0.0013538909,0.00076626835,0.0006690351,0.0013714208,0.0022292624,0.0014482402,0.0031875914],"category_scores_gemma":[0.004474268,0.0005133364,0.00093532394,0.0009798201,0.001061541,0.0017031496,0.0019260626,0.0030586033,0.00039012902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032432794,0.00027818469,0.0007918201,0.00034608395,0.00015582587,0.00012401256,0.00009991391,0.413777,0.0030427678,0.38395894,0.0075081466,0.18959294],"study_design_scores_gemma":[0.000021691605,0.00007269677,0.000091487906,0.000019456305,0.000014934619,0.00004136216,0.00001135854,0.92724866,0.00032968973,0.07018252,0.0019565464,0.0000096266385],"about_ca_topic_score_codex":0.0017396021,"about_ca_topic_score_gemma":0.0021565405,"teacher_disagreement_score":0.0031875914,"about_ca_system_score_codex":0.0008726627,"about_ca_system_score_gemma":0.001222004,"threshold_uncertainty_score":0.010663569},"labels":[],"label_agreement":null},{"id":"W2034888964","doi":"10.1016/j.bandc.2004.08.034","title":"Functional localization and double dissociations: The relationship between internal structure and behavior","year":2004,"lang":"en","type":"article","venue":"Brain and Cognition","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Alberta","funders":"","keywords":"Cognition; Psychology; Modularity (biology); Neuroscience; Cognitive psychology; Cognitive systems; Cognitive science","score_opus":0.03435379120927048,"score_gpt":0.266254441642272,"score_spread":0.23190065043300154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034888964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9494186,0.00093555206,0.04028786,0.00093798124,0.000037490165,0.000019507674,0.00024754507,0.00015698411,0.007958539],"genre_scores_gemma":[0.9963666,0.00012843953,0.0026015653,0.00006718069,0.000013078848,0.000011582466,0.00009916626,0.000042548178,0.00066982314],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997023,0.000050049715,0.000015485779,0.00010712674,0.00008411297,0.00004092905],"domain_scores_gemma":[0.99795675,0.00080439483,0.00041931556,0.00044025359,0.00014153961,0.00023762132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009416102,0.00027484904,0.00047356533,0.00076042966,0.000275192,0.0013956099,0.0006759569,0.00067325146,0.00389855],"category_scores_gemma":[0.0065314975,0.00040428794,0.00026320468,0.00046000522,0.0016533479,0.002653486,0.0014150505,0.0011589228,0.0003470848],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0050525563,0.0006320854,0.16068242,0.00074520987,0.0007646854,0.0012201904,0.0057438393,0.011038405,0.33133537,0.14889413,0.0038138428,0.33007726],"study_design_scores_gemma":[0.00026334543,0.0003808865,0.5340732,0.00006285595,0.00018120045,0.002479159,0.0014121269,0.024780089,0.018928561,0.41511917,0.00220512,0.000114355884],"about_ca_topic_score_codex":0.00084755453,"about_ca_topic_score_gemma":0.0006792917,"teacher_disagreement_score":0.00389855,"about_ca_system_score_codex":0.00033693097,"about_ca_system_score_gemma":0.00022710486,"threshold_uncertainty_score":0.013041973},"labels":[],"label_agreement":null},{"id":"W2035729910","doi":"10.1155/s1110865703306079","title":"Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent","year":2003,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Gradient descent; Nonlinear system; Artificial neural network; Convergence (economics); Computer science; Mean squared error; Filter (signal processing); Kalman filter; Extended Kalman filter; Control theory (sociology); Levenberg–Marquardt algorithm; Adaptive filter; Stochastic gradient descent; Kernel adaptive filter; Algorithm; Nonlinear conjugate gradient method; Gradient method; Artificial intelligence; Mathematics; Filter design; Computer vision; Statistics","score_opus":0.016125871334562798,"score_gpt":0.27179829719716897,"score_spread":0.2556724258626062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035729910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025047645,0.00039520202,0.9719561,0.00010889667,0.000047356516,0.000041544812,0.000026394144,0.0005158621,0.0018610349],"genre_scores_gemma":[0.6568129,0.00037297382,0.33874986,0.000110764515,0.000042893807,0.0002104059,0.00012641987,0.000053568656,0.0035200974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997801,0.000072086754,0.00001702126,0.000050576004,0.000060616127,0.000019477098],"domain_scores_gemma":[0.999474,0.00025953073,0.0000867205,0.000057702182,0.00010919135,0.00001286621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083319977,0.0005530648,0.0006088565,0.00037153982,0.00030145844,0.00046642392,0.0005161854,0.00069732236,0.00080140174],"category_scores_gemma":[0.0023014138,0.00032140684,0.00037885274,0.0004325628,0.00047557178,0.0008849233,0.0006210393,0.00063335325,0.00035697423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056656107,0.000034040524,0.00074050843,0.000072581985,0.000044488377,0.00007620757,0.000056455465,0.9008921,0.0030497452,0.0052275825,0.00057270454,0.089176975],"study_design_scores_gemma":[0.000002451199,0.000010052522,0.00007394688,0.0000022377114,0.0000019519957,0.000006671913,0.0000012412953,0.99848926,0.000375705,0.00086645683,0.00016758998,0.0000024513251],"about_ca_topic_score_codex":0.0045903344,"about_ca_topic_score_gemma":0.004787302,"teacher_disagreement_score":0.0045903344,"about_ca_system_score_codex":0.0003888032,"about_ca_system_score_gemma":0.0005443922,"threshold_uncertainty_score":0.0091272},"labels":[],"label_agreement":null},{"id":"W2035981989","doi":"10.5539/mas.v2n2p29","title":"A Recurrent Neural Network for Solving Convex Quadratic Program","year":2008,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Positive-definite matrix; Quadratic programming; Artificial neural network; Regular polygon; Set (abstract data type); Convex optimization; Recurrent neural network; Mathematical optimization; Computer science; Quadratic equation; Matrix (chemical analysis); Quadratically constrained quadratic program; Mathematics; Algorithm; Artificial intelligence","score_opus":0.039805266447745014,"score_gpt":0.28166285487565923,"score_spread":0.24185758842791422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035981989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076858266,0.0003374121,0.98946244,0.00013854787,0.000039821498,0.000020400586,0.0000211236,0.00030531458,0.001989081],"genre_scores_gemma":[0.5728222,0.00083391194,0.41760224,0.000245649,0.00012799888,0.00021937709,0.00020248414,0.00012734649,0.007818954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996842,0.00008293348,0.000019956342,0.00007679066,0.00010240704,0.000033779113],"domain_scores_gemma":[0.9997743,0.00009258063,0.000027849703,0.00001797673,0.000075202006,0.000012124703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068572885,0.0006076623,0.00063678296,0.00025937232,0.0002652748,0.00047123656,0.0009407664,0.0012008814,0.0017852613],"category_scores_gemma":[0.001375627,0.00026518933,0.0004606652,0.0004128477,0.00037512984,0.0009320834,0.00062060624,0.0011345682,0.0004308377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010087883,0.000083436156,0.0004141001,0.00021241614,0.00007944931,0.00024320044,0.00009158148,0.80223566,0.015971737,0.037740283,0.0026828416,0.1401444],"study_design_scores_gemma":[0.0000034458449,0.000017520952,0.000022888295,0.000002955815,0.0000051561437,0.000015021951,0.0000016203204,0.9974637,0.0005911929,0.0015045106,0.00036880962,0.0000030561935],"about_ca_topic_score_codex":0.0027904783,"about_ca_topic_score_gemma":0.0032487372,"teacher_disagreement_score":0.0027904783,"about_ca_system_score_codex":0.00039235453,"about_ca_system_score_gemma":0.0006215196,"threshold_uncertainty_score":0.005972266},"labels":[],"label_agreement":null},{"id":"W2036165337","doi":"10.1016/j.neunet.2009.06.011","title":"A new bidirectional heteroassociative memory encompassing correlational, competitive and topological properties","year":2009,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Representation (politics); Set (abstract data type); Topology (electrical circuits); Theoretical computer science; Space (punctuation); Feature (linguistics); Categorization; Unsupervised learning; Artificial intelligence; Cluster analysis; Competitive learning; Mathematics","score_opus":0.022895153780869423,"score_gpt":0.2403952759763743,"score_spread":0.21750012219550488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036165337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32080784,0.0022238544,0.6395455,0.0006958097,0.00073392625,0.00009406995,0.0006624707,0.0019392004,0.033297382],"genre_scores_gemma":[0.9269018,0.0006395804,0.061051138,0.00027231255,0.00013741806,0.00007529917,0.0003201132,0.000080414575,0.010521956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998616,0.000013763554,0.000009526101,0.00005333667,0.000037731472,0.000024007475],"domain_scores_gemma":[0.99966204,0.00005075034,0.000047234284,0.00009805613,0.00008874243,0.000053203705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019622192,0.0003015599,0.00062533596,0.00041729628,0.0003971374,0.0012461843,0.0011457627,0.00059962616,0.0038749822],"category_scores_gemma":[0.0005049603,0.00017230865,0.0003338225,0.0005152895,0.00064028037,0.0021787363,0.00082668447,0.00051480054,0.00066556496],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008180824,0.00035494746,0.0019828896,0.00056093803,0.0002452107,0.0005081121,0.00022745208,0.02675613,0.28284773,0.4175762,0.00842165,0.25970075],"study_design_scores_gemma":[0.00011644821,0.0006868574,0.0023760726,0.00006197731,0.00026865833,0.0015754518,0.00013433243,0.58802927,0.08533374,0.30249894,0.0187857,0.00013257319],"about_ca_topic_score_codex":0.00031426776,"about_ca_topic_score_gemma":0.0005241847,"teacher_disagreement_score":0.0038749822,"about_ca_system_score_codex":0.00022992,"about_ca_system_score_gemma":0.00036982525,"threshold_uncertainty_score":0.012963116},"labels":[],"label_agreement":null},{"id":"W2036170825","doi":"10.1139/s03-080","title":"Internal workings of feed-forward neural networks","year":2004,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Black box; Backpropagation; Computer science; Artificial intelligence; Perceptron; Process (computing); Machine learning; Component (thermodynamics); Key (lock)","score_opus":0.00447545247125535,"score_gpt":0.18307986854506014,"score_spread":0.17860441607380478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036170825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02834942,0.0013382675,0.9504597,0.0009842609,0.00011246414,0.00005652631,0.00006151381,0.00041904018,0.018218784],"genre_scores_gemma":[0.814925,0.0025674903,0.17397001,0.00033525864,0.0001392845,0.00021739412,0.00014289124,0.00015117908,0.0075515257],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983967,0.00062571885,0.00012334289,0.0002078675,0.0005534386,0.00009302272],"domain_scores_gemma":[0.9935916,0.004641907,0.00040098713,0.00055540144,0.000735553,0.00007456247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032867095,0.0009902914,0.0007813988,0.0008783801,0.0005964845,0.002877842,0.0016998474,0.0016437443,0.0034521045],"category_scores_gemma":[0.016841393,0.00075289956,0.00096949097,0.0007337669,0.003016638,0.0055651334,0.0016962278,0.0017803668,0.00061656826],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010415101,0.000059474056,0.0023844552,0.00036490045,0.00017394304,0.0002836035,0.0012257718,0.4112371,0.0035332053,0.46979302,0.00096724415,0.109873086],"study_design_scores_gemma":[0.00001348554,0.0000579113,0.00055439695,0.00015969036,0.00005918232,0.00013594881,0.00009486068,0.7028445,0.0032758773,0.28877416,0.003988176,0.00004188798],"about_ca_topic_score_codex":0.0017413476,"about_ca_topic_score_gemma":0.00089599716,"teacher_disagreement_score":0.0034521045,"about_ca_system_score_codex":0.001051439,"about_ca_system_score_gemma":0.00059105933,"threshold_uncertainty_score":0.017381966},"labels":[],"label_agreement":null},{"id":"W2036552982","doi":"10.1109/cec.2010.5585953","title":"Evolving ARTMAP neural networks using Multi-Objective Particle Swarm Optimization","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Particle swarm optimization; Artificial neural network; Computer science; Artificial intelligence; Machine learning; Fuzzy logic","score_opus":0.022643934668476307,"score_gpt":0.26716263946376345,"score_spread":0.24451870479528715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036552982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039862193,0.00035518213,0.95485246,0.00016185464,0.000053807595,0.00007590838,0.000031257965,0.00031898177,0.0042883223],"genre_scores_gemma":[0.5811788,0.00031275637,0.41294593,0.00021623184,0.00005141082,0.00036356592,0.000118992575,0.0000968349,0.004715485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997563,0.00009530482,0.000011872777,0.00004135518,0.00007269954,0.000022478804],"domain_scores_gemma":[0.9991947,0.000498476,0.000077853714,0.00004999154,0.00015505831,0.000023914115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011188409,0.0007365191,0.00074276334,0.0007432417,0.00035060753,0.0010202219,0.0010836135,0.001304608,0.0015120223],"category_scores_gemma":[0.0031838529,0.00041885476,0.0005908988,0.000514019,0.00056061323,0.0010169205,0.0006746552,0.00074355473,0.00031002707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024086316,0.000026353375,0.0003457005,0.000030284564,0.000029622717,0.000039060284,0.00004180446,0.9563883,0.00090836245,0.0029258358,0.00039010492,0.03885042],"study_design_scores_gemma":[0.0000025671106,0.000008613908,0.000027105703,0.0000024334186,0.000002009851,0.0000054941365,0.000003194845,0.99897516,0.00018162237,0.00066270615,0.00012742379,0.0000017627331],"about_ca_topic_score_codex":0.002853781,"about_ca_topic_score_gemma":0.0027945454,"teacher_disagreement_score":0.002853781,"about_ca_system_score_codex":0.0006712276,"about_ca_system_score_gemma":0.0005196265,"threshold_uncertainty_score":0.0059170723},"labels":[],"label_agreement":null},{"id":"W2036738287","doi":"10.1007/s11227-008-0215-z","title":"Prediction of dynamical, nonlinear, and unstable process behavior","year":2008,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Scheduling (production processes); Nonlinear system; Process (computing); Distributed computing; Mathematical optimization","score_opus":0.03187427107375176,"score_gpt":0.25064224053703243,"score_spread":0.21876796946328067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036738287","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8426598,0.0001829436,0.15416159,0.00039009156,0.0000744185,0.00003297958,0.00019803456,0.000368621,0.0019315593],"genre_scores_gemma":[0.99606603,0.000028475128,0.0033624726,0.000009015166,0.0000113225615,0.0000065200825,0.000053150976,0.000012579922,0.0004504921],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998857,0.000030707062,0.0000055187197,0.000032411106,0.000026288173,0.000019342964],"domain_scores_gemma":[0.9983694,0.0011077541,0.00014312385,0.0001109978,0.00017354773,0.00009512143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006610114,0.00040554555,0.000365968,0.00042107466,0.00029695086,0.00067937886,0.0004356767,0.0005586809,0.0011310088],"category_scores_gemma":[0.003790136,0.00023354766,0.00031855574,0.00020415641,0.00043327757,0.00073321216,0.00038579633,0.0007520119,0.00016186544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031715335,0.00012645409,0.01372588,0.000040118193,0.00006079265,0.00012843907,0.00006219882,0.9481848,0.004950949,0.0070795473,0.0007809286,0.024542795],"study_design_scores_gemma":[0.0000016824254,0.0000031891034,0.00027842505,3.1012414e-7,7.9087306e-7,0.0000024479143,0.0000010586573,0.9986873,0.00014461455,0.0008706207,0.00000871867,8.282068e-7],"about_ca_topic_score_codex":0.0051997225,"about_ca_topic_score_gemma":0.0033694424,"teacher_disagreement_score":0.0051997225,"about_ca_system_score_codex":0.00047345858,"about_ca_system_score_gemma":0.00047925333,"threshold_uncertainty_score":0.0103389025},"labels":[],"label_agreement":null},{"id":"W2037808679","doi":"10.1109/icassp.2013.6638110","title":"Reduced-complexity binary-weight-coded associative memories","year":2013,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Associative property; Content-addressable memory; Field-programmable gate array; Content-addressable storage; Lookup table; Binary number; Artificial neural network; Bidirectional associative memory; Arithmetic; Parallel computing; Theoretical computer science; Computer hardware; Artificial intelligence; Programming language; Mathematics","score_opus":0.030388721076248737,"score_gpt":0.2568609192583747,"score_spread":0.22647219818212594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037808679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36991236,0.0018967337,0.6002593,0.00047919562,0.00035723794,0.0001304163,0.00042060573,0.003124012,0.023420151],"genre_scores_gemma":[0.847054,0.0003654235,0.14338572,0.00014836698,0.00004388778,0.00006749516,0.00036084256,0.000043834865,0.00853026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998679,0.00001260277,0.000008338594,0.00001877367,0.0000715373,0.000020869627],"domain_scores_gemma":[0.9997049,0.00007445914,0.00003590418,0.000059371927,0.00011187654,0.000013481271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110809335,0.00029074488,0.00021430888,0.00027205964,0.00016269079,0.0004660486,0.0011182565,0.00034844628,0.0046924422],"category_scores_gemma":[0.00063047983,0.00012637272,0.00012454773,0.00032475273,0.00013638832,0.0007948694,0.00025369663,0.00035928545,0.0007151231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006904747,0.00027442042,0.0013497699,0.0005711057,0.000109800356,0.00038195183,0.00009064506,0.120476864,0.27894935,0.041152444,0.0077760196,0.5481771],"study_design_scores_gemma":[0.00014293146,0.0003506505,0.0016908018,0.000038236125,0.00007858753,0.0005713225,0.00004690014,0.77970326,0.18728904,0.014405683,0.01564035,0.00004227021],"about_ca_topic_score_codex":0.0014167326,"about_ca_topic_score_gemma":0.004355156,"teacher_disagreement_score":0.0046924422,"about_ca_system_score_codex":0.00034881703,"about_ca_system_score_gemma":0.00050684216,"threshold_uncertainty_score":0.015697777},"labels":[],"label_agreement":null},{"id":"W2038365451","doi":"10.1016/s0895-7177(02)00254-6","title":"A study of the transiently chaotic neural network for combinatorial optimization","year":2002,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nortel (Canada); University of Calgary; Raytheon Technologies (Canada)","funders":"","keywords":"Travelling salesman problem; Chaotic; Artificial neural network; Mathematics; Combinatorial optimization; Mathematical optimization; Computer science; Artificial intelligence","score_opus":0.04373199134758575,"score_gpt":0.2251811366691757,"score_spread":0.18144914532158996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038365451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4483337,0.005366596,0.4666252,0.0041581257,0.00046917686,0.000078775774,0.00017219671,0.00017571797,0.07462062],"genre_scores_gemma":[0.98368466,0.0010108105,0.008586558,0.00011038787,0.00014965246,0.00003458218,0.00004011802,0.000038775514,0.006344446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998913,0.000046160498,0.0000050501544,0.000016057946,0.000024303818,0.00001717792],"domain_scores_gemma":[0.9992632,0.00048335054,0.00008864486,0.000036238565,0.00007102371,0.000057495028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048153423,0.00033912054,0.00051326666,0.0004209657,0.0004365262,0.0008418586,0.0008200313,0.0009362427,0.0033362822],"category_scores_gemma":[0.0038194892,0.0002462528,0.0005334485,0.00045827555,0.001189847,0.0013738314,0.0006374825,0.00066314096,0.00012685734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079086145,0.000026147309,0.00083997543,0.00013328483,0.00004963733,0.00021071968,0.000088111614,0.55344045,0.0024967957,0.43348143,0.0013827462,0.00777166],"study_design_scores_gemma":[0.000007998069,0.000017594468,0.00018714031,0.0000062732734,0.0000072587154,0.000040695555,0.000015798769,0.9313238,0.0001755206,0.06771154,0.0005011828,0.00000504442],"about_ca_topic_score_codex":0.0017138778,"about_ca_topic_score_gemma":0.0009583174,"teacher_disagreement_score":0.0033362822,"about_ca_system_score_codex":0.0007007696,"about_ca_system_score_gemma":0.00048180678,"threshold_uncertainty_score":0.01116097},"labels":[],"label_agreement":null},{"id":"W2039086233","doi":"10.1016/s0303-2647(99)00091-x","title":"Computational dynamics of gradient bistable networks","year":2000,"lang":"en","type":"article","venue":"Biosystems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bistability; Spurious relationship; Attractor; Computer science; Artificial neural network; Memorization; Computation; Value (mathematics); Dynamics (music); Artificial intelligence; Algorithm; Mathematics; Machine learning; Physics","score_opus":0.008572044566254341,"score_gpt":0.21292009994388494,"score_spread":0.2043480553776306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039086233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5867388,0.0012577287,0.35410234,0.003789493,0.00029921302,0.00004410916,0.0002491494,0.0004632568,0.053055853],"genre_scores_gemma":[0.98837566,0.0002673066,0.0072495937,0.000080428494,0.000033471006,0.000027220844,0.000042484935,0.000035170666,0.003888691],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999229,0.00002528567,0.0000026021244,0.000013407185,0.000021178246,0.000014609178],"domain_scores_gemma":[0.9995753,0.00023715661,0.000048370694,0.000029433564,0.00004822565,0.00006145777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002620134,0.00019909788,0.00035761323,0.00038570346,0.0003300155,0.0009611108,0.0006962323,0.000734729,0.004383481],"category_scores_gemma":[0.0021173318,0.0002109637,0.00020599738,0.00023735833,0.0008442815,0.0014091651,0.00064101,0.0006276491,0.00025382417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071472896,0.000030358806,0.00083798217,0.00004135323,0.000024722476,0.00008224252,0.00009253647,0.47361624,0.004315624,0.5075347,0.001701968,0.011650752],"study_design_scores_gemma":[0.000007585434,0.0000058995925,0.0001601888,0.0000032392093,0.000002331529,0.0000131217885,0.0000122654965,0.89134365,0.00026308306,0.10780383,0.0003791271,0.0000056361237],"about_ca_topic_score_codex":0.0020452042,"about_ca_topic_score_gemma":0.0015688272,"teacher_disagreement_score":0.004383481,"about_ca_system_score_codex":0.0006379772,"about_ca_system_score_gemma":0.00035983027,"threshold_uncertainty_score":0.014664173},"labels":[],"label_agreement":null},{"id":"W2039315777","doi":"10.1142/s0129183100001309","title":"LEARNING, EXPLORATION AND CHAOTIC POLICIES","year":2000,"lang":"en","type":"article","venue":"International Journal of Modern Physics C","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Chaotic; Reinforcement learning; Computer science; Artificial neural network; Artificial intelligence; Chaotic systems; Test (biology)","score_opus":0.022046145349573516,"score_gpt":0.2711982281231609,"score_spread":0.24915208277358739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039315777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42889023,0.0020208717,0.5503903,0.0016892763,0.00017588533,0.00014323668,0.00015130037,0.00026369668,0.016275266],"genre_scores_gemma":[0.9778309,0.00047720765,0.018843723,0.00008950242,0.000045309112,0.00009383683,0.0000467579,0.00002505452,0.0025475381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993494,0.00026581893,0.00004154774,0.000105672974,0.000116440875,0.00012106273],"domain_scores_gemma":[0.99720556,0.0017835728,0.00043276284,0.00020484773,0.0001898129,0.00018345943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010993567,0.00074330857,0.000748049,0.00039141905,0.00036627776,0.0011775156,0.00071050815,0.0013576825,0.0012725409],"category_scores_gemma":[0.0076116445,0.00029977606,0.00067830394,0.0004802034,0.0017714369,0.0019950552,0.0011231059,0.0009954514,0.00012407347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011908121,0.00005853012,0.0009734033,0.000078399826,0.00006402543,0.00012187305,0.000060139675,0.92071694,0.0010968781,0.0687835,0.00041329986,0.0075139017],"study_design_scores_gemma":[0.00004781121,0.00009221408,0.00025594485,0.000010921151,0.00001689431,0.000041312764,0.000014275839,0.95958847,0.00054089003,0.03891555,0.0004610948,0.000014742226],"about_ca_topic_score_codex":0.0022704299,"about_ca_topic_score_gemma":0.0010049347,"teacher_disagreement_score":0.0022704299,"about_ca_system_score_codex":0.0010015363,"about_ca_system_score_gemma":0.00068827113,"threshold_uncertainty_score":0.0072667003},"labels":[],"label_agreement":null},{"id":"W2039727982","doi":"10.1117/12.420935","title":"&lt;title&gt;Optical-electronic shape recognition system based on synergetic associative memory&lt;/title&gt;","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Content-addressable memory; Spurious relationship; Realization (probability); Content-addressable storage; Artificial intelligence; Artificial neural network; Feature extraction; Hough transform; Distortion (music); Feature (linguistics); Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Object (grammar); Computer vision; Image (mathematics); Machine learning; Mathematics; Telecommunications","score_opus":0.011145088956196746,"score_gpt":0.21643608056380848,"score_spread":0.20529099160761172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039727982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27171803,0.0014029997,0.6129066,0.0015795161,0.0012982824,0.00018048628,0.00028565916,0.0060294583,0.104598984],"genre_scores_gemma":[0.8060056,0.00043965064,0.1226793,0.0004147305,0.00016200564,0.000083908475,0.0002689202,0.00008792342,0.06985794],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999162,0.000008275214,0.0000061762066,0.000018879286,0.00003772605,0.000012785212],"domain_scores_gemma":[0.99990284,0.000014744824,0.0000073896535,0.000021932206,0.00003813383,0.000014891907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015952723,0.0001358472,0.00016079034,0.00023710592,0.0002783824,0.00037465311,0.000521431,0.00040449324,0.011555186],"category_scores_gemma":[0.00023551974,0.000100929836,0.00014066616,0.00020143048,0.00028283813,0.00076262705,0.00031699645,0.00022653134,0.0022773903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003993769,0.0001896692,0.0012835239,0.00016068594,0.0000356854,0.00055928034,0.00009587971,0.005220014,0.4733413,0.023635797,0.012088105,0.4829907],"study_design_scores_gemma":[0.00015305917,0.0009112154,0.005378568,0.000041866286,0.00008989128,0.0017421908,0.00009142946,0.24561614,0.6223982,0.010792876,0.11268951,0.000095086645],"about_ca_topic_score_codex":0.0004937975,"about_ca_topic_score_gemma":0.0009725013,"teacher_disagreement_score":0.011555186,"about_ca_system_score_codex":0.00021101398,"about_ca_system_score_gemma":0.00019402622,"threshold_uncertainty_score":0.038655996},"labels":[],"label_agreement":null},{"id":"W2040139364","doi":"10.1117/12.395080","title":"&lt;title&gt;New pruning techniques for constructive neural networks with application to image compression&lt;/title&gt;","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pruning; Constructive; Artificial neural network; Computer science; Image (mathematics); Image compression; Artificial intelligence; Compression (physics); Data compression; Algorithm; Pattern recognition (psychology); Image processing; Process (computing)","score_opus":0.006930629213458162,"score_gpt":0.225825055356655,"score_spread":0.21889442614319685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040139364","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007502101,0.0008814107,0.98792404,0.00021433525,0.00013887945,0.000043208907,0.000029328121,0.00042525365,0.0028414163],"genre_scores_gemma":[0.12891799,0.0015524575,0.8608376,0.00024016525,0.00024289751,0.00011839529,0.000200687,0.00021715215,0.0076727206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950504,0.00014506985,0.000038000904,0.00005322737,0.00022930489,0.000029326959],"domain_scores_gemma":[0.9988279,0.00060205284,0.00011193297,0.00013656875,0.0002949367,0.000026585676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008376661,0.00072778953,0.00052821526,0.00079757505,0.0002903158,0.0006364074,0.0010247681,0.0007948887,0.0027479148],"category_scores_gemma":[0.0030784677,0.00026895117,0.00048049845,0.00082617224,0.0006367341,0.00085991935,0.000596677,0.0010720714,0.0008874806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016443901,0.000081875965,0.00074573554,0.00028210363,0.00008701632,0.00040205405,0.00013721809,0.1272472,0.05655862,0.036186367,0.0060331807,0.7720742],"study_design_scores_gemma":[0.000011939977,0.00008393588,0.0004154975,0.000041996507,0.000029451623,0.00033311296,0.000014707265,0.9562844,0.025531111,0.009139114,0.008094159,0.000020602864],"about_ca_topic_score_codex":0.0008748081,"about_ca_topic_score_gemma":0.0019169936,"teacher_disagreement_score":0.0027479148,"about_ca_system_score_codex":0.00036601143,"about_ca_system_score_gemma":0.0002855416,"threshold_uncertainty_score":0.009192705},"labels":[],"label_agreement":null},{"id":"W2040801373","doi":"10.1017/s0140525x12003111","title":"Realistic neurons can compute the operations needed by quantum probability theory and other vector symbolic architectures","year":2013,"lang":"en","type":"letter","venue":"Behavioral and Brain Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Quantum; Algebraic number; Algebraic operation; Theoretical computer science; Cognition; Cognitive architecture; Architecture; Algebraic structure; Artificial intelligence; Algebra over a field; Cognitive science; Mathematics; Pure mathematics; Psychology; Neuroscience; Physics","score_opus":0.04512589476692963,"score_gpt":0.29192332099909657,"score_spread":0.24679742623216694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040801373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12854777,0.0028692659,0.4739625,0.18447696,0.005178478,0.0001574149,0.00052990206,0.0017090463,0.20256868],"genre_scores_gemma":[0.9226708,0.0019065088,0.052416794,0.010319963,0.00094197155,0.00023862993,0.00024367438,0.00008995864,0.01117175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996532,0.000086441505,0.000015508234,0.000045211385,0.00015209882,0.000047577276],"domain_scores_gemma":[0.9991448,0.00047536098,0.000048459082,0.00019974593,0.00008063218,0.000050983446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059177686,0.00033642753,0.00031049357,0.0002006457,0.0005463887,0.0013837017,0.0008645785,0.0044903327,0.0049487324],"category_scores_gemma":[0.0044818697,0.00025314552,0.0003700797,0.00024943246,0.0019255878,0.003440701,0.00086581067,0.002929001,0.0020880646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088615336,0.00003339952,0.00025147587,0.000067174835,0.000015485217,0.00047117006,0.00007330807,0.013052215,0.004440708,0.94113815,0.019025723,0.021342572],"study_design_scores_gemma":[0.00005841663,0.000030422672,0.00014578727,0.000015498956,0.000005383581,0.00031579728,0.00003641881,0.08911415,0.001777125,0.89053845,0.01794404,0.000018327695],"about_ca_topic_score_codex":0.0008230339,"about_ca_topic_score_gemma":0.0008387151,"teacher_disagreement_score":0.0049487324,"about_ca_system_score_codex":0.0011218757,"about_ca_system_score_gemma":0.00042660214,"threshold_uncertainty_score":0.01655519},"labels":[],"label_agreement":null},{"id":"W2041516550","doi":"10.1088/0031-9155/52/21/014","title":"Myelin structure is a key difference in the x-ray scattering signature between meningioma, schwannoma and glioblastoma multiforme","year":2007,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Royal University Hospital Foundation; University of Saskatchewan","keywords":"Schwannoma; Meningioma; Glioblastoma; Myelin sheath; Myelin; Scattering; Pathology; Glioma; Medicine; Radiology; Physics; Optics; Internal medicine; Cancer research; Central nervous system","score_opus":0.0775030571185019,"score_gpt":0.33990678784303946,"score_spread":0.26240373072453754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041516550","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981729,0.00016715514,0.001284112,0.000021933343,0.0000024443773,0.0000037772734,0.00006055505,0.00001726721,0.00026980366],"genre_scores_gemma":[0.9975424,0.0001263649,0.0019726441,0.00000740424,0.0000019788176,0.00000460303,0.00011751218,0.0000055808323,0.00022139272],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99991775,0.000010357924,0.000008050421,0.00001347238,0.000032232747,0.000018144843],"domain_scores_gemma":[0.99969506,0.00006788896,0.000115972485,0.000018556377,0.00005190287,0.000050563216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017950007,0.00020717985,0.00018869793,0.0008023133,0.00023718596,0.00032337543,0.000095608906,0.00022074522,0.0005149642],"category_scores_gemma":[0.0005724184,0.00017390393,0.00012559486,0.0003939102,0.00036692346,0.000256203,0.00022213788,0.00016898016,0.00010920409],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032901604,0.000015433669,0.010962976,0.000045397865,0.000013835786,0.00026763155,0.00010719058,0.00022641856,0.9841936,0.0002529997,0.000035578014,0.0035499318],"study_design_scores_gemma":[0.0000201309,0.00035054766,0.37203988,0.000012961021,0.00006367202,0.004291624,0.0005619741,0.0063833115,0.61388093,0.0012454536,0.0011194196,0.000030146917],"about_ca_topic_score_codex":0.0008991786,"about_ca_topic_score_gemma":0.0015668657,"teacher_disagreement_score":0.0008991786,"about_ca_system_score_codex":0.00022265124,"about_ca_system_score_gemma":0.00024294337,"threshold_uncertainty_score":0.0017879009},"labels":[],"label_agreement":null},{"id":"W2041533575","doi":"10.1109/icif.2007.4408052","title":"A sequential ESM track association algorithm based on the use of information theoretic criteria","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Akaike information criterion; Computer science; Association (psychology); Track (disk drive); Cluster analysis; Data mining; Data association; Algorithm; Association rule learning; Artificial intelligence; Machine learning","score_opus":0.026914276519152524,"score_gpt":0.26090488545601026,"score_spread":0.23399060893685775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041533575","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027715813,0.00008728085,0.9964139,0.00003754171,0.00003974478,0.000036204365,0.000016825483,0.00024884494,0.00034806886],"genre_scores_gemma":[0.059021067,0.00011411786,0.9382463,0.00006175955,0.00006275378,0.0001548911,0.00015549347,0.00005955602,0.0021240013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790657,0.00028466608,0.00019572521,0.0005003715,0.0009870976,0.00012562591],"domain_scores_gemma":[0.9967045,0.0010118891,0.00037835195,0.00042339717,0.0013756546,0.00010625253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019337769,0.0009586653,0.0013437849,0.0021161605,0.0011288766,0.001392205,0.0020758891,0.0013080761,0.0021617953],"category_scores_gemma":[0.0053967144,0.0005752309,0.0008753092,0.0020686865,0.00069779897,0.0020960972,0.0016317329,0.0015367059,0.001330711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023354574,0.000109679386,0.0017356077,0.00011323147,0.0001095788,0.00009257323,0.00013166635,0.10616273,0.011146185,0.016892066,0.0029082817,0.86036485],"study_design_scores_gemma":[0.00003655744,0.00014701224,0.0007610689,0.000017248909,0.000041042393,0.00029872407,0.000040661875,0.9700678,0.0105194105,0.011117214,0.006912315,0.000040913117],"about_ca_topic_score_codex":0.002531493,"about_ca_topic_score_gemma":0.0027881984,"teacher_disagreement_score":0.002531493,"about_ca_system_score_codex":0.0007648006,"about_ca_system_score_gemma":0.0017411329,"threshold_uncertainty_score":0.010226965},"labels":[],"label_agreement":null},{"id":"W2041890119","doi":"10.1167/13.9.143","title":"The Influence of Task-Irrelevant Spatial Regularities on Statistical Learning","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Task (project management); Statistical learning; Perception; Computer science; Spatial analysis; Spatial ability; Artificial intelligence; Cognitive psychology; Spatial learning; Machine learning; Psychology; Cognition; Mathematics; Statistics","score_opus":0.0065841102711041155,"score_gpt":0.2590910012219614,"score_spread":0.25250689095085727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041890119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.995006,0.00011837868,0.0028961087,0.000042992902,0.00000976517,0.000038212213,0.00003547765,0.000041923344,0.0018112533],"genre_scores_gemma":[0.99746954,0.00007417372,0.001968376,0.000025273159,0.0000062037575,0.00003052915,0.000056934667,0.000029215757,0.0003397459],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99689144,0.0010830218,0.00030037493,0.0007534844,0.00075687247,0.00021474158],"domain_scores_gemma":[0.9600175,0.028524429,0.004408377,0.0041622324,0.0009129366,0.001974638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025175763,0.0006337983,0.00049621233,0.00035351425,0.00029662516,0.0015298605,0.0006986111,0.00059393706,0.0013800663],"category_scores_gemma":[0.037862226,0.00048689658,0.00038153643,0.00024345396,0.0011338546,0.00088481035,0.0010256926,0.0010453042,0.00019709441],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0076176897,0.0028613233,0.12121512,0.0007920218,0.0005233899,0.00058921415,0.00235028,0.019307151,0.6985561,0.002241043,0.0004366183,0.14350994],"study_design_scores_gemma":[0.000447412,0.009418797,0.7616831,0.000090065514,0.00049712986,0.0007789505,0.0006614739,0.0479537,0.16652457,0.009224675,0.0025028472,0.00021730138],"about_ca_topic_score_codex":0.0014483595,"about_ca_topic_score_gemma":0.0017811356,"teacher_disagreement_score":0.0025175763,"about_ca_system_score_codex":0.00045576514,"about_ca_system_score_gemma":0.0006096875,"threshold_uncertainty_score":0.013314426},"labels":[],"label_agreement":null},{"id":"W2042128965","doi":"10.1139/l05-050","title":"Modeling incremental pavement roughness using functional network","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Delaware; University of Delaware Research Foundation","keywords":"Computer science; Artificial neural network; Pavement management; Functional requirement; Surface finish; Set (abstract data type); Machine learning; Engineering; Civil engineering; Mechanical engineering","score_opus":0.021646718126474087,"score_gpt":0.20321258326582228,"score_spread":0.1815658651393482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042128965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14958468,0.00013826526,0.8469908,0.00016345801,0.000017245675,0.00002381297,0.00011155961,0.00020728617,0.0027628378],"genre_scores_gemma":[0.9594116,0.00014227157,0.03820575,0.00001767441,0.000011130932,0.000053304448,0.00008831182,0.000015958805,0.00205398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985814,0.000043600237,0.000007058282,0.000044743327,0.00002791381,0.000018565901],"domain_scores_gemma":[0.99963284,0.0002315322,0.000041225332,0.00002267522,0.000061524406,0.000010273557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045954244,0.0003411594,0.0002542948,0.00045302243,0.00021100654,0.00046063794,0.0006433244,0.0006169707,0.00091466156],"category_scores_gemma":[0.0015661205,0.0001924406,0.0003921223,0.0004137249,0.00044874486,0.0009813734,0.00027578583,0.00042006135,0.00010515627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007470106,0.000007122874,0.00051988487,0.000005960927,0.0000064393453,0.000012633539,0.000011093378,0.99093986,0.00042565455,0.0032913797,0.000055703,0.0047168736],"study_design_scores_gemma":[2.921613e-7,0.0000021012258,0.000075550335,3.7030628e-7,9.880317e-7,0.0000017080125,8.7474007e-7,0.9990048,0.000051034767,0.0008294627,0.00003203405,8.0874463e-7],"about_ca_topic_score_codex":0.016408026,"about_ca_topic_score_gemma":0.011790053,"teacher_disagreement_score":0.016408026,"about_ca_system_score_codex":0.0008120291,"about_ca_system_score_gemma":0.0004711389,"threshold_uncertainty_score":0.03262502},"labels":[],"label_agreement":null},{"id":"W2042190000","doi":"10.1115/dscc2011-5957","title":"Tuning of an Adaptive Neural Network Compensator for Position Control of a Pneumatic System","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Queen's University","funders":"","keywords":"Control theory (sociology); PID controller; Artificial neural network; Computer science; Adaptive control; Control engineering; Position (finance); Compensation (psychology); Controller (irrigation); Tracking (education); Control (management); Engineering; Artificial intelligence; Temperature control","score_opus":0.03347492949165477,"score_gpt":0.23233062454085962,"score_spread":0.19885569504920486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042190000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1913792,0.0006066459,0.79965734,0.00023102335,0.00020598162,0.00021584154,0.000043241816,0.001548691,0.006111995],"genre_scores_gemma":[0.92977935,0.00012698179,0.06817984,0.00006656464,0.000024858282,0.00008757705,0.000029190856,0.000025242449,0.0016804445],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984133,0.000026901173,0.000012161035,0.000037043126,0.000068427325,0.000014152607],"domain_scores_gemma":[0.9996886,0.00010254235,0.000051573825,0.000029562374,0.00011514873,0.00001253929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044949708,0.0003380687,0.0002590266,0.00022699898,0.00031635366,0.00025056943,0.000517708,0.0005688643,0.0008921509],"category_scores_gemma":[0.0012730969,0.00015755686,0.00013439247,0.0001377095,0.00022922372,0.00022700678,0.00015981955,0.00039420155,0.00021004885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005247549,0.00022465707,0.0020651054,0.00047340116,0.00010761487,0.00035417918,0.0001688231,0.2629281,0.38112226,0.0019729142,0.0014582492,0.3485998],"study_design_scores_gemma":[0.00009385858,0.00043482418,0.002584042,0.000026102092,0.000042618725,0.00019894549,0.000011404094,0.9300872,0.06313728,0.00027377452,0.0030798195,0.000030235675],"about_ca_topic_score_codex":0.0026435356,"about_ca_topic_score_gemma":0.0036601855,"teacher_disagreement_score":0.0026435356,"about_ca_system_score_codex":0.0002988885,"about_ca_system_score_gemma":0.0003855702,"threshold_uncertainty_score":0.005256295},"labels":[],"label_agreement":null},{"id":"W2042871988","doi":"10.3758/s13420-011-0050-6","title":"Predicting shifts in generalization gradients with perceptrons","year":2011,"lang":"en","type":"article","venue":"Learning & Behavior","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Generalization; Stimulus generalization; Psychology; Perceptron; Set (abstract data type); Cognitive psychology; Artificial intelligence; Artificial neural network; Stimulus (psychology); Perception; Machine learning; Pattern recognition (psychology); Computer science; Neuroscience; Mathematics","score_opus":0.026676081362604156,"score_gpt":0.24457527993760972,"score_spread":0.21789919857500556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042871988","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54920423,0.0006212769,0.44526204,0.0007706446,0.00023924257,0.00006149727,0.00016338828,0.0015321325,0.002145599],"genre_scores_gemma":[0.97069025,0.000079017685,0.027770862,0.0000616894,0.000030407979,0.000019801895,0.00008316787,0.000053474298,0.0012113503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975365,0.00006803761,0.00001432334,0.0000741961,0.000046793553,0.000042954514],"domain_scores_gemma":[0.99801517,0.0012643662,0.00016218334,0.00018242415,0.00027059249,0.00010529102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013480264,0.00054713787,0.0006434528,0.0005771335,0.00022666226,0.000639982,0.00062688,0.0009027666,0.00181081],"category_scores_gemma":[0.006401892,0.00044705372,0.00041380496,0.0003340072,0.0005324594,0.0012239616,0.000553474,0.0016635576,0.00035980798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007227086,0.0002997714,0.013411013,0.00009423518,0.00013325576,0.00009086534,0.000095327,0.79146916,0.011942184,0.0068317903,0.0027275945,0.17218205],"study_design_scores_gemma":[0.000005312197,0.000016899572,0.00062543486,0.0000033204035,0.0000035745445,0.0000055060127,0.0000035483442,0.9952003,0.00076486013,0.0033275315,0.000040328345,0.0000033530587],"about_ca_topic_score_codex":0.0033532605,"about_ca_topic_score_gemma":0.0041213646,"teacher_disagreement_score":0.0033532605,"about_ca_system_score_codex":0.00058325514,"about_ca_system_score_gemma":0.00048702772,"threshold_uncertainty_score":0.0071291924},"labels":[],"label_agreement":null},{"id":"W2043000136","doi":"10.1016/j.ijar.2005.06.016","title":"Fuzzy relational neural network","year":2005,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Moore–Penrose pseudoinverse; Defuzzification; Computer science; Artificial neural network; Benchmark (surveying); Artificial intelligence; Fuzzy logic; Backpropagation; Neuro-fuzzy; Relational database; Machine learning; Fuzzy set operations; Data mining; Fuzzy control system; Fuzzy number; Fuzzy set; Algorithm; Mathematics","score_opus":0.016123284640364277,"score_gpt":0.2654042246827835,"score_spread":0.24928094004241924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043000136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029143786,0.0046716006,0.93731046,0.0012171812,0.00060535624,0.000076512995,0.0004699172,0.0008070805,0.02569808],"genre_scores_gemma":[0.69456667,0.0032547314,0.27545112,0.00031510173,0.000285498,0.00008642141,0.00083524984,0.000066697336,0.025138551],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969447,0.00005799984,0.000023024739,0.000083475425,0.000120203,0.000020883588],"domain_scores_gemma":[0.99964595,0.00011078458,0.000023092649,0.000074179894,0.00012870076,0.000017234903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006045042,0.00028258908,0.0006496041,0.0005708394,0.0004468068,0.0010161846,0.00067256397,0.00075118715,0.0075492417],"category_scores_gemma":[0.002614123,0.00016750686,0.00040706602,0.0006640108,0.00040195018,0.0012463452,0.00050271966,0.0006666086,0.0013850096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030088378,0.000069059606,0.0015228292,0.00023272815,0.00013592739,0.0002310999,0.0001022679,0.13185874,0.0093247155,0.27700192,0.012249009,0.5669708],"study_design_scores_gemma":[0.000018320186,0.000039268263,0.0005905367,0.000035472738,0.00006609154,0.00017382721,0.000025915904,0.8796082,0.0035956868,0.10002495,0.015802141,0.000019629291],"about_ca_topic_score_codex":0.0024992323,"about_ca_topic_score_gemma":0.0021988426,"teacher_disagreement_score":0.0075492417,"about_ca_system_score_codex":0.0005549372,"about_ca_system_score_gemma":0.00046680067,"threshold_uncertainty_score":0.025254786},"labels":[],"label_agreement":null},{"id":"W2043473479","doi":"10.1002/qj.200212858311","title":"The impact of time‐averaging on the detectability of nonlinear empirical relations","year":2002,"lang":"en","type":"article","venue":"Quarterly Journal of the Royal Meteorological Society","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonlinear system; Precipitation; Variable (mathematics); Artificial neural network; Measure (data warehouse); Mathematics; Scale (ratio); Statistics; Econometrics; Meteorology; Environmental science; Applied mathematics; Computer science; Mathematical analysis; Data mining; Artificial intelligence; Geography","score_opus":0.025705995621013902,"score_gpt":0.2690127204619924,"score_spread":0.24330672484097854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043473479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9211411,0.00040659274,0.076315455,0.00041928457,0.00006364112,0.000016310663,0.000084368854,0.0002440642,0.001309089],"genre_scores_gemma":[0.9972025,0.00005373504,0.0025605867,0.000023410399,0.000026350304,0.0000048070146,0.000050913073,0.000022542521,0.00005516673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99677306,0.0014931437,0.0002629442,0.0005167948,0.0007445053,0.00020947175],"domain_scores_gemma":[0.8360915,0.14630337,0.008102571,0.0059822,0.002481513,0.0010388563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0089093875,0.00037257216,0.00055966026,0.0005915296,0.00054065307,0.000963646,0.00052653905,0.0007846106,0.00064519653],"category_scores_gemma":[0.09678049,0.00029027622,0.0005388994,0.0005730114,0.0013853288,0.002368142,0.0011891503,0.0011206324,0.00007613645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029082235,0.00027276613,0.22542536,0.000334802,0.00084111793,0.0020414777,0.0014027186,0.52460855,0.06843943,0.011025058,0.000976834,0.16172372],"study_design_scores_gemma":[0.000029217192,0.00050212146,0.13822904,0.000039126837,0.00019507267,0.0006530743,0.00023111961,0.82472414,0.0232349,0.011351421,0.0006949838,0.00011579583],"about_ca_topic_score_codex":0.0022288298,"about_ca_topic_score_gemma":0.0012737045,"teacher_disagreement_score":0.0089093875,"about_ca_system_score_codex":0.0005188074,"about_ca_system_score_gemma":0.00046356238,"threshold_uncertainty_score":0.04711789},"labels":[],"label_agreement":null},{"id":"W2043650904","doi":"10.1007/s10044-014-0367-9","title":"Topology-oriented self-organizing maps: a survey","year":2014,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Self-organizing map; Resource (disambiguation); Artificial intelligence; Tree (set theory); Asset (computer security); Architecture; Data science; Artificial neural network; Geography; Computer security; Mathematics","score_opus":0.010837478049906275,"score_gpt":0.24406133211178974,"score_spread":0.23322385406188345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043650904","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026966026,0.49291193,0.45283243,0.0017226945,0.00079609884,0.00012426701,0.00058588485,0.0006239532,0.02343677],"genre_scores_gemma":[0.26689494,0.5078923,0.21582985,0.0005716453,0.0026481787,0.00025003275,0.0012976292,0.00016573578,0.004449729],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999622,0.00005860232,0.0000378352,0.00009924046,0.00016165336,0.00002057445],"domain_scores_gemma":[0.9992126,0.00039872422,0.00006891538,0.00006862622,0.0002029157,0.00004802675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006026263,0.0007271466,0.0013672078,0.0027800207,0.00036751613,0.0019592077,0.001284543,0.00089955784,0.0012250306],"category_scores_gemma":[0.0018027374,0.0003979535,0.00057369046,0.0047390135,0.0007382963,0.0023667298,0.00088571844,0.00058656547,0.00069150794],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058586927,0.00016483782,0.0027850415,0.0042365096,0.00015551841,0.00014645427,0.00023638154,0.026286224,0.00262632,0.07800127,0.008986882,0.876316],"study_design_scores_gemma":[0.000042977696,0.00043235515,0.0077443123,0.0012366256,0.0002470125,0.002469499,0.0007271002,0.29454926,0.0063924375,0.46504736,0.22092469,0.0001864295],"about_ca_topic_score_codex":0.0009437253,"about_ca_topic_score_gemma":0.00080612476,"teacher_disagreement_score":0.0027800207,"about_ca_system_score_codex":0.0005524384,"about_ca_system_score_gemma":0.00073876785,"threshold_uncertainty_score":0.0040981174},"labels":[],"label_agreement":null},{"id":"W2044390790","doi":"10.1117/12.473101","title":"A flexible framework for developing a cooperative intelligent image analysis system","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Task (project management); Image processing; Software; Domain (mathematical analysis); Task analysis; Image (mathematics); Plan (archaeology); Interface (matter); Human–computer interaction; Software engineering; Artificial intelligence; Systems engineering; Programming language","score_opus":0.01752367896489721,"score_gpt":0.258904934165448,"score_spread":0.24138125520055081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044390790","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010605146,0.00004520162,0.99333787,0.000088001645,0.000011738444,0.0001589391,0.000038805178,0.0041760043,0.0010829247],"genre_scores_gemma":[0.0293733,0.000119285636,0.9668231,0.000084935455,0.000021836564,0.0005808585,0.00036844163,0.0005985139,0.0020295864],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967403,0.0006097196,0.0003834928,0.00060564873,0.0013187538,0.00034209478],"domain_scores_gemma":[0.9978974,0.00064815156,0.00015859722,0.00058469066,0.00043315295,0.00027810084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005803275,0.0014332155,0.0010433531,0.0016996558,0.0014802727,0.0042126644,0.0060876636,0.0021462261,0.003932315],"category_scores_gemma":[0.0054780277,0.0015545284,0.0025716496,0.0007771712,0.002895229,0.0045145475,0.0039358484,0.0028567242,0.0024227456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028166978,0.0004064009,0.0021006041,0.000699637,0.00021524979,0.0018054771,0.0026500188,0.22178547,0.035131387,0.4297322,0.020883858,0.284308],"study_design_scores_gemma":[0.00014542122,0.0002522887,0.000426549,0.00023806778,0.0001388469,0.00071311474,0.0002527189,0.70449066,0.019814959,0.13035385,0.14298491,0.00018857676],"about_ca_topic_score_codex":0.00814175,"about_ca_topic_score_gemma":0.006060227,"teacher_disagreement_score":0.00814175,"about_ca_system_score_codex":0.0016531369,"about_ca_system_score_gemma":0.0036525493,"threshold_uncertainty_score":0.030691028},"labels":[],"label_agreement":null},{"id":"W2044406471","doi":"10.1109/tcsi.2011.2177108","title":"A Prototype CVNS Distributed Neural Network Using Synapse-Neuron Modules","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial neural network; Scalability; Noise (video); Artificial intelligence","score_opus":0.028617182724762796,"score_gpt":0.2383163920788104,"score_spread":0.2096992093540476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044406471","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5171087,0.00051659567,0.44263476,0.00050679105,0.00040960644,0.00034538057,0.0006519094,0.006111564,0.03171461],"genre_scores_gemma":[0.91760486,0.000079665566,0.07285202,0.000097890195,0.000017910392,0.00011816372,0.0002076775,0.00003817292,0.008983638],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998878,0.000016573975,0.0000046500045,0.000032970274,0.000040903364,0.000017137932],"domain_scores_gemma":[0.99984896,0.00002300536,0.0000119247725,0.000022797072,0.000073129435,0.00002015608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001544015,0.00026475205,0.0002982617,0.00015218982,0.00024201444,0.00036055275,0.0019001269,0.000615578,0.0035470324],"category_scores_gemma":[0.00023668286,0.00015301866,0.00021929543,0.00016648204,0.0002083363,0.00047254126,0.00026703664,0.00031014858,0.00054535305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009640461,0.00048129592,0.0032252385,0.00081651466,0.00019729584,0.0010094175,0.00030771902,0.24422379,0.55671304,0.013855111,0.008429794,0.16977683],"study_design_scores_gemma":[0.00025526583,0.0019648888,0.0038802568,0.000054636796,0.00011042917,0.00084216037,0.00007253028,0.75469226,0.21560077,0.0031831942,0.019278126,0.000065454515],"about_ca_topic_score_codex":0.0019374569,"about_ca_topic_score_gemma":0.0024348649,"teacher_disagreement_score":0.0035470324,"about_ca_system_score_codex":0.0005056134,"about_ca_system_score_gemma":0.0005000319,"threshold_uncertainty_score":0.011866033},"labels":[],"label_agreement":null},{"id":"W2044606531","doi":"10.1117/12.527094","title":"Neural networks: different problems require different learning rate adaptive methods","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Backpropagation; Artificial neural network; Artificial intelligence; Task (project management); Benchmark (surveying); Heuristic; Word error rate; Set (abstract data type); Machine learning; Pattern recognition (psychology)","score_opus":0.019115246869090722,"score_gpt":0.2564058301148821,"score_spread":0.2372905832457914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044606531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019196367,0.001567414,0.9757436,0.00066086353,0.00013221438,0.00008074752,0.000016517666,0.0003652729,0.002237078],"genre_scores_gemma":[0.29524443,0.0021667266,0.6978593,0.0004667564,0.00038077243,0.00035620749,0.000085260996,0.00034607504,0.0030944005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860495,0.0005013475,0.00012966001,0.00030158905,0.00038663542,0.00007587723],"domain_scores_gemma":[0.9952828,0.0029173084,0.00028252392,0.00056766556,0.00084301515,0.00010666913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039680987,0.0011365381,0.00094631617,0.00067028886,0.00036251126,0.0013217133,0.0016678141,0.001935868,0.0010809946],"category_scores_gemma":[0.016232291,0.00044067472,0.0007389868,0.00074196747,0.0008910749,0.0031900317,0.0011914247,0.0025622572,0.00049021747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027539112,0.000174522,0.002403655,0.00045332234,0.00027546546,0.0001104562,0.00028858098,0.32550064,0.019230135,0.054970086,0.0025975215,0.5937202],"study_design_scores_gemma":[0.000030870404,0.00006178484,0.00067561376,0.00003522634,0.00003488913,0.00008247054,0.000032262928,0.97445786,0.0062758382,0.015526138,0.0027624527,0.000024582523],"about_ca_topic_score_codex":0.0010610962,"about_ca_topic_score_gemma":0.0012763871,"teacher_disagreement_score":0.0039680987,"about_ca_system_score_codex":0.00065351033,"about_ca_system_score_gemma":0.00043276866,"threshold_uncertainty_score":0.020985603},"labels":[],"label_agreement":null},{"id":"W2045377862","doi":"10.1109/coginf.2008.4639188","title":"Speech segmentation using multifractal measures and amplification of signal features","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Speech recognition; Utterance; Segmentation; SIGNAL (programming language); Speech processing; Spectrogram; Noise (video); Speech segmentation; Energy (signal processing); Waveform; Pattern recognition (psychology); Artificial intelligence; Boundary (topology); Dimension (graph theory); Mathematics","score_opus":0.049560886698848367,"score_gpt":0.27554537769345955,"score_spread":0.22598449099461118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045377862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05773956,0.00075227907,0.93937516,0.00007255564,0.000033599703,0.000039159833,0.000053617052,0.00050091377,0.0014330405],"genre_scores_gemma":[0.28887475,0.00060502975,0.70874226,0.000038837494,0.000107735825,0.000062806044,0.00015622521,0.00018935998,0.0012230268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996111,0.0000687478,0.000020725429,0.00010780411,0.0001605128,0.00003114319],"domain_scores_gemma":[0.9992536,0.0004037075,0.000112734386,0.00007567662,0.00012965282,0.0000247805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005382154,0.0004998685,0.00038360944,0.0017148417,0.00029632752,0.0007350256,0.00036727832,0.00051503023,0.0011447808],"category_scores_gemma":[0.001911057,0.0002512572,0.0005949566,0.00077214336,0.00056289387,0.0012437174,0.00045854188,0.00043003046,0.0004701416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021674977,0.000054489978,0.0012582885,0.00020047204,0.000051946372,0.00015065998,0.00036823552,0.022789214,0.4286058,0.0118778255,0.0006518892,0.5337743],"study_design_scores_gemma":[0.00003380389,0.00047255226,0.017786358,0.00006804437,0.000096499665,0.0008177408,0.00016260848,0.7507817,0.20219076,0.014643639,0.012827919,0.00011837038],"about_ca_topic_score_codex":0.00064083346,"about_ca_topic_score_gemma":0.00092546514,"teacher_disagreement_score":0.0017148417,"about_ca_system_score_codex":0.00040295665,"about_ca_system_score_gemma":0.00022222282,"threshold_uncertainty_score":0.0038295984},"labels":[],"label_agreement":null},{"id":"W2045963734","doi":"10.11113/jt.v70.3510","title":"Time Series Forecasting using Least Square Support Vector Machine for Canadian Lynx Data","year":2014,"lang":"en","type":"article","venue":"Jurnal Teknologi","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Universiti Teknologi Malaysia; Ministério da Ciência, Tecnologia e Inovação; Kementerian Sains, Teknologi dan Inovasi","keywords":"Autoregressive integrated moving average; Support vector machine; Artificial neural network; Mean squared error; Time series; Series (stratigraphy); Mean absolute percentage error; Computer science; Flexibility (engineering); Artificial intelligence; Machine learning; Autoregressive model; Data mining; Statistics; Mathematics","score_opus":0.07531674695125204,"score_gpt":0.27907685822231043,"score_spread":0.2037601112710584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045963734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88331944,0.0020677885,0.10732647,0.0007488348,0.00016131507,0.00005867521,0.0018374085,0.001130832,0.003349305],"genre_scores_gemma":[0.97579986,0.00054206414,0.020463908,0.000028293585,0.000020699143,0.000023921348,0.0016884982,0.000022506449,0.0014103223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996953,0.00004022941,0.000023610666,0.000057102432,0.0001388282,0.000045019067],"domain_scores_gemma":[0.99943024,0.00020123704,0.0000585641,0.00003195851,0.0002524699,0.000025462348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080237334,0.0005174502,0.00050242984,0.0011485235,0.00062280777,0.00068668695,0.0006205905,0.00044610695,0.00084735744],"category_scores_gemma":[0.003219603,0.00016816588,0.00049809914,0.002130188,0.00018523757,0.00052454934,0.0003322167,0.0006156402,0.00016430886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035871423,0.00012933665,0.0319601,0.0002916102,0.00012897712,0.00041651164,0.00023216457,0.66291845,0.005745605,0.0024935124,0.006167033,0.28915796],"study_design_scores_gemma":[0.000004181756,0.00001457804,0.0067387098,0.0000068894365,0.000009542822,0.000011504464,0.000040705614,0.99168795,0.00071083853,0.00021140111,0.0005521388,0.000011579413],"about_ca_topic_score_codex":0.552,"about_ca_topic_score_gemma":0.37255424,"teacher_disagreement_score":0.448,"about_ca_system_score_codex":0.0018904614,"about_ca_system_score_gemma":0.0029974012,"threshold_uncertainty_score":0.90127677},"labels":[],"label_agreement":null},{"id":"W2046061729","doi":"10.1109/optim.2008.4602503","title":"FPGA implementation of a self-organized map with on-chip learning","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"Computer science; Field-programmable gate array; Control reconfiguration; Artificial neural network; Computer architecture; Chip; Artificial intelligence; Embedded system","score_opus":0.014248789846391951,"score_gpt":0.2504570148770541,"score_spread":0.23620822503066213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046061729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13157563,0.00045574416,0.842457,0.00015470228,0.00023654499,0.00010977696,0.00012620667,0.003911061,0.020973342],"genre_scores_gemma":[0.7586477,0.00015614669,0.23458405,0.00008626332,0.000031328043,0.00006881881,0.00012932971,0.00006560365,0.0062308162],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988437,0.000021652735,0.0000054663324,0.000016483784,0.00005149373,0.000020535972],"domain_scores_gemma":[0.9999013,0.000022204591,0.00000958111,0.000024579273,0.000034242792,0.000008210397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009647253,0.00023385293,0.00019534827,0.00022101292,0.00015231002,0.000326921,0.0007006834,0.00025288016,0.0025526443],"category_scores_gemma":[0.00023296542,0.00010672491,0.00016600775,0.00016560777,0.00011761218,0.00033604557,0.00017372593,0.00021288736,0.00046467147],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041616362,0.00027971095,0.0027322976,0.00042553523,0.00016987587,0.0005607827,0.00014520434,0.0925043,0.20107491,0.017639393,0.0058517433,0.6782001],"study_design_scores_gemma":[0.00012079143,0.00075653964,0.0037344578,0.00004173071,0.00009639622,0.00092958886,0.000057159185,0.6692345,0.2920243,0.003279285,0.029686593,0.00003862932],"about_ca_topic_score_codex":0.0008117058,"about_ca_topic_score_gemma":0.0013261175,"teacher_disagreement_score":0.0025526443,"about_ca_system_score_codex":0.00020958553,"about_ca_system_score_gemma":0.00024791813,"threshold_uncertainty_score":0.008539498},"labels":[],"label_agreement":null},{"id":"W2046405980","doi":"10.1007/s00521-014-1563-7","title":"Advanced learning methods and exponent regularization applied to a high order neural network","year":2014,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Artificial neural network; Exponent; Conjugate gradient method; Backpropagation; Benchmark (surveying); Mean squared error; Regularization (linguistics); Computer science; Exponential function; Error function; Computational Science and Engineering; Algorithm; Applied mathematics; Mathematics; Artificial intelligence; Mathematical analysis; Statistics","score_opus":0.010412971559653667,"score_gpt":0.28630312644563044,"score_spread":0.2758901548859768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046405980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014571717,0.00076915364,0.9813363,0.00039647578,0.0002638271,0.00002355301,0.000026909129,0.000091081216,0.0025209214],"genre_scores_gemma":[0.42177847,0.0022100415,0.54691446,0.00031790772,0.00071713765,0.00017155225,0.00014779267,0.0003623852,0.027380342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971277,0.00013923584,0.00001877799,0.00003530732,0.00007505803,0.000018784256],"domain_scores_gemma":[0.99883014,0.0005886693,0.00009823828,0.00013355055,0.00029319836,0.000056243265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014868347,0.0006906873,0.0005924355,0.0007104676,0.00041250643,0.0008272609,0.0009852955,0.0012408699,0.0016660049],"category_scores_gemma":[0.0042008236,0.0003116291,0.00064059807,0.0005868129,0.0009848727,0.0015353507,0.0011848741,0.0021100973,0.00041575488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011369679,0.000086669854,0.0010135666,0.0002017944,0.00006975178,0.00019278112,0.00012833404,0.5732726,0.008623142,0.34121114,0.0029239347,0.07216271],"study_design_scores_gemma":[0.0000032657006,0.000008815608,0.000067961184,0.0000057344355,0.0000034965824,0.000014718354,0.0000035646012,0.978004,0.00033223518,0.020980733,0.0005711287,0.0000044229196],"about_ca_topic_score_codex":0.0021130815,"about_ca_topic_score_gemma":0.0028295212,"teacher_disagreement_score":0.0021130815,"about_ca_system_score_codex":0.00068314484,"about_ca_system_score_gemma":0.0008064582,"threshold_uncertainty_score":0.007863224},"labels":[],"label_agreement":null},{"id":"W2047719782","doi":"10.5539/mas.v5n4p217","title":"Artificial Bee Colony based Data Mining Algorithms for Classification Tasks","year":2011,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Naive Bayes classifier; Artificial bee colony algorithm; Data mining; Artificial intelligence; Algorithm; Statistical classification; Decision tree; Machine learning; Pattern recognition (psychology); Support vector machine","score_opus":0.25231052301955376,"score_gpt":0.3273377980124616,"score_spread":0.07502727499290784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047719782","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01063635,0.0020059522,0.9829525,0.00031626315,0.000103665414,0.00018549558,0.00010110585,0.000599106,0.003099496],"genre_scores_gemma":[0.11285642,0.0019542184,0.88126445,0.00024088762,0.00007911423,0.00054320827,0.00040672702,0.000072274066,0.0025826774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989505,0.00028334686,0.00012495171,0.000120161545,0.0004833518,0.00003775559],"domain_scores_gemma":[0.99839514,0.0008451571,0.00012741894,0.00014334063,0.00046010097,0.000028728884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013169196,0.00089324685,0.0012313928,0.0013604331,0.0004625745,0.0011871877,0.0014770981,0.0011086536,0.0014243631],"category_scores_gemma":[0.004942509,0.00031878726,0.00075354526,0.0025108913,0.00042240316,0.0010856754,0.000646013,0.001269295,0.0008657995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111370624,0.00022673243,0.0021381367,0.0006281816,0.00022560285,0.00017736097,0.00017997249,0.27892035,0.007968448,0.018522808,0.0071361577,0.6837648],"study_design_scores_gemma":[0.000039942533,0.00006689358,0.00056368415,0.000058912905,0.000040039213,0.0001536975,0.00003634926,0.9731271,0.003534451,0.011908768,0.010450408,0.000019756722],"about_ca_topic_score_codex":0.002078992,"about_ca_topic_score_gemma":0.0020161422,"teacher_disagreement_score":0.002078992,"about_ca_system_score_codex":0.0004411194,"about_ca_system_score_gemma":0.0006455222,"threshold_uncertainty_score":0.006964624},"labels":[],"label_agreement":null},{"id":"W2047738241","doi":"10.1139/s03-065","title":"Lime softening clarifier modeling with artificial neural networks","year":2004,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Clarifier; Artificial neural network; Lime; Softening; Environmental science; Effluent; Computer science; Process engineering; Environmental engineering; Engineering; Machine learning; Materials science; Mathematics; Statistics","score_opus":0.007534360084301139,"score_gpt":0.17998564646897067,"score_spread":0.17245128638466953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047738241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.241162,0.0006142621,0.75135195,0.00027147343,0.000043094155,0.000053524855,0.00012346912,0.00067162904,0.0057086563],"genre_scores_gemma":[0.9461425,0.00036571725,0.048109688,0.000036933212,0.000017468756,0.000106574626,0.00012668561,0.000025377361,0.0050690677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998684,0.000040611554,0.000008622727,0.000025897965,0.000042260763,0.000014137368],"domain_scores_gemma":[0.99971074,0.0001757137,0.000029620258,0.000013786199,0.00006523313,0.0000049140504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031824675,0.00046274293,0.00026381243,0.0002779451,0.00017639478,0.00051354733,0.0004908665,0.000706313,0.00084966735],"category_scores_gemma":[0.0012033227,0.00030194398,0.00034584253,0.00028691837,0.00017950675,0.00063941476,0.00019909388,0.000445526,0.00020326914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015758134,0.000012224485,0.00033577863,0.000012249518,0.000010185195,0.000012592424,0.000011039731,0.9907706,0.000848717,0.0002793753,0.000062911524,0.0076284944],"study_design_scores_gemma":[6.082319e-7,0.0000046501414,0.000063613654,9.2795693e-7,0.0000011420641,0.0000018013944,0.0000012664425,0.9994778,0.00024899983,0.00014500254,0.000053160074,0.0000010465318],"about_ca_topic_score_codex":0.010614055,"about_ca_topic_score_gemma":0.008027931,"teacher_disagreement_score":0.010614055,"about_ca_system_score_codex":0.00048460005,"about_ca_system_score_gemma":0.00038252232,"threshold_uncertainty_score":0.021104574},"labels":[],"label_agreement":null},{"id":"W2048683932","doi":"10.1007/s11760-014-0623-x","title":"A method for determining the number of features in the kernel space required for preserving classifiability","year":2014,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Kernel principal component analysis; Kernel (algebra); Pattern recognition (psychology); sort; Principal component analysis; Robustness (evolution); Kernel method; Computer science; Artificial intelligence; Feature vector; Mathematics; Algorithm; Support vector machine; Arithmetic; Combinatorics","score_opus":0.02874084438857532,"score_gpt":0.34337802506875625,"score_spread":0.31463718068018093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048683932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077894195,0.00010495492,0.9913555,0.000047416375,0.000024387105,0.00003539087,0.000035709792,0.0003492144,0.0002580811],"genre_scores_gemma":[0.10506447,0.000109135886,0.89303356,0.000044684406,0.000050338873,0.00014231681,0.00015563687,0.00014044832,0.0012593952],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985092,0.00028298196,0.00015522364,0.0003400418,0.0005877009,0.00012481541],"domain_scores_gemma":[0.99261016,0.0038275186,0.00046490162,0.001287467,0.0015773879,0.00023250612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002218164,0.0008663619,0.0011445677,0.0013730014,0.00082357135,0.0014401657,0.0014287137,0.0014026567,0.0019058654],"category_scores_gemma":[0.013388887,0.000511359,0.0007907787,0.00083250215,0.0010178481,0.0022544628,0.0012217986,0.0020676346,0.0008143845],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009790008,0.00036334057,0.0018728471,0.00036202723,0.00016048906,0.00018532772,0.00021450754,0.038338523,0.19379671,0.029165355,0.0033640526,0.7311978],"study_design_scores_gemma":[0.00015118107,0.00035614142,0.0028532562,0.000045691166,0.00013467601,0.0005320945,0.00006926315,0.8554295,0.10195222,0.03262354,0.005743949,0.000108526096],"about_ca_topic_score_codex":0.001477065,"about_ca_topic_score_gemma":0.0019034005,"teacher_disagreement_score":0.002218164,"about_ca_system_score_codex":0.0006315447,"about_ca_system_score_gemma":0.0013864995,"threshold_uncertainty_score":0.01173085},"labels":[],"label_agreement":null},{"id":"W2049147586","doi":"10.1109/ccece.2006.277425","title":"Can we work around Complex Numerical Methods? An Insight","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Stability (learning theory); Artificial neural network; Work (physics); Computer science; Set (abstract data type); Numerical stability; Numerical analysis; Van der Pol oscillator; Applied mathematics; Mathematical optimization; Control theory (sociology); Artificial intelligence; Mathematics; Machine learning; Nonlinear system; Engineering; Mathematical analysis; Control (management); Physics","score_opus":0.054988067119112846,"score_gpt":0.32766433601204936,"score_spread":0.2726762688929365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049147586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010192783,0.05637417,0.54822266,0.2505987,0.009262415,0.00008830342,0.00015305191,0.0007282442,0.1243797],"genre_scores_gemma":[0.46519074,0.07473444,0.34548354,0.046180632,0.014821627,0.0005084583,0.00016561909,0.0010569495,0.051857885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981275,0.0009064816,0.000075442535,0.00020855559,0.00055447314,0.00012753514],"domain_scores_gemma":[0.99500716,0.0028919762,0.00022877904,0.0010899231,0.00052426633,0.00025783153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046451273,0.00091533334,0.0010323816,0.0013375913,0.0012957087,0.005496829,0.0020748796,0.0030321635,0.010132851],"category_scores_gemma":[0.01430996,0.00043054787,0.0007528727,0.00070908637,0.010841865,0.01820183,0.0038199446,0.005747118,0.002499034],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009806203,0.000015371854,0.00013774753,0.0001296003,0.000011962115,0.00003844787,0.00044086124,0.0011083214,0.00014826954,0.9745954,0.005922646,0.01744154],"study_design_scores_gemma":[0.000008492144,0.000014298148,0.000066409004,0.00013320077,0.0000029897406,0.000059829898,0.0002012417,0.0029157957,0.00007798766,0.9518506,0.044656336,0.000012909024],"about_ca_topic_score_codex":0.0010458058,"about_ca_topic_score_gemma":0.0007802463,"teacher_disagreement_score":0.010132851,"about_ca_system_score_codex":0.0010336305,"about_ca_system_score_gemma":0.0012824251,"threshold_uncertainty_score":0.033897758},"labels":[],"label_agreement":null},{"id":"W2049944496","doi":"10.1177/02783640022067887","title":"Robotic System Sensitivity to Neural Network Learning Rate: Theory, Simulation, and Experiments","year":2000,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Sensitivity (control systems); Controller (irrigation); Computer science; PID controller; Artificial intelligence; Control theory (sociology); Robot; Control engineering; Robotics; Reinforcement learning; Engineering; Control (management)","score_opus":0.06838534475249715,"score_gpt":0.3862598059274142,"score_spread":0.3178744611749171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049944496","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81683606,0.00093660306,0.17448848,0.00036671362,0.00007261646,0.0003021087,0.00017525979,0.0008932391,0.0059289034],"genre_scores_gemma":[0.98948497,0.00015981273,0.009885628,0.000024630175,0.000003955222,0.00007966156,0.000042673724,0.000021395557,0.00029726798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894494,0.00036549848,0.0000799062,0.00009858897,0.00040546566,0.000105595354],"domain_scores_gemma":[0.99055123,0.007441429,0.00065153174,0.00048645528,0.0008046233,0.0000646875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028539817,0.00048858754,0.0005187283,0.00053443783,0.0002451443,0.0004481041,0.0006375785,0.000754135,0.0007296382],"category_scores_gemma":[0.010925387,0.0002749245,0.00043624666,0.00039179184,0.00064352766,0.0007776494,0.00045044158,0.00072999776,0.0000978159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023424781,0.00011934545,0.002977187,0.00019692995,0.000048739254,0.00008761495,0.0000859827,0.97212696,0.008091339,0.0009488367,0.00022740048,0.014855345],"study_design_scores_gemma":[0.000009553589,0.00021010361,0.0010514687,0.000012566629,0.000012397856,0.000032421154,0.000015228059,0.98998743,0.008143039,0.00038849216,0.0001247624,0.000012560423],"about_ca_topic_score_codex":0.0048215785,"about_ca_topic_score_gemma":0.002987687,"teacher_disagreement_score":0.0048215785,"about_ca_system_score_codex":0.00080942607,"about_ca_system_score_gemma":0.00040905084,"threshold_uncertainty_score":0.015093446},"labels":[],"label_agreement":null},{"id":"W2050270680","doi":"10.1186/1471-2202-8-s2-p72","title":"Principal dynamic mode analysis of hippocampal neuronal networks","year":2007,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hippocampal formation; Neuroscience; Neuron; Hippocampus; Pyramidal cell; Computer science; Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Biology","score_opus":0.021396080654491736,"score_gpt":0.29851633365290003,"score_spread":0.2771202529984083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050270680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1679618,0.00021335157,0.8296673,0.00008004393,0.000011958372,0.00002923021,0.00022931013,0.00022675705,0.0015800776],"genre_scores_gemma":[0.91516197,0.00036596836,0.081635855,0.000013582184,0.000011413723,0.00007048458,0.00026048438,0.00005107077,0.0024291086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99991834,0.000020762449,0.0000027799776,0.00002096458,0.00002618891,0.000010972379],"domain_scores_gemma":[0.9998031,0.00009560497,0.000025386922,0.000023236216,0.000041491356,0.000011185333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024590673,0.00032953796,0.00018370329,0.00043408375,0.00012403668,0.00028081486,0.00026738134,0.00018897971,0.00083968154],"category_scores_gemma":[0.001054687,0.00015691247,0.00029124005,0.00031339706,0.00020870153,0.00036395213,0.00021311034,0.00035088052,0.00015767243],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012765008,0.00003580293,0.0027714886,0.00011067589,0.00006299715,0.0001423893,0.00016732053,0.79045683,0.060042743,0.027642976,0.0010159896,0.11742313],"study_design_scores_gemma":[0.00000150676,0.000010194164,0.0012348753,0.0000025075103,0.0000025805873,0.000025183177,0.000008838381,0.99068165,0.00186391,0.005808643,0.00035482555,0.000005303699],"about_ca_topic_score_codex":0.0015591535,"about_ca_topic_score_gemma":0.0010874977,"teacher_disagreement_score":0.0015591535,"about_ca_system_score_codex":0.00018032127,"about_ca_system_score_gemma":0.00022320791,"threshold_uncertainty_score":0.0031000972},"labels":[],"label_agreement":null},{"id":"W2050527683","doi":"10.1007/s00521-003-0390-z","title":"Multiple neural networks for a long term time series forecast","year":2004,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Artificial neural network; Computer science; Term (time); Long-term prediction; Series (stratigraphy); Time series; Artificial intelligence; Machine learning; Telecommunications","score_opus":0.015154340905724468,"score_gpt":0.2520112016425414,"score_spread":0.23685686073681697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050527683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07081408,0.0029335362,0.92119235,0.0009921886,0.00046318836,0.000042750853,0.00018624323,0.0005502143,0.0028255],"genre_scores_gemma":[0.7800671,0.0012042577,0.20952873,0.00015357461,0.00051438506,0.000113237635,0.00024405995,0.00009038456,0.008084275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995746,0.00014116087,0.00003982757,0.00010095445,0.000103659346,0.000039859184],"domain_scores_gemma":[0.998863,0.0007142377,0.00009807956,0.00009308099,0.00018999643,0.000041506613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014630132,0.00079163106,0.0012830604,0.0008680893,0.000614099,0.00091641897,0.0012834527,0.0019377706,0.0028739024],"category_scores_gemma":[0.0049936906,0.0008178042,0.0009247969,0.0010044647,0.00042202888,0.002379871,0.000900164,0.0020462985,0.00042100283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015380309,0.00003404273,0.00047569565,0.000044087854,0.00010297382,0.00007673308,0.00001730859,0.9280731,0.0012434662,0.0072886175,0.0007651182,0.061725043],"study_design_scores_gemma":[0.0000024510803,0.000004251391,0.00005745446,0.0000014743463,0.00000661164,0.0000039103134,9.3378105e-7,0.99797827,0.00012990281,0.0017388463,0.00007356341,0.0000023872922],"about_ca_topic_score_codex":0.008351579,"about_ca_topic_score_gemma":0.008825488,"teacher_disagreement_score":0.008351579,"about_ca_system_score_codex":0.0009789108,"about_ca_system_score_gemma":0.0005931801,"threshold_uncertainty_score":0.016605914},"labels":[],"label_agreement":null},{"id":"W2051220245","doi":"10.1142/s0219649207001822","title":"Utilising Neural Network and Support Vector Machine for Gene Expression Classification","year":2007,"lang":"en","type":"article","venue":"Journal of Information & Knowledge Management","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Support vector machine; Computer science; Artificial neural network; Artificial intelligence; Data mining; Process (computing); Machine learning; Knowledge extraction; Set (abstract data type); Data set","score_opus":0.022806593258737344,"score_gpt":0.27781762644568564,"score_spread":0.2550110331869483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051220245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063653775,0.0031616183,0.92727506,0.0005487573,0.00018201725,0.00010124962,0.00025712818,0.0015790485,0.0032413583],"genre_scores_gemma":[0.4385132,0.0018380015,0.5551545,0.0002028017,0.0001885829,0.00027259561,0.0007010515,0.00006981681,0.0030594254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868554,0.00044236062,0.0001043866,0.00018536126,0.0005128743,0.000069480986],"domain_scores_gemma":[0.9973572,0.0017628615,0.00017768504,0.00017214222,0.00048773605,0.000042499832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018209257,0.00075343146,0.00085690885,0.0015199909,0.0002848602,0.00094419793,0.0006269523,0.0011059006,0.0010843503],"category_scores_gemma":[0.0061040586,0.00021531289,0.00052665675,0.0022388157,0.00043120157,0.0018827423,0.00046296086,0.0008713739,0.000616958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005330025,0.0002505656,0.0060532177,0.0003257325,0.00021858008,0.00018184808,0.00012256451,0.14074764,0.01757702,0.006613459,0.0022672664,0.8251091],"study_design_scores_gemma":[0.000020481886,0.00012556408,0.0019173892,0.000022219525,0.00004508132,0.000103208804,0.000039129245,0.9790943,0.009738987,0.006197175,0.0026657225,0.000030662064],"about_ca_topic_score_codex":0.002777555,"about_ca_topic_score_gemma":0.0022847631,"teacher_disagreement_score":0.002777555,"about_ca_system_score_codex":0.0004752795,"about_ca_system_score_gemma":0.00048542526,"threshold_uncertainty_score":0.009630084},"labels":[],"label_agreement":null},{"id":"W2052985314","doi":"10.1016/s0893-6080(00)00049-6","title":"Iterative fast orthogonal search algorithm for MDL-based training of generalized single-layer networks","year":2000,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Minimum description length; Algorithm; Basis (linear algebra); Basis function; Mathematics; Pruning; Selection (genetic algorithm); Computer science; Iterative method; Mathematical optimization; Artificial intelligence","score_opus":0.04963424689951592,"score_gpt":0.280924202035542,"score_spread":0.2312899551360261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052985314","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062675974,0.0001382228,0.991774,0.000072644056,0.000023115566,0.000040656072,0.000042455744,0.0005587615,0.0010825223],"genre_scores_gemma":[0.19614446,0.00012501987,0.79977864,0.00014486564,0.000032548098,0.00042188235,0.00030867942,0.00024401954,0.0027998583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994561,0.0002271195,0.000041011914,0.00007103267,0.00013419476,0.000070633716],"domain_scores_gemma":[0.99836487,0.0008956874,0.00010392028,0.00016642673,0.00040903135,0.00006005274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018824035,0.00079136225,0.0012556979,0.00067644566,0.0005206148,0.0007757635,0.001725166,0.0012615643,0.0057149366],"category_scores_gemma":[0.005528161,0.0006683484,0.00046855112,0.0010492302,0.000778251,0.0013309993,0.001690495,0.0014273837,0.0011683005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023746124,0.000083224484,0.0004365153,0.00014616147,0.000060601342,0.000051292776,0.00010976061,0.64351034,0.0030551564,0.017728442,0.0035031685,0.33107787],"study_design_scores_gemma":[0.000011028254,0.000012153175,0.000031337484,0.0000039740507,0.0000022595807,0.000005821094,0.0000040566542,0.997879,0.0003684115,0.0014108623,0.00026835973,0.0000027822302],"about_ca_topic_score_codex":0.008966816,"about_ca_topic_score_gemma":0.013181772,"teacher_disagreement_score":0.008966816,"about_ca_system_score_codex":0.0008272029,"about_ca_system_score_gemma":0.0024730854,"threshold_uncertainty_score":0.019118309},"labels":[],"label_agreement":null},{"id":"W2053870179","doi":"10.1142/s0217984901001458","title":"A SIMPLE NEURAL NETWORK APPROACH TO INVARIANT IMAGE RECOGNITION","year":2001,"lang":"en","type":"article","venue":"Modern Physics Letters B","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Artificial neural network; Computer science; Invariant (physics); Artificial intelligence; Pattern recognition (psychology); Time delay neural network; Cellular neural network; Computation; Probabilistic neural network; Image processing; Segmentation; Feature extraction; Computer vision; Image (mathematics); Algorithm; Mathematics","score_opus":0.03106452002172478,"score_gpt":0.23381153154670223,"score_spread":0.20274701152497746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053870179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025527037,0.0006529517,0.9918793,0.00012871786,0.00012327534,0.000032010415,0.000039850755,0.00045834726,0.004132863],"genre_scores_gemma":[0.25554246,0.0020417222,0.72337973,0.0003863157,0.00043442345,0.00022402983,0.00026942792,0.00015837354,0.017563514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965775,0.000056928737,0.000017761633,0.00008935834,0.00014769276,0.000030540243],"domain_scores_gemma":[0.99985874,0.000036221383,0.00001411285,0.00003202079,0.000049101378,0.000009887192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034264752,0.0005876334,0.00066067534,0.00068839674,0.00024396587,0.0008145808,0.0012236068,0.0009791161,0.0039108316],"category_scores_gemma":[0.00095336227,0.00027027423,0.00055767887,0.000610391,0.00072679325,0.0014044116,0.0007062306,0.00095526397,0.0015849378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010177812,0.00008666958,0.000524122,0.00037182865,0.00018223959,0.00025814478,0.00007127192,0.23491776,0.04118401,0.2374433,0.0048279157,0.480031],"study_design_scores_gemma":[0.000010365403,0.000053343723,0.00031579728,0.0000159773,0.000026772204,0.00013541958,0.000009114067,0.9082369,0.00681391,0.07244328,0.011912071,0.000026988411],"about_ca_topic_score_codex":0.0013749105,"about_ca_topic_score_gemma":0.0013681373,"teacher_disagreement_score":0.0039108316,"about_ca_system_score_codex":0.0005536756,"about_ca_system_score_gemma":0.00035450983,"threshold_uncertainty_score":0.013083041},"labels":[],"label_agreement":null},{"id":"W2054507989","doi":"10.1007/s10470-008-9135-3","title":"Robust low-sensitivity Adaline neuron based on Continuous Valued Number System","year":2008,"lang":"en","type":"article","venue":"Analog Integrated Circuits and Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Windsor","funders":"","keywords":"Computer science; Sensitivity (control systems); Artificial neural network; Redundancy (engineering); Modular design; Analogue electronics; Electronic engineering; Control theory (sociology); Algorithm; Electronic circuit; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.025542426220768397,"score_gpt":0.22284739475839016,"score_spread":0.19730496853762175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054507989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12504655,0.00087629934,0.8625521,0.0001807177,0.0002459001,0.00004933339,0.00007861493,0.001131065,0.009839367],"genre_scores_gemma":[0.9108595,0.00020169752,0.08478174,0.00008293563,0.000039217735,0.00003056792,0.000046828038,0.000025680993,0.0039319415],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998294,0.000024386592,0.000009815475,0.000053429132,0.00006887662,0.000014022991],"domain_scores_gemma":[0.99984455,0.000035799978,0.000023640236,0.000020197402,0.00006584649,0.000010081433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022830328,0.00032875568,0.00045198438,0.00015777288,0.00027514424,0.00053849246,0.00067865796,0.00045354638,0.0013081752],"category_scores_gemma":[0.00040586502,0.00011787164,0.00022610839,0.00020138476,0.00021362424,0.0005332957,0.00030924863,0.00031559693,0.00026981748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008990913,0.0001138383,0.0018250851,0.0005206408,0.00014631353,0.00050493114,0.00023263,0.11858917,0.56248605,0.046471704,0.0038887465,0.2643218],"study_design_scores_gemma":[0.000040879957,0.00033549688,0.0012412906,0.000021037116,0.00006490687,0.00036412894,0.000020411002,0.9173735,0.06927169,0.0064801425,0.004745981,0.00004054067],"about_ca_topic_score_codex":0.00096180884,"about_ca_topic_score_gemma":0.0013817541,"teacher_disagreement_score":0.0013081752,"about_ca_system_score_codex":0.00036687622,"about_ca_system_score_gemma":0.00021173226,"threshold_uncertainty_score":0.004376292},"labels":[],"label_agreement":null},{"id":"W2054526672","doi":"10.1117/12.781499","title":"A hardware neural network for target tracking","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Artificial neural network; IBM; Tracking (education); Set (abstract data type); Computer hardware; Energy (signal processing); Parallel computing; Algorithm; Artificial intelligence","score_opus":0.018323391927560814,"score_gpt":0.23333351202114525,"score_spread":0.21501012009358444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054526672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07818539,0.0020542322,0.90117776,0.0007245265,0.00028315588,0.00006474302,0.00020165149,0.0025923166,0.014716275],"genre_scores_gemma":[0.6658891,0.0008617888,0.32125694,0.00018787017,0.000057714235,0.000111707646,0.0002957042,0.000053768807,0.011285366],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999218,0.00001088163,0.0000035769826,0.000018567738,0.000034362667,0.0000108925215],"domain_scores_gemma":[0.99990034,0.00003393402,0.000006777193,0.000012191898,0.000041212144,0.00000551988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017415133,0.0001693652,0.00015122337,0.00022311196,0.00021566896,0.00030577806,0.00062046817,0.00035691945,0.0027040385],"category_scores_gemma":[0.00048358,0.00011596323,0.00016565784,0.0003872274,0.00023345683,0.00045230013,0.00020129299,0.0005454544,0.00038054475],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027879636,0.000113492526,0.0013232462,0.00018671309,0.00006460064,0.0001586723,0.00005017045,0.42268103,0.06587995,0.04315194,0.008146048,0.45796534],"study_design_scores_gemma":[0.000014058796,0.000054591626,0.00027145303,0.000008646097,0.000010081365,0.00003296014,0.000004358494,0.98594147,0.0067008403,0.0033039593,0.0036513733,0.0000061388937],"about_ca_topic_score_codex":0.0058346726,"about_ca_topic_score_gemma":0.0069487584,"teacher_disagreement_score":0.0058346726,"about_ca_system_score_codex":0.000556047,"about_ca_system_score_gemma":0.00070180243,"threshold_uncertainty_score":0.011601388},"labels":[],"label_agreement":null},{"id":"W2054708090","doi":"10.1016/j.cam.2003.11.014","title":"Existence and stability of equilibria of the continuous-time Hopfield neural network","year":2004,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Wuhan University; Ministry of Education of the People's Republic of China","keywords":"Mathematics; Uniqueness; Hopfield network; Stability (learning theory); Ordinary differential equation; Artificial neural network; Simple (philosophy); Lyapunov function; Applied mathematics; Comparison theorem; Equilibrium point; Set (abstract data type); Differential equation; Initial value problem; Mathematical analysis; Nonlinear system; Computer science","score_opus":0.015610723543693675,"score_gpt":0.22819902571195666,"score_spread":0.212588302168263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054708090","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8130903,0.0012345964,0.16181372,0.00078388734,0.00007198243,0.00006899725,0.00017673185,0.000092991715,0.022666678],"genre_scores_gemma":[0.99314183,0.00017429562,0.0039692395,0.000017108043,0.000009065655,0.000022885975,0.000029822819,0.000005371771,0.0026304694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99985564,0.000031462027,0.000007412766,0.00003645707,0.00003871084,0.000030254745],"domain_scores_gemma":[0.99921334,0.00037101028,0.000117418305,0.000024689038,0.00020806234,0.00006534926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006165818,0.00037071566,0.00035777554,0.00062837184,0.0007926706,0.0012768309,0.00085667294,0.00083820184,0.0027756128],"category_scores_gemma":[0.002813236,0.00025233923,0.00031017727,0.00030893303,0.0009814112,0.00096640753,0.00067265664,0.00048491725,0.00023928887],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012946895,0.00026859893,0.013398766,0.00051732984,0.00025744463,0.0011003129,0.0012272162,0.44074982,0.06267927,0.4055125,0.0028698193,0.07012413],"study_design_scores_gemma":[0.00008178457,0.00010578109,0.0016943029,0.000026904052,0.000035534627,0.00018322744,0.00017259737,0.9225847,0.005264083,0.06897256,0.0008512254,0.000027365806],"about_ca_topic_score_codex":0.0038947745,"about_ca_topic_score_gemma":0.002303196,"teacher_disagreement_score":0.0038947745,"about_ca_system_score_codex":0.0008636753,"about_ca_system_score_gemma":0.0006594911,"threshold_uncertainty_score":0.009285331},"labels":[],"label_agreement":null},{"id":"W2054806940","doi":"10.1109/coginf.2008.4639160","title":"Adaptive evaluation of complex time series using nonconventional neural units","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute of Automation, Chinese Academy of Sciences; University of Saskatchewan","keywords":"Computer science; Artificial neural network; Chaotic; Nonlinear system; Forcing (mathematics); Control theory (sociology); Adaptive system; Sensitivity (control systems); Scalability; Adaptation (eye); Control engineering; Artificial intelligence; Engineering; Mathematics; Electronic engineering","score_opus":0.2161977660710183,"score_gpt":0.3164942871789401,"score_spread":0.10029652110792184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054806940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10651912,0.00027967917,0.89135975,0.00003670195,0.00003846177,0.000066893386,0.000053621636,0.00034608896,0.0012996978],"genre_scores_gemma":[0.8327193,0.00018137957,0.16603684,0.000036847316,0.000028105518,0.00009924629,0.000065352775,0.0000372754,0.00079567585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996215,0.00007098375,0.000032624434,0.00011501852,0.00014076338,0.000019081],"domain_scores_gemma":[0.99953806,0.00018935362,0.00009307055,0.000081585975,0.00008188272,0.000016142172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004507199,0.00048686273,0.0003365507,0.00045998572,0.00014273626,0.0004821111,0.00059853983,0.0002672855,0.00082074275],"category_scores_gemma":[0.0018978141,0.00014810075,0.00028209348,0.00038842458,0.00034534605,0.00088566024,0.00049725996,0.00039898427,0.000104752144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040760197,0.00014103674,0.009472594,0.00040695257,0.00019491342,0.00042383678,0.00043669506,0.15836972,0.28531036,0.015545079,0.0005092314,0.528782],"study_design_scores_gemma":[0.0000066280168,0.00017521977,0.0051289983,0.000015395823,0.000030266267,0.0001387736,0.000035701512,0.9620217,0.028375247,0.0032456156,0.0008016212,0.000024876417],"about_ca_topic_score_codex":0.00060943764,"about_ca_topic_score_gemma":0.0009244375,"teacher_disagreement_score":0.00082074275,"about_ca_system_score_codex":0.000245016,"about_ca_system_score_gemma":0.00016053066,"threshold_uncertainty_score":0.002745688},"labels":[],"label_agreement":null},{"id":"W2055262709","doi":"10.1007/s11063-004-7775-6","title":"Speeding up the Self-Organizing Feature Map Using Dynamic Subset Selection","year":2005,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Computational intelligence; Self-organizing map; Set (abstract data type); Filter (signal processing); Feature (linguistics); Feature selection; Context (archaeology); Unsupervised learning; Pattern recognition (psychology); Machine learning; Selection (genetic algorithm); Overhead (engineering); Data set; Data mining; Artificial neural network","score_opus":0.0153514869666129,"score_gpt":0.2541891695184719,"score_spread":0.23883768255185903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055262709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13930082,0.000448352,0.85479933,0.00023787848,0.00017430585,0.000101225596,0.00016638664,0.0028719371,0.0018997185],"genre_scores_gemma":[0.6069695,0.00022169053,0.38853645,0.00013894183,0.000113741,0.00020050754,0.00065704715,0.00029502396,0.0028670125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999705,0.000056835845,0.000022045606,0.00006980608,0.00010371064,0.000042630116],"domain_scores_gemma":[0.99905175,0.000435168,0.000041577332,0.0001780506,0.00026010332,0.000033331282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006379003,0.00074309536,0.0013919504,0.0008553227,0.0005304362,0.000671763,0.0010566476,0.00050815067,0.002030618],"category_scores_gemma":[0.0020458184,0.00040619032,0.0006892196,0.0008776405,0.00029247944,0.0013127126,0.0009436839,0.0006985052,0.00066336885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002845337,0.00014707411,0.0013563344,0.00009814315,0.00012942782,0.00011083266,0.00013327104,0.09637197,0.030636953,0.002777517,0.0062932046,0.8616608],"study_design_scores_gemma":[0.000028852568,0.00009058725,0.0006884588,0.000004130644,0.00003666995,0.000076967925,0.00003393697,0.9831402,0.011299869,0.0031501686,0.0014394703,0.00001079335],"about_ca_topic_score_codex":0.002217894,"about_ca_topic_score_gemma":0.002864288,"teacher_disagreement_score":0.002217894,"about_ca_system_score_codex":0.00023665983,"about_ca_system_score_gemma":0.00065211573,"threshold_uncertainty_score":0.0067930818},"labels":[],"label_agreement":null},{"id":"W2055494787","doi":"10.1155/2011/301204","title":"Encoding Static and Temporal Patterns with a Bidirectional Heteroassociative Memory","year":2011,"lang":"en","type":"article","venue":"Journal of Applied Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bidirectional associative memory; Computer science; Encoding (memory); Generalization; Content-addressable memory; ENCODE; Associative property; Stability (learning theory); Adaptability; Pattern recognition (psychology); Artificial neural network; Attractor; Artificial intelligence; Binary number; Machine learning; Mathematics; Arithmetic","score_opus":0.031712559139137744,"score_gpt":0.23375905150461837,"score_spread":0.2020464923654806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055494787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3161783,0.00059056166,0.6628656,0.00045125582,0.00016871502,0.00006601917,0.00029604085,0.0014989127,0.017884675],"genre_scores_gemma":[0.94855,0.00036927868,0.046010956,0.000058249723,0.00002417078,0.000063029715,0.00010556739,0.000031365107,0.004787333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999466,0.000009955669,0.0000032046685,0.000011932956,0.000014794246,0.00001345537],"domain_scores_gemma":[0.9998573,0.000040702143,0.000020075255,0.00003975685,0.000024769142,0.000017419874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017430019,0.00018782841,0.00023115551,0.00021010013,0.00024625257,0.00047952507,0.00061496935,0.0003301074,0.00225809],"category_scores_gemma":[0.0004933952,0.00014096883,0.0002442123,0.00028828526,0.00033160285,0.0010235981,0.00044562813,0.00037220528,0.000521887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008246175,0.00043590533,0.003514838,0.00034199934,0.00013623718,0.00069090055,0.00041111748,0.3092471,0.20945199,0.14120209,0.0049081813,0.32883507],"study_design_scores_gemma":[0.000037425343,0.00017228763,0.0010516632,0.000022172386,0.000049561047,0.00026063106,0.00007363589,0.9151281,0.035865203,0.042314444,0.0049973894,0.00002741535],"about_ca_topic_score_codex":0.0007865607,"about_ca_topic_score_gemma":0.0010095706,"teacher_disagreement_score":0.00225809,"about_ca_system_score_codex":0.0001988008,"about_ca_system_score_gemma":0.00029732366,"threshold_uncertainty_score":0.0075540543},"labels":[],"label_agreement":null},{"id":"W2055513867","doi":"10.1016/s0893-6080(00)00067-8","title":"Nonlinear canonical correlation analysis by neural networks","year":2000,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institutes of Natural Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Canonical correlation; Canonical analysis; Nonlinear system; Artificial neural network; Feedforward neural network; Correlation; Mathematics; Residual; Feed forward; Computer science; Function (biology); Pattern recognition (psychology); Artificial intelligence; Algorithm; Statistics","score_opus":0.008297458051211012,"score_gpt":0.23512092490151587,"score_spread":0.22682346685030486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055513867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074261953,0.0008273923,0.98724073,0.0003064505,0.00015953727,0.000021778093,0.00007457998,0.00014213959,0.0038012017],"genre_scores_gemma":[0.6204952,0.0055249613,0.3448368,0.00047299702,0.00083380286,0.00030922343,0.0006753764,0.0006058893,0.026245845],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988366,0.00060830475,0.000043016236,0.00020902602,0.00022489813,0.00007817827],"domain_scores_gemma":[0.99787164,0.00094785535,0.00022831181,0.00034218826,0.0005024376,0.00010759517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017808015,0.0007631821,0.00090449885,0.0014215166,0.0006442665,0.0017267556,0.00081875705,0.0009399784,0.0031262957],"category_scores_gemma":[0.007942693,0.0005648717,0.0007669886,0.0020763474,0.00213332,0.0026291243,0.0020389354,0.0014501923,0.0009546822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006233342,0.000019834028,0.00065073225,0.00012179607,0.00007149493,0.00006690857,0.00007477214,0.16992989,0.0015133448,0.76687956,0.005304587,0.05530474],"study_design_scores_gemma":[0.0000059784084,0.000008868108,0.00022354406,0.000014498722,0.0000090086105,0.000029978772,0.000012781235,0.7750258,0.0006120311,0.22135457,0.0026797915,0.000023093759],"about_ca_topic_score_codex":0.0035829267,"about_ca_topic_score_gemma":0.0034178167,"teacher_disagreement_score":0.0035829267,"about_ca_system_score_codex":0.00089016184,"about_ca_system_score_gemma":0.0012113745,"threshold_uncertainty_score":0.010458469},"labels":[],"label_agreement":null},{"id":"W2056030999","doi":"10.1080/03610920601126001","title":"Estimation of the Entropy Functional from Dependent Samples","year":2007,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"Estimator; Histogram; Mathematics; Entropy (arrow of time); Entropy estimation; Rényi entropy; Minimax estimator; Applied mathematics; Statistics; Computer science; Principle of maximum entropy; Artificial intelligence; Minimum-variance unbiased estimator; Physics","score_opus":0.04284610281592455,"score_gpt":0.3757617240142625,"score_spread":0.33291562119833795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056030999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11160797,0.00019254274,0.88677645,0.00019056542,0.000025997459,0.000051961928,0.00009473648,0.00010820246,0.0009516175],"genre_scores_gemma":[0.91452056,0.00016813738,0.08336732,0.00006227958,0.000047464197,0.00008249673,0.00029347435,0.000039896808,0.0014184151],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984641,0.0008418769,0.00006919812,0.00022859905,0.00033355594,0.000062803076],"domain_scores_gemma":[0.98741156,0.01006366,0.0007631448,0.00077279063,0.0007893323,0.00019952992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037999484,0.00050177943,0.0008843278,0.0013932077,0.00030346197,0.00089492253,0.0010164813,0.0008683711,0.0014608429],"category_scores_gemma":[0.0228892,0.00038246377,0.00056298,0.0006278624,0.0014593308,0.0016686035,0.0016140359,0.0009698693,0.00015933614],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035952486,0.00010200048,0.0097492635,0.00026992807,0.00015611218,0.00028740862,0.00012497682,0.8196894,0.008144821,0.09507903,0.0009138279,0.065123744],"study_design_scores_gemma":[0.000008600163,0.000029304325,0.0018031658,0.000010126618,0.00000890935,0.00002950919,0.000008221544,0.97940516,0.002318014,0.016188905,0.0001755582,0.00001451119],"about_ca_topic_score_codex":0.0012562303,"about_ca_topic_score_gemma":0.0006443549,"teacher_disagreement_score":0.0037999484,"about_ca_system_score_codex":0.0009627848,"about_ca_system_score_gemma":0.0006221579,"threshold_uncertainty_score":0.020096242},"labels":[],"label_agreement":null},{"id":"W2056158129","doi":"10.1007/s10827-006-0007-5","title":"Functional organization within a neural network trained to update target representations across 3-D saccades","year":2006,"lang":"en","type":"article","venue":"Journal of Computational Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Canadian Institutes of Health Research","funders":"Canada Research Chairs","keywords":"Artificial neural network; Computer science; Theory of computation; Functional connectivity; Artificial intelligence; Neuroscience; Cognitive science; Machine learning; Psychology; Algorithm","score_opus":0.016873428491170187,"score_gpt":0.272620901615778,"score_spread":0.2557474731246078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056158129","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9372114,0.00019937535,0.058042873,0.00052052323,0.00011813522,0.0000354262,0.0002117843,0.00041627084,0.0032443346],"genre_scores_gemma":[0.99395245,0.000040811716,0.0047125774,0.000024211478,0.00000711141,0.00000905473,0.00005656104,0.000036274123,0.0011609076],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991965,0.000011208049,0.000004580356,0.000029017341,0.000013574356,0.000022035687],"domain_scores_gemma":[0.99952817,0.000171037,0.000047605183,0.00004641309,0.00013104297,0.00007564357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025433916,0.00024779147,0.0002757007,0.00025385924,0.00036465784,0.0006355882,0.00053278555,0.00095495273,0.001569126],"category_scores_gemma":[0.0017771239,0.00031833746,0.00035983662,0.00026421796,0.0003002692,0.0005290625,0.00032516386,0.00067060866,0.00018867059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009086167,0.00035259954,0.020446835,0.00014286717,0.00027254366,0.00071493565,0.0004539067,0.33266023,0.54774195,0.0067859055,0.002500168,0.08701955],"study_design_scores_gemma":[0.000017648661,0.00009409177,0.015888538,0.0000065008694,0.000030293659,0.000100634315,0.000041825027,0.9631632,0.018888095,0.0015347842,0.00022090587,0.000013508849],"about_ca_topic_score_codex":0.0073677115,"about_ca_topic_score_gemma":0.0052789045,"teacher_disagreement_score":0.0073677115,"about_ca_system_score_codex":0.000670825,"about_ca_system_score_gemma":0.000616566,"threshold_uncertainty_score":0.01464963},"labels":[],"label_agreement":null},{"id":"W2056318075","doi":"10.1142/s0129065701000552","title":"STOCHASTIC RADIAL BASIS FUNCTIONS","year":2001,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Gaussian; Bernoulli's principle; Binary number; Transfer function; Basis (linear algebra); Function (biology); Bernoulli distribution; Mathematics; Radial basis function; Applied mathematics; Algorithm; Computer science; Control theory (sociology); Artificial neural network; Random variable; Statistics; Control (management); Artificial intelligence; Physics; Arithmetic","score_opus":0.022738930733472655,"score_gpt":0.26634129567638726,"score_spread":0.2436023649429146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056318075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037418758,0.0009815531,0.98636985,0.00042260275,0.00016300123,0.000052639603,0.0002168347,0.0010745213,0.006977037],"genre_scores_gemma":[0.27868685,0.0036423511,0.68629754,0.00067521253,0.00028327308,0.00052962016,0.0011712931,0.00067448436,0.028039372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911505,0.00029850967,0.000041989093,0.00010897833,0.00035444912,0.00008091989],"domain_scores_gemma":[0.9988269,0.0003327667,0.00008092186,0.00018537372,0.00051151117,0.00006246625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015638169,0.0011569114,0.0010629066,0.00078836316,0.0004527222,0.0016416954,0.0017001,0.0019903698,0.007616],"category_scores_gemma":[0.0060176235,0.0004872613,0.0008347589,0.001300408,0.0005943431,0.0015474107,0.0008208437,0.0014599513,0.006644171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021737786,0.000121320954,0.0010099831,0.00039463135,0.00015005161,0.0001639498,0.000081701386,0.46184587,0.007331824,0.2160055,0.025228145,0.28744963],"study_design_scores_gemma":[0.000013899093,0.000017173646,0.00017949073,0.00002693952,0.0000085533675,0.00004485886,0.00000767832,0.9674087,0.0013978606,0.021749424,0.0091210045,0.000024355684],"about_ca_topic_score_codex":0.004684274,"about_ca_topic_score_gemma":0.0032546313,"teacher_disagreement_score":0.007616,"about_ca_system_score_codex":0.0008253708,"about_ca_system_score_gemma":0.0010642598,"threshold_uncertainty_score":0.025478125},"labels":[],"label_agreement":null},{"id":"W2056608535","doi":"10.1243/09596518jsce692","title":"Synchronous machine model identification using continuous wavelet NARX network","year":2009,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; McMaster University","funders":"","keywords":"Nonlinear autoregressive exogenous model; Wavelet; Computer science; Identification (biology); Autoregressive model; Polynomial; Stability (learning theory); System identification; Network model; Generator (circuit theory); Algorithm; Artificial intelligence; Control theory (sociology); Machine learning; Data mining; Mathematics; Artificial neural network; Control (management); Power (physics); Statistics","score_opus":0.008428329791160533,"score_gpt":0.19903613965802383,"score_spread":0.1906078098668633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056608535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020867396,0.000103031365,0.9774418,0.000056632594,0.00003187498,0.000021533431,0.000037819893,0.000366837,0.0010730909],"genre_scores_gemma":[0.7821804,0.0002983309,0.21246903,0.0000365739,0.00003879543,0.00015149619,0.00023996012,0.000066133565,0.0045192405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997776,0.00006597924,0.0000115746425,0.00006302607,0.00006641148,0.00001541564],"domain_scores_gemma":[0.9997825,0.00008899391,0.000035207893,0.000032917265,0.000053009193,0.0000074466448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000530371,0.0004454663,0.0004482639,0.0002850977,0.00020934526,0.00043052426,0.00047227603,0.0005053053,0.00091282633],"category_scores_gemma":[0.0011287653,0.00024929788,0.0004052451,0.00032614026,0.00023203394,0.0007112537,0.00032513842,0.0006484409,0.00035196386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094282404,0.000047899528,0.00060030015,0.0000877508,0.000047683217,0.00012317847,0.0000705234,0.8671865,0.016430184,0.010797291,0.0007037744,0.103810646],"study_design_scores_gemma":[0.0000017591143,0.00000926674,0.00007387148,0.0000010735606,0.0000015517276,0.000005807766,0.0000013389913,0.99864477,0.0005252745,0.0005831735,0.00015053421,0.0000016994175],"about_ca_topic_score_codex":0.0025239848,"about_ca_topic_score_gemma":0.0020001573,"teacher_disagreement_score":0.0025239848,"about_ca_system_score_codex":0.00028243178,"about_ca_system_score_gemma":0.0004038317,"threshold_uncertainty_score":0.0050185323},"labels":[],"label_agreement":null},{"id":"W2056717482","doi":"10.1088/1742-6596/341/1/012018","title":"Scalability of Self-organizing Maps on a GPU cluster using OpenCL and CUDA","year":2012,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"","keywords":"CUDA; Computer science; GPU cluster; Scalability; Parallel computing; Graphics processing unit; General-purpose computing on graphics processing units; Graphics; Code (set theory); Implementation; Computational science; Computer graphics (images); Operating system; Set (abstract data type)","score_opus":0.037269367423199855,"score_gpt":0.2679180052073575,"score_spread":0.23064863778415762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056717482","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8531695,0.00034972068,0.1277804,0.0003186892,0.00013448567,0.00016837666,0.0002507695,0.006649497,0.011178641],"genre_scores_gemma":[0.8904911,0.0001366732,0.10687864,0.00004392429,0.000016995802,0.00015758783,0.00042314758,0.0003834228,0.0014684642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988471,0.00023439807,0.00006602215,0.00015479034,0.0005432053,0.00015449285],"domain_scores_gemma":[0.99709916,0.0010157042,0.00012613916,0.00038097336,0.0011967123,0.00018133057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013352527,0.00077875773,0.0005975187,0.0007635207,0.0006385661,0.0011762041,0.002233323,0.0006123643,0.0018617096],"category_scores_gemma":[0.006188384,0.00036189612,0.00034290637,0.0010686183,0.00058886915,0.0017127971,0.001036062,0.00060180324,0.00038364454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001413784,0.00087365916,0.011338064,0.00040180908,0.0002987635,0.0004871,0.00055762025,0.7415383,0.059109867,0.0062038894,0.007574575,0.17020252],"study_design_scores_gemma":[0.000076802644,0.00016252004,0.0013509919,0.000010765039,0.00001747995,0.000029053359,0.00008103162,0.98074466,0.015227402,0.0011382276,0.0011429584,0.000018162964],"about_ca_topic_score_codex":0.015436642,"about_ca_topic_score_gemma":0.011207674,"teacher_disagreement_score":0.015436642,"about_ca_system_score_codex":0.0011990326,"about_ca_system_score_gemma":0.001342778,"threshold_uncertainty_score":0.03069359},"labels":[],"label_agreement":null},{"id":"W2057132034","doi":"10.1021/jp004504v","title":"Irreversible Thermodynamics of Neural Networks:  Calortropy Production in Logic Operations","year":2001,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry B","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Production (economics); Thermodynamics; Artificial neural network; Computer science; Physics; Artificial intelligence; Economics; Microeconomics","score_opus":0.013938702575276692,"score_gpt":0.24880582197491427,"score_spread":0.23486711939963759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057132034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17714882,0.00245544,0.77687526,0.0014340626,0.00024711984,0.000048121023,0.00010242065,0.00016012632,0.04152868],"genre_scores_gemma":[0.9699894,0.0009207057,0.02205061,0.000121695826,0.00010506143,0.0000711884,0.000034895405,0.000056877063,0.006649571],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99983454,0.000046199937,0.000007740204,0.000025202211,0.000056125482,0.000030115843],"domain_scores_gemma":[0.99969447,0.00015932438,0.00004559677,0.000036540223,0.00004028616,0.000023758184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004719012,0.00029067087,0.0003303177,0.00036003682,0.00041009998,0.0008538329,0.0005386567,0.0005306688,0.0020746083],"category_scores_gemma":[0.0020556287,0.00018494231,0.00040928644,0.00026435024,0.0017421196,0.0019366099,0.00085410953,0.0006459727,0.00017252285],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024445479,0.000013888847,0.00024908982,0.00004723665,0.000009884216,0.000070944356,0.00007994225,0.101240344,0.0058663217,0.8851326,0.0003522881,0.0069129844],"study_design_scores_gemma":[0.0000057786697,0.000015643456,0.00032262554,0.000010898169,0.0000044371845,0.00004314455,0.000025150808,0.40531164,0.0017603976,0.5913585,0.0011297439,0.000012009282],"about_ca_topic_score_codex":0.00062576134,"about_ca_topic_score_gemma":0.0006009246,"teacher_disagreement_score":0.0020746083,"about_ca_system_score_codex":0.0006829587,"about_ca_system_score_gemma":0.00041907263,"threshold_uncertainty_score":0.0069402456},"labels":[],"label_agreement":null},{"id":"W205741422","doi":"","title":"Context-Sensitive MTL Networks for Machine Lifelong Learning.","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Multi-task learning; Inductive transfer; Computer science; Transfer of learning; Task (project management); Artificial intelligence; Machine learning; Context (archaeology); Lifelong learning; Artificial neural network; Robot learning; Psychology; Engineering","score_opus":0.012687524687092477,"score_gpt":0.2554689349994403,"score_spread":0.24278141031234782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W205741422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069667897,0.0017627432,0.9864849,0.00064716535,0.0001108109,0.00008283088,0.00007800209,0.00064547063,0.0032213079],"genre_scores_gemma":[0.38925245,0.0013822232,0.598694,0.00051706954,0.00019441686,0.0008678097,0.00032675097,0.00013616098,0.008629052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996166,0.00016945947,0.000021253287,0.00007046654,0.0000970938,0.0000250798],"domain_scores_gemma":[0.9985936,0.00087931263,0.0001199476,0.00020108277,0.00013985924,0.00006609919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012859869,0.00056939805,0.0004402278,0.0003607348,0.00044362512,0.0007996758,0.001525692,0.0010205713,0.0041271853],"category_scores_gemma":[0.004918159,0.00029984865,0.00036313303,0.00050104386,0.00086207857,0.0019431805,0.0015013051,0.0020852557,0.00096304674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015942758,0.00020401338,0.0010834781,0.00034715811,0.0000955389,0.00021229558,0.00025674223,0.2063218,0.010779429,0.19820267,0.007949633,0.57438785],"study_design_scores_gemma":[0.00001300229,0.00006134995,0.00025536286,0.00002913743,0.000010279059,0.000053634376,0.000021260634,0.8227096,0.0034297497,0.16740736,0.005991027,0.000018268061],"about_ca_topic_score_codex":0.0012783831,"about_ca_topic_score_gemma":0.002939387,"teacher_disagreement_score":0.0041271853,"about_ca_system_score_codex":0.0011233942,"about_ca_system_score_gemma":0.00074585306,"threshold_uncertainty_score":0.01380676},"labels":[],"label_agreement":null},{"id":"W2058757475","doi":"10.4018/jcini.2007040103","title":"Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?","year":2007,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Entropy (arrow of time); Information theory; Multifractal system; Artificial intelligence; Joint entropy; Fractal; Data mining; Theoretical computer science; Statistical physics; Mathematics; Principle of maximum entropy; Statistics; Physics","score_opus":0.04688067716876313,"score_gpt":0.3483206180641463,"score_spread":0.3014399408953832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058757475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07344715,0.043917727,0.83897835,0.011479082,0.0017650609,0.000088973735,0.0008240265,0.00037468664,0.029125022],"genre_scores_gemma":[0.84048337,0.023225449,0.12706624,0.0016921206,0.0036409167,0.00025584042,0.00046623894,0.0001569292,0.0030129375],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977654,0.0006806572,0.0002688061,0.0003392767,0.00081518095,0.0001305448],"domain_scores_gemma":[0.9926974,0.0051120585,0.00068048673,0.00076358364,0.00060394296,0.00014252015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033463007,0.0008157452,0.0014560269,0.0038483997,0.0006057907,0.003023911,0.00089177187,0.0014126914,0.0012327436],"category_scores_gemma":[0.013875512,0.0003141569,0.0007492708,0.0025177703,0.0053616785,0.007613651,0.0013583157,0.0013098713,0.00047806956],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008934413,0.000042485775,0.0041782847,0.00072909816,0.00013564185,0.00025407976,0.0007475376,0.011129125,0.0055970177,0.8527132,0.003976419,0.120407775],"study_design_scores_gemma":[0.0000088151155,0.000062253705,0.0030988932,0.0001770219,0.000030411557,0.00039478607,0.0003097246,0.027252557,0.0020474934,0.95195377,0.014579273,0.00008493604],"about_ca_topic_score_codex":0.00048126027,"about_ca_topic_score_gemma":0.00031321187,"teacher_disagreement_score":0.0038483997,"about_ca_system_score_codex":0.0009390568,"about_ca_system_score_gemma":0.000451979,"threshold_uncertainty_score":0.017697096},"labels":[],"label_agreement":null},{"id":"W2058819127","doi":"10.1016/j.neucom.2010.06.004","title":"Chaotic time series prediction with residual analysis method using hybrid Elman–NARX neural networks","year":2010,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":248,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Residual; Chaotic; Nonlinear autoregressive exogenous model; Series (stratigraphy); Artificial neural network; Time series; Computer science; Embedding; Mathematics; Algorithm; Applied mathematics; Control theory (sociology); Artificial intelligence; Machine learning","score_opus":0.010028910704772619,"score_gpt":0.24573115851989272,"score_spread":0.2357022478151201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058819127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05238747,0.0003313773,0.94532555,0.00008554927,0.000108168235,0.00003381557,0.000023925833,0.0005062257,0.00119795],"genre_scores_gemma":[0.81869173,0.0002844827,0.17671332,0.00005214203,0.000058291593,0.00009207167,0.00009602816,0.000080520455,0.003931324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997632,0.0000692534,0.000024233797,0.00005612287,0.000068168294,0.000019125644],"domain_scores_gemma":[0.9996332,0.00015921223,0.000039020917,0.0000300828,0.00012644887,0.000012127872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082771387,0.0006016297,0.0007457699,0.00026806822,0.00036225215,0.00054905855,0.00063347945,0.00067820726,0.0012532591],"category_scores_gemma":[0.0011941645,0.0002877797,0.0006509685,0.0002912395,0.00027680583,0.00078835565,0.00036940258,0.0007026902,0.0002939908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004887073,0.00013281901,0.0017851756,0.00022453374,0.00024482518,0.00020543767,0.00016402178,0.71177435,0.026815534,0.005731765,0.00095696194,0.2514759],"study_design_scores_gemma":[0.000006839884,0.000026418948,0.0001917608,0.0000021849346,0.000012313422,0.0000113055985,0.0000037779082,0.9977495,0.001679422,0.00018202965,0.00012863091,0.0000058128944],"about_ca_topic_score_codex":0.0035851027,"about_ca_topic_score_gemma":0.0027911402,"teacher_disagreement_score":0.0035851027,"about_ca_system_score_codex":0.00025013523,"about_ca_system_score_gemma":0.0004544229,"threshold_uncertainty_score":0.007128477},"labels":[],"label_agreement":null},{"id":"W2058857490","doi":"10.1080/10629360410001724941","title":"Using fragment chemistry data mining and probabilistic neural networks in screening chemicals for acute toxicity to the fathead minnow","year":2004,"lang":"en","type":"article","venue":"SAR and QSAR in environmental research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Minnow; Probabilistic logic; Pimephales promelas; Computer science; Probabilistic neural network; Artificial neural network; Machine learning; Statistical model; Artificial intelligence; Gaussian process; Data mining; Quantitative structure–activity relationship; Fragment (logic); Gaussian; Chemistry; Fish <Actinopterygii>; Algorithm; Biology; Computational chemistry","score_opus":0.11457005099511426,"score_gpt":0.37960441357251634,"score_spread":0.2650343625774021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058857490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30961543,0.00063972914,0.6861791,0.00054999435,0.000018083261,0.00014730112,0.0004908698,0.0007327847,0.0016266146],"genre_scores_gemma":[0.73374945,0.000472737,0.2640436,0.00011933632,0.000020567824,0.00016068613,0.00066029694,0.000019422812,0.00075402344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950314,0.00023371917,0.000036121797,0.00009352087,0.00010373362,0.000029688028],"domain_scores_gemma":[0.99808335,0.0014924242,0.00012363849,0.0001451269,0.00013197535,0.000023570616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018696657,0.0005757622,0.0005024567,0.0007539895,0.00023516116,0.0005437497,0.0007232099,0.00068190246,0.00041245946],"category_scores_gemma":[0.005029106,0.00022864701,0.0005941234,0.0010156174,0.0004763708,0.001174815,0.00051618466,0.0005300051,0.000097009055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003462374,0.00016782543,0.009346711,0.00020277541,0.00013599319,0.00011361215,0.000070953276,0.6958,0.0063551115,0.003643642,0.00038165378,0.28343546],"study_design_scores_gemma":[0.000011845055,0.00009953342,0.0016328726,0.000008243222,0.000030681575,0.0000400908,0.000014821267,0.9891543,0.0039031187,0.004828784,0.00026387608,0.000011836473],"about_ca_topic_score_codex":0.0052455603,"about_ca_topic_score_gemma":0.0066258674,"teacher_disagreement_score":0.0052455603,"about_ca_system_score_codex":0.00060576526,"about_ca_system_score_gemma":0.00059394387,"threshold_uncertainty_score":0.010430038},"labels":[],"label_agreement":null},{"id":"W2059612120","doi":"10.1103/physreve.83.051121","title":"Boundary effect correction in<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-nearest-neighbor estimation","year":2011,"lang":"lv","type":"article","venue":"Physical Review E","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Bounded function; k-nearest neighbors algorithm; Boundary (topology); Mathematics; Algorithm; Computer science; Statistics; Artificial intelligence; Mathematical analysis","score_opus":0.02147186723678927,"score_gpt":0.2702876590663699,"score_spread":0.24881579182958066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059612120","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014168591,0.00027024405,0.9891726,0.00038283464,0.0010598147,0.00007088401,0.0006935063,0.0031253444,0.0038079626],"genre_scores_gemma":[0.06101115,0.000501659,0.8967632,0.000624041,0.00029111453,0.00039548703,0.0021871354,0.0068120942,0.031414118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99515045,0.0016649197,0.00033583696,0.0006239497,0.00200214,0.00022271535],"domain_scores_gemma":[0.99022335,0.0046133813,0.00047367177,0.0020964723,0.0024777662,0.00011529043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004223245,0.0010825373,0.0009970294,0.0013751698,0.0010297819,0.0019842517,0.0030817227,0.0018137842,0.056979757],"category_scores_gemma":[0.041143022,0.0008054206,0.0011936908,0.0017846726,0.0007621312,0.002321173,0.002244703,0.002646376,0.014828849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032043777,0.00015672557,0.0024942812,0.00090994703,0.00014263467,0.0006081159,0.0005177913,0.04279308,0.01770741,0.207064,0.21534231,0.5119432],"study_design_scores_gemma":[0.000060654416,0.000089121284,0.004676643,0.00026827172,0.00006992681,0.0008643613,0.00014446373,0.48497775,0.084148586,0.09962614,0.32484603,0.0002280154],"about_ca_topic_score_codex":0.005741129,"about_ca_topic_score_gemma":0.009379405,"teacher_disagreement_score":0.056979757,"about_ca_system_score_codex":0.0010980603,"about_ca_system_score_gemma":0.001982549,"threshold_uncertainty_score":0.19061631},"labels":[],"label_agreement":null},{"id":"W2059802069","doi":"10.1139/p04-080","title":"Identical thermodynamical processes and entropy","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Physics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Physics; Entropy (arrow of time); Statistical physics; Symmetry (geometry); Theoretical physics; Classical mechanics; Mathematical physics; Thermodynamics","score_opus":0.009848653427822174,"score_gpt":0.22101173290357307,"score_spread":0.2111630794757509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059802069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23196274,0.0026154025,0.5720596,0.0046232557,0.0009664065,0.00015168515,0.00032880937,0.0004413357,0.18685073],"genre_scores_gemma":[0.95060325,0.0008716027,0.03656448,0.00039304423,0.00049355644,0.00016057352,0.00015056002,0.00013836075,0.010624596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977641,0.00065817015,0.00015574532,0.00039082294,0.0007559737,0.00027519182],"domain_scores_gemma":[0.9963942,0.0014396218,0.0005400322,0.00095406704,0.00041512342,0.0002569032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023139492,0.0003901688,0.0006614714,0.0014418379,0.0012252412,0.0026061845,0.0013950233,0.0012030533,0.0049294434],"category_scores_gemma":[0.008480254,0.000290718,0.00088627136,0.0008562743,0.0068435315,0.006150059,0.0028973552,0.0020768642,0.00061270146],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048094357,0.0000058960563,0.000056446002,0.000009061806,0.0000024617455,0.000021366415,0.00003419032,0.0009484365,0.00023846203,0.99682605,0.00016833961,0.0016845285],"study_design_scores_gemma":[0.0000045953543,0.0000067701662,0.000098176206,0.0000056125477,0.0000024563403,0.00004197329,0.0000129913515,0.009189895,0.0003167121,0.9891868,0.0011273607,0.0000066418415],"about_ca_topic_score_codex":0.00070477865,"about_ca_topic_score_gemma":0.0003151976,"teacher_disagreement_score":0.0049294434,"about_ca_system_score_codex":0.0015516231,"about_ca_system_score_gemma":0.0011370085,"threshold_uncertainty_score":0.016490638},"labels":[],"label_agreement":null},{"id":"W2059932969","doi":"10.1016/s0893-6080(03)00118-7","title":"Automatic basis selection techniques for RBF networks","year":2003,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Basis (linear algebra); Radial basis function; Generalization; Computer science; Estimator; Basis function; Artificial neural network; Variance (accounting); Bayesian information criterion; Artificial intelligence; Radial basis function network; Model selection; Algorithm; Mathematics; Statistics","score_opus":0.013522199747673989,"score_gpt":0.25178545301779276,"score_spread":0.23826325327011877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059932969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023758763,0.00017787839,0.99648094,0.00004321278,0.000026052778,0.0000115702915,0.000015749783,0.00041700486,0.00045166057],"genre_scores_gemma":[0.11084796,0.0003729177,0.8847359,0.00005836035,0.000093809766,0.00011838733,0.00019293469,0.00031095243,0.0032688212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926263,0.0002583585,0.000037592967,0.00007978432,0.00030246362,0.000059069436],"domain_scores_gemma":[0.99861634,0.00060272234,0.00007229762,0.0001743507,0.00049732754,0.000036937912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012903874,0.0007197812,0.0011355571,0.0009770404,0.0005534867,0.0008353852,0.0011453393,0.0011218186,0.0034780025],"category_scores_gemma":[0.0045789382,0.0006596259,0.00080849486,0.00095763686,0.00041537956,0.0010430998,0.0008963306,0.0022496863,0.0019117012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000246189,0.00008734316,0.00033644965,0.00015431864,0.0000651897,0.000054240514,0.00008093576,0.14774203,0.02014077,0.020872016,0.005927879,0.8042925],"study_design_scores_gemma":[0.000014059291,0.000021080592,0.000119228825,0.000009039808,0.000010935225,0.000027738522,0.000006405753,0.988605,0.0033281026,0.0062728655,0.0015787091,0.000006922004],"about_ca_topic_score_codex":0.0020103278,"about_ca_topic_score_gemma":0.0027298904,"teacher_disagreement_score":0.0034780025,"about_ca_system_score_codex":0.000316865,"about_ca_system_score_gemma":0.00062748283,"threshold_uncertainty_score":0.011635065},"labels":[],"label_agreement":null},{"id":"W2061485277","doi":"10.1016/j.neunet.2006.05.045","title":"Capacity analysis for a two-level decoupled Hamming network for associative memory under a noisy environment","year":2007,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Hamming code; Content-addressable memory; Computer science; Hamming distance; Artificial neural network; Associative property; Noise (video); Hamming(7,4); Bidirectional associative memory; Content-addressable storage; Algorithm; Arithmetic; Parallel computing; Mathematics; Artificial intelligence; Decoding methods; Block code; Pure mathematics","score_opus":0.0440614358242014,"score_gpt":0.2755755221126617,"score_spread":0.23151408628846026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061485277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23367581,0.0005416422,0.75596505,0.00064463494,0.0000604844,0.000066246605,0.0003504301,0.00025784574,0.00843777],"genre_scores_gemma":[0.9746274,0.0002761213,0.019515224,0.00010213917,0.00004914793,0.000075363925,0.00015397542,0.000057784742,0.005142794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993767,0.00016492372,0.000029322971,0.00012289564,0.00013465316,0.0001715879],"domain_scores_gemma":[0.9962036,0.0024240236,0.0002369988,0.00037607332,0.00050219655,0.0002570986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014839395,0.0006804882,0.0011661324,0.00082215556,0.000609113,0.0012589439,0.0024642353,0.0013661348,0.0062146983],"category_scores_gemma":[0.0047522127,0.0004593935,0.000637673,0.0006649424,0.0018181881,0.0041916994,0.0020469343,0.0014433387,0.00048420453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040844487,0.00013674583,0.0006592583,0.00024074003,0.00009870646,0.0002187736,0.00018132955,0.5859286,0.016291171,0.37849554,0.0015798587,0.01576076],"study_design_scores_gemma":[0.000005402779,0.000014167904,0.0000981289,0.0000038935555,0.000009308621,0.00002274039,0.0000101580745,0.9620836,0.0008470473,0.03682244,0.00007130701,0.000011819205],"about_ca_topic_score_codex":0.0023484195,"about_ca_topic_score_gemma":0.0021068708,"teacher_disagreement_score":0.0062146983,"about_ca_system_score_codex":0.0013294821,"about_ca_system_score_gemma":0.00094485265,"threshold_uncertainty_score":0.020790279},"labels":[],"label_agreement":null},{"id":"W2062074603","doi":"10.1109/mcse.2006.45","title":"Noise and Signal Interaction","year":2006,"lang":"en","type":"article","venue":"Computing in Science & Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Randomness; Noise (video); Computer science; Complex system; SIGNAL (programming language); Artificial intelligence; Data science; Mathematics","score_opus":0.0072733015171388025,"score_gpt":0.22835714219929584,"score_spread":0.22108384068215703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062074603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10973735,0.028507374,0.5424189,0.027046695,0.0034486393,0.00016949578,0.0005792364,0.0008045423,0.2872878],"genre_scores_gemma":[0.9111912,0.010178905,0.038812675,0.0048701917,0.0022563115,0.00019617657,0.00031904914,0.0002277123,0.031947825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99772686,0.0008070319,0.00009213411,0.00048617594,0.0006168876,0.00027084706],"domain_scores_gemma":[0.9954861,0.0031423795,0.00037218267,0.00030631965,0.00044639182,0.000246548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015319999,0.0007981692,0.0011110796,0.0013413063,0.0013631833,0.0050289817,0.0010682325,0.002654526,0.010717701],"category_scores_gemma":[0.008043579,0.0004627833,0.0006709577,0.0007563813,0.005108334,0.004711562,0.0034087868,0.0022723447,0.0017699425],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025591347,0.000055358105,0.0016373473,0.000310988,0.00011789844,0.0006524462,0.0006786363,0.0108836945,0.009357923,0.9311339,0.0053484594,0.039567333],"study_design_scores_gemma":[0.0000404259,0.00014364877,0.0019586028,0.00012149657,0.0000631862,0.00062140956,0.00033521556,0.017197361,0.002406652,0.9497884,0.027253872,0.00006977486],"about_ca_topic_score_codex":0.0011980953,"about_ca_topic_score_gemma":0.0006660498,"teacher_disagreement_score":0.010717701,"about_ca_system_score_codex":0.0010747341,"about_ca_system_score_gemma":0.00070158514,"threshold_uncertainty_score":0.03585422},"labels":[],"label_agreement":null},{"id":"W2062610520","doi":"10.1121/1.3588881","title":"Efference copy and context effects.","year":2011,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Efference copy; Context (archaeology); Psychology; Categorization; Syllable; Sensory system; Cognitive psychology; Communication; Audiology; Speech recognition; Computer science; Artificial intelligence; Medicine","score_opus":0.015567264792399615,"score_gpt":0.23223241904021463,"score_spread":0.21666515424781502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062610520","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9423388,0.0029147754,0.02091599,0.0004058009,0.0002953548,0.0003190027,0.00060080824,0.00057135377,0.03163813],"genre_scores_gemma":[0.98799187,0.00035940268,0.0075936285,0.00034055675,0.00007671699,0.00011351907,0.0002739819,0.0001563338,0.0030941076],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988111,0.00015223949,0.00009266722,0.00041036276,0.0004556224,0.00007793115],"domain_scores_gemma":[0.9932126,0.0039688284,0.0009777055,0.0009921091,0.00038260448,0.00046614587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009108581,0.0009995735,0.00053128274,0.00041928323,0.000283585,0.00076179317,0.00082984776,0.0008419895,0.014822141],"category_scores_gemma":[0.012022837,0.0005417722,0.0005289614,0.00013364309,0.00067515817,0.0017353303,0.0015425863,0.001136306,0.00073176384],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002786115,0.0004135515,0.008187832,0.0010701758,0.000142758,0.00079371827,0.00044816223,0.0006601784,0.93491226,0.0041151163,0.00032774307,0.04614232],"study_design_scores_gemma":[0.0006610871,0.005626359,0.50750947,0.00020042181,0.00081235426,0.0061730347,0.0003313833,0.0063214046,0.4423548,0.022183998,0.007649308,0.00017629955],"about_ca_topic_score_codex":0.00038456052,"about_ca_topic_score_gemma":0.0005425112,"teacher_disagreement_score":0.014822141,"about_ca_system_score_codex":0.00035552145,"about_ca_system_score_gemma":0.00031745643,"threshold_uncertainty_score":0.049585044},"labels":[],"label_agreement":null},{"id":"W2063300059","doi":"10.1016/s0893-6080(02)00090-4","title":"A feed-forward network for input that is both categorical and quantitative","year":2002,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kamloops Art Gallery","funders":"","keywords":"Categorical variable; Variable (mathematics); Mathematics; Function (biology); Continuous variable; Sigmoid function; Perceptron; Computer science; Pattern recognition (psychology); Artificial intelligence; Artificial neural network; Statistics","score_opus":0.0439221356258542,"score_gpt":0.2695092125570767,"score_spread":0.2255870769312225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063300059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014442803,0.00030971738,0.97189516,0.0008085839,0.0003170606,0.00007100277,0.0008818295,0.00212962,0.009144247],"genre_scores_gemma":[0.48945364,0.0005432826,0.48732764,0.000661477,0.00012257203,0.00029689874,0.0013716293,0.00015796605,0.020064846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994772,0.00007686282,0.000033022494,0.0002129525,0.00014453265,0.00005549553],"domain_scores_gemma":[0.9991604,0.00027770284,0.000050428138,0.00016643199,0.00028871128,0.00005631302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008699446,0.0006896664,0.00055093656,0.00056526595,0.00054363674,0.0018146961,0.001470915,0.0016641429,0.008031566],"category_scores_gemma":[0.0044278568,0.00038362297,0.00086447067,0.000855233,0.0009351581,0.0036750818,0.001703431,0.0016922833,0.002072755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040519095,0.00024219435,0.0044751726,0.00056666136,0.00021204478,0.00041556518,0.00046687343,0.0761624,0.03781072,0.25244436,0.014506787,0.6122921],"study_design_scores_gemma":[0.000028320597,0.00013154052,0.0017605032,0.00014091717,0.0001198355,0.00025029943,0.000103199505,0.74417937,0.018935895,0.21712208,0.01716342,0.0000646114],"about_ca_topic_score_codex":0.0036789807,"about_ca_topic_score_gemma":0.0054264674,"teacher_disagreement_score":0.008031566,"about_ca_system_score_codex":0.00087833486,"about_ca_system_score_gemma":0.00110899,"threshold_uncertainty_score":0.026868284},"labels":[],"label_agreement":null},{"id":"W2063705560","doi":"10.1167/8.6.596","title":"Exploring the spatiotemporal properties of fractal rotation","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Rotation (mathematics); Luminance; Stimulus (psychology); Fractal; Physics; Artificial intelligence; Perception; Motion perception; Optics; Computer vision; Mathematics; Computer science; Motion (physics); Psychology; Mathematical analysis; Neuroscience","score_opus":0.0620924756053989,"score_gpt":0.2767432247643703,"score_spread":0.2146507491589714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063705560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9832169,0.00052621803,0.014618164,0.000041671017,0.000009887602,0.00002387244,0.00008310627,0.000038990707,0.0014413554],"genre_scores_gemma":[0.9959585,0.00018269627,0.0035787816,0.000008958633,0.000006549265,0.000011944156,0.00006918983,0.0000062155864,0.0001771187],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994016,0.000009380921,0.00000460836,0.000014264527,0.00001743122,0.000014152386],"domain_scores_gemma":[0.9995442,0.00017934864,0.00012881363,0.00005939013,0.000052932253,0.000035338417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016660114,0.000105371684,0.00015163778,0.0005400294,0.00007287275,0.00023343567,0.00011449047,0.00016185417,0.00050023524],"category_scores_gemma":[0.0010147551,0.00012376637,0.00018378878,0.00023569359,0.00020085627,0.00028901233,0.00018307535,0.00020496527,0.000065033004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001416451,0.000017254095,0.0053701187,0.00008660714,0.000012411562,0.00020157322,0.00012501459,0.0011541586,0.9775784,0.0016362949,0.0000721539,0.013604351],"study_design_scores_gemma":[0.00005058821,0.0008146269,0.5319396,0.000060286533,0.000091694565,0.004487437,0.00023096662,0.08658036,0.36616445,0.0062372736,0.0032770815,0.00006558041],"about_ca_topic_score_codex":0.00025064565,"about_ca_topic_score_gemma":0.00021521866,"teacher_disagreement_score":0.0005400294,"about_ca_system_score_codex":0.00016546666,"about_ca_system_score_gemma":0.000085129315,"threshold_uncertainty_score":0.00167346},"labels":[],"label_agreement":null},{"id":"W2064087236","doi":"10.1007/s00521-009-0309-4","title":"MLP bilinear separation","year":2009,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Sigmoid function; Bilinear interpolation; Computer science; Activation function; Artificial neural network; Computational Science and Engineering; Multilayer perceptron; Boundary (topology); Function (biology); Perceptron; Boundary value problem; Applied mathematics; Decision boundary; Algorithm; Artificial intelligence; Mathematics; Machine learning; Support vector machine; Mathematical analysis","score_opus":0.014624235361634413,"score_gpt":0.3007019258672761,"score_spread":0.2860776905056417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064087236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045257467,0.00059306104,0.9637296,0.0004874072,0.0005282159,0.000058474554,0.000422157,0.001521779,0.028133437],"genre_scores_gemma":[0.358583,0.0017052221,0.4260491,0.00096737663,0.001105729,0.00038671918,0.002410044,0.0012903197,0.2075024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935955,0.00012536604,0.00004357334,0.00015182434,0.0002290418,0.000090631904],"domain_scores_gemma":[0.99940515,0.000102796395,0.00003353243,0.00018938587,0.00020356968,0.00006558122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006306268,0.0010444083,0.000887265,0.0006357142,0.00072218815,0.0017094836,0.00086004345,0.0011301544,0.03638306],"category_scores_gemma":[0.0019518555,0.00048759097,0.0006944961,0.0007465507,0.0005473181,0.0019563148,0.002955953,0.0020688924,0.018928904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068988063,0.00013577935,0.00065414514,0.00040952436,0.00010247991,0.00019462503,0.00009345695,0.07826054,0.046177182,0.13887867,0.032223064,0.7021807],"study_design_scores_gemma":[0.000033767625,0.00012007737,0.00060644525,0.00006325689,0.000045304,0.00035980414,0.000047233403,0.8015434,0.04582811,0.0769704,0.07432818,0.000053989592],"about_ca_topic_score_codex":0.0011853889,"about_ca_topic_score_gemma":0.0014146463,"teacher_disagreement_score":0.03638306,"about_ca_system_score_codex":0.0005225233,"about_ca_system_score_gemma":0.0009151079,"threshold_uncertainty_score":0.12171346},"labels":[],"label_agreement":null},{"id":"W2064991996","doi":"10.1002/atr.5670340205","title":"Estimation of AADT from short period counts in hong kong — A comparison between neural network method and regression analysis","year":2000,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Statistics; Regression analysis; Estimation; Artificial neural network; Regression; Mathematics; Computer science; Engineering; Artificial intelligence","score_opus":0.015877929392588527,"score_gpt":0.3170991963002309,"score_spread":0.3012212669076424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064991996","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95899945,0.0002432817,0.038755327,0.00006632842,0.000026925863,0.000025652418,0.0005018107,0.00016068059,0.0012204687],"genre_scores_gemma":[0.9922253,0.000105326784,0.0066015553,0.0000048761026,0.000006521102,0.000015078109,0.00034376202,0.000007318857,0.0006902742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969435,0.00011079396,0.00003387549,0.00007070056,0.000056479108,0.000033795277],"domain_scores_gemma":[0.99854386,0.0006395462,0.00020226826,0.00007222745,0.0004979855,0.00004427133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092335563,0.0004693481,0.00037124826,0.0010289125,0.00016249545,0.0004956125,0.00044879108,0.00019999497,0.0005599679],"category_scores_gemma":[0.0023919654,0.00016681447,0.00023623287,0.0010849413,0.0001532748,0.00043269724,0.00022148184,0.0002425735,0.00013526362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000588807,0.00020758466,0.2144726,0.00024675517,0.00028361665,0.00038087962,0.00028618067,0.5928462,0.0047347113,0.0010825228,0.0015574798,0.1833126],"study_design_scores_gemma":[0.000004832235,0.000032696116,0.04978101,0.0000092747805,0.000022041084,0.000021420623,0.00011957176,0.9483502,0.0011822267,0.00012692383,0.00033754663,0.000012359258],"about_ca_topic_score_codex":0.21956764,"about_ca_topic_score_gemma":0.089933716,"teacher_disagreement_score":0.21956764,"about_ca_system_score_codex":0.0010862009,"about_ca_system_score_gemma":0.0007819415,"threshold_uncertainty_score":0.4365793},"labels":[],"label_agreement":null},{"id":"W2065005304","doi":"10.1016/j.neunet.2004.02.002","title":"New training strategies for constructive neural networks with application to regression problems","year":2004,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Constructive; Computer science; Pruning; Artificial neural network; Feed forward; Generalization; Artificial intelligence; Feedforward neural network; Process (computing); Machine learning; Training (meteorology); Mathematics; Control engineering; Engineering","score_opus":0.020266458456182895,"score_gpt":0.2586138086660905,"score_spread":0.23834735020990763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065005304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011594531,0.00026917845,0.9968624,0.000074924086,0.00004365156,0.000022167613,0.000008320898,0.000104409504,0.0014555288],"genre_scores_gemma":[0.08880808,0.00094499363,0.90379465,0.00024882416,0.00026754985,0.00034599428,0.00008646137,0.00043549537,0.005067974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875486,0.00067522464,0.000078816614,0.00010925283,0.00031630576,0.0000655173],"domain_scores_gemma":[0.99490476,0.003648067,0.00021472464,0.00028177362,0.00082875876,0.00012195849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032308963,0.001865267,0.0012237787,0.0015277963,0.0007226181,0.0013311425,0.0026380573,0.00214416,0.0043686144],"category_scores_gemma":[0.014917515,0.001060601,0.0010420141,0.0013412188,0.0014110417,0.0022513932,0.0023733692,0.0030135722,0.0014121494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012488975,0.0001531406,0.00059372146,0.0004101242,0.00012047628,0.00023788378,0.00034379892,0.43724772,0.009087437,0.210408,0.0059789354,0.3352939],"study_design_scores_gemma":[0.000013912516,0.000028365024,0.000049251863,0.0000351013,0.00002143548,0.000060533526,0.000015043548,0.96155626,0.0015114963,0.03458054,0.0021165193,0.000011553994],"about_ca_topic_score_codex":0.0011834821,"about_ca_topic_score_gemma":0.0025361425,"teacher_disagreement_score":0.0043686144,"about_ca_system_score_codex":0.00062951434,"about_ca_system_score_gemma":0.00067530107,"threshold_uncertainty_score":0.017086864},"labels":[],"label_agreement":null},{"id":"W2065021034","doi":"10.1016/j.fss.2009.04.014","title":"Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms","year":2009,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fuzzy logic; Discretization; Computer science; Pruning; Theoretical computer science; Artificial intelligence; Cluster analysis; Mathematics","score_opus":0.02396526518399888,"score_gpt":0.24462056126633092,"score_spread":0.22065529608233203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065021034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011634599,0.001159536,0.979111,0.00037120935,0.000062082414,0.000021979386,0.00003502118,0.000052378648,0.0075521376],"genre_scores_gemma":[0.77837384,0.00358917,0.21031465,0.00020359937,0.00025353002,0.00011061211,0.000084472005,0.00006135046,0.007008811],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987293,0.00059322955,0.00006891398,0.0001838735,0.000358901,0.00006574532],"domain_scores_gemma":[0.9952679,0.003606961,0.00029851752,0.00020334114,0.0005555874,0.000067561836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030300913,0.00070805656,0.00082162616,0.00079333474,0.000585694,0.0019180222,0.0015453714,0.0010345047,0.001869712],"category_scores_gemma":[0.01108933,0.00033892112,0.0007860778,0.0015576622,0.0019029467,0.0035581312,0.00064190564,0.0013802883,0.00019383169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031872,0.000033403925,0.00036604333,0.00012152328,0.00003382115,0.00010444887,0.00025001343,0.27871096,0.00067265594,0.6914693,0.00065036735,0.027555536],"study_design_scores_gemma":[0.0000038069363,0.000018100909,0.00005785401,0.000019914798,0.000013384425,0.00003332135,0.000027018415,0.80406505,0.00026577234,0.19447865,0.0010085459,0.0000086417995],"about_ca_topic_score_codex":0.005344873,"about_ca_topic_score_gemma":0.0028333634,"teacher_disagreement_score":0.005344873,"about_ca_system_score_codex":0.001273474,"about_ca_system_score_gemma":0.00081721717,"threshold_uncertainty_score":0.016024828},"labels":[],"label_agreement":null},{"id":"W2065509247","doi":"10.1016/j.ijar.2010.06.002","title":"Decision making with imprecise parameters","year":2010,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Benchmark (surveying); Machine learning; Interval (graph theory); Artificial intelligence; Process (computing); Data mining; Mathematics","score_opus":0.008210408865902656,"score_gpt":0.27327613585861926,"score_spread":0.26506572699271663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065509247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06473301,0.0013099299,0.92683506,0.0014384803,0.0001139848,0.00008146103,0.00025323266,0.00017652106,0.0050583975],"genre_scores_gemma":[0.81821495,0.0007751971,0.17833625,0.00022838675,0.00013816892,0.00013853415,0.0002496996,0.00004826505,0.0018704643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931338,0.00310616,0.00058425224,0.0012296805,0.0015292082,0.00041682296],"domain_scores_gemma":[0.974293,0.02177428,0.0010440253,0.0017357239,0.0008132296,0.0003397773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007814373,0.001042394,0.0024539218,0.0012132378,0.00074870547,0.005393797,0.0021118955,0.0024218976,0.0039807875],"category_scores_gemma":[0.037110996,0.0010886748,0.0015601157,0.0017174672,0.002436465,0.009735529,0.0022643781,0.003942183,0.000490511],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083994423,0.00014475275,0.0017771515,0.00040174136,0.00036074733,0.0004770861,0.0004231534,0.7533044,0.0010478297,0.15789753,0.0017179488,0.08160763],"study_design_scores_gemma":[0.000050123374,0.000043844022,0.00017449704,0.000048074406,0.00006574158,0.00007329661,0.00006495578,0.71054935,0.0007775312,0.28738713,0.0007394143,0.000026014106],"about_ca_topic_score_codex":0.0018146123,"about_ca_topic_score_gemma":0.0015371623,"teacher_disagreement_score":0.007814373,"about_ca_system_score_codex":0.0017238903,"about_ca_system_score_gemma":0.0012274852,"threshold_uncertainty_score":0.04132688},"labels":[],"label_agreement":null},{"id":"W2066073395","doi":"10.1109/jstsp.2014.2318078","title":"Introduction to the Issue on Perception Inspired Video Processing","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Perception; Artificial intelligence; Computer vision; Psychology","score_opus":0.0136193724607914,"score_gpt":0.26232331756431054,"score_spread":0.24870394510351915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066073395","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006821255,0.065796174,0.022511268,0.034762774,0.83679426,0.00015557816,0.0004679751,0.0005406762,0.038289096],"genre_scores_gemma":[0.0050337613,0.06941707,0.0077779135,0.02132362,0.78877664,0.00013847234,0.0007801013,0.00056579453,0.10618659],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99920076,0.00007085933,0.000077957906,0.0001480924,0.00042792127,0.00007438455],"domain_scores_gemma":[0.9963871,0.0010607297,0.00017354889,0.0002000847,0.0016065174,0.0005720129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010860282,0.0011837657,0.0012064076,0.0025461658,0.00097520545,0.0037728355,0.0010573273,0.0030224663,0.035303],"category_scores_gemma":[0.0033018102,0.0005024533,0.00094530685,0.001690664,0.0009970545,0.0028040905,0.0013985704,0.006264946,0.01778727],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047383648,0.00005147254,0.00012521476,0.0006267002,0.000025600593,0.00013423158,0.0000289709,0.00024247068,0.002730936,0.0072460244,0.8922391,0.09650195],"study_design_scores_gemma":[0.000005827287,0.00005216978,0.00025591123,0.00015637573,0.000013334176,0.0002619795,0.000014955889,0.00049547956,0.00058487256,0.002975256,0.99516666,0.000017204768],"about_ca_topic_score_codex":0.00045319696,"about_ca_topic_score_gemma":0.0009174507,"teacher_disagreement_score":0.035303,"about_ca_system_score_codex":0.0007771216,"about_ca_system_score_gemma":0.0009822638,"threshold_uncertainty_score":0.118100286},"labels":[],"label_agreement":null},{"id":"W2067184091","doi":"10.1049/ip-cds:20000237","title":"Sensitivity study and improvements on a nonlinear resistive-type neuron circuit","year":2000,"lang":"en","type":"article","venue":"IEE Proceedings - Circuits Devices and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"NMOS logic; PMOS logic; Resistor; Nonlinear system; Very-large-scale integration; CMOS; Computer science; Sensitivity (control systems); Electronic engineering; Interconnection; Multiplier (economics); Transistor; Topology (electrical circuits); Electrical engineering; Engineering; Physics; Voltage; Telecommunications","score_opus":0.028122959127225858,"score_gpt":0.2534808239073338,"score_spread":0.22535786478010794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067184091","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4927871,0.0007900748,0.48715264,0.0004864227,0.00009000936,0.00017698364,0.00015458238,0.0014107508,0.01695145],"genre_scores_gemma":[0.96531415,0.00015403944,0.03223307,0.00007481411,0.000013985108,0.000026839463,0.0000447669,0.00004427177,0.0020940115],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999408,0.00017452204,0.00002480885,0.000099940975,0.00023831389,0.000054445478],"domain_scores_gemma":[0.9985006,0.0010132747,0.00008656411,0.00015315441,0.00022340757,0.000022989941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006666817,0.0006102959,0.00029960863,0.00030418986,0.00016254101,0.00043124205,0.0007573312,0.0006829085,0.002443568],"category_scores_gemma":[0.0036139402,0.00021654407,0.0005022335,0.00019793832,0.00031789162,0.0005820307,0.00039890475,0.0004956408,0.0003797086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006257408,0.00008955219,0.0018331956,0.00044293728,0.00014438134,0.00061350915,0.00017626987,0.45253742,0.47501075,0.01079135,0.0005041042,0.05723082],"study_design_scores_gemma":[0.000026611453,0.000728397,0.0017377267,0.00003183574,0.00010837781,0.00046507368,0.000027076188,0.854422,0.13671486,0.002931361,0.0027691321,0.000037603328],"about_ca_topic_score_codex":0.001157649,"about_ca_topic_score_gemma":0.0010281227,"teacher_disagreement_score":0.002443568,"about_ca_system_score_codex":0.0007580872,"about_ca_system_score_gemma":0.00018847316,"threshold_uncertainty_score":0.008174598},"labels":[],"label_agreement":null},{"id":"W2068298892","doi":"10.1007/s00034-004-0514-4","title":"The Design of Genetically Optimized Self-Organizing Neural Networks with Polynomial and Fuzzy Polynomial Neurons","year":2005,"lang":"en","type":"article","venue":"Circuits Systems and Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Polynomial; Flexibility (engineering); Computer science; Genetic algorithm; Perceptron; Fuzzy logic; Selection (genetic algorithm); Multilayer perceptron; Mathematics; Algorithm; Mathematical optimization; Artificial intelligence; Machine learning","score_opus":0.014687766803619575,"score_gpt":0.2127659967189391,"score_spread":0.1980782299153195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068298892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2724228,0.00053830026,0.7177649,0.00026207452,0.00010172835,0.00007792466,0.00003934775,0.00028320545,0.008509678],"genre_scores_gemma":[0.92761236,0.00010757421,0.070624344,0.000038874135,0.0000120354125,0.00005660373,0.000017664392,0.000017698285,0.0015127993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998996,0.000020872341,0.0000049600653,0.000024045004,0.000030145562,0.00002026995],"domain_scores_gemma":[0.9997607,0.00007174669,0.00004605756,0.000018935561,0.000082318715,0.000020267289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003099275,0.0002765721,0.00035577881,0.00021311644,0.0002632077,0.0004557308,0.0007715369,0.0006457997,0.000544082],"category_scores_gemma":[0.000951722,0.00024738166,0.00029788318,0.00019761999,0.00040962486,0.00035422944,0.00031228072,0.00037768832,0.000101072095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058115846,0.000038815015,0.0003911887,0.000041275674,0.000021205438,0.00005125781,0.000045249486,0.9450351,0.012844944,0.009364394,0.00032153825,0.031786922],"study_design_scores_gemma":[0.00000874435,0.000028583441,0.00008685429,0.0000020767297,0.0000059416316,0.000011851953,0.0000043462987,0.9970078,0.0014965808,0.0011052398,0.00023829065,0.0000037443717],"about_ca_topic_score_codex":0.0017506251,"about_ca_topic_score_gemma":0.0023285046,"teacher_disagreement_score":0.0017506251,"about_ca_system_score_codex":0.000672882,"about_ca_system_score_gemma":0.00060792756,"threshold_uncertainty_score":0.0048820972},"labels":[],"label_agreement":null},{"id":"W2070372194","doi":"10.3402/tellusa.v53i5.12230","title":"Nonlinear principal component analysis by neural networks","year":2001,"lang":"en","type":"article","venue":"Tellus A Dynamic Meteorology and Oceanography","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Principal component analysis; Maxima and minima; Nonlinear system; Artificial neural network; Regularization (linguistics); Mathematics; Bottleneck; Applied mathematics; Computer science; Mathematical analysis; Statistics; Artificial intelligence; Physics","score_opus":0.007072254337937034,"score_gpt":0.23106170946467555,"score_spread":0.2239894551267385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070372194","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015011277,0.0007177975,0.996084,0.00014527916,0.000059481546,0.000041155283,0.00008264111,0.0004575682,0.0009108447],"genre_scores_gemma":[0.10856989,0.0031623102,0.8797782,0.00010510742,0.00026354214,0.0004742624,0.0005747875,0.00030946088,0.006762384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987882,0.00056179555,0.00006487917,0.00024791397,0.0002775504,0.000059714854],"domain_scores_gemma":[0.99914,0.00041051587,0.00011414791,0.00012594227,0.00018245864,0.000026891175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015199974,0.0014426201,0.0010314382,0.001416425,0.00044994312,0.0012537494,0.0008560613,0.0010113019,0.0033545948],"category_scores_gemma":[0.005109694,0.0006299459,0.0010763941,0.0024514557,0.0010331642,0.0015798874,0.001461251,0.0018008987,0.0023612573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000120670506,0.00004077692,0.0006499584,0.00041740868,0.0002380992,0.00010148779,0.00011086789,0.5475739,0.0058873105,0.06301364,0.006931514,0.37491432],"study_design_scores_gemma":[0.000007908912,0.000012696174,0.00022524368,0.000025378804,0.0000126168015,0.000027768998,0.000009875775,0.9591281,0.0011724317,0.034814432,0.004543628,0.000019905354],"about_ca_topic_score_codex":0.0038257446,"about_ca_topic_score_gemma":0.0034165275,"teacher_disagreement_score":0.0038257446,"about_ca_system_score_codex":0.00083558453,"about_ca_system_score_gemma":0.0010901401,"threshold_uncertainty_score":0.011222303},"labels":[],"label_agreement":null},{"id":"W2070816016","doi":"10.1016/j.cageo.2008.12.002","title":"Windows-based software for optimising entropy-based groupings of textural data","year":2009,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Computer science; Software; Data mining; Operating system","score_opus":0.04033156847777994,"score_gpt":0.2879354024067988,"score_spread":0.24760383392901888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070816016","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014162031,0.0001400372,0.8544025,0.0000654536,0.00015228508,0.0004265071,0.0022372142,0.12703419,0.0013798116],"genre_scores_gemma":[0.05593384,0.00013171676,0.9192123,0.00005356539,0.000042567764,0.0010591358,0.002823623,0.018070307,0.0026728744],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998911,0.0001232982,0.00018225075,0.0003307123,0.0003222851,0.00013056214],"domain_scores_gemma":[0.99712366,0.0017029269,0.00021030699,0.0002461344,0.0006004605,0.000116521296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033304114,0.0023395736,0.0015283673,0.002399754,0.000862146,0.00233193,0.0029074305,0.0010622477,0.038619045],"category_scores_gemma":[0.01065433,0.001412179,0.001779444,0.0020403855,0.00057278975,0.002258766,0.0016378999,0.0023469946,0.0063310126],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031397103,0.00037420614,0.0038445066,0.0015140203,0.0006193663,0.00044192368,0.0017147994,0.02886032,0.0673598,0.008573768,0.05278285,0.8307747],"study_design_scores_gemma":[0.0006533761,0.0004718009,0.0066639786,0.00019738615,0.00035730493,0.00055296626,0.00040072776,0.7032895,0.22582832,0.011664535,0.04965361,0.00026645858],"about_ca_topic_score_codex":0.0041165804,"about_ca_topic_score_gemma":0.005806674,"teacher_disagreement_score":0.038619045,"about_ca_system_score_codex":0.00089719397,"about_ca_system_score_gemma":0.001406061,"threshold_uncertainty_score":0.12919354},"labels":[],"label_agreement":null},{"id":"W2071013248","doi":"10.1049/iet-cta:20050259","title":"Online neural identification of multi-input multi-output systems","year":2006,"lang":"en","type":"article","venue":"IET Control Theory and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Control theory (sociology); Artificial neural network; Bounded function; Identification (biology); Computer science; Nonlinear system; Feed forward; Feedforward neural network; Adaptive control; Lyapunov function; Control (management); Mathematics; Control engineering; Artificial intelligence; Engineering","score_opus":0.013298784998596875,"score_gpt":0.2534049069776856,"score_spread":0.2401061219790887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071013248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015547068,0.0002441717,0.98164684,0.00008344892,0.000043339816,0.000021846628,0.000017133752,0.0002914879,0.002104725],"genre_scores_gemma":[0.86544937,0.00022777381,0.13072935,0.00007212483,0.0000309133,0.00012630192,0.000057713616,0.000028261598,0.00327814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997336,0.000058403984,0.000018416858,0.000059805418,0.000099196186,0.000030469304],"domain_scores_gemma":[0.9995648,0.00019628524,0.00007605665,0.000051817573,0.00009749024,0.000013558401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005043326,0.00047851022,0.00046239307,0.0002751188,0.00033191658,0.0005090679,0.0006452869,0.00095519435,0.0009854889],"category_scores_gemma":[0.0015976918,0.00028451227,0.00025857036,0.0002696007,0.00033313234,0.00080418243,0.00075462926,0.0008006041,0.00020414124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007106261,0.0000454453,0.00050224486,0.00011412675,0.000033303775,0.00015627568,0.000062367384,0.853658,0.01660493,0.006953993,0.00048244643,0.12131569],"study_design_scores_gemma":[0.0000021350966,0.000008410146,0.00010468058,0.0000036402405,0.0000016502871,0.000011747794,0.0000017508675,0.99789715,0.0009151822,0.00085655914,0.00019459605,0.0000023795908],"about_ca_topic_score_codex":0.0017641169,"about_ca_topic_score_gemma":0.0020086113,"teacher_disagreement_score":0.0017641169,"about_ca_system_score_codex":0.00033720667,"about_ca_system_score_gemma":0.00040153388,"threshold_uncertainty_score":0.0035076737},"labels":[],"label_agreement":null},{"id":"W2071296557","doi":"10.1115/msec2009-84299","title":"Modeling of Thermal Dynamics of Dies Using Neural Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Finite element method; Thermal; Computer science; Temperature measurement; Line (geometry); System identification; Identification (biology); Observer (physics); Algorithm; Control theory (sociology); Artificial intelligence; Engineering; Data modeling; Mathematics; Physics; Structural engineering","score_opus":0.025429929238625096,"score_gpt":0.25779541398361727,"score_spread":0.2323654847449922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071296557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09001619,0.00048182282,0.9043202,0.00013150451,0.000034529152,0.000040402712,0.0001410372,0.0005050472,0.0043292386],"genre_scores_gemma":[0.95597196,0.00051463436,0.038812663,0.000029258692,0.000017337432,0.00013218063,0.00015396625,0.000029008828,0.0043390235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998448,0.000031018255,0.000009512015,0.000045294284,0.00005263964,0.000016625423],"domain_scores_gemma":[0.99979717,0.000095833064,0.00003737144,0.000016448366,0.00004732558,0.0000058179176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002619377,0.0005297849,0.0003950668,0.0003270089,0.00020113472,0.000550455,0.00053213193,0.0005574806,0.0009481781],"category_scores_gemma":[0.0007663103,0.00033599895,0.00036336572,0.00028183812,0.00036025402,0.00083266915,0.00027796126,0.00045267152,0.00022161852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010467933,0.0000050050553,0.00018279563,0.000010723258,0.0000056586277,0.000013100151,0.000009404636,0.993822,0.0013429895,0.0006350147,0.00003627777,0.003926642],"study_design_scores_gemma":[6.1991557e-7,0.0000031169664,0.00008346555,0.0000011476208,0.000001217399,0.0000026205842,0.0000012332725,0.9991903,0.00028693024,0.00035752048,0.00007044207,0.0000013042974],"about_ca_topic_score_codex":0.006903662,"about_ca_topic_score_gemma":0.0065864706,"teacher_disagreement_score":0.006903662,"about_ca_system_score_codex":0.000521072,"about_ca_system_score_gemma":0.00038082656,"threshold_uncertainty_score":0.01372695},"labels":[],"label_agreement":null},{"id":"W2071413690","doi":"10.1142/s0129183101001407","title":"ASSOCIATIVE MEMORY USING SYNCHRONIZATION IN A CHAOTIC NEURAL NETWORK","year":2001,"lang":"en","type":"article","venue":"International Journal of Modern Physics C","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Content-addressable memory; Bidirectional associative memory; Computer science; Artificial neural network; Chaotic; Synchronization (alternating current); Simple (philosophy); Associative property; Content-addressable storage; Artificial intelligence; Mathematics; Computer network","score_opus":0.026742199314487132,"score_gpt":0.28509373774398605,"score_spread":0.25835153842949893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071413690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14818011,0.0037337437,0.82529646,0.00072710105,0.00039868875,0.000052671832,0.00011423876,0.0003743081,0.02112262],"genre_scores_gemma":[0.947674,0.0027498542,0.04215175,0.00010207802,0.00030289867,0.00010270041,0.000078987876,0.000047486443,0.006790346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998528,0.00004541069,0.000011358347,0.00002891601,0.000047246587,0.000014318327],"domain_scores_gemma":[0.99981743,0.00008074843,0.000036559813,0.000020469879,0.000031173036,0.000013591167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003320932,0.00032977838,0.00037282868,0.00040902046,0.0002890804,0.0005402229,0.00043371978,0.00056883716,0.0013789586],"category_scores_gemma":[0.0008657739,0.00017945746,0.00034361682,0.00065085466,0.00071410055,0.0011904519,0.0007503594,0.0004150722,0.00019873012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011164505,0.000034974226,0.00071749487,0.00019422483,0.00008687921,0.0005337095,0.00017241106,0.23963653,0.036006656,0.6968736,0.001357792,0.024274101],"study_design_scores_gemma":[0.000020779878,0.0000829544,0.0003389241,0.000019534486,0.00003092357,0.00015892508,0.000023643875,0.86244804,0.0028369243,0.13142969,0.0025841305,0.000025517773],"about_ca_topic_score_codex":0.00054882857,"about_ca_topic_score_gemma":0.00038926752,"teacher_disagreement_score":0.0013789586,"about_ca_system_score_codex":0.00024668215,"about_ca_system_score_gemma":0.00024969087,"threshold_uncertainty_score":0.004613042},"labels":[],"label_agreement":null},{"id":"W2071443735","doi":"10.1016/s0375-9601(00)00726-x","title":"Robust chaos in neural networks","year":2000,"lang":"en","type":"article","venue":"Physics Letters A","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Robustness (evolution); Physics; Artificial neural network; Chaotic; Statistical physics; CHAOS (operating system); Chaotic systems; Topology (electrical circuits); Computer science; Artificial intelligence; Combinatorics; Mathematics","score_opus":0.01997829559782242,"score_gpt":0.21224158246201955,"score_spread":0.19226328686419714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071443735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09268771,0.010053657,0.8720321,0.0027470652,0.0005472951,0.000046422843,0.00012475447,0.00047386353,0.021287084],"genre_scores_gemma":[0.96524173,0.0018829529,0.025354471,0.00016011174,0.000489396,0.0000609594,0.00006735144,0.000112564834,0.006630438],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992482,0.0003025305,0.000043214306,0.00012366383,0.00021918718,0.00006314337],"domain_scores_gemma":[0.9975042,0.0015363053,0.00037390206,0.00022768794,0.00026469628,0.00009324559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014450881,0.00052318367,0.0010401292,0.00092654326,0.000476154,0.001624571,0.00088456465,0.0014344491,0.0015302337],"category_scores_gemma":[0.007950418,0.00051169936,0.00053789513,0.000677416,0.0021338095,0.0017495936,0.001287297,0.0013109623,0.00027913533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096928205,0.000017901331,0.00042689714,0.0001384104,0.000100148776,0.000074790965,0.000095530275,0.48468226,0.003358213,0.48809463,0.0016029567,0.021311373],"study_design_scores_gemma":[0.00000866278,0.000017676337,0.00017880273,0.000010566221,0.000009365753,0.000016953349,0.000008386403,0.7540017,0.00042719702,0.24451247,0.00079550577,0.0000128303955],"about_ca_topic_score_codex":0.0014961973,"about_ca_topic_score_gemma":0.00087407196,"teacher_disagreement_score":0.001624571,"about_ca_system_score_codex":0.001024022,"about_ca_system_score_gemma":0.0004380957,"threshold_uncertainty_score":0.007642448},"labels":[],"label_agreement":null},{"id":"W2071708530","doi":"10.1142/s0218488502001703","title":"COMMENTS ON THE PAPER ON POSSIBILISTIC ENTROPIES BY A. SGARRO AND L. P. DINU","year":2002,"lang":"en","type":"article","venue":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Mathematics; Environmental science","score_opus":0.021505167363693373,"score_gpt":0.24885268907828842,"score_spread":0.22734752171459505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071708530","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00076608016,0.012108746,0.0054968596,0.840244,0.102141924,0.000090662295,0.00096741586,0.00017771345,0.038006697],"genre_scores_gemma":[0.04805392,0.03527171,0.012247511,0.3422455,0.18223149,0.00042576622,0.0018750838,0.001457124,0.37619188],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945568,0.0017760866,0.00048377833,0.0006107733,0.0023318904,0.00024075352],"domain_scores_gemma":[0.9616453,0.019013928,0.0012555495,0.001129226,0.015704904,0.0012511103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077409493,0.001416891,0.0013998576,0.0021899054,0.0028766321,0.0048360536,0.0023889078,0.006036331,0.089736156],"category_scores_gemma":[0.06798659,0.0004942281,0.0016804873,0.0030970958,0.0023407426,0.007091887,0.0019914869,0.010412804,0.029849414],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027664899,0.00001123952,0.0000707103,0.000095762414,0.0000060538596,0.00007941716,0.0001645347,0.00010738975,0.00007406773,0.013976429,0.97773904,0.0076475763],"study_design_scores_gemma":[0.000015916088,0.000018138266,0.00050569314,0.00028813427,0.000011441253,0.00015966022,0.00028109082,0.00036950278,0.0002572806,0.023728715,0.97430587,0.000058563437],"about_ca_topic_score_codex":0.009508271,"about_ca_topic_score_gemma":0.0069963816,"teacher_disagreement_score":0.089736156,"about_ca_system_score_codex":0.0055725956,"about_ca_system_score_gemma":0.0034547758,"threshold_uncertainty_score":0.30019736},"labels":[],"label_agreement":null},{"id":"W2072146242","doi":"10.5963/lsmr0306003","title":"Hidden Neural Network for Complex Pattern Recognition: A Comparison Study with Multi- Neural Network Based Approach","year":2013,"lang":"en","type":"article","venue":"International Journal of Life Science and Medical Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Artificial neural network; Computer science; Time delay neural network; Artificial intelligence; Pattern recognition (psychology); Probabilistic neural network","score_opus":0.20561931195438446,"score_gpt":0.4288782236113564,"score_spread":0.22325891165697195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072146242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21405534,0.021221993,0.7501882,0.00086218823,0.0004545482,0.0001340703,0.00009868674,0.0006840784,0.012300877],"genre_scores_gemma":[0.9114846,0.0047594393,0.079935044,0.00010827324,0.00012362695,0.00005948203,0.0001146614,0.00004433079,0.003370536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942565,0.00019368049,0.000040492734,0.00008704475,0.00021361842,0.000039502735],"domain_scores_gemma":[0.9991104,0.00046908186,0.00003857861,0.00006604156,0.00028898413,0.000026973083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014855971,0.00039818956,0.0005283668,0.0006939165,0.00018681944,0.0008370433,0.0006330461,0.0007195189,0.001779207],"category_scores_gemma":[0.0023543325,0.00011488866,0.00041507653,0.00062301825,0.00018993678,0.0014172102,0.00038631083,0.00044635113,0.00031242124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009199643,0.00038470922,0.006188211,0.00051334465,0.0003386981,0.00017136794,0.00013864807,0.33054593,0.0070429035,0.007848026,0.0013109641,0.6445971],"study_design_scores_gemma":[0.000011116211,0.00016850294,0.0017036335,0.000016527336,0.00003532492,0.0000428101,0.000022882145,0.99536395,0.001125064,0.00089542207,0.0006056791,0.000009141043],"about_ca_topic_score_codex":0.0036522,"about_ca_topic_score_gemma":0.0025671695,"teacher_disagreement_score":0.0036522,"about_ca_system_score_codex":0.00046173017,"about_ca_system_score_gemma":0.0003557902,"threshold_uncertainty_score":0.007856727},"labels":[],"label_agreement":null},{"id":"W2072279880","doi":"10.1167/13.9.1003","title":"Time to wave goodbye to phase scrambling - creating unrecognizable control stimuli using a diffeomorphic transform.","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Scrambling; Perception; Computer science; Pattern recognition (psychology); Image warping; Computer vision; Visual processing; Visual perception; Visual cortex; Cognitive neuroscience of visual object recognition; Object (grammar); Psychology; Neuroscience; Algorithm","score_opus":0.03342904546324094,"score_gpt":0.31555441453240934,"score_spread":0.2821253690691684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072279880","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6874733,0.00029028844,0.30548263,0.00012513019,0.0001598048,0.00048486074,0.00027028171,0.0008341738,0.0048795803],"genre_scores_gemma":[0.8849903,0.00011651295,0.11033582,0.00007343427,0.0000136378285,0.0003012502,0.00030661994,0.00017339265,0.0036891403],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998821,0.000030503401,0.0000066411985,0.0000359883,0.00003176099,0.000012964501],"domain_scores_gemma":[0.99959594,0.00019145868,0.000053151805,0.00009130625,0.000040479554,0.00002761108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031970916,0.00033248888,0.00016985912,0.00017298375,0.000099671175,0.00024124177,0.00032127477,0.00020836391,0.003209522],"category_scores_gemma":[0.0028031305,0.00014273028,0.00025980617,0.00013760384,0.00032923243,0.00032386262,0.00035954942,0.00026488485,0.00033309677],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010839382,0.00023927122,0.0013135005,0.00032719347,0.000067612724,0.00023302721,0.0003697008,0.015275174,0.84072256,0.006483392,0.0012613361,0.13262333],"study_design_scores_gemma":[0.00031126587,0.0023610091,0.022461725,0.00004004243,0.00008897478,0.0015627439,0.00015943302,0.30918074,0.63737136,0.010866391,0.015486338,0.00010999751],"about_ca_topic_score_codex":0.0007411855,"about_ca_topic_score_gemma":0.0008789029,"teacher_disagreement_score":0.003209522,"about_ca_system_score_codex":0.00016910202,"about_ca_system_score_gemma":0.00014940626,"threshold_uncertainty_score":0.010736942},"labels":[],"label_agreement":null},{"id":"W2072499402","doi":"10.1109/tnn.2005.860855","title":"A Sequential Dynamic Heteroassociative Memory for Multistep Pattern Recognition and One-to-Many Association","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Backpropagation; Artificial intelligence; Bidirectional associative memory; Pattern recognition (psychology); Associative property; Content-addressable memory; Sequence learning; Artificial neural network; Feed forward; Association (psychology); Context (archaeology); Set (abstract data type); Task (project management); Content-addressable storage; Feedforward neural network; Machine learning; Mathematics","score_opus":0.0214230759761563,"score_gpt":0.24619839756790232,"score_spread":0.224775321591746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072499402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041168425,0.002000839,0.9395738,0.0005761803,0.0007726736,0.00016261742,0.00028628216,0.0026459415,0.012813187],"genre_scores_gemma":[0.6643967,0.001658217,0.3079212,0.00062374864,0.0002620788,0.00036185456,0.00063263054,0.00011714581,0.024026422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997694,0.000028652774,0.00001614169,0.0000826402,0.00007297349,0.00003024977],"domain_scores_gemma":[0.9996997,0.000060094622,0.00003105507,0.000090885405,0.00008195362,0.00003620437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003777699,0.00050386117,0.0006035359,0.00038780426,0.0004426438,0.0007419537,0.0017894828,0.0008305672,0.00696843],"category_scores_gemma":[0.0007849707,0.00025307937,0.00056230085,0.000597995,0.00055786496,0.0017553363,0.00073572877,0.0009812944,0.0016632623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075176894,0.0006261265,0.0018563053,0.00070490676,0.00025548248,0.00070591515,0.00024053059,0.06694922,0.15824422,0.1450663,0.013145147,0.6114541],"study_design_scores_gemma":[0.00011237192,0.0006148337,0.0012759725,0.000062477506,0.00013274963,0.0014478333,0.00005528331,0.82850766,0.05326432,0.07305048,0.041403566,0.00007240775],"about_ca_topic_score_codex":0.0010608657,"about_ca_topic_score_gemma":0.0015914023,"teacher_disagreement_score":0.00696843,"about_ca_system_score_codex":0.00043152433,"about_ca_system_score_gemma":0.00073442765,"threshold_uncertainty_score":0.023311734},"labels":[],"label_agreement":null},{"id":"W2072813327","doi":"10.1016/s0009-2509(00)00034-8","title":"A neural adaptive controller","year":2000,"lang":"en","type":"article","venue":"Chemical Engineering Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Imperial Oil Limited","keywords":"Control theory (sociology); Artificial neural network; Controller (irrigation); Adaptive control; Nonlinear system; Process (computing); Computer science; Control engineering; Continuous stirred-tank reactor; Control (management); Engineering; Artificial intelligence","score_opus":0.007820450071880845,"score_gpt":0.20365294591138758,"score_spread":0.19583249583950674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072813327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022187958,0.0008891804,0.92598826,0.00073226227,0.0010307092,0.00013677988,0.00008674902,0.0016223127,0.047325764],"genre_scores_gemma":[0.78209853,0.00071437884,0.16642857,0.0005771247,0.000290107,0.00025156772,0.0001224878,0.00007848031,0.049438804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998584,0.000014473145,0.0000068104955,0.00005199905,0.00005602778,0.000012292995],"domain_scores_gemma":[0.99987113,0.00003101487,0.000009833049,0.000017014727,0.000059217287,0.000011747694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001882544,0.0004033635,0.0003857656,0.00029234533,0.000555539,0.00065151503,0.00065322855,0.0009915441,0.0054295436],"category_scores_gemma":[0.0005789704,0.00016288793,0.00029745663,0.0002617702,0.00033803147,0.00047992583,0.0006594757,0.00070617173,0.0011074513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002480792,0.0001621806,0.0007307206,0.00027262827,0.00010258182,0.0003053429,0.00011470206,0.3017734,0.10815759,0.06621972,0.008938511,0.51297456],"study_design_scores_gemma":[0.000030453093,0.00009423512,0.00036130662,0.000016241185,0.000030215044,0.00008911355,0.000010565328,0.97051305,0.009502785,0.00840736,0.010926088,0.00001852705],"about_ca_topic_score_codex":0.0019484616,"about_ca_topic_score_gemma":0.0017936656,"teacher_disagreement_score":0.0054295436,"about_ca_system_score_codex":0.00031559996,"about_ca_system_score_gemma":0.0004946183,"threshold_uncertainty_score":0.018163621},"labels":[],"label_agreement":null},{"id":"W2073312178","doi":"10.5555/1639809.1639939","title":"Parallel tracing of multiple trajectories in gradient descent algorithm with Cell Broadband Engine","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Algorithm; Gradient descent; Saddle point; Saddle; Conjugate gradient method; Computer science; Function (biology); Stochastic gradient descent; Sublinear function; Tracing; Function approximation; Mathematics; Applied mathematics; Mathematical optimization; Artificial neural network; Mathematical analysis; Artificial intelligence; Geometry","score_opus":0.010300911935598383,"score_gpt":0.2106773247110732,"score_spread":0.20037641277547483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073312178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03693598,0.000038906925,0.9587718,0.00006575799,0.000022018416,0.000033654378,0.00004423,0.00136544,0.002722243],"genre_scores_gemma":[0.3713419,0.00006098372,0.6236982,0.00004114553,0.000007782635,0.00013620076,0.00015165037,0.0002708535,0.0042913305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998204,0.000031472624,0.000010015757,0.000035895217,0.0000765858,0.000025615602],"domain_scores_gemma":[0.9995951,0.00013944149,0.00003235065,0.000102301565,0.000104645806,0.000026264672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038066268,0.0004420773,0.00046170378,0.000502449,0.0004241513,0.00067151105,0.0010304545,0.0006946116,0.003267035],"category_scores_gemma":[0.0014814177,0.0002832726,0.00028172205,0.00048260883,0.00035478183,0.00073161663,0.0007366749,0.0005679793,0.0009858218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040455078,0.00011850543,0.005144846,0.00014140982,0.00007236737,0.0004351031,0.000637977,0.5772053,0.05977439,0.05162939,0.0031773562,0.3012588],"study_design_scores_gemma":[0.000006161951,0.000014984077,0.00012285003,0.000003304766,0.0000025550312,0.000030065528,0.000010244217,0.9901904,0.0058224774,0.0025910025,0.001201,0.0000049981304],"about_ca_topic_score_codex":0.004543633,"about_ca_topic_score_gemma":0.005100048,"teacher_disagreement_score":0.004543633,"about_ca_system_score_codex":0.00058194995,"about_ca_system_score_gemma":0.00076787284,"threshold_uncertainty_score":0.0109292865},"labels":[],"label_agreement":null},{"id":"W2074130315","doi":"10.1142/s0218001403002411","title":"SCALING LARGE LEARNING PROBLEMS WITH HARD PARALLEL MIXTURES","year":2003,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; National Science Foundation","keywords":"Computer science; Generalization; Probabilistic logic; Parallelizable manifold; Support vector machine; Generative model; Machine learning; Upper and lower bounds; Artificial intelligence; Function (biology); Algorithm; Generative grammar; Mathematics","score_opus":0.06775681660741593,"score_gpt":0.2902994059329209,"score_spread":0.22254258932550497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074130315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03819895,0.0006761623,0.95388436,0.0013306303,0.00016788258,0.0001443171,0.00006615761,0.0013861255,0.0041453377],"genre_scores_gemma":[0.47809988,0.0007722965,0.50867707,0.0007488553,0.0006726056,0.00083202415,0.00041169487,0.00074202596,0.009043625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964228,0.0012859183,0.00025314713,0.00081783126,0.00082200597,0.00039827343],"domain_scores_gemma":[0.989244,0.006782417,0.0005993396,0.002073898,0.00082671794,0.00047375145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005305107,0.0019787862,0.0023555525,0.0011591171,0.0012487265,0.0031033254,0.0027500847,0.001980613,0.006091687],"category_scores_gemma":[0.026720027,0.0017462416,0.0018255302,0.0015148838,0.0026828388,0.0076013035,0.0077400487,0.0044435677,0.001711771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040887616,0.0002652129,0.0012491592,0.0002751906,0.00019074226,0.00016247801,0.00025881443,0.80761594,0.0032414682,0.074523464,0.0044719805,0.107336655],"study_design_scores_gemma":[0.00004465985,0.000033104017,0.00008724682,0.0000070444476,0.000013715103,0.000025421723,0.000020925445,0.92305046,0.00064423715,0.07519652,0.00086606346,0.000010546185],"about_ca_topic_score_codex":0.002551262,"about_ca_topic_score_gemma":0.0026685596,"teacher_disagreement_score":0.006091687,"about_ca_system_score_codex":0.0014730267,"about_ca_system_score_gemma":0.0011645883,"threshold_uncertainty_score":0.028056443},"labels":[],"label_agreement":null},{"id":"W2074287220","doi":"10.1109/tac.2007.908342","title":"Development and Analysis of a Neural Dynamical Approach to Nonlinear Programming Problems","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nonlinear programming; Dynamical systems theory; Convergence (economics); Mathematical optimization; Nonlinear system; Artificial neural network; Mathematics; Convex optimization; Dynamical system (definition); Rate of convergence; Computer science; Convex analysis; Applied mathematics; Regular polygon; Artificial intelligence","score_opus":0.014363315387184628,"score_gpt":0.24725107715336206,"score_spread":0.23288776176617743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074287220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031881726,0.0005527321,0.9881847,0.0003826037,0.000079255406,0.000026964393,0.000027966644,0.000020410376,0.0075372383],"genre_scores_gemma":[0.49373895,0.004207456,0.47242686,0.0004886281,0.0007326326,0.00071640004,0.00022610865,0.0001338056,0.027329136],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999569,0.0001789475,0.000020587608,0.00006849139,0.00013677504,0.000026143936],"domain_scores_gemma":[0.99946016,0.0002887156,0.000052592903,0.000026229789,0.00014264026,0.000029720772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010890212,0.0007895078,0.000557891,0.00067520165,0.00043722417,0.0008689819,0.0010631194,0.0013037527,0.0025927864],"category_scores_gemma":[0.002581923,0.0004150415,0.00073958986,0.0004720458,0.0011753791,0.0014369347,0.0017048118,0.0018188356,0.00040902218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015970596,0.00003751457,0.0002865135,0.00014829081,0.000031556803,0.0001395647,0.00010316019,0.4588376,0.0025846572,0.5136812,0.0013613077,0.02277268],"study_design_scores_gemma":[0.000003936045,0.00002081864,0.00006486666,0.000012930556,0.000003701774,0.000025294212,0.000009429221,0.94306314,0.0001978343,0.054328274,0.0022634764,0.000006297374],"about_ca_topic_score_codex":0.0018629495,"about_ca_topic_score_gemma":0.0016776857,"teacher_disagreement_score":0.0025927864,"about_ca_system_score_codex":0.00082435255,"about_ca_system_score_gemma":0.0008334149,"threshold_uncertainty_score":0.008673668},"labels":[],"label_agreement":null},{"id":"W2075303777","doi":"10.1109/eit.2009.5189648","title":"Artificial neural networks activation function HDL coder","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Field-programmable gate array; Sigmoid function; Hyperbolic function; Artificial neural network; Application-specific integrated circuit; Activation function; Toolbox; MATLAB; Function approximation; Code (set theory); Range (aeronautics); Computer hardware; Computer engineering; Artificial intelligence; Mathematics","score_opus":0.02001076917127448,"score_gpt":0.2416462751716112,"score_spread":0.2216355060003367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075303777","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01004025,0.00031013272,0.93466,0.00015560864,0.00022369233,0.00018021917,0.0012934403,0.03591338,0.017223254],"genre_scores_gemma":[0.36812037,0.00091759436,0.5507358,0.0006536815,0.00012698035,0.0017659174,0.0051498725,0.004779857,0.06774986],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970764,0.000058018864,0.000030681753,0.000042228865,0.00013066214,0.00003080407],"domain_scores_gemma":[0.99950683,0.00015839706,0.000036188823,0.000061994055,0.0002260262,0.000010530011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037219512,0.00070105615,0.0003695336,0.0006185772,0.00020500417,0.00081440347,0.0010434437,0.00052259205,0.025402611],"category_scores_gemma":[0.0017273896,0.00022600061,0.00027753704,0.00039995703,0.00020034671,0.00039127748,0.00032835628,0.00083848706,0.008842284],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010863807,0.00013188523,0.0014369967,0.0013401393,0.00010462832,0.0011189352,0.00034410795,0.14339913,0.07088776,0.05954522,0.08402947,0.63657534],"study_design_scores_gemma":[0.00020935368,0.00013703038,0.0006495678,0.0001564545,0.000037122732,0.0007350838,0.00003507022,0.75462055,0.12323438,0.0063442956,0.11378908,0.000051957766],"about_ca_topic_score_codex":0.0010025731,"about_ca_topic_score_gemma":0.00083983154,"teacher_disagreement_score":0.025402611,"about_ca_system_score_codex":0.00036763126,"about_ca_system_score_gemma":0.0004841215,"threshold_uncertainty_score":0.08498019},"labels":[],"label_agreement":null},{"id":"W2077598507","doi":"10.1145/1370256.1370262","title":"Study of best algorithm combinations for speech processing tasks in machine learning using median vs. mean clusters in MARF","year":2008,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Speech recognition; Artificial intelligence; Speech processing; Support vector machine; Algorithm; Pattern recognition (psychology); Machine learning","score_opus":0.05356206613146202,"score_gpt":0.31815168455805665,"score_spread":0.2645896184265946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077598507","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63782525,0.008354357,0.3354589,0.0015072359,0.00033372777,0.00046562683,0.0012818251,0.0031710067,0.01160207],"genre_scores_gemma":[0.7727896,0.00065322866,0.2212563,0.00014923506,0.00011002444,0.00027627425,0.0018120296,0.0007688115,0.0021845477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99484575,0.0026231268,0.00032586086,0.0008520868,0.0009450775,0.0004081006],"domain_scores_gemma":[0.96813166,0.027289942,0.0006419401,0.0014580193,0.0019601402,0.00051837176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0107119735,0.0013862738,0.0022308212,0.0034136015,0.001300727,0.0024453308,0.002278525,0.0024513763,0.003543726],"category_scores_gemma":[0.039704785,0.0005400435,0.0014039456,0.0021588358,0.0007489395,0.0033175508,0.0012820588,0.0016661906,0.000753917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033991535,0.00080487,0.014350355,0.0011809005,0.000834451,0.00019570664,0.00057926105,0.5795366,0.0037322463,0.009836226,0.011323058,0.37422717],"study_design_scores_gemma":[0.0001305784,0.00044971102,0.0034808326,0.0000683429,0.0001713503,0.00010879292,0.0003710118,0.98163706,0.0047085355,0.007312379,0.001526611,0.000034734192],"about_ca_topic_score_codex":0.006825348,"about_ca_topic_score_gemma":0.007222677,"teacher_disagreement_score":0.0107119735,"about_ca_system_score_codex":0.0018231587,"about_ca_system_score_gemma":0.0018781126,"threshold_uncertainty_score":0.056650996},"labels":[],"label_agreement":null},{"id":"W2080290724","doi":"10.1142/s0218001408006715","title":"NONLINEAR FEATURE EXTRACTION USING FISHER CRITERION","year":2008,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of California, Irvine; Ryerson University","keywords":"Nonlinear system; Feature (linguistics); Pattern recognition (psychology); Feature extraction; Computer science; Class (philosophy); Algorithm; Mathematics; Mathematical optimization; Artificial intelligence","score_opus":0.1428690490826122,"score_gpt":0.3478391469912609,"score_spread":0.2049700979086487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080290724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004871399,0.00030085552,0.99396026,0.000060586317,0.000018153498,0.000016815342,0.000030128576,0.00014912685,0.0005927582],"genre_scores_gemma":[0.26097727,0.0013080847,0.73322594,0.00010615911,0.000111800706,0.000130894,0.0003565065,0.00013169298,0.0036515663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995716,0.00008179719,0.00003468873,0.000093269155,0.00017750356,0.00004114045],"domain_scores_gemma":[0.9994624,0.0002639271,0.000079783684,0.000049172482,0.00012850654,0.000016064827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083902566,0.0008683371,0.0012575202,0.0014436631,0.00034534538,0.00079821976,0.00061093754,0.00080074795,0.00168139],"category_scores_gemma":[0.0023713817,0.00032828742,0.0006817377,0.0012885567,0.00061776,0.0018624398,0.0008682912,0.0005419486,0.0010430292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023369862,0.00005759198,0.0013624387,0.00046033927,0.00011033216,0.00025608926,0.000121782425,0.11156096,0.09301304,0.021676024,0.0032915578,0.76785624],"study_design_scores_gemma":[0.000016317015,0.0000828652,0.0019333521,0.000042327676,0.000041734842,0.00027290013,0.00004187486,0.948499,0.022841346,0.021566818,0.004602327,0.00005908772],"about_ca_topic_score_codex":0.0010583835,"about_ca_topic_score_gemma":0.00070037594,"teacher_disagreement_score":0.00168139,"about_ca_system_score_codex":0.0003893631,"about_ca_system_score_gemma":0.00047698806,"threshold_uncertainty_score":0.0056248307},"labels":[],"label_agreement":null},{"id":"W2080328041","doi":"10.5539/cis.v2n3p15","title":"Machine Learning Emulation in Nature-inspired Computation Systems","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Emulation; Artificial intelligence; Computation; Artificial neural network; Machine learning; Fuzzy logic; Frame (networking); Process (computing); Algorithm","score_opus":0.008936729043113174,"score_gpt":0.2546277486818277,"score_spread":0.2456910196387145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080328041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036117032,0.0005917047,0.94418585,0.0004903065,0.00009453966,0.000043013224,0.000019608222,0.00034996914,0.018107949],"genre_scores_gemma":[0.84867203,0.0005801871,0.14162958,0.00016711779,0.000066573004,0.00012608012,0.00004236168,0.000046030822,0.008670036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997241,0.000096838,0.000012746405,0.00003991635,0.00010636233,0.00002011589],"domain_scores_gemma":[0.99960655,0.00021861141,0.000032972388,0.00005259072,0.00007858678,0.000010692853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054952607,0.00021346888,0.00027222052,0.00026827736,0.0002939109,0.00067523867,0.00049356173,0.00045712,0.0014695423],"category_scores_gemma":[0.0018899416,0.00011210372,0.00029339577,0.00026597656,0.0007214067,0.001033948,0.00045385986,0.0005282732,0.00025346165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009589618,0.000058761656,0.0011755598,0.00014837261,0.00004037697,0.00017669404,0.00029676344,0.55304545,0.012435452,0.3207289,0.0016090663,0.11018872],"study_design_scores_gemma":[0.000010047093,0.000033613887,0.00018297693,0.000009185119,0.000004281011,0.000041912906,0.000017146856,0.96049196,0.003731569,0.032349005,0.0031209113,0.0000074627],"about_ca_topic_score_codex":0.0007077745,"about_ca_topic_score_gemma":0.0003652756,"teacher_disagreement_score":0.0014695423,"about_ca_system_score_codex":0.0006213644,"about_ca_system_score_gemma":0.0003451339,"threshold_uncertainty_score":0.0049161315},"labels":[],"label_agreement":null},{"id":"W2081075625","doi":"10.1002/isaf.200","title":"Predicting direction shifts on Canadian–US exchange rates with artificial neural networks","year":2001,"lang":"en","type":"article","venue":"Intelligent Systems in Accounting Finance & Management","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Backpropagation; Artificial intelligence; Computer science; Machine learning; Fuzzy logic","score_opus":0.018910684352484514,"score_gpt":0.2427696472913218,"score_spread":0.22385896293883728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081075625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98461896,0.00031625386,0.009228619,0.00039950508,0.00007133116,0.000038223152,0.0013576723,0.0003964914,0.003572958],"genre_scores_gemma":[0.9925568,0.000105742474,0.0048583425,0.000024956951,0.000012697225,0.000009264371,0.0011902169,0.000012318467,0.0012296725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996308,0.00009486343,0.000017699502,0.00005531942,0.00013440498,0.00006683954],"domain_scores_gemma":[0.9985526,0.00041455412,0.00013818147,0.00006908583,0.00073376397,0.00009181059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016229253,0.0007719292,0.00036163972,0.0016812074,0.000594163,0.0012697267,0.00060501177,0.0005032267,0.0012468962],"category_scores_gemma":[0.005149456,0.000257003,0.00043423977,0.0018272514,0.00023527708,0.00049880135,0.00047792585,0.000846402,0.00035130713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005038414,0.0001502358,0.117243394,0.000045718636,0.0001359755,0.00011265467,0.00010125195,0.7960898,0.0012410837,0.0008527655,0.004523235,0.07900003],"study_design_scores_gemma":[0.0000126242985,0.00001898004,0.026173377,0.000008806401,0.000012991195,0.000008883286,0.000054190412,0.97173506,0.0009909668,0.0002856714,0.0006805475,0.000017868073],"about_ca_topic_score_codex":0.7254366,"about_ca_topic_score_gemma":0.7067094,"teacher_disagreement_score":0.27456337,"about_ca_system_score_codex":0.0047530658,"about_ca_system_score_gemma":0.003493704,"threshold_uncertainty_score":0.55236065},"labels":[],"label_agreement":null},{"id":"W2082028343","doi":"10.1504/ijitm.2012.044061","title":"Dealing with biometric multi-dimensionality through chaotic neural network methodology","year":2011,"lang":"en","type":"article","venue":"International Journal of Information Technology and Management","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Chaotic; Curse of dimensionality; Dimensionality reduction; Artificial neural network; Computer science; Dimension (graph theory); Fingerprint (computing); Data mining; Linear subspace; Associative property; Artificial intelligence; Pattern recognition (psychology); Content-addressable memory; Machine learning; Mathematics","score_opus":0.06038198197880419,"score_gpt":0.2969288995294264,"score_spread":0.23654691755062224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082028343","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020766437,0.00040077194,0.97680795,0.00013008021,0.00003172434,0.000017964918,0.000012053795,0.00009360333,0.0017393606],"genre_scores_gemma":[0.7475191,0.0012091367,0.24679422,0.00006709656,0.000082025166,0.0001427485,0.000054123375,0.000030490975,0.004101065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983394,0.00006178412,0.000010298471,0.000030999116,0.00004774256,0.000015178076],"domain_scores_gemma":[0.9997608,0.0001097302,0.000041453215,0.00003114892,0.00004882477,0.000007994085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037874008,0.00027723552,0.00038365295,0.0003008067,0.0002674048,0.00043814437,0.00052042323,0.00046392618,0.00071497075],"category_scores_gemma":[0.0008160076,0.00017617006,0.00031592205,0.0003122435,0.00043464854,0.0007197315,0.0005211473,0.0005569678,0.00013512954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008644514,0.000034982066,0.0016578548,0.00021413935,0.000108885855,0.00027648068,0.00021020128,0.76057017,0.026997332,0.06317256,0.0007519196,0.14591898],"study_design_scores_gemma":[0.0000020395992,0.000015156356,0.00013437941,0.0000040325567,0.0000063283187,0.00005297212,0.000007523986,0.992417,0.0018670128,0.005027741,0.00046000883,0.0000057574407],"about_ca_topic_score_codex":0.0008422915,"about_ca_topic_score_gemma":0.0006423291,"teacher_disagreement_score":0.0008422915,"about_ca_system_score_codex":0.00026247045,"about_ca_system_score_gemma":0.00033256656,"threshold_uncertainty_score":0.0023917556},"labels":[],"label_agreement":null},{"id":"W2082441502","doi":"10.1109/ijcnn.2013.6706974","title":"Spreading activation and sparseness in a bidirectional associative memory","year":2013,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Bidirectional associative memory; Content-addressable memory; Associative property; Computer science; Artificial neural network; Attractor; Artificial intelligence; Mathematics","score_opus":0.014023980459777175,"score_gpt":0.2270362860288624,"score_spread":0.2130123055690852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082441502","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81282294,0.0005336158,0.181547,0.00021841523,0.00002710036,0.00001942091,0.000041398926,0.0001517632,0.004638302],"genre_scores_gemma":[0.9934987,0.000116035924,0.005692014,0.000015436379,0.000009270316,0.000009580636,0.000014902371,0.000007811498,0.000636297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997917,0.00008369263,0.000010964148,0.000033551856,0.00005128372,0.000028843024],"domain_scores_gemma":[0.9981117,0.0012287106,0.00024015877,0.00014287636,0.00018392321,0.00009267911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006711736,0.00020131911,0.00033329654,0.00032762677,0.00024915626,0.0005920553,0.0003465188,0.0005111159,0.0010725203],"category_scores_gemma":[0.0031207232,0.00021046786,0.00020037673,0.00022610989,0.0008324903,0.0009970645,0.00057018176,0.00033805257,0.00015399774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010821517,0.00016628808,0.005125195,0.0002901442,0.0001358649,0.00083028834,0.0007084501,0.5443999,0.22185087,0.1639349,0.0007639527,0.06071201],"study_design_scores_gemma":[0.000032235515,0.00014148733,0.0018523752,0.000013524752,0.000031706077,0.00021379357,0.000069778354,0.9126394,0.009399152,0.075122826,0.00046295696,0.000020786969],"about_ca_topic_score_codex":0.0007417801,"about_ca_topic_score_gemma":0.0006224176,"teacher_disagreement_score":0.0010725203,"about_ca_system_score_codex":0.00021607506,"about_ca_system_score_gemma":0.00021752037,"threshold_uncertainty_score":0.0035879612},"labels":[],"label_agreement":null},{"id":"W2082933678","doi":"10.1103/physreve.67.016118","title":"Bistable gradient networks. I. Attractors and pattern retrieval at low loading in the thermodynamic limit","year":2003,"lang":"en","type":"article","venue":"Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Attractor; Bistability; Spurious relationship; Content-addressable memory; Statistical physics; Hopfield network; Physics; Artificial neural network; Coupling (piping); Hebbian theory; Attraction; Topology (electrical circuits); Computer science; Mathematics; Mathematical analysis; Quantum mechanics; Artificial intelligence; Materials science; Combinatorics","score_opus":0.012408850535858459,"score_gpt":0.28150019893752887,"score_spread":0.2690913484016704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082933678","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8289286,0.0029241196,0.15195172,0.0007221108,0.000037611946,0.000046836467,0.00015079441,0.00026254004,0.014975689],"genre_scores_gemma":[0.99417263,0.00031986833,0.004321792,0.000033081564,0.000015417841,0.000020129495,0.000029372999,0.000019120193,0.0010686215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994004,0.000015857144,0.0000033757883,0.000009667928,0.000015601763,0.000015438376],"domain_scores_gemma":[0.9996816,0.00013198916,0.00009081064,0.00002246731,0.000037317943,0.00003585537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025185657,0.00022433336,0.00025159665,0.0004590693,0.00025759227,0.000572902,0.000412589,0.0005186655,0.0013225614],"category_scores_gemma":[0.0015317671,0.0001639322,0.00019986686,0.00024024239,0.0009441574,0.0013883329,0.00045910786,0.0002580052,0.00013585965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024512637,0.00007366148,0.008696694,0.00033454626,0.000100752746,0.00081341196,0.00047722648,0.47137105,0.06769555,0.41407216,0.0020070728,0.034112785],"study_design_scores_gemma":[0.00001678087,0.000034637986,0.0028441353,0.000016752552,0.000011605293,0.00019989342,0.00005748772,0.857822,0.0030436972,0.13515203,0.0007844406,0.00001646234],"about_ca_topic_score_codex":0.00093420566,"about_ca_topic_score_gemma":0.0007936478,"teacher_disagreement_score":0.0013225614,"about_ca_system_score_codex":0.00046546475,"about_ca_system_score_gemma":0.00012346238,"threshold_uncertainty_score":0.004424453},"labels":[],"label_agreement":null},{"id":"W2083226902","doi":"10.1007/s11071-011-0173-5","title":"Fuzzy identification of cutting acoustic emission with extended subtractive cluster analysis","year":2011,"lang":"en","type":"article","venue":"Nonlinear Dynamics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Subtractive color; Identification (biology); Fuzzy logic; Machining; Cluster (spacecraft); Process (computing); Simple (philosophy); Algorithm; Computer science; Square (algebra); Mathematics; Engineering; Artificial intelligence; Mechanical engineering; Physics; Geometry","score_opus":0.013712970985204775,"score_gpt":0.2497697200556232,"score_spread":0.23605674907041843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083226902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044564318,0.00008650436,0.9536959,0.00002979026,0.000021165071,0.00002964487,0.000015492082,0.000106800595,0.0014503987],"genre_scores_gemma":[0.79580116,0.00011351599,0.20142238,0.00002117966,0.000017881794,0.000089404966,0.000073128205,0.000049058228,0.0024122817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994091,0.00012064511,0.000028162885,0.00012594316,0.00026755081,0.00004868626],"domain_scores_gemma":[0.99937445,0.00022068423,0.000058675538,0.00006684369,0.00025998443,0.000019319601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008665864,0.00064678834,0.00074072083,0.000840249,0.0006791338,0.0007663114,0.001007082,0.0006193305,0.00090799906],"category_scores_gemma":[0.0018724593,0.0003173354,0.0008274004,0.00074907765,0.0006576148,0.0008976768,0.0007659671,0.00062692957,0.0002092405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064944394,0.00014780155,0.0020372062,0.0003177054,0.00018645506,0.0002038549,0.0004778934,0.64236957,0.079782896,0.016592827,0.0007864806,0.25644785],"study_design_scores_gemma":[0.0000037903478,0.000021061676,0.00043842397,0.000004222402,0.0000095571195,0.000022669396,0.000026164227,0.9930515,0.0043526716,0.001860389,0.00019977389,0.000009743339],"about_ca_topic_score_codex":0.0029318668,"about_ca_topic_score_gemma":0.003011543,"teacher_disagreement_score":0.0029318668,"about_ca_system_score_codex":0.00040493094,"about_ca_system_score_gemma":0.0006405373,"threshold_uncertainty_score":0.0058295727},"labels":[],"label_agreement":null},{"id":"W2083231156","doi":"10.1016/s0966-6362(00)00094-1","title":"A review of analytical techniques for gait data. Part 1: fuzzy, statistical and fractal methods","year":2001,"lang":"en","type":"review","venue":"Gait & Posture","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":381,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gait; Fuzzy logic; Multivariate statistics; Computer science; Gait analysis; Fractal analysis; Fractal; Data mining; Artificial intelligence; Machine learning; Mathematics; Physical medicine and rehabilitation; Fractal dimension; Medicine","score_opus":0.1139975489929698,"score_gpt":0.4550222332615494,"score_spread":0.34102468426857957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083231156","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074333214,0.94412935,0.049489927,0.0007618031,0.0009674679,0.000045355056,0.00021663909,0.000102255406,0.0035439015],"genre_scores_gemma":[0.0057853884,0.9480679,0.040630944,0.00044179548,0.0017724175,0.00010510739,0.00030232285,0.000046995414,0.0028471693],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9993761,0.00010053504,0.000094724826,0.00010167095,0.00030230332,0.000024711955],"domain_scores_gemma":[0.99810493,0.0011242186,0.00011067141,0.00008955728,0.0005343807,0.000036239395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011716015,0.0010583693,0.0019301095,0.0054316344,0.00041329308,0.0013921321,0.001660438,0.0013339542,0.003098838],"category_scores_gemma":[0.0029579648,0.0007442644,0.0007281812,0.0060185874,0.0010633472,0.0020972476,0.00058727956,0.0013592403,0.0030311446],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004286093,0.00008559993,0.00054741,0.007602684,0.00009374973,0.00016263327,0.0001223959,0.0019754132,0.0033106513,0.012852487,0.03707533,0.9361288],"study_design_scores_gemma":[0.00002329675,0.00022312123,0.0053584753,0.004654292,0.00017625699,0.003533703,0.00020147079,0.010672767,0.004281024,0.049051978,0.9216234,0.00020013223],"about_ca_topic_score_codex":0.0017344573,"about_ca_topic_score_gemma":0.0024211626,"teacher_disagreement_score":0.0054316344,"about_ca_system_score_codex":0.00069419265,"about_ca_system_score_gemma":0.00084465684,"threshold_uncertainty_score":0.010366678},"labels":[],"label_agreement":null},{"id":"W2083415932","doi":"10.1007/s00500-007-0244-1","title":"Conceptual duplication","year":2007,"lang":"en","type":"article","venue":"Soft Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Adaptive resonance theory; Cluster analysis; Stability (learning theory); Computer science; Artificial intelligence; Artificial neural network; Plasticity; Quality (philosophy); Task (project management); Machine learning; Engineering; Materials science; Physics","score_opus":0.01945906045802956,"score_gpt":0.2710991355941179,"score_spread":0.2516400751360883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083415932","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076404563,0.0008519814,0.10081792,0.016053533,0.0026928345,0.00016443591,0.0006412741,0.00060539076,0.87053204],"genre_scores_gemma":[0.4624851,0.00148479,0.04740791,0.0052747796,0.002132333,0.0004952846,0.0017938517,0.0012720507,0.47765392],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99614537,0.001407647,0.0001728699,0.0007624119,0.0011865372,0.000325083],"domain_scores_gemma":[0.9946384,0.0012864873,0.00025866108,0.0018090954,0.0017248439,0.00028246598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021566718,0.00082539476,0.00052597123,0.0023129531,0.0029312132,0.0066952514,0.0022743987,0.0022550216,0.08429653],"category_scores_gemma":[0.01554721,0.0004238856,0.0006823907,0.0020021887,0.007597445,0.011880938,0.005026751,0.003707489,0.020659626],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009544414,0.00000492633,0.00005069292,0.000025559182,0.0000023467007,0.000072261944,0.0007723967,0.000046481186,0.0001265873,0.9774655,0.011792439,0.009631278],"study_design_scores_gemma":[0.000013211759,0.000010844011,0.00017416413,0.000063537766,0.000010788803,0.0005718369,0.0011821236,0.0006376647,0.00052662607,0.6298978,0.36689577,0.000015747875],"about_ca_topic_score_codex":0.0021244476,"about_ca_topic_score_gemma":0.0014967474,"teacher_disagreement_score":0.08429653,"about_ca_system_score_codex":0.0042916546,"about_ca_system_score_gemma":0.00297487,"threshold_uncertainty_score":0.28199995},"labels":[],"label_agreement":null},{"id":"W2083629661","doi":"10.1117/12.380560","title":"&lt;title&gt;Adaptive structure feed-forward neural networks using polynomial activation functions&lt;/title&gt;","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Activation function; Constructive; Artificial neural network; Computer science; Orthonormal basis; Adaptation (eye); Sigmoid function; Artificial intelligence; Mathematics","score_opus":0.010775279404378503,"score_gpt":0.2164546495523488,"score_spread":0.2056793701479703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083629661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010140593,0.0035267847,0.9275555,0.0011532537,0.0025285373,0.00016739457,0.00046394044,0.0039779893,0.050485976],"genre_scores_gemma":[0.28523105,0.004740298,0.51884896,0.0009275035,0.0009185973,0.00023964516,0.0021254348,0.0014330406,0.18553554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998522,0.000026975635,0.000008912399,0.00004277552,0.000054241435,0.000014917477],"domain_scores_gemma":[0.99976355,0.00005699289,0.000020606389,0.000053496482,0.00009063353,0.000014779483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033533608,0.00063391577,0.00045065244,0.00032728555,0.00023608583,0.00064777845,0.0010566007,0.00078203384,0.020541444],"category_scores_gemma":[0.0007465396,0.000166433,0.00031501436,0.00088989554,0.0005319012,0.00089132076,0.000394747,0.000995119,0.009419799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002493813,0.000063803476,0.00038594057,0.00041649994,0.00005178985,0.0003106511,0.000056413865,0.046542432,0.037665617,0.07870132,0.06257032,0.7729859],"study_design_scores_gemma":[0.000051308165,0.00017853486,0.0006805838,0.00010079402,0.00004143053,0.00033624642,0.000017677377,0.7492963,0.06507576,0.024168741,0.15999189,0.00006074539],"about_ca_topic_score_codex":0.0016862466,"about_ca_topic_score_gemma":0.0031216017,"teacher_disagreement_score":0.020541444,"about_ca_system_score_codex":0.0004726836,"about_ca_system_score_gemma":0.00034716638,"threshold_uncertainty_score":0.068718016},"labels":[],"label_agreement":null},{"id":"W2083914959","doi":"10.1016/j.neunet.2015.04.008","title":"Set selection dynamical system neural networks with partial memories, with applications to Sudoku and KenKen puzzles","year":2015,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Set (abstract data type); Artificial neural network; Computer science; Heuristic; Selection (genetic algorithm); Solver; Problem solver; Artificial intelligence; Theoretical computer science; Machine learning; Algorithm","score_opus":0.016922813073623557,"score_gpt":0.2379846997646934,"score_spread":0.22106188669106985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083914959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34680933,0.0033692226,0.6294934,0.0023190475,0.00045749292,0.00007393465,0.0002465116,0.00024844916,0.016982578],"genre_scores_gemma":[0.9403432,0.0008631793,0.05133793,0.0001416401,0.00017029703,0.00013210105,0.000105746076,0.000047044396,0.006858943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983835,0.00007926254,0.00000867075,0.00003134561,0.00002513625,0.000017128295],"domain_scores_gemma":[0.9987789,0.000827899,0.00011782785,0.000092973074,0.000107393105,0.00007502997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074104394,0.00045207707,0.0009778697,0.0004869558,0.0007154288,0.0010571998,0.0011123635,0.0011046744,0.0027986744],"category_scores_gemma":[0.004658509,0.00039872475,0.0005973291,0.00071053155,0.001181116,0.0018333338,0.0013858897,0.0011503274,0.00017389662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015122959,0.00004963436,0.0012877582,0.0001299439,0.0001184398,0.0001686356,0.00017613631,0.64489627,0.0013223245,0.31053686,0.002420061,0.038742777],"study_design_scores_gemma":[0.000010057145,0.00001481011,0.00013032058,0.0000056493805,0.000007734796,0.000012894542,0.0000135085065,0.9201647,0.00012324593,0.07923068,0.00027809734,0.0000083292725],"about_ca_topic_score_codex":0.0017274176,"about_ca_topic_score_gemma":0.0023754903,"teacher_disagreement_score":0.0027986744,"about_ca_system_score_codex":0.00043681776,"about_ca_system_score_gemma":0.0004391146,"threshold_uncertainty_score":0.009362519},"labels":[],"label_agreement":null},{"id":"W2084111464","doi":"10.1142/s0129065705000402","title":"HARDWARE IMPLEMENTATION OF A NEW ARTIFICIAL NEURON","year":2005,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Field-programmable gate array; Computer science; Artificial neuron; Artificial neural network; Function (biology); Software; Field (mathematics); SIGNAL (programming language); Computer hardware; Activation function; Embedded system; Artificial intelligence; Mathematics; Operating system","score_opus":0.03710349710960794,"score_gpt":0.3323794781561206,"score_spread":0.2952759810465127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084111464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07080077,0.00045060183,0.89724654,0.00031844672,0.000645449,0.00013289128,0.00014242095,0.00420825,0.026054591],"genre_scores_gemma":[0.5148363,0.0004593968,0.46896756,0.000252946,0.00007625785,0.00012187817,0.00020558579,0.00007816465,0.015001948],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984586,0.00001620422,0.000012000705,0.00002931358,0.000073856645,0.000022697579],"domain_scores_gemma":[0.9998833,0.000023265511,0.000009503777,0.000022220333,0.00004838209,0.000013267646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015494839,0.00021337505,0.0002334348,0.00018074938,0.00021429746,0.0005194237,0.00091205566,0.00034908648,0.00400089],"category_scores_gemma":[0.00029823586,0.00013076469,0.00020611429,0.00013082704,0.00019103271,0.00040157174,0.00024466234,0.00033460028,0.0009064185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069335336,0.00019685406,0.0031029284,0.00069501007,0.00017506108,0.0012352889,0.00027876438,0.115446456,0.3951507,0.061953455,0.0074538277,0.41361833],"study_design_scores_gemma":[0.00016451781,0.0008630914,0.0025224763,0.00009789027,0.00013834631,0.0017256847,0.00006699158,0.67561334,0.20134705,0.006512228,0.11087747,0.0000708306],"about_ca_topic_score_codex":0.00091447384,"about_ca_topic_score_gemma":0.0012360765,"teacher_disagreement_score":0.00400089,"about_ca_system_score_codex":0.00030311092,"about_ca_system_score_gemma":0.00038934062,"threshold_uncertainty_score":0.013384342},"labels":[],"label_agreement":null},{"id":"W2084961869","doi":"10.1109/isse.2007.4432921","title":"A Generic Control Block for Feedforward Neural Network with On-Chip Delta Rule Learning Algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"Reconfigurability; Computer science; Artificial neural network; Feedforward neural network; Feed forward; Block (permutation group theory); Algorithm; Field-programmable gate array; Artificial intelligence; Embedded system; Control engineering; Engineering","score_opus":0.012747636383754942,"score_gpt":0.2352295414018239,"score_spread":0.22248190501806894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084961869","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00826077,0.00019545911,0.98352987,0.00004722782,0.00008255686,0.00014516231,0.00008687759,0.0031633035,0.004488761],"genre_scores_gemma":[0.43379688,0.00030912532,0.5537827,0.00021368524,0.00007296167,0.0005432649,0.0004295072,0.00031354357,0.010538341],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967813,0.00004284453,0.000028700091,0.000082615086,0.000128833,0.000038888702],"domain_scores_gemma":[0.999826,0.000031339754,0.000019737743,0.000051205287,0.000059743503,0.000012001074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029574978,0.00051156036,0.0003368704,0.0003484262,0.00024571715,0.0005532343,0.0013766263,0.0006032268,0.004136026],"category_scores_gemma":[0.00041783365,0.00020387299,0.00036589964,0.00017072036,0.0002594564,0.00051850436,0.00031756843,0.0005499885,0.001075028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005729244,0.0002754415,0.00204909,0.00090513576,0.0001659191,0.0006414323,0.0002665623,0.1279547,0.22914611,0.040658887,0.008622304,0.5887414],"study_design_scores_gemma":[0.00018377212,0.0007728209,0.0021731786,0.000100852194,0.00016302904,0.0012024454,0.00003258737,0.7048507,0.21050976,0.005747448,0.07419945,0.000064017826],"about_ca_topic_score_codex":0.001445766,"about_ca_topic_score_gemma":0.0014394885,"teacher_disagreement_score":0.004136026,"about_ca_system_score_codex":0.00036606274,"about_ca_system_score_gemma":0.00049588113,"threshold_uncertainty_score":0.013836384},"labels":[],"label_agreement":null},{"id":"W2085193043","doi":"10.1109/hpcsim.2014.6903755","title":"A GPU-based associative memory using sparse Neural Networks","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Speedup; Content-addressable memory; Multiplication (music); Associative property; Artificial neural network; Matrix multiplication; Parallel computing; Variety (cybernetics); Scheme (mathematics); Content-addressable storage; Matrix (chemical analysis); Theoretical computer science; Algorithm; Artificial intelligence; Mathematics","score_opus":0.04101163181320631,"score_gpt":0.2805326911741783,"score_spread":0.23952105936097198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085193043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17354414,0.0009490275,0.7873334,0.0008656054,0.00050277077,0.00012538763,0.00036691036,0.008129971,0.028182747],"genre_scores_gemma":[0.6124545,0.00037148155,0.37057075,0.00032723768,0.00008510372,0.00013300664,0.00046531195,0.0002167735,0.015375816],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986553,0.000022318778,0.000007839695,0.000028369204,0.000057371566,0.000018560233],"domain_scores_gemma":[0.9997937,0.00003819259,0.000012514778,0.00006698406,0.00006415225,0.000024471476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017873578,0.00029635918,0.0003943137,0.0004247364,0.00038642297,0.000707758,0.0014813923,0.00059232424,0.0048782476],"category_scores_gemma":[0.0007064241,0.00020975806,0.0002525243,0.0007052046,0.000350409,0.0011498574,0.00086614984,0.0004969013,0.0008886639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007887103,0.00037342135,0.002306705,0.0003484938,0.00015936513,0.0004682585,0.00019896546,0.17204665,0.1296833,0.0600469,0.026275424,0.60730374],"study_design_scores_gemma":[0.00007682669,0.00014348121,0.00036541137,0.000016903468,0.000027094044,0.0001605775,0.000023024502,0.94448286,0.029679697,0.012001311,0.01300397,0.000018842566],"about_ca_topic_score_codex":0.0024570813,"about_ca_topic_score_gemma":0.00401612,"teacher_disagreement_score":0.0048782476,"about_ca_system_score_codex":0.0004221429,"about_ca_system_score_gemma":0.00044774247,"threshold_uncertainty_score":0.016319394},"labels":[],"label_agreement":null},{"id":"W2085889198","doi":"10.1016/j.jfranklin.2013.08.015","title":"Preservation of relevant properties of interconnected dynamical systems over complex networks","year":2013,"lang":"en","type":"article","venue":"Journal of the Franklin Institute","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"Computer science; Distributed computing","score_opus":0.03010828103074449,"score_gpt":0.23052496594928587,"score_spread":0.20041668491854137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085889198","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.548363,0.0011216658,0.4274943,0.00082054175,0.00012757846,0.000056683533,0.00059120596,0.00036231213,0.021062814],"genre_scores_gemma":[0.98877966,0.00043748505,0.00856872,0.000056849636,0.00007965489,0.000024801164,0.00017283756,0.00006381216,0.0018160943],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995634,0.00012246554,0.000039215345,0.00012403516,0.00009459803,0.00005622039],"domain_scores_gemma":[0.99559754,0.0019071967,0.0008488458,0.000892449,0.00047533587,0.00027870925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011911375,0.0005164667,0.0004650415,0.0011890512,0.0005958416,0.0021603084,0.0007284891,0.0004674744,0.0030207224],"category_scores_gemma":[0.006288126,0.00030517022,0.000595516,0.00087507913,0.0013048582,0.0038010115,0.0012596155,0.0013520863,0.00022599932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025809297,0.00011101066,0.003340384,0.00022697632,0.000117232616,0.00050818134,0.00044995436,0.049653314,0.017109886,0.8937977,0.0012767467,0.03315056],"study_design_scores_gemma":[0.000025973934,0.000103166974,0.0018590017,0.000023942688,0.00004201612,0.00022328208,0.000120932535,0.12872954,0.0046941754,0.86277544,0.0013863199,0.000016165337],"about_ca_topic_score_codex":0.00040262105,"about_ca_topic_score_gemma":0.0003888184,"teacher_disagreement_score":0.0030207224,"about_ca_system_score_codex":0.00054832373,"about_ca_system_score_gemma":0.00044068712,"threshold_uncertainty_score":0.0101053715},"labels":[],"label_agreement":null},{"id":"W2085961337","doi":"10.1016/j.neunet.2010.03.006","title":"Classification of 2-dimensional array patterns: Assembling many small neural networks is better than using a large one","year":2010,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"Jilin University","keywords":"Artificial neural network; Time delay neural network; Computer science; Stochastic neural network; Nervous system network models; Types of artificial neural networks; Artificial intelligence; Physical neural network; Pattern recognition (psychology); Winner-take-all","score_opus":0.04018571857650472,"score_gpt":0.2675530410632497,"score_spread":0.22736732248674496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085961337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3095852,0.0016574579,0.67743707,0.0021653704,0.00054755324,0.00017447442,0.00013212921,0.002585441,0.005715324],"genre_scores_gemma":[0.7164616,0.00078127946,0.27574372,0.00093881926,0.00043451253,0.00016309082,0.0003652154,0.00029406056,0.004817725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914217,0.0002495299,0.00007059146,0.0002830428,0.00016361622,0.000090893656],"domain_scores_gemma":[0.9965676,0.001805418,0.000302235,0.0006519056,0.0005370638,0.00013570297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018117927,0.0011575972,0.0014087491,0.0007363959,0.00057029614,0.0018977012,0.00082778564,0.001732293,0.0038849602],"category_scores_gemma":[0.007914155,0.00065943203,0.0008396759,0.0010301479,0.0008537344,0.004921702,0.0011811239,0.0015619819,0.0015706463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008908104,0.00035947264,0.010089557,0.00028835036,0.0002197796,0.00015780132,0.00025043762,0.10056406,0.059873387,0.0028854047,0.0043190215,0.82010204],"study_design_scores_gemma":[0.00005434563,0.00044491203,0.0062415637,0.000050584247,0.00015913087,0.00016718764,0.00027337766,0.9547531,0.021471499,0.012566118,0.0037672457,0.0000509875],"about_ca_topic_score_codex":0.0022313995,"about_ca_topic_score_gemma":0.003191239,"teacher_disagreement_score":0.0038849602,"about_ca_system_score_codex":0.0004234098,"about_ca_system_score_gemma":0.00043468378,"threshold_uncertainty_score":0.012996495},"labels":[],"label_agreement":null},{"id":"W2086214213","doi":"10.1016/s0925-2312(01)00575-6","title":"Dynamics of stochastic artificial neurons","year":2001,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Bernoulli's principle; Sigmoid function; Gaussian; Stochastic process; Statistical physics; Artificial neural network; State (computer science); Mathematics; Convergence (economics); Computer science; Algorithm; Physics; Artificial intelligence; Quantum mechanics","score_opus":0.020985027821481025,"score_gpt":0.25336963946141805,"score_spread":0.232384611639937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086214213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50053877,0.0027753406,0.39589822,0.0058483025,0.00086205965,0.000080046135,0.00040412636,0.00036286918,0.09323036],"genre_scores_gemma":[0.9794573,0.00064248487,0.0064829914,0.00014185767,0.00009747179,0.000036263147,0.000084162595,0.000050109524,0.013007318],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99976057,0.000078572215,0.000011797161,0.000034361914,0.00007571313,0.00003907128],"domain_scores_gemma":[0.99898463,0.00039233305,0.00015948052,0.000055220215,0.00024953813,0.0001589024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062392,0.00033182703,0.0005717131,0.0005759828,0.0005878264,0.0015157004,0.0010534168,0.0012208935,0.004744358],"category_scores_gemma":[0.004547195,0.00037011606,0.00044111078,0.00035779207,0.0014921394,0.0014490687,0.0010965138,0.0010176703,0.0004274069],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005550202,0.000027750413,0.0008650788,0.000047438996,0.00003170037,0.00011979876,0.000106108295,0.42614418,0.003224878,0.5628753,0.0019384841,0.0045637875],"study_design_scores_gemma":[0.000011440888,0.000012285301,0.00031546556,0.000008124766,0.0000050423673,0.000034221815,0.000026715494,0.903572,0.00022237227,0.09499105,0.00078924507,0.000012155983],"about_ca_topic_score_codex":0.0040831105,"about_ca_topic_score_gemma":0.0025833026,"teacher_disagreement_score":0.004744358,"about_ca_system_score_codex":0.0012195389,"about_ca_system_score_gemma":0.00076889864,"threshold_uncertainty_score":0.015871406},"labels":[],"label_agreement":null},{"id":"W2086791809","doi":"10.4018/jcini.2008070101","title":"Language Independent Recognition of Human Emotion using Artificial Neural Networks","year":2008,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Curse of dimensionality; Modular design; Feature selection; Selection (genetic algorithm); Consistency (knowledge bases); Feature (linguistics); Pattern recognition (psychology); Identification (biology); Machine learning; Natural language processing","score_opus":0.045754725136149935,"score_gpt":0.3194228397995744,"score_spread":0.2736681146634245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086791809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23113047,0.0014251174,0.7600681,0.0002717618,0.00017682122,0.00008393575,0.00016416029,0.001733845,0.004945777],"genre_scores_gemma":[0.8336334,0.0006455469,0.16118988,0.00016495348,0.000109440014,0.00011030773,0.00031276082,0.000059137576,0.0037745484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997309,0.00007920295,0.000016765434,0.00006957122,0.00008027042,0.000023223372],"domain_scores_gemma":[0.9997954,0.000091637754,0.00003076649,0.000021704001,0.00005378625,0.0000068202507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037754508,0.00043292253,0.00033587485,0.00032914203,0.00012430499,0.0004175193,0.00044051115,0.00040117383,0.0007319064],"category_scores_gemma":[0.0013150114,0.00013187915,0.00034038082,0.0002894019,0.00019449873,0.00051474164,0.00035172398,0.0003917149,0.00035259876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005106415,0.00017180343,0.0027511837,0.00019770625,0.00014628666,0.00032371347,0.00025502875,0.07666327,0.2960725,0.0024885873,0.0019168371,0.6185025],"study_design_scores_gemma":[0.000017677701,0.000115231756,0.0049032946,0.000019737929,0.000045900677,0.00012977421,0.000053072035,0.95865446,0.031519316,0.0029930647,0.001522141,0.000026432801],"about_ca_topic_score_codex":0.0006548895,"about_ca_topic_score_gemma":0.0009875497,"teacher_disagreement_score":0.0007319064,"about_ca_system_score_codex":0.00017496223,"about_ca_system_score_gemma":0.00012059857,"threshold_uncertainty_score":0.0024484992},"labels":[],"label_agreement":null},{"id":"W2086934679","doi":"10.1007/s11063-012-9223-3","title":"A Chaotic Feature Extracting BAM and Its Application in Implementing Memory Search","year":2012,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Attractor; Chaotic; Lyapunov exponent; Feature (linguistics); Computer science; Aperiodic graph; Content-addressable memory; Function (biology); Fixed point; Control theory (sociology); Mathematics; Artificial neural network; Artificial intelligence; Control (management); Mathematical analysis","score_opus":0.027056660672921142,"score_gpt":0.29343144331973986,"score_spread":0.26637478264681874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086934679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07211187,0.00058771676,0.9205659,0.00024895696,0.00019579098,0.000062266554,0.0001235214,0.0018893422,0.004214621],"genre_scores_gemma":[0.6861709,0.0002789581,0.30951348,0.0000846061,0.00004596994,0.00008397844,0.000082070015,0.00008860313,0.0036513796],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999423,0.000014311049,0.0000042377355,0.000012736112,0.000017622611,0.000008657364],"domain_scores_gemma":[0.99969316,0.00013168466,0.000021808008,0.00005221077,0.00007807558,0.000023049932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024639373,0.00030328205,0.00037072873,0.00031514262,0.00034915537,0.00040095052,0.0005646279,0.00056339224,0.0024294585],"category_scores_gemma":[0.0008527365,0.00017045831,0.00016593591,0.0005846042,0.00023984163,0.00061448774,0.00030001366,0.00029275674,0.0005945839],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012682641,0.00015299505,0.0022675989,0.0004609363,0.00007761809,0.0005820427,0.00020989506,0.06217355,0.41248587,0.027510991,0.0030394695,0.4897708],"study_design_scores_gemma":[0.00004201767,0.00024924043,0.0010640695,0.00002588994,0.0000431477,0.0004581755,0.000027511906,0.8758556,0.11225244,0.0057332464,0.0042190435,0.000029623605],"about_ca_topic_score_codex":0.0008818998,"about_ca_topic_score_gemma":0.00079508795,"teacher_disagreement_score":0.0024294585,"about_ca_system_score_codex":0.00026219033,"about_ca_system_score_gemma":0.00022017852,"threshold_uncertainty_score":0.008127332},"labels":[],"label_agreement":null},{"id":"W2087824310","doi":"10.1007/s00332-005-0647-z","title":"Co-Existence of Chaos and Stable Periodic Orbits in a Simple Discrete Neural Network","year":2005,"lang":"en","type":"article","venue":"Journal of Nonlinear Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Guangdong Province","keywords":"Simple (philosophy); CHAOS (operating system); Chaotic; Periodic orbits; Mathematics; Series (stratigraphy); Artificial neural network; Control theory (sociology); Statistical physics; Mathematical analysis; Pure mathematics; Topology (electrical circuits); Computer science; Physics; Combinatorics; Artificial intelligence","score_opus":0.017728547599359448,"score_gpt":0.2916860968013685,"score_spread":0.27395754920200904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087824310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97017586,0.00015019468,0.02670536,0.0001950949,0.000038129867,0.00001308022,0.000023513387,0.000042225725,0.002656444],"genre_scores_gemma":[0.99807465,0.000026782947,0.0013345846,0.000008892432,0.000008683987,0.0000055786686,0.000009740373,0.0000036370325,0.0005274183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988055,0.000027097403,0.000010054833,0.000025141197,0.000037905455,0.000019309286],"domain_scores_gemma":[0.9987086,0.00060564885,0.0002568828,0.00007468418,0.00014778135,0.00020642806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035167136,0.00022832068,0.0005981894,0.0007763159,0.0007024117,0.0007955874,0.00054096826,0.00086641597,0.0011263668],"category_scores_gemma":[0.002983582,0.00040494272,0.00042317135,0.0003328536,0.0012703968,0.00084422075,0.0008596108,0.00040563854,0.00009312205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013985422,0.00025117418,0.029703941,0.00043710775,0.00049583334,0.0035924097,0.0013981395,0.5454324,0.09924092,0.29116133,0.0013798911,0.025508262],"study_design_scores_gemma":[0.000039811704,0.00007015187,0.002440006,0.0000068322984,0.00003092993,0.00025335234,0.000060859325,0.95212686,0.0015465777,0.043222092,0.00018101788,0.00002138267],"about_ca_topic_score_codex":0.001067678,"about_ca_topic_score_gemma":0.0009836559,"teacher_disagreement_score":0.0011263668,"about_ca_system_score_codex":0.0004633143,"about_ca_system_score_gemma":0.00028085022,"threshold_uncertainty_score":0.0037680864},"labels":[],"label_agreement":null},{"id":"W2088196995","doi":"10.1186/1471-2202-14-s1-p61","title":"Neural correlations code for stimulus variance","year":2013,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Stimulus (psychology); Neural coding; Sensory system; Correlation; Vestibular system; Coding (social sciences); Perception; Computer science; Pattern recognition (psychology); Speech recognition; Mathematics; Neuroscience; Communication; Artificial intelligence; Psychology; Statistics; Cognitive psychology","score_opus":0.045467115984207045,"score_gpt":0.2809449997593551,"score_spread":0.23547788377514806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088196995","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62871677,0.0003373711,0.36438873,0.00025233807,0.00005896285,0.000045961104,0.000310629,0.0008172168,0.005072027],"genre_scores_gemma":[0.9868334,0.00011159412,0.012369437,0.00003433765,0.000017365797,0.00002521783,0.00008317524,0.000085143954,0.00044015126],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997118,0.00004673371,0.000015377684,0.00009463536,0.000095504285,0.000035964225],"domain_scores_gemma":[0.9988176,0.0006007536,0.00022642332,0.00019312842,0.00010357512,0.000058449215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003350076,0.00032024912,0.00034345486,0.0005004379,0.00019222072,0.0008809311,0.00047706216,0.00045103516,0.00093128445],"category_scores_gemma":[0.0037883339,0.00040243033,0.0004616406,0.00048931135,0.0006504203,0.000956766,0.0004904631,0.00067827414,0.0002160215],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026392637,0.00009384773,0.013122977,0.00022883744,0.00013515697,0.00041098334,0.0003647775,0.12041424,0.695714,0.10556978,0.0008959621,0.062785506],"study_design_scores_gemma":[0.000026162488,0.00013688934,0.035248198,0.00003437355,0.0000559003,0.00052109105,0.000043466163,0.8324119,0.063000605,0.06691544,0.001521039,0.0000849714],"about_ca_topic_score_codex":0.00066793483,"about_ca_topic_score_gemma":0.00053113437,"teacher_disagreement_score":0.00093128445,"about_ca_system_score_codex":0.0006066967,"about_ca_system_score_gemma":0.00048555693,"threshold_uncertainty_score":0.004401922},"labels":[],"label_agreement":null},{"id":"W2088974536","doi":"10.1007/s005000100153","title":"An fMUX architecture: data modularization and mixed-mode system modeling","year":2002,"lang":"en","type":"article","venue":"Soft Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; University of Alberta","funders":"","keywords":"Multiplexer; Modular programming; Computer science; Modular design; Theoretical computer science; Fuzzy logic; Process (computing); Scheme (mathematics); Mode (computer interface); Granularity; Variable (mathematics); Architecture; Artificial intelligence; Distributed computing; Computer architecture; Topology (electrical circuits); Multiplexing; Mathematics; Programming language; Human–computer interaction","score_opus":0.03676392619399044,"score_gpt":0.264909366984472,"score_spread":0.22814544079048155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088974536","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017572427,0.000070603346,0.9760199,0.00010151845,0.000042746142,0.000039340553,0.00008190006,0.0037673207,0.002304301],"genre_scores_gemma":[0.5963257,0.00009742572,0.39575627,0.00015934273,0.00005661371,0.00016225631,0.00028017577,0.0002619336,0.0069003636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999762,0.000070014816,0.000014591499,0.000060008166,0.00006142155,0.000032044638],"domain_scores_gemma":[0.99969137,0.000081692546,0.000017729297,0.000102269376,0.00009063987,0.000016283224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058231724,0.000626481,0.00038859103,0.00039693125,0.0004412761,0.0008375128,0.0013908698,0.00045997676,0.0064042974],"category_scores_gemma":[0.0010925807,0.0002803013,0.00042481412,0.00025302608,0.00030945716,0.001209069,0.000682051,0.00063205196,0.00093508244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011606356,0.0002582817,0.0030066294,0.00024221913,0.00019326354,0.00028912403,0.00031840743,0.26695895,0.046527695,0.061514713,0.011711222,0.6078189],"study_design_scores_gemma":[0.000027443606,0.000098546254,0.00020855379,0.00001202799,0.000028184233,0.00006704874,0.000017357494,0.9699228,0.015289776,0.010411751,0.0039030854,0.00001343776],"about_ca_topic_score_codex":0.002841561,"about_ca_topic_score_gemma":0.004188594,"teacher_disagreement_score":0.0064042974,"about_ca_system_score_codex":0.0005080279,"about_ca_system_score_gemma":0.00047620464,"threshold_uncertainty_score":0.021424532},"labels":[],"label_agreement":null},{"id":"W2089392845","doi":"10.1109/icnc.2011.6022163","title":"MVN_CNN and UBN_CNN for endocardial edge detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen Elizabeth II Health Sciences Centre; Dalhousie University","funders":"","keywords":"Cellular neural network; Convolutional neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Smoothness; Edge detection; Brightness; Boolean function; Image (mathematics); Enhanced Data Rates for GSM Evolution; Computer vision; Filter (signal processing); Artificial neural network; Mathematics; Image processing; Algorithm; Physics","score_opus":0.03913984350706438,"score_gpt":0.2289740077599235,"score_spread":0.18983416425285912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089392845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047088277,0.003228361,0.9344452,0.00039995761,0.00040651378,0.000102346916,0.0006551218,0.0033676405,0.01030667],"genre_scores_gemma":[0.41904798,0.0015524841,0.55524755,0.000437773,0.00011176926,0.00019135623,0.0014714403,0.00020291665,0.021736763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974173,0.000035883524,0.000017436923,0.00006959375,0.000090865484,0.000044533797],"domain_scores_gemma":[0.9996854,0.00006456029,0.00002851481,0.00005707094,0.00014550313,0.000019046456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005447232,0.00057525944,0.00034538424,0.0006125616,0.00019933665,0.00059519144,0.0008611181,0.0008302428,0.003128069],"category_scores_gemma":[0.0014456745,0.00033108288,0.00039587944,0.0005775746,0.00021983472,0.0010074697,0.0005381669,0.0004944074,0.00093776436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029766446,0.00007188416,0.0023386134,0.00029343006,0.00010920096,0.00021150048,0.000052623138,0.07723682,0.06481043,0.0120530315,0.009468337,0.8330564],"study_design_scores_gemma":[0.000011989593,0.000069151,0.002069002,0.00002846791,0.000043437612,0.0001826209,0.000019134079,0.94371146,0.039208587,0.003433964,0.011195745,0.000026395335],"about_ca_topic_score_codex":0.0054616337,"about_ca_topic_score_gemma":0.010221087,"teacher_disagreement_score":0.0054616337,"about_ca_system_score_codex":0.00066094525,"about_ca_system_score_gemma":0.00045129278,"threshold_uncertainty_score":0.010859668},"labels":[],"label_agreement":null},{"id":"W2089629718","doi":"10.1016/j.ijar.2006.01.003","title":"The design of self-organizing neural networks based on PNs and FPNs with the aid of genetic optimization and extended GMDH method","year":2006,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Economy; Canada Research Chairs","keywords":"Artificial neural network; Flexibility (engineering); Computer science; Genetic algorithm; Perceptron; Parametric statistics; Fuzzy logic; Group method of data handling; Process (computing); Artificial intelligence; Neuro-fuzzy; Machine learning; Fuzzy control system; Mathematics","score_opus":0.007360264778536558,"score_gpt":0.23500494700057242,"score_spread":0.22764468222203588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089629718","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022081107,0.00029755413,0.97429764,0.00009097661,0.00005648602,0.000049553822,0.000019418125,0.00016705367,0.0029402552],"genre_scores_gemma":[0.6674234,0.00036557813,0.32920712,0.000083525985,0.000045572775,0.00026728903,0.00005365777,0.000035392808,0.0025184401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986815,0.000035316207,0.000007708991,0.000033566106,0.00003570907,0.00001945993],"domain_scores_gemma":[0.99973685,0.00008614603,0.000032310432,0.0000136550025,0.000115076225,0.000015899892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059522747,0.0004059805,0.0005098678,0.00033378403,0.00033849376,0.0004751438,0.0010433557,0.00068051805,0.0006774696],"category_scores_gemma":[0.00089988625,0.00035612175,0.00040402386,0.00033154845,0.00043120465,0.00059776794,0.00038612634,0.0005328902,0.00014509697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044562705,0.000025040383,0.00037196625,0.00008395485,0.000032311964,0.000055977318,0.000056700013,0.92324114,0.005199435,0.011601957,0.0005418721,0.058745075],"study_design_scores_gemma":[0.0000060455213,0.00001838436,0.000065638815,0.0000049137598,0.000008752234,0.000015046864,0.000005252129,0.996617,0.0008361865,0.0019892126,0.00042972973,0.0000038048709],"about_ca_topic_score_codex":0.003338852,"about_ca_topic_score_gemma":0.0034726833,"teacher_disagreement_score":0.003338852,"about_ca_system_score_codex":0.00054949726,"about_ca_system_score_gemma":0.0008160453,"threshold_uncertainty_score":0.006638825},"labels":[],"label_agreement":null},{"id":"W2091212769","doi":"10.1142/s0129065700000235","title":"COOPERATIVE COEVOLUTION OF NEURAL REPRESENTATIONS","year":2000,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Pattern recognition (psychology); Encoding (memory); Computer science; Artificial intelligence; Chromosome; Artificial neural network; Perceptron; Fitness function; Feature (linguistics); Set (abstract data type); Multilayer perceptron; Detector; Task (project management); Feature extraction; Genetic algorithm; Representation (politics); Machine learning; Biology; Gene","score_opus":0.020200532606702716,"score_gpt":0.29757925325959694,"score_spread":0.2773787206528942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091212769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23277958,0.0004669716,0.7464909,0.00033507185,0.00007191656,0.00014315895,0.000047001624,0.0005176309,0.019147757],"genre_scores_gemma":[0.8890771,0.000248857,0.102609135,0.00012717162,0.000017019445,0.00021967522,0.00007710355,0.00006680104,0.007557057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994153,0.00018158823,0.000027523758,0.00012246432,0.00016491514,0.00008822828],"domain_scores_gemma":[0.99882346,0.00050266663,0.00008700367,0.0002497855,0.00024462785,0.00009230102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011424675,0.0006401016,0.00082909525,0.00078593474,0.00053983944,0.0015023871,0.0016754247,0.0013146363,0.0019358211],"category_scores_gemma":[0.0055852938,0.00041900692,0.0006640504,0.0008244696,0.0010628307,0.0012948674,0.0017843549,0.00089304644,0.00043408974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013860677,0.00014128705,0.002842707,0.000115256094,0.00020904103,0.00051184883,0.00067769614,0.75846756,0.026297836,0.09123618,0.0016957156,0.11766626],"study_design_scores_gemma":[0.000023179959,0.00007666647,0.0004098897,0.000011883415,0.000031746276,0.00011453991,0.00007337918,0.97341806,0.0032353778,0.020145359,0.0024407394,0.000019200408],"about_ca_topic_score_codex":0.0021978396,"about_ca_topic_score_gemma":0.0017460488,"teacher_disagreement_score":0.0021978396,"about_ca_system_score_codex":0.0007547595,"about_ca_system_score_gemma":0.00061907084,"threshold_uncertainty_score":0.0064759254},"labels":[],"label_agreement":null},{"id":"W2091641666","doi":"10.1167/13.9.1090","title":"The time-course of rapid stimulus-specific perceptual learning","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Stimulus (psychology); Perception; Perceptual learning; Psychology; Audiology; Cognitive psychology; Neuroscience; Medicine","score_opus":0.01096599932817837,"score_gpt":0.2595053601107972,"score_spread":0.24853936078261885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091641666","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914009,0.000970337,0.005156627,0.000088256245,0.000035588528,0.000116900585,0.00014006053,0.000114833085,0.0019765445],"genre_scores_gemma":[0.9951007,0.0003582722,0.0026087998,0.000058072263,0.000019692836,0.00014012384,0.0002438893,0.000028919829,0.0014415359],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99935955,0.00007229955,0.000067586894,0.00017685127,0.0002229212,0.00010087383],"domain_scores_gemma":[0.9971283,0.00090808125,0.0006555395,0.0005060904,0.00038699672,0.0004149998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074000726,0.00033002495,0.00075750734,0.00023318142,0.0001708995,0.00052784535,0.0005216206,0.0004898466,0.0034594678],"category_scores_gemma":[0.0037751787,0.00040833867,0.0003710367,0.00014635897,0.00034340835,0.00060263206,0.0007873208,0.0015522187,0.0007638522],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013454554,0.0012574365,0.008219064,0.00036878206,0.00009780992,0.00007997346,0.00013959662,0.0011305467,0.9218068,0.00029405122,0.00025522072,0.06500527],"study_design_scores_gemma":[0.00024986413,0.014235408,0.6593949,0.000102652055,0.00015269229,0.0005688723,0.00012490318,0.011442833,0.3082657,0.0015748434,0.0038005563,0.00008681892],"about_ca_topic_score_codex":0.0007572063,"about_ca_topic_score_gemma":0.0014318867,"teacher_disagreement_score":0.0034594678,"about_ca_system_score_codex":0.00034590156,"about_ca_system_score_gemma":0.0004603737,"threshold_uncertainty_score":0.011573017},"labels":[],"label_agreement":null},{"id":"W2092309188","doi":"10.4028/www.scientific.net/amr.314-316.2569","title":"Interval Analysis Method of Fault Tree Based on Convex Model","year":2011,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Association of Emergency Physicians","funders":"","keywords":"Interval (graph theory); Fault tree analysis; Monte Carlo method; Regular polygon; Tree (set theory); Mathematics; Computer science; Interval arithmetic; Algorithm; Applied mathematics; Statistics; Combinatorics; Engineering; Reliability engineering; Geometry","score_opus":0.14511370643705293,"score_gpt":0.4230665167503539,"score_spread":0.277952810313301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092309188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00089864567,0.00014736071,0.99832684,0.00003574223,0.000018198052,0.00000901596,0.00003148314,0.00006785012,0.0004648808],"genre_scores_gemma":[0.31787315,0.001734612,0.67437416,0.0001314775,0.0002973461,0.00029534698,0.0007454979,0.00029165455,0.0042567276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986203,0.00040493975,0.000085852276,0.0003120883,0.00047964032,0.00009725035],"domain_scores_gemma":[0.99795234,0.0010090491,0.00022704524,0.00015622965,0.00056980207,0.00008535335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015512384,0.00085434556,0.001225673,0.002169763,0.0005374708,0.0017035065,0.0016279256,0.00067437044,0.003833811],"category_scores_gemma":[0.005946611,0.00037044077,0.0012803455,0.0017936912,0.00073470135,0.0027871393,0.0011355787,0.001895202,0.0006793312],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119774144,0.00004393589,0.0013724755,0.0003046357,0.00013261227,0.00022271062,0.0002821345,0.45609486,0.005278945,0.2828387,0.0055955388,0.24771369],"study_design_scores_gemma":[0.000008628183,0.000029197958,0.00020487423,0.000015444799,0.00002186413,0.000094186296,0.000019106023,0.9428131,0.00087961875,0.05334258,0.00255018,0.000021190148],"about_ca_topic_score_codex":0.0028848655,"about_ca_topic_score_gemma":0.0012048745,"teacher_disagreement_score":0.003833811,"about_ca_system_score_codex":0.0010918457,"about_ca_system_score_gemma":0.0010888028,"threshold_uncertainty_score":0.01282537},"labels":[],"label_agreement":null},{"id":"W2092405628","doi":"10.1016/s0952-1976(02)00024-6","title":"Local learning framework for handwritten character recognition","year":2002,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Character (mathematics); Character recognition; Artificial intelligence; Intelligent character recognition; Natural language processing; Speech recognition; Pattern recognition (psychology); Image (mathematics)","score_opus":0.04074951893294332,"score_gpt":0.2628596708928362,"score_spread":0.22211015195989287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092405628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037799107,0.0003344975,0.9937204,0.000050285285,0.000022599523,0.000021004249,0.00007823191,0.0012770224,0.00071608677],"genre_scores_gemma":[0.25986895,0.00091102795,0.7159571,0.00022024696,0.00016862457,0.00028432327,0.000919938,0.0005321921,0.021137508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966085,0.00007332404,0.000015712678,0.00009890852,0.00010680363,0.00004447379],"domain_scores_gemma":[0.999653,0.00008881968,0.000032355394,0.00008691771,0.000109007895,0.000029842739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000504808,0.0005137716,0.00121813,0.0008067699,0.00045310234,0.001001047,0.0018719424,0.0008734963,0.005048635],"category_scores_gemma":[0.00087236793,0.0002983186,0.00073738006,0.00097608997,0.00052049,0.0011899768,0.00080693676,0.0010049294,0.002179741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028339107,0.00019423901,0.00044596026,0.000225616,0.00010198533,0.00013619527,0.00007450698,0.289485,0.03194846,0.043226637,0.007919696,0.62595826],"study_design_scores_gemma":[0.000010888091,0.00003849524,0.00012694948,0.00000578368,0.000017608245,0.000032003893,0.0000094551915,0.983988,0.0040706783,0.009546528,0.0021429844,0.00001067548],"about_ca_topic_score_codex":0.007825561,"about_ca_topic_score_gemma":0.009988185,"teacher_disagreement_score":0.007825561,"about_ca_system_score_codex":0.00081231166,"about_ca_system_score_gemma":0.0010362956,"threshold_uncertainty_score":0.016889334},"labels":[],"label_agreement":null},{"id":"W2092520467","doi":"10.1007/s00521-015-1875-2","title":"Artificial neural network training utilizing the smooth variable structure filter estimation strategy","year":2015,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ford Motor Company (Canada); McMaster University","funders":"","keywords":"Benchmark (surveying); Computer science; Artificial neural network; Nonlinear system; Filter (signal processing); Multilayer perceptron; Control theory (sociology); Variable (mathematics); Kalman filter; Perceptron; Extended Kalman filter; State variable; Algorithm; Artificial intelligence; Mathematics; Control (management)","score_opus":0.06201852723807622,"score_gpt":0.28983335797010773,"score_spread":0.22781483073203151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092520467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013591982,0.00011311868,0.9846377,0.00007352404,0.000042769785,0.000023546376,0.000013547167,0.00023450988,0.001269309],"genre_scores_gemma":[0.5259639,0.00020338321,0.46825647,0.00011247987,0.000057015564,0.0001259548,0.000110036504,0.000071425406,0.0050993063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998229,0.00005157249,0.000013069846,0.000038522052,0.000053217595,0.000020626527],"domain_scores_gemma":[0.99947304,0.00028540185,0.000038080878,0.000044605633,0.00014575497,0.0000131304505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007390312,0.0003839471,0.0004986792,0.00034841854,0.00030281112,0.00046445627,0.0005973157,0.0008960876,0.0017033474],"category_scores_gemma":[0.0023002645,0.00028806104,0.00036665544,0.00042082646,0.00027639972,0.00066351454,0.00035368316,0.0008402922,0.00034353082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001517761,0.000092414426,0.0012587415,0.000097361226,0.000092411516,0.00008893945,0.00008120457,0.67266417,0.01849318,0.019070541,0.0017434671,0.2861657],"study_design_scores_gemma":[0.000004309475,0.00001592853,0.00012138855,0.0000027354833,0.000005464921,0.000007716467,0.0000017580039,0.99778795,0.0009826432,0.0008444836,0.0002224694,0.0000030986448],"about_ca_topic_score_codex":0.0047107083,"about_ca_topic_score_gemma":0.0076218806,"teacher_disagreement_score":0.0047107083,"about_ca_system_score_codex":0.00028512484,"about_ca_system_score_gemma":0.0007646497,"threshold_uncertainty_score":0.009366572},"labels":[],"label_agreement":null},{"id":"W2092908668","doi":"10.1080/10255810305034","title":"Geometric Representations for High-Dimensional Data Using a Spherical SOFM","year":2003,"lang":"en","type":"article","venue":"International Journal of Smart Engineering System Design","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Visualization; Scientific visualization; Data visualization; Artificial intelligence; Theoretical computer science; Topology (electrical circuits); Pattern recognition (psychology); Data mining; Mathematics; Combinatorics","score_opus":0.06223028648870047,"score_gpt":0.29147544859369373,"score_spread":0.22924516210499327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092908668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005619685,0.000056879428,0.9932493,0.00007024167,0.000014965573,0.000015759577,0.000083285595,0.00022175988,0.0006681797],"genre_scores_gemma":[0.23771527,0.0003371209,0.75944835,0.000075766715,0.00005517379,0.0001600928,0.00068472436,0.00012867217,0.0013949088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933606,0.00018267361,0.000060004855,0.00009975533,0.00028034468,0.000041082927],"domain_scores_gemma":[0.999129,0.00029220758,0.00010010964,0.00020492445,0.0002239188,0.000049855706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008156473,0.00044986894,0.0005791445,0.0019580706,0.00038261584,0.0020038986,0.0008848352,0.0004939568,0.0018261098],"category_scores_gemma":[0.003610741,0.0002403535,0.0011564449,0.0019421458,0.0011152974,0.0019972564,0.0016997983,0.00073457987,0.0007615054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014808362,0.000040693893,0.0018191506,0.00023838648,0.000079862526,0.0003400673,0.00089045055,0.35130155,0.015338524,0.34288493,0.0052818493,0.2816364],"study_design_scores_gemma":[0.000008007241,0.000041829935,0.0005593283,0.000017125583,0.000008687079,0.00019160612,0.00013790792,0.9196784,0.0018207069,0.07168415,0.005825906,0.000026299766],"about_ca_topic_score_codex":0.0021427271,"about_ca_topic_score_gemma":0.0016493441,"teacher_disagreement_score":0.0021427271,"about_ca_system_score_codex":0.00052118825,"about_ca_system_score_gemma":0.0006469467,"threshold_uncertainty_score":0.0061089396},"labels":[],"label_agreement":null},{"id":"W2093275257","doi":"10.5430/air.v1n2p11","title":"Interpretable support vector regression","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Social Fund; European Commission","keywords":"Support vector machine; Interpretability; Data mining; Fuzzy rule; Kernel (algebra); Identification (biology); Computer science; Artificial intelligence; Reduction (mathematics); Least squares support vector machine; Fuzzy logic; Kernel method; Relevance vector machine; Mathematics; Pattern recognition (psychology); Machine learning; Fuzzy set","score_opus":0.24615392452537876,"score_gpt":0.4550938221825369,"score_spread":0.20893989765715815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093275257","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007034077,0.00020274761,0.9905748,0.0001071044,0.00004579809,0.000023275106,0.00005882992,0.0004194177,0.001534006],"genre_scores_gemma":[0.43049327,0.0009671359,0.5602641,0.00019248537,0.000198935,0.00018278777,0.00084179535,0.00023952518,0.00661992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986557,0.00047947327,0.00008398157,0.00018768969,0.0005335743,0.000059648253],"domain_scores_gemma":[0.99820876,0.0008778168,0.00020017714,0.00034604533,0.0003431442,0.000024035131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014478624,0.0010114623,0.0009850455,0.0009101219,0.0001721702,0.0013933511,0.0009930033,0.0011757301,0.003280129],"category_scores_gemma":[0.0064999782,0.00036800627,0.00092394225,0.00068135775,0.0004675582,0.0015457409,0.0009119922,0.0016532667,0.0011814062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018395751,0.00013751579,0.0012125077,0.0004263223,0.00015795007,0.00060334284,0.0003104338,0.48777983,0.028839713,0.08413742,0.0033209235,0.39289013],"study_design_scores_gemma":[0.0000104382225,0.00006224153,0.00030073008,0.000022438513,0.000015780872,0.00009677832,0.000030593685,0.97232664,0.0044789123,0.019317387,0.0033228074,0.000015257991],"about_ca_topic_score_codex":0.0005262385,"about_ca_topic_score_gemma":0.0004727357,"teacher_disagreement_score":0.003280129,"about_ca_system_score_codex":0.0002852706,"about_ca_system_score_gemma":0.00038305635,"threshold_uncertainty_score":0.0109731555},"labels":[],"label_agreement":null},{"id":"W2093541146","doi":"10.3390/e15125439","title":"Consistency and Generalization Bounds for Maximum Entropy Density Estimation","year":2013,"lang":"en","type":"article","venue":"Entropy","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; University of Alberta","keywords":"Mathematics; Principle of maximum entropy; Bounded function; Entropy (arrow of time); Consistency (knowledge bases); Applied mathematics; Generalization; Maximum entropy spectral estimation; Maximum entropy probability distribution; Statistical physics; Statistics; Discrete mathematics; Mathematical analysis; Thermodynamics; Physics","score_opus":0.011193156991453719,"score_gpt":0.23445279662714663,"score_spread":0.22325963963569292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093541146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025904588,0.0018320197,0.96691227,0.0014439052,0.000099388075,0.00006292609,0.00020273581,0.00022556436,0.0033164907],"genre_scores_gemma":[0.80811685,0.0031764954,0.18111502,0.001218038,0.0011856256,0.00059669727,0.0012192549,0.000628583,0.0027434232],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98608357,0.0068046334,0.00079577486,0.0023933554,0.003150543,0.0007720704],"domain_scores_gemma":[0.81497204,0.1530537,0.008659138,0.012303614,0.009496749,0.0015147522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032210343,0.0018257665,0.003287744,0.003744782,0.0015593056,0.003269826,0.0049392716,0.0034418667,0.0026162683],"category_scores_gemma":[0.18436423,0.0011635215,0.0028195048,0.0028903864,0.00769533,0.009615254,0.0077961413,0.006978237,0.0005173844],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029088263,0.00009715761,0.0070526483,0.0005632769,0.0003941824,0.00046638143,0.000599387,0.29351053,0.0039045024,0.6582157,0.002115649,0.032789666],"study_design_scores_gemma":[0.000018070712,0.00006498162,0.0015215818,0.00012599297,0.000042375617,0.00017989201,0.000047682963,0.6121587,0.001987357,0.38312384,0.0006739806,0.00005549766],"about_ca_topic_score_codex":0.0020006124,"about_ca_topic_score_gemma":0.0009574695,"teacher_disagreement_score":0.032210343,"about_ca_system_score_codex":0.0031086314,"about_ca_system_score_gemma":0.0016571996,"threshold_uncertainty_score":0.17034656},"labels":[],"label_agreement":null},{"id":"W2094296396","doi":"10.1142/s0219876207001266","title":"A LOCAL-PATCH BASED MULTI-STAGE ARTIFICIAL-NEURAL-NETWORK TRAINING PROCEDURE AND ITS APPLICATION TO MATERIAL CHARACTERIZATION","year":2007,"lang":"en","type":"article","venue":"International Journal of Computational Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Backpropagation; Computer science; Training (meteorology); Generalization; Training set; Stage (stratigraphy); Finite element method; Artificial intelligence; Set (abstract data type); Multi stage; Machine learning; Algorithm; Mathematics; Engineering; Industrial engineering; Structural engineering","score_opus":0.06572859403367784,"score_gpt":0.3967993802493886,"score_spread":0.33107078621571073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094296396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008701657,0.00005126607,0.99022865,0.000030894924,0.000012523146,0.000040591793,0.000014477839,0.00043882892,0.00048107],"genre_scores_gemma":[0.2269431,0.00009475446,0.77079606,0.000042288364,0.000018786854,0.00014040912,0.000081903745,0.00010672955,0.0017760153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996358,0.000092186616,0.00001950475,0.000113823655,0.00011615223,0.000022377497],"domain_scores_gemma":[0.99910563,0.0003675975,0.00009274363,0.00017627364,0.00023282239,0.000024860357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000783947,0.00059116015,0.00071960635,0.00050869514,0.00034360614,0.00035499397,0.0012511112,0.00096939906,0.0020207998],"category_scores_gemma":[0.0017125362,0.00039111302,0.00054738537,0.00046607904,0.0004893624,0.0008180474,0.00048020453,0.00079365197,0.0005439864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024804263,0.00015715013,0.002360188,0.0002618479,0.00007149134,0.0002503872,0.00018891587,0.4096358,0.11225281,0.0041173296,0.0012638724,0.46919218],"study_design_scores_gemma":[0.000005777811,0.00007288193,0.00062806945,0.000006629741,0.00001086052,0.00009349153,0.000008506345,0.977985,0.019845132,0.00048963405,0.00083777454,0.000016211847],"about_ca_topic_score_codex":0.0013588804,"about_ca_topic_score_gemma":0.0023980937,"teacher_disagreement_score":0.0020207998,"about_ca_system_score_codex":0.0003113897,"about_ca_system_score_gemma":0.00038262698,"threshold_uncertainty_score":0.006760299},"labels":[],"label_agreement":null},{"id":"W2095271244","doi":"10.1016/j.conengprac.2009.05.006","title":"An adaptive system for modelling and simulation of electrical arc furnaces","year":2009,"lang":"en","type":"article","venue":"Control Engineering Practice","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Ontario Centres of Excellence","keywords":"Arc (geometry); Electric arc; Computer science; Engineering; Mechanical engineering; Physics; Electrode","score_opus":0.015339244398665842,"score_gpt":0.25730479626778824,"score_spread":0.24196555186912239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095271244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11878466,0.0002632194,0.8720439,0.00023002258,0.00010425576,0.000104135135,0.0001512025,0.0016057108,0.006712879],"genre_scores_gemma":[0.9492231,0.00011671577,0.048146438,0.00002975882,0.000015892314,0.00012968908,0.00008681632,0.000041592288,0.0022100224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998424,0.00005450167,0.000011502687,0.00003405518,0.00004456585,0.000012869726],"domain_scores_gemma":[0.99970263,0.00016056471,0.00003092778,0.000029708823,0.00006235452,0.000013780541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003256888,0.0004156049,0.00048698898,0.00029876432,0.0003680273,0.0005169035,0.00082544313,0.00108071,0.0019488476],"category_scores_gemma":[0.0014231998,0.000285294,0.0003490938,0.00028951038,0.0003897376,0.00046577636,0.00049621257,0.0006945762,0.00022752446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002548459,0.000011213332,0.00017865353,0.000016741918,0.000007932145,0.000020231058,0.000019864652,0.99215925,0.0013013321,0.0012082149,0.00008000425,0.0049711717],"study_design_scores_gemma":[0.00000330741,0.000006729373,0.00003921772,9.280288e-7,0.0000019292224,0.000002301202,0.0000013177138,0.99938273,0.00022585291,0.0001937882,0.00014062834,0.0000012776824],"about_ca_topic_score_codex":0.009738136,"about_ca_topic_score_gemma":0.0060375985,"teacher_disagreement_score":0.009738136,"about_ca_system_score_codex":0.0004927431,"about_ca_system_score_gemma":0.0005855157,"threshold_uncertainty_score":0.019362926},"labels":[],"label_agreement":null},{"id":"W2095572051","doi":"10.1167/4.8.302","title":"Isolating the top-down component of perceptual learning","year":2004,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Perception; Perceptual learning; Session (web analytics); Psychology; Logarithm; Mathematics; Statistics; Cognitive psychology; Audiology; Computer science; Artificial intelligence; Medicine","score_opus":0.01362246643868244,"score_gpt":0.2760010608236343,"score_spread":0.26237859438495187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095572051","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899211,0.00026905607,0.0081277145,0.000042806158,0.00002097765,0.00006236199,0.00007890253,0.00005750242,0.0014195794],"genre_scores_gemma":[0.99210536,0.00011648213,0.0063168313,0.000060590213,0.000027473207,0.00008771963,0.000157536,0.000050905623,0.0010772302],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910444,0.00010911237,0.000050267543,0.00035188883,0.00025894115,0.00012530488],"domain_scores_gemma":[0.99744415,0.0010453631,0.0003321796,0.000644807,0.00024329283,0.00029021117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007274772,0.00058651686,0.0011897893,0.00027195437,0.0003009969,0.0006401788,0.00044929207,0.00051257265,0.004618181],"category_scores_gemma":[0.0025964454,0.00031233905,0.00033278632,0.00019854355,0.00082714186,0.00083731697,0.0011629503,0.0011445666,0.00048884045],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018025942,0.0007476228,0.0081567485,0.00025127202,0.000042594424,0.00006058532,0.00032669617,0.00027266293,0.95170957,0.00020744507,0.00009373898,0.036328502],"study_design_scores_gemma":[0.00029854488,0.010081666,0.46197572,0.0000243064,0.00023547151,0.00054636155,0.0002158215,0.004928214,0.51660126,0.0028316493,0.0021731942,0.00008775287],"about_ca_topic_score_codex":0.0008200001,"about_ca_topic_score_gemma":0.00088697125,"teacher_disagreement_score":0.004618181,"about_ca_system_score_codex":0.00024176746,"about_ca_system_score_gemma":0.00036193267,"threshold_uncertainty_score":0.0154492855},"labels":[],"label_agreement":null},{"id":"W2095797787","doi":"10.1109/mwscas.1997.662315","title":"Quantization noise improvement in a distributed-neuron architecture","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"CMC Microsystems","keywords":"Quantization (signal processing); Scaling; Computer science; Sigmoid function; Artificial neural network; Noise shaping; Noise (video); Architecture; Mathematics; Algorithm; Artificial intelligence","score_opus":0.008681973596040128,"score_gpt":0.23299255124029825,"score_spread":0.22431057764425813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095797787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2907618,0.0011781673,0.69513047,0.00059998804,0.00019904549,0.000026434818,0.000054534557,0.0011903704,0.010859228],"genre_scores_gemma":[0.94596314,0.00016110872,0.05123945,0.00008205003,0.000031090745,0.000011286264,0.00002626774,0.000026022144,0.0024595472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997316,0.000041599797,0.000017450271,0.00006836939,0.00011649141,0.000024530962],"domain_scores_gemma":[0.9992939,0.00026806424,0.000048093087,0.000111035646,0.00024531604,0.000033506927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044635005,0.00023127678,0.00036399948,0.00018716621,0.00022528484,0.00048909965,0.0006390084,0.0005762604,0.0020140288],"category_scores_gemma":[0.0022480194,0.00015812954,0.00016952823,0.00026948148,0.00035880058,0.000812942,0.0004918088,0.0004239686,0.00030827956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008038692,0.00019807681,0.0016792783,0.00026557667,0.00006719054,0.0004465399,0.00018936074,0.39012462,0.26676536,0.059986625,0.0018188854,0.2776545],"study_design_scores_gemma":[0.000052482323,0.00021600403,0.0007603182,0.000014972758,0.00002873905,0.00015455658,0.000017491613,0.9507493,0.031018093,0.015348038,0.0016250522,0.000014925515],"about_ca_topic_score_codex":0.00073260145,"about_ca_topic_score_gemma":0.0013198911,"teacher_disagreement_score":0.0020140288,"about_ca_system_score_codex":0.00037735075,"about_ca_system_score_gemma":0.0002833312,"threshold_uncertainty_score":0.0067376494},"labels":[],"label_agreement":null},{"id":"W2096163154","doi":"10.1109/iscas.1997.608912","title":"Digital implementation of neural networks with quantized neurons","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Very-large-scale integration; Artificial neural network; Feedforward neural network; Feed forward; Quantization (signal processing); Computer architecture; Electronic engineering; Artificial intelligence; Control engineering; Embedded system; Algorithm; Engineering","score_opus":0.019705459307106068,"score_gpt":0.2518198839990128,"score_spread":0.2321144246919067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096163154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034557562,0.00068557647,0.9540358,0.0002788413,0.00029929518,0.000046803532,0.000096240256,0.0008505181,0.009149388],"genre_scores_gemma":[0.8928784,0.00048546825,0.100305125,0.00013490123,0.000048438676,0.00008689071,0.00007552234,0.000022599817,0.005962592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998747,0.000027232789,0.000009751372,0.000022070786,0.0000487823,0.000017503107],"domain_scores_gemma":[0.9998894,0.000034407993,0.0000129736245,0.000020153819,0.000034464672,0.000008616423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017583994,0.00024712613,0.00028408994,0.00014273055,0.00023929891,0.0005664366,0.0007732615,0.0005341038,0.0021513237],"category_scores_gemma":[0.0005027012,0.00014792278,0.00026536308,0.00017313522,0.0003228056,0.0008338538,0.00031841244,0.0005248497,0.00026883272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002251147,0.000104393155,0.0006941213,0.00029228683,0.000075591684,0.00032257434,0.00012757527,0.70743144,0.055048265,0.10100813,0.0022100525,0.13246049],"study_design_scores_gemma":[0.000019158328,0.000059635084,0.00012190944,0.000011262685,0.0000142774925,0.00004702158,0.000008267128,0.9828237,0.005592307,0.008667426,0.0026252442,0.000009693165],"about_ca_topic_score_codex":0.0018964886,"about_ca_topic_score_gemma":0.0023139468,"teacher_disagreement_score":0.0021513237,"about_ca_system_score_codex":0.00042866482,"about_ca_system_score_gemma":0.0003573004,"threshold_uncertainty_score":0.0071968436},"labels":[],"label_agreement":null},{"id":"W2096356503","doi":"10.1007/s00500-006-0052-z","title":"Image Compression and Reconstruction Using pi t -Sigma Neural Networks","year":2006,"lang":"en","type":"article","venue":"Soft Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Feedforward neural network; Perceptron; Algorithm; Computer science; Convergence (economics); Multilayer perceptron; Mean squared error; Conjugate gradient method; Feed forward; Backpropagation; Layer (electronics); Lossy compression; Image compression; Sigma; Pattern recognition (psychology); Artificial intelligence; Mathematics; Image (mathematics); Image processing; Physics; Statistics","score_opus":0.01259578683317587,"score_gpt":0.24156595702320433,"score_spread":0.22897017019002847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096356503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05603025,0.00080796785,0.9375467,0.0002920255,0.00014263194,0.000052619736,0.00004854316,0.000697769,0.004381609],"genre_scores_gemma":[0.6020016,0.0010666621,0.38714376,0.00020902812,0.000076300916,0.000057072615,0.00011137475,0.00007490415,0.00925926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998586,0.000020409358,0.00001019518,0.000022016404,0.00007651499,0.000012146664],"domain_scores_gemma":[0.99974364,0.000090684814,0.00002792696,0.000049051454,0.00007660549,0.000012041228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029121424,0.00033345644,0.00030322393,0.0004386601,0.0002290422,0.0005816386,0.00037879604,0.0005988305,0.002854427],"category_scores_gemma":[0.0009903985,0.00019960711,0.00028817973,0.0006671123,0.00034207848,0.00068538164,0.0003870621,0.00065485516,0.0004710906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064701814,0.000113586735,0.0012322818,0.00020789243,0.00010372603,0.00024861068,0.000079434074,0.15446809,0.11033685,0.015663423,0.0020382223,0.7148608],"study_design_scores_gemma":[0.0000147412375,0.0000853432,0.0004147608,0.000014857689,0.00001962772,0.0001634727,0.00001585226,0.94746023,0.048014298,0.0027209723,0.0010641608,0.000011634295],"about_ca_topic_score_codex":0.0014883024,"about_ca_topic_score_gemma":0.0021422338,"teacher_disagreement_score":0.002854427,"about_ca_system_score_codex":0.00025723517,"about_ca_system_score_gemma":0.00031914032,"threshold_uncertainty_score":0.009549022},"labels":[],"label_agreement":null},{"id":"W2096575660","doi":"10.1109/tsp.2007.894379","title":"Cooperative Recurrent Neural Networks for the Constrained $L_{1}$ Estimator","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Estimator; Artificial neural network; Computer science; Convergence (economics); Artificial intelligence; Algorithm; Mathematics; Statistics","score_opus":0.028859370346108748,"score_gpt":0.2921786131692397,"score_spread":0.26331924282313096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096575660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005699498,0.00021073036,0.9925638,0.000095621544,0.000021828986,0.000021983415,0.00001946902,0.00022843346,0.001138592],"genre_scores_gemma":[0.4529314,0.0004697731,0.5365472,0.00030749236,0.000113403876,0.00026680378,0.00026177874,0.00017081444,0.008931436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891365,0.00033903867,0.00006545726,0.00032016393,0.0002835189,0.00007815626],"domain_scores_gemma":[0.9987017,0.0006101765,0.00016467986,0.00012760583,0.00035541825,0.000040488336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002142775,0.0009337675,0.0009261695,0.00048058984,0.0003894139,0.00083873316,0.0018691951,0.0011741711,0.0023835578],"category_scores_gemma":[0.005449226,0.00048673677,0.00075226533,0.0005589994,0.0006217642,0.001514485,0.0011879366,0.0012657342,0.00067445333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023627897,0.00011207367,0.0011199573,0.00019740632,0.0001549955,0.00013243062,0.000182843,0.5884729,0.009963724,0.056221176,0.0040479894,0.33915818],"study_design_scores_gemma":[0.000007677871,0.00002353382,0.00009436098,0.0000061397723,0.000013342592,0.000014350227,0.0000051435613,0.9945163,0.0010784263,0.0035277964,0.0007049791,0.000008008644],"about_ca_topic_score_codex":0.005997623,"about_ca_topic_score_gemma":0.0071097156,"teacher_disagreement_score":0.005997623,"about_ca_system_score_codex":0.0007810925,"about_ca_system_score_gemma":0.0013098483,"threshold_uncertainty_score":0.011925459},"labels":[],"label_agreement":null},{"id":"W2097129343","doi":"10.1109/isuma.2003.1236154","title":"Development of quadratic neural unit with applications to pattern classification","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Quadratic equation; Computer science; Nervous system network models; Computation; Time delay neural network; Matrix (chemical analysis); Artificial intelligence; Algorithm; Unit (ring theory); Stochastic neural network; Types of artificial neural networks; Mathematics","score_opus":0.047440922397590166,"score_gpt":0.27654608084544746,"score_spread":0.22910515844785728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097129343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008088511,0.000768417,0.98560774,0.00021855421,0.000118627155,0.000047931666,0.000027850067,0.00033914705,0.004783185],"genre_scores_gemma":[0.32339868,0.001198059,0.6664151,0.00022650632,0.00014984988,0.00015062989,0.00012584303,0.00009466952,0.008240687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973387,0.000060423557,0.000024721367,0.00005344589,0.00010588293,0.000021715605],"domain_scores_gemma":[0.99957436,0.000117784686,0.000024473062,0.000034548044,0.00022546298,0.00002337415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006390757,0.00036683655,0.00044538375,0.00044968995,0.00022969651,0.0004918899,0.00083017396,0.00058105565,0.0024772165],"category_scores_gemma":[0.0014359587,0.00024474028,0.00031364275,0.00076779374,0.00047810146,0.00088149856,0.00053336535,0.0006365442,0.00069988286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016594357,0.00010113278,0.0007742884,0.00055764074,0.00006230604,0.00032979672,0.00019065992,0.21911664,0.06509085,0.17152175,0.0046831225,0.53740585],"study_design_scores_gemma":[0.000007298423,0.000100634854,0.00015786491,0.000013914872,0.000007784304,0.0001316309,0.000008372824,0.9663408,0.010737493,0.01468727,0.007795962,0.000011072109],"about_ca_topic_score_codex":0.001032445,"about_ca_topic_score_gemma":0.0010121507,"teacher_disagreement_score":0.0024772165,"about_ca_system_score_codex":0.0003892794,"about_ca_system_score_gemma":0.0004442984,"threshold_uncertainty_score":0.008287132},"labels":[],"label_agreement":null},{"id":"W2097719214","doi":"10.1109/icnn.1997.616193","title":"On viewing the transform performed by a hidden layer in a feedforward ANN, as a complex Mobius mapping","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Feed forward; Layer (electronics); Artificial intelligence; Process (computing); Feedforward neural network; Artificial neural network; Conformal map; Deep learning; Pattern recognition (psychology); Mathematics; Engineering; Control engineering; Geometry","score_opus":0.07504053225354533,"score_gpt":0.2741181599911632,"score_spread":0.19907762773761783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097719214","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03062882,0.0003570926,0.9547564,0.00048538684,0.00015212908,0.000037228892,0.00004651934,0.00013744268,0.013398913],"genre_scores_gemma":[0.6038277,0.0028567496,0.36602846,0.0005648125,0.0003596788,0.00021522919,0.00014959493,0.00017795103,0.02581979],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99975747,0.00008247883,0.0000120149425,0.0000556514,0.00007292503,0.000019393938],"domain_scores_gemma":[0.999508,0.00022694918,0.00007099789,0.000085469605,0.000082070306,0.000026414247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010426576,0.0010490722,0.0003507028,0.00049814896,0.00035520777,0.0012572795,0.00070474774,0.0010735829,0.0031958309],"category_scores_gemma":[0.002737114,0.00024571034,0.0005335761,0.00060431816,0.0027075924,0.0025349932,0.0008323241,0.001373866,0.0008459258],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014114886,0.000060425613,0.0013334025,0.00026724927,0.000053582397,0.0002548333,0.00048401637,0.18721284,0.021599665,0.7202466,0.0010445585,0.06730164],"study_design_scores_gemma":[0.000018551806,0.0001705184,0.0007755384,0.0000537733,0.000024245315,0.00018869735,0.00009586282,0.5605726,0.00559367,0.42909124,0.0033720345,0.000043176136],"about_ca_topic_score_codex":0.0009564848,"about_ca_topic_score_gemma":0.0008418218,"teacher_disagreement_score":0.0031958309,"about_ca_system_score_codex":0.00047027136,"about_ca_system_score_gemma":0.0005824016,"threshold_uncertainty_score":0.010691106},"labels":[],"label_agreement":null},{"id":"W2097793565","doi":"10.1109/icassp.1995.479701","title":"A high-order temporal neural network for word recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Time delay neural network; Speech recognition; Feature (linguistics); Layer (electronics); Word (group theory); Pattern recognition (psychology); Speech processing; Recurrent neural network","score_opus":0.043087918188621496,"score_gpt":0.24395505600492365,"score_spread":0.20086713781630217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097793565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015678423,0.000942694,0.9787733,0.00016148709,0.000130833,0.000047149355,0.00017185244,0.0010443771,0.0030498477],"genre_scores_gemma":[0.4142788,0.0011654496,0.56982195,0.00022797145,0.00014869675,0.00022180127,0.0007904308,0.00012779342,0.013217149],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982953,0.00003654844,0.000012192442,0.00005608641,0.0000462759,0.000019355552],"domain_scores_gemma":[0.99984217,0.000053322918,0.000016329195,0.00002129812,0.000057288707,0.000009546051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003775746,0.0004086769,0.0003107577,0.00028465333,0.00029632924,0.00044787308,0.00062750495,0.0006690236,0.0032249787],"category_scores_gemma":[0.0009419852,0.0001838312,0.00033205395,0.00050257525,0.00023984551,0.0009075155,0.00031441488,0.0006705297,0.0010021677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032526688,0.00012190273,0.00077534333,0.00021189489,0.00007031391,0.00021028082,0.000084774336,0.23432,0.073980406,0.020542879,0.006734774,0.66262215],"study_design_scores_gemma":[0.0000054373013,0.00003280013,0.0002194678,0.000006012841,0.0000090073045,0.00004902011,0.000004861904,0.9920637,0.0038817842,0.0021633073,0.0015573732,0.0000072736993],"about_ca_topic_score_codex":0.0041695256,"about_ca_topic_score_gemma":0.0060592964,"teacher_disagreement_score":0.0041695256,"about_ca_system_score_codex":0.00043905515,"about_ca_system_score_gemma":0.00053868216,"threshold_uncertainty_score":0.0107886195},"labels":[],"label_agreement":null},{"id":"W2097897306","doi":"10.1109/imtc.2008.4547083","title":"Selective Vision Sensing with Neural Gas Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Computer science; Sampling (signal processing); Cloud computing; Artificial intelligence; Artificial neural network; Data collection; Real-time computing; Computer vision","score_opus":0.010789060017741773,"score_gpt":0.22290288540101041,"score_spread":0.21211382538326864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097897306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086564206,0.0016956071,0.9044157,0.0005522213,0.000083615174,0.000057030546,0.00005828024,0.0011511004,0.0054220627],"genre_scores_gemma":[0.81708616,0.0006602035,0.17790799,0.00020388779,0.000046258614,0.00010529391,0.00007473431,0.000047352893,0.0038681126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983835,0.000049263414,0.0000054824945,0.00003344528,0.000053559143,0.000019865362],"domain_scores_gemma":[0.9997181,0.00017321514,0.000027100394,0.000026534386,0.00004489978,0.000010072911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003520162,0.00038560329,0.0003821625,0.00035376844,0.00022707952,0.00047684961,0.0005907817,0.0007191234,0.0008683094],"category_scores_gemma":[0.0012597673,0.00023767064,0.0002632963,0.00041792903,0.00047421287,0.0007921208,0.0005964854,0.00051244104,0.00017531778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026132504,0.000055105218,0.00077601254,0.00011764505,0.00005059281,0.00015096202,0.00009931727,0.77958757,0.036290098,0.016296439,0.0017858758,0.164529],"study_design_scores_gemma":[0.0000047539606,0.000012920966,0.00008598994,0.000002720626,0.0000025622173,0.000015140739,0.0000053699137,0.9939494,0.0026061917,0.0028447453,0.00046652195,0.0000036357321],"about_ca_topic_score_codex":0.0032989774,"about_ca_topic_score_gemma":0.0027918331,"teacher_disagreement_score":0.0032989774,"about_ca_system_score_codex":0.00056114915,"about_ca_system_score_gemma":0.00028297407,"threshold_uncertainty_score":0.0065595508},"labels":[],"label_agreement":null},{"id":"W2098087097","doi":"10.1109/lsp.2002.804133","title":"Natural gradient learning neural networks for adaptive inversion of Hammerstein systems","year":2002,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Gradient descent; Backpropagation; Adaptive filter; Artificial neural network; Kernel adaptive filter; Computer science; Multilayer perceptron; Nonlinear system; Activation function; Adaptive system; Inversion (geology); Perceptron; Algorithm; Adaptive algorithm; Adaptive learning; Filter (signal processing); Control theory (sociology); Artificial intelligence; Filter design","score_opus":0.02519008230801976,"score_gpt":0.2214355908204638,"score_spread":0.19624550851244404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098087097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009122817,0.0018724215,0.9823887,0.0005578432,0.00019791399,0.000032116972,0.000027083684,0.0002783775,0.0055227797],"genre_scores_gemma":[0.54487884,0.0025518844,0.44012892,0.00041822487,0.0002585553,0.000195186,0.00011287878,0.00011037904,0.011345149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999856,0.0000519784,0.000008471672,0.000019421741,0.000053592787,0.0000104306555],"domain_scores_gemma":[0.99975115,0.00013593928,0.000019197816,0.000026628883,0.00005975259,0.0000074403197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053060206,0.00036155782,0.00029553304,0.00018957835,0.00018919806,0.00038798343,0.0005085238,0.000663666,0.0017127596],"category_scores_gemma":[0.0018392,0.0001461693,0.0002088817,0.0003140994,0.00042247705,0.0008750436,0.00038045822,0.0009729958,0.0004561739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010456907,0.0000539424,0.00048354507,0.0003185075,0.00004200145,0.00013620597,0.000121715115,0.50603986,0.009817835,0.24844494,0.0062406696,0.22819619],"study_design_scores_gemma":[0.0000060502593,0.000016426247,0.00007055498,0.000009432008,0.000003591473,0.000024819834,0.000003542857,0.9596953,0.0012838136,0.03552421,0.003356585,0.000005767519],"about_ca_topic_score_codex":0.0012882407,"about_ca_topic_score_gemma":0.001983404,"teacher_disagreement_score":0.0017127596,"about_ca_system_score_codex":0.00047911066,"about_ca_system_score_gemma":0.00040876458,"threshold_uncertainty_score":0.0057297945},"labels":[],"label_agreement":null},{"id":"W2098232303","doi":"10.1109/tnn.2006.885040","title":"A Recurrent Neural Network for Hierarchical Control of Interconnected Dynamic Systems","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Subnetwork; Computer science; Artificial neural network; Recurrent neural network; Bounded function; Stability (learning theory); Decomposition; Stochastic neural network; Artificial intelligence; Mathematics; Machine learning","score_opus":0.013220099711196883,"score_gpt":0.2574756441492499,"score_spread":0.244255544438053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098232303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010080292,0.00052104925,0.9861435,0.00009864406,0.000049928243,0.00002409923,0.000037642665,0.00052658876,0.0025183025],"genre_scores_gemma":[0.782282,0.0007362688,0.21126135,0.00010799906,0.00007956231,0.00017084357,0.00019874718,0.000071855124,0.005091369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997116,0.00006900736,0.000021244787,0.00008119941,0.00008894806,0.00002802732],"domain_scores_gemma":[0.9998554,0.000046127905,0.000027108537,0.00001640308,0.000046692574,0.00000826643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055393425,0.0004950141,0.0005480478,0.00023022403,0.00023475254,0.00051255996,0.00077687873,0.0007214452,0.0015197123],"category_scores_gemma":[0.00097347517,0.00021640907,0.00047047716,0.0003327118,0.0003491975,0.00055460253,0.00051338214,0.0007550576,0.0003070251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007181115,0.000034827717,0.00033691022,0.0001245568,0.000059346068,0.0001501011,0.000066175526,0.8666252,0.016176993,0.027957603,0.0013111018,0.08708531],"study_design_scores_gemma":[0.0000035552937,0.000015404541,0.000041852643,0.000003036347,0.000006669292,0.000010173617,0.0000015015562,0.9978904,0.00052267715,0.0010747757,0.0004266611,0.0000032172222],"about_ca_topic_score_codex":0.0045140805,"about_ca_topic_score_gemma":0.004427134,"teacher_disagreement_score":0.0045140805,"about_ca_system_score_codex":0.0004890299,"about_ca_system_score_gemma":0.00054837833,"threshold_uncertainty_score":0.008975565},"labels":[],"label_agreement":null},{"id":"W2098412053","doi":"10.1109/72.896800","title":"Experiments on the application of IOHMMs to model financial returns series","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Université de Montréal","keywords":"Hidden Markov model; Hidden semi-Markov model; Gaussian; Moment (physics); Variable-order Markov model; Series (stratigraphy); Conditional probability distribution; Generalization; Conditional expectation; Markov chain; Mathematics; Markov model; Forward algorithm; Econometrics; Applied mathematics; Mixture model; Computer science; Artificial intelligence; Statistics","score_opus":0.02458885608029223,"score_gpt":0.2592432467594541,"score_spread":0.23465439067916186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098412053","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9509608,0.0018391418,0.03741282,0.000874769,0.00022028034,0.0002991242,0.0018392067,0.0029857568,0.003568021],"genre_scores_gemma":[0.96544325,0.00036938937,0.030695627,0.00016707725,0.000048134676,0.00019531819,0.0018609216,0.00008460171,0.0011357905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979588,0.0009833851,0.00026023886,0.00044209644,0.00019621466,0.00015931053],"domain_scores_gemma":[0.979618,0.016884474,0.0005698695,0.0013492958,0.0012251361,0.00035317754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075547565,0.0017932284,0.0012195389,0.0008278251,0.0006851661,0.0008358111,0.0013439059,0.0024577559,0.0019282942],"category_scores_gemma":[0.020043792,0.00062381354,0.0011166603,0.001094801,0.0006736119,0.0018877687,0.0009765299,0.002465269,0.00065056875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017720663,0.0016570733,0.009179841,0.00050993665,0.00048673118,0.00022332651,0.0003974037,0.89162487,0.0039047953,0.0016310622,0.0032607573,0.08535218],"study_design_scores_gemma":[0.00009037426,0.00035080995,0.0019530171,0.000018981722,0.000048181446,0.00003725614,0.00005928274,0.9927273,0.0032811055,0.0010423637,0.00036596815,0.00002538887],"about_ca_topic_score_codex":0.029500563,"about_ca_topic_score_gemma":0.020901266,"teacher_disagreement_score":0.029500563,"about_ca_system_score_codex":0.0014088375,"about_ca_system_score_gemma":0.00088465016,"threshold_uncertainty_score":0.058657706},"labels":[],"label_agreement":null},{"id":"W2098779848","doi":"10.1109/ccece.1993.332303","title":"Gradient descent decoding of binary cyclic codes","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Code word; Decoding methods; Parity-check matrix; Maxima and minima; Permutation (music); Mathematics; Hamming distance; BCH code; List decoding; Algorithm; Hamming code; Hopfield network; Binary code; Discrete mathematics; Binary number; Computer science; Artificial neural network; Block code; Concatenated error correction code; Arithmetic; Artificial intelligence","score_opus":0.036848884077252134,"score_gpt":0.23996546704964097,"score_spread":0.20311658297238883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098779848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16158158,0.0005076905,0.81695765,0.00049654505,0.00009547521,0.00008947631,0.00015389189,0.000767447,0.019350218],"genre_scores_gemma":[0.817562,0.00019986522,0.17359845,0.00012500174,0.000027434347,0.000059272203,0.00012391323,0.000090200476,0.00821396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979395,0.000057887468,0.000010481181,0.000030978426,0.00007159603,0.000035082074],"domain_scores_gemma":[0.99956745,0.00021258637,0.000049863887,0.000038096936,0.00011379981,0.000018228297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035814152,0.00024249787,0.00031728606,0.00024708724,0.0002643699,0.00041719904,0.00039555613,0.00050374406,0.001832737],"category_scores_gemma":[0.002195137,0.00015049952,0.00013043094,0.00034611023,0.00053904834,0.00052790844,0.0003832222,0.00031520062,0.0002963671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008445343,0.000033766282,0.0006294955,0.00006754502,0.000015535223,0.0000829859,0.00005937271,0.8622729,0.0070703025,0.045858186,0.0021179868,0.08170755],"study_design_scores_gemma":[0.0000049092537,0.00001117432,0.00009769481,0.0000039876795,8.2310515e-7,0.00001718714,0.0000037065472,0.9912793,0.0015500957,0.0065856236,0.00044271312,0.0000027147798],"about_ca_topic_score_codex":0.0057690167,"about_ca_topic_score_gemma":0.0055763624,"teacher_disagreement_score":0.0057690167,"about_ca_system_score_codex":0.00078474934,"about_ca_system_score_gemma":0.00079867936,"threshold_uncertainty_score":0.011470854},"labels":[],"label_agreement":null},{"id":"W2099141349","doi":"10.1080/09540090701528951","title":"A systematic comparison of flat and standard cascade-correlation using a student–teacher network approximation task","year":2007,"lang":"en","type":"article","venue":"Connection Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Cascade; Computer science; Task (project management); Artificial intelligence; Connection (principal bundle); Layer (electronics); Algorithm; Mathematics; Geometry","score_opus":0.0323057393688994,"score_gpt":0.3358260682264963,"score_spread":0.3035203288575969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099141349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8625018,0.00061707885,0.11961465,0.00020765356,0.00015983528,0.0010466108,0.0016617142,0.0031836992,0.011006921],"genre_scores_gemma":[0.922317,0.00018315471,0.067905396,0.00015407958,0.000026259468,0.00064253266,0.0037112946,0.0003606845,0.0046995156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692196,0.0012865391,0.0003026301,0.0007361051,0.0005679029,0.00018487361],"domain_scores_gemma":[0.9797972,0.012761742,0.00062478054,0.0037064995,0.0025042375,0.0006056282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060226824,0.0011901917,0.0007473287,0.0007482404,0.0005355393,0.0008627936,0.001930204,0.0010617974,0.0037372464],"category_scores_gemma":[0.038396,0.000582792,0.0006138816,0.0007370028,0.00060231023,0.0033696874,0.0015901914,0.0014964695,0.0010357955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.019052854,0.0037737056,0.030975906,0.00233708,0.0010832597,0.00059185165,0.0012500234,0.17610893,0.052480422,0.008785951,0.017946446,0.6856136],"study_design_scores_gemma":[0.0010992547,0.007478084,0.04084531,0.000102994025,0.00036626155,0.00053677673,0.00027734812,0.8912253,0.044059906,0.008354093,0.0054857326,0.00016898163],"about_ca_topic_score_codex":0.0067327586,"about_ca_topic_score_gemma":0.009408193,"teacher_disagreement_score":0.0067327586,"about_ca_system_score_codex":0.0008035957,"about_ca_system_score_gemma":0.0012297156,"threshold_uncertainty_score":0.03185135},"labels":[],"label_agreement":null},{"id":"W2099391893","doi":"10.1109/cdc.2010.5717599","title":"On-line nonlinear systems identification via dynamic neural networks with multi-time scales","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Nonlinear system; Identification (biology); Computer science; Line (geometry); Control theory (sociology); Nonlinear system identification; Function (biology); Lyapunov function; System identification; Algorithm; Artificial intelligence; Mathematics; Data modeling; Physics; Control (management)","score_opus":0.009716652801928013,"score_gpt":0.24869767875513785,"score_spread":0.23898102595320983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099391893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009260283,0.0001991023,0.9889159,0.00005292638,0.000033178527,0.000021411883,0.000008762372,0.00020880155,0.0012995482],"genre_scores_gemma":[0.7249728,0.0003942091,0.27050507,0.00013871495,0.000050194023,0.00011449884,0.000062961604,0.00007113234,0.00369047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995521,0.000107035594,0.00002988988,0.000098208926,0.00017516302,0.00003762566],"domain_scores_gemma":[0.9995235,0.00017617625,0.00009244264,0.00007837145,0.00011605359,0.000013369659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054120447,0.00074683776,0.0006266127,0.00039046703,0.00041211024,0.0008063767,0.0009678634,0.0008306203,0.0011919348],"category_scores_gemma":[0.0014831122,0.0003147736,0.00038706395,0.0003404813,0.00050640805,0.0014876825,0.0008930222,0.0009637687,0.00033179796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019906802,0.00009821218,0.0009829451,0.0001460307,0.00008488589,0.00017163336,0.00015162244,0.65858,0.019017108,0.012681344,0.0006813575,0.30720574],"study_design_scores_gemma":[0.0000055339815,0.000028960536,0.0001567471,0.00000637119,0.0000084084795,0.000040114035,0.00000765649,0.99433976,0.0031261349,0.0015476024,0.0007259597,0.000006799699],"about_ca_topic_score_codex":0.0016325908,"about_ca_topic_score_gemma":0.0018682729,"teacher_disagreement_score":0.0016325908,"about_ca_system_score_codex":0.00045543507,"about_ca_system_score_gemma":0.00032395864,"threshold_uncertainty_score":0.0039874315},"labels":[],"label_agreement":null},{"id":"W2099661768","doi":"","title":"CsMTL MLP For WEKA: Neural Network Learning with Inductive Transfer","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Transfer of learning; Inductive transfer; Artificial intelligence; Artificial neural network; Machine learning; Task (project management); Multi-task learning; Context (archaeology); Perceptron; Suite; Multilayer perceptron; Inductive bias; Engineering; Robot learning","score_opus":0.011345019571932078,"score_gpt":0.22724693944169744,"score_spread":0.21590191986976537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099661768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007693376,0.00024980668,0.85268974,0.0003210453,0.00015231105,0.00032373925,0.0014315445,0.13527463,0.0018638498],"genre_scores_gemma":[0.18223655,0.00022077307,0.79818237,0.0004337835,0.000078937075,0.002360445,0.0048688943,0.0062963306,0.005321877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975132,0.001064429,0.00022374543,0.0004946519,0.0005167984,0.00018716321],"domain_scores_gemma":[0.99470973,0.003054862,0.0002536577,0.0012640369,0.00060768507,0.00010995887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034339502,0.0020123234,0.00096455816,0.001721758,0.0006832425,0.0013527006,0.0037946647,0.001907948,0.022310149],"category_scores_gemma":[0.016186288,0.0013663963,0.002163655,0.0012882868,0.00083383115,0.0035128652,0.0030721626,0.0051706103,0.010183308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007782757,0.00045615816,0.0030595078,0.0012514316,0.0011722583,0.0004102111,0.00023796459,0.21152465,0.012747627,0.016815374,0.082982555,0.66856396],"study_design_scores_gemma":[0.0001017778,0.00013721584,0.0007503561,0.000045535082,0.00005876397,0.00007942057,0.000022454924,0.9574134,0.016728632,0.014823969,0.00977393,0.00006455461],"about_ca_topic_score_codex":0.0032070333,"about_ca_topic_score_gemma":0.0054199086,"teacher_disagreement_score":0.022310149,"about_ca_system_score_codex":0.0010441393,"about_ca_system_score_gemma":0.0016912387,"threshold_uncertainty_score":0.07463485},"labels":[],"label_agreement":null},{"id":"W2099673030","doi":"10.1109/ccece.1993.332291","title":"Fast contingency screening through optimizing Hopfield neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ranking (information retrieval); Hopfield network; Computer science; Artificial neural network; Scheme (mathematics); Artificial intelligence; Maxima and minima; Machine learning; Contingency; Contingency table; Mathematical optimization; Data mining; Mathematics","score_opus":0.03843153998369575,"score_gpt":0.24395687624830592,"score_spread":0.20552533626461017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099673030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070275,0.0002284577,0.92451257,0.000109527115,0.000021277921,0.000067568624,0.000056672547,0.0009852733,0.0037436828],"genre_scores_gemma":[0.8171743,0.00011844058,0.18057902,0.000045360783,0.000014879376,0.00007046577,0.00008980588,0.00004778104,0.0018599553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997644,0.000071731694,0.0000141555665,0.00004122533,0.000071747905,0.000036762016],"domain_scores_gemma":[0.99950385,0.00026245415,0.00006032399,0.00004267144,0.00010855512,0.000022165283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007497125,0.00070651533,0.00058961776,0.0005644293,0.00037564264,0.0005610152,0.000681715,0.00042909148,0.0020568303],"category_scores_gemma":[0.001922276,0.0003152962,0.00026876046,0.00037834005,0.0003686508,0.001092346,0.00050421746,0.00040918344,0.00027169212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006585589,0.000020959189,0.0004663115,0.000029097786,0.000016131004,0.000031417952,0.000028885086,0.8918014,0.002321012,0.0026474555,0.00048319082,0.10208819],"study_design_scores_gemma":[0.000005961155,0.000013932445,0.00008201604,0.0000022380805,0.0000032386442,0.000005022621,0.0000041110297,0.99741554,0.0008034925,0.0015532876,0.000108175416,0.000003033484],"about_ca_topic_score_codex":0.008315637,"about_ca_topic_score_gemma":0.009456582,"teacher_disagreement_score":0.008315637,"about_ca_system_score_codex":0.00074235257,"about_ca_system_score_gemma":0.00081891316,"threshold_uncertainty_score":0.016534448},"labels":[],"label_agreement":null},{"id":"W2099707194","doi":"10.1109/iembs.1990.692254","title":"Information Based Classification Of Motor Unit Action Potentials","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Probabilistic logic; Pattern recognition (psychology); Template matching; Artificial intelligence; Inference; Matching (statistics); Motor unit; Speech recognition; Mathematics; Statistics; Image (mathematics)","score_opus":0.04289090032586611,"score_gpt":0.2806200107245804,"score_spread":0.2377291103987143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099707194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11691845,0.0004917052,0.8773913,0.00018032348,0.000100269266,0.000097537144,0.0003251148,0.0010318557,0.0034634985],"genre_scores_gemma":[0.68346596,0.0003329627,0.3135049,0.00007172107,0.00005594001,0.00009299878,0.0005073783,0.00007865661,0.0018895336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945253,0.00010350366,0.00004160044,0.00012315264,0.00022745293,0.000051752988],"domain_scores_gemma":[0.9983954,0.0008670895,0.00016263442,0.00013890538,0.00038030357,0.000055728273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013349081,0.00034254318,0.00040496682,0.0009885461,0.00016959937,0.00074907986,0.00059441983,0.0007303255,0.0015166346],"category_scores_gemma":[0.0067476677,0.0001555758,0.00039191195,0.00067109487,0.00025728028,0.0010650682,0.00039959003,0.00045896493,0.0006318932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006486686,0.000093879054,0.003959446,0.00013800657,0.000058680984,0.00010295151,0.00007404782,0.04271982,0.06557953,0.0055113197,0.0012142054,0.8798994],"study_design_scores_gemma":[0.00003723139,0.00023529101,0.01704315,0.000026574422,0.00006928774,0.00032525155,0.000025803922,0.9052301,0.067106456,0.007786275,0.0020607288,0.00005392086],"about_ca_topic_score_codex":0.0006594823,"about_ca_topic_score_gemma":0.00060529093,"teacher_disagreement_score":0.0015166346,"about_ca_system_score_codex":0.00036151338,"about_ca_system_score_gemma":0.00031681213,"threshold_uncertainty_score":0.007059753},"labels":[],"label_agreement":null},{"id":"W2099759795","doi":"10.1109/tnn.2005.851786","title":"Constructive Feedforward Neural Networks Using Hermite Polynomial Activation Functions","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Activation function; Constructive; Sigmoid function; Hermite polynomials; Artificial neural network; Computer science; Feedforward neural network; Orthonormal basis; Polynomial; Adaptation (eye); Function (biology); Constructive proof; Topology (electrical circuits); Mathematics; Artificial intelligence; Discrete mathematics; Pure mathematics; Combinatorics; Mathematical analysis","score_opus":0.01768520251766669,"score_gpt":0.2410274838492189,"score_spread":0.2233422813315522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099759795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004922759,0.00012585547,0.99341667,0.000044852262,0.000023087086,0.000013818639,0.000009732321,0.00025173582,0.0011915133],"genre_scores_gemma":[0.5174772,0.00056591816,0.4753587,0.00017033228,0.000068710746,0.00014275838,0.000093359144,0.00010593777,0.0060171285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994715,0.0002095742,0.000019990646,0.00007048185,0.0001886838,0.00003975152],"domain_scores_gemma":[0.9989441,0.0006172421,0.000091005546,0.00009020782,0.00022851425,0.00002893404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00110199,0.0009758775,0.0004992142,0.00042290078,0.00026755527,0.0007228243,0.0015093668,0.0011148502,0.00140234],"category_scores_gemma":[0.003307554,0.00044563497,0.0005185933,0.00043500832,0.00078554876,0.0014719891,0.0010277188,0.0011022582,0.0007661518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010899345,0.000051325536,0.00035156801,0.00013753067,0.000054234974,0.00019368391,0.00011393368,0.7953232,0.020004543,0.040600557,0.00093903986,0.14212143],"study_design_scores_gemma":[0.0000038590447,0.000027080876,0.000043216016,0.000007512977,0.0000074088534,0.000035952162,0.0000043255127,0.990364,0.0042492715,0.004561508,0.0006888492,0.000006973434],"about_ca_topic_score_codex":0.0009381393,"about_ca_topic_score_gemma":0.0016352892,"teacher_disagreement_score":0.0015093668,"about_ca_system_score_codex":0.0005393652,"about_ca_system_score_gemma":0.0005060744,"threshold_uncertainty_score":0.0058279037},"labels":[],"label_agreement":null},{"id":"W2099764232","doi":"10.1109/iscas.2005.1465198","title":"Three-Layer Symmetrical and Asymmetrical Associative Memories for Image Applications","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Content-addressable memory; Bidirectional associative memory; Associative property; Feed forward; Layer (electronics); Recall; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Content-addressable storage; Numeral system; Binary number; Feedforward neural network; Arithmetic; Mathematics; Psychology; Cognitive psychology; Engineering","score_opus":0.022476455910266698,"score_gpt":0.28701864422306506,"score_spread":0.26454218831279835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099764232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09429402,0.0048849676,0.8644914,0.00066337135,0.0003803257,0.00007317981,0.00018670375,0.0013779268,0.03364808],"genre_scores_gemma":[0.79083306,0.0023200964,0.19100223,0.00034541436,0.00010706664,0.000109881235,0.00017172135,0.000046369165,0.015064119],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999331,0.000011508822,0.0000059294457,0.000013226224,0.00002396756,0.000012161954],"domain_scores_gemma":[0.99981993,0.000048087255,0.000019074068,0.000042005257,0.000056938192,0.000013987371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019295745,0.00024442314,0.00018332638,0.00016578693,0.00018698748,0.0004974908,0.00071222405,0.0004720924,0.0061026183],"category_scores_gemma":[0.00055387325,0.00008684272,0.00022081744,0.00026568738,0.00024922777,0.000897159,0.00046198358,0.00037714397,0.0010625047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038121647,0.00011654165,0.0009365479,0.0006090236,0.00006382077,0.00032712478,0.00011909653,0.053200595,0.069526136,0.18072937,0.0068410663,0.68714947],"study_design_scores_gemma":[0.000064242384,0.00028193448,0.0010636991,0.000081756116,0.00009797513,0.0008866294,0.00009635705,0.5960474,0.06900688,0.27911776,0.05320387,0.000051466348],"about_ca_topic_score_codex":0.00041487342,"about_ca_topic_score_gemma":0.0006934379,"teacher_disagreement_score":0.0061026183,"about_ca_system_score_codex":0.00016769016,"about_ca_system_score_gemma":0.00024173931,"threshold_uncertainty_score":0.020415306},"labels":[],"label_agreement":null},{"id":"W2099791674","doi":"10.1109/nnsp.1997.622441","title":"A self-scaling neural hardware structure that reduces the effect of some implementation errors","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Sigmoid function; Realization (probability); Scaling; Artificial neural network; Computer science; Very-large-scale integration; Noise (video); Property (philosophy); Topology (electrical circuits); Algorithm; Artificial intelligence; Mathematics; Embedded system","score_opus":0.016517701784612406,"score_gpt":0.26942714511273924,"score_spread":0.2529094433281268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099791674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12276695,0.0003655912,0.86641145,0.00024701352,0.00011000282,0.000047028352,0.000035920497,0.0013421077,0.008673852],"genre_scores_gemma":[0.69747615,0.00013873454,0.2981996,0.00014286106,0.000057607635,0.000050751125,0.000045937526,0.00008309696,0.00380518],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989045,0.000013465205,0.0000072853704,0.000030510928,0.000051423864,0.000006935492],"domain_scores_gemma":[0.9997296,0.000063203435,0.000042982203,0.00007639167,0.00007335769,0.00001443624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013778832,0.00022240513,0.00020819432,0.0002090678,0.00016758178,0.00031810143,0.0006178575,0.0003839887,0.0017566786],"category_scores_gemma":[0.00079339975,0.00014127993,0.0001758542,0.00019540975,0.00024181056,0.00060861453,0.0002472522,0.00033410112,0.00045494692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001395622,0.00016920059,0.0009326966,0.00022102207,0.00006040396,0.00024847194,0.00011087032,0.10862979,0.5022722,0.05455986,0.001455376,0.3312005],"study_design_scores_gemma":[0.00006022437,0.0006665652,0.0017755189,0.0000320396,0.0000701081,0.0008663798,0.000024732513,0.81016576,0.1573931,0.013329639,0.015585743,0.00003013179],"about_ca_topic_score_codex":0.00019444032,"about_ca_topic_score_gemma":0.0003621912,"teacher_disagreement_score":0.0017566786,"about_ca_system_score_codex":0.00017167022,"about_ca_system_score_gemma":0.00021440168,"threshold_uncertainty_score":0.0058766603},"labels":[],"label_agreement":null},{"id":"W2099851844","doi":"10.1109/ijcnn.2004.1380864","title":"A new mixed-signal feed-forward neural network with on-chip learning","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Robustness (evolution); Computer science; Artificial neural network; Multiplexing; Resistive touchscreen; Chip; Mixed-signal integrated circuit; Scaling; Digital signal processing; SIGNAL (programming language); Network architecture; Electronic engineering; Artificial intelligence; Computer hardware; Computer network; Integrated circuit; Engineering; Mathematics; Telecommunications; Computer vision","score_opus":0.01181231715305695,"score_gpt":0.22269726630966452,"score_spread":0.21088494915660758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099851844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015442362,0.0005425415,0.97875243,0.00016044012,0.00018530672,0.000052906074,0.000063265354,0.0008725271,0.003928252],"genre_scores_gemma":[0.45743638,0.0005696586,0.52609175,0.00056839146,0.00015757776,0.00027140926,0.00026727765,0.00007981609,0.014557699],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996119,0.000069992086,0.000019825344,0.00007965572,0.00017512875,0.00004355899],"domain_scores_gemma":[0.999694,0.00009030895,0.000028365961,0.000035661295,0.00013218338,0.00001956793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050157524,0.00060113857,0.00056595576,0.0003239222,0.00025239697,0.0007791612,0.0016711631,0.0010871512,0.002763104],"category_scores_gemma":[0.0008479535,0.00034636585,0.0003757589,0.00043613688,0.000345725,0.0013330996,0.0005935741,0.0008198704,0.0008421579],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004283881,0.00022401236,0.0012427063,0.00037184003,0.00018495294,0.0003039487,0.00008366995,0.24518046,0.080783315,0.017172176,0.0048497985,0.6491748],"study_design_scores_gemma":[0.0000231236,0.00012042532,0.0001835829,0.000014170644,0.00003183018,0.00011190789,0.0000043676364,0.97956014,0.014136988,0.0017507549,0.004048141,0.000014666665],"about_ca_topic_score_codex":0.0011031243,"about_ca_topic_score_gemma":0.0026047346,"teacher_disagreement_score":0.002763104,"about_ca_system_score_codex":0.00042091572,"about_ca_system_score_gemma":0.00044736254,"threshold_uncertainty_score":0.009243488},"labels":[],"label_agreement":null},{"id":"W2099878906","doi":"10.1109/ccece.1999.808034","title":"Automatic detection of acoustic sub-word boundaries for single digit recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Numerical digit; Computer science; Word (group theory); Speech recognition; Digit recognition; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Word recognition; Function (biology); Arithmetic; Mathematics","score_opus":0.02705295358286952,"score_gpt":0.2357747329060169,"score_spread":0.20872177932314737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099878906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19658329,0.00081997825,0.7953633,0.00008494644,0.00015369292,0.00008600237,0.00021274145,0.0040329117,0.002663136],"genre_scores_gemma":[0.5234552,0.00033189467,0.4726193,0.00007200728,0.00004564233,0.00009731868,0.00044965383,0.00032405977,0.002604963],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996582,0.00007003673,0.000024939047,0.00010208435,0.000117025666,0.000027742959],"domain_scores_gemma":[0.99878806,0.00061934144,0.00012982609,0.00010882948,0.00030188062,0.000052213967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038302277,0.00046176472,0.00053923903,0.00085767306,0.00020352493,0.00059864396,0.00057296065,0.0006095881,0.00247482],"category_scores_gemma":[0.0023744395,0.00019702583,0.0002600984,0.00031365833,0.0002551414,0.00086356176,0.0004067801,0.00041057606,0.0022743964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046627497,0.00007700305,0.0013942316,0.00021852962,0.000023879722,0.00012296056,0.00010371809,0.0037780416,0.3620192,0.0011148184,0.00074197364,0.6299395],"study_design_scores_gemma":[0.000038150753,0.0005178613,0.014287081,0.000048752616,0.000075694974,0.0012043333,0.00014687364,0.38811645,0.5851722,0.0035361764,0.0067973523,0.00005915021],"about_ca_topic_score_codex":0.00030135066,"about_ca_topic_score_gemma":0.0004494144,"teacher_disagreement_score":0.00247482,"about_ca_system_score_codex":0.0001380811,"about_ca_system_score_gemma":0.0002472706,"threshold_uncertainty_score":0.008279085},"labels":[],"label_agreement":null},{"id":"W2100266218","doi":"10.1093/bioinformatics/btu849","title":"An automatic tool to analyze and cluster macromolecular conformations based on self-organizing maps","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"Computer science; Cluster (spacecraft); Data mining; Artificial intelligence; Programming language","score_opus":0.006141447108255647,"score_gpt":0.2239100227889064,"score_spread":0.21776857568065075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100266218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005922677,0.00016911136,0.7476628,0.00020627058,0.00011082452,0.00030681555,0.013463916,0.22944187,0.002715802],"genre_scores_gemma":[0.04666815,0.0002598664,0.9177896,0.0001682382,0.000058619655,0.0012721487,0.017325498,0.012007549,0.004450442],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995073,0.00005471,0.00004087863,0.00012277439,0.00022698303,0.000047309375],"domain_scores_gemma":[0.99908173,0.00035555364,0.00010721493,0.0001336279,0.00023476615,0.000087125154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009639238,0.001979662,0.0011648635,0.0038823849,0.0009875506,0.001799746,0.0026692639,0.0009987968,0.022398613],"category_scores_gemma":[0.0034922548,0.0010246983,0.0016484838,0.0023862305,0.00053462305,0.0015566841,0.00217398,0.0013302827,0.009929393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005615108,0.00038467927,0.006137539,0.0018737385,0.00053902925,0.00083270523,0.0005957305,0.036927115,0.025862014,0.015026526,0.28733706,0.6239224],"study_design_scores_gemma":[0.0002564344,0.00010093338,0.0049975323,0.00020544002,0.000098282464,0.00077631394,0.00017492128,0.79771376,0.04313335,0.04553824,0.106804,0.00020075183],"about_ca_topic_score_codex":0.002899042,"about_ca_topic_score_gemma":0.0043413597,"teacher_disagreement_score":0.022398613,"about_ca_system_score_codex":0.0006982709,"about_ca_system_score_gemma":0.0016936961,"threshold_uncertainty_score":0.07493085},"labels":[],"label_agreement":null},{"id":"W2100283156","doi":"10.1109/icnn.1996.549082","title":"Modular neural network architectures for classification","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'96)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Modular design; Computer science; Artificial neural network; Divide and conquer algorithms; Modular neural network; Artificial intelligence; Classifier (UML); Machine learning; Task (project management); Time delay neural network; Decomposition; Engineering; Algorithm","score_opus":0.058983145013753346,"score_gpt":0.2745676832261193,"score_spread":0.21558453821236595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100283156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069452315,0.0026670387,0.984749,0.0003112524,0.0001661636,0.000049972266,0.00010472864,0.000907229,0.004099358],"genre_scores_gemma":[0.35297984,0.0051507833,0.62954843,0.00031423982,0.0004919998,0.00032496772,0.00062519027,0.00014723482,0.0104173785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996247,0.00009857415,0.00002225513,0.000085331834,0.00013248569,0.00003654221],"domain_scores_gemma":[0.9995604,0.00015401498,0.000041615764,0.00007461904,0.00014873447,0.000020639212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007831473,0.00085634895,0.00059315696,0.0007278003,0.00026142225,0.00081263843,0.0011452913,0.0008523587,0.00406476],"category_scores_gemma":[0.0021677085,0.00027679765,0.00054104254,0.0012012178,0.00040569945,0.0014850476,0.00081739796,0.0012027983,0.0015913404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000123686,0.00006062496,0.0007821051,0.0003383963,0.00012637448,0.0001039531,0.000086059175,0.4067025,0.007802705,0.08170473,0.008655484,0.49351344],"study_design_scores_gemma":[0.000008394471,0.000038151225,0.00024686338,0.000029370223,0.000019776333,0.000044276632,0.000008526795,0.9515074,0.0014286861,0.040932044,0.005724257,0.0000122224355],"about_ca_topic_score_codex":0.0016622221,"about_ca_topic_score_gemma":0.002169478,"teacher_disagreement_score":0.00406476,"about_ca_system_score_codex":0.0006157623,"about_ca_system_score_gemma":0.0004825203,"threshold_uncertainty_score":0.013598025},"labels":[],"label_agreement":null},{"id":"W2100495367","doi":"10.1126/science.1127647","title":"Reducing the Dimensionality of Data with Neural Networks","year":2006,"lang":"en","type":"article","venue":"Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20953,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Autoencoder; Curse of dimensionality; Initialization; Gradient descent; Artificial neural network; Computer science; Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Layer (electronics); High dimensional; Principal (computer security); Algorithm; Materials science; Nanotechnology","score_opus":0.029731573255271218,"score_gpt":0.276790050995563,"score_spread":0.24705847774029177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100495367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008521799,0.0011751311,0.9870107,0.00081496267,0.0001230324,0.000047989415,0.00020433475,0.0008380173,0.0012639181],"genre_scores_gemma":[0.15979284,0.0029358882,0.8319172,0.00058736355,0.00035443777,0.00032597873,0.0011442946,0.00027738398,0.002664616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978896,0.00057114946,0.00021671299,0.00036641952,0.0008588225,0.00009720357],"domain_scores_gemma":[0.99631184,0.0018026539,0.0002146634,0.0010835492,0.00051813765,0.000069168054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00184146,0.0013522169,0.0014621406,0.0013398195,0.0007345267,0.0021548942,0.0011499878,0.0012736935,0.0017755091],"category_scores_gemma":[0.014108884,0.0008932269,0.0011765839,0.0018643022,0.001395105,0.00465274,0.0032251473,0.003590527,0.0012297836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002555628,0.00014821198,0.002491978,0.0007146577,0.00033684782,0.00022013848,0.00042907934,0.29519558,0.01777397,0.067101575,0.014420494,0.6009119],"study_design_scores_gemma":[0.000022382033,0.000047034795,0.0006714924,0.00006663339,0.00004416724,0.000093565104,0.0000744223,0.8408451,0.008794585,0.13842796,0.010871364,0.000041330622],"about_ca_topic_score_codex":0.0023119585,"about_ca_topic_score_gemma":0.0026946217,"teacher_disagreement_score":0.0023119585,"about_ca_system_score_codex":0.000821901,"about_ca_system_score_gemma":0.0011130669,"threshold_uncertainty_score":0.009738684},"labels":[],"label_agreement":null},{"id":"W2100585289","doi":"10.1109/cibcb.2006.330988","title":"Detecting Similarities between Families of Bio-sequences using the Steady-State of a PCA-Neural Network","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Artificial neural network; Robustness (evolution); Attractor; Pattern recognition (psychology); ENCODE; Computer science; Artificial intelligence; Principal component analysis; Similarity (geometry); Property (philosophy); Mathematics; Algorithm; Biology","score_opus":0.034952766206103304,"score_gpt":0.26191069036248843,"score_spread":0.22695792415638513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100585289","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058905907,0.00020756658,0.93939555,0.00009092853,0.000022940943,0.000049868173,0.000044141667,0.0005026517,0.0007805309],"genre_scores_gemma":[0.59422874,0.00025256077,0.4037888,0.00007742829,0.000025981151,0.00012054454,0.00021372021,0.0000447731,0.0012474604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994796,0.00010249334,0.000037492275,0.00021007776,0.00013264472,0.00003776054],"domain_scores_gemma":[0.99839705,0.0008353816,0.0002608482,0.00012325357,0.00031284953,0.000070462505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013547228,0.00044134192,0.0006233767,0.0015636472,0.00060425105,0.00094503944,0.0007757705,0.0007876772,0.0010781212],"category_scores_gemma":[0.004443238,0.00034642217,0.0006744366,0.00083582074,0.0009535767,0.0018953045,0.0008037086,0.0006592598,0.00030963658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051824324,0.00027071108,0.022899054,0.0003145063,0.00025935477,0.0005496471,0.0007222405,0.2656783,0.08167602,0.064034,0.0012855148,0.56179243],"study_design_scores_gemma":[0.0000065220574,0.00007735713,0.0026267006,0.000011109152,0.000022044529,0.00021650903,0.000046102265,0.9749545,0.006757422,0.01449734,0.0007574596,0.000026870452],"about_ca_topic_score_codex":0.0014179474,"about_ca_topic_score_gemma":0.0009066456,"teacher_disagreement_score":0.0015636472,"about_ca_system_score_codex":0.00064743933,"about_ca_system_score_gemma":0.0006107806,"threshold_uncertainty_score":0.007164538},"labels":[],"label_agreement":null},{"id":"W2101192906","doi":"10.1109/cca.1998.728499","title":"Identification of a turbogenerator system using adaptive time delay neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Nonlinear system; Artificial neural network; Computer science; Identifier; A priori and a posteriori; Nonlinear system identification; Identification (biology); Control theory (sociology); System identification; Set (abstract data type); Adaptive system; MIMO; Control engineering; Engineering; Artificial intelligence; Data modeling","score_opus":0.025496297564208487,"score_gpt":0.2213727394956898,"score_spread":0.19587644193148132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101192906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06688905,0.0003021537,0.9296945,0.00008830772,0.000039386312,0.00003532479,0.000047730446,0.00039903657,0.0025045483],"genre_scores_gemma":[0.92363316,0.0002583684,0.07246416,0.000032163196,0.000012729376,0.00008382509,0.000066962166,0.00002183978,0.003426786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998859,0.000023694121,0.000008359023,0.000035193203,0.00003558806,0.000011207308],"domain_scores_gemma":[0.9998104,0.00007974025,0.000038470684,0.000017328623,0.00004703253,0.000007029497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022668905,0.00048086274,0.00033864548,0.00024037319,0.0002501524,0.00035730822,0.0003302138,0.000533312,0.00072409806],"category_scores_gemma":[0.0006282467,0.00020687764,0.00027592736,0.00020160167,0.00031817483,0.0005444006,0.00029393332,0.00042346207,0.0001939378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011732657,0.000025268053,0.0010724439,0.00012317703,0.00004668482,0.00019182384,0.00008574034,0.87545246,0.04277033,0.0034977072,0.0002600418,0.07635709],"study_design_scores_gemma":[0.000005792095,0.00003694389,0.0003903238,0.0000065954355,0.000007914191,0.00004745585,0.000008224466,0.98987854,0.008172293,0.0008186951,0.0006191394,0.000008072032],"about_ca_topic_score_codex":0.0024677494,"about_ca_topic_score_gemma":0.0023933894,"teacher_disagreement_score":0.0024677494,"about_ca_system_score_codex":0.00036994426,"about_ca_system_score_gemma":0.0004190918,"threshold_uncertainty_score":0.0049067736},"labels":[],"label_agreement":null},{"id":"W2101230057","doi":"10.1109/iembs.1989.96585","title":"Need for knowledge-based subsystem in evoked potential neural-net recognition system","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Evoked potential; Speech recognition; Perception; Waveform; Pattern recognition (psychology); Neuroscience; Psychology","score_opus":0.026074586022783484,"score_gpt":0.24227034613347304,"score_spread":0.21619576011068956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101230057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015094824,0.0007884984,0.9604651,0.0012041456,0.00015569915,0.00030573417,0.00036067946,0.012001188,0.009624119],"genre_scores_gemma":[0.22542536,0.0007426888,0.75793797,0.00073552394,0.00021479133,0.00038789966,0.0012733334,0.0003978014,0.012884617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913293,0.00015299938,0.0000989508,0.00022863832,0.00029726984,0.00008921676],"domain_scores_gemma":[0.9980761,0.0005968975,0.000081755854,0.00035946275,0.0007450166,0.00014077972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014078692,0.00064596126,0.00088926416,0.0011697677,0.0005811271,0.0029144997,0.0020137096,0.0014006243,0.011574984],"category_scores_gemma":[0.003408565,0.0005247419,0.0005464095,0.00070234644,0.0005671351,0.0034321034,0.001003817,0.0012104403,0.006024144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007727382,0.0005766347,0.004956197,0.0010784386,0.00019821592,0.001455128,0.0004175648,0.022797838,0.10963356,0.042342905,0.017376736,0.79839414],"study_design_scores_gemma":[0.00027215577,0.00096816395,0.009841345,0.00056604657,0.00066462735,0.003796665,0.00057125866,0.6834383,0.13866916,0.06417287,0.0967483,0.00029106945],"about_ca_topic_score_codex":0.0035388027,"about_ca_topic_score_gemma":0.0030368692,"teacher_disagreement_score":0.011574984,"about_ca_system_score_codex":0.0009536472,"about_ca_system_score_gemma":0.0014050679,"threshold_uncertainty_score":0.038722157},"labels":[],"label_agreement":null},{"id":"W2101444506","doi":"10.1109/wescan.1997.627153","title":"ATM data classification with an artificial neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Manitoba","funders":"","keywords":"Computer science; Supervisor; Artificial neural network; Artificial intelligence; Time delay neural network; Machine learning; Competitive learning; Data classification; Data mining; Pattern recognition (psychology)","score_opus":0.15236177511449034,"score_gpt":0.28801844941331195,"score_spread":0.1356566742988216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101444506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06398406,0.00044095455,0.9258054,0.0004031283,0.0002612082,0.0002821938,0.00037345762,0.004114219,0.0043353876],"genre_scores_gemma":[0.5442202,0.0002724115,0.44625294,0.00024253632,0.00015708157,0.000439053,0.0007161198,0.000089349385,0.0076102787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994547,0.00012036055,0.000053632826,0.00015270223,0.00014901177,0.00006954099],"domain_scores_gemma":[0.9990158,0.00040376684,0.00006891225,0.00007739543,0.00040614075,0.000027933336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012490638,0.0007273919,0.00063899986,0.0014114596,0.0006336591,0.0012984761,0.0013763483,0.001671635,0.0031420174],"category_scores_gemma":[0.003533033,0.00043967483,0.00077293545,0.0013957721,0.0003824315,0.0010658436,0.0007145747,0.0012878138,0.0010335201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038996717,0.00022551703,0.003166427,0.00009778908,0.000112189104,0.00015298498,0.00007528759,0.5034174,0.0077369227,0.003608592,0.003973809,0.4770431],"study_design_scores_gemma":[0.0000025444717,0.000015676507,0.00015710804,0.0000035119806,0.0000050924073,0.000009109827,0.0000048952,0.99805033,0.0008922091,0.0005228187,0.0003329281,0.0000037377495],"about_ca_topic_score_codex":0.008220347,"about_ca_topic_score_gemma":0.0061757662,"teacher_disagreement_score":0.008220347,"about_ca_system_score_codex":0.0009242799,"about_ca_system_score_gemma":0.0007695492,"threshold_uncertainty_score":0.016344965},"labels":[],"label_agreement":null},{"id":"W2101827833","doi":"10.1109/iscas.1993.394239","title":"A fuzzy neural classifier for pattern classification","year":2002,"lang":"en","type":"article","venue":"1993 IEEE International Symposium on Circuits and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Classifier (UML); Artificial intelligence; Fuzzy logic; Computer science; Artificial neural network; Machine learning; Membership function; Fuzzy classification; Fuzzy set; Pattern recognition (psychology)","score_opus":0.07631004241905755,"score_gpt":0.27897085967965823,"score_spread":0.2026608172606007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101827833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003078387,0.00093639543,0.99245363,0.00020841985,0.00027029985,0.00009076163,0.00014652857,0.0008518355,0.0019636913],"genre_scores_gemma":[0.07937708,0.0011782662,0.9094242,0.00036120284,0.00036123142,0.00035775252,0.0005374536,0.000083332234,0.008319371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898595,0.00014060039,0.00006914791,0.00023000558,0.000497799,0.00007645987],"domain_scores_gemma":[0.9993749,0.00016081837,0.00004114244,0.00007703474,0.00031670654,0.000029473136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011203298,0.00064947223,0.0011584989,0.0015873535,0.00071293366,0.0013128307,0.0013668499,0.0021085609,0.0034450986],"category_scores_gemma":[0.0029461556,0.0003033344,0.0007015002,0.0016961996,0.00044346758,0.0018228403,0.0006601926,0.0013732263,0.0022749265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012167436,0.00009676905,0.00076377613,0.00022147065,0.00006446288,0.00020681821,0.00004666495,0.038960867,0.023638714,0.018075634,0.014282137,0.9035211],"study_design_scores_gemma":[0.000017593571,0.000056582176,0.0005353122,0.000041615738,0.000029365676,0.000334511,0.000015023542,0.9640305,0.008284064,0.009139459,0.01748424,0.00003170188],"about_ca_topic_score_codex":0.003399293,"about_ca_topic_score_gemma":0.0033701502,"teacher_disagreement_score":0.0034450986,"about_ca_system_score_codex":0.0007573691,"about_ca_system_score_gemma":0.0010362046,"threshold_uncertainty_score":0.011524975},"labels":[],"label_agreement":null},{"id":"W2102323492","doi":"10.1109/tvcg.2008.85","title":"Glimmer: Multilevel MDS on the GPU","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":186,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Graphics processing unit; Parallel computing; CUDA; Benchmark (surveying); General-purpose computing on graphics processing units; Computation; Graphics; Memory hierarchy; Maxima and minima; Algorithm; Computational science; Computer graphics (images); Mathematics","score_opus":0.039512195038094486,"score_gpt":0.2667145617690557,"score_spread":0.22720236673096122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102323492","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009612871,0.00015943662,0.97661716,0.00021053103,0.00008483693,0.000049159207,0.0006020755,0.009534311,0.0031297063],"genre_scores_gemma":[0.07514013,0.00014255116,0.91898245,0.00009846602,0.000036153462,0.00014739651,0.0010124947,0.0015205167,0.0029198735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969304,0.000042682324,0.00001746665,0.000054160908,0.0001644274,0.00002818932],"domain_scores_gemma":[0.9995807,0.000089812616,0.00004345938,0.00014897852,0.00010575086,0.000031296826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003605615,0.0007827679,0.0007889648,0.0008909791,0.0006178472,0.0010012611,0.0013016212,0.0008110119,0.008637014],"category_scores_gemma":[0.001902704,0.000412133,0.0008312688,0.0009589293,0.00037797054,0.0009794057,0.0019607814,0.001260179,0.0028307885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002516995,0.000058642163,0.001920196,0.00031471846,0.00018432965,0.00022577985,0.00031676344,0.34255314,0.03365056,0.043324023,0.05106656,0.52613354],"study_design_scores_gemma":[0.000036927897,0.000039275066,0.0002608059,0.000018662604,0.000010425154,0.00007258193,0.000025317631,0.95811653,0.010787978,0.012252818,0.018353881,0.00002477142],"about_ca_topic_score_codex":0.0034312408,"about_ca_topic_score_gemma":0.006166069,"teacher_disagreement_score":0.008637014,"about_ca_system_score_codex":0.0004789116,"about_ca_system_score_gemma":0.0009909936,"threshold_uncertainty_score":0.02889371},"labels":[],"label_agreement":null},{"id":"W2102340788","doi":"10.1109/ijcnn.1998.682305","title":"Network generating attribute grammar encoding","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Encoding (memory); Computer science; ENCODE; Parsing; Parse tree; Grammar; Artificial neural network; Tree (set theory); Rule-based machine translation; Artificial intelligence; Grammar induction; Representation (politics); Theoretical computer science; Mathematics","score_opus":0.047945743018864354,"score_gpt":0.2325059632233347,"score_spread":0.18456022020447033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102340788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02228689,0.00010386584,0.9596683,0.00032970193,0.00011552669,0.000085580345,0.00060906773,0.0027058076,0.014095268],"genre_scores_gemma":[0.42346716,0.00024826213,0.5627268,0.00020569343,0.00006012693,0.0002298443,0.0019602738,0.0008581731,0.010243643],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952996,0.00010869833,0.000036091194,0.00012369188,0.00015148283,0.000050158764],"domain_scores_gemma":[0.99885976,0.00040998255,0.000048157635,0.00034084602,0.00030197803,0.000039370956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005580601,0.00032236334,0.00037753658,0.0005924781,0.00042689283,0.0011393331,0.0009530435,0.0006321531,0.0066810315],"category_scores_gemma":[0.0037756916,0.00018867779,0.0006506894,0.0006850195,0.00086371857,0.0015122052,0.0009661935,0.0009386833,0.0012533756],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012429082,0.00005639347,0.00088501826,0.00015094453,0.000027665117,0.00030743226,0.0003200586,0.098929,0.019563064,0.66411775,0.00660695,0.2089115],"study_design_scores_gemma":[0.000024007113,0.000040549636,0.00022581617,0.000032502416,0.000033127668,0.00022461898,0.000056662142,0.5938199,0.027909946,0.35034037,0.027265018,0.000027445578],"about_ca_topic_score_codex":0.0012574166,"about_ca_topic_score_gemma":0.0017004383,"teacher_disagreement_score":0.0066810315,"about_ca_system_score_codex":0.0010588827,"about_ca_system_score_gemma":0.0010017067,"threshold_uncertainty_score":0.022350252},"labels":[],"label_agreement":null},{"id":"W2102473090","doi":"10.1109/aamas.2004.130","title":"From Global Selective Perception to Local Selective Perception","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Reinforcement learning; Computer science; Perception; Task (project management); Artificial intelligence; Machine learning; Human–computer interaction; Engineering; Psychology","score_opus":0.009403027730984318,"score_gpt":0.26104252843607945,"score_spread":0.2516395007050951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102473090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016956357,0.0011707665,0.96851146,0.00076338573,0.00007889011,0.000025595193,0.000022435615,0.0003566462,0.01211454],"genre_scores_gemma":[0.79601127,0.0013011049,0.19550534,0.0005273386,0.00014698273,0.0000942853,0.000059074355,0.000164559,0.006190072],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999537,0.00014107683,0.000015104471,0.00013416595,0.00010887775,0.000063775544],"domain_scores_gemma":[0.99913365,0.00044306373,0.00007358076,0.00014916819,0.00011230624,0.00008816565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076983345,0.00056096975,0.0004161925,0.00023453667,0.00037698128,0.001236482,0.0009646891,0.00075703673,0.0024261754],"category_scores_gemma":[0.0021272525,0.00029759054,0.0003909177,0.0002551217,0.0023559986,0.0027490754,0.0017383156,0.0017957115,0.0003726478],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034817233,0.00009590741,0.0014962066,0.00033020662,0.00011080453,0.00023338966,0.000844689,0.19472459,0.018416917,0.5123587,0.004404861,0.26663557],"study_design_scores_gemma":[0.00004750868,0.00017861597,0.0006821891,0.00004120829,0.000055040862,0.0001282589,0.0001223953,0.66978574,0.0062856814,0.31351757,0.0091218455,0.00003389902],"about_ca_topic_score_codex":0.0015506101,"about_ca_topic_score_gemma":0.0011793509,"teacher_disagreement_score":0.0024261754,"about_ca_system_score_codex":0.0006820279,"about_ca_system_score_gemma":0.0005341338,"threshold_uncertainty_score":0.0081163645},"labels":[],"label_agreement":null},{"id":"W2102854440","doi":"10.1109/wescan.1993.270529","title":"Neural equalization of communication channels","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Impulse (physics); Channel (broadcasting); Infinite impulse response; SIGNAL (programming language); Inverse; Equalization (audio); Nonlinear system; Distortion (music); Artificial neural network; Algorithm; Topology (electrical circuits); Artificial intelligence; Physics; Mathematics; Digital filter; Computer vision; Telecommunications; Combinatorics; Filter (signal processing)","score_opus":0.04934760969187874,"score_gpt":0.26096008662297887,"score_spread":0.21161247693110014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102854440","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009937891,0.0005647363,0.97877765,0.0002351048,0.00015107657,0.000024565557,0.00002524866,0.00041113622,0.009872702],"genre_scores_gemma":[0.62814265,0.0016373681,0.34042385,0.00038578262,0.0003084407,0.00010773604,0.00013052221,0.00014139246,0.028722316],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997899,0.000042351043,0.000009738371,0.000052318956,0.00007316725,0.00003254493],"domain_scores_gemma":[0.9997795,0.00009603878,0.000017752602,0.00003152261,0.00006527838,0.000009812224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023906653,0.000362194,0.00027397048,0.00030705496,0.00033701866,0.0005498433,0.00056184945,0.0005318383,0.0032053492],"category_scores_gemma":[0.001255534,0.00013808071,0.00025976085,0.00030178393,0.00041717832,0.0010480428,0.00070875726,0.0005730404,0.0006897834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001805751,0.00006856349,0.0004448869,0.00015495438,0.000049936923,0.00016100494,0.00009773166,0.233157,0.043513834,0.13139386,0.0035001908,0.5872774],"study_design_scores_gemma":[0.00001476617,0.00007425417,0.00028301528,0.000023584866,0.000019924191,0.00018857289,0.00002505377,0.9242846,0.02608695,0.034763876,0.014212661,0.000022829607],"about_ca_topic_score_codex":0.0011328177,"about_ca_topic_score_gemma":0.0013827559,"teacher_disagreement_score":0.0032053492,"about_ca_system_score_codex":0.00042050285,"about_ca_system_score_gemma":0.00032856798,"threshold_uncertainty_score":0.010722995},"labels":[],"label_agreement":null},{"id":"W2102899874","doi":"10.1109/ijcnn.2000.860808","title":"Application of feedforward neuro-fuzzy networks for current prediction in electric arc furnaces","year":2000,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Feed forward; Neuro-fuzzy; Computer science; Adaptive neuro fuzzy inference system; Fuzzy control system; Fuzzy logic; Feedforward neural network; Nonlinear system; Novelty; Artificial neural network; Artificial intelligence; Control engineering; Engineering","score_opus":0.010655081811597878,"score_gpt":0.2454524227019993,"score_spread":0.2347973408904014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102899874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08949707,0.0013741282,0.9038759,0.00025314116,0.000099976474,0.000050361396,0.000063620595,0.00050923007,0.0042766565],"genre_scores_gemma":[0.94926786,0.000384313,0.048867214,0.000034173667,0.000023862207,0.000035590274,0.000041697196,0.000009913892,0.0013353871],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998559,0.000043740736,0.000010615617,0.000025160867,0.00005095365,0.0000136475655],"domain_scores_gemma":[0.99936765,0.0004148543,0.0000478011,0.000027207196,0.00013089966,0.000011606953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063202094,0.00057988416,0.00035238,0.00028926344,0.00031122292,0.0005231445,0.00058490434,0.00084278494,0.00084787153],"category_scores_gemma":[0.0020242454,0.00021313636,0.00025334882,0.00032894942,0.00030887825,0.00048759443,0.00028413412,0.0005061306,0.00016693889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010806044,0.000038729555,0.0010874323,0.00008151574,0.000024590057,0.0001109021,0.00004824119,0.88398737,0.004335398,0.0019646473,0.00033835284,0.10787484],"study_design_scores_gemma":[0.0000032507378,0.00002343788,0.00020748451,0.0000094529505,0.000005026527,0.000013386826,0.0000046655487,0.99670357,0.0017165012,0.0011086598,0.00020048162,0.0000040369023],"about_ca_topic_score_codex":0.004366145,"about_ca_topic_score_gemma":0.004470975,"teacher_disagreement_score":0.004366145,"about_ca_system_score_codex":0.00030722478,"about_ca_system_score_gemma":0.00028694954,"threshold_uncertainty_score":0.008681476},"labels":[],"label_agreement":null},{"id":"W2103121832","doi":"10.1109/icnn.1993.298744","title":"Design of fault tolerant neurocontrollers using immunization technique","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Fault tolerance; Nonlinear system; Computer science; Artificial neural network; Fault (geology); Noise (video); Control theory (sociology); Control (management); Artificial intelligence; Architecture; Control engineering; Engineering; Distributed computing","score_opus":0.09625146585140336,"score_gpt":0.2964927239642496,"score_spread":0.2002412581128462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103121832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033977985,0.0001897125,0.9601364,0.00014829428,0.000088270885,0.00012234949,0.00001989366,0.0015899598,0.0037271082],"genre_scores_gemma":[0.8518061,0.0001329872,0.14478154,0.00016722,0.000029847866,0.00031734415,0.000035772617,0.00004423007,0.002684911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998049,0.0000273486,0.000014540823,0.000046824167,0.00007157435,0.00003471622],"domain_scores_gemma":[0.9995888,0.00009114178,0.000100457044,0.000036511246,0.00015530466,0.000027905198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043909953,0.000494365,0.00039436633,0.00039463842,0.00033628114,0.0005750522,0.00080468075,0.00082794466,0.0012172387],"category_scores_gemma":[0.0011195719,0.00027479636,0.0003210069,0.00021421615,0.0004004868,0.00035929278,0.00031141436,0.00066507637,0.000364678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016396696,0.0001572368,0.0010149468,0.00014776835,0.00009120559,0.00021753501,0.00017988133,0.71702826,0.06600536,0.014689669,0.00193683,0.19836737],"study_design_scores_gemma":[0.000041591982,0.00014895202,0.00034211378,0.000014608766,0.000022665376,0.000057783695,0.00001241497,0.98316276,0.012451046,0.0022632598,0.0014716776,0.000011082403],"about_ca_topic_score_codex":0.0018723559,"about_ca_topic_score_gemma":0.0017947681,"teacher_disagreement_score":0.0018723559,"about_ca_system_score_codex":0.00050600007,"about_ca_system_score_gemma":0.0006611758,"threshold_uncertainty_score":0.00407207},"labels":[],"label_agreement":null},{"id":"W2103139761","doi":"10.1109/iscas.2009.5118448","title":"Current-mode multiple-valued dynamic memory","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Dram; Converters; Modular design; Computer science; Noise margin; Margin (machine learning); Dynamic random-access memory; Reduction (mathematics); Electronic engineering; Noise (video); Computer hardware; Semiconductor memory; Voltage; Engineering; Electrical engineering; Artificial intelligence","score_opus":0.014145203772010036,"score_gpt":0.29551769684233603,"score_spread":0.28137249307032597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103139761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22884265,0.0103769805,0.71388376,0.0008207251,0.0014863004,0.00014064343,0.00038138533,0.0033505447,0.040716976],"genre_scores_gemma":[0.90726644,0.0012413348,0.08047462,0.00033914627,0.00015316317,0.000039641804,0.0002143923,0.000040792343,0.010230446],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999043,0.000009195499,0.000007117925,0.000027007287,0.000039357437,0.000012967312],"domain_scores_gemma":[0.9998596,0.000021402924,0.00001889551,0.00003585286,0.000050257582,0.000013880078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011167406,0.00018136432,0.00024289105,0.00020820882,0.00023587277,0.00066578196,0.0014580645,0.00028632235,0.0039114817],"category_scores_gemma":[0.00021222464,0.000098963246,0.0001549435,0.00028484987,0.0002078839,0.000965004,0.0002924022,0.0002932498,0.00078188896],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004450481,0.00015929663,0.0021309548,0.0007592619,0.00010671481,0.0010646175,0.00015533208,0.011854567,0.41267484,0.08763448,0.007923129,0.47509167],"study_design_scores_gemma":[0.00011733789,0.0019503551,0.0016707666,0.00014195516,0.00023697209,0.008000203,0.0001644742,0.26103085,0.55376893,0.022949426,0.14982694,0.00014173953],"about_ca_topic_score_codex":0.00013283353,"about_ca_topic_score_gemma":0.00029086685,"teacher_disagreement_score":0.0039114817,"about_ca_system_score_codex":0.00023610453,"about_ca_system_score_gemma":0.00020473811,"threshold_uncertainty_score":0.013085246},"labels":[],"label_agreement":null},{"id":"W2103255058","doi":"","title":"Relative Density Nets: A New Way to Combine Backpropagation with HMM's","year":2001,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hidden Markov model; Discriminative model; Computer science; Backpropagation; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Feedforward neural network; Speech recognition; Feed forward; Point (geometry); Machine learning; Mathematics; Engineering","score_opus":0.01587924644970704,"score_gpt":0.2339953062531079,"score_spread":0.21811605980340087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103255058","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018803371,0.00019378934,0.995404,0.00006647192,0.00007294569,0.00003319407,0.000032395208,0.0015818977,0.00073504844],"genre_scores_gemma":[0.10458073,0.00053155265,0.887506,0.00022255872,0.00014866696,0.00020607615,0.00021128546,0.00047249324,0.006120641],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909055,0.00023489163,0.00005065771,0.00020271917,0.00035370397,0.00006748369],"domain_scores_gemma":[0.99891925,0.0005497921,0.00007899674,0.00015043207,0.00024338625,0.000058192076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001967414,0.0015191089,0.0010908616,0.0010630277,0.00043755962,0.0013470742,0.002723686,0.001335224,0.004197994],"category_scores_gemma":[0.004823716,0.0010854783,0.0008653891,0.0011044613,0.0008026448,0.00335327,0.002090392,0.0024835262,0.0019434006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020152732,0.00019544715,0.00088427245,0.00024585857,0.00030208117,0.00016764003,0.00016523457,0.32870522,0.010076244,0.040845804,0.006217844,0.61199284],"study_design_scores_gemma":[0.00001168428,0.00003349121,0.00010300378,0.000012761179,0.000021320711,0.000030496216,0.000005504602,0.9834157,0.0027681193,0.011215631,0.0023678334,0.0000144279265],"about_ca_topic_score_codex":0.0044454327,"about_ca_topic_score_gemma":0.006182783,"teacher_disagreement_score":0.0044454327,"about_ca_system_score_codex":0.0006760239,"about_ca_system_score_gemma":0.00082449435,"threshold_uncertainty_score":0.014043629},"labels":[],"label_agreement":null},{"id":"W2103287642","doi":"10.5539/mas.v3n1p95","title":"A Proposed Technique for Analyzing Experiments Of Type","year":2008,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Type (biology); Geology","score_opus":0.045005147585343214,"score_gpt":0.29388293073549104,"score_spread":0.24887778315014783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103287642","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008501601,0.00007800983,0.9811932,0.00008299338,0.00014229405,0.0005698046,0.001152925,0.0066149356,0.0016642582],"genre_scores_gemma":[0.096018106,0.000057975656,0.8940431,0.00017610735,0.00016781541,0.003405906,0.0019638282,0.00066819234,0.0034989528],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9903334,0.00274695,0.00080398924,0.0034041537,0.002403771,0.0003077569],"domain_scores_gemma":[0.94807225,0.032318592,0.002945224,0.010504546,0.005594074,0.000565149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073997946,0.0014328067,0.0015934956,0.0029240004,0.00079296785,0.0019277646,0.0017433182,0.0017106648,0.019605046],"category_scores_gemma":[0.03282168,0.00045819362,0.0018345051,0.0016674339,0.00091400946,0.0019975805,0.0009136051,0.0016028427,0.004719921],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017288419,0.00048184633,0.008268209,0.00075418793,0.00043181205,0.00022035534,0.00029669618,0.0063721873,0.087642945,0.014170156,0.010007048,0.86962575],"study_design_scores_gemma":[0.0005103505,0.0020756675,0.032839864,0.00018483716,0.00074446475,0.0013974332,0.00050422223,0.6686333,0.17071086,0.07330645,0.048863046,0.00022949606],"about_ca_topic_score_codex":0.0007087063,"about_ca_topic_score_gemma":0.0010440014,"teacher_disagreement_score":0.019605046,"about_ca_system_score_codex":0.0008359218,"about_ca_system_score_gemma":0.0014922993,"threshold_uncertainty_score":0.065585375},"labels":[],"label_agreement":null},{"id":"W2103351277","doi":"10.1109/mwscas.1993.343335","title":"A multi-layer neural network architecture with external weight memory","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Very-large-scale integration; Computer science; Architecture; Artificial neural network; Layer (electronics); Computer architecture; Artificial intelligence; Chip; Classifier (UML); Computer hardware; Embedded system","score_opus":0.02276131971544176,"score_gpt":0.22549087144753185,"score_spread":0.2027295517320901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103351277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029647402,0.0011170186,0.9522862,0.0003022073,0.00025622576,0.000070739276,0.00019719126,0.004605995,0.011517059],"genre_scores_gemma":[0.44023862,0.000819,0.5333462,0.00042017942,0.00011480386,0.00018460049,0.00048807298,0.00013828615,0.024250131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998723,0.000013244354,0.000008183276,0.00003622001,0.00004610995,0.000023816747],"domain_scores_gemma":[0.99989974,0.0000164936,0.000009860178,0.000018098684,0.000043467862,0.000012263082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001901071,0.00038657602,0.00027510332,0.00030235274,0.0002951276,0.00067862554,0.0015699473,0.00075103756,0.0057805344],"category_scores_gemma":[0.00035002455,0.00027605417,0.0002707875,0.00040543443,0.00021327338,0.0010790516,0.00052242074,0.0006098517,0.0019287815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039208785,0.00020274936,0.0011302296,0.00047383466,0.0001345017,0.00034452663,0.00010213153,0.09883541,0.21241842,0.021214318,0.008782494,0.6559693],"study_design_scores_gemma":[0.00006889758,0.00033764614,0.0012326025,0.000073309406,0.00012324456,0.00038902176,0.00001827196,0.8714939,0.09214127,0.0063633034,0.027704226,0.000054237615],"about_ca_topic_score_codex":0.0017955285,"about_ca_topic_score_gemma":0.0038023049,"teacher_disagreement_score":0.0057805344,"about_ca_system_score_codex":0.0004044329,"about_ca_system_score_gemma":0.0004537161,"threshold_uncertainty_score":0.019337833},"labels":[],"label_agreement":null},{"id":"W2103431437","doi":"10.1109/ccece.2003.1226270","title":"An algorithm based on evolutionary programming for training artificial neural networks with nonconventional neurons","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Toronto","funders":"","keywords":"Artificial neural network; Computer science; Evolutionary algorithm; Artificial intelligence; Evolutionary programming; Evolutionary acquisition of neural topologies; Evolutionary computation; Training (meteorology); Machine learning; Algorithm","score_opus":0.02952367676772761,"score_gpt":0.268564208788235,"score_spread":0.2390405320205074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103431437","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004408581,0.00007141885,0.99447614,0.000036191133,0.000025103029,0.00003112937,0.000004790289,0.00009714945,0.0008495239],"genre_scores_gemma":[0.13110392,0.00018236486,0.8661626,0.000088731016,0.000031456602,0.00039641777,0.000048585407,0.000071468996,0.001914379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997216,0.000086305896,0.000019128296,0.000056644527,0.00009626255,0.000019981695],"domain_scores_gemma":[0.99969316,0.0001867952,0.000022840193,0.000024688836,0.00006120197,0.000011238064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007892595,0.00063104194,0.0005886442,0.0004112551,0.00030532887,0.00045638482,0.0008148063,0.0009787794,0.0018326282],"category_scores_gemma":[0.0017130474,0.0003223403,0.00039417204,0.00049623736,0.00049469917,0.0005571324,0.00071329414,0.0010064834,0.00026150953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054304564,0.000084182466,0.0006096413,0.00015109756,0.00007882009,0.00011259005,0.00014275442,0.68396944,0.008300039,0.03562087,0.0010136894,0.2698625],"study_design_scores_gemma":[0.000015810823,0.000053231972,0.000096659074,0.000012262294,0.000012766805,0.000047799404,0.0000062912104,0.99306947,0.001277426,0.0038542934,0.0015466743,0.000007221918],"about_ca_topic_score_codex":0.0007363945,"about_ca_topic_score_gemma":0.00091727235,"teacher_disagreement_score":0.0018326282,"about_ca_system_score_codex":0.00021241412,"about_ca_system_score_gemma":0.00043504048,"threshold_uncertainty_score":0.006130755},"labels":[],"label_agreement":null},{"id":"W2103761081","doi":"10.1109/acc.2002.1025361","title":"Comparison of fault detection techniques: problem and solution","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Orthonormal basis; Boundary (topology); Computer science; Artificial intelligence; Projection (relational algebra); Haar; Pattern recognition (psychology); Mathematics; Algorithm; Mathematical analysis; Wavelet","score_opus":0.034529177661534155,"score_gpt":0.2852956830862606,"score_spread":0.25076650542472645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103761081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015029776,0.0019715894,0.97962725,0.00046361942,0.000057324534,0.00011500019,0.00006961173,0.0005081509,0.0021576083],"genre_scores_gemma":[0.25691247,0.0029670964,0.7358315,0.00012824794,0.00014876667,0.00020690859,0.0003048261,0.00013212503,0.0033680627],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980281,0.0006439658,0.00013816658,0.00035294224,0.0007301252,0.00010675047],"domain_scores_gemma":[0.9957924,0.0027114374,0.0002779316,0.00026500446,0.00088151026,0.00007176801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031573214,0.001082207,0.0017733047,0.0023907651,0.0005705832,0.0017683577,0.0014906611,0.0028339117,0.0025230295],"category_scores_gemma":[0.010378848,0.00045261995,0.0008907222,0.0019008233,0.00054436916,0.002519813,0.0012797249,0.0010419308,0.0008652672],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002929126,0.0001555987,0.0017560802,0.00043566752,0.00008741782,0.00006597536,0.00009526372,0.138004,0.0027859863,0.008489181,0.0017005975,0.8461313],"study_design_scores_gemma":[0.000036099045,0.00024893857,0.0015257206,0.00007334892,0.0000448877,0.000308653,0.00015480239,0.9756565,0.005685021,0.012420384,0.0038174507,0.00002815456],"about_ca_topic_score_codex":0.0018370985,"about_ca_topic_score_gemma":0.001168286,"teacher_disagreement_score":0.0031573214,"about_ca_system_score_codex":0.00074168574,"about_ca_system_score_gemma":0.0011044771,"threshold_uncertainty_score":0.016697705},"labels":[],"label_agreement":null},{"id":"W2104197126","doi":"10.1109/icsmc.1995.538037","title":"Techniques for confident and reliable fault detection in large scale engineering plants","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Fault detection and isolation; Scale (ratio); Domain (mathematical analysis); Fault (geology); Algorithm; Artificial intelligence; Mathematics; Actuator","score_opus":0.010494034906611476,"score_gpt":0.22095778982390013,"score_spread":0.21046375491728866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104197126","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029297494,0.000104455656,0.99614245,0.000040651437,0.00000792473,0.0000101072155,0.000009229091,0.00044392195,0.00031150726],"genre_scores_gemma":[0.44128424,0.00049491687,0.5558415,0.000091969676,0.000058612382,0.0001301843,0.00007815137,0.00014239083,0.0018780825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990133,0.00021103615,0.000055365286,0.00016390963,0.0004958416,0.000060490434],"domain_scores_gemma":[0.9967069,0.0014283286,0.0005982713,0.0008615415,0.00034946794,0.000055587352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011932919,0.00083009095,0.0008754636,0.0006298916,0.00037625714,0.000730978,0.0016658765,0.0011696841,0.0014544964],"category_scores_gemma":[0.007153026,0.00065653364,0.0005956648,0.0005924959,0.00089013815,0.0021713132,0.0014620374,0.0018843146,0.0005013379],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023459371,0.00009625204,0.0007933815,0.00026656353,0.00008471917,0.00027046102,0.0003577869,0.5903679,0.069226205,0.050078776,0.0018721501,0.28635123],"study_design_scores_gemma":[0.000016006405,0.000057197456,0.00022496497,0.000013526917,0.000013505212,0.0001228663,0.00001786524,0.967307,0.009000703,0.021808445,0.0013945652,0.000023407261],"about_ca_topic_score_codex":0.0010155269,"about_ca_topic_score_gemma":0.0014109141,"teacher_disagreement_score":0.0016658765,"about_ca_system_score_codex":0.0005633104,"about_ca_system_score_gemma":0.0005679257,"threshold_uncertainty_score":0.006310761},"labels":[],"label_agreement":null},{"id":"W2104260617","doi":"10.1109/ijcnn.1991.155371","title":"On fuzzy neuron models","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Fuzzy logic; Artificial intelligence; Computer science; Neuro-fuzzy; Artificial neural network; Biological neuron model; Fuzzy set; Neuron; Fuzzy control system; Mathematics; Neuroscience; Psychology","score_opus":0.04183838465852903,"score_gpt":0.22563276539831376,"score_spread":0.1837943807397847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104260617","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007966333,0.016088927,0.89522,0.0028698018,0.001240826,0.000049058817,0.00020696591,0.00016469177,0.07619335],"genre_scores_gemma":[0.58771807,0.04486121,0.24901046,0.0021568278,0.0023693235,0.00039563607,0.0006982609,0.0002731213,0.11251708],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995747,0.00011863598,0.000021392112,0.00007168881,0.00016016598,0.000053368123],"domain_scores_gemma":[0.999567,0.00020558323,0.000034477158,0.000045218563,0.00011764562,0.000030184374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075494865,0.0007541469,0.00081406254,0.00091058516,0.0007833652,0.0016927095,0.0015762097,0.0018932023,0.0058873864],"category_scores_gemma":[0.0024560043,0.00038691322,0.001064557,0.0009842282,0.0014908123,0.0030807252,0.0014916471,0.002112788,0.001585457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013304503,0.000011700227,0.00017484812,0.00010825396,0.000026871683,0.000102679456,0.00011404494,0.05868918,0.00080810254,0.9169898,0.0037406718,0.019220605],"study_design_scores_gemma":[0.0000059074823,0.00001564616,0.00009323119,0.00007950824,0.000010674659,0.00009798669,0.000028665194,0.19966862,0.00031542382,0.777665,0.022000615,0.000018681907],"about_ca_topic_score_codex":0.0048312484,"about_ca_topic_score_gemma":0.003435788,"teacher_disagreement_score":0.0058873864,"about_ca_system_score_codex":0.0013458245,"about_ca_system_score_gemma":0.000903133,"threshold_uncertainty_score":0.019695282},"labels":[],"label_agreement":null},{"id":"W2104592023","doi":"10.1109/acc.2001.946194","title":"The Kalman filter based recursive algorithm: windup and its avoidance","year":2001,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Kalman filter; Covariance matrix; Algorithm; Control theory (sociology); Covariance; Extended Kalman filter; Estimator; Mathematics; Matrix (chemical analysis); Covariance intersection; Constant (computer programming); Recursive least squares filter; Least-squares function approximation; Computer science; Adaptive filter; Estimation of covariance matrices; Statistics; Artificial intelligence","score_opus":0.01644764842112028,"score_gpt":0.24089403193146697,"score_spread":0.22444638351034668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104592023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015138539,0.00033194915,0.9968406,0.000062761326,0.00004218397,0.000011978654,0.000005616526,0.00023873743,0.00095242576],"genre_scores_gemma":[0.28693655,0.0018046483,0.6982537,0.00023214222,0.00018564153,0.00020157175,0.00011609187,0.00026516026,0.012004451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992642,0.00017083193,0.000037465827,0.00015198119,0.0003148256,0.000060653987],"domain_scores_gemma":[0.99935,0.0002701069,0.00007390279,0.000085822554,0.00020360835,0.000016589343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010758959,0.0005814828,0.00097543397,0.00039109326,0.00052693416,0.001009787,0.0010346037,0.0012517519,0.0015754866],"category_scores_gemma":[0.003520127,0.000439231,0.00050352537,0.0005485025,0.0008386368,0.0015311007,0.0010223269,0.0013732158,0.0010131114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012228341,0.000037158858,0.0010558685,0.00021637077,0.000109566805,0.00017335943,0.00028295576,0.2987284,0.009937046,0.15794365,0.0039786715,0.52741474],"study_design_scores_gemma":[0.000017764394,0.00007196234,0.00038327294,0.00003222977,0.000030501094,0.00015531921,0.000016230802,0.9681792,0.003863363,0.018194718,0.00901679,0.00003861116],"about_ca_topic_score_codex":0.0048525077,"about_ca_topic_score_gemma":0.004003661,"teacher_disagreement_score":0.0048525077,"about_ca_system_score_codex":0.00045370654,"about_ca_system_score_gemma":0.0010996012,"threshold_uncertainty_score":0.009648502},"labels":[],"label_agreement":null},{"id":"W2104618927","doi":"10.1109/ijcnn.1999.831108","title":"A self-scaling procedure in unsupervised correlational neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Vigilance (psychology); Categorization; Artificial neural network; Artificial intelligence; Computer science; Machine learning; Unsupervised learning; Multidimensional scaling; Scaling; Cognitive psychology; Psychology; Mathematics","score_opus":0.010729680871529755,"score_gpt":0.22180174745228645,"score_spread":0.2110720665807567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104618927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049600508,0.00018940412,0.94711787,0.00019640593,0.00005052166,0.00014609407,0.000043331656,0.0006544111,0.0020014422],"genre_scores_gemma":[0.5757906,0.00019507471,0.42078677,0.00022643224,0.000080375255,0.0005481448,0.00010712495,0.00021497517,0.0020505015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894816,0.00043955471,0.00009093044,0.0002735498,0.00019368141,0.000054194923],"domain_scores_gemma":[0.99475104,0.0022320324,0.00048373808,0.0017758068,0.00062368024,0.00013377475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031377266,0.00044054014,0.0006196735,0.00067329826,0.00070895016,0.00077711313,0.0011027702,0.00076261105,0.0016619309],"category_scores_gemma":[0.016899966,0.0003689855,0.00074322324,0.00081984344,0.0017336824,0.0017623621,0.0014759684,0.0014369442,0.00041641906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003019797,0.00029452893,0.0042111897,0.00034617117,0.00022798513,0.00030876097,0.000983125,0.21126744,0.061470438,0.22904208,0.0034196547,0.48812667],"study_design_scores_gemma":[0.00002918584,0.00013143508,0.0017243935,0.00003306022,0.000023178261,0.00013961161,0.00004028452,0.90883166,0.00936813,0.07744683,0.0021955625,0.000036634763],"about_ca_topic_score_codex":0.0006707397,"about_ca_topic_score_gemma":0.0007639705,"teacher_disagreement_score":0.0031377266,"about_ca_system_score_codex":0.0004910736,"about_ca_system_score_gemma":0.00059862057,"threshold_uncertainty_score":0.016594052},"labels":[],"label_agreement":null},{"id":"W2105011932","doi":"10.1142/s0217979200002065","title":"SELF-ORGANIZING NEURAL NETWORK MODEL OF THE SENSORY-MOTOR MECHANISM","year":2000,"lang":"en","type":"article","venue":"International Journal of Modern Physics B","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Mechanism (biology); Sensory system; Artificial neural network; Feature (linguistics); Artificial intelligence; Connection (principal bundle); Neuroscience; Psychology","score_opus":0.019014081676539688,"score_gpt":0.23863562067257882,"score_spread":0.21962153899603915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105011932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084989324,0.0012186029,0.86547196,0.0010687747,0.0002932818,0.000107013664,0.00045694795,0.0005487385,0.045845393],"genre_scores_gemma":[0.89700675,0.00081994565,0.0761376,0.00017122178,0.00010153255,0.00032952876,0.0002084515,0.00003772534,0.025187185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989057,0.000024339128,0.0000045711545,0.000028166942,0.00003691654,0.000015453255],"domain_scores_gemma":[0.9999341,0.000018209574,0.000010700396,0.000008046713,0.000018804261,0.000010080469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017763501,0.00030827723,0.0003931389,0.0002260558,0.00026773414,0.0005638739,0.0013799749,0.0009933198,0.00284534],"category_scores_gemma":[0.00031917013,0.00016955943,0.0003686566,0.00029218316,0.00053364347,0.00083534257,0.00037053085,0.00053671486,0.00058234774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005254768,0.000049301078,0.00033463803,0.00009927212,0.00004165575,0.00028556754,0.0000794611,0.81129795,0.009389309,0.16475318,0.0015208825,0.0120962765],"study_design_scores_gemma":[0.00001413574,0.000018539644,0.00010322,0.000004151926,0.000007243533,0.000046191286,0.0000068742847,0.9776693,0.00046918023,0.020050565,0.0016035944,0.000007027097],"about_ca_topic_score_codex":0.0018875415,"about_ca_topic_score_gemma":0.0014405127,"teacher_disagreement_score":0.00284534,"about_ca_system_score_codex":0.00039698312,"about_ca_system_score_gemma":0.00066717487,"threshold_uncertainty_score":0.009518623},"labels":[],"label_agreement":null},{"id":"W2105098876","doi":"10.1109/ccece.1997.614788","title":"Visual speech recognition by recurrent neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Speech recognition; Recurrent neural network; Artificial neural network; Word (group theory); Artificial intelligence; Segmentation; Time delay neural network; Pattern recognition (psychology); Sequence (biology); Mathematics","score_opus":0.028388055562913378,"score_gpt":0.2531358160263043,"score_spread":0.22474776046339093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105098876","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02674398,0.0015306459,0.96027815,0.0002568029,0.00021572775,0.00007531983,0.00025158815,0.0061838054,0.0044638873],"genre_scores_gemma":[0.52568597,0.0011359547,0.46090415,0.0003285095,0.00018025693,0.000167736,0.0009430671,0.0002640086,0.010390375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996094,0.00009018771,0.000027152804,0.00011385963,0.00011731976,0.000042005824],"domain_scores_gemma":[0.9995999,0.00013881111,0.000055279896,0.000066863104,0.000117573785,0.000021516047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005042641,0.0006067652,0.00057262107,0.00043639992,0.00021392039,0.0007858778,0.00087631185,0.00063477486,0.002659129],"category_scores_gemma":[0.0016193105,0.0002788898,0.0004852895,0.00036018685,0.00033312914,0.00088479515,0.0005783596,0.0006333032,0.001563368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057923415,0.00017153591,0.0011252626,0.00036009637,0.00018999827,0.0003873744,0.00018377214,0.07418351,0.18959077,0.020517608,0.009302679,0.7034083],"study_design_scores_gemma":[0.000028029104,0.000125367,0.00064540945,0.000026452322,0.00006584494,0.00021879599,0.000016791588,0.9405644,0.041955724,0.008436078,0.00788463,0.00003257314],"about_ca_topic_score_codex":0.0022976233,"about_ca_topic_score_gemma":0.0035622213,"teacher_disagreement_score":0.002659129,"about_ca_system_score_codex":0.00043556618,"about_ca_system_score_gemma":0.00025493058,"threshold_uncertainty_score":0.008895695},"labels":[],"label_agreement":null},{"id":"W2105189995","doi":"10.1109/iembs.1990.692253","title":"Classification Performance Of Different Motor Unit Action Potential Feature Space Representations: A Simulation Study","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Action (physics); Space (punctuation); Feature (linguistics); Unit (ring theory); Artificial intelligence; Motor unit; Feature vector; Pattern recognition (psychology); Mathematics; Psychology; Physics; Neuroscience","score_opus":0.0466846732869447,"score_gpt":0.3240385977579554,"score_spread":0.2773539244710107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105189995","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9567512,0.0002602299,0.040748235,0.00017145791,0.000021535963,0.00006448365,0.00024720072,0.0002740577,0.0014616383],"genre_scores_gemma":[0.9868751,0.00006750092,0.012206925,0.000026165573,0.000004879553,0.00004997205,0.00028331135,0.000023443643,0.00046280018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999471,0.00022530554,0.000054450986,0.00007457601,0.000102504484,0.00007223717],"domain_scores_gemma":[0.9864218,0.01153675,0.00043530352,0.00047806618,0.0009935199,0.00013452434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002138253,0.00053056894,0.00054811366,0.00059923594,0.00023218642,0.0005771448,0.00042542449,0.00092045794,0.0008901296],"category_scores_gemma":[0.016557159,0.00022558942,0.0005417996,0.00057644554,0.00035259238,0.0006952987,0.00031375972,0.0004476061,0.00024472954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016533879,0.0002703505,0.010155833,0.00016915149,0.0001929447,0.00018349283,0.00016567443,0.9062322,0.00785793,0.0012865539,0.00069229986,0.07114027],"study_design_scores_gemma":[0.000036484667,0.0003489906,0.0031610234,0.000008280542,0.000021775311,0.00006846671,0.00002441605,0.9918149,0.003936155,0.0004547485,0.00011082213,0.000013977328],"about_ca_topic_score_codex":0.00791305,"about_ca_topic_score_gemma":0.0037614394,"teacher_disagreement_score":0.00791305,"about_ca_system_score_codex":0.00068215077,"about_ca_system_score_gemma":0.0003931989,"threshold_uncertainty_score":0.015734017},"labels":[],"label_agreement":null},{"id":"W2105287172","doi":"10.1109/icpr.1992.201720","title":"An optimal pairing scheme in associative memory classifier and its application in character recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pairing; Associative property; Content-addressable memory; Computer science; Artificial intelligence; Scheme (mathematics); Classifier (UML); Character (mathematics); Pattern recognition (psychology); Theoretical computer science; Artificial neural network; Mathematics","score_opus":0.02991668285155088,"score_gpt":0.26460247079491084,"score_spread":0.23468578794335995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105287172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033341106,0.0005533843,0.961543,0.00019559675,0.00016327717,0.00008625052,0.000060582246,0.0003989325,0.0036578022],"genre_scores_gemma":[0.43704605,0.00057523244,0.5537159,0.00018330384,0.00021450847,0.0003033954,0.00023193884,0.00007412391,0.007655581],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886143,0.00045029738,0.00009768353,0.0002245154,0.00025472877,0.00011139043],"domain_scores_gemma":[0.99896085,0.0003056694,0.0000743647,0.0002697829,0.00032157477,0.00006780521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001500252,0.0003558268,0.0007744133,0.00084172265,0.00095258374,0.0011969956,0.00096523983,0.0010309588,0.0039511374],"category_scores_gemma":[0.003316137,0.00023590785,0.00034428915,0.0011703722,0.00095570873,0.0022583748,0.0010719232,0.0008816162,0.0012381945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040144025,0.00023666068,0.0012759882,0.00013881974,0.00004429597,0.00014862919,0.00023699223,0.08054889,0.02151634,0.22720493,0.0057629547,0.662484],"study_design_scores_gemma":[0.0000354463,0.0003130675,0.0004040791,0.000036525547,0.000029042822,0.00028133905,0.000073145515,0.8628864,0.020074591,0.10671599,0.009105638,0.000044723063],"about_ca_topic_score_codex":0.0009266545,"about_ca_topic_score_gemma":0.00068383367,"teacher_disagreement_score":0.0039511374,"about_ca_system_score_codex":0.00070427504,"about_ca_system_score_gemma":0.0008038216,"threshold_uncertainty_score":0.013217866},"labels":[],"label_agreement":null},{"id":"W2105442939","doi":"10.1109/iros.1993.583125","title":"Aggregating interdependent sensory data in multisensor systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Hong Kong University of Science and Technology","keywords":"Interdependence; Weighting; Computer science; Group (periodic table); Scheme (mathematics); Artificial intelligence; Data mining; Quality (philosophy); Machine learning; Mathematics","score_opus":0.10161679289089848,"score_gpt":0.28681550778842185,"score_spread":0.18519871489752338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105442939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018073073,0.0005995242,0.97972566,0.00023738646,0.00008851015,0.000040795258,0.00002298612,0.00012274958,0.0010893227],"genre_scores_gemma":[0.7103431,0.001420659,0.2839642,0.00019529226,0.00030603656,0.00017666134,0.00014562481,0.00007414487,0.0033743493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985043,0.0005339164,0.00009672242,0.00025481434,0.0005111621,0.00009906826],"domain_scores_gemma":[0.99551964,0.0029819862,0.00029621288,0.00055353413,0.00054639444,0.000102272796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025265217,0.0011541849,0.0018275452,0.0011836771,0.0007093428,0.0014990232,0.0012872663,0.0015235738,0.0010894072],"category_scores_gemma":[0.0069495616,0.00072848174,0.00082282926,0.0018254712,0.001004479,0.0028239985,0.002116938,0.0013623312,0.00026492728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023151985,0.0000902479,0.00094518985,0.00027179948,0.0002667892,0.00025359166,0.00038189266,0.8026862,0.007062951,0.0323225,0.0014470841,0.15404028],"study_design_scores_gemma":[0.000013118505,0.00007495046,0.0002228055,0.00001086853,0.000032245294,0.000033922806,0.000040039737,0.96735024,0.0020696165,0.029182693,0.0009535282,0.000015891204],"about_ca_topic_score_codex":0.001415194,"about_ca_topic_score_gemma":0.0013334239,"teacher_disagreement_score":0.0025265217,"about_ca_system_score_codex":0.00055784686,"about_ca_system_score_gemma":0.0004821405,"threshold_uncertainty_score":0.013361692},"labels":[],"label_agreement":null},{"id":"W2105497425","doi":"10.1109/wescan.1995.494071","title":"A hybrid architecture for Hopfield neural networks and its GaAs implementation using CCDs as storage elements","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hopfield network; Artificial neural network; Computer science; Multiplication (music); Architecture; Activation function; Function (biology); Artificial neuron; Parallel computing; Computer architecture; Artificial intelligence; Mathematics","score_opus":0.03320487164198635,"score_gpt":0.2998526693932582,"score_spread":0.2666477977512719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105497425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051423848,0.0026385176,0.9277828,0.00037295872,0.00023875094,0.00010413548,0.00018349453,0.001099861,0.016155759],"genre_scores_gemma":[0.57309854,0.00091451744,0.41711065,0.00021198163,0.00005745245,0.000120220386,0.000108258275,0.000037000744,0.008341452],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986494,0.000027604503,0.000007751416,0.00002442039,0.000061256236,0.000014063468],"domain_scores_gemma":[0.99988604,0.000028150622,0.0000083875,0.0000146278935,0.000053095537,0.000009831414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018179126,0.0002476162,0.00021240034,0.00024352454,0.00038682873,0.0007561096,0.00079745677,0.0005823622,0.0022817173],"category_scores_gemma":[0.00028544772,0.00016854005,0.00022474249,0.00052441587,0.00026388967,0.00075453834,0.00030137826,0.0003093209,0.00059613783],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058406946,0.00012218635,0.0020306706,0.0006003329,0.00024390854,0.0006682638,0.00027060002,0.09981953,0.35516405,0.101414934,0.0077492325,0.4313322],"study_design_scores_gemma":[0.00008969583,0.000456687,0.0017115215,0.000080952865,0.00014288396,0.00079586724,0.00012540664,0.77855116,0.15099157,0.021676254,0.045299914,0.0000780323],"about_ca_topic_score_codex":0.0025228458,"about_ca_topic_score_gemma":0.0044720275,"teacher_disagreement_score":0.0025228458,"about_ca_system_score_codex":0.000605739,"about_ca_system_score_gemma":0.00031930069,"threshold_uncertainty_score":0.0076331496},"labels":[],"label_agreement":null},{"id":"W2105597488","doi":"10.1109/ijcnn.1993.714000","title":"Indirect adaptive neurocontrol using localized polynomial networks with CLI cells","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Control theory (sociology); Nonlinear system; Polynomial; Computer science; Stability (learning theory); Adaptive control; Scheme (mathematics); Adaptation (eye); Representation (politics); Exponential stability; Artificial neural network; Mathematics; Control (management); Artificial intelligence; Law","score_opus":0.01669235924650195,"score_gpt":0.22803810832107205,"score_spread":0.2113457490745701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105597488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059740856,0.00044186253,0.9297086,0.00015411762,0.00006150028,0.00007604417,0.000029909306,0.00051989063,0.009267332],"genre_scores_gemma":[0.9391272,0.00022040094,0.056775987,0.000050980187,0.000024341673,0.00012445245,0.000028951697,0.000011936654,0.003635849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998629,0.000020073108,0.0000071906675,0.000034561348,0.000053546974,0.000021818701],"domain_scores_gemma":[0.99980944,0.000046684814,0.00003130106,0.000027551845,0.00007020315,0.000014775553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024955877,0.00046936757,0.00033091215,0.0001620129,0.0003537074,0.0005262724,0.00094845553,0.00045417174,0.0013496245],"category_scores_gemma":[0.00057040213,0.00019601213,0.00030422438,0.00027550833,0.0005329631,0.0005794833,0.0007094469,0.00063033774,0.00023981211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021771071,0.000109050234,0.0013015217,0.00023723692,0.00006818126,0.0002881919,0.00036335035,0.6875405,0.07997565,0.053886004,0.0016131811,0.1743994],"study_design_scores_gemma":[0.000020971322,0.00013029571,0.00016510632,0.000007634489,0.000014943143,0.00004857024,0.000013389097,0.99019647,0.005222628,0.002988449,0.0011809106,0.0000106121715],"about_ca_topic_score_codex":0.0032004637,"about_ca_topic_score_gemma":0.0036905187,"teacher_disagreement_score":0.0032004637,"about_ca_system_score_codex":0.00050614047,"about_ca_system_score_gemma":0.00056833314,"threshold_uncertainty_score":0.00636369},"labels":[],"label_agreement":null},{"id":"W2105686536","doi":"10.1109/icnn.1994.374492","title":"Training artificial neural networks using variable precision incremental communication","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Variable (mathematics); Computer science; Artificial neural network; Scheme (mathematics); Convergence (economics); Node (physics); Feed forward; Computation; Degree (music); Artificial intelligence; Computer engineering; Algorithm; Mathematics; Control engineering","score_opus":0.11231165799256763,"score_gpt":0.2866832201117676,"score_spread":0.17437156211919996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105686536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08519543,0.00021930665,0.9106691,0.00016237168,0.000055674554,0.000045064484,0.000018271065,0.0006991939,0.002935634],"genre_scores_gemma":[0.81171006,0.0001690652,0.18645152,0.00006717263,0.000032518456,0.000102337086,0.000046273042,0.000031803025,0.0013892018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973077,0.00008358066,0.000018368919,0.000033931825,0.00009877423,0.000034544657],"domain_scores_gemma":[0.99902225,0.00058062724,0.00009314858,0.00013008161,0.00015433335,0.000019607853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006596454,0.0003341607,0.0003767643,0.00029904416,0.0002934064,0.00044120644,0.0010183112,0.0005108923,0.00114579],"category_scores_gemma":[0.0032142664,0.0001998196,0.00021263337,0.00042969338,0.0005039652,0.0009989548,0.0006670636,0.00072184723,0.00015759472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017800443,0.000044638826,0.00071182934,0.00006694221,0.000029381952,0.00008151308,0.00015933497,0.83211136,0.011285807,0.012348301,0.00068509544,0.1422978],"study_design_scores_gemma":[0.00001519685,0.00004940925,0.000105305815,0.000004787847,0.00000627114,0.000015762835,0.0000070900087,0.9936772,0.0034480344,0.0021655045,0.0005011777,0.0000043818627],"about_ca_topic_score_codex":0.0025078466,"about_ca_topic_score_gemma":0.0026173885,"teacher_disagreement_score":0.0025078466,"about_ca_system_score_codex":0.00039069826,"about_ca_system_score_gemma":0.0005129759,"threshold_uncertainty_score":0.0049865246},"labels":[],"label_agreement":null},{"id":"W2105720696","doi":"10.1504/ijmic.2012.045693","title":"Machine vibration prediction using ANFIS and wavelet packet decomposition","year":2012,"lang":"en","type":"article","venue":"International Journal of Modelling Identification and Control","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Syncrude","keywords":"Adaptive neuro fuzzy inference system; Wavelet packet decomposition; Wavelet; SIGNAL (programming language); Computer science; Vibration; Series (stratigraphy); Decomposition; Fuzzy logic; Artificial intelligence; Wavelet transform; Fuzzy control system; Acoustics; Physics","score_opus":0.02100117685958766,"score_gpt":0.27416907879596536,"score_spread":0.2531679019363777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105720696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023125755,0.00026943375,0.97461367,0.00007726668,0.000050292296,0.000025975802,0.000051536725,0.0007364634,0.0010495645],"genre_scores_gemma":[0.7342074,0.00054778095,0.26214784,0.000045000168,0.000044389322,0.00012951528,0.00021771587,0.000060245366,0.0026001225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997285,0.000057086847,0.000020880794,0.000050467595,0.00012419035,0.000018934807],"domain_scores_gemma":[0.9997665,0.00012722056,0.000027204196,0.000018669269,0.000053126263,0.000007228855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052966445,0.00057224184,0.000728057,0.00050955656,0.0002220481,0.00041140764,0.0004694148,0.0005959592,0.0007225461],"category_scores_gemma":[0.001079683,0.00033477705,0.0005882532,0.00041281662,0.00020094629,0.0006862633,0.00025214753,0.00074867584,0.0002399814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008589243,0.00006188748,0.0010090567,0.00011916206,0.00009296296,0.00011063654,0.00005301751,0.8211687,0.009557289,0.0028410389,0.00076722424,0.16413316],"study_design_scores_gemma":[0.0000017213945,0.000009130777,0.000116775125,0.0000024503543,0.0000036014471,0.0000058207593,0.0000018289336,0.9988746,0.00053055637,0.00030255268,0.00014913036,0.00000185457],"about_ca_topic_score_codex":0.005596853,"about_ca_topic_score_gemma":0.003996805,"teacher_disagreement_score":0.005596853,"about_ca_system_score_codex":0.00029467297,"about_ca_system_score_gemma":0.00032580682,"threshold_uncertainty_score":0.011128545},"labels":[],"label_agreement":null},{"id":"W2105921217","doi":"10.1109/ijcnn.2002.1007508","title":"Time series models discovery with similarity-based neuro-fuzzy networks and evolutionary algorithms","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Dependency (UML); Computer science; Autoregressive model; Similarity (geometry); Artificial intelligence; Series (stratigraphy); Neuro-fuzzy; Algorithm; Time series; Multivariate statistics; Fuzzy logic; Data mining; Machine learning; Mathematics; Fuzzy control system; Image (mathematics); Statistics","score_opus":0.010780440515094889,"score_gpt":0.20124797808191375,"score_spread":0.19046753756681886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105921217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01936196,0.00058443187,0.97858024,0.00022827694,0.000022280685,0.000041640014,0.000022729233,0.0000819919,0.001076397],"genre_scores_gemma":[0.526444,0.00072756625,0.4702241,0.0001040459,0.00011826172,0.00020889979,0.0001347709,0.00003532116,0.002003038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987226,0.00061036326,0.00008609016,0.00018597905,0.0003482669,0.00004671018],"domain_scores_gemma":[0.996863,0.002194185,0.00030731724,0.00022733228,0.00035027397,0.0000578562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033769193,0.00063568604,0.00142644,0.00228909,0.00061423774,0.0014656523,0.0014999284,0.0016553364,0.0009084291],"category_scores_gemma":[0.013023015,0.00057307974,0.001211139,0.0024401613,0.0010328271,0.0023707624,0.0016357548,0.0010915066,0.00020664382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108198285,0.000091389615,0.0012227491,0.00010312033,0.00021609785,0.00013735045,0.00013605732,0.805788,0.0010519016,0.06601656,0.0005864448,0.124542125],"study_design_scores_gemma":[0.000007674027,0.000008170276,0.00009903402,0.0000045662778,0.000011253527,0.000018097116,0.0000070239066,0.97910416,0.00031153808,0.020259343,0.00016311779,0.000005979286],"about_ca_topic_score_codex":0.0033600926,"about_ca_topic_score_gemma":0.0027240163,"teacher_disagreement_score":0.0033769193,"about_ca_system_score_codex":0.0010635767,"about_ca_system_score_gemma":0.00086032454,"threshold_uncertainty_score":0.017859042},"labels":[],"label_agreement":null},{"id":"W2106004777","doi":"10.1111/j.1467-8640.2010.00366.x","title":"DECISION TREES DO NOT GENERALIZE TO NEW VARIATIONS","year":2010,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Canadian Institute for Advanced Research","keywords":"Decision tree; Curse of dimensionality; Machine learning; Computer science; Artificial intelligence; Partition (number theory); Representation (politics); Decision tree learning; Set (abstract data type); Mathematics; Theoretical computer science; Combinatorics","score_opus":0.03635067146760383,"score_gpt":0.3192401414954662,"score_spread":0.28288947002786236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106004777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029541157,0.001607697,0.95854217,0.0023476353,0.00021131823,0.000074619296,0.00023404807,0.00052560266,0.0069157635],"genre_scores_gemma":[0.7055092,0.004360326,0.2749266,0.0032856846,0.0011057352,0.00055710255,0.00097036146,0.00068450585,0.0086006075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936109,0.0020291454,0.00048005668,0.0016703852,0.0016723904,0.00053712545],"domain_scores_gemma":[0.9772794,0.014131371,0.0016940043,0.0047587175,0.0016104076,0.0005260189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008943257,0.0012280828,0.0023090928,0.0017351527,0.0012170335,0.003267176,0.0030473785,0.0026672422,0.0027288585],"category_scores_gemma":[0.038582213,0.0009701567,0.0024417287,0.0014994101,0.0060935295,0.012883125,0.0036240434,0.007747021,0.0011377714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096832424,0.00004654994,0.0026585658,0.00028491142,0.00018982317,0.00028618617,0.0005608038,0.19939882,0.001367663,0.7010711,0.004474253,0.08956454],"study_design_scores_gemma":[0.000017148815,0.000034167293,0.0003870864,0.00005379238,0.000021717804,0.00012613086,0.000040101648,0.2047199,0.0002349557,0.79049873,0.0038441634,0.000022107573],"about_ca_topic_score_codex":0.0022900542,"about_ca_topic_score_gemma":0.0023300725,"teacher_disagreement_score":0.008943257,"about_ca_system_score_codex":0.0014703501,"about_ca_system_score_gemma":0.0010537278,"threshold_uncertainty_score":0.04729706},"labels":[],"label_agreement":null},{"id":"W2106036443","doi":"10.1109/mwscas.1994.519065","title":"Fuzzy clustering neural network system design and implementation","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Very-large-scale integration; Computer architecture; Fuzzy logic; Cluster analysis; Artificial neural network; Architecture; Feed forward; Feedforward neural network; Artificial intelligence; Parallel computing; Embedded system; Control engineering; Engineering","score_opus":0.03556301112506028,"score_gpt":0.2606193682828856,"score_spread":0.22505635715782535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106036443","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046004145,0.00016417626,0.9901084,0.00011264725,0.000051046078,0.00007631768,0.000028773205,0.00050453615,0.004353623],"genre_scores_gemma":[0.28187734,0.00038912232,0.7094824,0.0001565668,0.000062426756,0.00034294647,0.00015041727,0.000057726396,0.007481098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997032,0.000051629304,0.000018191087,0.00006167667,0.00013345711,0.000031676995],"domain_scores_gemma":[0.9998086,0.000035814704,0.000012096673,0.000022907316,0.00011366195,0.000006953501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048947625,0.0003196412,0.00032587795,0.00030524415,0.00035148585,0.0006385904,0.0011123556,0.0007268334,0.0035237113],"category_scores_gemma":[0.00076413364,0.00019339731,0.0002816654,0.00031204053,0.00029063263,0.0005790598,0.000321992,0.00047491695,0.00096338254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015679632,0.00007551555,0.00066196854,0.00030121935,0.00008685647,0.00014220088,0.00014040204,0.46529293,0.036893778,0.07519674,0.0038368527,0.41721466],"study_design_scores_gemma":[0.000019281,0.00008785186,0.00017236636,0.000015526144,0.00002032,0.00008163633,0.000019100435,0.9676646,0.015438617,0.0063318587,0.010135539,0.000013335385],"about_ca_topic_score_codex":0.004013197,"about_ca_topic_score_gemma":0.005142767,"teacher_disagreement_score":0.004013197,"about_ca_system_score_codex":0.0008623826,"about_ca_system_score_gemma":0.00092797185,"threshold_uncertainty_score":0.011787951},"labels":[],"label_agreement":null},{"id":"W2106282354","doi":"10.1109/icsmc.1998.728081","title":"Architecture of integrated distributed intelligent multimedia system for on-line real-time process monitoring","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude","keywords":"Computer science; Architecture; Condition monitoring; Process (computing); Fault (geology); Fault detection and isolation; Heuristic; Case-based reasoning; Hypermedia; Intelligent agent; Real-time computing; Multimedia; Artificial intelligence; Engineering; Operating system","score_opus":0.033617811015136126,"score_gpt":0.2789192237931025,"score_spread":0.2453014127779664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106282354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07816896,0.00059550576,0.89814705,0.00036031177,0.0001268021,0.00026391257,0.00024841545,0.011460686,0.010628357],"genre_scores_gemma":[0.77384156,0.0002615192,0.2178927,0.0002687772,0.00008929015,0.00027380954,0.00051446183,0.00007936831,0.0067785745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978524,0.000031508636,0.000014089893,0.000070930946,0.000065380744,0.000032863623],"domain_scores_gemma":[0.9998166,0.0000241831,0.000017036205,0.000029779669,0.00008409107,0.000028239898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003288055,0.00037227036,0.00040266232,0.00060223986,0.00046846582,0.0006955261,0.001425634,0.00086720474,0.0035209067],"category_scores_gemma":[0.00038056707,0.00014845663,0.00023993998,0.0004018948,0.00022508173,0.00068250066,0.0005110277,0.00040660542,0.0007336861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012805955,0.0008889366,0.005911968,0.00042464663,0.0002613875,0.0009066832,0.0004789708,0.0897651,0.2068266,0.018853206,0.014998983,0.65940297],"study_design_scores_gemma":[0.00026361717,0.00063570135,0.0043032193,0.000045431938,0.00038212148,0.00081778935,0.00010974652,0.87733704,0.08100627,0.008577358,0.026443502,0.00007827336],"about_ca_topic_score_codex":0.0017303589,"about_ca_topic_score_gemma":0.0016488698,"teacher_disagreement_score":0.0035209067,"about_ca_system_score_codex":0.0006388401,"about_ca_system_score_gemma":0.0005560051,"threshold_uncertainty_score":0.011778593},"labels":[],"label_agreement":null},{"id":"W2106485397","doi":"10.1109/tnn.2005.852861","title":"NDRAM: Nonlinear Dynamic Recurrent Associative Memory for Learning Bipolar and Nonbipolar Correlated Patterns","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Hebbian theory; Bidirectional associative memory; Content-addressable memory; Attractor; Computer science; Artificial neural network; Content-addressable storage; Hopfield network; Learning rule; Nonlinear system; Spurious relationship; Leabra; Artificial intelligence; Recurrent neural network; Competitive learning; Machine learning; Mathematics; Wake-sleep algorithm","score_opus":0.010190180093398218,"score_gpt":0.24679394345844005,"score_spread":0.23660376336504182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106485397","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09599647,0.0021797705,0.88240993,0.00028460694,0.00031687642,0.000082557766,0.00026155985,0.002720626,0.015747579],"genre_scores_gemma":[0.77332443,0.0008391734,0.2103091,0.00020778232,0.00009329432,0.00009022385,0.00044601332,0.0001031247,0.01458689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999354,0.000008761652,0.000005937652,0.000021378028,0.000020805834,0.000007641566],"domain_scores_gemma":[0.9999074,0.00002186302,0.000017703775,0.000022200025,0.000022251279,0.000008522936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019319457,0.00031913238,0.00032643141,0.00022407812,0.00013627172,0.00032867512,0.0009542125,0.00035147314,0.0025365562],"category_scores_gemma":[0.0004297792,0.00009961853,0.00021612305,0.0002638192,0.0002171604,0.0007801608,0.00045104141,0.00033323048,0.0006640017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037682158,0.00019269816,0.001840323,0.00049859297,0.00015628245,0.00049611117,0.00012874385,0.102835625,0.11617463,0.055135585,0.006430067,0.7157344],"study_design_scores_gemma":[0.000036168563,0.00021418634,0.0006866484,0.000022241724,0.000052396997,0.00046140281,0.000018780027,0.9483394,0.024800302,0.015616111,0.009724695,0.00002772656],"about_ca_topic_score_codex":0.0005545338,"about_ca_topic_score_gemma":0.0013797081,"teacher_disagreement_score":0.0025365562,"about_ca_system_score_codex":0.00019939993,"about_ca_system_score_gemma":0.00017963962,"threshold_uncertainty_score":0.008485675},"labels":[],"label_agreement":null},{"id":"W2106700089","doi":"10.1109/mwscas.2005.1594125","title":"Design and implementation of novel multi-layer mixed-signal on-chip neural networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial neural network; Computer science; Multiplexing; Electronic engineering; CMOS; Mixed-signal integrated circuit; Node (physics); Chip; Digital signal processing; Limiting; Computer hardware; Engineering; Integrated circuit; Artificial intelligence; Telecommunications","score_opus":0.05495100391520042,"score_gpt":0.30960221369112223,"score_spread":0.2546512097759218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106700089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062330928,0.0006918475,0.92736346,0.00027088026,0.0002424492,0.00013070961,0.00009819338,0.0017601679,0.007111355],"genre_scores_gemma":[0.63918626,0.00036452687,0.35406658,0.00024632542,0.00006281073,0.00020282534,0.00015854262,0.00003680966,0.005675362],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979657,0.00003392571,0.000015685913,0.00003327714,0.00008704111,0.00003347021],"domain_scores_gemma":[0.9998141,0.00003191674,0.000026915992,0.00002204162,0.00009177871,0.000013282932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034161063,0.00039926427,0.00025086824,0.00022624682,0.00021528066,0.00052551244,0.0013516125,0.0005082738,0.0014676633],"category_scores_gemma":[0.00045457386,0.0002517968,0.00022165423,0.00016175867,0.00016328525,0.00065814523,0.00026642656,0.00042797462,0.0003580963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047243014,0.00022205414,0.001610165,0.0005561167,0.00018780668,0.0004820665,0.00015545775,0.16671436,0.29282242,0.02313017,0.0047989525,0.508848],"study_design_scores_gemma":[0.000043609252,0.00025923483,0.0008340811,0.000024344396,0.00006502223,0.00022818279,0.000013999968,0.8989335,0.08899672,0.0015741385,0.009008149,0.000019052599],"about_ca_topic_score_codex":0.000869198,"about_ca_topic_score_gemma":0.0031720933,"teacher_disagreement_score":0.0014676633,"about_ca_system_score_codex":0.0004937749,"about_ca_system_score_gemma":0.00040230705,"threshold_uncertainty_score":0.004909873},"labels":[],"label_agreement":null},{"id":"W2107014821","doi":"10.1109/mwscas.1992.271071","title":"LBAQ: a pattern recognition neural network that learns by asking questions","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Backpropagation; Computer science; Session (web analytics); Artificial neural network; Artificial intelligence; Set (abstract data type); Machine learning; World Wide Web","score_opus":0.030338955681919366,"score_gpt":0.2443813757573672,"score_spread":0.21404242007544785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107014821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017311802,0.0005251931,0.9421909,0.00052467297,0.00022205291,0.00027806,0.0017704236,0.028382035,0.008794861],"genre_scores_gemma":[0.17794831,0.00053418044,0.7750006,0.00072466687,0.0000668001,0.0005492969,0.0071589234,0.00084114954,0.037176084],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996332,0.000072400784,0.0000211018,0.000121628116,0.00011863036,0.00003293235],"domain_scores_gemma":[0.99951804,0.00016940945,0.00003633378,0.00010508817,0.00013338277,0.00003775961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091915036,0.00081856316,0.00045320624,0.00054815155,0.00025859132,0.0008194872,0.0023302175,0.0013393341,0.015482839],"category_scores_gemma":[0.0029806474,0.00050186744,0.0003752563,0.0006889139,0.00048604247,0.0019192812,0.0012103382,0.0012030882,0.005350976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046031998,0.0002762609,0.0017366507,0.0004620731,0.00009406343,0.00017037967,0.00015117635,0.04879524,0.024670169,0.01100429,0.06185526,0.8503242],"study_design_scores_gemma":[0.0001418089,0.00027676826,0.001387955,0.000057464564,0.000043235865,0.0001624559,0.000048073834,0.8879568,0.027616357,0.018017452,0.06424083,0.000050796476],"about_ca_topic_score_codex":0.00581617,"about_ca_topic_score_gemma":0.009332937,"teacher_disagreement_score":0.015482839,"about_ca_system_score_codex":0.00043743508,"about_ca_system_score_gemma":0.0005701445,"threshold_uncertainty_score":0.051795244},"labels":[],"label_agreement":null},{"id":"W2107757737","doi":"10.1109/ijcnn.2004.1381114","title":"Classifying cognitive states from fMRI data using neural networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Voxel; Functional magnetic resonance imaging; Computer science; Backpropagation; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Cognition; Point (geometry); Brain activity and meditation; Machine learning; Mathematics; Neuroscience; Psychology","score_opus":0.09105938483906399,"score_gpt":0.31911569647116955,"score_spread":0.22805631163210555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107757737","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5210147,0.0017321467,0.47127908,0.0007507509,0.00021136146,0.00021368971,0.00078591367,0.0018525139,0.0021598309],"genre_scores_gemma":[0.8952431,0.0005259322,0.10167743,0.0001355166,0.000112589405,0.0001949287,0.0008522487,0.000036656067,0.0012215027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948907,0.00014346403,0.000040297862,0.00013544547,0.00011383723,0.00007791127],"domain_scores_gemma":[0.9979188,0.0014483114,0.0002022255,0.00013030898,0.00024625438,0.000054163196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013787282,0.001276989,0.0008053807,0.0016554703,0.00035629884,0.0010263382,0.0006562596,0.0012478776,0.0007848716],"category_scores_gemma":[0.0063544502,0.00030468302,0.0005422503,0.0010702417,0.00039103566,0.0015110393,0.0005238528,0.0012628669,0.0004170742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097060634,0.00071709754,0.02166252,0.0002040386,0.00029171625,0.0003261025,0.00029330663,0.2540392,0.02851952,0.0013534826,0.0022813408,0.68934107],"study_design_scores_gemma":[0.000012067551,0.000079245954,0.005808614,0.00001752015,0.00003375152,0.00005619519,0.000040195835,0.9828294,0.0066016545,0.0041584997,0.00033968408,0.000023238375],"about_ca_topic_score_codex":0.0035795697,"about_ca_topic_score_gemma":0.0044051856,"teacher_disagreement_score":0.0035795697,"about_ca_system_score_codex":0.0004898053,"about_ca_system_score_gemma":0.00037847064,"threshold_uncertainty_score":0.007291496},"labels":[],"label_agreement":null},{"id":"W2107999479","doi":"10.1109/icnn.1996.549206","title":"The effects of reduced precision bit lengths on feedforward neural networks for speech recognition","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'96)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Artificial neural network; Computer science; Feedforward neural network; Feed forward; Fixed-point arithmetic; Speech recognition; Fixed point; Floating point; Set (abstract data type); Arithmetic; A priori and a posteriori; Time delay neural network; Precision and recall; Independence (probability theory); Point (geometry); Algorithm; Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.03712323545250512,"score_gpt":0.26961427705257773,"score_spread":0.2324910416000726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107999479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3865935,0.0047820997,0.59892607,0.0010046915,0.00030144496,0.00023103681,0.00013944901,0.0033826914,0.004638974],"genre_scores_gemma":[0.69493663,0.0011623183,0.3009793,0.00020551053,0.00013224932,0.00015390986,0.000115958734,0.00017775228,0.0021363418],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974947,0.0008332324,0.00030801387,0.00026483065,0.0009607629,0.00013845935],"domain_scores_gemma":[0.9586891,0.03386775,0.0024909612,0.0024965692,0.0023247327,0.0001308956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030801087,0.0011999301,0.00044072638,0.0007964024,0.000589286,0.0009970963,0.0015448314,0.0008894141,0.002368944],"category_scores_gemma":[0.03530487,0.0004996336,0.00035618182,0.00093281246,0.000869529,0.0037636007,0.00069411995,0.0013512161,0.00042677217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003480494,0.00024780267,0.003140431,0.00063840736,0.00010602083,0.0006544234,0.00035117497,0.32097882,0.083457485,0.020221744,0.0013880369,0.5653351],"study_design_scores_gemma":[0.00013095027,0.0025351963,0.0024017536,0.00020859326,0.0001683349,0.000785662,0.00012277871,0.80091536,0.17257796,0.015939824,0.004122086,0.000091472444],"about_ca_topic_score_codex":0.002679154,"about_ca_topic_score_gemma":0.003585786,"teacher_disagreement_score":0.0030801087,"about_ca_system_score_codex":0.001334244,"about_ca_system_score_gemma":0.00070730754,"threshold_uncertainty_score":0.016289413},"labels":[],"label_agreement":null},{"id":"W2108064091","doi":"10.1109/tsmcb.2006.890293","title":"Desynchronizing a Chaotic Pattern Recognition Neural Network to Model Inaccurate Perception","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Chaotic; Computer science; Rendering (computer graphics); Artificial intelligence; Perception; Lyapunov exponent; Noise (video); Artificial neural network; Image (mathematics); Computer vision; Psychology","score_opus":0.03841195354700823,"score_gpt":0.25524168044264706,"score_spread":0.21682972689563884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108064091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29844794,0.0005165637,0.6872099,0.000714675,0.00013138933,0.000062828934,0.00010664369,0.0003335407,0.012476393],"genre_scores_gemma":[0.97805756,0.00017437736,0.01816012,0.000053546326,0.00002474759,0.000051645886,0.000034334284,0.000023580353,0.0034201345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998627,0.000038289094,0.00000783755,0.000038514252,0.00003479469,0.00001783494],"domain_scores_gemma":[0.9996964,0.00012544477,0.00007796542,0.000036319267,0.000049926137,0.000014043005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034288756,0.00038538701,0.00028209566,0.0002477461,0.0001795706,0.00043797775,0.00062432303,0.00056114787,0.0012482282],"category_scores_gemma":[0.0013841465,0.00019773818,0.00037666626,0.00020950701,0.0005073371,0.00060635706,0.00058141246,0.00072555663,0.00013835207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046364927,0.000022737315,0.0006148248,0.000026904636,0.000022576363,0.00009248861,0.00008370916,0.9699402,0.00608546,0.0162713,0.00021237909,0.006581131],"study_design_scores_gemma":[0.0000015412503,0.000007174342,0.00006157542,8.0813936e-7,0.000001405254,0.000005514999,0.0000015470213,0.99861515,0.0001232691,0.0011041522,0.000076632794,0.0000011749032],"about_ca_topic_score_codex":0.004459054,"about_ca_topic_score_gemma":0.00289929,"teacher_disagreement_score":0.004459054,"about_ca_system_score_codex":0.000658678,"about_ca_system_score_gemma":0.00036337748,"threshold_uncertainty_score":0.008866191},"labels":[],"label_agreement":null},{"id":"W2108264206","doi":"10.1109/adfsp.1998.685709","title":"Adaptive non-linear modeling","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Kalman filter; Computer science; Convergence (economics); Process (computing); Linearity; Adaptation (eye); Simple (philosophy); Adaptive filter; Filter (signal processing); Artificial neural network; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Engineering; Electronic engineering","score_opus":0.05976263155691578,"score_gpt":0.24828069037277736,"score_spread":0.1885180588158616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108264206","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019989477,0.0005362726,0.9898775,0.0002356403,0.000101250436,0.000026007627,0.00006792889,0.00045941406,0.0066969576],"genre_scores_gemma":[0.6460614,0.004647644,0.28934005,0.00040758718,0.00033602482,0.0004797854,0.00059752044,0.00022906205,0.05790097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970144,0.000072754025,0.000013164826,0.000067218745,0.00012279567,0.000022732727],"domain_scores_gemma":[0.99978215,0.00009057275,0.000024770185,0.000041362324,0.00005091448,0.000010247876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003250876,0.00062589766,0.000660511,0.0002628799,0.00027111996,0.00090778666,0.0009695496,0.00089922926,0.0038451622],"category_scores_gemma":[0.0009804121,0.0003019503,0.0005538701,0.0003637721,0.000415355,0.00080162054,0.0008972085,0.0012162444,0.001404617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052436477,0.000045619727,0.00051720993,0.0001971322,0.000082977116,0.00014118734,0.00011603125,0.75632715,0.011508417,0.11766235,0.0043859137,0.108963616],"study_design_scores_gemma":[0.0000050314748,0.000016767615,0.00013765023,0.000009936937,0.000008172725,0.000032657153,0.000006659489,0.9732608,0.0008586878,0.018780455,0.006874978,0.0000081618355],"about_ca_topic_score_codex":0.0024161635,"about_ca_topic_score_gemma":0.0023786747,"teacher_disagreement_score":0.0038451622,"about_ca_system_score_codex":0.00041203343,"about_ca_system_score_gemma":0.00050495716,"threshold_uncertainty_score":0.012863338},"labels":[],"label_agreement":null},{"id":"W2108401489","doi":"10.1109/pacrim.1997.620377","title":"On the selection of nodes in linear-in-the-weight neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Artificial neural network; Computer science; Convergence (economics); Selection (genetic algorithm); Construct (python library); Algorithm; Artificial intelligence; Machine learning; Mathematical optimization; Mathematics","score_opus":0.022064867026541927,"score_gpt":0.23235021222990923,"score_spread":0.2102853452033673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108401489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013476923,0.000545103,0.9845201,0.00024582935,0.00004004448,0.00006238087,0.00001181901,0.00009605934,0.0010017812],"genre_scores_gemma":[0.42366865,0.0013348961,0.5691032,0.0003176849,0.00018975405,0.00040844057,0.00008667703,0.00018870068,0.0047019967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99868935,0.0007487205,0.00006192847,0.0001911694,0.00020172074,0.00010716496],"domain_scores_gemma":[0.9948703,0.004018562,0.00034603878,0.00016248903,0.0005234868,0.00007913935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040995865,0.0014680234,0.0012742274,0.0006692424,0.00081634824,0.0010688942,0.0020048784,0.0022059493,0.0014305183],"category_scores_gemma":[0.022289043,0.00070389797,0.00031133628,0.0010276815,0.0021941764,0.0026298072,0.0018944102,0.0016958751,0.0004981022],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019744971,0.00008283248,0.00076085003,0.00024311339,0.000047155892,0.00007326774,0.00022576662,0.8084025,0.0030010736,0.068087764,0.0010702766,0.117807955],"study_design_scores_gemma":[0.000023874156,0.00004058212,0.00006685349,0.000022385364,0.000011016072,0.000012084787,0.0000142573235,0.97190046,0.0010258589,0.026468392,0.00040497835,0.000009194166],"about_ca_topic_score_codex":0.002985882,"about_ca_topic_score_gemma":0.0034078879,"teacher_disagreement_score":0.0040995865,"about_ca_system_score_codex":0.0007406065,"about_ca_system_score_gemma":0.0008309995,"threshold_uncertainty_score":0.021680951},"labels":[],"label_agreement":null},{"id":"W2108563286","doi":"10.1109/icassp.2013.6639349","title":"Advances in optimizing recurrent networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":486,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Recurrent neural network; Artificial intelligence; Machine learning; Artificial neural network; Clipping (morphology); Deep learning","score_opus":0.009799538304525268,"score_gpt":0.24278434951980285,"score_spread":0.2329848112152776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108563286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010686712,0.0052752704,0.9763856,0.0008841149,0.0001581098,0.00003112503,0.00008057304,0.000730438,0.0057681096],"genre_scores_gemma":[0.4692262,0.012121909,0.5023857,0.00087573956,0.00082857546,0.0002348471,0.000796442,0.00088830077,0.012642301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990715,0.00029700875,0.00007780256,0.00018698836,0.00029949265,0.000067226625],"domain_scores_gemma":[0.9984529,0.0008871657,0.00013112629,0.00019060065,0.00029632053,0.000041815696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025041339,0.0015096565,0.0012812836,0.0005140307,0.00031978934,0.0013144541,0.0012867919,0.0012536836,0.002472435],"category_scores_gemma":[0.009077747,0.00062213035,0.00069455995,0.0007470438,0.00059065555,0.0022828921,0.0013371297,0.0022628661,0.001180313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086445456,0.00006747529,0.0008137122,0.000288976,0.00013994439,0.00007678258,0.00009098741,0.7360991,0.004478611,0.051569853,0.0040923455,0.20219567],"study_design_scores_gemma":[0.000007983867,0.00003072883,0.00012761037,0.000025558416,0.000017274831,0.000025840534,0.000006705213,0.9800198,0.0015484883,0.014913126,0.0032674817,0.000009363606],"about_ca_topic_score_codex":0.0033885336,"about_ca_topic_score_gemma":0.003524951,"teacher_disagreement_score":0.0033885336,"about_ca_system_score_codex":0.00089323195,"about_ca_system_score_gemma":0.0007992812,"threshold_uncertainty_score":0.013243318},"labels":[],"label_agreement":null},{"id":"W2108581046","doi":"10.1207/s15516709cog0000_76","title":"Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation","year":2006,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":137,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Gatsby Charitable Foundation","keywords":"Artificial neural network; Energy (signal processing); Nonlinear system; Backpropagation; Computer science; Artificial intelligence; Pattern recognition (psychology); Unit vector; Connection (principal bundle); Mathematics; Physics; Statistics","score_opus":0.014939100614361824,"score_gpt":0.2612231394455777,"score_spread":0.24628403883121586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108581046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030627659,0.00011103418,0.96739966,0.00016344534,0.000022610817,0.000033270346,0.00007771434,0.0006937936,0.0008707434],"genre_scores_gemma":[0.45902076,0.0001755529,0.53775704,0.00014093902,0.000046363202,0.00010624896,0.00035442255,0.00015851716,0.0022402275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963534,0.00007416779,0.000017123168,0.00010363065,0.00013594725,0.00003373944],"domain_scores_gemma":[0.99882036,0.0005209134,0.00017547369,0.00020711456,0.00023107325,0.00004493859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009979744,0.00088558084,0.0006424969,0.0011842641,0.00045280444,0.00092185213,0.0014609611,0.0008769646,0.0008911917],"category_scores_gemma":[0.0046002786,0.00061327347,0.00066267565,0.00087543187,0.000760825,0.0017441947,0.0011485061,0.0014216779,0.0003249138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012645508,0.00012679743,0.003737346,0.000109752764,0.00014361908,0.00024664612,0.00016902367,0.6697879,0.026578208,0.026715925,0.0028842979,0.2693741],"study_design_scores_gemma":[0.0000028777115,0.000006191499,0.00012502194,0.0000024907154,0.0000028756317,0.0000131108045,0.000002884762,0.992751,0.0017456926,0.005094555,0.0002499243,0.0000033728345],"about_ca_topic_score_codex":0.0032178161,"about_ca_topic_score_gemma":0.007158653,"teacher_disagreement_score":0.0032178161,"about_ca_system_score_codex":0.0008560206,"about_ca_system_score_gemma":0.0009197692,"threshold_uncertainty_score":0.006398201},"labels":[],"label_agreement":null},{"id":"W2108677974","doi":"","title":"Practical Variational Inference for Neural Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1066,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial neural network; Heuristic; Simple (philosophy); Pruning; Inference; Perspective (graphical); Artificial intelligence; Function (biology); TIMIT; Algorithm; Hidden Markov model","score_opus":0.08821316975451085,"score_gpt":0.3227024568937303,"score_spread":0.23448928713921943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108677974","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009952781,0.0004954222,0.9960289,0.00041853767,0.000035406214,0.000022380222,0.000072011055,0.00007970829,0.0018524886],"genre_scores_gemma":[0.19438688,0.0022261313,0.78934807,0.0004924268,0.0005380263,0.00059118384,0.00075176154,0.00051678356,0.011148764],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975249,0.001548875,0.000104518855,0.00031498302,0.0004026647,0.00010404815],"domain_scores_gemma":[0.99329823,0.005479633,0.00022693643,0.00040481746,0.00045389973,0.00013642242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005700448,0.0012645207,0.001452387,0.0010496436,0.000847678,0.0018460258,0.0022955954,0.0023252831,0.0051749484],"category_scores_gemma":[0.019064715,0.001151557,0.0014009378,0.0011627438,0.0024159371,0.0026093854,0.0029985541,0.0040297955,0.00089540536],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026856804,0.000018737308,0.0003694713,0.0001828809,0.000086954824,0.00007304977,0.000099823774,0.354087,0.000660307,0.6132384,0.002936335,0.028220262],"study_design_scores_gemma":[0.0000070951282,0.000005719056,0.000054291737,0.000018564657,0.0000045783304,0.000014662552,0.000008463525,0.7102256,0.00012805068,0.28744525,0.0020807874,0.000006898208],"about_ca_topic_score_codex":0.0074710953,"about_ca_topic_score_gemma":0.008777454,"teacher_disagreement_score":0.0074710953,"about_ca_system_score_codex":0.0023222044,"about_ca_system_score_gemma":0.002025899,"threshold_uncertainty_score":0.030147254},"labels":[],"label_agreement":null},{"id":"W2108692534","doi":"10.1109/iscas.1992.230205","title":"A hybrid architecture for feed-forward multi-layer neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; CMOS; Latency (audio); Layer (electronics); Generality; Chip; Artificial neural network; Architecture; Electronic circuit; Reliability (semiconductor); Computer architecture; Embedded system; Computer hardware; Computer engineering; Electronic engineering; Electrical engineering; Artificial intelligence; Engineering; Telecommunications","score_opus":0.023774884915635307,"score_gpt":0.26165354143284264,"score_spread":0.23787865651720733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108692534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024266923,0.0010598431,0.9618941,0.0002709569,0.00020643335,0.00006440438,0.00015841928,0.00379098,0.008287925],"genre_scores_gemma":[0.47570392,0.00062402955,0.49940965,0.0004221211,0.00011221337,0.00026785352,0.00053296366,0.00013043883,0.022796722],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983823,0.000024621992,0.000010934522,0.00004462759,0.00006020317,0.000021323824],"domain_scores_gemma":[0.99985445,0.000032894528,0.000010226483,0.00003010955,0.000058755755,0.000013575891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035188967,0.00047125772,0.0003421709,0.00037784624,0.00032469514,0.0008589149,0.0018634214,0.0010692389,0.006702784],"category_scores_gemma":[0.00043795604,0.00030907267,0.00039710331,0.00046204147,0.00027252643,0.0013759703,0.0006545965,0.00068517687,0.0020489115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050491915,0.00021600975,0.0009380714,0.00042931782,0.00032202428,0.0003291218,0.0001609766,0.24320127,0.107766055,0.03418095,0.008602056,0.60334927],"study_design_scores_gemma":[0.000039729104,0.00019375526,0.00031613075,0.000030996933,0.00006960636,0.00014742119,0.00001561178,0.9630289,0.017504144,0.009254479,0.009371818,0.00002743726],"about_ca_topic_score_codex":0.0014992252,"about_ca_topic_score_gemma":0.005068696,"teacher_disagreement_score":0.006702784,"about_ca_system_score_codex":0.00043270347,"about_ca_system_score_gemma":0.00034803725,"threshold_uncertainty_score":0.022423029},"labels":[],"label_agreement":null},{"id":"W2108885350","doi":"10.1002/sim.1107","title":"A new approach to training back‐propagation artificial neural networks: empirical evaluation on ten data sets from clinical studies","year":2002,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Initialization; Artificial neural network; Backpropagation; Computer science; Logistic regression; Artificial intelligence; Deviance (statistics); Training set; Machine learning; Regularization (linguistics); Data mining; Statistics; Mathematics","score_opus":0.5224149863755513,"score_gpt":0.4928430757635104,"score_spread":0.029571910612040897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108885350","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78252465,0.003924978,0.20811352,0.0008795253,0.00029689813,0.0005227211,0.0009282022,0.00096468715,0.0018448049],"genre_scores_gemma":[0.84646493,0.00091805664,0.1493164,0.00018249896,0.00009137378,0.0004433646,0.0018006951,0.00008534763,0.0006973937],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994079,0.0040643034,0.00047804415,0.00040536813,0.000880519,0.00009279257],"domain_scores_gemma":[0.96191174,0.03081494,0.001098186,0.0021255217,0.0037106755,0.00033894533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020385373,0.0013889015,0.0009974227,0.0016562784,0.0003815106,0.0009108424,0.0012974358,0.0013343933,0.0007042006],"category_scores_gemma":[0.048876483,0.00035051594,0.0006648679,0.0013995516,0.00075167627,0.00092510815,0.0010902415,0.0015181986,0.00029812468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002137031,0.0013423132,0.10580238,0.001115985,0.0015739929,0.00045254387,0.0005068971,0.3384116,0.0038190803,0.0009664296,0.004092784,0.53977895],"study_design_scores_gemma":[0.0002068375,0.0013037889,0.022324188,0.00018374485,0.00027745334,0.0003064122,0.00015453137,0.96767676,0.0045333593,0.0014640228,0.0015043982,0.00006449472],"about_ca_topic_score_codex":0.0031320103,"about_ca_topic_score_gemma":0.004541339,"teacher_disagreement_score":0.020385373,"about_ca_system_score_codex":0.00051598373,"about_ca_system_score_gemma":0.00074477954,"threshold_uncertainty_score":0.107809424},"labels":[],"label_agreement":null},{"id":"W2109498142","doi":"10.1109/ijcnn.1993.714172","title":"MES classification using artificial neural networks and chaos theory","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"CHAOS (operating system); Artificial neural network; Computer science; Artificial intelligence; Chaos theory; Physical neural network; Types of artificial neural networks; Machine learning; Time delay neural network; Chaotic","score_opus":0.04592543333909305,"score_gpt":0.27802530892064564,"score_spread":0.2320998755815526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109498142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053997725,0.0012227938,0.93829966,0.0004996343,0.000121099074,0.00010641993,0.00013404294,0.0007711557,0.004847494],"genre_scores_gemma":[0.71238405,0.0009307782,0.28012374,0.00016284065,0.00025258298,0.00018977781,0.0004339277,0.0000780004,0.0054443423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995969,0.00011572855,0.00004267464,0.0000687543,0.00013375061,0.000042257507],"domain_scores_gemma":[0.9992017,0.00045988854,0.00008787558,0.000048475984,0.00017846939,0.000023583507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008582008,0.0004468888,0.0005932842,0.0026701768,0.0003603811,0.0011448523,0.00054604065,0.00079970824,0.0015243022],"category_scores_gemma":[0.0027741946,0.00020841857,0.00053700834,0.0011647253,0.0004436505,0.001045125,0.0006133722,0.000537175,0.0004051553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017846814,0.00006993706,0.005165696,0.00014011706,0.00012283213,0.00015998849,0.00011049806,0.42040202,0.0072806454,0.028545216,0.0026426604,0.53518194],"study_design_scores_gemma":[0.000003464827,0.000011305674,0.0005355591,0.0000098900855,0.000006655117,0.000020149739,0.000012506556,0.9910392,0.0008341359,0.006781671,0.00073912716,0.0000063365515],"about_ca_topic_score_codex":0.0020355976,"about_ca_topic_score_gemma":0.0017897665,"teacher_disagreement_score":0.0026701768,"about_ca_system_score_codex":0.0005775746,"about_ca_system_score_gemma":0.00041404527,"threshold_uncertainty_score":0.0050992966},"labels":[],"label_agreement":null},{"id":"W2109619323","doi":"10.1109/ijcnn.2004.1380100","title":"Sharing training patterns in neural network ensembles","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Classifier (UML); Artificial neural network; Machine learning; Training set; Training (meteorology); Artificial intelligence; Perspective (graphical); Information sharing; Data mining","score_opus":0.04158060109186897,"score_gpt":0.2638412185350935,"score_spread":0.22226061744322453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109619323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43355075,0.00049083837,0.56246656,0.00032313057,0.000063274245,0.00011729763,0.000054119886,0.00080844254,0.0021256232],"genre_scores_gemma":[0.9236444,0.000101772996,0.07490709,0.0001041992,0.000028895636,0.00013854202,0.00010876579,0.00006704491,0.00089928607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956995,0.0020936679,0.0002969107,0.0006408909,0.00085013587,0.00041897327],"domain_scores_gemma":[0.9796093,0.0111421775,0.001229589,0.0050908136,0.0023795536,0.00054857496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009798535,0.00082193356,0.0019625877,0.0009891414,0.0011325841,0.0013242636,0.0017370865,0.0013670003,0.00079338433],"category_scores_gemma":[0.031452,0.0010873703,0.0006658473,0.0011607092,0.0014080548,0.003751717,0.003923198,0.0014197276,0.00034163863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056680525,0.00024108484,0.007451784,0.00007919381,0.00021058456,0.00013418264,0.0005591274,0.79784966,0.0051690764,0.004901183,0.0005960986,0.18224113],"study_design_scores_gemma":[0.000028038638,0.00019297664,0.0010640335,0.00001624565,0.00005001804,0.000052329848,0.00009753585,0.98349047,0.0046976134,0.009780906,0.0005119139,0.000017874545],"about_ca_topic_score_codex":0.002303128,"about_ca_topic_score_gemma":0.0041507175,"teacher_disagreement_score":0.009798535,"about_ca_system_score_codex":0.0007731841,"about_ca_system_score_gemma":0.0009615266,"threshold_uncertainty_score":0.05182028},"labels":[],"label_agreement":null},{"id":"W2109692094","doi":"10.1002/9780470050118.ecse271","title":"Neural Network Architectures","year":2008,"lang":"en","type":"other","venue":"Wiley Encyclopedia of Computer Science and Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Interpretability; Network topology; Computer science; Artificial neural network; Suite; Artificial intelligence; Deep neural networks; Distributed computing; Computer architecture; Computer network","score_opus":0.005539051035810803,"score_gpt":0.1950214526714487,"score_spread":0.18948240163563787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109692094","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021667514,0.008318936,0.8633226,0.002585387,0.0006744321,0.00011550316,0.0006122886,0.0013272011,0.10137616],"genre_scores_gemma":[0.6068414,0.015157193,0.30494022,0.000833642,0.00047900577,0.000411883,0.0014361795,0.0001819905,0.06971856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99977666,0.00006824486,0.000018832365,0.000052264502,0.0000615952,0.000022373648],"domain_scores_gemma":[0.99973875,0.00008552293,0.000019098457,0.000054617874,0.00008671602,0.000015261408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036569007,0.00040484854,0.0003201838,0.00052193744,0.0003031736,0.0011851054,0.00087707286,0.0007985313,0.008364408],"category_scores_gemma":[0.0014322761,0.00017536944,0.00026126206,0.00070641603,0.00055026246,0.0013006774,0.0007120751,0.0007974485,0.0016630627],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050932445,0.00003159709,0.0005528103,0.0003443609,0.00006710483,0.00017435306,0.0000852457,0.2444708,0.00532054,0.54231954,0.012113235,0.1944694],"study_design_scores_gemma":[0.0000148269355,0.000034767505,0.00022061121,0.00009620658,0.000029806479,0.00014290556,0.000034189074,0.60958046,0.0031103452,0.32780817,0.05891153,0.000016136766],"about_ca_topic_score_codex":0.0011350055,"about_ca_topic_score_gemma":0.0016793878,"teacher_disagreement_score":0.008364408,"about_ca_system_score_codex":0.00055752235,"about_ca_system_score_gemma":0.0004474199,"threshold_uncertainty_score":0.027981758},"labels":[],"label_agreement":null},{"id":"W2109731683","doi":"10.1109/ccece.2000.849559","title":"New approach for solving optimization problems in economic load dispatch using Hopfield neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Hopfield network; Artificial neural network; Mathematical optimization; Penalty method; Computer science; Linear inequality; Quadratic equation; Quadratic programming; Optimization problem; Function (biology); Constrained optimization problem; Mathematics; Inequality; Artificial intelligence","score_opus":0.04050547944500768,"score_gpt":0.24083715765603328,"score_spread":0.2003316782110256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109731683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025527244,0.00037082055,0.99449956,0.00012648638,0.0000478828,0.000020722848,0.00001703755,0.00007427871,0.002290492],"genre_scores_gemma":[0.15205467,0.0015407946,0.83664626,0.00017429222,0.00022417586,0.00021416688,0.00012465741,0.00008443377,0.008936552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974316,0.00005256361,0.000019396693,0.00003896374,0.00012699662,0.000018802248],"domain_scores_gemma":[0.9998221,0.00008650575,0.000012814524,0.000013603756,0.000055983444,0.000008955403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006687801,0.0008035612,0.000662628,0.00047556616,0.00035821804,0.0007011062,0.0010282461,0.000852163,0.0017159521],"category_scores_gemma":[0.00082029076,0.00034252866,0.00056371134,0.00068638864,0.00053321465,0.0014601856,0.00085928326,0.0013272568,0.00032316247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057893652,0.00007278255,0.00035711273,0.00021842733,0.00012013967,0.00017412766,0.000111271576,0.63135046,0.010067018,0.08950359,0.003023438,0.26494372],"study_design_scores_gemma":[0.000012056735,0.000012366276,0.00006411175,0.0000075542716,0.000008872148,0.000017057146,0.000005078101,0.9786511,0.0009887781,0.017798515,0.0024270413,0.000007369298],"about_ca_topic_score_codex":0.0038504149,"about_ca_topic_score_gemma":0.0051075923,"teacher_disagreement_score":0.0038504149,"about_ca_system_score_codex":0.0006618166,"about_ca_system_score_gemma":0.00074777205,"threshold_uncertainty_score":0.007655978},"labels":[],"label_agreement":null},{"id":"W2109734802","doi":"10.1109/iscas.2000.856333","title":"Complex EKF neural network for adaptive equalization","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial neural network; Equalization (audio); Adaptive equalizer; Channel (broadcasting); Convergence (economics); Backpropagation; Extended Kalman filter; Adaptive filter; Blind equalization; Kalman filter; Adaptive system; Control theory (sociology); Artificial intelligence; Algorithm; Telecommunications","score_opus":0.10812531221619674,"score_gpt":0.28396206020741516,"score_spread":0.1758367479912184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109734802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004892184,0.0012486797,0.99000204,0.00026961465,0.00020748132,0.000027273518,0.000050885115,0.00035164313,0.0029502418],"genre_scores_gemma":[0.5637063,0.0026716471,0.4127113,0.0003458019,0.00023334887,0.0002332294,0.0002752258,0.000082909,0.019740243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998054,0.000045553887,0.000012434751,0.000041028674,0.000078943776,0.000016694183],"domain_scores_gemma":[0.9997347,0.00009869391,0.000017906108,0.000024629566,0.00011951866,0.000004515463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039254682,0.00036438278,0.00039034797,0.00027591182,0.00019913679,0.00052465335,0.00039277945,0.00080805505,0.0025915662],"category_scores_gemma":[0.0015998428,0.000120520286,0.0001898095,0.00034261812,0.00025305647,0.0007188439,0.00024149998,0.00073912024,0.00056244957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019570124,0.000049593935,0.0008808429,0.00022553094,0.000055870885,0.00012337379,0.000047925183,0.48963922,0.016947519,0.042804662,0.00648127,0.44254845],"study_design_scores_gemma":[0.000010392587,0.00001364192,0.00019854282,0.000009046137,0.000007113184,0.00004879418,0.0000028607456,0.9896578,0.0022817808,0.004045882,0.0037153878,0.000008823557],"about_ca_topic_score_codex":0.0039352784,"about_ca_topic_score_gemma":0.0031935757,"teacher_disagreement_score":0.0039352784,"about_ca_system_score_codex":0.00047801036,"about_ca_system_score_gemma":0.00052434887,"threshold_uncertainty_score":0.008669674},"labels":[],"label_agreement":null},{"id":"W2110307520","doi":"10.1109/cec.2011.5949741","title":"Fitness functions for searching the Mandelbrot set","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mandelbrot set; Set (abstract data type); Fractal; Mathematics; Complex quadratic polynomial; Boundary (topology); Computer science; Mathematical analysis","score_opus":0.11411018989391827,"score_gpt":0.2892585841548077,"score_spread":0.17514839426088943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110307520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31351304,0.0016925012,0.674016,0.00082932453,0.000102577935,0.00010889321,0.00014134671,0.00030784402,0.009288499],"genre_scores_gemma":[0.7280205,0.0004929183,0.26842976,0.000117565956,0.00003809887,0.0001831184,0.0002156144,0.00009656953,0.0024058055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994747,0.00021007656,0.000033412463,0.00007321508,0.00015103896,0.00005761567],"domain_scores_gemma":[0.997332,0.002029637,0.0001436474,0.000101217214,0.0003198186,0.000073779294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001977297,0.0006969921,0.0007941403,0.0018101411,0.000776717,0.00096466613,0.00087167916,0.0014587006,0.0015746123],"category_scores_gemma":[0.00929274,0.0002512802,0.0005761786,0.0008179153,0.00075150724,0.0009890378,0.00059561845,0.0007274547,0.00020896955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010825076,0.00008732595,0.0033989635,0.000094682546,0.00006747248,0.00012290048,0.000114410854,0.8816402,0.0042430884,0.032587506,0.00121175,0.07632348],"study_design_scores_gemma":[0.000025181262,0.00005659806,0.00080184825,0.00001802284,0.000018426994,0.00006873091,0.000026397389,0.98928934,0.0010929436,0.007960636,0.00062301673,0.00001890632],"about_ca_topic_score_codex":0.0028046505,"about_ca_topic_score_gemma":0.0020467143,"teacher_disagreement_score":0.0028046505,"about_ca_system_score_codex":0.0010991807,"about_ca_system_score_gemma":0.0007681868,"threshold_uncertainty_score":0.010457039},"labels":[],"label_agreement":null},{"id":"W2110663814","doi":"10.1177/0255761405052401","title":"The nature of music","year":2005,"lang":"en","type":"article","venue":"International Journal of Music Education","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Cognitive science; Cognition; Trait; Music psychology; Psychology; Music and emotion; Cognitive psychology; Music education; Communication; Music history; Neuroscience; Computer science","score_opus":0.013611425887240422,"score_gpt":0.29528459887252795,"score_spread":0.28167317298528755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110663814","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0850372,0.09939223,0.031205766,0.038377244,0.0046910066,0.0000484172,0.0007667176,0.00034246984,0.74013895],"genre_scores_gemma":[0.83881897,0.04062578,0.0094796065,0.008172559,0.005572532,0.000067130335,0.00047860615,0.00015964627,0.09662516],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995534,0.00006784362,0.00001611498,0.00011685426,0.00018827719,0.00005757868],"domain_scores_gemma":[0.9996438,0.00009415955,0.000043762084,0.00006167448,0.00008254067,0.000074038806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032621928,0.00017941024,0.00023201977,0.00054209837,0.0009808148,0.0044344156,0.0006324686,0.0012151807,0.008997019],"category_scores_gemma":[0.0018395091,0.00016126508,0.00015761245,0.00049210613,0.0039297217,0.0027490219,0.0012050638,0.0012054874,0.0028482827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001624249,0.000032894586,0.003152681,0.00047003504,0.000039319275,0.00038789454,0.0026597956,0.0007552909,0.014824706,0.67108554,0.023908421,0.282521],"study_design_scores_gemma":[0.0000220267,0.000059089798,0.010974904,0.0004017593,0.000015392812,0.0012878465,0.0012417238,0.00087113923,0.0021376747,0.32907534,0.65386754,0.00004567412],"about_ca_topic_score_codex":0.0011614702,"about_ca_topic_score_gemma":0.0011621995,"teacher_disagreement_score":0.008997019,"about_ca_system_score_codex":0.00064039783,"about_ca_system_score_gemma":0.00065147434,"threshold_uncertainty_score":0.030098021},"labels":[],"label_agreement":null},{"id":"W2110772377","doi":"10.1109/milcom.1996.569416","title":"Nonlinear state estimation using neural filters","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Nonlinear system; Radial basis function; Artificial neural network; Nonlinear dynamical systems; Computer science; Control theory (sociology); State (computer science); Basis (linear algebra); Estimation theory; Artificial intelligence; Algorithm; Applied mathematics; Mathematics; Physics","score_opus":0.04293610855209108,"score_gpt":0.2641662744025062,"score_spread":0.2212301658504151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110772377","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00821569,0.00033453645,0.99022067,0.000051348743,0.000037091893,0.000010083348,0.000013623747,0.00040732292,0.0007096163],"genre_scores_gemma":[0.4517648,0.00091740704,0.5426115,0.00014154695,0.00014766553,0.00007429502,0.00013445753,0.000098482466,0.004109865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995902,0.00009662338,0.000022739043,0.00011666848,0.00013588447,0.000037866957],"domain_scores_gemma":[0.9993267,0.00034081418,0.00010383327,0.000071305396,0.00014092911,0.000016350532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007635655,0.00056234235,0.0006043625,0.0007875301,0.000344322,0.00070581946,0.00057290465,0.0009826678,0.0010900529],"category_scores_gemma":[0.002789909,0.00028256106,0.00038200957,0.0006062849,0.0003802253,0.0012665971,0.00063579687,0.00069100957,0.0007873271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025159135,0.00008365358,0.0012901479,0.00016315716,0.000099083256,0.0000874179,0.000116272706,0.20224307,0.04521804,0.010179858,0.0011636927,0.73910403],"study_design_scores_gemma":[0.000007777746,0.000037169015,0.0005400502,0.000014230748,0.000017367105,0.000039190672,0.000008201613,0.98388463,0.010100774,0.003939926,0.0013900232,0.000020667067],"about_ca_topic_score_codex":0.003260758,"about_ca_topic_score_gemma":0.0029591646,"teacher_disagreement_score":0.003260758,"about_ca_system_score_codex":0.00038558178,"about_ca_system_score_gemma":0.00034940796,"threshold_uncertainty_score":0.006483555},"labels":[],"label_agreement":null},{"id":"W2110922215","doi":"10.1109/ijcnn.2001.938847","title":"Decision fusion in neural network ensembles","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Machine learning; Focus (optics); Training set; Time delay neural network; Architecture; Ensemble learning; Network architecture; Computer network","score_opus":0.023427320707179653,"score_gpt":0.23775210733832958,"score_spread":0.21432478663114993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110922215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034329068,0.0018523047,0.960011,0.0002373049,0.00010780668,0.000054012817,0.000062822175,0.0002950008,0.0030506128],"genre_scores_gemma":[0.71385396,0.0012703817,0.28179768,0.00016381108,0.0002480693,0.00012875152,0.00023781833,0.000085432956,0.0022141838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968495,0.0013589155,0.00017961845,0.000387582,0.001014271,0.00021005595],"domain_scores_gemma":[0.9949986,0.0031317852,0.00030205515,0.0005985068,0.0008615704,0.000107486114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005893736,0.0009721076,0.0018620782,0.0010242835,0.0006477615,0.0014488305,0.0014007183,0.0012408735,0.0016427437],"category_scores_gemma":[0.011238582,0.0003960225,0.0010931013,0.0015342713,0.00084146723,0.0023379927,0.0021340917,0.001546396,0.00039201582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024921383,0.00006525487,0.0010741322,0.00015174352,0.00035399152,0.000076791854,0.00014773304,0.7835112,0.0026542249,0.025628293,0.0011573085,0.1849301],"study_design_scores_gemma":[0.000012794378,0.000082725644,0.0002754961,0.000014703056,0.000058439815,0.000026072292,0.000012513942,0.97735095,0.002215078,0.019005323,0.0009299502,0.000015896125],"about_ca_topic_score_codex":0.0013831676,"about_ca_topic_score_gemma":0.0014490582,"teacher_disagreement_score":0.005893736,"about_ca_system_score_codex":0.0009320958,"about_ca_system_score_gemma":0.0005232228,"threshold_uncertainty_score":0.031169415},"labels":[],"label_agreement":null},{"id":"W2111139746","doi":"10.1109/ccece.1997.614812","title":"The effects of limited precision implementation on the performance of radial basis functions","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Radial basis function; Computer science; Basis (linear algebra); Expression (computer science); Radial basis function network; Algorithm; Function (biology); Basis function; Speech recognition; Artificial intelligence; Artificial neural network; Mathematics","score_opus":0.016299819374364363,"score_gpt":0.24060737863525322,"score_spread":0.22430755926088886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111139746","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6572148,0.0036374263,0.32912534,0.00071789057,0.0002183303,0.00006704583,0.000068799025,0.0019993973,0.006950812],"genre_scores_gemma":[0.9637534,0.0004783898,0.0348679,0.00007316084,0.000034582765,0.000022235428,0.00003508799,0.00008894129,0.0006463805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808985,0.00058917736,0.00014612699,0.00020046029,0.0007880731,0.00018634075],"domain_scores_gemma":[0.9783544,0.016942244,0.0013671886,0.0016406146,0.0015738901,0.0001216524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002001699,0.0009501459,0.0005288137,0.00045352493,0.0004843368,0.0009451012,0.000951566,0.0009493306,0.001369182],"category_scores_gemma":[0.03213483,0.00041500948,0.00028501995,0.00064106286,0.00071356754,0.0017838277,0.0008173851,0.0008750353,0.0003884368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003794068,0.00035341928,0.005145161,0.0006274758,0.00015119367,0.0011849849,0.00037926476,0.58804494,0.13594054,0.012385437,0.0014267825,0.2505667],"study_design_scores_gemma":[0.00013003434,0.0022952624,0.0044486467,0.00015303478,0.0001768504,0.00084213936,0.000110912115,0.75707096,0.22723864,0.00493635,0.0024801213,0.00011697529],"about_ca_topic_score_codex":0.0013751894,"about_ca_topic_score_gemma":0.0010148484,"teacher_disagreement_score":0.002001699,"about_ca_system_score_codex":0.0005940803,"about_ca_system_score_gemma":0.0003535953,"threshold_uncertainty_score":0.010586143},"labels":[],"label_agreement":null},{"id":"W2111222766","doi":"10.1109/ijcnn.1993.714200","title":"System identification using a new dynamic neural network","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Chaining; Artificial neural network; A priori and a posteriori; Identification (biology); Modal; System dynamics; Artificial intelligence; Gradient descent; System identification; Machine learning; Data mining","score_opus":0.019441703170675697,"score_gpt":0.2633334799109031,"score_spread":0.2438917767402274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111222766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028906711,0.00016873327,0.9944705,0.00009203365,0.000044925117,0.000016064347,0.00002876724,0.00023526733,0.0020530296],"genre_scores_gemma":[0.5125933,0.0010817801,0.47226632,0.00029358835,0.00013234062,0.0003934447,0.00034314994,0.000116615665,0.012779393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995938,0.000080482154,0.000023711067,0.00011804089,0.00015031357,0.000033728258],"domain_scores_gemma":[0.9997068,0.00009935656,0.000038088372,0.000033420765,0.0001070296,0.000015271315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004928516,0.0005574455,0.00080409856,0.0005657257,0.0003420302,0.0009207061,0.0010681358,0.0009937496,0.0019194202],"category_scores_gemma":[0.0014692792,0.0003901316,0.0005316685,0.00053852453,0.00042968066,0.0014655887,0.0009275516,0.0010170295,0.0005125966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059350055,0.00003915705,0.0006507555,0.00011866011,0.00008308218,0.00018451316,0.00006324593,0.8323851,0.009455245,0.025018655,0.0013983544,0.13054396],"study_design_scores_gemma":[0.0000029438781,0.000010123604,0.00005796929,0.000005580482,0.0000049150926,0.000031626892,0.0000025616696,0.9962154,0.0004305698,0.0019852817,0.0012473342,0.0000056227814],"about_ca_topic_score_codex":0.0035179968,"about_ca_topic_score_gemma":0.0027144083,"teacher_disagreement_score":0.0035179968,"about_ca_system_score_codex":0.0005828353,"about_ca_system_score_gemma":0.00062822876,"threshold_uncertainty_score":0.0069950223},"labels":[],"label_agreement":null},{"id":"W2111322737","doi":"10.1121/1.3588190","title":"Using spiking onset neurons and a recurrent neural network for sound identification.","year":2011,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Spiral ganglion; Acoustics; Computer science; SIGNAL (programming language); Filter (signal processing); Sound (geography); Filter bank; Waveform; Speech recognition; Pattern recognition (psychology); Channel (broadcasting); Physics; Cochlea; Neuroscience; Artificial intelligence; Biology; Telecommunications","score_opus":0.06277529740745805,"score_gpt":0.2958399521088183,"score_spread":0.23306465470136026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111322737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0902568,0.0008871673,0.905016,0.00026221038,0.00008388551,0.0000658685,0.00017871647,0.0014268638,0.0018223883],"genre_scores_gemma":[0.76931065,0.00047199795,0.22604497,0.00008568196,0.000053194442,0.00012672215,0.00041015985,0.00007954223,0.0034170554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997836,0.000071029186,0.000014496608,0.00006054002,0.00004579612,0.000024589954],"domain_scores_gemma":[0.9995173,0.00025556228,0.00007162026,0.00005104854,0.00008068949,0.000023774597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075235096,0.0007924843,0.00050741405,0.00043340793,0.00017159429,0.00064688973,0.0009870948,0.00072493183,0.0010985574],"category_scores_gemma":[0.0020133283,0.00034979658,0.00067996635,0.00038889344,0.00032670167,0.0011383714,0.00046436238,0.0007588146,0.0003671175],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002483635,0.00008487893,0.0016923043,0.0001107126,0.00014027694,0.00020053217,0.000063506,0.87826043,0.014610509,0.005073745,0.000682691,0.09883204],"study_design_scores_gemma":[0.000002653636,0.000017958646,0.00007096174,0.0000019527333,0.000006169542,0.00000911827,0.0000022278593,0.99839336,0.00055198563,0.0008654165,0.000074929245,0.0000032408766],"about_ca_topic_score_codex":0.0051666135,"about_ca_topic_score_gemma":0.0075389273,"teacher_disagreement_score":0.0051666135,"about_ca_system_score_codex":0.00064842793,"about_ca_system_score_gemma":0.00046106713,"threshold_uncertainty_score":0.010273099},"labels":[],"label_agreement":null},{"id":"W2111377264","doi":"10.1109/icnn.1996.548874","title":"An adaptive and fully sparse training approach for multilayer perceptrons","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'96)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Backpropagation; Computer science; Artificial neural network; Artificial intelligence; Feed forward; Perceptron; Process (computing); Sparse approximation; Training (meteorology); Multilayer perceptron; Set (abstract data type); Pattern recognition (psychology); Feedforward neural network; Machine learning; Engineering; Control engineering","score_opus":0.10408522941878491,"score_gpt":0.2857363058533977,"score_spread":0.1816510764346128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111377264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002350002,0.00013910918,0.99616265,0.00004805147,0.00002483554,0.0000184216,0.000015497608,0.00021632804,0.0010250647],"genre_scores_gemma":[0.19961679,0.0005952877,0.792965,0.00017435272,0.00014762097,0.00017579562,0.00015346859,0.000125057,0.0060464717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975747,0.000059292546,0.000014001951,0.000040496987,0.00010366813,0.000025111432],"domain_scores_gemma":[0.99970824,0.00011913632,0.000027167094,0.000051360395,0.00007872959,0.000015300082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042156485,0.00049915665,0.0004659258,0.00034503703,0.0002869085,0.00041938014,0.0009108399,0.0007253423,0.0019669833],"category_scores_gemma":[0.001120832,0.0003289303,0.0004817592,0.00041866198,0.0003534595,0.0007974162,0.00067231536,0.0010704589,0.00054801756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084811596,0.000082566534,0.000433571,0.00016893685,0.00007357366,0.00009683839,0.000101620964,0.3514486,0.04114019,0.03464989,0.0028324116,0.56888694],"study_design_scores_gemma":[0.000004795455,0.000031345775,0.000109042194,0.000007050301,0.000008548496,0.00003924123,0.0000028688708,0.98924446,0.0032684936,0.0049033626,0.0023731992,0.000007521493],"about_ca_topic_score_codex":0.0013360573,"about_ca_topic_score_gemma":0.0021398694,"teacher_disagreement_score":0.0019669833,"about_ca_system_score_codex":0.0002467668,"about_ca_system_score_gemma":0.0003927018,"threshold_uncertainty_score":0.0065802336},"labels":[],"label_agreement":null},{"id":"W2111697789","doi":"10.1109/iwfhr.2002.1030925","title":"Empirical error based optimization of SVM kernels: application to digit image recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Concordia University","funders":"","keywords":"Support vector machine; Kernel (algebra); Polynomial; Computer science; Convergence (economics); NIST; Gradient descent; Algorithm; Kernel method; Mathematical optimization; Artificial intelligence; Pattern recognition (psychology); Mathematics; Artificial neural network; Speech recognition","score_opus":0.03821892080945861,"score_gpt":0.29809860566094365,"score_spread":0.25987968485148505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111697789","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03665607,0.00029043574,0.96204907,0.00010532196,0.000017229153,0.000017283939,0.0000165054,0.0003788907,0.00046922348],"genre_scores_gemma":[0.5133674,0.00025482802,0.48493874,0.00003827923,0.000023726172,0.00003937813,0.00006363134,0.00017671102,0.0010973498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993919,0.00021515871,0.000043840853,0.000097514654,0.00022220335,0.0000294246],"domain_scores_gemma":[0.9978682,0.001033295,0.00023917755,0.00031404983,0.00050657213,0.000038558544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019849846,0.00050615636,0.0009276311,0.00044171902,0.00019929826,0.0006462165,0.00064151146,0.0007833634,0.00061189145],"category_scores_gemma":[0.0072913207,0.00027560815,0.00029198462,0.0007673816,0.000456394,0.00086311874,0.0006454121,0.00077278784,0.00022866184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009749526,0.00011287205,0.0010306705,0.00010818524,0.000037139234,0.000044980196,0.00007489228,0.76964134,0.010508709,0.0073916987,0.00068625144,0.21026586],"study_design_scores_gemma":[0.0000024654648,0.000014471035,0.0001424104,0.0000017932549,0.000001616808,0.000009641489,0.000002347666,0.9958674,0.0028613985,0.0009370097,0.00015561271,0.0000038659573],"about_ca_topic_score_codex":0.0016439948,"about_ca_topic_score_gemma":0.0011705154,"teacher_disagreement_score":0.0019849846,"about_ca_system_score_codex":0.0004575821,"about_ca_system_score_gemma":0.0004421917,"threshold_uncertainty_score":0.010497749},"labels":[],"label_agreement":null},{"id":"W2112314142","doi":"10.1109/ijcnn.2007.4371210","title":"FEBAM: A Feature-Extracting Bidirectional Associative Memory","year":2007,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Université du Québec en Outaouais; Institut national de psychiatrie légale Philippe-Pinel; University of Ottawa","funders":"","keywords":"Computer science; Principal component analysis; Pattern recognition (psychology); Feature extraction; Dimensionality reduction; Artificial intelligence; Curse of dimensionality; Associative property; Set (abstract data type); Content-addressable memory; Feature (linguistics); Independent component analysis; Bidirectional associative memory; Artificial neural network; Mathematics","score_opus":0.07072273590437704,"score_gpt":0.33756045397066964,"score_spread":0.2668377180662926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112314142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08920386,0.0015614148,0.87141496,0.00092537154,0.0008930579,0.000119797034,0.00044358397,0.004379025,0.031058963],"genre_scores_gemma":[0.82103235,0.0010676276,0.15437785,0.000524056,0.00012531849,0.00024054895,0.00044237243,0.00015648147,0.022033496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990106,0.000013911066,0.000006422962,0.000029956229,0.000027944734,0.000020742635],"domain_scores_gemma":[0.9997931,0.00005130473,0.000021884716,0.00004974314,0.000056334276,0.000027498396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002405846,0.00039282948,0.0004987424,0.00026842934,0.00040980236,0.0009868363,0.0019027692,0.00070941314,0.0051413598],"category_scores_gemma":[0.0006372696,0.0001998316,0.0004479111,0.00028619,0.00045553537,0.002079548,0.0007284227,0.00071234757,0.0014311253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010304357,0.00042495035,0.0028805083,0.0008096463,0.00026494497,0.0010660026,0.00036038356,0.124046125,0.11921265,0.24454248,0.015692763,0.48966917],"study_design_scores_gemma":[0.000089573456,0.0005804396,0.00082650426,0.00006703056,0.00015942342,0.0008863943,0.000089118534,0.79484946,0.047783267,0.11956775,0.035030976,0.000070144],"about_ca_topic_score_codex":0.0008405044,"about_ca_topic_score_gemma":0.00090310466,"teacher_disagreement_score":0.0051413598,"about_ca_system_score_codex":0.00028623856,"about_ca_system_score_gemma":0.00049443776,"threshold_uncertainty_score":0.017199516},"labels":[],"label_agreement":null},{"id":"W2112503633","doi":"10.1109/icsmc.1990.142133","title":"A counter-propagation neural network for function approximation","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Function approximation; Function (biology); Computer science; Artificial neural network; Artificial intelligence; Inverse; Architecture; Inverse function; Network architecture; Kinematics; Mathematics; Computer network","score_opus":0.028250711627184337,"score_gpt":0.2286709340798451,"score_spread":0.20042022245266075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112503633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020570427,0.00034560304,0.9951043,0.0000865662,0.000110270536,0.00002838755,0.00003926998,0.0004621637,0.0017663876],"genre_scores_gemma":[0.13793686,0.0009638739,0.84988976,0.00025491035,0.000121215795,0.00026994137,0.00027890044,0.00017517108,0.010109326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960893,0.000070369635,0.00002255343,0.00009238233,0.00017763466,0.000028198054],"domain_scores_gemma":[0.9995547,0.00015494843,0.00003335474,0.000060912862,0.00017614478,0.000019981748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072208146,0.0009220427,0.0004826728,0.0007402467,0.00043672931,0.0010655193,0.0018892311,0.0014369163,0.0042344616],"category_scores_gemma":[0.0019884366,0.00034367773,0.0004323332,0.0009623358,0.0005966315,0.0019096506,0.0007588367,0.0015953773,0.0014218843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018315995,0.00009569399,0.00088977633,0.00026129108,0.000096102434,0.00024223709,0.000075982556,0.42008623,0.03189398,0.08065591,0.007514784,0.4580048],"study_design_scores_gemma":[0.0000040330897,0.000013732822,0.00006085537,0.000008439111,0.000006994481,0.00004588005,0.0000017256673,0.99067944,0.0033513817,0.0028960619,0.0029251627,0.0000063136135],"about_ca_topic_score_codex":0.0044087004,"about_ca_topic_score_gemma":0.0050710146,"teacher_disagreement_score":0.0044087004,"about_ca_system_score_codex":0.0008853674,"about_ca_system_score_gemma":0.0008331537,"threshold_uncertainty_score":0.0141656995},"labels":[],"label_agreement":null},{"id":"W2112636696","doi":"10.1109/icnn.1996.549056","title":"Limited precision incremental communication: error analysis","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'96)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Backpropagation; Computer science; Convergence (economics); Error analysis; Perceptron; Nonlinear system; Propagation of uncertainty; Node (physics); Algorithm; Artificial neural network; Perturbation (astronomy); Artificial intelligence; Mathematics; Engineering","score_opus":0.0593627372812839,"score_gpt":0.286303340884466,"score_spread":0.22694060360318213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112636696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02687482,0.0005684076,0.96791303,0.00018001812,0.0000571153,0.000044188688,0.000029020855,0.0003611589,0.003972242],"genre_scores_gemma":[0.85119563,0.0006967595,0.14284043,0.000112847054,0.00007304453,0.00017049865,0.000077749704,0.00017522454,0.004657766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804795,0.00030726913,0.00008227414,0.00015599643,0.0012901768,0.00011631973],"domain_scores_gemma":[0.98891836,0.0074404352,0.000751677,0.0012304833,0.0015649996,0.00009410029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022596065,0.00063541165,0.00054277544,0.0007567551,0.0003541312,0.0010251049,0.0013563773,0.0010107004,0.0024243006],"category_scores_gemma":[0.020627446,0.00029516022,0.0003868334,0.00062077487,0.00096969143,0.0017010495,0.0013709416,0.0013512148,0.00041530273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025034492,0.000046316265,0.0006192723,0.00024110796,0.00004337988,0.000190651,0.00017786208,0.87188566,0.011644286,0.037872665,0.0009021425,0.0761264],"study_design_scores_gemma":[0.000004078385,0.000040627234,0.00014097171,0.000013159214,0.000006788463,0.00004105618,0.000005867299,0.9892151,0.005087552,0.0051182327,0.0003185332,0.0000081064145],"about_ca_topic_score_codex":0.0019322019,"about_ca_topic_score_gemma":0.0010278187,"teacher_disagreement_score":0.0024243006,"about_ca_system_score_codex":0.00069849327,"about_ca_system_score_gemma":0.0006673784,"threshold_uncertainty_score":0.011950135},"labels":[],"label_agreement":null},{"id":"W2112765921","doi":"10.1109/icsmc.2001.973520","title":"A new view on regularization theory","year":2001,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"occam; Regularization (linguistics); Regularization perspectives on support vector machines; Computer science; Algorithm; Linear map; Fourier transform; Applied mathematics; Mathematics; Artificial intelligence; Inverse problem; Mathematical analysis; Tikhonov regularization; Pure mathematics","score_opus":0.015008899471237926,"score_gpt":0.24286086485452815,"score_spread":0.22785196538329022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112765921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043064,0.013071089,0.86746675,0.036911156,0.002530412,0.00003024072,0.0001768341,0.00022522976,0.075281866],"genre_scores_gemma":[0.3539619,0.033081304,0.4794331,0.034550283,0.02981522,0.00050638773,0.0005205132,0.0010377457,0.06709359],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966336,0.00106868,0.00015568329,0.00062796817,0.0013151993,0.00019892759],"domain_scores_gemma":[0.9965585,0.0020365173,0.0001955449,0.00044201917,0.00054288097,0.0002244905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004220557,0.0009614726,0.0016918209,0.0022781682,0.0014729727,0.0046877437,0.002304237,0.004382682,0.004905366],"category_scores_gemma":[0.006394383,0.00055816973,0.0012552583,0.001424971,0.0089069335,0.008417637,0.0032665518,0.010205681,0.0013262792],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034750478,0.0000059409604,0.000026974918,0.00003220858,0.0000070116134,0.000015323976,0.00004984287,0.00066323346,0.00014635306,0.9923071,0.0022373372,0.0045052026],"study_design_scores_gemma":[0.000007199503,0.00001280935,0.00003814524,0.000029414914,0.0000044290646,0.000051455398,0.000018236758,0.0045980057,0.00007730193,0.9782492,0.016904434,0.000009308128],"about_ca_topic_score_codex":0.00088314223,"about_ca_topic_score_gemma":0.0007570108,"teacher_disagreement_score":0.004905366,"about_ca_system_score_codex":0.0016290142,"about_ca_system_score_gemma":0.0013445863,"threshold_uncertainty_score":0.022320747},"labels":[],"label_agreement":null},{"id":"W2112830891","doi":"10.1109/72.883420","title":"On-line learning of dynamical systems in the presence of model mismatch and disturbances","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Computer science; Line (geometry); Nonlinear dynamical systems; Artificial intelligence; Nonlinear system; Mathematics; Physics","score_opus":0.018987192055971096,"score_gpt":0.2507532648143782,"score_spread":0.2317660727584071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112830891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074263394,0.00018355765,0.9232687,0.00011769502,0.000032150074,0.000039669692,0.0000144036285,0.0003580295,0.0017222774],"genre_scores_gemma":[0.95972556,0.00012310033,0.038411357,0.00007312424,0.000027247093,0.00005342094,0.000046631823,0.000048474165,0.0014910981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993156,0.00026079704,0.000050708273,0.00013618819,0.00016703877,0.00006964274],"domain_scores_gemma":[0.9971263,0.001795332,0.0004038661,0.00023416347,0.00037491976,0.000065384644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018605933,0.00066384696,0.00090872304,0.0002725462,0.00029023195,0.00103958,0.0011293773,0.001312327,0.0008356251],"category_scores_gemma":[0.008008409,0.00041457373,0.00039631518,0.00027626744,0.00083548547,0.0014969712,0.0011779924,0.0010589724,0.00023737802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011723125,0.00006429056,0.0007812116,0.00006263995,0.00005010221,0.00020226555,0.0001258016,0.9290532,0.0033890526,0.0042399815,0.00027884924,0.061635416],"study_design_scores_gemma":[0.0000035375724,0.000025029454,0.00008096267,0.0000025034242,0.0000032268872,0.000015378317,0.0000046507475,0.9980116,0.00063222303,0.0011117161,0.000106629865,0.0000025507557],"about_ca_topic_score_codex":0.0028939794,"about_ca_topic_score_gemma":0.0022069004,"teacher_disagreement_score":0.0028939794,"about_ca_system_score_codex":0.00044269275,"about_ca_system_score_gemma":0.0004101209,"threshold_uncertainty_score":0.009839833},"labels":[],"label_agreement":null},{"id":"W2112952476","doi":"10.1109/iscas.1990.111966","title":"A network that uses the outer product rule, hidden neurons, and peaks in the energy landscape","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hopfield network; Computer science; Orthogonality; Artificial neural network; Flexibility (engineering); Process (computing); Content-addressable memory; Product (mathematics); Outer product; Artificial intelligence; Theoretical computer science; Mathematics; Operating system","score_opus":0.028919525547341948,"score_gpt":0.2139446636946682,"score_spread":0.18502513814732624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112952476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067021996,0.00033084798,0.9154765,0.00022988049,0.00012382425,0.000065446984,0.00023558072,0.0006295509,0.015886452],"genre_scores_gemma":[0.5829986,0.00043841885,0.40078294,0.00009605458,0.000034641107,0.00016162264,0.0003944594,0.00011967372,0.014973643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998896,0.000023147608,0.0000067361516,0.000032185053,0.00003492019,0.000013280427],"domain_scores_gemma":[0.9997719,0.00009878036,0.000017884042,0.00003420122,0.00005843045,0.000018903007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033683464,0.00032481807,0.00040661165,0.00031236972,0.0003609663,0.0009027425,0.00079035875,0.000729292,0.003080892],"category_scores_gemma":[0.0015401717,0.00023740747,0.0003643307,0.000491288,0.0005070693,0.0013630255,0.00043924735,0.00056505617,0.00040216136],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004911788,0.000024204477,0.00052797585,0.000058783833,0.000034945046,0.000097513366,0.000033839162,0.9136036,0.004173828,0.03783527,0.0008568456,0.042704087],"study_design_scores_gemma":[0.000010550418,0.000013385794,0.000104600964,0.000004847708,0.000007540813,0.000026974396,0.000004472362,0.9839941,0.001720091,0.012835635,0.0012712067,0.0000065672048],"about_ca_topic_score_codex":0.0048981654,"about_ca_topic_score_gemma":0.006107543,"teacher_disagreement_score":0.0048981654,"about_ca_system_score_codex":0.0006282868,"about_ca_system_score_gemma":0.0006825432,"threshold_uncertainty_score":0.010306656},"labels":[],"label_agreement":null},{"id":"W2113206221","doi":"10.1109/icmic.2011.5973678","title":"Survey: Dimension reduction by pattern decomposition","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Computer science; Curse of dimensionality; Dimensionality reduction; Dimension (graph theory); Inference; Focus (optics); Stability (learning theory); Computational complexity theory; Field (mathematics); Decomposition; Point (geometry); Reduction (mathematics); Key (lock); Artificial intelligence; Data mining; Algorithm; Machine learning; Mathematics","score_opus":0.032866889925376785,"score_gpt":0.258149891939786,"score_spread":0.2252830020144092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113206221","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007342504,0.42004767,0.53522396,0.0029071271,0.0015904384,0.00016423286,0.00072229595,0.0013521742,0.030649602],"genre_scores_gemma":[0.08428864,0.5002906,0.38730195,0.0021384787,0.0033763878,0.00045029278,0.0035815134,0.00060760864,0.017964631],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991068,0.0001690877,0.00009281399,0.00017735844,0.00039848228,0.000055461845],"domain_scores_gemma":[0.99893194,0.0004391265,0.00005896103,0.0002422163,0.00029598497,0.00003176832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006889221,0.0012698433,0.0018141227,0.0021544131,0.00047177818,0.0018198703,0.0013290441,0.0009393857,0.00616014],"category_scores_gemma":[0.0024386158,0.0005354862,0.0011471813,0.004651437,0.00060863345,0.0023217548,0.0012488246,0.001365203,0.004026192],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005437795,0.00009077095,0.000574048,0.002548924,0.00012460336,0.000084027975,0.000067644054,0.008398709,0.0030933137,0.031528395,0.039588876,0.91384625],"study_design_scores_gemma":[0.00007941137,0.00030694046,0.0036211729,0.0016791337,0.00033661167,0.002332872,0.0002723579,0.118890725,0.011582517,0.22953813,0.631231,0.0001291329],"about_ca_topic_score_codex":0.0009030931,"about_ca_topic_score_gemma":0.0008679516,"teacher_disagreement_score":0.00616014,"about_ca_system_score_codex":0.00051478384,"about_ca_system_score_gemma":0.0007878116,"threshold_uncertainty_score":0.02060771},"labels":[],"label_agreement":null},{"id":"W2113315014","doi":"10.1109/iscas.1992.230201","title":"Ring unidirectional associative memories","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hebbian theory; Backpropagation; Associative property; Content-addressable storage; Content-addressable memory; Computer science; Sequence (biology); Recall; Convergence (economics); Artificial neural network; Artificial intelligence; Ring (chemistry); Bidirectional associative memory; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.014236077156463876,"score_gpt":0.23698553100414702,"score_spread":0.22274945384768313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113315014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110823624,0.0028845943,0.8385836,0.0004974903,0.0007351644,0.00012053441,0.00062312314,0.005501442,0.040230498],"genre_scores_gemma":[0.7609152,0.0015359734,0.1967042,0.00046247992,0.0002171506,0.00018517808,0.0005780713,0.00016455793,0.039237186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980634,0.000031063835,0.000016636219,0.000053812702,0.000053599633,0.000038435395],"domain_scores_gemma":[0.99954885,0.0000859594,0.000059327118,0.00013152685,0.00013488033,0.000039385497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027696026,0.000337583,0.0004194044,0.00030577715,0.00040451757,0.0008826274,0.001424005,0.0005082461,0.009711649],"category_scores_gemma":[0.0009584256,0.00017873425,0.00026472274,0.00036893997,0.00031687738,0.0017960108,0.0009011732,0.0005062997,0.0024324453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000951666,0.0002219896,0.0014684446,0.00078667176,0.00015165846,0.0005944745,0.00020187706,0.063616484,0.17184906,0.16405444,0.017805606,0.5782977],"study_design_scores_gemma":[0.000110098634,0.0007489406,0.00093917514,0.00008338229,0.00018825123,0.0015039382,0.00010379351,0.62417716,0.18709815,0.093154974,0.09178058,0.00011146127],"about_ca_topic_score_codex":0.00040174177,"about_ca_topic_score_gemma":0.0008060502,"teacher_disagreement_score":0.009711649,"about_ca_system_score_codex":0.000280597,"about_ca_system_score_gemma":0.0002953829,"threshold_uncertainty_score":0.032488704},"labels":[],"label_agreement":null},{"id":"W2113457899","doi":"10.1109/ijcnn.2007.4371193","title":"Performance Evaluation of Two Distributed BackPropagation Implementations","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Backpropagation; Workstation; Exploit; Artificial neural network; Implementation; Set (abstract data type); Artificial intelligence; Parallel computing; Machine learning; Distributed computing; Theoretical computer science; Software engineering; Operating system; Programming language","score_opus":0.048583902941459256,"score_gpt":0.35656806808482616,"score_spread":0.3079841651433669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113457899","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8660382,0.0014788744,0.10799725,0.00062086794,0.00047287205,0.0002853371,0.0003178413,0.00927577,0.013513063],"genre_scores_gemma":[0.90837765,0.00037522585,0.08608358,0.000108055865,0.000040537343,0.00015430551,0.00048420863,0.00031649086,0.0040598665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986994,0.00026645546,0.00011554704,0.00031704048,0.00041533037,0.00018601163],"domain_scores_gemma":[0.9958131,0.0014843123,0.00018036243,0.0006211201,0.0016371866,0.0002639886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019555544,0.0014415714,0.0013010225,0.0008677155,0.0007470919,0.0011358416,0.0024656926,0.0015045932,0.0030001868],"category_scores_gemma":[0.006596496,0.0004530624,0.000430396,0.0013070418,0.0007158226,0.0019514498,0.0008440812,0.0011990486,0.00077838224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0069591757,0.0019334086,0.006815968,0.0007099012,0.00047573686,0.0003649205,0.00037629128,0.5327541,0.029831853,0.0042935614,0.006886612,0.40859857],"study_design_scores_gemma":[0.0005682846,0.00058348355,0.0016476277,0.000020171701,0.00007348565,0.000075210235,0.00013054177,0.9695724,0.02487236,0.0012041961,0.00122683,0.00002539228],"about_ca_topic_score_codex":0.010923619,"about_ca_topic_score_gemma":0.00699594,"teacher_disagreement_score":0.010923619,"about_ca_system_score_codex":0.001789614,"about_ca_system_score_gemma":0.0015731908,"threshold_uncertainty_score":0.021720111},"labels":[],"label_agreement":null},{"id":"W2114023229","doi":"10.5267/j.msl.2011.02.002","title":"An Intelligent technical analysis using neural network","year":2011,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.040386321987832825,"score_gpt":0.27770644384535503,"score_spread":0.23732012185752221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114023229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047130976,0.00074708235,0.94053364,0.00050046697,0.000106738844,0.000099182704,0.00023084192,0.0013906754,0.009260442],"genre_scores_gemma":[0.6603686,0.0012725422,0.325021,0.00023917225,0.0001750671,0.00018170103,0.0005806191,0.000091033384,0.012070256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967504,0.000048664537,0.00002212981,0.00009142257,0.00013095292,0.000031701],"domain_scores_gemma":[0.99965346,0.00011009848,0.00005603067,0.00003414669,0.000132706,0.000013534642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046856026,0.000674907,0.0004907479,0.0016387671,0.00038586877,0.0011727404,0.00066340837,0.0007437337,0.0024326653],"category_scores_gemma":[0.0014321301,0.00018614641,0.0006731655,0.001087534,0.00029105964,0.0013726105,0.00054736837,0.00046300614,0.0005504413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015118867,0.00014862498,0.007985568,0.00024172972,0.00021459203,0.00029342502,0.00012101428,0.37620088,0.017585078,0.017672548,0.005688776,0.57369655],"study_design_scores_gemma":[0.000003475833,0.00001927676,0.001547156,0.000010554596,0.000025059815,0.000040960105,0.000015262414,0.98887676,0.0020004034,0.0060064634,0.0014424807,0.000012109193],"about_ca_topic_score_codex":0.003206643,"about_ca_topic_score_gemma":0.0030388304,"teacher_disagreement_score":0.003206643,"about_ca_system_score_codex":0.0006327291,"about_ca_system_score_gemma":0.00051617925,"threshold_uncertainty_score":0.00813812},"labels":[],"label_agreement":null},{"id":"W2114116576","doi":"10.1109/asap.1990.145503","title":"Systolic-based computing machinery for radar signal processing studies","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Radar; Computer science; Signal processing; Digital signal processing; Data processing; Process (computing); Real-time computing; Radar signal processing; SIGNAL (programming language); Radar systems; Parallel processing; Software; Data processing system; Artificial intelligence; Computer hardware; Computer graphics (images); Telecommunications; Database; Operating system","score_opus":0.06342649580980407,"score_gpt":0.30226240247763714,"score_spread":0.23883590666783305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114116576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014132267,0.0023173306,0.964143,0.00094906567,0.0007811043,0.00006719219,0.00018459209,0.0023688592,0.027775576],"genre_scores_gemma":[0.061422344,0.008237512,0.8884436,0.0013457708,0.0024446202,0.0005642313,0.0010119005,0.00095886807,0.035571128],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991216,0.00021895133,0.00010774084,0.00014417937,0.00035375849,0.000053712138],"domain_scores_gemma":[0.99875176,0.00046784815,0.00007579278,0.00034640962,0.00028425123,0.00007394255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095652894,0.00095211464,0.00069560856,0.001400505,0.0007905421,0.0019772302,0.001488084,0.00078179064,0.019740384],"category_scores_gemma":[0.0036894702,0.00046230602,0.0006832233,0.0021937098,0.002050452,0.0032866767,0.0015293157,0.0028543263,0.012458614],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028955792,0.00003224195,0.00023688597,0.0002724222,0.000021317172,0.00014518629,0.00026360413,0.0046741106,0.005965612,0.83560395,0.028024899,0.124730736],"study_design_scores_gemma":[0.000036017216,0.00006691632,0.00033331753,0.00017188866,0.000024943183,0.000413212,0.00005640004,0.042191178,0.006257615,0.48003247,0.47037622,0.000039862553],"about_ca_topic_score_codex":0.00067900115,"about_ca_topic_score_gemma":0.00078999094,"teacher_disagreement_score":0.019740384,"about_ca_system_score_codex":0.00071640237,"about_ca_system_score_gemma":0.0010062433,"threshold_uncertainty_score":0.06603813},"labels":[],"label_agreement":null},{"id":"W2114126036","doi":"10.1109/cca.1993.348269","title":"Neural network based controllers for non-linear systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial neural network; Inverted pendulum; Computer science; Controller (irrigation); Nonlinear system; Term (time); Control theory (sociology); Ranking (information retrieval); Artificial intelligence; Scheme (mathematics); Control (management); Control engineering; Mathematics; Engineering","score_opus":0.030285630782081022,"score_gpt":0.24233785365462474,"score_spread":0.21205222287254372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114126036","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064714104,0.002137921,0.97981024,0.00035933976,0.00028292564,0.00006617282,0.000044119126,0.0008102698,0.010017584],"genre_scores_gemma":[0.76095814,0.0033225159,0.2063186,0.0004698454,0.00037541098,0.00066513463,0.00021784902,0.00012614485,0.02754636],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996562,0.00007607361,0.000026430866,0.000059955997,0.00015269718,0.00002860803],"domain_scores_gemma":[0.99960536,0.00020427271,0.000046252204,0.000024375757,0.00011018593,0.00000961602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005928198,0.00085787085,0.0005674796,0.00037772575,0.00037844124,0.0010017495,0.0008352339,0.0010421511,0.004203669],"category_scores_gemma":[0.0015751903,0.00024058437,0.0003201676,0.00041691834,0.00065729144,0.00070649077,0.0005922116,0.0011324822,0.0006570513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007107923,0.0000550446,0.00018270532,0.00030924712,0.00004613751,0.00011085443,0.000073942996,0.8550237,0.0053337067,0.042884085,0.0022246246,0.0936849],"study_design_scores_gemma":[0.000013299028,0.000025088604,0.000058955495,0.000012361716,0.0000058390156,0.000011941186,0.0000033700428,0.99173826,0.0007346316,0.004958236,0.0024326427,0.000005547176],"about_ca_topic_score_codex":0.0052503673,"about_ca_topic_score_gemma":0.0056606117,"teacher_disagreement_score":0.0052503673,"about_ca_system_score_codex":0.0007125112,"about_ca_system_score_gemma":0.0006035886,"threshold_uncertainty_score":0.014062703},"labels":[],"label_agreement":null},{"id":"W2114508814","doi":"","title":"Low precision arithmetic for deep learning","year":2014,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"MNIST database; Computer science; Benchmark (surveying); Computation; Artificial neural network; Point (geometry); Set (abstract data type); Floating point; Artificial intelligence; Training set; Deep learning; Algorithm; Machine learning; Arithmetic; Mathematics","score_opus":0.03463392661948356,"score_gpt":0.17420071414242494,"score_spread":0.13956678752294138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114508814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4002736,0.0039399555,0.5285333,0.0021990726,0.0011466339,0.00032548248,0.0068588927,0.018030055,0.03869303],"genre_scores_gemma":[0.7612242,0.0007212254,0.22696583,0.0005685491,0.00008229209,0.00058947137,0.0032802597,0.0011679061,0.005400271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911994,0.00023076855,0.000067645684,0.00015575081,0.00031111788,0.00011478651],"domain_scores_gemma":[0.9975836,0.0014580355,0.0000995168,0.00041366828,0.0003995722,0.000045597928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016578994,0.0010165495,0.0006023642,0.0006626772,0.0005389798,0.0011526918,0.0018342651,0.0011068791,0.011808079],"category_scores_gemma":[0.011236335,0.00038358456,0.0005564489,0.001291935,0.00084756775,0.0021307557,0.0009878507,0.0025590665,0.0021199186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012420957,0.00028634304,0.003297634,0.00075053005,0.00013145337,0.00021606384,0.00021285124,0.7915534,0.0073752133,0.06949396,0.03192097,0.093519494],"study_design_scores_gemma":[0.00023204667,0.0002660872,0.0006850272,0.00009681313,0.000041512878,0.000093359275,0.00006459708,0.9028299,0.02146941,0.06306367,0.011128287,0.00002927215],"about_ca_topic_score_codex":0.004794687,"about_ca_topic_score_gemma":0.004410666,"teacher_disagreement_score":0.011808079,"about_ca_system_score_codex":0.0010120344,"about_ca_system_score_gemma":0.0012522914,"threshold_uncertainty_score":0.039501905},"labels":[],"label_agreement":null},{"id":"W2114828655","doi":"10.1139/v09-025","title":"An improved neural network method for solving the Schrödinger equation","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Montréal; Queen's University; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Basis function; Hamiltonian (control theory); Curse of dimensionality; Artificial neural network; Nonlinear system; Basis (linear algebra); Wave function; Schrödinger equation; Applied mathematics; Radial basis function; Mathematics; Algorithm; Mathematical analysis; Computer science; Mathematical optimization; Artificial intelligence; Quantum mechanics; Physics; Geometry","score_opus":0.02041845681541388,"score_gpt":0.27001409445482794,"score_spread":0.24959563763941406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114828655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041130306,0.00022010699,0.9924379,0.00015966903,0.00006724802,0.00003130364,0.000044711942,0.0001915104,0.0027345773],"genre_scores_gemma":[0.103347845,0.00036426392,0.88574,0.00019339124,0.000107822634,0.00030316444,0.00021851347,0.00014393574,0.0095810415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996112,0.00011872202,0.000018566256,0.00004674371,0.0001761519,0.000028616401],"domain_scores_gemma":[0.99971896,0.00011345726,0.00001569535,0.000023572255,0.00011579457,0.000012446062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006754118,0.00042016926,0.00056853687,0.0004722603,0.00041571166,0.00044067702,0.0012028636,0.0009670583,0.0037209517],"category_scores_gemma":[0.001254039,0.00027492607,0.0005310848,0.00063962623,0.00037438388,0.0010654132,0.00066371856,0.001131882,0.0009719937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007200223,0.00005824533,0.00046143192,0.00014925656,0.000058309965,0.000102493446,0.00006549429,0.7466984,0.007150916,0.058638427,0.004518169,0.18202692],"study_design_scores_gemma":[0.000004919134,0.000006003563,0.000037922633,0.0000028071609,0.0000026611447,0.000008558537,0.0000013332825,0.99562633,0.00032438882,0.003100562,0.00088050717,0.000004027735],"about_ca_topic_score_codex":0.0057706716,"about_ca_topic_score_gemma":0.006426324,"teacher_disagreement_score":0.0057706716,"about_ca_system_score_codex":0.00050203176,"about_ca_system_score_gemma":0.0009868771,"threshold_uncertainty_score":0.012447774},"labels":[],"label_agreement":null},{"id":"W2114918782","doi":"10.1109/ijcnn.1998.685972","title":"EVOL: ensembles voting online","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Voting; Computer science; Artificial intelligence; Modular design; Focus (optics); Scheme (mathematics); Machine learning; Artificial neural network; Process (computing); Mathematics","score_opus":0.03892456644358199,"score_gpt":0.24095964695964825,"score_spread":0.20203508051606625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114918782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008523093,0.00021555836,0.97620934,0.0004279509,0.00027307327,0.00016648167,0.0002774585,0.0060549616,0.007852017],"genre_scores_gemma":[0.4316182,0.00023734964,0.54077804,0.00073929084,0.0004615087,0.0007377561,0.0016970712,0.001584547,0.022146255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973099,0.0010553875,0.00012532494,0.00045830244,0.0007952009,0.0002558868],"domain_scores_gemma":[0.99641114,0.0013027894,0.00015577566,0.0014349439,0.0005085441,0.00018690208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034663542,0.0010383258,0.0014618943,0.0012360846,0.0011656892,0.0016400113,0.0026198956,0.0017875273,0.016089892],"category_scores_gemma":[0.010742838,0.0006316584,0.0009769138,0.001303985,0.0009454723,0.0038266368,0.0041707987,0.0022478644,0.0053477916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005221343,0.00023237037,0.0019415633,0.00012693285,0.00021080908,0.00019577608,0.00017619587,0.19100606,0.0038283735,0.08299531,0.044817444,0.6739471],"study_design_scores_gemma":[0.000059041922,0.000060706636,0.00025338278,0.000014557941,0.00002250578,0.00007065989,0.000023098648,0.9019062,0.003008288,0.07904785,0.015515997,0.00001766824],"about_ca_topic_score_codex":0.0012929179,"about_ca_topic_score_gemma":0.0025523119,"teacher_disagreement_score":0.016089892,"about_ca_system_score_codex":0.00076405425,"about_ca_system_score_gemma":0.000849412,"threshold_uncertainty_score":0.053826034},"labels":[],"label_agreement":null},{"id":"W2114928916","doi":"10.1023/a:1008313828824","title":"Using Extra Output Learning to Insert a Symbolic Theory into a Connectionist Network","year":2000,"lang":"en","type":"article","venue":"Minds and Machines","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Connectionism; Computer science; Artificial intelligence; Interpretation (philosophy); Theory of computation; Decision tree; Tree (set theory); Machine learning; Translation (biology); Artificial neural network; Algorithm; Mathematics","score_opus":0.017761251668335105,"score_gpt":0.2678624336770148,"score_spread":0.2501011820086797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114928916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008844954,0.00008530736,0.98288995,0.00035529264,0.00020660464,0.00002666745,0.000049475333,0.0013595297,0.0061822534],"genre_scores_gemma":[0.47361246,0.00023453376,0.5127315,0.00054105284,0.00022826376,0.00013063046,0.00025234252,0.00049008505,0.011779295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994717,0.00014832693,0.000038140948,0.00013484729,0.00016405006,0.000042934455],"domain_scores_gemma":[0.99794024,0.0011187575,0.00007002807,0.0005068769,0.00027732638,0.00008676209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010227355,0.0007337155,0.00062274013,0.0006406341,0.0005920942,0.0015853137,0.0021143158,0.001058569,0.011683387],"category_scores_gemma":[0.005449479,0.0005139874,0.00090600917,0.00054304145,0.0018064601,0.004122974,0.0025068664,0.0029166264,0.0023900284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031967994,0.00023535063,0.0012359029,0.00035583993,0.00023382553,0.0004047065,0.00039190386,0.1495621,0.01530993,0.37219572,0.0055483324,0.4542068],"study_design_scores_gemma":[0.000026123695,0.0000690459,0.00015257951,0.00004069417,0.00005569936,0.000091172486,0.000024645546,0.713721,0.0068533802,0.273768,0.0051734047,0.00002422352],"about_ca_topic_score_codex":0.0012240246,"about_ca_topic_score_gemma":0.002544789,"teacher_disagreement_score":0.011683387,"about_ca_system_score_codex":0.0006866464,"about_ca_system_score_gemma":0.00071071775,"threshold_uncertainty_score":0.039084792},"labels":[],"label_agreement":null},{"id":"W2115101923","doi":"10.1109/72.963787","title":"Compound binomial processes in neural integration","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Artificial neural network; Computer science; Stochastic process; Bernoulli's principle; Stochastic neural network; Bernoulli process; Event (particle physics); Multiplexing; Bernoulli trial; Algorithm; Mathematics; Artificial intelligence; Recurrent neural network; Statistics; Telecommunications","score_opus":0.02193154123387744,"score_gpt":0.2515548845564565,"score_spread":0.22962334332257908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115101923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018038273,0.0029918654,0.9620154,0.0011314417,0.00021982685,0.00004316466,0.00005554458,0.00009016291,0.015414338],"genre_scores_gemma":[0.7433452,0.0080294935,0.21634337,0.0014274879,0.0009801857,0.00027890477,0.00014601368,0.00017451402,0.029274939],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918944,0.0003294098,0.000039906725,0.00012375815,0.00021342805,0.000103988255],"domain_scores_gemma":[0.99555176,0.0037176574,0.0002313259,0.00012926395,0.00025360673,0.000116392206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003235105,0.0007031568,0.0010277991,0.0013542467,0.00075960177,0.0013795149,0.0011340259,0.0017380401,0.0039989124],"category_scores_gemma":[0.010110377,0.00052362593,0.0009353488,0.0012075182,0.0022896586,0.0033315178,0.0014091885,0.001975368,0.00058946153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053931097,0.000038210685,0.0007042466,0.000065020075,0.000040774717,0.00021225192,0.0001786277,0.108333424,0.0012396808,0.86702585,0.001404169,0.0207037],"study_design_scores_gemma":[0.000018744304,0.00004745537,0.00038212552,0.000029070785,0.000017887767,0.00012201625,0.000021719547,0.53144366,0.00059798924,0.46456897,0.0027247975,0.000025586243],"about_ca_topic_score_codex":0.0028177854,"about_ca_topic_score_gemma":0.0024235547,"teacher_disagreement_score":0.0039989124,"about_ca_system_score_codex":0.001338532,"about_ca_system_score_gemma":0.00067769364,"threshold_uncertainty_score":0.017109036},"labels":[],"label_agreement":null},{"id":"W2115455785","doi":"10.1109/ipps.1991.153787","title":"On a binary relation inference network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Relation (database); Computer science; Inference; Binary number; Binary relation; Artificial intelligence; Mathematics; Data mining; Arithmetic; Discrete mathematics","score_opus":0.030472751587647222,"score_gpt":0.2450229200664563,"score_spread":0.21455016847880906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115455785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021737883,0.00028132132,0.96232176,0.0012334019,0.00007863982,0.00008732523,0.00035981747,0.00046025656,0.013439545],"genre_scores_gemma":[0.4060762,0.0011454431,0.57243824,0.00060156174,0.00023988096,0.0003738364,0.0013449437,0.00017481176,0.017605158],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99878556,0.0003668029,0.000078914636,0.00039531745,0.00028190968,0.00009146892],"domain_scores_gemma":[0.99763155,0.0015336819,0.00017019162,0.0002536322,0.00031010623,0.00010079693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014725534,0.000491259,0.000609555,0.0013551919,0.0012159663,0.0024294043,0.0017110296,0.0013691228,0.009860591],"category_scores_gemma":[0.008262345,0.00058997754,0.00086610724,0.001576347,0.0019943367,0.00686343,0.0016279,0.0014792756,0.0011840736],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025425907,0.000052599436,0.00075772847,0.00016408654,0.000049397946,0.00021364172,0.00026845007,0.13201368,0.002757299,0.7982845,0.00392302,0.06126136],"study_design_scores_gemma":[0.000033728524,0.000022212678,0.0002190176,0.00002647501,0.0000318402,0.000080823615,0.00003654586,0.51964253,0.0011714472,0.47222662,0.0064912173,0.000017501818],"about_ca_topic_score_codex":0.0067062327,"about_ca_topic_score_gemma":0.0059304987,"teacher_disagreement_score":0.009860591,"about_ca_system_score_codex":0.00159646,"about_ca_system_score_gemma":0.0010407254,"threshold_uncertainty_score":0.032987},"labels":[],"label_agreement":null},{"id":"W2115555550","doi":"10.1109/iscas.2005.1465417","title":"Two-level Decoupled Hamming Network for Associative Memory Under Noisy Environment","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Noise (video); Hamming distance; Hamming code; Associative property; Computer science; Content-addressable memory; Hamming weight; Content-addressable storage; Hamming(7,4); Bidirectional associative memory; Algorithm; Hamming bound; Mathematics; Theoretical computer science; Decoding methods; Artificial intelligence; Block code; Artificial neural network","score_opus":0.03287060306631676,"score_gpt":0.26615710761253425,"score_spread":0.2332865045462175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115555550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22109172,0.0004373336,0.7731703,0.00020016675,0.00006540717,0.000053424363,0.00012291795,0.00034416854,0.0045145415],"genre_scores_gemma":[0.9473825,0.0002470029,0.048150145,0.00007415552,0.000026512422,0.000059544167,0.00007464937,0.000020680765,0.003964734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999757,0.000046284815,0.000018108703,0.00006719017,0.00007079622,0.000040642673],"domain_scores_gemma":[0.9994906,0.0001707369,0.000056043555,0.00014407728,0.00009916858,0.000039377454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000407034,0.0003708238,0.00057542283,0.00032248386,0.00031182464,0.00062321423,0.001051935,0.0006116296,0.003255583],"category_scores_gemma":[0.0010681002,0.00020315328,0.0003562197,0.0003590627,0.0006862077,0.0022821473,0.0008239736,0.00067117455,0.00038359995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007047632,0.00018087875,0.0009638062,0.0004054557,0.00011581485,0.00024626643,0.00014204648,0.5182442,0.10719546,0.28835624,0.0014392877,0.08200571],"study_design_scores_gemma":[0.000015116966,0.000110233486,0.00026840015,0.000006043088,0.000021293836,0.000082883336,0.000012240529,0.94654495,0.011356696,0.04095807,0.0006013008,0.000022779986],"about_ca_topic_score_codex":0.00070629857,"about_ca_topic_score_gemma":0.0010374972,"teacher_disagreement_score":0.003255583,"about_ca_system_score_codex":0.00047429584,"about_ca_system_score_gemma":0.00045246098,"threshold_uncertainty_score":0.010890961},"labels":[],"label_agreement":null},{"id":"W2115587236","doi":"10.1109/icnn.1997.616165","title":"On the implementation of backpropagation on the Alex AVX-2 parallel system","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Backpropagation; Computer science; Speedup; Block (permutation group theory); MIMD; Parallel computing; Process (computing); Parallel processing; Overhead (engineering); Artificial neural network; Artificial intelligence; Operating system","score_opus":0.055242168859400975,"score_gpt":0.27194165516289176,"score_spread":0.21669948630349078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115587236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18569937,0.00067399617,0.79068357,0.00050552096,0.00023727257,0.00012111787,0.00010813485,0.0071280207,0.014843049],"genre_scores_gemma":[0.5618944,0.0003542909,0.4288218,0.00010660095,0.000044956985,0.00011531686,0.00018188005,0.00016168495,0.008319063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998099,0.00004566623,0.000010592618,0.000023824236,0.00008156428,0.000028464561],"domain_scores_gemma":[0.9997067,0.000096103984,0.000018572082,0.000070818984,0.00008624931,0.000021458058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039393312,0.0003246308,0.00030863538,0.00020697786,0.0002620576,0.00038598193,0.0009051778,0.00037873286,0.004002298],"category_scores_gemma":[0.0012398574,0.00019413614,0.00017846262,0.0003265805,0.00024700645,0.000694059,0.00038575346,0.00055277173,0.00091940997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001177637,0.00025144432,0.0025650123,0.00034300354,0.00012890607,0.00043837368,0.00021574246,0.31212276,0.096005045,0.03785079,0.007968872,0.54093254],"study_design_scores_gemma":[0.000043623968,0.00013072988,0.0006621421,0.000009103499,0.000017530327,0.00006450643,0.000014655071,0.96250165,0.028813338,0.0030649933,0.0046681203,0.000009573122],"about_ca_topic_score_codex":0.005531268,"about_ca_topic_score_gemma":0.0064473907,"teacher_disagreement_score":0.005531268,"about_ca_system_score_codex":0.00043847485,"about_ca_system_score_gemma":0.0007309978,"threshold_uncertainty_score":0.013389051},"labels":[],"label_agreement":null},{"id":"W2115654888","doi":"10.3233/978-1-61499-621-7-35","title":"A Many-Core Machine Model for Designing Algorithms with Minimum Parallelism Overheads","year":2016,"lang":"en","type":"book-chapter","venue":"Advances in parallel computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Parallelism (grammar); Computer science; Parallel computing; Core (optical fiber); Many core; Data parallelism; Instruction-level parallelism; Multi-core processor; Task parallelism; Algorithm; Telecommunications","score_opus":0.03179537465127065,"score_gpt":0.287560293246253,"score_spread":0.25576491859498235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115654888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033226404,0.0005569565,0.9909967,0.0002880898,0.000046415098,0.000046912002,0.000036264446,0.00018487893,0.0045211813],"genre_scores_gemma":[0.16058527,0.0016727293,0.82444555,0.00033997753,0.00015270275,0.0005959081,0.0002140048,0.00027828614,0.011715643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994734,0.0001323871,0.00002421236,0.00010225419,0.00023194277,0.000035784058],"domain_scores_gemma":[0.9994017,0.00027680837,0.000041203304,0.00013474695,0.00011261118,0.00003293169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070595136,0.00088466535,0.0007231116,0.00048750825,0.00041037018,0.0011884527,0.0022079938,0.0012615313,0.0030620613],"category_scores_gemma":[0.0018458131,0.00041024876,0.00077314326,0.00092390034,0.0009403931,0.0023942112,0.00072563335,0.0022690012,0.0012919662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005378459,0.00009036157,0.00029482698,0.00028853698,0.00003584504,0.000101645295,0.000116148345,0.5223169,0.009283051,0.420077,0.004381934,0.042959977],"study_design_scores_gemma":[0.00001002239,0.00003762074,0.00007737819,0.00001795486,0.000010050069,0.00004867218,0.000007323571,0.8869653,0.0015029178,0.10453157,0.0067826016,0.000008655907],"about_ca_topic_score_codex":0.00076607085,"about_ca_topic_score_gemma":0.0010223449,"teacher_disagreement_score":0.0030620613,"about_ca_system_score_codex":0.0010341859,"about_ca_system_score_gemma":0.0011204766,"threshold_uncertainty_score":0.010243654},"labels":[],"label_agreement":null},{"id":"W2115861312","doi":"10.1109/iscas.1993.394263","title":"Analog neural network building blocks based on current mode subthreshold operation","year":2002,"lang":"en","type":"article","venue":"1993 IEEE International Symposium on Circuits and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Subthreshold conduction; Artificial neural network; Comparator; Computer science; Electronic engineering; Current (fluid); Analog signal; Analog signal processing; Amplifier; Operational amplifier; Electronic circuit; Multiplier (economics); Field-programmable analog array; Analogue electronics; Feed forward; Electrical engineering; Analog multiplier; Transistor; Artificial intelligence; CMOS; Voltage; Engineering; Computer hardware; Control engineering; Digital signal processing","score_opus":0.0419612648578287,"score_gpt":0.2799327835935417,"score_spread":0.23797151873571298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115861312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010050052,0.0007272415,0.966683,0.00015967207,0.00012269703,0.000108865286,0.00024716038,0.0018415486,0.02005975],"genre_scores_gemma":[0.34606105,0.0014228605,0.6269406,0.00027581266,0.00008672952,0.0005279313,0.00070154737,0.00026560295,0.023717849],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985707,0.000021213662,0.000010434259,0.00002273336,0.00007358138,0.000014964227],"domain_scores_gemma":[0.9998795,0.00003583532,0.000011652591,0.00001857336,0.00004773213,0.000006789653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019501869,0.00040887017,0.00022891533,0.0002877114,0.00026428144,0.00065120833,0.00075929926,0.00046325006,0.0064570038],"category_scores_gemma":[0.0005305204,0.00020483632,0.0003021438,0.00027437776,0.00026416456,0.00081595004,0.0002912065,0.0007599826,0.0021284653],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021251578,0.0001322824,0.00042351283,0.0005352665,0.00010281512,0.00041628254,0.00015172949,0.15885906,0.2148624,0.30582026,0.007116593,0.3113672],"study_design_scores_gemma":[0.000051092127,0.00026634656,0.00057598215,0.0001066806,0.00006741743,0.00043403468,0.000023680985,0.73126876,0.12911867,0.06108405,0.07695446,0.00004884804],"about_ca_topic_score_codex":0.0010710281,"about_ca_topic_score_gemma":0.0019973656,"teacher_disagreement_score":0.0064570038,"about_ca_system_score_codex":0.00037915644,"about_ca_system_score_gemma":0.00036283248,"threshold_uncertainty_score":0.021600842},"labels":[],"label_agreement":null},{"id":"W2115872360","doi":"10.1007/0-306-47015-2_55","title":"Network Parallel Computing for SOM Neural Networks","year":2005,"lang":"en","type":"book-chapter","venue":"Kluwer Academic Publishers eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Speedup; Java; Artificial neural network; Workstation; Distributed computing; Computation; Parallel computing; Load balancing (electrical power); Self-organizing map; Artificial intelligence; Operating system; Algorithm","score_opus":0.030506119061609667,"score_gpt":0.25537390673803584,"score_spread":0.22486778767642618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115872360","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006559177,0.010825518,0.90009034,0.00073249673,0.0008273587,0.00009446485,0.00029508543,0.0029385386,0.07763704],"genre_scores_gemma":[0.12918371,0.014288607,0.7687905,0.0003933116,0.00064813293,0.00046184773,0.0010334561,0.0010306637,0.084169894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998023,0.00003234899,0.00000847618,0.000030101608,0.00011385011,0.000013042309],"domain_scores_gemma":[0.9998747,0.000043283522,0.0000048018424,0.00003183453,0.000036880483,0.000008506677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020403946,0.0005740605,0.00042573357,0.0004051209,0.00036072446,0.00095869263,0.0008788288,0.0005286381,0.0096805375],"category_scores_gemma":[0.00072509516,0.00026526698,0.0004025754,0.0012024358,0.00036714794,0.0008764514,0.0006931561,0.0010085799,0.0039010658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010488981,0.00007686734,0.00031712273,0.00091940805,0.00009027608,0.00026763228,0.00015462819,0.10818917,0.010575533,0.1960783,0.05160134,0.6316247],"study_design_scores_gemma":[0.00003778053,0.000039265004,0.00031492888,0.00012328642,0.000026208621,0.00032643497,0.000047812293,0.55301726,0.008160863,0.21284409,0.22504158,0.000020577936],"about_ca_topic_score_codex":0.0009198819,"about_ca_topic_score_gemma":0.0017135801,"teacher_disagreement_score":0.0096805375,"about_ca_system_score_codex":0.0005163331,"about_ca_system_score_gemma":0.00042425768,"threshold_uncertainty_score":0.032384634},"labels":[],"label_agreement":null},{"id":"W2115958133","doi":"10.1024/1421-0185.63.3.151","title":"Visual Learning: An Overview","year":2004,"lang":"en","type":"article","venue":"Swiss Journal of Psychology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Machine learning; Computer science; Artificial neural network; Psychology","score_opus":0.06644055297602335,"score_gpt":0.4194167422772394,"score_spread":0.35297618930121605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115958133","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00081308244,0.96003836,0.018312091,0.0011533138,0.00066990766,0.000029019533,0.00012002331,0.0001820698,0.018682119],"genre_scores_gemma":[0.015257818,0.9517871,0.018968634,0.0014677903,0.0025581892,0.000108668144,0.00040353366,0.00008608886,0.009362162],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99951005,0.00008795452,0.000044166245,0.00014929257,0.00016942977,0.000039085193],"domain_scores_gemma":[0.9994536,0.00029295386,0.000025887099,0.000035161018,0.00014203851,0.000050388124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071463257,0.0009784773,0.0009306796,0.004029945,0.0005529035,0.002795231,0.0013186553,0.0018640388,0.0071982574],"category_scores_gemma":[0.0012085842,0.0005767343,0.00060674845,0.003216093,0.0013398742,0.0038675368,0.0013718787,0.0019883106,0.004025922],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066008324,0.00008408822,0.00053166103,0.003701899,0.00008085057,0.0001290544,0.0001463222,0.0020144926,0.0011148534,0.04205509,0.039425302,0.91065025],"study_design_scores_gemma":[0.0000147363,0.00011473158,0.0015489295,0.00246388,0.00004784772,0.0012846736,0.0001463152,0.0028923247,0.0007322737,0.09233052,0.89836586,0.00005792269],"about_ca_topic_score_codex":0.0025167274,"about_ca_topic_score_gemma":0.00139905,"teacher_disagreement_score":0.0071982574,"about_ca_system_score_codex":0.0014329862,"about_ca_system_score_gemma":0.00093197555,"threshold_uncertainty_score":0.024080515},"labels":[],"label_agreement":null},{"id":"W2116049283","doi":"10.1109/iscas.1996.541632","title":"Design of IIR filters with arbitrary amplitude and phase responses by feedback neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Infinite impulse response; 2D Filters; Artificial neural network; Computer science; Amplitude; Digital filter; Linear phase; Very-large-scale integration; Filter (signal processing); Phase (matter); Control theory (sociology); Analogue filter; Finite impulse response; Pole–zero plot; Transfer function; Algorithm; Artificial intelligence; Engineering; Physics","score_opus":0.03097436089383127,"score_gpt":0.24791091528096,"score_spread":0.21693655438712872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116049283","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008140843,0.00029057843,0.98903775,0.00007213946,0.00003951088,0.00004791393,0.000017024353,0.00024603918,0.0021081436],"genre_scores_gemma":[0.4447267,0.0006816791,0.54990524,0.00023212204,0.0000788974,0.00031887696,0.00006990635,0.00007667643,0.0039098416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997646,0.0000426051,0.000016612235,0.00005306459,0.000093067414,0.00003000921],"domain_scores_gemma":[0.9997713,0.000085807085,0.000054146964,0.000017670855,0.00005889566,0.000012262279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054301554,0.0006759105,0.00035431556,0.00029101997,0.00028492892,0.0005485216,0.00083950633,0.00089201366,0.001214837],"category_scores_gemma":[0.0012354384,0.00029587193,0.0003351446,0.00025083832,0.00046201,0.00059488777,0.00028502918,0.00044089888,0.0003977576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024455562,0.00010640862,0.0007772784,0.00039265826,0.00007989598,0.00025670914,0.00023441498,0.46823975,0.16938,0.038937572,0.0017699423,0.31958076],"study_design_scores_gemma":[0.00004342877,0.0001629378,0.00024668378,0.00003079302,0.00003179396,0.0001187003,0.000012468897,0.9742313,0.015510603,0.005864995,0.0037279394,0.0000183802],"about_ca_topic_score_codex":0.0014926695,"about_ca_topic_score_gemma":0.0021978258,"teacher_disagreement_score":0.0014926695,"about_ca_system_score_codex":0.00051095977,"about_ca_system_score_gemma":0.0004704108,"threshold_uncertainty_score":0.0040640235},"labels":[],"label_agreement":null},{"id":"W2116198287","doi":"10.1109/wescan.1993.270588","title":"Neural networks in control systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial neural network; Control (management); Control system; Artificial intelligence; Engineering","score_opus":0.016461341396193846,"score_gpt":0.2099819224821837,"score_spread":0.19352058108598985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116198287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003291915,0.38172993,0.4640366,0.008698169,0.004948843,0.00015869207,0.00048030916,0.0007804456,0.13587506],"genre_scores_gemma":[0.26552066,0.46069574,0.18790117,0.0039354987,0.007385397,0.0009469612,0.0010937912,0.00025360915,0.07226717],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991792,0.00028748417,0.00006844673,0.00014300898,0.00027774036,0.00004400727],"domain_scores_gemma":[0.9994993,0.0002945198,0.000044977416,0.000033841312,0.00010723489,0.000020139409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069311383,0.0012391065,0.0013010795,0.0010773132,0.00046912872,0.0022187002,0.0012539469,0.0026468956,0.0070124925],"category_scores_gemma":[0.0019823944,0.0002913881,0.000441413,0.0018715024,0.001590763,0.0021829128,0.0008960241,0.0023523709,0.0020643577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034671742,0.000029311586,0.00030582226,0.0015634217,0.000075447606,0.00023883871,0.00022926841,0.06492894,0.0008446867,0.69827837,0.026974957,0.20649628],"study_design_scores_gemma":[0.000020961961,0.00005901442,0.0003884787,0.0007103169,0.00003321958,0.00023802462,0.000121200406,0.060608704,0.0004710079,0.631149,0.306159,0.000041066975],"about_ca_topic_score_codex":0.0027842566,"about_ca_topic_score_gemma":0.001992412,"teacher_disagreement_score":0.0070124925,"about_ca_system_score_codex":0.0013830496,"about_ca_system_score_gemma":0.0010396027,"threshold_uncertainty_score":0.023459077},"labels":[],"label_agreement":null},{"id":"W2116221794","doi":"10.1109/icnn.1993.298618","title":"Dynamic neural controller with somatic adaptation","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Sigmoid function; Artificial neural network; Computer science; Nonlinear system; Artificial intelligence; Controller (irrigation); Adaptation (eye); Control theory (sociology); Neuroscience; Biology; Control (management)","score_opus":0.04864879469509891,"score_gpt":0.26886183443905093,"score_spread":0.220213039743952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116221794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026286036,0.0008482086,0.9314814,0.00037151526,0.0005251571,0.00017345611,0.000079039986,0.0022916272,0.037943542],"genre_scores_gemma":[0.9045762,0.00043328232,0.074011825,0.0003227496,0.00012432951,0.00031899172,0.00010372696,0.000054715867,0.020054178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999828,0.000018433955,0.000010478459,0.00004595956,0.00007538148,0.000021797516],"domain_scores_gemma":[0.99983764,0.000032657197,0.000021270882,0.000019308896,0.00007619772,0.000012968821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002668445,0.00039102638,0.0003699351,0.0003286704,0.00038823485,0.00073947146,0.0012168996,0.0008160391,0.0037290533],"category_scores_gemma":[0.00064358744,0.00016478742,0.00035042004,0.00031707683,0.00040340057,0.00048648022,0.0007051249,0.00062689045,0.0008048843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026021007,0.00021044239,0.00083516946,0.00041273094,0.00012774632,0.0005094491,0.00014646146,0.48871294,0.06077458,0.03685637,0.00659612,0.40455782],"study_design_scores_gemma":[0.00004590375,0.000113996866,0.00035301066,0.000016592909,0.000019579791,0.00010392696,0.0000063657876,0.98804665,0.0045937896,0.0022452348,0.0044413353,0.000013675156],"about_ca_topic_score_codex":0.0020969782,"about_ca_topic_score_gemma":0.00240195,"teacher_disagreement_score":0.0037290533,"about_ca_system_score_codex":0.00047222784,"about_ca_system_score_gemma":0.00048538944,"threshold_uncertainty_score":0.0124748945},"labels":[],"label_agreement":null},{"id":"W2117319878","doi":"10.1109/tnn.2004.824248","title":"Branching Competitive Learning Network: A Novel Self-Creating Model","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Competitive learning; Vector quantization; Cluster analysis; Computer science; Learning vector quantization; Quantization (signal processing); Adaptability; Artificial intelligence; Artificial neural network; Branching (polymer chemistry); Algorithm; Theoretical computer science","score_opus":0.014163992524662574,"score_gpt":0.23384284334311212,"score_spread":0.21967885081844954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117319878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04139706,0.00034588785,0.94683665,0.00041357576,0.00006717778,0.000058458983,0.000036819092,0.00025312597,0.010591347],"genre_scores_gemma":[0.86423963,0.00047285043,0.12574977,0.00028497813,0.00009303823,0.00015407556,0.000062091945,0.00005899382,0.008884551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994892,0.00015775478,0.000016924238,0.00008273641,0.00019118754,0.00006212529],"domain_scores_gemma":[0.999186,0.00033923087,0.00010450665,0.00007350578,0.00018471052,0.00011205194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001043212,0.00036466465,0.00063168607,0.0005669851,0.00062945887,0.0011934817,0.0022445833,0.0014182221,0.0024074547],"category_scores_gemma":[0.0023439887,0.00024023827,0.000480987,0.00065576914,0.0013981429,0.0015696648,0.0010049988,0.0010211742,0.0004078833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089119836,0.00008380733,0.0010643716,0.00008153526,0.000045275472,0.00020376674,0.00022123312,0.6401084,0.009325528,0.28919718,0.0020179423,0.057561804],"study_design_scores_gemma":[0.000008665351,0.000022382714,0.00004278847,0.0000025124712,0.0000039669126,0.000034421922,0.000004980712,0.9830033,0.00038190634,0.015810892,0.000678071,0.0000061908104],"about_ca_topic_score_codex":0.0025599648,"about_ca_topic_score_gemma":0.0013325809,"teacher_disagreement_score":0.0025599648,"about_ca_system_score_codex":0.0010776073,"about_ca_system_score_gemma":0.00073642575,"threshold_uncertainty_score":0.00805378},"labels":[],"label_agreement":null},{"id":"W2117472944","doi":"10.1109/ijcnn.1992.227062","title":"A distortion invariant feature extraction algorithm used with associative memory classifier","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Feature extraction; Associative property; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Content-addressable memory; Artificial neural network; Invariant (physics); Algorithm; Mathematics","score_opus":0.015136312727952992,"score_gpt":0.24268726580529576,"score_spread":0.22755095307734277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117472944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007343686,0.00013878135,0.99019074,0.000049601847,0.00006808174,0.00009464264,0.00006521756,0.0010918278,0.0009574347],"genre_scores_gemma":[0.11016149,0.0002186194,0.8833664,0.00012008968,0.00007093485,0.00030650434,0.0005328386,0.00013311912,0.005089973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990791,0.00007323358,0.0001050321,0.00023811507,0.00041371668,0.000090775466],"domain_scores_gemma":[0.99926645,0.00012098885,0.000060326227,0.00015262618,0.00037694475,0.000022717419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080002414,0.0007348197,0.001114536,0.0018395432,0.0005685767,0.0012590338,0.0014214774,0.0011537353,0.0031952278],"category_scores_gemma":[0.0021061671,0.00036183748,0.00080168433,0.0023416793,0.0005344572,0.0016460755,0.0008050227,0.0008900453,0.0024098172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014740121,0.00008542195,0.0008747716,0.00007260246,0.000057282134,0.00010534772,0.000049771283,0.012376672,0.044664927,0.006680853,0.0032150913,0.93166983],"study_design_scores_gemma":[0.00007047238,0.00024606308,0.0028907165,0.000025138199,0.00009666278,0.0008782017,0.000052489424,0.79360986,0.17253502,0.0074121384,0.022101332,0.000081940925],"about_ca_topic_score_codex":0.0025631264,"about_ca_topic_score_gemma":0.0016051212,"teacher_disagreement_score":0.0031952278,"about_ca_system_score_codex":0.0005268749,"about_ca_system_score_gemma":0.00074182043,"threshold_uncertainty_score":0.01068908},"labels":[],"label_agreement":null},{"id":"W2117580362","doi":"10.1109/isspa.2007.4555420","title":"Hybrid learning scheme for modular-based phoneme recognizer","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Modular design; Computer science; Scheme (mathematics); Classifier (UML); Artificial neural network; Modular neural network; Cluster analysis; TIMIT; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Hidden Markov model; Time delay neural network; Mathematics","score_opus":0.019827984547066017,"score_gpt":0.2648709941372823,"score_spread":0.2450430095902163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117580362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010092969,0.00009039728,0.98832625,0.00003445656,0.000025559271,0.000049289014,0.00002430123,0.00080420566,0.00055261096],"genre_scores_gemma":[0.34729627,0.00014090561,0.64766264,0.00014883478,0.000083959996,0.00025064606,0.0002861177,0.000099963356,0.004030664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998963,0.00018738164,0.00008593253,0.00031772107,0.0003588308,0.00008714878],"domain_scores_gemma":[0.9988757,0.00023579196,0.00008824011,0.00031721682,0.0004194408,0.000063727144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011823925,0.0005784282,0.00077290455,0.0008438484,0.00047761656,0.0005408803,0.0021753842,0.0009088668,0.0026854363],"category_scores_gemma":[0.0019160258,0.0002851833,0.0005644455,0.00053915265,0.00053480396,0.0016104945,0.0012374457,0.0007467523,0.0016478592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003872448,0.00013921056,0.00196595,0.00013485676,0.000113447124,0.00014770353,0.00019508936,0.05450376,0.085564464,0.014028041,0.0029817706,0.83983845],"study_design_scores_gemma":[0.000036815345,0.00023274186,0.0010210488,0.000014018885,0.000059016867,0.00034070734,0.000030234765,0.9490545,0.03579388,0.007981814,0.0053851823,0.000050076742],"about_ca_topic_score_codex":0.0008834153,"about_ca_topic_score_gemma":0.00092457014,"teacher_disagreement_score":0.0026854363,"about_ca_system_score_codex":0.00045022758,"about_ca_system_score_gemma":0.00041451008,"threshold_uncertainty_score":0.008983672},"labels":[],"label_agreement":null},{"id":"W2117955612","doi":"10.1109/icsmc.1990.142087","title":"Problem decomposition and subgoaling in artificial neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Perceptron; Separable space; Computer science; Artificial neural network; Convergence (economics); Decomposition; Artificial intelligence; Divide and conquer algorithms; Algorithm; Connection (principal bundle); Mathematics","score_opus":0.021156678003789568,"score_gpt":0.24581295665222508,"score_spread":0.22465627864843551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117955612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00518613,0.0010495556,0.9870069,0.0005931344,0.00005154477,0.000056052682,0.000028071265,0.00029414694,0.0057343612],"genre_scores_gemma":[0.22094144,0.0021449728,0.76515627,0.00063805195,0.00015691422,0.00068498176,0.0002306844,0.00017008836,0.009876576],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985966,0.0006097923,0.000101428814,0.00025797074,0.0003291271,0.000105023726],"domain_scores_gemma":[0.99874485,0.0006751664,0.000119765034,0.00017856531,0.00022000847,0.00006162865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017679663,0.0010874148,0.0007408907,0.0006633989,0.00061460666,0.0023323216,0.0010376786,0.0013797758,0.0033003483],"category_scores_gemma":[0.004605863,0.0004755426,0.00083973317,0.00083445595,0.0030001826,0.003563017,0.0018664296,0.002338233,0.0009875355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007736211,0.000067851535,0.0007204505,0.00048134074,0.00006043451,0.00013958753,0.0007317435,0.1443445,0.00422177,0.6389882,0.0048361192,0.2053307],"study_design_scores_gemma":[0.000029791858,0.000070370836,0.000224447,0.00009937545,0.000015020486,0.00008810426,0.00008605578,0.41411775,0.002268492,0.56909466,0.013885093,0.000020831696],"about_ca_topic_score_codex":0.0014865657,"about_ca_topic_score_gemma":0.0012242566,"teacher_disagreement_score":0.0033003483,"about_ca_system_score_codex":0.0010613757,"about_ca_system_score_gemma":0.0009693441,"threshold_uncertainty_score":0.011040747},"labels":[],"label_agreement":null},{"id":"W2118105760","doi":"10.1109/pacrim.1995.519577","title":"Design of CMOS quadratic neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"CMOS; Quadratic equation; Artificial neural network; Electronic engineering; Transistor; Computer science; Mathematics; Engineering; Artificial intelligence; Electrical engineering; Voltage","score_opus":0.041312166333615355,"score_gpt":0.23126190420613799,"score_spread":0.18994973787252262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118105760","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031294134,0.00042930822,0.9577354,0.00021297623,0.000110322566,0.00007854821,0.0000556619,0.000429642,0.009653896],"genre_scores_gemma":[0.80970544,0.00027685595,0.1839655,0.00026370885,0.000056872166,0.00014250615,0.0000736062,0.000035417725,0.0054801195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998084,0.00003509583,0.000009827762,0.000046976624,0.000067611,0.000032238688],"domain_scores_gemma":[0.99981374,0.0000392022,0.00002311036,0.000011015463,0.00010095366,0.000011976653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035885992,0.00029727546,0.00021100252,0.00013992797,0.00028960506,0.00040800014,0.0010574633,0.00046053348,0.0021372796],"category_scores_gemma":[0.00062619627,0.00016010035,0.00015697248,0.00016510465,0.00029304074,0.0004901764,0.00035658816,0.0004598796,0.00037565967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000330008,0.00014005626,0.0012102649,0.00039372026,0.00008413418,0.00022592024,0.00017470105,0.48841223,0.10000475,0.06404546,0.004172188,0.34080657],"study_design_scores_gemma":[0.000027261238,0.00015731086,0.00025921388,0.000013166891,0.000019930412,0.000076236516,0.000014349752,0.9721747,0.017297827,0.004799578,0.005150735,0.000009684355],"about_ca_topic_score_codex":0.0011580543,"about_ca_topic_score_gemma":0.0023397808,"teacher_disagreement_score":0.0021372796,"about_ca_system_score_codex":0.000524942,"about_ca_system_score_gemma":0.00032942803,"threshold_uncertainty_score":0.007149875},"labels":[],"label_agreement":null},{"id":"W2118226819","doi":"10.1109/ccece.1993.332193","title":"Optimum learning rate for backpropagation neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Backpropagation; Artificial neural network; Computer science; Artificial intelligence; Process (computing); Constant (computer programming); Range (aeronautics); Types of artificial neural networks; Machine learning; Time delay neural network; Engineering","score_opus":0.026078864897819142,"score_gpt":0.23610239931201876,"score_spread":0.21002353441419963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118226819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007659762,0.0021439588,0.9836283,0.00030140983,0.00016063728,0.00003999173,0.00003134189,0.00046398764,0.005570632],"genre_scores_gemma":[0.49554917,0.0044773957,0.48922265,0.00027238077,0.00026541998,0.00025994104,0.00015901426,0.00051052356,0.009283548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864525,0.00039964434,0.000086904976,0.00022100935,0.00053505617,0.000112201036],"domain_scores_gemma":[0.99778205,0.0010525042,0.00020212842,0.00014219244,0.00076617184,0.000055027547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018526026,0.0007850074,0.0009913982,0.00082668924,0.0004250651,0.0015727279,0.0009842593,0.0016844864,0.0024441795],"category_scores_gemma":[0.00972217,0.00048293744,0.00067212444,0.0007071168,0.00072579586,0.0021565002,0.0006845089,0.001463195,0.0014192774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000258062,0.00008551705,0.0009515456,0.0005990947,0.00007389671,0.00021929956,0.00018369705,0.6653702,0.015285725,0.11530352,0.0059075714,0.19576184],"study_design_scores_gemma":[0.000011408883,0.000028759747,0.00014867117,0.00004848685,0.0000142358585,0.000107260545,0.000014196351,0.97404397,0.003968041,0.018512925,0.0030838985,0.000018184322],"about_ca_topic_score_codex":0.0011271021,"about_ca_topic_score_gemma":0.00077628664,"teacher_disagreement_score":0.0024441795,"about_ca_system_score_codex":0.0009222188,"about_ca_system_score_gemma":0.0009252592,"threshold_uncertainty_score":0.009797633},"labels":[],"label_agreement":null},{"id":"W2118597821","doi":"10.1109/nafips.2004.1337367","title":"Hardware design issues of fuzzy neural networks","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Exploit; Interpretability; Computer science; Realization (probability); Fuzzy logic; Transparency (behavior); Artificial neural network; Neuro-fuzzy; Fuzzy control system; Computer architecture; Artificial intelligence; Embedded system; Distributed computing; Computer security","score_opus":0.016558344202922688,"score_gpt":0.2518491931598035,"score_spread":0.2352908489568808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118597821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02591294,0.0043075257,0.935301,0.002439087,0.00039325506,0.00013383135,0.000057321362,0.0007256936,0.030729337],"genre_scores_gemma":[0.6206293,0.0030707086,0.36369804,0.0005769077,0.00024572006,0.00022777118,0.00008384941,0.00007581704,0.011391894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961203,0.00009669103,0.00003200123,0.000045162382,0.00018002419,0.000033994675],"domain_scores_gemma":[0.9991954,0.0003790982,0.000050721424,0.00009376246,0.00026408746,0.000016964193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084795954,0.00035039283,0.00026772826,0.0003267403,0.0003826773,0.0013609728,0.0012971997,0.0008377878,0.004733158],"category_scores_gemma":[0.0030554438,0.0003509946,0.00018056833,0.0003105341,0.00045991162,0.0013912527,0.00034678564,0.00078385917,0.0009447414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029711885,0.000093785144,0.0010654065,0.001063536,0.000069014866,0.0003283494,0.00027874438,0.15822145,0.041973222,0.31458807,0.0055457177,0.47647548],"study_design_scores_gemma":[0.00012505641,0.0004300703,0.00082400936,0.0002778464,0.00008339193,0.00069817354,0.00016402072,0.6931723,0.06741439,0.14519294,0.091571644,0.000046146342],"about_ca_topic_score_codex":0.0012666096,"about_ca_topic_score_gemma":0.0021829458,"teacher_disagreement_score":0.004733158,"about_ca_system_score_codex":0.00084977905,"about_ca_system_score_gemma":0.00063971616,"threshold_uncertainty_score":0.015833974},"labels":[],"label_agreement":null},{"id":"W2118897319","doi":"10.1109/ccece.1999.808197","title":"Fuzzy neurons: a coarse-grained reconfigurable element for computational intelligence","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Fuzzy logic; Computational intelligence; Fuzzy electronics; Interface (matter); Computer architecture; Reconfigurable computing; Fuzzy control system; Neuro-fuzzy; Artificial neural network; Artificial intelligence; Embedded system; Field-programmable gate array; Parallel computing","score_opus":0.03505120530682525,"score_gpt":0.278618889991179,"score_spread":0.24356768468435375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118897319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08875972,0.0018301082,0.8834732,0.00056134665,0.00039783271,0.00015164954,0.00020431115,0.0031201378,0.021501658],"genre_scores_gemma":[0.56300455,0.00066588947,0.4212083,0.00029571573,0.000058365535,0.00009273552,0.00014503462,0.00009575193,0.014433598],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988616,0.000012575771,0.0000068036343,0.000021818605,0.000056810048,0.000015799398],"domain_scores_gemma":[0.9999211,0.00002126211,0.00000831511,0.000019491383,0.00001928224,0.000010524203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014534872,0.00021883905,0.00023348152,0.00023040477,0.00025793829,0.00048993464,0.0010381938,0.00059145154,0.0032062512],"category_scores_gemma":[0.00028660148,0.00014839464,0.00023644221,0.00016990886,0.0004043373,0.0005988925,0.00035045078,0.00043589933,0.0007999479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004327635,0.000105150444,0.0009405216,0.0003117148,0.00008352751,0.0004739174,0.0001728914,0.073803335,0.56192833,0.063815504,0.005569619,0.2923627],"study_design_scores_gemma":[0.00010424593,0.00060907163,0.0015766349,0.00007232332,0.000105519015,0.0009727141,0.00007111179,0.54957443,0.29018167,0.030334689,0.1263039,0.00009373275],"about_ca_topic_score_codex":0.000908063,"about_ca_topic_score_gemma":0.0013802719,"teacher_disagreement_score":0.0032062512,"about_ca_system_score_codex":0.00030496664,"about_ca_system_score_gemma":0.00028060665,"threshold_uncertainty_score":0.010725975},"labels":[],"label_agreement":null},{"id":"W2119023550","doi":"10.1109/iscas.1992.230014","title":"Mutual information training and size minimization of adaptive probabilistic neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Heuristic; Artificial neural network; Probabilistic logic; Mutual information; Pruning; Artificial intelligence; Computer science; Maximization; Minification; Benchmark (surveying); Machine learning; Minimum description length; Upper and lower bounds; Bayesian information criterion; Bayes' theorem; Pattern recognition (psychology); Mathematics; Mathematical optimization; Bayesian probability","score_opus":0.01895349243249383,"score_gpt":0.21687507032389042,"score_spread":0.19792157789139658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119023550","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03543595,0.00032336233,0.9611184,0.00026133328,0.000021087208,0.000039465285,0.00004413672,0.0003327003,0.0024235968],"genre_scores_gemma":[0.6980084,0.0002921372,0.29716003,0.00018068544,0.00011765524,0.00037464933,0.00026567932,0.0002205853,0.0033801585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971513,0.0014026117,0.00011741918,0.00041711534,0.0007556701,0.00015590832],"domain_scores_gemma":[0.99195135,0.0059423097,0.00065687415,0.00059905375,0.00070803886,0.00014229574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039042153,0.00088217,0.0013390541,0.0008288686,0.00050805043,0.0009600783,0.0020096581,0.0015154016,0.0013194731],"category_scores_gemma":[0.018041238,0.00070535415,0.000584336,0.0007424488,0.0017542545,0.0022070212,0.0023409352,0.0013035081,0.00025387545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009760243,0.000029790464,0.0004271503,0.00006887193,0.000036650443,0.000054170392,0.000057312653,0.9220568,0.002496349,0.021369658,0.00071404845,0.052591585],"study_design_scores_gemma":[0.0000035142928,0.000018878993,0.00009760803,0.0000044285443,0.0000037509765,0.000010237407,0.0000019633394,0.99124736,0.0008057246,0.007690514,0.000113296315,0.000002718335],"about_ca_topic_score_codex":0.0015313713,"about_ca_topic_score_gemma":0.0016919104,"teacher_disagreement_score":0.0039042153,"about_ca_system_score_codex":0.0013577098,"about_ca_system_score_gemma":0.00081544294,"threshold_uncertainty_score":0.020647705},"labels":[],"label_agreement":null},{"id":"W2119123815","doi":"10.1109/ijcnn.1992.226860","title":"Neural network channel equalization","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Intersymbol interference; Perceptron; Adaptive equalizer; Computer science; Backpropagation; Channel (broadcasting); Interference (communication); Multilayer perceptron; Equalization (audio); Noise (video); Bit error rate; Artificial neural network; Algorithm; Electronic engineering; Artificial intelligence; Telecommunications; Engineering","score_opus":0.025036275996848913,"score_gpt":0.2499997345150426,"score_spread":0.2249634585181937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119123815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08222659,0.00093620276,0.89839315,0.00040025584,0.00033931746,0.000058176844,0.00012955342,0.0011553727,0.01636136],"genre_scores_gemma":[0.8915592,0.00039213372,0.09448401,0.0001652943,0.00005099098,0.00006467187,0.00012763786,0.000041034775,0.013115102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998073,0.00004190162,0.000008623196,0.00002944573,0.00007493324,0.00003787702],"domain_scores_gemma":[0.99959797,0.00021584393,0.000019998419,0.000027801594,0.00013008574,0.000008293672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038727504,0.00025005417,0.00028425414,0.00024019268,0.00019825053,0.00034201506,0.0003764695,0.0004928154,0.0034937933],"category_scores_gemma":[0.0015006663,0.00012112629,0.00018310784,0.0003207328,0.00023481903,0.00065859355,0.00027771544,0.000339415,0.00054377253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013733358,0.00005146101,0.00041792422,0.000053219926,0.00003664494,0.000048084323,0.000016867743,0.9151462,0.0028426044,0.007744861,0.0016568736,0.07184796],"study_design_scores_gemma":[0.000007410414,0.000013830858,0.00007679814,0.0000016429053,0.000003635821,0.000009734268,0.0000014290658,0.9973712,0.001230913,0.000854786,0.00042620735,0.0000024418782],"about_ca_topic_score_codex":0.003850342,"about_ca_topic_score_gemma":0.0055325953,"teacher_disagreement_score":0.003850342,"about_ca_system_score_codex":0.00045430375,"about_ca_system_score_gemma":0.00031245107,"threshold_uncertainty_score":0.011687875},"labels":[],"label_agreement":null},{"id":"W2119248427","doi":"10.1109/iscas.2006.1693201","title":"A Full-Differential Analog Design of an Indirect Inverse Control Law Based on Neural Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; CMC Microsystems","keywords":"Computer science; Artificial neural network; Differential (mechanical device); Inverse; Analogue electronics; Control theory (sociology); Electronic circuit; Control engineering; Control (management); Electronic engineering; Engineering; Artificial intelligence; Mathematics; Electrical engineering","score_opus":0.01492862633367079,"score_gpt":0.21965292106399661,"score_spread":0.20472429473032583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119248427","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007871404,0.00018114419,0.98611975,0.00009027463,0.00010097646,0.000058162135,0.000015211908,0.0003776014,0.0051855077],"genre_scores_gemma":[0.7534732,0.00032036548,0.23980567,0.00023221511,0.000069644935,0.00019382972,0.00004497983,0.000041076724,0.005819097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999742,0.000032475346,0.000016698441,0.00005962209,0.00013146897,0.000017756101],"domain_scores_gemma":[0.9998072,0.000060570153,0.000021126176,0.000024070818,0.000079365585,0.000007708896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036056875,0.00040202448,0.00028838968,0.00022611703,0.00025896632,0.0005772923,0.00090887194,0.00058675866,0.00169161],"category_scores_gemma":[0.00071141944,0.00019727022,0.0002724046,0.00013304612,0.0004430573,0.00046044413,0.00037770183,0.00048078233,0.00028393074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025018162,0.00017521314,0.00073118665,0.0005922228,0.000114122195,0.00037433114,0.00031686813,0.2790938,0.2241014,0.064957835,0.0025266449,0.42676622],"study_design_scores_gemma":[0.00005284923,0.0003187434,0.00037620173,0.00003749034,0.000049538623,0.00025431387,0.000011752301,0.9448814,0.039548755,0.0040166182,0.010427023,0.000025249354],"about_ca_topic_score_codex":0.0009791744,"about_ca_topic_score_gemma":0.0011708969,"teacher_disagreement_score":0.00169161,"about_ca_system_score_codex":0.00035675292,"about_ca_system_score_gemma":0.00037594093,"threshold_uncertainty_score":0.005658984},"labels":[],"label_agreement":null},{"id":"W2119485531","doi":"10.5539/cis.v4n6p28","title":"Cascade-Correlation Algorithm with Trainable Activation Functions","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Jinan","keywords":"Cascade; Computer science; Activation function; Generalization; Convergence (economics); Algorithm; Base (topology); Series (stratigraphy); Correlation; Artificial neural network; Artificial intelligence; Mathematics; Chemical engineering","score_opus":0.01572855704734097,"score_gpt":0.2057236963631498,"score_spread":0.18999513931580883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119485531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007648758,0.00023568596,0.9891851,0.00006966891,0.00004719036,0.00006071903,0.000041098345,0.0007664702,0.0019453432],"genre_scores_gemma":[0.3111281,0.0005078658,0.6756615,0.00019486764,0.00010157766,0.00036960642,0.00041838278,0.00024897818,0.011369098],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993837,0.000105307576,0.00003830164,0.00016799817,0.0002144042,0.00009034103],"domain_scores_gemma":[0.99922323,0.00022166492,0.000056692905,0.00013313288,0.00031682372,0.000048389455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011954659,0.0011154154,0.0011032079,0.00090824213,0.0007289068,0.0007769521,0.002103363,0.0014132741,0.004725693],"category_scores_gemma":[0.0025382098,0.0005916173,0.00089295494,0.0012948841,0.000598361,0.0015901278,0.0011200599,0.0015567888,0.0015025731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032606663,0.00014270844,0.0014301102,0.0001375679,0.00012843151,0.0001770539,0.00012289395,0.39196754,0.012831482,0.03263847,0.009024329,0.5510734],"study_design_scores_gemma":[0.000009363676,0.000025470183,0.00011397333,0.0000039804163,0.000010474436,0.000029388726,0.000002682439,0.9947201,0.0022716455,0.0019489866,0.0008569479,0.0000069172797],"about_ca_topic_score_codex":0.007384833,"about_ca_topic_score_gemma":0.0075603463,"teacher_disagreement_score":0.007384833,"about_ca_system_score_codex":0.0010315301,"about_ca_system_score_gemma":0.001980972,"threshold_uncertainty_score":0.015809},"labels":[],"label_agreement":null},{"id":"W2119561118","doi":"10.1109/ijcnn.2008.4634267","title":"A comparison of fuzzy ARTMAP and Gaussian ARTMAP neural networks for incremental learning","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Artificial intelligence; Artificial neural network; Computer science; Machine learning; Gaussian process; Generalization; Block (permutation group theory); Fuzzy logic; Gaussian; Incremental learning; Pattern recognition (psychology); Semi-supervised learning; Mathematics","score_opus":0.0385790887708196,"score_gpt":0.2976934267304521,"score_spread":0.2591143379596325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119561118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06965029,0.0028748312,0.91523385,0.00037083687,0.00023185143,0.0001934483,0.00013292235,0.0022672468,0.0090447515],"genre_scores_gemma":[0.5607494,0.0009863735,0.43252218,0.0002683725,0.00008261667,0.00028971976,0.00027425983,0.00016606785,0.00466097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990295,0.0002602375,0.00005221006,0.000163879,0.00041530898,0.000078934514],"domain_scores_gemma":[0.99620074,0.0019568175,0.00012046592,0.00046573774,0.0011719471,0.000084389605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029538933,0.00071648264,0.00089019915,0.0010660634,0.0003660992,0.0012097434,0.0019515612,0.0016047789,0.0019834067],"category_scores_gemma":[0.010611551,0.0003899879,0.0006246765,0.0010078744,0.00065846334,0.002699426,0.00092601334,0.0012442268,0.0006296353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007628773,0.0002152927,0.0014231018,0.0002352176,0.00016883199,0.00008785668,0.00011642493,0.24723643,0.0058426154,0.0119408425,0.0033892854,0.7285813],"study_design_scores_gemma":[0.0000147683295,0.00011077726,0.00053908525,0.000011467611,0.000020345668,0.00005584917,0.000014387914,0.9921767,0.0033990378,0.0025521999,0.0010873607,0.000017932744],"about_ca_topic_score_codex":0.005118936,"about_ca_topic_score_gemma":0.0062043257,"teacher_disagreement_score":0.005118936,"about_ca_system_score_codex":0.0010313803,"about_ca_system_score_gemma":0.0010318303,"threshold_uncertainty_score":0.015621841},"labels":[],"label_agreement":null},{"id":"W2120199131","doi":"10.1023/a:1024068626366","title":"Inference for the Generalization Error","year":2003,"lang":"en","type":"article","venue":"Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":959,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal; Health Canada; Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Estimator; Variance (accounting); Generalization; Statistical hypothesis testing; Null hypothesis; Statistics; Set (abstract data type); Generalization error; Mathematics; Computer science; Randomness; Econometrics; Artificial intelligence; Algorithm; Machine learning; Artificial neural network","score_opus":0.026905038304977553,"score_gpt":0.2926671986209821,"score_spread":0.2657621603160046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120199131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007718812,0.0009090361,0.9868902,0.0016381581,0.00025766535,0.00004174915,0.000186517,0.00040864202,0.0019491949],"genre_scores_gemma":[0.537145,0.0025627182,0.43595627,0.0018298099,0.0023642087,0.0005487381,0.001691206,0.000848743,0.01705328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99493307,0.0018209587,0.0003032599,0.001783751,0.0008458542,0.00031304848],"domain_scores_gemma":[0.970258,0.023096763,0.00094077346,0.0030888456,0.0022217482,0.00039386723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010626746,0.0014690141,0.002672326,0.0027956231,0.0011750646,0.002985655,0.003942511,0.0032219375,0.010475791],"category_scores_gemma":[0.06362353,0.0012811322,0.002650067,0.0016175298,0.0032529365,0.007932821,0.0030607283,0.0094533665,0.0021286183],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002940844,0.00019271963,0.004397948,0.0005446464,0.0006175779,0.00018425824,0.00020303737,0.2876836,0.0019168272,0.5232614,0.011792799,0.16891108],"study_design_scores_gemma":[0.000030963656,0.000023433213,0.00067543605,0.0000905094,0.00006163275,0.00006346669,0.000017591385,0.66292787,0.0008330438,0.33407128,0.0011784988,0.000026272799],"about_ca_topic_score_codex":0.005624606,"about_ca_topic_score_gemma":0.0049629374,"teacher_disagreement_score":0.010626746,"about_ca_system_score_codex":0.002752115,"about_ca_system_score_gemma":0.002385402,"threshold_uncertainty_score":0.056200266},"labels":[],"label_agreement":null},{"id":"W2120241573","doi":"10.1109/icnn.1993.298670","title":"A cascaded recurrent neural network for real-time nonlinear adaptive filtering","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial neural network; Nonlinear system; Chaotic; Recurrent neural network; Artificial intelligence; Feature (linguistics); Relation (database); Time domain; Domain (mathematical analysis); Mathematics; Data mining; Computer vision","score_opus":0.08572803928964218,"score_gpt":0.3038610795383356,"score_spread":0.2181330402486934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120241573","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025402014,0.0015672429,0.9905811,0.00015151977,0.00017605691,0.000034273675,0.000088404304,0.0009848806,0.0038762528],"genre_scores_gemma":[0.22101559,0.003775234,0.7511979,0.00031828607,0.00033326045,0.00022483969,0.0006672233,0.00025443442,0.022213286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997292,0.00004591214,0.0000217974,0.000078020574,0.000105872954,0.00001914538],"domain_scores_gemma":[0.9998505,0.000039279545,0.00002079808,0.00002619333,0.000054584714,0.000008652814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042195627,0.00059367844,0.00056809484,0.00032051178,0.0002688894,0.00062082865,0.0009212716,0.0011353537,0.004622077],"category_scores_gemma":[0.00073250267,0.00026731836,0.00067980436,0.0004867825,0.00031555208,0.0009867722,0.00035017708,0.00094483915,0.0016292956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020572971,0.00010997389,0.0007922244,0.00043707684,0.00015827648,0.0006323613,0.00011410811,0.33533716,0.086863786,0.0856684,0.012706517,0.47697446],"study_design_scores_gemma":[0.000015537935,0.00007613211,0.0002502798,0.000027596112,0.00004468948,0.0002576666,0.00000768627,0.9584252,0.008766912,0.009525205,0.022572067,0.000031044427],"about_ca_topic_score_codex":0.0021343234,"about_ca_topic_score_gemma":0.003570655,"teacher_disagreement_score":0.004622077,"about_ca_system_score_codex":0.00049904414,"about_ca_system_score_gemma":0.0003914534,"threshold_uncertainty_score":0.0154623985},"labels":[],"label_agreement":null},{"id":"W2120270028","doi":"10.1109/ijcnn.2000.859369","title":"Classification of incomplete data using the fuzzy ARTMAP neural network","year":2000,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Department of National Defence","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Missing data; Artificial neural network; Artificial intelligence; Computer science; Fuzzy logic; Task (project management); Data mining; Class (philosophy); Machine learning; Pattern recognition (psychology); Identification (biology); Radar; Engineering","score_opus":0.11318621365492809,"score_gpt":0.3094950660830403,"score_spread":0.1963088524281122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120270028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06909359,0.0006511769,0.9278976,0.00022361784,0.000075157106,0.00003988444,0.000265228,0.0007033808,0.0010503357],"genre_scores_gemma":[0.66409034,0.0006489538,0.33124423,0.00014749722,0.00014019052,0.00014107597,0.0010698409,0.000064793305,0.0024529933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991351,0.00018360664,0.00006866475,0.00018474674,0.00035225973,0.00007553779],"domain_scores_gemma":[0.997248,0.0015070988,0.00018416705,0.00036552816,0.00064824824,0.000046976907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021307687,0.00054690504,0.0015100461,0.0011342436,0.0003787942,0.0012429742,0.0010181866,0.0012652207,0.0011201553],"category_scores_gemma":[0.007976158,0.00036720358,0.0008372467,0.001263362,0.0005376857,0.0019921304,0.0008308546,0.00115042,0.00037360055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046166574,0.000080287864,0.002388258,0.00013800502,0.00013812484,0.00035286305,0.00016797533,0.5000782,0.008305944,0.004237483,0.0026209804,0.4810302],"study_design_scores_gemma":[0.0000040077407,0.000020896114,0.00061314215,0.000008895793,0.0000109343455,0.000034560533,0.000013421567,0.9942198,0.0019327897,0.0027891838,0.0003403538,0.000012036087],"about_ca_topic_score_codex":0.0029070047,"about_ca_topic_score_gemma":0.0023450893,"teacher_disagreement_score":0.0029070047,"about_ca_system_score_codex":0.00045850954,"about_ca_system_score_gemma":0.000550123,"threshold_uncertainty_score":0.011268675},"labels":[],"label_agreement":null},{"id":"W2120435280","doi":"10.1109/coginf.2009.5250750","title":"Amplification of signal features using variance fractal dimension trajectory","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Trajectory; Dimension (graph theory); Fractal dimension; Fractal; Variance (accounting); SIGNAL (programming language); Computer science; Mathematics; Artificial intelligence; Mathematical analysis; Physics","score_opus":0.020664270410212047,"score_gpt":0.2636922936286005,"score_spread":0.24302802321838848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120435280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088309616,0.00021947421,0.90824133,0.000089858484,0.000035445602,0.000045821576,0.00005669888,0.000810878,0.0021907336],"genre_scores_gemma":[0.4830918,0.0002690424,0.5144553,0.00004068325,0.000048543337,0.00007434001,0.00020148171,0.0001982487,0.0016205956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996773,0.000050048606,0.000018960762,0.00005548769,0.0001712793,0.000026981335],"domain_scores_gemma":[0.9986494,0.00075668085,0.00012589099,0.00017074619,0.00026480187,0.000032595315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005636259,0.00043541033,0.00033373374,0.0010005758,0.0002317905,0.00052321854,0.0002861892,0.00030243173,0.0013427979],"category_scores_gemma":[0.0033606181,0.00018206585,0.00035954273,0.00052413676,0.0003091325,0.00086547906,0.0004780024,0.00041110476,0.0004179872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030790633,0.000053667358,0.0028582506,0.00011764596,0.00003803829,0.00026464954,0.00030659817,0.043128815,0.41603154,0.0124087,0.0008311311,0.52365303],"study_design_scores_gemma":[0.000030079971,0.00028041267,0.009403018,0.00003247353,0.00004835124,0.0009900808,0.00007079673,0.70107394,0.26675296,0.009635441,0.011592523,0.00008979827],"about_ca_topic_score_codex":0.00031060635,"about_ca_topic_score_gemma":0.00030839362,"teacher_disagreement_score":0.0013427979,"about_ca_system_score_codex":0.0003209255,"about_ca_system_score_gemma":0.00017702815,"threshold_uncertainty_score":0.004492104},"labels":[],"label_agreement":null},{"id":"W2120439140","doi":"10.1109/icnn.1993.298746","title":"Minimum description length pruning and maximum mutual information training of adaptive probabilistic neural networks","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Minimum description length; Probabilistic logic; Pruning; Artificial neural network; Computer science; Artificial intelligence; Gaussian; Benchmark (surveying); Mutual information; Probabilistic neural network; Machine learning; Pattern recognition (psychology); Algorithm; Time delay neural network","score_opus":0.0888738075565767,"score_gpt":0.2649945237197897,"score_spread":0.176120716163213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120439140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033005837,0.00024860122,0.9652578,0.00016802056,0.000019999408,0.000034856912,0.000057366935,0.00038856207,0.00081890554],"genre_scores_gemma":[0.5901935,0.00018064222,0.40645128,0.00018965948,0.00006931751,0.0002762444,0.00048939505,0.00018647742,0.0019635241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981902,0.0007844771,0.00011834055,0.0002486461,0.00054851786,0.00010971173],"domain_scores_gemma":[0.9940456,0.0042152214,0.00046400612,0.0004909105,0.00065635605,0.00012777656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029537827,0.0006685899,0.0013914057,0.0008897807,0.00046407187,0.000895413,0.0023420758,0.0014177805,0.0012183064],"category_scores_gemma":[0.014397073,0.0007241481,0.0007355644,0.00086360576,0.0010230191,0.0022166881,0.0016330789,0.0012079696,0.0002540389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015792159,0.00005319173,0.0009895161,0.000083918254,0.00006019927,0.000084550054,0.00007226427,0.8620691,0.003178464,0.010762644,0.0009281807,0.121560104],"study_design_scores_gemma":[0.0000031233,0.00001338091,0.00007151389,0.0000023201499,0.0000026833732,0.000007919115,0.0000016623893,0.99775773,0.00063958357,0.00141301,0.000084699415,0.0000023845625],"about_ca_topic_score_codex":0.0025339685,"about_ca_topic_score_gemma":0.0026443806,"teacher_disagreement_score":0.0029537827,"about_ca_system_score_codex":0.0012007596,"about_ca_system_score_gemma":0.0012101837,"threshold_uncertainty_score":0.0156213045},"labels":[],"label_agreement":null},{"id":"W2120641842","doi":"10.1109/mwscas.2000.952884","title":"Recurrent neural network design for temporal sequence learning","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Recurrent neural network; Artificial neural network; Time delay neural network; Feed forward; Feedforward neural network; Gradient descent; Genetic algorithm; Convergence (economics); Layer (electronics); Artificial intelligence; Sequence (biology); Probabilistic neural network; Phone; Algorithm; Machine learning; Engineering","score_opus":0.11431875017048576,"score_gpt":0.28940718483600014,"score_spread":0.17508843466551438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120641842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036302933,0.00026080493,0.99422014,0.00004004309,0.000045183944,0.000028145663,0.000028860695,0.0003086302,0.0014377873],"genre_scores_gemma":[0.39918098,0.0008073368,0.5914906,0.00015396181,0.00010302741,0.0004075283,0.00033256132,0.00015511565,0.0073688393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961346,0.000097665004,0.00003755162,0.000090674475,0.00012607935,0.000034574052],"domain_scores_gemma":[0.99959475,0.00014568266,0.000058316287,0.000047036287,0.00013839996,0.000015897633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086465647,0.0006778428,0.0005489209,0.0003987244,0.00021958437,0.00054492464,0.0010324169,0.0007529025,0.002778645],"category_scores_gemma":[0.0019958112,0.00028568893,0.0005443033,0.00044590092,0.00034686035,0.0009616722,0.0003280506,0.0006945715,0.000890968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016805124,0.00008001698,0.00051828625,0.00026641105,0.0001377367,0.00020103931,0.00009331106,0.61954224,0.031402268,0.06328207,0.0025388752,0.28176972],"study_design_scores_gemma":[0.000012730618,0.00007096825,0.00007251277,0.000010560104,0.000021003518,0.00005852768,0.0000032591172,0.98548096,0.0057652206,0.0054264306,0.0030675249,0.000010394313],"about_ca_topic_score_codex":0.0016246119,"about_ca_topic_score_gemma":0.0024222317,"teacher_disagreement_score":0.002778645,"about_ca_system_score_codex":0.00066882983,"about_ca_system_score_gemma":0.0005065678,"threshold_uncertainty_score":0.009295523},"labels":[],"label_agreement":null},{"id":"W2120645653","doi":"10.1109/ijcnn.2000.857875","title":"Building a 2D-compatible multilayer neural network","year":2000,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial neural network; Computer science; Time delay neural network; Computation; Simple (philosophy); Physical neural network; Set (abstract data type); Network topology; Artificial intelligence; Computer engineering; Computer architecture; Computer hardware; Types of artificial neural networks; Algorithm; Computer network","score_opus":0.017414652921885123,"score_gpt":0.2614561900616278,"score_spread":0.24404153713974266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120645653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031631853,0.00013616988,0.9577845,0.00013984379,0.000093280505,0.000046224697,0.00012925077,0.0015051438,0.008533745],"genre_scores_gemma":[0.32495144,0.00022372608,0.6667667,0.00013883096,0.000026872422,0.00015926523,0.00025529956,0.00010479752,0.007373026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998839,0.000015160331,0.000007754135,0.000029832185,0.00004456974,0.000018826016],"domain_scores_gemma":[0.9999347,0.000013535171,0.0000060697894,0.000013687825,0.00002448821,0.000007630376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019737559,0.00034748923,0.00030116425,0.00024757232,0.0002479382,0.0004970325,0.0007060916,0.0005688448,0.002674191],"category_scores_gemma":[0.00045293177,0.0002946162,0.00041399986,0.00020120703,0.0002263422,0.00076277024,0.0006866172,0.00039670375,0.0011442766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015125677,0.00008371582,0.0014864493,0.00030745871,0.00008373482,0.00042489148,0.00018925009,0.49656597,0.15474205,0.038198743,0.0048686163,0.30289787],"study_design_scores_gemma":[0.000013168069,0.00006344008,0.0003814261,0.000016561033,0.00001677086,0.00009763181,0.00001573721,0.96148074,0.02035844,0.007249522,0.010287457,0.000019116975],"about_ca_topic_score_codex":0.0017552126,"about_ca_topic_score_gemma":0.0025569361,"teacher_disagreement_score":0.002674191,"about_ca_system_score_codex":0.0003658168,"about_ca_system_score_gemma":0.0004040096,"threshold_uncertainty_score":0.008946061},"labels":[],"label_agreement":null},{"id":"W2120787117","doi":"10.1109/icassp.1997.595504","title":"Nonlinear adaptive filtering with FIR synapses and adaptive activation functions","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Activation function; Sigmoid function; Finite impulse response; Transfer function; Nonlinear system; Control theory (sociology); Artificial neural network; Computer science; Multilayer perceptron; Adaptive filter; Backpropagation; Linear filter; Convergence (economics); Impulse response; Filter (signal processing); Impulse (physics); Algorithm; Mathematics; Artificial intelligence; Engineering; Physics; Mathematical analysis","score_opus":0.03085334014115754,"score_gpt":0.21168150766726468,"score_spread":0.18082816752610714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120787117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027624989,0.0012744437,0.96566784,0.000121983256,0.000114515875,0.000019652622,0.00002254405,0.00034128968,0.0048126713],"genre_scores_gemma":[0.7164411,0.0020837733,0.26741323,0.00015359191,0.00014387157,0.00006090308,0.000076102144,0.00004593651,0.013581478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997911,0.00004519029,0.000015667141,0.000049694627,0.00007518987,0.000023209805],"domain_scores_gemma":[0.9998178,0.00008299631,0.000030471085,0.000027637338,0.000034670742,0.0000064235915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003842309,0.00035819394,0.00038500217,0.00017634417,0.00015741479,0.00052438665,0.00068468566,0.0010325217,0.0012317924],"category_scores_gemma":[0.0010731719,0.00025126728,0.00041540802,0.00039276035,0.00047208025,0.00092227024,0.00036545968,0.00063040864,0.00043487313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017639183,0.00008052143,0.0010552413,0.00029840347,0.000088753935,0.00036226868,0.000105370666,0.66383237,0.077038504,0.07924886,0.0008564063,0.176857],"study_design_scores_gemma":[0.000007947326,0.00005102178,0.0002610672,0.000011036948,0.00001695609,0.000098530116,0.000005651149,0.982627,0.0060077147,0.008650016,0.002252638,0.000010415864],"about_ca_topic_score_codex":0.0013080325,"about_ca_topic_score_gemma":0.0013745623,"teacher_disagreement_score":0.0013080325,"about_ca_system_score_codex":0.00039641096,"about_ca_system_score_gemma":0.0003051935,"threshold_uncertainty_score":0.004120767},"labels":[],"label_agreement":null},{"id":"W2120915519","doi":"10.1109/ccece.2002.1013048","title":"Hopfield-genetic approach for solving the routing problem in computer networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Hopfield network; Computer science; Convergence (economics); Genetic algorithm; Computation; Mathematical optimization; Routing (electronic design automation); Artificial neural network; Population; Artificial intelligence; Algorithm; Machine learning; Mathematics; Computer network","score_opus":0.017502216009483106,"score_gpt":0.22455754539657927,"score_spread":0.20705532938709617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120915519","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037227625,0.0017300255,0.98770624,0.00023200527,0.00010490379,0.00005558507,0.000028228278,0.00015873314,0.0062614903],"genre_scores_gemma":[0.13775982,0.0035586937,0.8484157,0.00021475162,0.00013832453,0.00026113854,0.00010405437,0.000054613403,0.009492995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998411,0.000036130084,0.000008470109,0.000024058118,0.00007643991,0.0000138123705],"domain_scores_gemma":[0.9999064,0.00003983722,0.000010726256,0.000008812851,0.00002780988,0.0000064241976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004295443,0.0006346793,0.0004645377,0.000682232,0.0004778457,0.0005076225,0.0008315353,0.0009994255,0.0015154673],"category_scores_gemma":[0.000625889,0.0001934945,0.0006241552,0.00090194977,0.00061993673,0.00074999797,0.00038603213,0.00081301405,0.00031300558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029735784,0.000052874453,0.00058594346,0.0003020482,0.00012929046,0.0002090629,0.000107945176,0.6070511,0.0080123935,0.16703531,0.0029395437,0.21354479],"study_design_scores_gemma":[0.00003900932,0.000068767775,0.000262875,0.000051083065,0.000057514862,0.0001802062,0.000033521195,0.8772263,0.004624593,0.09808423,0.019340696,0.00003118993],"about_ca_topic_score_codex":0.006025518,"about_ca_topic_score_gemma":0.00727145,"teacher_disagreement_score":0.006025518,"about_ca_system_score_codex":0.0008775807,"about_ca_system_score_gemma":0.0012820135,"threshold_uncertainty_score":0.011980891},"labels":[],"label_agreement":null},{"id":"W2120974754","doi":"10.1109/ijcnn.1992.287072","title":"Probability of error, maximum mutual information, and size minimization of neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Mutual information; Heuristic; Computer science; Maximization; Artificial intelligence; Pruning; Minification; Probabilistic logic; Machine learning; Upper and lower bounds; Benchmark (surveying); Minimum description length; Bayes' theorem; Algorithm; Pattern recognition (psychology); Mathematics; Mathematical optimization; Bayesian probability","score_opus":0.01887496976729302,"score_gpt":0.22869276521010848,"score_spread":0.20981779544281545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120974754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012458272,0.0011997292,0.9831209,0.0004218218,0.00003684959,0.000029443512,0.000082906525,0.0002579733,0.002392026],"genre_scores_gemma":[0.61817175,0.0022756637,0.37109,0.000284363,0.0005389036,0.00070834265,0.0006220811,0.00048922555,0.005819654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9904185,0.004986255,0.00040052156,0.001252261,0.0026396224,0.0003028527],"domain_scores_gemma":[0.96557283,0.027727842,0.0028562124,0.0017697178,0.0017478936,0.00032555958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008483109,0.0014426694,0.0017503393,0.0023772053,0.0008434738,0.0025560437,0.0022813026,0.0023267902,0.0015858541],"category_scores_gemma":[0.05386267,0.0010043776,0.0008632269,0.0022140415,0.003929869,0.0047500054,0.003022139,0.0019197388,0.00044974525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012813487,0.00002784465,0.0009829019,0.000179156,0.0000981356,0.000098972276,0.00010668529,0.8342939,0.0012927378,0.11082473,0.0015313003,0.050435465],"study_design_scores_gemma":[0.0000060977964,0.00002220184,0.00033614188,0.000025539568,0.000010541715,0.00003736447,0.000008296982,0.8852538,0.0010128245,0.1128764,0.0003985872,0.0000122492165],"about_ca_topic_score_codex":0.0021347834,"about_ca_topic_score_gemma":0.0015416215,"teacher_disagreement_score":0.008483109,"about_ca_system_score_codex":0.002162498,"about_ca_system_score_gemma":0.0011553311,"threshold_uncertainty_score":0.044863522},"labels":[],"label_agreement":null},{"id":"W2121235350","doi":"10.1109/tnn.2006.873285","title":"OR/AND neurons and the development of interpretable logic models","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta Hospital; Alberta Hospital Edmonton; University of Alberta","funders":"","keywords":"Fuzzy logic; Computer science; Theoretical computer science; Modular design; Mathematics; Artificial intelligence","score_opus":0.021930472644109546,"score_gpt":0.23477964709518126,"score_spread":0.2128491744510717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121235350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014336155,0.0007485506,0.9687765,0.0008667123,0.000054917142,0.00005930161,0.00013459648,0.00024622015,0.0147770075],"genre_scores_gemma":[0.41566908,0.0018434874,0.56889224,0.00040370453,0.000094920615,0.00029978366,0.00035498652,0.00012563156,0.01231621],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992563,0.00027630737,0.000049062855,0.00015821592,0.00020442937,0.000055658682],"domain_scores_gemma":[0.99900913,0.0005851741,0.000103407634,0.00012197231,0.00014449678,0.000035811638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014891805,0.00049788743,0.00041060228,0.0006680625,0.0003002649,0.0021460853,0.0012177646,0.0008259809,0.0025516208],"category_scores_gemma":[0.0035776075,0.00037991657,0.0008035673,0.000372591,0.0020644276,0.002695443,0.0012871915,0.0014252708,0.0004896281],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027134278,0.000023141478,0.0003033571,0.00011572715,0.00003273543,0.00014500204,0.00026407474,0.14840518,0.0022009232,0.8255565,0.00054259395,0.022383716],"study_design_scores_gemma":[0.000013469106,0.000025156885,0.000100294616,0.000059211365,0.00001940182,0.000056117533,0.000050131264,0.4022115,0.0016685452,0.58554256,0.010240096,0.00001353439],"about_ca_topic_score_codex":0.0017383503,"about_ca_topic_score_gemma":0.0017825925,"teacher_disagreement_score":0.0025516208,"about_ca_system_score_codex":0.0013157869,"about_ca_system_score_gemma":0.0009817834,"threshold_uncertainty_score":0.009546757},"labels":[],"label_agreement":null},{"id":"W2121284732","doi":"10.1016/s0895-7177(03)00085-2","title":"Critical temperature of the transiently chaotic neural network","year":2003,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nortel (Canada); University of Calgary","funders":"","keywords":"Chaotic; Artificial neural network; Weighting; Computer science; Applied mathematics; Control theory (sociology); Statistical physics; Mathematics; Algorithm; Physics; Artificial intelligence","score_opus":0.019947703436721864,"score_gpt":0.22231491973826617,"score_spread":0.2023672163015443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121284732","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9150845,0.001015215,0.056345914,0.0019966257,0.00022274212,0.00004946415,0.00013077972,0.0002750902,0.024879584],"genre_scores_gemma":[0.9972269,0.00013339863,0.0008801435,0.00004524498,0.000038154558,0.000017647546,0.00002743569,0.000031734548,0.0015994315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998807,0.000033997683,0.0000048061315,0.000021718524,0.00002053948,0.000038313086],"domain_scores_gemma":[0.9983267,0.0008185188,0.00020605474,0.000084653126,0.00027372243,0.00029042337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006115104,0.0003197622,0.00061699626,0.0008093802,0.0007976411,0.0011685409,0.0006843523,0.00092240993,0.0033835114],"category_scores_gemma":[0.0057813646,0.00029759933,0.00029121176,0.00021234363,0.0022213832,0.0015772862,0.00088904466,0.00092023954,0.00021945691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005437094,0.00007980033,0.0036239633,0.0002290837,0.00006178035,0.000524135,0.00048444438,0.36275798,0.024200799,0.5982125,0.003509071,0.0057726796],"study_design_scores_gemma":[0.000039375333,0.00005047828,0.0013962185,0.00003201021,0.000019029521,0.000093371375,0.00009608647,0.85750425,0.001816228,0.13842726,0.00049279974,0.000032941985],"about_ca_topic_score_codex":0.0021684957,"about_ca_topic_score_gemma":0.0008689008,"teacher_disagreement_score":0.0033835114,"about_ca_system_score_codex":0.0011054997,"about_ca_system_score_gemma":0.00070318696,"threshold_uncertainty_score":0.011318982},"labels":[],"label_agreement":null},{"id":"W2121315965","doi":"10.1007/978-3-319-09274-4_6","title":"Increasing Accuracy in a Bidirectional Associative Memory through Expended Databases","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Recall; Computer science; Association (psychology); Associative property; Association rule learning; Artificial intelligence; Precision and recall; Point (geometry); Artificial neural network; Process (computing); Machine learning; Pattern recognition (psychology); Natural language processing; Cognitive psychology; Psychology; Mathematics","score_opus":0.03794630838851651,"score_gpt":0.29481448363673035,"score_spread":0.2568681752482138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121315965","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5480857,0.0024167292,0.4244249,0.0014790755,0.00060721696,0.00006327086,0.00063925993,0.004178257,0.018105624],"genre_scores_gemma":[0.94471836,0.0005343165,0.047902323,0.00018460029,0.000120574834,0.000033092212,0.00025373514,0.00010868901,0.0061441776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934536,0.0000814614,0.00008598043,0.00019831298,0.00018472536,0.000104116625],"domain_scores_gemma":[0.99459064,0.0021615266,0.0003651415,0.0018762858,0.00074308075,0.00026336798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010257068,0.00047631585,0.0010004592,0.0008402458,0.0006145551,0.002141016,0.0025772585,0.0009470761,0.0066658775],"category_scores_gemma":[0.0066434285,0.0005399246,0.00033420976,0.0016038441,0.0008486179,0.00668217,0.0021419474,0.0014298529,0.0013689173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002016244,0.00078421546,0.003628895,0.00042140062,0.00018839461,0.00029882902,0.00031743653,0.07586537,0.049160678,0.09426574,0.009630408,0.76342237],"study_design_scores_gemma":[0.00011421042,0.0005233213,0.001643847,0.000066309796,0.00021967784,0.00045705526,0.00015694069,0.7223346,0.05615364,0.21330917,0.0049485695,0.000072597875],"about_ca_topic_score_codex":0.0017636367,"about_ca_topic_score_gemma":0.0023317426,"teacher_disagreement_score":0.0066658775,"about_ca_system_score_codex":0.00050511915,"about_ca_system_score_gemma":0.0007432916,"threshold_uncertainty_score":0.022299588},"labels":[],"label_agreement":null},{"id":"W2121472132","doi":"10.1109/tnn.2010.2046497","title":"Realization of the Conscience Mechanism in CMOS Implementation of Winner-Takes-All Self-Organizing Neural Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Computer science; CMOS; Realization (probability); MATLAB; Winner-take-all; Mechanism (biology); Quantization (signal processing); Electronic engineering; Artificial intelligence; Control engineering; Engineering; Algorithm","score_opus":0.012075696766829336,"score_gpt":0.2522204979835713,"score_spread":0.24014480121674198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121472132","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35450634,0.0006486562,0.6174144,0.00049145333,0.00030745554,0.000089378795,0.000051924602,0.0010999343,0.02539043],"genre_scores_gemma":[0.9648528,0.00006155307,0.033777922,0.00005525554,0.000011490884,0.000017460254,0.000008028481,0.000007150874,0.0012082733],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991655,0.000014453076,0.000005963862,0.000013743299,0.000034557896,0.000014703251],"domain_scores_gemma":[0.9999039,0.000027215825,0.000012849006,0.000015540629,0.00002975595,0.000010617027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015259262,0.00013985728,0.00011455655,0.0001071392,0.00020709274,0.0003138405,0.00063271343,0.00025390403,0.0010411895],"category_scores_gemma":[0.0003203111,0.00006518904,0.00013270389,0.00007553564,0.00020617286,0.0002679747,0.0001932749,0.00025020394,0.00013234324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032208624,0.00029284362,0.0023747282,0.00034427806,0.00011408743,0.00086601987,0.00049054105,0.122228585,0.4398522,0.17014307,0.0021924037,0.2607792],"study_design_scores_gemma":[0.00008411069,0.0005863851,0.0014935118,0.000026887694,0.00004160145,0.00035946534,0.00005770647,0.82894707,0.13824864,0.019346362,0.010772613,0.000035589852],"about_ca_topic_score_codex":0.0005137248,"about_ca_topic_score_gemma":0.00087024225,"teacher_disagreement_score":0.0010411895,"about_ca_system_score_codex":0.00021584424,"about_ca_system_score_gemma":0.00020175416,"threshold_uncertainty_score":0.0034831762},"labels":[],"label_agreement":null},{"id":"W2121707362","doi":"10.1109/ccece.1993.332191","title":"Modified backpropagation algorithms for training the multilayer feedforward neural networks with hard-limiting neurons","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Backpropagation; Artificial neural network; Rprop; Algorithm; Limiting; Feed forward; Computer science; Feedforward neural network; Sigmoid function; Process (computing); Artificial intelligence; Time delay neural network; Types of artificial neural networks; Engineering; Control engineering","score_opus":0.09341877860737569,"score_gpt":0.25794710652303016,"score_spread":0.16452832791565447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121707362","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019264845,0.0005418868,0.99572635,0.00007893558,0.00010596256,0.00005440095,0.000038865892,0.00054571853,0.0009814642],"genre_scores_gemma":[0.051793884,0.0011838593,0.94072735,0.00012968955,0.000103294034,0.00041183017,0.00022861971,0.00018522682,0.005236155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940026,0.00012391608,0.00007094895,0.000082305596,0.0002829988,0.000039531165],"domain_scores_gemma":[0.9991836,0.0003166444,0.00008652185,0.000088923975,0.0002978238,0.000026527385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011627482,0.0014798611,0.00081427704,0.0008891039,0.00044151148,0.0007752296,0.0017995293,0.0014204753,0.0035784591],"category_scores_gemma":[0.004175521,0.00056690624,0.0007824178,0.0009698652,0.00044188852,0.0017882652,0.0007433149,0.001584151,0.0016599307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010100076,0.00007434549,0.00054471404,0.0004519634,0.00013298514,0.00014074282,0.0001059309,0.36954495,0.008639601,0.018339269,0.006242635,0.5956819],"study_design_scores_gemma":[0.00002345399,0.000041854462,0.00022521596,0.00004845156,0.000023505558,0.00008811387,0.00000892641,0.9781925,0.005100291,0.008098633,0.008124772,0.00002425876],"about_ca_topic_score_codex":0.003101327,"about_ca_topic_score_gemma":0.004328135,"teacher_disagreement_score":0.0035784591,"about_ca_system_score_codex":0.00055505836,"about_ca_system_score_gemma":0.0009244781,"threshold_uncertainty_score":0.011971116},"labels":[],"label_agreement":null},{"id":"W2121709672","doi":"10.1023/a:1012443410163","title":"The Computing Capacity of Three-Input Multiple-Valued One-Threshold Perceptrons","year":2001,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Set (abstract data type); Mathematics; Computational intelligence; Point (geometry); Discrete mathematics; Space (punctuation); Combinatorics; Algorithm; Computer science; Artificial intelligence; Geometry","score_opus":0.04709573901816167,"score_gpt":0.26201229344758836,"score_spread":0.2149165544294267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121709672","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55621964,0.0029480231,0.3782503,0.0032857808,0.00030652908,0.00003627527,0.0006783201,0.0010829853,0.057192113],"genre_scores_gemma":[0.98965925,0.00025603324,0.0082230605,0.00009129573,0.000039739803,0.0000134896945,0.00006742429,0.00003696224,0.0016127054],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996032,0.000090023874,0.000030217152,0.00007787018,0.000090639915,0.000107920525],"domain_scores_gemma":[0.9962852,0.00276515,0.00015521237,0.00034013906,0.00027133175,0.0001828594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010398602,0.00030381687,0.000770149,0.00039488115,0.00044875406,0.0022780653,0.0014297074,0.000985557,0.0056464206],"category_scores_gemma":[0.005954365,0.00027857383,0.00044884754,0.0005821524,0.0017772127,0.0035620283,0.0013401028,0.0011743394,0.0006218111],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068550406,0.000103804974,0.0007914827,0.00038749064,0.00007191039,0.0002499978,0.00031374532,0.20734361,0.031111974,0.68574023,0.003240443,0.06995987],"study_design_scores_gemma":[0.000018698765,0.00002139886,0.000351445,0.000024678797,0.00001330278,0.00005332781,0.000028153767,0.67493606,0.004832913,0.3190605,0.0006328985,0.000026552189],"about_ca_topic_score_codex":0.0013546641,"about_ca_topic_score_gemma":0.00073642394,"teacher_disagreement_score":0.0056464206,"about_ca_system_score_codex":0.00067845033,"about_ca_system_score_gemma":0.0006110429,"threshold_uncertainty_score":0.01888913},"labels":[],"label_agreement":null},{"id":"W2121794035","doi":"10.1109/pacrim.1991.160775","title":"OSNet: A neural network implementation of order statistic filters","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Constant (computer programming); Artificial neural network; Statistic; Order statistic; Computer science; Algorithm; Order (exchange); Value (mathematics); Artificial intelligence; Mathematics; Statistics; Machine learning","score_opus":0.02256623661745341,"score_gpt":0.27381171353660183,"score_spread":0.25124547691914845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121794035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009302415,0.00009934837,0.9814788,0.000105225205,0.00009534232,0.000040843748,0.00022362552,0.0048046685,0.0038496885],"genre_scores_gemma":[0.27264136,0.00023166668,0.71206,0.0002217936,0.000074363146,0.00020527701,0.0008845343,0.0002920687,0.013388965],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980396,0.000033706823,0.000015699052,0.00004096895,0.00007803846,0.000027640408],"domain_scores_gemma":[0.9997482,0.000074273565,0.000027335811,0.000034162404,0.00009892546,0.00001722569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003817573,0.00044372177,0.00036331377,0.00046415196,0.00024568412,0.00081240607,0.001114469,0.00065234443,0.005946335],"category_scores_gemma":[0.0012056844,0.00020814252,0.0002791721,0.00055774796,0.00040294652,0.0011412052,0.00036818194,0.0005201929,0.0014510528],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006579413,0.00013489454,0.0021494678,0.00019966716,0.000096663294,0.00019291781,0.00010820388,0.39208496,0.027025476,0.050301787,0.018640326,0.5084078],"study_design_scores_gemma":[0.000016508317,0.00004578481,0.00019874916,0.000008081215,0.000009245564,0.000038735452,0.000008392001,0.9804049,0.007330927,0.0054292833,0.0064998083,0.000009605158],"about_ca_topic_score_codex":0.0064526526,"about_ca_topic_score_gemma":0.01080009,"teacher_disagreement_score":0.0064526526,"about_ca_system_score_codex":0.0007118189,"about_ca_system_score_gemma":0.00064662873,"threshold_uncertainty_score":0.019892454},"labels":[],"label_agreement":null},{"id":"W2121994657","doi":"10.1109/icnn.1997.611669","title":"CMNN: cooperative modular neural network","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Modular design; Benchmark (surveying); Computer science; Artificial neural network; Voting; Artificial intelligence; Range (aeronautics); Architecture; Routing (electronic design automation); Machine learning; Engineering; Programming language; Computer network","score_opus":0.048950504522394626,"score_gpt":0.25993124280333035,"score_spread":0.21098073828093572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121994657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032478433,0.00082430127,0.9582288,0.0003423235,0.00014796549,0.000109099485,0.00020452039,0.0025991637,0.005065405],"genre_scores_gemma":[0.7384631,0.00055377383,0.25119242,0.000385213,0.00019426965,0.00036097624,0.0007568562,0.00014508735,0.007948274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994837,0.00012554206,0.0000219185,0.00012551153,0.00017354106,0.00006973487],"domain_scores_gemma":[0.99931645,0.00019285668,0.000093906085,0.00012050836,0.00023369267,0.000042566917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010062242,0.0007785944,0.0006500652,0.0006271731,0.0003374604,0.0005253835,0.0018845344,0.00085270073,0.0030536042],"category_scores_gemma":[0.0023553586,0.0002526394,0.00043668892,0.00075348275,0.00055355235,0.0011171172,0.0011600598,0.00071887195,0.0009484636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028300204,0.00013129388,0.0019045711,0.00025591278,0.00015531623,0.00019689162,0.00009332648,0.44428644,0.015080677,0.018544065,0.011159985,0.50790846],"study_design_scores_gemma":[0.000015493739,0.00007126433,0.00037607193,0.00000881537,0.000015797412,0.00005456553,0.00000655946,0.98655254,0.0024119623,0.007459003,0.0030176884,0.000010247574],"about_ca_topic_score_codex":0.0030433554,"about_ca_topic_score_gemma":0.0036650472,"teacher_disagreement_score":0.0030536042,"about_ca_system_score_codex":0.0006099255,"about_ca_system_score_gemma":0.0008036657,"threshold_uncertainty_score":0.010215342},"labels":[],"label_agreement":null},{"id":"W2122122698","doi":"10.1109/ccece.2002.1012989","title":"Testing a cognitive packet concept on a LAN","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer network; Computer science; Network packet; Acknowledgement; Router; Packet analyzer; Source routing; Quality of service; Cognitive network; Routing protocol; Routing table; Cognitive radio; Telecommunications","score_opus":0.04212981268543985,"score_gpt":0.27139436863840277,"score_spread":0.2292645559529629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122122698","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88405854,0.00020094447,0.088823214,0.00072465854,0.00031915095,0.00026946075,0.000116247844,0.0006058619,0.0248819],"genre_scores_gemma":[0.9625946,0.00021810556,0.034217957,0.00019290566,0.000086688415,0.00016197166,0.0000757536,0.00006451678,0.002387515],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978763,0.0009558416,0.00006786712,0.0002258633,0.0006529758,0.00022119221],"domain_scores_gemma":[0.9904109,0.0065442836,0.00033744943,0.0010122388,0.0013679228,0.00032727892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038037389,0.0006579301,0.00030301788,0.0007338018,0.0006082849,0.001490016,0.0016141244,0.0010202666,0.0029324247],"category_scores_gemma":[0.0126373535,0.0001896344,0.00038727338,0.00045355372,0.0019063296,0.004002665,0.0016466171,0.0013236551,0.000313254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008469245,0.0064040394,0.027796213,0.0011522342,0.00036998896,0.0016108261,0.0037746108,0.28215516,0.07046668,0.32858616,0.0074861404,0.26172873],"study_design_scores_gemma":[0.00044376514,0.0077740033,0.004226848,0.00012751514,0.00019186613,0.00028553203,0.0012510014,0.8785882,0.04153197,0.058677718,0.0068039256,0.00009766945],"about_ca_topic_score_codex":0.00333734,"about_ca_topic_score_gemma":0.0010278393,"teacher_disagreement_score":0.0038037389,"about_ca_system_score_codex":0.000862533,"about_ca_system_score_gemma":0.0007566192,"threshold_uncertainty_score":0.020116389},"labels":[],"label_agreement":null},{"id":"W2122160657","doi":"10.1007/s10032-005-0013-6","title":"Feature selection for ensembles applied to handwriting recognition","year":2006,"lang":"en","type":"article","venue":"International Journal on Document Analysis and Recognition (IJDAR)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Artificial intelligence; Random subspace method; Computer science; Feature selection; Pattern recognition (psychology); Boosting (machine learning); Perceptron; Machine learning; Robustness (evolution); Cascading classifiers; Feature (linguistics); Artificial neural network; Hidden Markov model; Classifier (UML)","score_opus":0.01284765546056195,"score_gpt":0.2665302875074339,"score_spread":0.25368263204687197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122160657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059659686,0.0008336931,0.93725085,0.00012261006,0.00017088579,0.00007558809,0.00015473974,0.0010565642,0.0006753748],"genre_scores_gemma":[0.71788317,0.0005438796,0.27587774,0.00009719721,0.00020737678,0.00025100226,0.00090063707,0.00023268507,0.0040062587],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924767,0.0002540123,0.00008360092,0.00013744792,0.00019343097,0.00008387587],"domain_scores_gemma":[0.9977908,0.0011022239,0.00010261381,0.00025758884,0.0006593676,0.00008737555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019787995,0.0007636899,0.0016156851,0.0009129412,0.0006004188,0.0007659358,0.0009000547,0.00065992336,0.0016588705],"category_scores_gemma":[0.0037571264,0.00041107048,0.0010867418,0.0010633644,0.00025317568,0.0008212356,0.0009932446,0.001126802,0.00063561514],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003503788,0.0001941943,0.0025244553,0.00008942215,0.00026689374,0.00008995665,0.00007840437,0.17192435,0.01479146,0.001897092,0.0031869707,0.80460656],"study_design_scores_gemma":[0.000009417725,0.00007484534,0.0011360922,0.00000812043,0.000038372626,0.000035418685,0.000012580575,0.9927301,0.0037004268,0.0015795106,0.0006669623,0.000008147719],"about_ca_topic_score_codex":0.0030690623,"about_ca_topic_score_gemma":0.0038059964,"teacher_disagreement_score":0.0030690623,"about_ca_system_score_codex":0.0003345346,"about_ca_system_score_gemma":0.00065768184,"threshold_uncertainty_score":0.010465026},"labels":[],"label_agreement":null},{"id":"W2122548695","doi":"10.1109/fuzz.2002.1006730","title":"Hybrid soft computing techniques for heterogeneous data classification","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Data mining; Data classification; Set (abstract data type); Data set; Fuzzy set; Statistical classification; One-class classification; Fuzzy logic; Machine learning; Artificial intelligence; Support vector machine","score_opus":0.0786015815895519,"score_gpt":0.31567041473517243,"score_spread":0.23706883314562055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122548695","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004476777,0.00056345004,0.99330646,0.0001417343,0.00005303436,0.00004855409,0.000024778885,0.00024407932,0.0011411873],"genre_scores_gemma":[0.2049106,0.0010831482,0.78956026,0.00022850078,0.00019465407,0.00037697633,0.00024661626,0.00008154193,0.0033177685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791497,0.000571353,0.00014980874,0.00023269889,0.0010312316,0.00009997961],"domain_scores_gemma":[0.99806005,0.0010493202,0.00014759261,0.00028414163,0.0004093499,0.000049452752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023452176,0.0008366749,0.0013974187,0.0032435574,0.00064715865,0.0016496307,0.0013828184,0.0009515446,0.002396056],"category_scores_gemma":[0.004360229,0.00038439853,0.0012043725,0.0041060746,0.00075390114,0.0018631482,0.0018892067,0.0012675873,0.0010079137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016015011,0.00013829602,0.0008843907,0.00031532496,0.00027215315,0.0002354474,0.00020215005,0.14178114,0.010427251,0.041803584,0.0030572866,0.80072284],"study_design_scores_gemma":[0.00002123774,0.000064284344,0.00046505293,0.000045791523,0.000052366282,0.00017221407,0.000070358954,0.9467527,0.0056502614,0.04097207,0.005703572,0.000030008112],"about_ca_topic_score_codex":0.0011348589,"about_ca_topic_score_gemma":0.0015530267,"teacher_disagreement_score":0.0032435574,"about_ca_system_score_codex":0.0005762799,"about_ca_system_score_gemma":0.0006544867,"threshold_uncertainty_score":0.0124028325},"labels":[],"label_agreement":null},{"id":"W2123049073","doi":"10.1109/icmlc.2002.1175375","title":"Achieving self-organization by lateral inhibition","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Novelty; Computer science; Self-organization; Cluster analysis; Network topology; Artificial intelligence; Unsupervised learning; Self-organizing network; Theoretical computer science; Topology (electrical circuits); Machine learning; Mathematics; Computer network","score_opus":0.004394121554296156,"score_gpt":0.19075311741695167,"score_spread":0.1863589958626555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123049073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04893013,0.0006269746,0.9322873,0.000698948,0.00014907612,0.000057808797,0.000062073275,0.00066517247,0.016522504],"genre_scores_gemma":[0.9278148,0.0005762501,0.062345743,0.0002788129,0.00008480451,0.0001923509,0.000091715476,0.00011406851,0.008501518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997758,0.00005416844,0.000009554816,0.000041882482,0.000072279465,0.000046397497],"domain_scores_gemma":[0.9996363,0.00013307417,0.000064565735,0.00004634592,0.000058999794,0.000060727692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040983577,0.00050730474,0.0005008729,0.0002754424,0.0004338498,0.00094322546,0.0015083044,0.0007625852,0.0014047372],"category_scores_gemma":[0.0010038113,0.00023683613,0.00058146287,0.00021975717,0.00089401746,0.0015463207,0.0011334346,0.0008233089,0.00045706838],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001402921,0.00011851673,0.0010064492,0.00015259457,0.000097650736,0.00031755943,0.0002764164,0.6175507,0.034910038,0.28337944,0.004930196,0.05712009],"study_design_scores_gemma":[0.000013689259,0.000037831658,0.00007168917,0.0000056956906,0.000008406799,0.000045853212,0.000011865802,0.9482234,0.0020876196,0.04747704,0.002005566,0.000011365139],"about_ca_topic_score_codex":0.00087879825,"about_ca_topic_score_gemma":0.0008785855,"teacher_disagreement_score":0.0015083044,"about_ca_system_score_codex":0.0006181691,"about_ca_system_score_gemma":0.00048550928,"threshold_uncertainty_score":0.00469929},"labels":[],"label_agreement":null},{"id":"W2123434711","doi":"10.1109/icnn.1997.611663","title":"Identification of a class of nonlinear systems using dynamic neural network structures","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Nonlinear system; Artificial neural network; Identification (biology); Class (philosophy); Computer science; Nonlinear system identification; Selection (genetic algorithm); Nonlinear dynamical systems; System identification; Artificial intelligence; Data mining; Physics","score_opus":0.043372502128329285,"score_gpt":0.27782354541501925,"score_spread":0.23445104328668998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123434711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026753476,0.0004282475,0.9697007,0.00013513383,0.000038268197,0.000034994217,0.00003431226,0.00013263704,0.0027421997],"genre_scores_gemma":[0.77206606,0.0011881278,0.22100478,0.00013380658,0.00012486034,0.00019880543,0.00022065522,0.000037091213,0.005025836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968505,0.000062245475,0.000017788087,0.00008923934,0.00011772114,0.00002793462],"domain_scores_gemma":[0.9995691,0.0002260405,0.000060520626,0.000047723956,0.00008674208,0.000009829272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004350239,0.00049577287,0.00046097356,0.0004988724,0.00030686328,0.00061232026,0.00050090445,0.0009792346,0.0009549309],"category_scores_gemma":[0.0014540671,0.00022685294,0.00030812353,0.00028931722,0.00055147486,0.0011098979,0.00055873476,0.00065139483,0.00025300845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013507917,0.000114552546,0.0018391459,0.00031076313,0.000078226745,0.00047047774,0.00020977971,0.63984215,0.048687298,0.10226596,0.0011554447,0.20489119],"study_design_scores_gemma":[0.000006346018,0.000047423146,0.00038784792,0.000017342074,0.000008497378,0.00010517251,0.000017680153,0.9779254,0.003802389,0.015886575,0.0017849786,0.000010434206],"about_ca_topic_score_codex":0.00070464367,"about_ca_topic_score_gemma":0.0008466517,"teacher_disagreement_score":0.0009792346,"about_ca_system_score_codex":0.00029194955,"about_ca_system_score_gemma":0.00026944705,"threshold_uncertainty_score":0.0031945705},"labels":[],"label_agreement":null},{"id":"W2123701148","doi":"10.1109/ijcnn.2000.857806","title":"Bias learning, knowledge sharing","year":2000,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Affine transformation; Manifold (fluid mechanics); Task (project management); Point (geometry); Class (philosophy); Artificial intelligence; Space (punctuation); Range (aeronautics); Domain (mathematical analysis); Function (biology); Field (mathematics); Machine learning; Variance (accounting); Mathematics","score_opus":0.041868448700852066,"score_gpt":0.2760101978923276,"score_spread":0.23414174919147554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123701148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039466366,0.00459404,0.9450069,0.0014425761,0.00016552201,0.00027337193,0.0002478702,0.0016215768,0.007181804],"genre_scores_gemma":[0.7697826,0.0027813881,0.21765886,0.0006640412,0.0004277012,0.00059780927,0.0007836842,0.00027334242,0.007030549],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99408394,0.0026611295,0.00025842324,0.00151901,0.0011692316,0.00030819327],"domain_scores_gemma":[0.9843518,0.007959627,0.0012493866,0.00469235,0.0013068402,0.0004399592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007621441,0.0014316842,0.0018782727,0.0015741024,0.0009927384,0.0026312047,0.0026742623,0.0028680488,0.004144633],"category_scores_gemma":[0.03604497,0.0005242322,0.00095280743,0.002227585,0.00246153,0.006577696,0.0053189397,0.002110913,0.001987724],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006901123,0.0004419811,0.0046274825,0.0007950165,0.0004424011,0.00020959022,0.00050336565,0.18379833,0.0070652943,0.10183257,0.007164575,0.6924293],"study_design_scores_gemma":[0.00013651371,0.00033820997,0.0015348863,0.00011953506,0.0001282736,0.00029960656,0.00012405345,0.46492195,0.007155281,0.5138841,0.011291878,0.000065691296],"about_ca_topic_score_codex":0.0020238794,"about_ca_topic_score_gemma":0.0012772084,"teacher_disagreement_score":0.007621441,"about_ca_system_score_codex":0.0016613418,"about_ca_system_score_gemma":0.0017683796,"threshold_uncertainty_score":0.04030651},"labels":[],"label_agreement":null},{"id":"W2123847705","doi":"10.1109/tnn.2011.2163169","title":"A New Formulation for Feedforward Neural Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Computer science; Feedforward neural network; Regularization (linguistics); Stochastic neural network; Artificial intelligence; Backpropagation; Time delay neural network; Types of artificial neural networks; Generalization; Measure (data warehouse); Probabilistic neural network; Feed forward; Machine learning; Algorithm; Mathematics; Data mining","score_opus":0.033822963046535905,"score_gpt":0.24762515996303097,"score_spread":0.21380219691649507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123847705","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093502115,0.0005899343,0.99475515,0.00026429503,0.00013641719,0.00002223891,0.00006684158,0.000046877805,0.0031832417],"genre_scores_gemma":[0.18334612,0.0037172711,0.7900551,0.0009209925,0.000992992,0.00053023093,0.0004542398,0.0001725902,0.01981057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990288,0.00030962855,0.00007709881,0.0002149618,0.00032052316,0.000049059265],"domain_scores_gemma":[0.99945444,0.00022527819,0.00005893643,0.000041583426,0.0001989854,0.000020713882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014210992,0.0012153243,0.0006869579,0.00073754665,0.0003587426,0.0012932817,0.0015972278,0.0016632876,0.003755886],"category_scores_gemma":[0.002623364,0.0003751529,0.00074499997,0.00080113753,0.001020354,0.0028274339,0.0009861554,0.0022546882,0.0008038902],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003581277,0.00003335304,0.0002953162,0.00036010292,0.000056985904,0.00018864256,0.00014280871,0.30603147,0.0048895734,0.59401387,0.005018281,0.08893383],"study_design_scores_gemma":[0.000009738882,0.00005632013,0.00012363464,0.000051713767,0.000019409314,0.00012744249,0.000017970173,0.8294362,0.0011673315,0.15004186,0.018931648,0.000016709135],"about_ca_topic_score_codex":0.0014649036,"about_ca_topic_score_gemma":0.0018909574,"teacher_disagreement_score":0.003755886,"about_ca_system_score_codex":0.0008903337,"about_ca_system_score_gemma":0.0008398876,"threshold_uncertainty_score":0.012564719},"labels":[],"label_agreement":null},{"id":"W2124261449","doi":"10.1109/nafips.2009.5156405","title":"Design of a robust neuro-controller for complex dynamic systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Overshoot (microwave communication); Control theory (sociology); Bandwidth (computing); Computer science; Step response; Controller (irrigation); Position (finance); Control engineering; Artificial intelligence; Control (management); Engineering; Telecommunications","score_opus":0.05163011650752495,"score_gpt":0.264181689734164,"score_spread":0.21255157322663906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124261449","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039782384,0.00038729594,0.99169475,0.00014913443,0.00006238015,0.00007717692,0.000026203492,0.00058796734,0.003036806],"genre_scores_gemma":[0.6097542,0.000880133,0.38115025,0.00037469968,0.00014141167,0.00070325076,0.0001565589,0.00012252279,0.0067170057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994079,0.00008513744,0.000043130003,0.00018375243,0.00022889677,0.000051225576],"domain_scores_gemma":[0.99955493,0.00011773815,0.00006705703,0.000030195297,0.00021018641,0.000019869563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007741488,0.0008362847,0.00082375726,0.0004122322,0.0004818008,0.0012065414,0.001238122,0.0015788797,0.0023246882],"category_scores_gemma":[0.0013532502,0.00037602888,0.00059074274,0.0003007683,0.00058696675,0.00061252946,0.0007245941,0.0010816441,0.00096269994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013713102,0.000057247424,0.0003713247,0.0004101526,0.00013883617,0.00035527837,0.000110548805,0.814919,0.03721014,0.014965398,0.0024502755,0.12887475],"study_design_scores_gemma":[0.000019945493,0.00006830932,0.00013434255,0.000018160441,0.000017325656,0.00005651921,0.000008615993,0.99153465,0.0039098337,0.0016725867,0.00254563,0.000014107276],"about_ca_topic_score_codex":0.0032213158,"about_ca_topic_score_gemma":0.0021336917,"teacher_disagreement_score":0.0032213158,"about_ca_system_score_codex":0.00063282985,"about_ca_system_score_gemma":0.0011804344,"threshold_uncertainty_score":0.007776916},"labels":[],"label_agreement":null},{"id":"W2124484847","doi":"10.1109/coase.2008.4626454","title":"Nonlinear systems identification using dynamic multi-time scales neural networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Identification (biology); Computer science; Nonlinear system; Nonlinear system identification; Control theory (sociology); System identification; Lyapunov function; Algorithm; Artificial intelligence; Data mining; Control (management); Physics","score_opus":0.02917265978631378,"score_gpt":0.26845335164849454,"score_spread":0.23928069186218076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124484847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0118578505,0.00026882047,0.9861731,0.0000621266,0.00003552962,0.000016135442,0.0000099103345,0.00018201987,0.0013945118],"genre_scores_gemma":[0.72156125,0.0005647628,0.27447534,0.000105127685,0.00005918025,0.000092147035,0.00006901738,0.00005251096,0.0030206894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997217,0.00006264939,0.000016073362,0.00006975441,0.00010779281,0.000022055858],"domain_scores_gemma":[0.999746,0.00010016832,0.000047440957,0.000038110393,0.000060077924,0.000008232439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034647516,0.00050135824,0.00048180696,0.0003507994,0.0003099405,0.0005679752,0.0005917501,0.00057816255,0.00087264145],"category_scores_gemma":[0.0010371805,0.00025183617,0.0003381812,0.00031693236,0.0003192809,0.0009769249,0.0006049987,0.00059592223,0.00023663143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010106443,0.00006160436,0.00088001246,0.0001065725,0.000078856996,0.00018013694,0.00010456632,0.7102463,0.019471398,0.018349452,0.0005831596,0.24983677],"study_design_scores_gemma":[0.0000028723318,0.000014228019,0.00013663166,0.0000038721696,0.0000050977014,0.000027966753,0.0000048523993,0.995789,0.001574191,0.0018244206,0.0006122743,0.0000045740057],"about_ca_topic_score_codex":0.0011962348,"about_ca_topic_score_gemma":0.0012069305,"teacher_disagreement_score":0.0011962348,"about_ca_system_score_codex":0.00029867375,"about_ca_system_score_gemma":0.00023875992,"threshold_uncertainty_score":0.0029192567},"labels":[],"label_agreement":null},{"id":"W2124537004","doi":"10.1016/j.tics.2007.09.004","title":"Learning multiple layers of representation","year":2007,"lang":"en","type":"review","venue":"Trends in Cognitive Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1013,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Backpropagation; Representation (politics); Artificial neural network; Computer science; Artificial intelligence; Perception; Generative model; Object (grammar); Generative grammar; Machine learning; Sensory system; Feature learning; Layer (electronics); Pattern recognition (psychology); Psychology; Neuroscience","score_opus":0.40443597567934647,"score_gpt":0.5100302918961084,"score_spread":0.10559431621676191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124537004","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016561506,0.2585571,0.6827744,0.006714892,0.0015614348,0.00008183226,0.0003723538,0.0015558719,0.031820614],"genre_scores_gemma":[0.24353649,0.4292297,0.2860208,0.0021440585,0.001328894,0.00017301468,0.0013670968,0.00018591812,0.03601414],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99983394,0.000026345992,0.000014739365,0.00005513703,0.00005271759,0.00001712956],"domain_scores_gemma":[0.9997081,0.00012833004,0.00003361083,0.000061988445,0.00005534607,0.000012628233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054403715,0.00078491535,0.0006142816,0.000475524,0.00014919214,0.0011475218,0.0015243536,0.0010891649,0.002869368],"category_scores_gemma":[0.0012441677,0.00032485864,0.00044892757,0.0007767477,0.00071262906,0.0036818606,0.0009758808,0.0012328763,0.0011735964],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027125858,0.000024197345,0.0003093756,0.0010182539,0.00011372787,0.000042589338,0.000024194138,0.011247946,0.0025233964,0.028538905,0.007828353,0.948302],"study_design_scores_gemma":[0.00004928569,0.00027611657,0.00269949,0.0018385744,0.00046581854,0.0017021997,0.00011213944,0.1885613,0.03382477,0.35549197,0.41486537,0.000112874404],"about_ca_topic_score_codex":0.00077249255,"about_ca_topic_score_gemma":0.0011598223,"teacher_disagreement_score":0.002869368,"about_ca_system_score_codex":0.000508072,"about_ca_system_score_gemma":0.0007075442,"threshold_uncertainty_score":0.00959897},"labels":[],"label_agreement":null},{"id":"W2124551777","doi":"10.1016/j.biosystems.2005.09.004","title":"Generative topographic mapping applied to clustering and visualization of motor unit action potentials","year":2005,"lang":"en","type":"article","venue":"Biosystems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Governo Brasil","keywords":"Visualization; Cluster analysis; Self-organizing map; Computer science; Pattern recognition (psychology); Artificial intelligence; Grid; Principal component analysis; Motor unit; Neuroscience; Mathematics; Biology","score_opus":0.042962105439870946,"score_gpt":0.2840534867386454,"score_spread":0.24109138129877444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124551777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032304727,0.00019602198,0.9628967,0.00019912235,0.000043200453,0.000047573823,0.00017771014,0.00222938,0.0019055562],"genre_scores_gemma":[0.6174725,0.00046061547,0.37870553,0.000053733413,0.000038026246,0.000095045805,0.00028023197,0.0006397724,0.00225454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999876,0.000037607326,0.0000056977697,0.000024861405,0.000038400336,0.000017396476],"domain_scores_gemma":[0.99957913,0.0002334411,0.000029084966,0.000060803435,0.00007251281,0.00002493384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030770642,0.0003750056,0.00031492367,0.0009341634,0.00034378702,0.00081323675,0.0004849695,0.00043731212,0.0030932233],"category_scores_gemma":[0.0021822616,0.00031130746,0.00053041143,0.0015133173,0.00038288644,0.0004929702,0.0007157176,0.00061555515,0.00045459231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020503462,0.0000429374,0.0015566982,0.00021002561,0.0000743896,0.00034642432,0.00063376594,0.32788926,0.055500526,0.03002742,0.00459743,0.57891613],"study_design_scores_gemma":[0.000007912981,0.000016054464,0.0010463392,0.000009237038,0.000009269513,0.00014228292,0.000040587423,0.9744863,0.0073409225,0.015279709,0.0016086015,0.000012809592],"about_ca_topic_score_codex":0.006637226,"about_ca_topic_score_gemma":0.007648371,"teacher_disagreement_score":0.006637226,"about_ca_system_score_codex":0.0004569059,"about_ca_system_score_gemma":0.00069983985,"threshold_uncertainty_score":0.013197184},"labels":[],"label_agreement":null},{"id":"W2124581141","doi":"10.1162/089976603321780281","title":"A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case","year":2003,"lang":"en","type":"review","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Connectionism; Computer science; Artificial intelligence; Taxonomy (biology); Generative grammar; Machine learning; Unsupervised learning; Artificial neural network","score_opus":0.11913249949567337,"score_gpt":0.337637395656113,"score_spread":0.21850489616043964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124581141","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055154976,0.6727553,0.19075292,0.02018671,0.0014660045,0.00013405184,0.00027213397,0.00022228177,0.10869518],"genre_scores_gemma":[0.14583702,0.74001217,0.08126251,0.0053225714,0.0026366133,0.0005176811,0.0004295302,0.00008492869,0.023896923],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993782,0.00019770181,0.000052532097,0.00012364444,0.00021331046,0.0000346057],"domain_scores_gemma":[0.99877375,0.0006824932,0.00007724197,0.000119203985,0.00030514237,0.000042091724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014285096,0.00067422393,0.001066608,0.0034049838,0.00070103275,0.0026655393,0.0016445653,0.0024153017,0.0023991948],"category_scores_gemma":[0.002563358,0.00042602324,0.00042889907,0.0040191077,0.003776488,0.008903285,0.001095295,0.0029094466,0.0011074989],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009950518,0.000014654166,0.00032098722,0.0009790085,0.000021405996,0.00013492118,0.0004060632,0.0024362605,0.00031471965,0.8190841,0.008706258,0.16757163],"study_design_scores_gemma":[0.000005076901,0.000033378663,0.00064927,0.0007205733,0.00001540948,0.0008931214,0.000342014,0.006006,0.0003032869,0.6716396,0.3193624,0.00002986454],"about_ca_topic_score_codex":0.0031775185,"about_ca_topic_score_gemma":0.003229972,"teacher_disagreement_score":0.0034049838,"about_ca_system_score_codex":0.0021954942,"about_ca_system_score_gemma":0.0013537762,"threshold_uncertainty_score":0.01592946},"labels":[],"label_agreement":null},{"id":"W2124620066","doi":"10.1109/ijcnn.2000.859437","title":"Input-side training in constructive neural networks based on error scaling and pruning","year":2000,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Constructive; Pruning; Computer science; Generalization; Artificial neural network; Scaling; Feedforward neural network; Artificial intelligence; Algorithm; Feed forward; Piecewise; Machine learning; Mathematics; Process (computing); Engineering","score_opus":0.021825852936410647,"score_gpt":0.2511428781424905,"score_spread":0.22931702520607983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124620066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0112606175,0.00014561521,0.9868901,0.000051825373,0.00002655094,0.00003105055,0.000006796687,0.00020344339,0.0013841059],"genre_scores_gemma":[0.33415127,0.00029135152,0.6634015,0.00016778894,0.00009296323,0.0001812588,0.00005586442,0.00016042107,0.0014976274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772686,0.0009873407,0.00013249773,0.00021263408,0.00081908784,0.00012154548],"domain_scores_gemma":[0.9935789,0.0041803634,0.00050567824,0.0007456533,0.0008778078,0.000111611385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034750507,0.0012432223,0.0010433651,0.0008901725,0.0004400786,0.00079129473,0.0021587887,0.001214976,0.0011416255],"category_scores_gemma":[0.013897007,0.00057376287,0.00059853215,0.0007410655,0.001890871,0.0020316357,0.0017556897,0.0013500713,0.0003845312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027761387,0.00012393651,0.0014991071,0.00027778296,0.00011035836,0.00039209484,0.00034692718,0.523628,0.031473972,0.070727214,0.0010894765,0.3700536],"study_design_scores_gemma":[0.000017850698,0.00011792849,0.00024657126,0.000036613495,0.00002872459,0.00020139267,0.000016922491,0.96604955,0.016064826,0.015937202,0.0012629692,0.000019464796],"about_ca_topic_score_codex":0.0005992045,"about_ca_topic_score_gemma":0.0010344209,"teacher_disagreement_score":0.0034750507,"about_ca_system_score_codex":0.00044522394,"about_ca_system_score_gemma":0.00056209264,"threshold_uncertainty_score":0.018378079},"labels":[],"label_agreement":null},{"id":"W2124751123","doi":"10.1186/2042-1001-1-12","title":"Machine learning for neuroscience","year":2011,"lang":"en","type":"article","venue":"Neural Systems & Circuits","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Graphical model; Computer science; Artificial intelligence; Machine learning; Inference; Key (lock); Variable (mathematics); Probabilistic logic; Hidden variable theory; Computational learning theory; Graph; Latent variable; Computation; Unsupervised learning; Theoretical computer science; Algorithm; Mathematics","score_opus":0.06957827306190785,"score_gpt":0.25403019565944346,"score_spread":0.18445192259753562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124751123","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00270207,0.3435112,0.20946464,0.16780774,0.019328697,0.00030094033,0.003699607,0.0056901914,0.24749494],"genre_scores_gemma":[0.16465299,0.42730916,0.19374193,0.060464863,0.040277634,0.0020014362,0.005942752,0.0028709208,0.10273829],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995432,0.0018704511,0.00027296442,0.0009804348,0.0012010379,0.0002432169],"domain_scores_gemma":[0.9927946,0.004337295,0.00039158677,0.0010429972,0.0009852286,0.00044827323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038442307,0.0016621422,0.002321239,0.0026852626,0.0012491441,0.007095149,0.0028600811,0.0058289086,0.038026527],"category_scores_gemma":[0.013001606,0.00045951654,0.000988685,0.0024210669,0.0062674074,0.008109233,0.002955801,0.0068739755,0.018281844],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004484273,0.00004423213,0.00057845836,0.0016394061,0.00011413443,0.00011894594,0.0003164245,0.002177811,0.00038645568,0.6073289,0.1835381,0.20371225],"study_design_scores_gemma":[0.0000141048795,0.000026975185,0.00032981118,0.0007317941,0.000013786369,0.00010330542,0.000117209056,0.0016869513,0.00010690994,0.6582111,0.33862695,0.000031001084],"about_ca_topic_score_codex":0.0022714692,"about_ca_topic_score_gemma":0.0011046082,"teacher_disagreement_score":0.038026527,"about_ca_system_score_codex":0.0033813398,"about_ca_system_score_gemma":0.004063621,"threshold_uncertainty_score":0.12721145},"labels":[],"label_agreement":null},{"id":"W2125833791","doi":"10.1007/s00521-009-0277-8","title":"Nonlinear Poisson regression using neural networks: a simulation study","year":2009,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"University of Tehran; Dalhousie University","keywords":"Artificial neural network; Poisson regression; Nonlinear system; Poisson distribution; Computer science; Perceptron; Nonlinear regression; Regression; Regression analysis; Artificial intelligence; Applied mathematics; Machine learning; Algorithm; Mathematics; Statistics; Population","score_opus":0.031514077975329964,"score_gpt":0.3327938700853901,"score_spread":0.3012797921100601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125833791","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83583933,0.0025837244,0.1391107,0.0041526924,0.00029817052,0.00020669508,0.0012911608,0.00038767493,0.016129915],"genre_scores_gemma":[0.9811952,0.00086795073,0.011311664,0.00024750846,0.0000840656,0.00014338453,0.00052589196,0.00009799045,0.005526209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865216,0.00082790403,0.00005530681,0.0001532611,0.00013878933,0.0001725121],"domain_scores_gemma":[0.95114607,0.04321645,0.0017364029,0.00104807,0.0021573002,0.0006958051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064856843,0.0008242677,0.0021646784,0.0015651105,0.001064421,0.0019035392,0.0029009834,0.004337989,0.005130218],"category_scores_gemma":[0.033528343,0.0010483973,0.0017717244,0.0023364983,0.0016174928,0.0029987209,0.0012318399,0.0031035997,0.00059688033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015153749,0.0001220591,0.002271825,0.00004583638,0.000050247883,0.0001119759,0.000047804777,0.98407435,0.0000969042,0.010416714,0.0008041574,0.0018065999],"study_design_scores_gemma":[0.000029505023,0.000018176564,0.00023147355,0.0000067136216,0.000013436511,0.000021421925,0.000020317919,0.99748284,0.000045582365,0.002036632,0.00008426267,0.000009580433],"about_ca_topic_score_codex":0.049294077,"about_ca_topic_score_gemma":0.025608396,"teacher_disagreement_score":0.049294077,"about_ca_system_score_codex":0.0028293205,"about_ca_system_score_gemma":0.0016004561,"threshold_uncertainty_score":0.098014295},"labels":[],"label_agreement":null},{"id":"W2125952379","doi":"10.3389/fninf.2014.00031","title":"Machine learning patterns for neuroimaging-genetic studies in the cloud","year":2014,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto; Montreal Neurological Institute and Hospital; Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Agence Nationale de la Recherche; Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Computer science; Neuroinformatics; Cloud computing; Scalability; Neuroimaging; Machine learning; Artificial intelligence; Big data; Deep learning; Software deployment; Reliability (semiconductor); Data science; Data mining; Power (physics); Software engineering; Database","score_opus":0.027972951984505685,"score_gpt":0.27036791952999445,"score_spread":0.24239496754548875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125952379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021031955,0.00054670154,0.9271588,0.0041434327,0.00027836082,0.0005133783,0.006686877,0.036226764,0.0034136865],"genre_scores_gemma":[0.1596085,0.00039647456,0.82990915,0.00057377527,0.00013346128,0.0005794671,0.0059985514,0.0013021551,0.0014984627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790037,0.00054693146,0.00021266784,0.00052697794,0.00065372506,0.0001593834],"domain_scores_gemma":[0.9921895,0.002187858,0.00060464634,0.0031794661,0.0012265317,0.0006119736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033301883,0.00085913105,0.0010645189,0.0020201595,0.0010474699,0.002509214,0.0031264753,0.0012147953,0.0068788277],"category_scores_gemma":[0.016363535,0.0006543972,0.0014030955,0.0036970202,0.0008714645,0.0030554784,0.0029738022,0.0016604562,0.0044828635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017284943,0.0009348158,0.034267217,0.0006852257,0.00039762043,0.0015093472,0.00058201735,0.07273837,0.020562558,0.07730435,0.13736969,0.6519203],"study_design_scores_gemma":[0.00021833136,0.0001037958,0.008632587,0.00008337022,0.000067138426,0.00059004925,0.00032072375,0.6594694,0.01589423,0.26874164,0.045811225,0.0000674839],"about_ca_topic_score_codex":0.004719209,"about_ca_topic_score_gemma":0.007322699,"teacher_disagreement_score":0.0068788277,"about_ca_system_score_codex":0.001474982,"about_ca_system_score_gemma":0.002813612,"threshold_uncertainty_score":0.023011982},"labels":[],"label_agreement":null},{"id":"W2126041711","doi":"10.1109/coginf.2006.365538","title":"Context Dependent Controller by Overlapped Neural Networks for Performance Metrics Revision","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Artificial neural network; Context (archaeology); Relation (database); Controller (irrigation); State (computer science); Artificial intelligence; Control (management); Machine learning; Data mining; Algorithm","score_opus":0.010935256060348833,"score_gpt":0.22901734963608278,"score_spread":0.21808209357573394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126041711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010116012,0.00011517199,0.9882284,0.000042602427,0.000016211066,0.000030051573,0.000008925457,0.00021994287,0.0012227166],"genre_scores_gemma":[0.77427346,0.00015280124,0.2234774,0.000084029176,0.00006772913,0.00024198406,0.000056652876,0.0001109108,0.0015350523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782985,0.0006846766,0.0001362227,0.00048168373,0.0007039183,0.0001636524],"domain_scores_gemma":[0.99770963,0.0010511791,0.0002976767,0.0003553223,0.00050979515,0.00007634166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025998347,0.0011541393,0.0007304094,0.00069497904,0.00036670244,0.0010505168,0.001549351,0.0009439614,0.0015782882],"category_scores_gemma":[0.0075039933,0.0003119507,0.00045560743,0.0005639489,0.0009344136,0.0018619879,0.0015699025,0.0012512285,0.0002029463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022917466,0.00011801064,0.0011075777,0.00013437211,0.00008754264,0.00013759894,0.0001872829,0.788767,0.015536191,0.044318438,0.00077459693,0.14860219],"study_design_scores_gemma":[0.000003782469,0.000028603154,0.00006075359,0.0000036697747,0.000005507991,0.000009200456,0.0000049461723,0.99468,0.0015097447,0.0034379116,0.00025191033,0.000004033962],"about_ca_topic_score_codex":0.0014970809,"about_ca_topic_score_gemma":0.0012141665,"teacher_disagreement_score":0.0025998347,"about_ca_system_score_codex":0.0008837791,"about_ca_system_score_gemma":0.00087747374,"threshold_uncertainty_score":0.013749421},"labels":[],"label_agreement":null},{"id":"W2126299630","doi":"10.1109/nafips.2004.1336288","title":"Dynamic neural network based training for support vector machines","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Support vector machine; Computer science; Artificial neural network; Artificial intelligence; Kernel (algebra); Structured support vector machine; Dual (grammatical number); Relevance vector machine; Machine learning; Kernel method; Least squares support vector machine; Mathematics","score_opus":0.01486319486230855,"score_gpt":0.258010899036116,"score_spread":0.24314770417380746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126299630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006527505,0.0003854935,0.9913078,0.00014918207,0.00006230718,0.000038141192,0.000026965698,0.00028075758,0.0012218616],"genre_scores_gemma":[0.47670916,0.00083580863,0.51743346,0.00021263763,0.00014045175,0.00040221596,0.000302169,0.00010537495,0.0038586925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907565,0.00031011208,0.00007542482,0.00017676175,0.0002925765,0.00006938157],"domain_scores_gemma":[0.9982767,0.0010488787,0.00010130759,0.00012992183,0.00040908437,0.00003409849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013488341,0.0007078899,0.00086078054,0.0007768473,0.00043481574,0.0008088432,0.0012219364,0.0011836704,0.002690817],"category_scores_gemma":[0.0074174814,0.00041033712,0.0004450171,0.0010426755,0.00058061833,0.0015847137,0.0007951141,0.0018904255,0.00070484483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017389847,0.00009793985,0.0009048454,0.00022690179,0.000068719884,0.000103781655,0.00007204668,0.51745427,0.006574665,0.026197119,0.0022458832,0.4458799],"study_design_scores_gemma":[0.000004098572,0.00001708499,0.00007229361,0.000009142059,0.0000031323618,0.000014468611,0.0000031591126,0.9952193,0.0011588458,0.0028015925,0.0006924759,0.0000043749233],"about_ca_topic_score_codex":0.0018029149,"about_ca_topic_score_gemma":0.0013935321,"teacher_disagreement_score":0.002690817,"about_ca_system_score_codex":0.0006947277,"about_ca_system_score_gemma":0.0005849785,"threshold_uncertainty_score":0.009001672},"labels":[],"label_agreement":null},{"id":"W2126865562","doi":"10.1109/icpr.1994.576980","title":"On kernel and radial basis function techniques for classification and function recovering","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Radial basis function; Radial basis function kernel; Kernel (algebra); Convergence (economics); Estimator; Computer science; Variable kernel density estimation; Radial basis function network; Artificial intelligence; Context (archaeology); Kernel method; Function (biology); Rate of convergence; Basis (linear algebra); Polynomial kernel; Mathematics; Mathematical optimization; Applied mathematics; Pattern recognition (psychology); Artificial neural network; Support vector machine; Statistics; Discrete mathematics","score_opus":0.035669391615589054,"score_gpt":0.23435251585498978,"score_spread":0.19868312423940074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126865562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00073941564,0.0040935706,0.9922356,0.00026437512,0.00015032214,0.000025741867,0.000024764684,0.00011499862,0.0023511858],"genre_scores_gemma":[0.055824462,0.01578723,0.91670483,0.00065028365,0.0011064673,0.00029805084,0.00023272203,0.00023061891,0.009165285],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643505,0.0014033408,0.00022391537,0.00042879945,0.0013153361,0.00019358034],"domain_scores_gemma":[0.9969092,0.0016436526,0.0002392302,0.00050809566,0.0006376355,0.00006219831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00529467,0.0018578131,0.0016847188,0.0023188728,0.00068457087,0.0020891312,0.0022813722,0.0034024734,0.002473835],"category_scores_gemma":[0.012115775,0.0007228788,0.0014594571,0.0036014616,0.0032910893,0.0057879766,0.0028029268,0.0043175365,0.00286782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007643615,0.00006726811,0.00023706371,0.00047038184,0.000074618525,0.00018386546,0.00016973245,0.045817543,0.005509679,0.77586126,0.0048377807,0.16669446],"study_design_scores_gemma":[0.0000284366,0.00010934901,0.00038009437,0.0002278325,0.00004830586,0.000483534,0.00003526587,0.32938346,0.0043217614,0.6168383,0.048044298,0.00009936819],"about_ca_topic_score_codex":0.0016287427,"about_ca_topic_score_gemma":0.0011071296,"teacher_disagreement_score":0.00529467,"about_ca_system_score_codex":0.0010489104,"about_ca_system_score_gemma":0.0010115611,"threshold_uncertainty_score":0.02800119},"labels":[],"label_agreement":null},{"id":"W2127108863","doi":"10.1109/ccece.1997.614792","title":"Fast and robust on-line system identification based on multi-layer recurrent neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Recurrent neural network; Identification (biology); Recursive least squares filter; Nonlinear system identification; Nonlinear system; System identification; Time delay neural network; Process (computing); Adaptive system; Forgetting; Artificial intelligence; Algorithm; Control theory (sociology); Data modeling; Adaptive filter; Control (management)","score_opus":0.0738469793201842,"score_gpt":0.26609601924461335,"score_spread":0.19224903992442915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127108863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014874997,0.00023037197,0.9825194,0.000060300892,0.000028277991,0.000021641246,0.000030043057,0.0012499554,0.0009849862],"genre_scores_gemma":[0.77233505,0.00031369601,0.22511894,0.00006029078,0.000039510862,0.00009013438,0.00016270006,0.00012191763,0.0017577575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993662,0.00015275383,0.000054413922,0.00013375827,0.00023365326,0.000059181064],"domain_scores_gemma":[0.99915326,0.00031498642,0.00015510127,0.00017456626,0.0001856158,0.00001658902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008891544,0.0007513218,0.00092344754,0.00038867327,0.00028525293,0.0006805316,0.0008564595,0.0007692178,0.0009667976],"category_scores_gemma":[0.0024657946,0.00041184024,0.00060721126,0.00031709223,0.0003604388,0.001053639,0.0005928811,0.00087903853,0.00046572863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023944992,0.00006754388,0.0007920902,0.00014194801,0.00014806584,0.00019855582,0.00012326671,0.6820361,0.03627474,0.0065812883,0.0013355634,0.27206147],"study_design_scores_gemma":[0.000004737059,0.00001606272,0.000151504,0.0000036318766,0.000008666873,0.000028102506,0.0000019999177,0.9958617,0.0029002733,0.0007687049,0.0002484266,0.000006177329],"about_ca_topic_score_codex":0.003004267,"about_ca_topic_score_gemma":0.0028093823,"teacher_disagreement_score":0.003004267,"about_ca_system_score_codex":0.00035350837,"about_ca_system_score_gemma":0.00040779836,"threshold_uncertainty_score":0.005973518},"labels":[],"label_agreement":null},{"id":"W2127224811","doi":"10.1109/ijcnn.1992.226895","title":"A neural network solution for bipedal gait synthesis","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial neural network; Sagittal plane; Computer science; Set (abstract data type); Feedforward neural network; Artificial intelligence; Robot; Torque; Gait; Control theory (sociology); Control (management); Control engineering; Engineering","score_opus":0.02592575950267554,"score_gpt":0.24907353493860523,"score_spread":0.2231477754359297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127224811","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021245128,0.00045854898,0.9870379,0.0001633185,0.00008422508,0.000038842256,0.00008100202,0.00023889035,0.009772848],"genre_scores_gemma":[0.27587837,0.0013045638,0.68318325,0.00020333815,0.0001363836,0.0006630415,0.00040755083,0.0001977874,0.03802576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988437,0.000019145922,0.00000765578,0.000024269371,0.000052654777,0.000011928118],"domain_scores_gemma":[0.9999151,0.000027732602,0.000008478502,0.000005467418,0.000037587713,0.000005710009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024771516,0.0005637503,0.0004602664,0.00048939785,0.0003753817,0.00062686985,0.0006286971,0.0009966352,0.00679274],"category_scores_gemma":[0.00069687824,0.00037407153,0.00048541694,0.00060543313,0.0003258798,0.00051080977,0.00058656017,0.0008751104,0.001245074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023387998,0.000017877543,0.00011533151,0.00011294547,0.000023208226,0.000069668284,0.000041873765,0.8821516,0.0028103122,0.035162825,0.0025003052,0.076970704],"study_design_scores_gemma":[0.0000064561937,0.0000056737563,0.000030019035,0.0000098008195,0.000002887383,0.000009402544,0.000004442794,0.99001503,0.00022234667,0.007328077,0.0023626531,0.0000032827102],"about_ca_topic_score_codex":0.0061303508,"about_ca_topic_score_gemma":0.0061049093,"teacher_disagreement_score":0.00679274,"about_ca_system_score_codex":0.0006441038,"about_ca_system_score_gemma":0.000927856,"threshold_uncertainty_score":0.022724032},"labels":[],"label_agreement":null},{"id":"W2127423726","doi":"10.1109/iros.1990.262513","title":"Intelligent adaptive identification for control systems: case studies","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Identification (biology); Computer science; Expert system; Lisp; Intelligent decision support system; Control (management); Intelligent control; Artificial intelligence; Programming language","score_opus":0.09409566902786794,"score_gpt":0.3075511154081996,"score_spread":0.2134554463803317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127423726","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7090084,0.017316015,0.15242453,0.0055634305,0.0003106194,0.0015756994,0.0006893608,0.00050640607,0.11260549],"genre_scores_gemma":[0.9433938,0.005467173,0.041903604,0.00033974464,0.000099747005,0.0006402956,0.00021770818,0.0000414943,0.007896336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970253,0.0018356619,0.0001438405,0.00015182678,0.0005503182,0.00029304283],"domain_scores_gemma":[0.99166346,0.0067612375,0.00032255825,0.0004963097,0.00051725085,0.00023923907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033785305,0.0007125723,0.0004982703,0.0012008008,0.0013548738,0.0019614962,0.0019628634,0.0042567654,0.004041942],"category_scores_gemma":[0.010441346,0.00029817442,0.0007708013,0.0022197203,0.0012764874,0.0012628074,0.0016108934,0.0014256828,0.0005008087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014546111,0.005220544,0.025101842,0.0050494084,0.0003698289,0.060970105,0.009415984,0.32978913,0.0041807787,0.18058476,0.036979336,0.34088358],"study_design_scores_gemma":[0.0016195828,0.0043203766,0.025162974,0.0017564694,0.0003812319,0.028386144,0.017661823,0.51936424,0.019503685,0.09390861,0.287629,0.00030587593],"about_ca_topic_score_codex":0.004200981,"about_ca_topic_score_gemma":0.005591651,"teacher_disagreement_score":0.0042567654,"about_ca_system_score_codex":0.0012917473,"about_ca_system_score_gemma":0.0005912993,"threshold_uncertainty_score":0.017867565},"labels":[],"label_agreement":null},{"id":"W2127659476","doi":"10.1109/icassp.2005.1416324","title":"EMG Signal Decomposition by Loopy Belief Propagation","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Northeastern States Research Cooperative; McGill University","keywords":"Belief propagation; Factor graph; SIGNAL (programming language); Computer science; Decomposition; Graph; Graphical model; Algorithm; Speech recognition; Signal processing; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science; Telecommunications","score_opus":0.005164890963461438,"score_gpt":0.2280575948860462,"score_spread":0.22289270392258478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127659476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002512635,0.000049881775,0.9970721,0.000034849036,0.000007402698,0.000009640621,0.000007608082,0.00014594484,0.00015992258],"genre_scores_gemma":[0.33733478,0.00026565086,0.6595073,0.000130243,0.00007456714,0.00017174247,0.00018048355,0.00013439992,0.002200842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999151,0.00025111198,0.000045715366,0.00020368275,0.00025684052,0.000091671485],"domain_scores_gemma":[0.997288,0.0019268984,0.0001929493,0.00014800315,0.00037654515,0.00006768863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016363631,0.0010835795,0.0010504513,0.00089092547,0.00041829917,0.0011590343,0.0012534669,0.0015939699,0.0019509391],"category_scores_gemma":[0.007485479,0.00070554344,0.00084316276,0.0010316549,0.000982656,0.0021405811,0.001240227,0.0017532102,0.00056170864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019131642,0.00004991701,0.00042099145,0.00007899016,0.00008415759,0.00006141233,0.00010995056,0.8222096,0.0053766584,0.012850466,0.000862554,0.15770404],"study_design_scores_gemma":[0.0000055292207,0.000012031842,0.000029336012,0.0000019990116,0.0000041887756,0.000006677169,0.0000020908624,0.9947955,0.0004903262,0.0045330143,0.00011598446,0.000003344013],"about_ca_topic_score_codex":0.005488616,"about_ca_topic_score_gemma":0.0037633665,"teacher_disagreement_score":0.005488616,"about_ca_system_score_codex":0.00082019344,"about_ca_system_score_gemma":0.0008499576,"threshold_uncertainty_score":0.010913312},"labels":[],"label_agreement":null},{"id":"W2128236145","doi":"10.1109/icassp.2004.1326365","title":"Modified local discriminant bases and its applications in signal classification [biomedical signal examples]","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Western University","funders":"","keywords":"Orthonormal basis; SIGNAL (programming language); Set (abstract data type); Computer science; Discriminant; Pruning; Pattern recognition (psychology); Basis (linear algebra); Basis function; Artificial intelligence; Selection (genetic algorithm); Linear discriminant analysis; Measure (data warehouse); Data mining; Machine learning; Mathematics","score_opus":0.046699082984084275,"score_gpt":0.2722544081108822,"score_spread":0.22555532512679793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128236145","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014891153,0.009788683,0.97030324,0.00068256323,0.00021065431,0.00005122968,0.000119004704,0.00046645352,0.0034870433],"genre_scores_gemma":[0.19146639,0.007927004,0.7945947,0.00023432873,0.0004626811,0.00015143446,0.00036297872,0.00013159927,0.004668896],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897003,0.0003353938,0.000067330606,0.0001503839,0.00044206314,0.000034850633],"domain_scores_gemma":[0.9982687,0.0009517831,0.00012978334,0.00019889988,0.00040042246,0.000050275237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014718798,0.0006252001,0.00095693144,0.0023210612,0.00033688342,0.0009482467,0.00075886596,0.0009554775,0.002199911],"category_scores_gemma":[0.0052850703,0.00023530604,0.0005550999,0.0038809374,0.00091307465,0.0008784086,0.0006906899,0.0010005373,0.0010903983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002993169,0.00009919304,0.0014735057,0.0006496748,0.000116703995,0.00024678966,0.00018814433,0.0735843,0.024776302,0.056958575,0.00699177,0.8346157],"study_design_scores_gemma":[0.000043925484,0.00019677046,0.0032159751,0.00013545649,0.00008487441,0.00082539686,0.00007047041,0.86476654,0.014605087,0.08582769,0.030150954,0.00007692814],"about_ca_topic_score_codex":0.00079205143,"about_ca_topic_score_gemma":0.00063665735,"teacher_disagreement_score":0.0023210612,"about_ca_system_score_codex":0.0003806409,"about_ca_system_score_gemma":0.00030505736,"threshold_uncertainty_score":0.007784128},"labels":[],"label_agreement":null},{"id":"W2128268297","doi":"10.1186/1471-2202-8-s2-p199","title":"Biological plausibility of kernel-based learning","year":2007,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Kernel (algebra); Artificial intelligence; Artificial neural network; Feature (linguistics); Nonlinear system; Linear model; Process (computing); Computation; Machine learning; Algorithm; Mathematics","score_opus":0.06555577376161884,"score_gpt":0.3023593596242143,"score_spread":0.23680358586259545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128268297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045297123,0.0024397792,0.9098013,0.014215366,0.0003589593,0.000037412516,0.0001894305,0.0009820447,0.026678588],"genre_scores_gemma":[0.92240626,0.00091215933,0.06886265,0.001112013,0.000336029,0.00007512895,0.00017160064,0.00016402193,0.005960109],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99791604,0.00072983187,0.00009672851,0.00055251207,0.00049870956,0.00020616512],"domain_scores_gemma":[0.9923165,0.0041041477,0.0005411994,0.0018664642,0.0009018468,0.0002698687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004042017,0.0006882072,0.0007147306,0.00083701266,0.0009133362,0.0031525192,0.0026062997,0.003123013,0.0044517005],"category_scores_gemma":[0.019971877,0.00036727238,0.0007922608,0.00050249306,0.0068344437,0.0066511235,0.002643655,0.0031552794,0.0014145303],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007764787,0.000039335133,0.0012519249,0.000115689036,0.000042252548,0.00012893902,0.0001967661,0.043216843,0.0015557592,0.9268153,0.0018330459,0.02472661],"study_design_scores_gemma":[0.000018757217,0.000046485035,0.0003889604,0.000027420547,0.000009598146,0.00015447824,0.000042154665,0.117104925,0.0018790002,0.8757873,0.004518001,0.000022903254],"about_ca_topic_score_codex":0.0010804855,"about_ca_topic_score_gemma":0.00033219802,"teacher_disagreement_score":0.0044517005,"about_ca_system_score_codex":0.0012460912,"about_ca_system_score_gemma":0.00072316057,"threshold_uncertainty_score":0.02137649},"labels":[],"label_agreement":null},{"id":"W2128640226","doi":"10.5555/2429759.2430068","title":"Allocation of simulation effort for neural network vs. regression metamodels","year":2012,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Metamodeling; Computer science; Artificial neural network; Point (geometry); Regression; Sample (material); Regression analysis; Machine learning; Statistics; Mathematics","score_opus":0.06712412315872954,"score_gpt":0.33184821505090945,"score_spread":0.26472409189217994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128640226","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10620313,0.00015771032,0.88594985,0.0004795066,0.00004191648,0.00024370203,0.00012221943,0.0005542659,0.006247787],"genre_scores_gemma":[0.7793919,0.00016377162,0.21771523,0.00011894112,0.000014727011,0.00056250585,0.00015312564,0.00020416216,0.0016755513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99679095,0.0021692377,0.00018146823,0.00031522653,0.0003563669,0.00018674682],"domain_scores_gemma":[0.9908817,0.0058985106,0.0006500625,0.0017080427,0.00065628224,0.00020541236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060755922,0.0007139365,0.0006756719,0.00077401823,0.0003257795,0.0009946006,0.0012732168,0.0008714852,0.0037843464],"category_scores_gemma":[0.029176285,0.00042953962,0.00061667,0.00064401515,0.00049743155,0.0018266992,0.0013295498,0.0010958272,0.00042112204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003579803,0.00011831157,0.0014010724,0.00007040192,0.00004105094,0.000031012627,0.0000820009,0.9405433,0.0023547418,0.019602438,0.00025620355,0.03514146],"study_design_scores_gemma":[0.000029686027,0.00006444508,0.0003087973,0.000016453747,0.000013412249,0.000008406373,0.000022832985,0.99027926,0.0014830392,0.007112851,0.00065348635,0.000007332812],"about_ca_topic_score_codex":0.0021169097,"about_ca_topic_score_gemma":0.0028013173,"teacher_disagreement_score":0.0060755922,"about_ca_system_score_codex":0.0014951911,"about_ca_system_score_gemma":0.0011659458,"threshold_uncertainty_score":0.032131195},"labels":[],"label_agreement":null},{"id":"W2128822792","doi":"10.1186/1471-2202-8-s2-p205","title":"Unsupervised learning is crucial to learning the names of objects","year":2007,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Unsupervised learning; Discriminative model; Machine learning; Artificial neural network; Supervised learning; Competitive learning; Exploit; Pattern recognition (psychology)","score_opus":0.027733473813780267,"score_gpt":0.28058688872664234,"score_spread":0.25285341491286206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128822792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1454471,0.0013196688,0.8279369,0.0035821842,0.00030814388,0.00005805889,0.00053716084,0.0015443508,0.019266434],"genre_scores_gemma":[0.7159395,0.001852298,0.26940036,0.0006359695,0.00017872787,0.0000861482,0.0011132606,0.00041655783,0.010377181],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991166,0.0002067315,0.000049525926,0.00040923432,0.00017704011,0.000040938598],"domain_scores_gemma":[0.99622595,0.0016572843,0.0008485404,0.0007039016,0.00040487177,0.00015954036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072054856,0.00039795082,0.00034842698,0.00036109367,0.0006146254,0.0015560337,0.0010378071,0.0011428936,0.0028658626],"category_scores_gemma":[0.007324266,0.0005368523,0.0005783436,0.0004927055,0.0025737516,0.0051516695,0.0010530325,0.0017598086,0.0020455897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021860364,0.00019518848,0.02604239,0.0006005878,0.00019302599,0.00051638996,0.001618532,0.025399802,0.11265279,0.23912627,0.012138504,0.58129793],"study_design_scores_gemma":[0.000026681333,0.00019530825,0.025104038,0.000108648404,0.0000672132,0.0012876954,0.00057474297,0.12320685,0.06530727,0.7411724,0.042796917,0.00015225165],"about_ca_topic_score_codex":0.00085120986,"about_ca_topic_score_gemma":0.0014708723,"teacher_disagreement_score":0.0028658626,"about_ca_system_score_codex":0.00040710517,"about_ca_system_score_gemma":0.00059943757,"threshold_uncertainty_score":0.009587228},"labels":[],"label_agreement":null},{"id":"W2128948883","doi":"10.1109/fuzzy.1995.409881","title":"Paralysis free fast learning: smart neural nets","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Types of artificial neural networks; Feedforward neural network; Backpropagation; Time delay neural network; Machine learning","score_opus":0.021366306931060487,"score_gpt":0.21577748632718022,"score_spread":0.19441117939611974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128948883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005551761,0.00047086817,0.98868644,0.00017649728,0.00010452731,0.000037130932,0.0000794609,0.0013097834,0.003583555],"genre_scores_gemma":[0.43323004,0.0013558895,0.5452456,0.0007731987,0.00017940799,0.00029171875,0.00056862284,0.00036953104,0.01798599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996451,0.00007257565,0.000026525011,0.00006261873,0.0001452343,0.000047848218],"domain_scores_gemma":[0.99911004,0.00036664557,0.000087295295,0.00015540831,0.00024084943,0.00003978253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008404852,0.0007505382,0.00067003083,0.00038948838,0.00031713335,0.0009560985,0.001323049,0.0011983647,0.00447238],"category_scores_gemma":[0.0026164078,0.00049217953,0.00043874973,0.00054681697,0.0008390077,0.0020388656,0.0010959907,0.0014734609,0.0015190525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017216295,0.000056446694,0.000600359,0.0002055757,0.00006815891,0.00014218847,0.000060045604,0.6252146,0.006156429,0.06836219,0.0073933043,0.29156858],"study_design_scores_gemma":[0.00000972634,0.000027746608,0.00007836302,0.000014097658,0.000008058054,0.00003399044,0.0000046161067,0.9730123,0.0019032429,0.022287505,0.0026116625,0.000008630935],"about_ca_topic_score_codex":0.0021770378,"about_ca_topic_score_gemma":0.0029521019,"teacher_disagreement_score":0.00447238,"about_ca_system_score_codex":0.0006574199,"about_ca_system_score_gemma":0.0006941563,"threshold_uncertainty_score":0.0149616},"labels":[],"label_agreement":null},{"id":"W2129160118","doi":"10.1109/ccece.2000.849560","title":"Isolated digit recognition using a block diagonal recurrent neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Recurrent neural network; TIMIT; Time delay neural network; Diagonal; Block (permutation group theory); Speech recognition; Artificial neural network; Feedforward neural network; Pattern recognition (psychology); Multilayer perceptron; Feed forward; Artificial intelligence; Mathematics; Hidden Markov model; Engineering","score_opus":0.0628771188617158,"score_gpt":0.25082774848954775,"score_spread":0.18795062962783193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129160118","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14376502,0.0006598398,0.8484103,0.00014369782,0.00013711769,0.00005088682,0.00021799252,0.0027966648,0.0038184468],"genre_scores_gemma":[0.74284786,0.0003162456,0.2509665,0.00009731509,0.00003721633,0.00004205588,0.00037018722,0.000054197903,0.005268451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980015,0.000048047797,0.000014298006,0.000055369223,0.000058654703,0.000023499782],"domain_scores_gemma":[0.9997073,0.000104437204,0.000038479266,0.000039875504,0.000095684634,0.000014178415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003669077,0.00030327422,0.00032677673,0.00027332111,0.00011165895,0.00037474508,0.00048679058,0.00035153478,0.0015918715],"category_scores_gemma":[0.000955578,0.00016059664,0.0002779557,0.0002510369,0.00015573298,0.0004900355,0.00023659217,0.00034201096,0.00076898845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000563852,0.00014834342,0.002062972,0.00018775825,0.00012700894,0.00035075253,0.00009531246,0.13839462,0.17745261,0.0048144585,0.0025789288,0.6732234],"study_design_scores_gemma":[0.00001211371,0.00012614965,0.00095592765,0.0000079748725,0.00003461128,0.0001280612,0.000009924207,0.97412145,0.022581529,0.0010206449,0.0009872017,0.000014379365],"about_ca_topic_score_codex":0.0018448451,"about_ca_topic_score_gemma":0.0032326533,"teacher_disagreement_score":0.0018448451,"about_ca_system_score_codex":0.00019996581,"about_ca_system_score_gemma":0.00022837534,"threshold_uncertainty_score":0.0053253174},"labels":[],"label_agreement":null},{"id":"W2129518077","doi":"10.1109/tsmca.2005.853498","title":"Constructing a model hierarchy with background knowledge for structural risk minimization: application to biological treatment of wastewater","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Toronto Metropolitan University","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Black box; Empirical risk minimization; Structural risk minimization; Hierarchy; Feature (linguistics); Artificial neural network; Statistical learning theory; Statistical model; Minification; Support vector machine","score_opus":0.032361111997653735,"score_gpt":0.2569077479966901,"score_spread":0.22454663599903635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129518077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012280039,0.00017405904,0.9853264,0.00026246737,0.000012265407,0.000031571642,0.000018722536,0.00015589359,0.0017386627],"genre_scores_gemma":[0.3901028,0.0005180406,0.60496026,0.00019143263,0.00008651781,0.00024218237,0.00017104587,0.00019876745,0.0035289405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992436,0.00035327536,0.000031765867,0.00010815882,0.00020021008,0.00006289738],"domain_scores_gemma":[0.9983853,0.0010862475,0.00015019142,0.00010583978,0.00018861829,0.00008372209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022682801,0.00085245416,0.001563828,0.0009242002,0.001022832,0.0015401659,0.0014059505,0.0019867094,0.0018693713],"category_scores_gemma":[0.005452873,0.00066839304,0.0015026282,0.0006745945,0.0012068995,0.0016302903,0.0030566133,0.002393781,0.000396797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004072994,0.000091163114,0.00047819933,0.0001354376,0.000037794765,0.00014765741,0.00026046002,0.8546589,0.0032940905,0.10012225,0.00060925656,0.040124025],"study_design_scores_gemma":[0.0000037485875,0.000020441577,0.00003399395,0.000006659334,0.0000035318849,0.000007691169,0.000012598472,0.9771721,0.00022854596,0.02202018,0.00048523193,0.0000053906665],"about_ca_topic_score_codex":0.0050819316,"about_ca_topic_score_gemma":0.0041448204,"teacher_disagreement_score":0.0050819316,"about_ca_system_score_codex":0.0016315761,"about_ca_system_score_gemma":0.0016833432,"threshold_uncertainty_score":0.011995971},"labels":[],"label_agreement":null},{"id":"W2130005880","doi":"10.1109/ccece.1996.548223","title":"On-line identification of synchronous generator using neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Identifier; Computer science; Artificial neural network; Generator (circuit theory); Identification (biology); Feed forward; Nonlinear system; Control theory (sociology); Complement (music); Feedforward neural network; Adaptive control; Line (geometry); Permanent magnet synchronous generator; Control engineering; Artificial intelligence; Control (management); Engineering; Mathematics; Power (physics)","score_opus":0.040670944483142894,"score_gpt":0.2654515941931806,"score_spread":0.2247806497100377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130005880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036976706,0.00013442003,0.95991415,0.00007011009,0.000029193085,0.000026155538,0.000027076907,0.0005375207,0.002284696],"genre_scores_gemma":[0.9026227,0.00019095367,0.093448326,0.000046046698,0.000019577948,0.00006941415,0.00007422299,0.000048966132,0.003479837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998073,0.00005264826,0.000010962511,0.000047718397,0.0000622437,0.000019165855],"domain_scores_gemma":[0.9996749,0.00012274651,0.00006851224,0.00004000371,0.000083602616,0.000010210976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003510778,0.00043140157,0.00035111877,0.0002978744,0.0002381304,0.00037015573,0.0003903232,0.0005445957,0.0008932005],"category_scores_gemma":[0.0012647784,0.00016238935,0.000191685,0.00025725135,0.00029207315,0.00067738816,0.00039414046,0.0004454521,0.00028913937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018087654,0.00005023326,0.0016123162,0.00013276619,0.000034644174,0.00024727135,0.00015291762,0.7118768,0.033245977,0.00915347,0.0010836154,0.24222916],"study_design_scores_gemma":[0.000004382407,0.000016897111,0.00029165077,0.0000050112367,0.000003706631,0.000033144705,0.000005663403,0.9934822,0.0039188545,0.0017345696,0.000499781,0.0000040625528],"about_ca_topic_score_codex":0.0020459206,"about_ca_topic_score_gemma":0.0023164735,"teacher_disagreement_score":0.0020459206,"about_ca_system_score_codex":0.00027936057,"about_ca_system_score_gemma":0.00032807904,"threshold_uncertainty_score":0.004068017},"labels":[],"label_agreement":null},{"id":"W2130036064","doi":"10.1061/(asce)0887-3801(2000)14:1(9)","title":"Artificial Neural Network for Measuring Organizational Effectiveness","year":2000,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Operationalization; Flexibility (engineering); Computer science; Artificial intelligence; Context (archaeology); Set (abstract data type); Machine learning; Data mining; Operations research; Industrial engineering; Engineering; Mathematics; Statistics","score_opus":0.01324290052618696,"score_gpt":0.22334173086201706,"score_spread":0.2100988303358301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130036064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2402105,0.0020238613,0.7349754,0.00061486335,0.00029774915,0.00076107815,0.0032640265,0.0011205095,0.016732037],"genre_scores_gemma":[0.7899315,0.000924225,0.2022703,0.00013820546,0.000052583346,0.0013083989,0.0015868616,0.000027275928,0.0037607444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987645,0.00043902695,0.000124015,0.00014586211,0.000453726,0.00007276095],"domain_scores_gemma":[0.99821496,0.0009863938,0.00024753506,0.00008825923,0.0004197446,0.000043239463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018257926,0.00074069225,0.00060204446,0.0016851239,0.00026069125,0.0008622516,0.0006921689,0.0009511667,0.001758238],"category_scores_gemma":[0.007664667,0.00016749183,0.00037437872,0.0025383702,0.00025956874,0.0010067971,0.00046820307,0.0008785232,0.00039400713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055250863,0.0007074326,0.05884775,0.0006728285,0.00043928696,0.00024116492,0.00032086245,0.42845076,0.007421969,0.020357236,0.0065348158,0.4754534],"study_design_scores_gemma":[0.000026169859,0.0003057745,0.022447772,0.000101991005,0.000058477588,0.00007676199,0.0001292144,0.9633624,0.003366363,0.006542534,0.0035392863,0.000043304026],"about_ca_topic_score_codex":0.0033278817,"about_ca_topic_score_gemma":0.0025757453,"teacher_disagreement_score":0.0033278817,"about_ca_system_score_codex":0.00086292427,"about_ca_system_score_gemma":0.00060206425,"threshold_uncertainty_score":0.009655833},"labels":[],"label_agreement":null},{"id":"W2130515936","doi":"10.1109/icsmc.1995.538345","title":"Network analyses: the case of first and second person pronouns","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Referent; Computer science; Feedforward neural network; Net (polyhedron); Function (biology); Limit (mathematics); Nonlinear system; Artificial intelligence; Feed forward; Artificial neural network; Cascade; Algorithm; Theoretical computer science; Mathematics; Linguistics","score_opus":0.06120682144104664,"score_gpt":0.2697112761450614,"score_spread":0.20850445470401477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130515936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28091446,0.0014375639,0.6486092,0.0025608954,0.00013028475,0.00005549832,0.00019663687,0.000601683,0.065493785],"genre_scores_gemma":[0.93175954,0.00038104577,0.055557273,0.0000990858,0.000070650865,0.000027831855,0.00009702579,0.00008153499,0.011926008],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99925965,0.00026006662,0.000022011567,0.00016917873,0.00021142262,0.00007757254],"domain_scores_gemma":[0.9970782,0.0018455624,0.00031025746,0.0002972986,0.00039657106,0.00007229035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013168193,0.00030845273,0.00036949842,0.0010938066,0.0008726286,0.0012474873,0.00095295504,0.0014560533,0.005601937],"category_scores_gemma":[0.012913694,0.00027364443,0.0005240733,0.0011693893,0.0014914967,0.0035400956,0.0008647042,0.00095582026,0.00043041533],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001430771,0.000024734558,0.0049946243,0.00011650516,0.000047546386,0.001307906,0.0015013218,0.100180775,0.003446406,0.8254917,0.0027900971,0.059955288],"study_design_scores_gemma":[0.000007860447,0.000016847418,0.002107794,0.00001883445,0.000017792043,0.0003276678,0.00026728844,0.7057295,0.00212364,0.28244686,0.0069180485,0.00001794856],"about_ca_topic_score_codex":0.006695413,"about_ca_topic_score_gemma":0.005355584,"teacher_disagreement_score":0.006695413,"about_ca_system_score_codex":0.0014153698,"about_ca_system_score_gemma":0.000502416,"threshold_uncertainty_score":0.018740356},"labels":[],"label_agreement":null},{"id":"W2130539867","doi":"","title":"Generalization Error and Algorithmic Convergence of Median Boosting","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Boosting (machine learning); AdaBoost; Margin (machine learning); Generalization; Generalization error; Computer science; Confidence interval; Algorithm; Extension (predicate logic); Artificial intelligence; Convergence (economics); Mathematics; Pattern recognition (psychology); Machine learning; Statistics; Support vector machine; Artificial neural network","score_opus":0.015165389621429853,"score_gpt":0.2459375295064859,"score_spread":0.23077213988505604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130539867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017739031,0.0007736372,0.97811615,0.00048713273,0.000044699573,0.000027317084,0.000029855384,0.00022134342,0.0025608488],"genre_scores_gemma":[0.7176809,0.0013470324,0.27497897,0.00063654035,0.00032608258,0.00032870183,0.0002718433,0.00038485508,0.0040451344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99568164,0.0018078765,0.00017327952,0.000712258,0.0013435708,0.00028133875],"domain_scores_gemma":[0.9715385,0.020491188,0.0017738737,0.0021317902,0.0036877263,0.00037701573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013206537,0.0008876387,0.0016736973,0.001377398,0.00080315245,0.0016113251,0.0017758438,0.001735161,0.0017265511],"category_scores_gemma":[0.05784245,0.0006382497,0.00094371225,0.0010676088,0.00321949,0.0031810603,0.0034254,0.0031558974,0.00044823866],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022676497,0.00005669162,0.0028508513,0.00016795266,0.00012553282,0.00009642521,0.00019509604,0.70752513,0.0026604503,0.21514659,0.0018768728,0.06907172],"study_design_scores_gemma":[0.000017468547,0.000057666137,0.00052591745,0.000034722434,0.000014492627,0.000049128415,0.000010635826,0.8929916,0.0013278641,0.104112595,0.00084461825,0.000013249293],"about_ca_topic_score_codex":0.0010772122,"about_ca_topic_score_gemma":0.00081279984,"teacher_disagreement_score":0.013206537,"about_ca_system_score_codex":0.0015200126,"about_ca_system_score_gemma":0.001113271,"threshold_uncertainty_score":0.06984371},"labels":[],"label_agreement":null},{"id":"W2130602610","doi":"10.4018/jcini.2007100103","title":"A Unified Approach To Fractal Dimensions","year":2007,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Fractal; Computer science; Fractal dimension; Fractal analysis; Fractal transform; Fractal dimension on networks; Cellular automaton; Theoretical computer science; Entropy (arrow of time); Algorithm; Statistical physics; Pattern recognition (psychology); Artificial intelligence; Mathematics; Image processing; Image compression; Image (mathematics); Mathematical analysis","score_opus":0.019964325438512617,"score_gpt":0.3045899903557838,"score_spread":0.2846256649172712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130602610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016287468,0.02509327,0.8992861,0.00546808,0.0010039979,0.0000945379,0.00027385805,0.0002610965,0.052231554],"genre_scores_gemma":[0.36495003,0.024892332,0.5933241,0.00153465,0.0032242783,0.00053029455,0.0004036179,0.00016751351,0.010973256],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982462,0.0005266583,0.00017175199,0.00035355572,0.00058086216,0.00012102865],"domain_scores_gemma":[0.99793977,0.00094011816,0.00016719724,0.0003089688,0.0005557526,0.00008813222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024291778,0.0008955854,0.0010681337,0.0052272473,0.0013088344,0.004336642,0.0012436723,0.0014456483,0.0016239369],"category_scores_gemma":[0.0050727925,0.00038348165,0.0010831666,0.0025544236,0.005818837,0.0067770686,0.0025755982,0.0032179202,0.0005153944],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032034775,0.000003809938,0.00009360658,0.00004595525,0.00000612298,0.00002220685,0.00016740987,0.0020016362,0.00035236642,0.98396724,0.0007920491,0.0125444615],"study_design_scores_gemma":[0.0000034798668,0.000012182308,0.00019012624,0.00004495419,0.000004655169,0.000076422264,0.00006444078,0.0075185844,0.00018077793,0.970696,0.021191364,0.000017128015],"about_ca_topic_score_codex":0.00074279134,"about_ca_topic_score_gemma":0.00055235653,"teacher_disagreement_score":0.0052272473,"about_ca_system_score_codex":0.0019120086,"about_ca_system_score_gemma":0.00090517045,"threshold_uncertainty_score":0.013872683},"labels":[],"label_agreement":null},{"id":"W2131726112","doi":"10.1109/icnn.1997.611676","title":"Incorporating functional knowledge into neural networks","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial neural network; Generalization; Embedding; Artificial intelligence; Nonlinear system; Knowledge engineering; Reliability (semiconductor); Machine learning; Mathematics","score_opus":0.05726490680278796,"score_gpt":0.26580692322463845,"score_spread":0.2085420164218505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131726112","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007649423,0.00056558545,0.986896,0.00031690515,0.000054573073,0.000016353197,0.000034649645,0.00014694499,0.0043195793],"genre_scores_gemma":[0.7182738,0.001924259,0.27274987,0.00030465992,0.00021367588,0.00010491573,0.00015816517,0.000082869345,0.0061878804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967146,0.00012085253,0.000018907309,0.00006198712,0.00010201339,0.000024780107],"domain_scores_gemma":[0.99913543,0.0005415107,0.00006777917,0.00009505934,0.00013472134,0.000025543648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009954552,0.0005308239,0.00042651023,0.00047989818,0.00026000838,0.0008311824,0.0008323844,0.0011616998,0.0016053697],"category_scores_gemma":[0.0041831178,0.00030886647,0.00038297076,0.00048984383,0.0008341355,0.0022842654,0.0010885104,0.0010257317,0.00037760462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028658667,0.000028270539,0.00037398044,0.000083131614,0.00003869649,0.00007837822,0.000051940784,0.8269491,0.002045327,0.077793434,0.00075844623,0.09177063],"study_design_scores_gemma":[0.0000018236495,0.000014205787,0.00006996208,0.000009626298,0.000005672187,0.000015289615,0.0000031338209,0.95522857,0.00040710918,0.0431747,0.0010647703,0.000005229736],"about_ca_topic_score_codex":0.002335978,"about_ca_topic_score_gemma":0.0027195094,"teacher_disagreement_score":0.002335978,"about_ca_system_score_codex":0.00049989775,"about_ca_system_score_gemma":0.00040988676,"threshold_uncertainty_score":0.0053704977},"labels":[],"label_agreement":null},{"id":"W2131960614","doi":"10.1109/ijcnn.2000.861390","title":"An easily calculated bound on condition for orthogonal algorithms","year":2000,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Cholesky decomposition; Algorithm; Diagonal; Matrix (chemical analysis); Limit (mathematics); Orthogonal functions; Orthogonal matrix; Upper and lower bounds; Function (biology); Symmetric matrix; Computer science; Mathematics; Orthogonal basis; Applied mathematics; Diagonal matrix; Eigenvalues and eigenvectors; Mathematical analysis","score_opus":0.018410745046034484,"score_gpt":0.2997235660553022,"score_spread":0.28131282100926774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131960614","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01269619,0.0025037704,0.90954393,0.0032760613,0.00096191146,0.00016726389,0.0005707426,0.00086684927,0.06941332],"genre_scores_gemma":[0.31752703,0.004852613,0.6430398,0.0029963243,0.0015971772,0.001604383,0.0014355178,0.0024890152,0.024458064],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99445146,0.0019001903,0.00036669555,0.0010055783,0.001786776,0.0004892998],"domain_scores_gemma":[0.94614685,0.042834867,0.0015493825,0.003314323,0.0051205065,0.0010340853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006634947,0.0025524888,0.0013639054,0.0018961582,0.0013756087,0.0027700171,0.0023156947,0.0025773628,0.025422521],"category_scores_gemma":[0.07956195,0.0006726374,0.0009814537,0.0016361292,0.003950321,0.009302209,0.005211306,0.008134937,0.00626042],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000291531,0.0001356062,0.0009991734,0.00042559736,0.000026109812,0.00021878687,0.00024753643,0.022955876,0.014431143,0.85461766,0.016785745,0.08886527],"study_design_scores_gemma":[0.00004877157,0.00018792546,0.0008408792,0.00044641749,0.000037788122,0.0004150926,0.00013998289,0.32533264,0.018831676,0.63057715,0.023015408,0.00012629623],"about_ca_topic_score_codex":0.0009172187,"about_ca_topic_score_gemma":0.0012092377,"teacher_disagreement_score":0.025422521,"about_ca_system_score_codex":0.0022095302,"about_ca_system_score_gemma":0.001989812,"threshold_uncertainty_score":0.08504677},"labels":[],"label_agreement":null},{"id":"W2131990751","doi":"10.1109/icnn.1997.611662","title":"On the properties of periodic perceptrons","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Perceptron; Sigmoid function; Benchmark (surveying); Computer science; Artificial intelligence; Activation function; Multilayer perceptron; Pattern recognition (psychology); Function (biology); Power (physics); Basis (linear algebra); Artificial neural network; Machine learning; Algorithm; Mathematics","score_opus":0.07086277701121951,"score_gpt":0.2449313435306359,"score_spread":0.17406856651941638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131990751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14170368,0.0030433019,0.82244426,0.0019537536,0.00032321145,0.00007776778,0.00029916927,0.0007639712,0.029390994],"genre_scores_gemma":[0.9316936,0.0018962604,0.05855985,0.00041691182,0.0004152595,0.000115922914,0.0002493436,0.00020105741,0.0064517013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99895954,0.0003180729,0.00007251369,0.0002382765,0.00029352258,0.000118038806],"domain_scores_gemma":[0.9879628,0.008948579,0.0009483966,0.0011304831,0.0007451977,0.00026462832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002765921,0.0005823011,0.00084603793,0.00097450975,0.00070309883,0.0017986431,0.0011356376,0.001300478,0.004391703],"category_scores_gemma":[0.021629442,0.00063542434,0.0008238484,0.00076528604,0.002416982,0.004334886,0.0013257401,0.0020883936,0.00059413584],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035407138,0.000054121538,0.0026059789,0.00033134053,0.00010071459,0.0005108111,0.00029554515,0.23732899,0.007756213,0.68218184,0.0025110235,0.06596939],"study_design_scores_gemma":[0.000021567812,0.00008580364,0.00066691934,0.000048303318,0.000021486945,0.00022545835,0.000026026642,0.5272631,0.0013527004,0.46873614,0.001533845,0.00001859533],"about_ca_topic_score_codex":0.0010695272,"about_ca_topic_score_gemma":0.0006220707,"teacher_disagreement_score":0.004391703,"about_ca_system_score_codex":0.00065588823,"about_ca_system_score_gemma":0.00054514466,"threshold_uncertainty_score":0.0146917105},"labels":[],"label_agreement":null},{"id":"W2132248566","doi":"10.1109/mwscas.2000.952888","title":"Supervised fuzzy inference network for invariant pattern recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Adaptive neuro fuzzy inference system; Pattern recognition (psychology); Artificial intelligence; Inference; Computer science; Feature extraction; Fuzzy logic; Invariant (physics); Fuzzy control system; Mathematics","score_opus":0.06860867751210871,"score_gpt":0.25451407871229564,"score_spread":0.18590540120018695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132248566","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044062175,0.0002091751,0.9934743,0.000044556848,0.0000346334,0.000024305229,0.00004168583,0.00036796968,0.001397075],"genre_scores_gemma":[0.49691495,0.00055126054,0.4940958,0.00015824183,0.0001037056,0.0003151667,0.0005005026,0.00008804679,0.007272269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995333,0.00010878746,0.000025473037,0.00012451591,0.00017567028,0.00003233301],"domain_scores_gemma":[0.99940765,0.00026394412,0.000048565435,0.00006619598,0.0001985797,0.000015039375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007800828,0.000440065,0.0006551738,0.00047604326,0.0002993063,0.00057414034,0.0008265781,0.00064260437,0.0022028082],"category_scores_gemma":[0.0019044989,0.00020903052,0.00050581584,0.0004622344,0.00039086535,0.0008516723,0.00040193676,0.0009259343,0.0005916297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016143682,0.00009726898,0.0008783528,0.00015967218,0.000102324666,0.00015065947,0.00008609184,0.5664071,0.009548949,0.028575383,0.0032944153,0.39053833],"study_design_scores_gemma":[0.0000044920857,0.000015301133,0.00009999587,0.0000037097034,0.0000072844527,0.000021164475,0.0000027470282,0.9936753,0.0008740422,0.0043446524,0.0009466282,0.000004664407],"about_ca_topic_score_codex":0.0034590159,"about_ca_topic_score_gemma":0.0036934584,"teacher_disagreement_score":0.0034590159,"about_ca_system_score_codex":0.00054171204,"about_ca_system_score_gemma":0.00079610007,"threshold_uncertainty_score":0.007369101},"labels":[],"label_agreement":null},{"id":"W2132608063","doi":"10.1007/11539087_15","title":"A Game-Theoretic Approach to Competitive Learning in Self-Organizing Maps","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Self-organizing map; Cluster analysis; Competitive learning; Artificial intelligence; Process (computing); Quality (philosophy); Machine learning; Game theory; Feature (linguistics); Data mining; Unsupervised learning; Mathematics","score_opus":0.010786045639498002,"score_gpt":0.22288940402546117,"score_spread":0.21210335838596317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132608063","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011246593,0.0017605353,0.948784,0.0020356125,0.00020091116,0.00009267009,0.00010449729,0.0000844369,0.03569083],"genre_scores_gemma":[0.7666767,0.0031267502,0.20486273,0.00069925364,0.0007509065,0.0005412798,0.00014509502,0.00009929691,0.023097979],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984744,0.00089039904,0.00005724625,0.0001386313,0.00031408275,0.0001252329],"domain_scores_gemma":[0.99768746,0.0017583268,0.000102578626,0.000089176436,0.00021567871,0.00014691972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001998147,0.0010592343,0.0016264074,0.000993787,0.0011610544,0.0034833632,0.0037679435,0.0028487407,0.006426321],"category_scores_gemma":[0.0060806507,0.0006166247,0.0012701893,0.0015767614,0.0035713473,0.003950111,0.0022515291,0.0024594748,0.0005615039],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009785232,0.000026380447,0.00007713273,0.00006531709,0.000024600726,0.000027913411,0.00007497079,0.06234315,0.00015776802,0.930473,0.001383002,0.005336893],"study_design_scores_gemma":[0.000012241903,0.00001601122,0.000042030148,0.000012088968,0.000008360124,0.000017718614,0.000025555108,0.22690517,0.00005155307,0.7714165,0.0014825148,0.000010281687],"about_ca_topic_score_codex":0.0037726113,"about_ca_topic_score_gemma":0.0036302044,"teacher_disagreement_score":0.006426321,"about_ca_system_score_codex":0.0024804354,"about_ca_system_score_gemma":0.0016504594,"threshold_uncertainty_score":0.021498203},"labels":[],"label_agreement":null},{"id":"W2132656519","doi":"10.1109/scima.2003.1215928","title":"Neural network model for wastewater treatment plant control","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministère de la Transition écologique et Solidaire","keywords":"Artificial neural network; Sewage treatment; Computer science; Control (management); Wastewater; Artificial intelligence; Waste management; Engineering","score_opus":0.028198188126833133,"score_gpt":0.23896708049843984,"score_spread":0.2107688923716067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132656519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014699406,0.0017772928,0.9671245,0.0003945538,0.00019285838,0.000037130965,0.00027253368,0.000738466,0.0147632435],"genre_scores_gemma":[0.9121067,0.0020839912,0.050481163,0.00019168714,0.00016128967,0.00034372794,0.0005615013,0.00010365508,0.033966318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997886,0.000042449406,0.000011221723,0.000047991784,0.00008848018,0.000021305956],"domain_scores_gemma":[0.9998586,0.00004962249,0.000016296155,0.0000068561403,0.00006408431,0.000004445195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026280826,0.0005436225,0.0006887187,0.0002669208,0.00023338805,0.0006303179,0.0008216696,0.00090183294,0.0023765054],"category_scores_gemma":[0.0006996911,0.0002011144,0.00036015906,0.00042452692,0.00026653783,0.0006690166,0.00034741702,0.00091041723,0.000592995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022815591,0.000009799294,0.00009944232,0.00004337603,0.000013631155,0.000019559144,0.000008929074,0.9825981,0.0011061409,0.004408701,0.00050347554,0.011166057],"study_design_scores_gemma":[0.0000023980926,0.000007883527,0.000061565865,0.0000029714051,0.0000034682441,0.0000042282313,0.0000011139582,0.9973363,0.00021425,0.0018007401,0.00056208734,0.0000030035367],"about_ca_topic_score_codex":0.013121742,"about_ca_topic_score_gemma":0.007892033,"teacher_disagreement_score":0.013121742,"about_ca_system_score_codex":0.0007576398,"about_ca_system_score_gemma":0.0006407394,"threshold_uncertainty_score":0.026090741},"labels":[],"label_agreement":null},{"id":"W2132711467","doi":"10.1109/iembs.2002.1053147","title":"A connectionist perspective on detection and control of epileptic seizures","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Connectionism; Chaotic; Perspective (graphical); Artificial neural network; Computer science; Artificial intelligence; Epilepsy; Nonlinear system; Neuroscience; Cognitive science; Psychology; Physics","score_opus":0.01162707347691335,"score_gpt":0.22357827446567308,"score_spread":0.21195120098875972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132711467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023822876,0.039984297,0.8058868,0.017359784,0.0014813303,0.000067483685,0.00028758417,0.00041268812,0.11069712],"genre_scores_gemma":[0.88840926,0.022295423,0.062394675,0.0011909993,0.0027970367,0.00015477264,0.00014350117,0.00006435421,0.022550061],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996006,0.00010539293,0.000027049493,0.000103645136,0.00013119615,0.00003217566],"domain_scores_gemma":[0.9994635,0.0003313885,0.000047684793,0.000039976567,0.00009188846,0.000025601013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006348735,0.00061848876,0.0005635018,0.00072800706,0.0003288638,0.00221964,0.0011039359,0.0018095743,0.0024863959],"category_scores_gemma":[0.0017683643,0.0002987421,0.00036554804,0.0006968579,0.002007051,0.002145788,0.00054086023,0.0015673499,0.00037724283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008176597,0.000036687397,0.0003665587,0.00025606816,0.000075324184,0.00024146949,0.00016051454,0.10227729,0.0029292845,0.84318084,0.002950935,0.047443394],"study_design_scores_gemma":[0.000031540752,0.00007288425,0.00053409167,0.00008932017,0.000026476371,0.000103707534,0.000034412293,0.23761724,0.0012924902,0.7395786,0.020584688,0.000034640772],"about_ca_topic_score_codex":0.0030564258,"about_ca_topic_score_gemma":0.0018624406,"teacher_disagreement_score":0.0030564258,"about_ca_system_score_codex":0.0012931641,"about_ca_system_score_gemma":0.00042837646,"threshold_uncertainty_score":0.009382606},"labels":[],"label_agreement":null},{"id":"W2133105369","doi":"10.1109/ijcnn.2002.1005474","title":"Input partitioning to mixture of experts","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Transparency (behavior); Modular design; Context (archaeology); Scheme (mathematics); Artificial neural network; Artificial intelligence; Machine learning; Space (punctuation); Architecture; Mathematics","score_opus":0.014295999398857166,"score_gpt":0.24815861238144246,"score_spread":0.2338626129825853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133105369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022263208,0.0002653259,0.9955675,0.00011908609,0.00004536361,0.00006676921,0.000052619896,0.00029846106,0.001358683],"genre_scores_gemma":[0.15878753,0.00063260086,0.8316497,0.00044686816,0.00028402582,0.00063758646,0.00063785614,0.00036496844,0.006558818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99541205,0.0025217158,0.00019924124,0.0008527377,0.0006998146,0.00031436805],"domain_scores_gemma":[0.9947067,0.0029707698,0.00025106277,0.0008439457,0.0010463862,0.00018121077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048045353,0.0020298027,0.0019918885,0.0012682992,0.0009896485,0.0022883324,0.0027516258,0.0026001602,0.007625481],"category_scores_gemma":[0.01789415,0.0009563677,0.002165322,0.0014795836,0.0014272648,0.0029436997,0.003921624,0.0026248456,0.002564242],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009376917,0.00016354914,0.00088468654,0.0006288228,0.00032051612,0.00022163066,0.0007402979,0.41506624,0.007077648,0.14649422,0.013865279,0.41359946],"study_design_scores_gemma":[0.00003595926,0.00005795176,0.00016955225,0.000051105726,0.0000504099,0.00006891412,0.000056085322,0.91976297,0.0044867466,0.06799383,0.0072404617,0.000026058899],"about_ca_topic_score_codex":0.0025886884,"about_ca_topic_score_gemma":0.002425034,"teacher_disagreement_score":0.007625481,"about_ca_system_score_codex":0.0015671058,"about_ca_system_score_gemma":0.001410693,"threshold_uncertainty_score":0.025509834},"labels":[],"label_agreement":null},{"id":"W2133211738","doi":"10.24908/pceea.v0i0.4920","title":"TEACHING SELF-AFFINE AND DYNAMICAL SYSTEMS ENGINEERING","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Multifractal system; Fractal; Fractal analysis; Attractor; Correlation dimension; Fractal dimension; Computer science; Wavelet; Dynamical systems theory; Lyapunov exponent; Algorithm; Statistical physics; Mathematics; Theoretical computer science; Artificial intelligence; Chaotic; Mathematical analysis","score_opus":0.0024048171875942063,"score_gpt":0.16821774852262938,"score_spread":0.1658129313350352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133211738","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015043075,0.043658562,0.34098247,0.05726098,0.017673284,0.00030228213,0.0011308576,0.002694863,0.5212536],"genre_scores_gemma":[0.1545984,0.077179156,0.19284993,0.01244666,0.023504863,0.0005970753,0.0019830102,0.0010823172,0.5357586],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99910396,0.00014400519,0.000047592872,0.00018949933,0.0004493393,0.00006543721],"domain_scores_gemma":[0.99836713,0.00069739035,0.00008794565,0.0001736455,0.0003532494,0.0003206651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011921444,0.0014308058,0.00084226223,0.0010404215,0.00087858393,0.002408332,0.00096647016,0.0012898626,0.044819154],"category_scores_gemma":[0.0039597494,0.0002909448,0.0006745601,0.00087481143,0.0012300418,0.0027085582,0.0021293834,0.003379674,0.01977743],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060087372,0.00035118012,0.001378988,0.00074773387,0.00003468644,0.00019637399,0.00097031106,0.004862793,0.003767715,0.1656561,0.325928,0.49604607],"study_design_scores_gemma":[0.000017410177,0.00019757019,0.0013165901,0.00045347196,0.000012129967,0.0003760539,0.00029399002,0.009825982,0.0018805459,0.19876507,0.7868385,0.000022687711],"about_ca_topic_score_codex":0.00066638784,"about_ca_topic_score_gemma":0.000736002,"teacher_disagreement_score":0.044819154,"about_ca_system_score_codex":0.0014632605,"about_ca_system_score_gemma":0.001949148,"threshold_uncertainty_score":0.149935},"labels":[],"label_agreement":null},{"id":"W2133539287","doi":"10.1109/tcomm.2005.843416","title":"Kalman Filter-Trained Recurrent Neural Equalizers for Time-Varying Channels","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Ottawa","funders":"","keywords":"Kalman filter; Recurrent neural network; Extended Kalman filter; Computer science; Convergence (economics); Control theory (sociology); Equalization (audio); Channel (broadcasting); Artificial neural network; Gradient descent; Invariant extended Kalman filter; Artificial intelligence; Telecommunications","score_opus":0.06137283117714596,"score_gpt":0.31443673816023904,"score_spread":0.25306390698309306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133539287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012374189,0.00041516504,0.98469824,0.00006417153,0.00007498965,0.000026283053,0.00002182273,0.00060363975,0.0017214145],"genre_scores_gemma":[0.62261313,0.0009355709,0.36556807,0.00014094691,0.000116903386,0.00012276573,0.00016354446,0.00010395896,0.010235109],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996086,0.000109468514,0.000038943705,0.000083729996,0.00011377128,0.000045495646],"domain_scores_gemma":[0.99940526,0.00028615977,0.00006001874,0.000072178984,0.00016567486,0.000010611005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008573172,0.0005893739,0.0005916458,0.0002470863,0.00028838063,0.0005126961,0.00077880407,0.0006448458,0.0019709922],"category_scores_gemma":[0.0021709795,0.00025778587,0.00036878505,0.00029223724,0.00034265537,0.0011989853,0.0003743241,0.00077538664,0.0006840892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039360696,0.00013114503,0.00077586534,0.0002197458,0.00019494577,0.00018388135,0.00021040163,0.4372275,0.05407723,0.027567808,0.0029595436,0.4760584],"study_design_scores_gemma":[0.000022563596,0.00009620658,0.00016273498,0.0000126639,0.000034537366,0.00007228591,0.00000976151,0.97808444,0.017469775,0.0019382496,0.0020829034,0.00001377576],"about_ca_topic_score_codex":0.0023204994,"about_ca_topic_score_gemma":0.005377741,"teacher_disagreement_score":0.0023204994,"about_ca_system_score_codex":0.00037871586,"about_ca_system_score_gemma":0.0004217037,"threshold_uncertainty_score":0.0065936446},"labels":[],"label_agreement":null},{"id":"W2133677789","doi":"10.1109/ccece.1998.685563","title":"Predicting and modelling of nonstationary temporal signals with fractal characteristics","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Manitoba Hydro","keywords":"Fractal; Chaotic; Computer science; Matching (statistics); Series (stratigraphy); Tracking (education); Feature (linguistics); Term (time); SIGNAL (programming language); Time series; Artificial intelligence; Algorithm; Machine learning; Mathematics","score_opus":0.03296268677845732,"score_gpt":0.21674013164981173,"score_spread":0.18377744487135442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133677789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06885449,0.00014092517,0.9300775,0.000049896433,0.000020581905,0.000017429964,0.000042284602,0.00019422929,0.00060271285],"genre_scores_gemma":[0.81505173,0.0002785544,0.18331577,0.0000174787,0.000033337295,0.000045264795,0.00011152744,0.000026828711,0.0011194766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998914,0.000024000135,0.000009061932,0.00003023999,0.00003250307,0.000012691342],"domain_scores_gemma":[0.99949133,0.00026498848,0.000111561065,0.000058519276,0.000058016823,0.000015542335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041618632,0.00042119392,0.00028873523,0.0004480142,0.00018487056,0.0003587727,0.0004626973,0.0004217172,0.00040535638],"category_scores_gemma":[0.0013553061,0.00016670697,0.00036241434,0.00028038066,0.00033016814,0.00067987276,0.00019120329,0.00048254742,0.00008729048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006354765,0.000024364124,0.0018488732,0.000040758634,0.000029682671,0.000110296074,0.000056014058,0.9348147,0.016738236,0.008662098,0.00016580455,0.037445713],"study_design_scores_gemma":[8.2864705e-7,0.000009435298,0.00024096052,0.0000011691593,0.0000034551338,0.000012938075,0.0000016930019,0.9974131,0.0011967089,0.0009780942,0.00013914837,0.000002565865],"about_ca_topic_score_codex":0.0021949576,"about_ca_topic_score_gemma":0.0023585574,"teacher_disagreement_score":0.0021949576,"about_ca_system_score_codex":0.00031243198,"about_ca_system_score_gemma":0.00027314425,"threshold_uncertainty_score":0.0043643117},"labels":[],"label_agreement":null},{"id":"W2133702739","doi":"10.1109/tnn.2002.1031938","title":"A recurrent neural network for solving Sylvester equation with time-varying coefficients","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":660,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"iNano Medical (Canada)","funders":"","keywords":"Recurrent neural network; Artificial neural network; Computer science; Sylvester equation; Control theory (sociology); Nonlinear system; Convergence (economics); Inverted pendulum; Applied mathematics; Time delay neural network; Mathematics; Artificial intelligence","score_opus":0.031826097085271364,"score_gpt":0.24180964027847188,"score_spread":0.2099835431932005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133702739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003519452,0.00040753226,0.993346,0.00008388448,0.000074793556,0.000023576666,0.000026768603,0.00026509538,0.0022529021],"genre_scores_gemma":[0.3310238,0.0014809782,0.6560083,0.00017822799,0.00015413496,0.00029427104,0.0002651677,0.00011998114,0.010475076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998109,0.000042296473,0.000017192811,0.000049209,0.00006000223,0.00002049553],"domain_scores_gemma":[0.99986815,0.000045310684,0.00002018359,0.000015715248,0.000042805335,0.000007832347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048234005,0.00072301866,0.00054249226,0.0002731794,0.00027542797,0.0005251449,0.00090619177,0.0010815773,0.0024188382],"category_scores_gemma":[0.0010096879,0.0003311378,0.0006102104,0.00044358303,0.00040451888,0.001072765,0.00048150378,0.0012896863,0.0008981528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102248814,0.000076986275,0.00060797896,0.000362902,0.00012261799,0.0003650611,0.00017466964,0.6352366,0.041929305,0.09179268,0.0036436198,0.22558536],"study_design_scores_gemma":[0.000008091125,0.000047983227,0.00006097245,0.000009715288,0.000014961433,0.000054278036,0.0000041977805,0.9892212,0.0032366419,0.004675928,0.00265314,0.000012766499],"about_ca_topic_score_codex":0.0025867969,"about_ca_topic_score_gemma":0.0038064956,"teacher_disagreement_score":0.0025867969,"about_ca_system_score_codex":0.00039863127,"about_ca_system_score_gemma":0.000570484,"threshold_uncertainty_score":0.008091867},"labels":[],"label_agreement":null},{"id":"W2133714906","doi":"10.1109/ccece.1996.548226","title":"Induction motor speed estimation using artificial neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial neural network; Control theory (sociology); Stator; Computer science; Induction motor; Scheme (mathematics); Electronic speed control; Control engineering; Control (management); Artificial intelligence; Engineering; Mathematics","score_opus":0.06775976888937398,"score_gpt":0.26989519600265016,"score_spread":0.2021354271132762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133714906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039722808,0.0009978032,0.9555819,0.0001432655,0.00006851532,0.000022795886,0.00004570023,0.0007413541,0.0026759433],"genre_scores_gemma":[0.80115676,0.0007936111,0.19464873,0.000059495924,0.00006569239,0.000059207476,0.00016410503,0.000042172593,0.0030102497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975604,0.00006244117,0.00002284738,0.00005288892,0.00008442844,0.000021371583],"domain_scores_gemma":[0.9996007,0.00016050166,0.00006201388,0.000027509182,0.00014120158,0.000008167185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046923695,0.00037948496,0.00043134607,0.0007445006,0.00016659939,0.0005323422,0.00035083937,0.0005526161,0.0007082496],"category_scores_gemma":[0.0019976604,0.00024408054,0.00018440299,0.0005126572,0.00014039803,0.00060880627,0.00022190713,0.0004965857,0.0003540527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019302934,0.000057541063,0.0024708814,0.00014548971,0.00007011029,0.00009820406,0.00003953408,0.39747062,0.02274159,0.004115254,0.001448751,0.57114905],"study_design_scores_gemma":[0.0000074390623,0.000017613222,0.0010115465,0.000012156758,0.000009373932,0.000024650524,0.000004194953,0.9931606,0.0032971296,0.0013897516,0.0010569098,0.000008660264],"about_ca_topic_score_codex":0.0016002427,"about_ca_topic_score_gemma":0.0012590183,"teacher_disagreement_score":0.0016002427,"about_ca_system_score_codex":0.0002945491,"about_ca_system_score_gemma":0.00021158496,"threshold_uncertainty_score":0.0031818748},"labels":[],"label_agreement":null},{"id":"W2133729357","doi":"10.1023/a:1009673915255","title":"Learning with Permutably Homogeneous Multiple-Valued Multiple-Threshold Perceptrons","year":2000,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières; University of Ottawa; Lakehead University","funders":"","keywords":"Perceptron; Separable space; Hyperplane; Homogeneous; Computational intelligence; Convergence (economics); Partition (number theory); Computer science; Mathematics; Multilayer perceptron; Artificial intelligence; Artificial neural network; Combinatorics; Mathematical analysis","score_opus":0.01127275004731886,"score_gpt":0.2201004917583075,"score_spread":0.20882774171098864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133729357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050514,0.00022895823,0.94688976,0.00019181565,0.000059605325,0.000017793394,0.00004800827,0.0003765917,0.0016734392],"genre_scores_gemma":[0.83358,0.00021405253,0.16227372,0.00013233251,0.00010394132,0.00004652881,0.00012936867,0.000115137955,0.0034048823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938524,0.00021929502,0.00005356633,0.00014748306,0.00010950879,0.00008481735],"domain_scores_gemma":[0.99874145,0.0006540823,0.00012280955,0.00024624588,0.00014433412,0.00009117889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015141316,0.00044016782,0.0011340912,0.00040857558,0.00033043046,0.001347325,0.0013131573,0.0008984648,0.0022626831],"category_scores_gemma":[0.0051957923,0.000485034,0.0007112195,0.00064791733,0.0010964636,0.002707501,0.0016708568,0.0012574334,0.00045748663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00104591,0.0001607176,0.0012333321,0.00023479397,0.00019108642,0.00028013997,0.0001452064,0.472292,0.030328382,0.27591932,0.0021118054,0.21605736],"study_design_scores_gemma":[0.000017789978,0.000042880456,0.00009146408,0.0000046556697,0.000012478935,0.000025696381,0.00000621329,0.9265413,0.0036477682,0.06933923,0.00026356996,0.00000694511],"about_ca_topic_score_codex":0.00044301106,"about_ca_topic_score_gemma":0.00055917026,"teacher_disagreement_score":0.0022626831,"about_ca_system_score_codex":0.00059768365,"about_ca_system_score_gemma":0.00044887495,"threshold_uncertainty_score":0.008007646},"labels":[],"label_agreement":null},{"id":"W2133884199","doi":"10.1109/72.883433","title":"Asynchronous self-organizing maps","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Asynchronous communication; MIMD; Computer science; Convergence (economics); Computation; Artificial neural network; Function (biology); Set (abstract data type); Algorithm; Theoretical computer science; Topology (electrical circuits); Artificial intelligence; Mathematics; Parallel computing","score_opus":0.008797966435843,"score_gpt":0.21109895450788146,"score_spread":0.20230098807203847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133884199","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032026082,0.00024669807,0.95784926,0.0002562039,0.00014138337,0.000052131163,0.00007933793,0.00026342823,0.009085514],"genre_scores_gemma":[0.82053906,0.00036488834,0.16779241,0.00018170524,0.0001782001,0.00020516162,0.00014864531,0.00009692961,0.010493021],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997167,0.000085495034,0.000013807229,0.000059190923,0.00009765013,0.000027168826],"domain_scores_gemma":[0.99944085,0.00025221484,0.00005728013,0.000085525586,0.00011568995,0.000048410515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005074677,0.00027479426,0.0003373226,0.0003977125,0.0004560089,0.00075790763,0.0007984518,0.0005855028,0.002036486],"category_scores_gemma":[0.0021985937,0.00015298072,0.00029778894,0.00036404133,0.0005706145,0.00095066795,0.0008102699,0.0005259266,0.0004032393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000766347,0.00005438932,0.0005862026,0.0000923184,0.00003789592,0.00018768343,0.00012382417,0.39467123,0.011034654,0.5238618,0.004130359,0.06514304],"study_design_scores_gemma":[0.00000763534,0.000013715866,0.00011921684,0.0000034625937,0.000003903717,0.00003652883,0.000009174552,0.9244674,0.0010667682,0.07234269,0.0019237036,0.000005799151],"about_ca_topic_score_codex":0.00047384933,"about_ca_topic_score_gemma":0.00054619974,"teacher_disagreement_score":0.002036486,"about_ca_system_score_codex":0.0003933807,"about_ca_system_score_gemma":0.0003359072,"threshold_uncertainty_score":0.0068126917},"labels":[],"label_agreement":null},{"id":"W2133983852","doi":"10.1109/leos.1992.694007","title":"Optical Neural Networks Using Competitive Learning","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.01962989335459668,"score_gpt":0.26187544558275505,"score_spread":0.24224555222815836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133983852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032815915,0.0019943977,0.925296,0.0008867787,0.00062074605,0.00016689622,0.00011941945,0.000862545,0.03723723],"genre_scores_gemma":[0.6869761,0.00084672315,0.28759536,0.0005533856,0.0004266094,0.00024882625,0.00021713074,0.00025488908,0.022881119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947995,0.00012359124,0.000020453956,0.00008453241,0.0002159181,0.00007560718],"domain_scores_gemma":[0.99867034,0.0004995513,0.0001339797,0.00015277795,0.00044108758,0.00010238711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013187132,0.00069485325,0.0009719506,0.000956822,0.0009280372,0.0019196084,0.0025891168,0.0017812643,0.005144963],"category_scores_gemma":[0.0039071515,0.00047244577,0.000621724,0.0011226502,0.00097157055,0.0020158822,0.0015439527,0.0011969615,0.0011001969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032578743,0.0002164203,0.0009375719,0.0002768095,0.00018516212,0.00013062444,0.00009355511,0.39003375,0.015149232,0.28841132,0.011185814,0.293054],"study_design_scores_gemma":[0.000014003784,0.000026806958,0.00007763586,0.0000061208057,0.0000117452855,0.000018968487,0.000006149346,0.9740738,0.0017807932,0.022494903,0.001479361,0.00000968231],"about_ca_topic_score_codex":0.0056088367,"about_ca_topic_score_gemma":0.0058993744,"teacher_disagreement_score":0.0056088367,"about_ca_system_score_codex":0.0014520821,"about_ca_system_score_gemma":0.0009832386,"threshold_uncertainty_score":0.017211556},"labels":[],"label_agreement":null},{"id":"W2134163741","doi":"10.1109/ijcnn.2004.1379927","title":"Connectionist based Dempster-Shafer evidential reasoning for data fusion","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Dempster–Shafer theory; Evidential reasoning approach; Artificial intelligence; Computer science; Connectionism; Benchmark (surveying); Machine learning; Artificial neural network; Process (computing); Case-based reasoning; Realization (probability); Sensor fusion; Voting; Convergence (economics); Decision support system; Mathematics","score_opus":0.05990120423156841,"score_gpt":0.32212963290808344,"score_spread":0.262228428676515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134163741","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006133869,0.00023148855,0.99179816,0.00015269456,0.000023781888,0.0000317935,0.00003711918,0.00008925012,0.0015018695],"genre_scores_gemma":[0.58330107,0.0005720383,0.41362435,0.00011002822,0.00006724659,0.00018536558,0.00016593545,0.000024471652,0.0019495643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999199,0.00027389108,0.00007702353,0.00010922701,0.00030646857,0.000034428067],"domain_scores_gemma":[0.9984604,0.00091680454,0.00013441594,0.00019338848,0.00026066854,0.000034408695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024987676,0.0006433198,0.000819754,0.0008634495,0.0006442306,0.0013843583,0.0014164506,0.0012578424,0.0018278987],"category_scores_gemma":[0.00583532,0.00036347087,0.000807594,0.0010501442,0.0009838106,0.0022333984,0.0012909214,0.0015839015,0.0003436684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009428597,0.00005551282,0.0005659673,0.00017432912,0.00012229563,0.00019792885,0.0001165902,0.7137812,0.003650447,0.17116787,0.001230139,0.108843386],"study_design_scores_gemma":[0.0000044356752,0.000013530981,0.0000743243,0.000006327827,0.000007877226,0.000029028002,0.000004525927,0.94827634,0.0010299459,0.050114486,0.00043107365,0.000008110854],"about_ca_topic_score_codex":0.0016087573,"about_ca_topic_score_gemma":0.002512712,"teacher_disagreement_score":0.0024987676,"about_ca_system_score_codex":0.0012089988,"about_ca_system_score_gemma":0.00097222615,"threshold_uncertainty_score":0.013214886},"labels":[],"label_agreement":null},{"id":"W2134214966","doi":"10.1109/ccece.1993.332251","title":"Neural networks and document classification","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation, Science and Economic Development Canada","funders":"","keywords":"Computer science; Artificial neural network; Relevance (law); Artificial intelligence; Natural language; Backpropagation; Natural language processing; Document classification; Convergence (economics); Machine learning","score_opus":0.02799713016883222,"score_gpt":0.23559463318556656,"score_spread":0.20759750301673435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134214966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026072392,0.10978725,0.75026834,0.018006425,0.0026878351,0.00016622426,0.0011919948,0.0011140697,0.09070554],"genre_scores_gemma":[0.50968355,0.09519813,0.31046462,0.003612908,0.0063928445,0.00041830423,0.002838067,0.00026877955,0.07112286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913603,0.0002426274,0.00007351893,0.00017998462,0.00028089952,0.00008699106],"domain_scores_gemma":[0.9980623,0.0013191827,0.00018037719,0.000117678086,0.00027364108,0.00004676788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013441517,0.0006370485,0.00055923924,0.0040959925,0.0006554858,0.0025671816,0.0007822687,0.001965634,0.0040020435],"category_scores_gemma":[0.004979722,0.0003002795,0.0005513668,0.005749306,0.0014941365,0.0031368437,0.00068933127,0.001474984,0.0016204648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009825097,0.00010009387,0.0039056838,0.0006253666,0.00017336597,0.00027128402,0.00032261864,0.062974095,0.0016823924,0.326666,0.026713567,0.5764674],"study_design_scores_gemma":[0.000023069762,0.000059265934,0.0038950671,0.000362871,0.00006990324,0.0005487205,0.00016793095,0.29818878,0.003252607,0.56796587,0.1253983,0.000067721914],"about_ca_topic_score_codex":0.0046724416,"about_ca_topic_score_gemma":0.0050982023,"teacher_disagreement_score":0.0046724416,"about_ca_system_score_codex":0.00181349,"about_ca_system_score_gemma":0.0005327701,"threshold_uncertainty_score":0.013388157},"labels":[],"label_agreement":null},{"id":"W2134443264","doi":"10.1109/icpr.1988.28252","title":"Planning, neural networks and Markov models for automatic speech recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; McGill University","funders":"","keywords":"Hidden Markov model; Speech recognition; Feature (linguistics); Computer science; Markov model; Markov process; Artificial neural network; Markov chain; Artificial intelligence; Dynamic programming; Pattern recognition (psychology); Natural language processing; Machine learning; Algorithm; Mathematics; Linguistics; Statistics","score_opus":0.037721023634309576,"score_gpt":0.26389108366781866,"score_spread":0.2261700600335091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134443264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007525534,0.005778298,0.9783775,0.0010135737,0.00016340482,0.000028500683,0.00019926812,0.00092480803,0.005989006],"genre_scores_gemma":[0.525733,0.012197406,0.4367247,0.00043342245,0.0005238723,0.00040185344,0.00088736555,0.0003316523,0.022766776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996941,0.00012688688,0.000017983484,0.00006433835,0.00006911232,0.00002755216],"domain_scores_gemma":[0.999132,0.0006790157,0.00006623745,0.000048377973,0.000053432363,0.000020933716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006111297,0.00062879326,0.0004723366,0.0005414284,0.00032906546,0.0010569401,0.00072597055,0.0008120667,0.0037691398],"category_scores_gemma":[0.002613704,0.0004553311,0.00054112554,0.0009552212,0.00094471767,0.0017762748,0.0005619089,0.001201303,0.0005409672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010033007,0.00003108365,0.00039139017,0.00017922856,0.00006814632,0.00010069279,0.0000729139,0.7021813,0.0012938357,0.17504537,0.0045004394,0.11603525],"study_design_scores_gemma":[0.000006896819,0.000010420036,0.0001061216,0.000017686478,0.0000087068465,0.000022845368,0.000010348693,0.81580955,0.00046034565,0.18083556,0.0026999398,0.000011551618],"about_ca_topic_score_codex":0.009796648,"about_ca_topic_score_gemma":0.010992473,"teacher_disagreement_score":0.009796648,"about_ca_system_score_codex":0.0012958173,"about_ca_system_score_gemma":0.0009058383,"threshold_uncertainty_score":0.019479275},"labels":[],"label_agreement":null},{"id":"W2134530666","doi":"10.1109/tim.2009.2025070","title":"The refinement of models with the aid of the fuzzy k-nearest neighbors approach","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Nonparametric statistics; Parametric statistics; Computer science; k-nearest neighbors algorithm; Variable (mathematics); Iterative and incremental development; Quadratic equation; Fuzzy logic; Parametric model; Process (computing); Component (thermodynamics); Artificial intelligence; Data mining; Nonparametric regression; Machine learning; Algorithm; Mathematics; Regression analysis; Statistics","score_opus":0.03135848829748607,"score_gpt":0.22109779358598872,"score_spread":0.18973930528850264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134530666","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033116885,0.00008131066,0.99593145,0.00004587673,0.000011178088,0.000016661244,0.00001225974,0.000077783356,0.0005118615],"genre_scores_gemma":[0.3522233,0.0004186691,0.6442025,0.000092509814,0.00006549189,0.00020309823,0.00013069948,0.00011424821,0.0025494457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980609,0.000909698,0.0001000506,0.0003226493,0.0005237235,0.00008296385],"domain_scores_gemma":[0.99695885,0.0018121559,0.00028889143,0.00041250142,0.00047445038,0.000053177617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00353596,0.0007909363,0.0011076613,0.0007113666,0.0005074486,0.001144632,0.0022891108,0.001085981,0.0015445536],"category_scores_gemma":[0.010364913,0.000864056,0.001518725,0.00045718867,0.00089770276,0.0025602144,0.0014081005,0.0015405685,0.0005029838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005774378,0.000038246137,0.001001479,0.00011006376,0.00007464544,0.00006402452,0.00019408326,0.89337724,0.0016292422,0.04314665,0.000363128,0.05994335],"study_design_scores_gemma":[0.000005834623,0.000030382842,0.000084753294,0.000009654179,0.000012078512,0.000018456918,0.00001318166,0.9857054,0.00047464433,0.012650334,0.000984876,0.000010384461],"about_ca_topic_score_codex":0.0065977364,"about_ca_topic_score_gemma":0.0068216515,"teacher_disagreement_score":0.0065977364,"about_ca_system_score_codex":0.0009119466,"about_ca_system_score_gemma":0.0011183141,"threshold_uncertainty_score":0.018700123},"labels":[],"label_agreement":null},{"id":"W2134842679","doi":"10.48550/arxiv.1305.6663","title":"Generalized Denoising Auto-Encoders as Generative Models","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":210,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Noise (video); Infinitesimal; Computer science; Algorithm; Noise reduction; Gaussian noise; Limit (mathematics); Gaussian; Mathematics; Applied mathematics; Artificial intelligence; Mathematical optimization","score_opus":0.0706505008980403,"score_gpt":0.1846081007012172,"score_spread":0.11395759980317689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134842679","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010732513,0.000441316,0.9849187,0.0003856704,0.000044664437,0.000016793147,0.0001367917,0.00030134854,0.0030222004],"genre_scores_gemma":[0.6783229,0.0018392932,0.29002607,0.00079813227,0.00025518148,0.00023627748,0.0008560659,0.00059869967,0.027067285],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990478,0.0004165266,0.00004203218,0.00019501413,0.00021947807,0.00007904232],"domain_scores_gemma":[0.9971578,0.0018752583,0.0002122157,0.00043157043,0.00022591453,0.00009721272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022310663,0.00077266886,0.0010291017,0.00089497416,0.0002802401,0.0013661777,0.0018658682,0.0015580188,0.0023918108],"category_scores_gemma":[0.0063011423,0.00095936336,0.0013134186,0.0007652897,0.002295032,0.002035091,0.0018863056,0.0027308513,0.00072264316],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044167562,0.000026035686,0.00042246855,0.00007526882,0.00004663174,0.00010766215,0.00015832165,0.44706535,0.001455512,0.5291977,0.0012166473,0.02018414],"study_design_scores_gemma":[0.000005773766,0.000008152316,0.00007720154,0.000016897113,0.000007899634,0.000034573884,0.000009262026,0.83133334,0.00041051002,0.16708198,0.0010047436,0.000009710222],"about_ca_topic_score_codex":0.0028320246,"about_ca_topic_score_gemma":0.0042975796,"teacher_disagreement_score":0.0028320246,"about_ca_system_score_codex":0.0012680872,"about_ca_system_score_gemma":0.0008132077,"threshold_uncertainty_score":0.011799097},"labels":[],"label_agreement":null},{"id":"W2135526517","doi":"10.1109/ijcnn.1991.155672","title":"A neuro-controller with learning and adaptation","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Adaptation (eye); Artificial neural network; Computer science; Artificial intelligence; Controller (irrigation); Scheme (mathematics); Range (aeronautics); Inverse; Inverse dynamics; Machine learning; Engineering; Mathematics; Psychology; Neuroscience","score_opus":0.01797235293951808,"score_gpt":0.19963049759934118,"score_spread":0.1816581446598231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135526517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008994011,0.00469134,0.7840978,0.0016248089,0.0031833262,0.0005175094,0.0005343195,0.0061004763,0.19025646],"genre_scores_gemma":[0.46089426,0.0039142394,0.17254579,0.0011609327,0.0012036314,0.0009778304,0.0009185095,0.00041558224,0.35796934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982786,0.000018014147,0.000012734897,0.00005095266,0.00007637849,0.00001395517],"domain_scores_gemma":[0.9998462,0.00003241845,0.000011461012,0.000023851517,0.00007083719,0.000015237828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019258543,0.0005377403,0.0006033838,0.00025068963,0.00038482368,0.0009368802,0.00090690097,0.0010327342,0.04327811],"category_scores_gemma":[0.0005222355,0.00013045358,0.00034343143,0.0003183836,0.00040007848,0.0004419098,0.0004967111,0.00082551234,0.011010725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031303053,0.00014543561,0.00040563825,0.0010033005,0.00010899171,0.0005405372,0.000114051545,0.17516588,0.02607181,0.08155983,0.057864696,0.6567068],"study_design_scores_gemma":[0.00009482917,0.00033162013,0.00068286376,0.00013972938,0.00010219709,0.00029030538,0.000027371996,0.71649396,0.01383561,0.02059947,0.24734055,0.000061517494],"about_ca_topic_score_codex":0.0019335552,"about_ca_topic_score_gemma":0.0024045294,"teacher_disagreement_score":0.04327811,"about_ca_system_score_codex":0.00047522833,"about_ca_system_score_gemma":0.0005312448,"threshold_uncertainty_score":0.14477968},"labels":[],"label_agreement":null},{"id":"W2136112593","doi":"10.1109/dnsr.2004.1344728","title":"Network intrusion detection using an improved competitive learning neural network","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Intrusion detection system; Computer science; Cluster analysis; Artificial neural network; Competitive learning; Artificial intelligence; Computation; Unsupervised learning; Data mining; Self-organizing map; Set (abstract data type); Machine learning; Pattern recognition (psychology); Computational intelligence; Algorithm","score_opus":0.01888728781584186,"score_gpt":0.2469473102205735,"score_spread":0.22806002240473164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136112593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042016774,0.000832619,0.94963354,0.00031552062,0.00026382704,0.00016325389,0.000072019415,0.0017851065,0.0049172947],"genre_scores_gemma":[0.55496395,0.0004722301,0.43986154,0.00039256553,0.000297429,0.00018343198,0.00025104312,0.00010746278,0.0034703193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980427,0.0003152265,0.00007842516,0.00029860987,0.001093295,0.00017175103],"domain_scores_gemma":[0.9980326,0.0005110922,0.00018361563,0.00013364042,0.0010518122,0.000087212466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019147049,0.0009065056,0.0016258385,0.0018162756,0.0005470294,0.0011748838,0.0022457733,0.0013682982,0.0012705892],"category_scores_gemma":[0.0035184398,0.0003442375,0.00085890223,0.0014100309,0.0006149415,0.0013042918,0.0010506766,0.0009993064,0.00060225767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004937286,0.0005730548,0.004450691,0.00021543357,0.0003295703,0.0003688586,0.000106180625,0.27385062,0.031300873,0.0095277745,0.005443922,0.67333925],"study_design_scores_gemma":[0.000010370458,0.00007541119,0.00035938484,0.000002669144,0.00001994314,0.000059152553,0.0000053862113,0.9953264,0.0029943124,0.00045199814,0.00068250427,0.000012405045],"about_ca_topic_score_codex":0.006552028,"about_ca_topic_score_gemma":0.0061097196,"teacher_disagreement_score":0.006552028,"about_ca_system_score_codex":0.0010607535,"about_ca_system_score_gemma":0.0008161441,"threshold_uncertainty_score":0.013027787},"labels":[],"label_agreement":null},{"id":"W2136195389","doi":"10.1142/s021800140900734x","title":"COMPOUND DIVERSITY FUNCTIONS FOR ENSEMBLE SELECTION","year":2009,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Classifier (UML); Artificial intelligence; Computer science; Random subspace method; Machine learning; Correlation; Pattern recognition (psychology); Ensemble learning; Data mining; Mathematics","score_opus":0.10676546017781806,"score_gpt":0.31486624723339585,"score_spread":0.20810078705557777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136195389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019971237,0.00079015136,0.97663224,0.00015090035,0.000065878485,0.00009707411,0.000091145565,0.00030101725,0.0019004078],"genre_scores_gemma":[0.5003668,0.0009938413,0.49319726,0.0002309096,0.00026753254,0.0008227233,0.0005499294,0.00018633425,0.0033847145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673706,0.0016373382,0.00016543295,0.0002998931,0.0009972117,0.00016305916],"domain_scores_gemma":[0.99167925,0.005439221,0.00042633305,0.00070228055,0.0015843519,0.0001685372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00725909,0.0013171968,0.0018267193,0.0027870995,0.0008016076,0.0013092034,0.0011651581,0.0013474738,0.0021101937],"category_scores_gemma":[0.01465216,0.0004410603,0.0012656003,0.0017829422,0.00088532944,0.0019738418,0.0016404166,0.0015756763,0.00062212936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016938115,0.00009696603,0.0027917724,0.00014182075,0.00027834447,0.00009830722,0.00012899535,0.7308347,0.0032057439,0.041479316,0.002834033,0.21794054],"study_design_scores_gemma":[0.000016213071,0.000106106236,0.00054720475,0.000021053049,0.000027802007,0.00006611517,0.000011851775,0.9791966,0.001287965,0.01685686,0.0018426643,0.000019544186],"about_ca_topic_score_codex":0.001050721,"about_ca_topic_score_gemma":0.0007844749,"teacher_disagreement_score":0.00725909,"about_ca_system_score_codex":0.0011097287,"about_ca_system_score_gemma":0.0008858002,"threshold_uncertainty_score":0.03839016},"labels":[],"label_agreement":null},{"id":"W2136291763","doi":"10.1109/icip.1994.413716","title":"Self-organizing segmentor and feature extractor","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Hebbian theory; Artificial intelligence; Computer science; Modular design; Pattern recognition (psychology); Extractor; Segmentation; Entropy (arrow of time); Support vector machine; Feature extraction; Artificial neural network; Engineering","score_opus":0.013206089328667587,"score_gpt":0.20212030326192928,"score_spread":0.1889142139332617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136291763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004806265,0.00019179487,0.9908174,0.0001003035,0.000039833,0.00005799173,0.000056519868,0.0019598126,0.0019700227],"genre_scores_gemma":[0.112238936,0.00034705448,0.870309,0.0003988246,0.00016766653,0.00026015993,0.00040368133,0.00042085483,0.015453871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994516,0.00006945746,0.000023320159,0.00016499395,0.00022078968,0.000069886155],"domain_scores_gemma":[0.99962986,0.000090944,0.000038734335,0.00008898617,0.00012298,0.000028451728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078347203,0.00075564825,0.00086072844,0.0014516984,0.0005698218,0.001026045,0.0016832575,0.0013965088,0.0034952045],"category_scores_gemma":[0.0007636623,0.000608961,0.0010136657,0.0012470976,0.0009510906,0.0016828313,0.00088233897,0.0009460805,0.0020705617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021258283,0.00017394674,0.000749402,0.00014386898,0.00019303072,0.00018352631,0.00019095969,0.053461898,0.053187236,0.051602088,0.007775371,0.8321261],"study_design_scores_gemma":[0.000036717927,0.00015829285,0.0009734833,0.000018567289,0.0000768075,0.00017145403,0.00003417328,0.8855815,0.061806288,0.024324631,0.026762417,0.00005565852],"about_ca_topic_score_codex":0.0028246508,"about_ca_topic_score_gemma":0.0049057105,"teacher_disagreement_score":0.0034952045,"about_ca_system_score_codex":0.00087952457,"about_ca_system_score_gemma":0.00077784684,"threshold_uncertainty_score":0.011692643},"labels":[],"label_agreement":null},{"id":"W2136295606","doi":"10.1109/icsmc.2007.4414023","title":"Chaotic time series prediction using knowledge based Green&amp;#x2019;s Kernel and least-squares support vector machines","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Kernel (algebra); Chaotic; Support vector machine; Kernel embedding of distributions; Computer science; Benchmark (surveying); Kernel method; Variable kernel density estimation; Polynomial kernel; Radial basis function kernel; Series (stratigraphy); Regularization (linguistics); Time series; Algorithm; Fourier transform; Artificial intelligence; Mathematics; Machine learning","score_opus":0.023214699781060034,"score_gpt":0.26728939342749347,"score_spread":0.24407469364643344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136295606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033704992,0.0001364277,0.96539813,0.000102696904,0.000012953963,0.000007902914,0.000015062902,0.0001536671,0.00046813107],"genre_scores_gemma":[0.8432477,0.00023056768,0.15456183,0.000044967666,0.000038202088,0.000032623015,0.00009250396,0.000039492752,0.0017120752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964726,0.00009029043,0.000021168977,0.00007372868,0.00013395638,0.000033583314],"domain_scores_gemma":[0.99888617,0.00067528634,0.00014765422,0.000101546124,0.00015850565,0.000030818584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007087988,0.00038976863,0.0006230141,0.00047674624,0.000188425,0.0006231204,0.00082640495,0.00079643045,0.0005633023],"category_scores_gemma":[0.0040198006,0.00021638801,0.00052674813,0.0004878713,0.0006204628,0.0014503483,0.0005307093,0.0006347389,0.00018430015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013800507,0.000081714476,0.0010456635,0.000064883454,0.00007269818,0.00011298256,0.00007661357,0.8101938,0.008896408,0.027577415,0.00056307897,0.15117674],"study_design_scores_gemma":[0.0000014334684,0.0000075806033,0.00007776705,0.0000013877708,0.0000020570392,0.0000054100856,0.0000014771927,0.99741155,0.0007256554,0.0016951005,0.00006793898,0.000002648098],"about_ca_topic_score_codex":0.0021754568,"about_ca_topic_score_gemma":0.0012347437,"teacher_disagreement_score":0.0021754568,"about_ca_system_score_codex":0.0005095235,"about_ca_system_score_gemma":0.0005020818,"threshold_uncertainty_score":0.0043255687},"labels":[],"label_agreement":null},{"id":"W2136451213","doi":"10.1162/089976601300014538","title":"Spatiotemporal Connectionist Networks: A Taxonomy and Review","year":2001,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Connectionism; Computer science; Artificial intelligence; Machine learning; Artificial neural network; Cognitive science; Theoretical computer science; Psychology","score_opus":0.034829288968460013,"score_gpt":0.2654551433796236,"score_spread":0.2306258544111636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136451213","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019998841,0.94631517,0.035657775,0.0036589743,0.0008316501,0.00004034704,0.0001470722,0.00012787241,0.011221282],"genre_scores_gemma":[0.015340879,0.96676666,0.013960139,0.0005157448,0.00094855635,0.000066806424,0.00019322471,0.000027388422,0.0021805728],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99969244,0.00006388933,0.000041330302,0.000067902576,0.000113468755,0.000020947835],"domain_scores_gemma":[0.9989557,0.00063335994,0.000079614896,0.00004318382,0.00024385867,0.000044141078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000978882,0.00086497783,0.0008835698,0.0035516487,0.00047266379,0.0017948475,0.001949946,0.00154355,0.0023503688],"category_scores_gemma":[0.0029643646,0.00043093445,0.00034636274,0.0059786155,0.0010748355,0.005151938,0.0008042258,0.0012177583,0.0010620392],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049908183,0.000052532978,0.0011280009,0.005863953,0.00008108968,0.00026392643,0.00026965706,0.008350269,0.00063175417,0.14835428,0.028582914,0.80637175],"study_design_scores_gemma":[0.000012460635,0.00008816651,0.0012649876,0.0030432271,0.00006170077,0.0013582975,0.00028177572,0.013922189,0.0006704659,0.20435448,0.7748768,0.00006560745],"about_ca_topic_score_codex":0.0028040921,"about_ca_topic_score_gemma":0.0029359916,"teacher_disagreement_score":0.0035516487,"about_ca_system_score_codex":0.0012614736,"about_ca_system_score_gemma":0.0014549555,"threshold_uncertainty_score":0.009152651},"labels":[],"label_agreement":null},{"id":"W2136755056","doi":"10.1109/ccece.1999.807186","title":"Application of Hopfield neural network in routing for computer networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial neural network; Node (physics); Shortest path problem; Geographic routing; Travelling salesman problem; Routing (electronic design automation); Computer network; Link-state routing protocol; Routing table; Routing protocol; Distributed computing; Algorithm; Artificial intelligence; Theoretical computer science; Engineering","score_opus":0.013152320136245468,"score_gpt":0.24314375771477253,"score_spread":0.22999143757852708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136755056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04087603,0.00856636,0.9295217,0.0010892737,0.0003288812,0.00007784209,0.00007551776,0.0007487711,0.018715583],"genre_scores_gemma":[0.7021586,0.007948603,0.2796912,0.00035139208,0.0001636255,0.00008865362,0.00010762492,0.000056950277,0.009433287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997702,0.00006564497,0.00001596512,0.000035512105,0.00009489566,0.000017783716],"domain_scores_gemma":[0.99964726,0.0001886855,0.000019881767,0.00002281859,0.000113302296,0.0000079888705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057100866,0.00042489654,0.0002701529,0.00044076695,0.00031843063,0.0006124729,0.0005361129,0.0010218385,0.0016766837],"category_scores_gemma":[0.0015686637,0.0001964507,0.00021448244,0.0007760566,0.0004816536,0.0010257546,0.00029522384,0.0005137706,0.00031306708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001074197,0.000045484965,0.0009082707,0.00022761471,0.00005461773,0.00021884253,0.00009173402,0.6859249,0.014955962,0.02641136,0.0019679382,0.26908588],"study_design_scores_gemma":[0.000010726964,0.00005129451,0.00027849787,0.000028115115,0.00001920091,0.000080089456,0.00001857052,0.9706458,0.0071986984,0.016709166,0.004944699,0.000015180078],"about_ca_topic_score_codex":0.0066902298,"about_ca_topic_score_gemma":0.004624274,"teacher_disagreement_score":0.0066902298,"about_ca_system_score_codex":0.00075743615,"about_ca_system_score_gemma":0.0004526111,"threshold_uncertainty_score":0.013302565},"labels":[],"label_agreement":null},{"id":"W2137161313","doi":"10.1109/ijcnn.2006.246925","title":"A Novel Cooperative Neural Learning Algorithm for Data Fusion","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Algorithm; Computer science; Least absolute deviations; Artificial neural network; Modular design; Sensor fusion; Fusion; Image fusion; Gaussian; Variance (accounting); Artificial intelligence; Image (mathematics); Mathematics; Estimator","score_opus":0.07620864815218481,"score_gpt":0.2962242574833673,"score_spread":0.22001560933118247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137161313","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013153706,0.00006631945,0.99801046,0.000040436476,0.0000149777015,0.000015831285,0.0000046773944,0.00014920368,0.00038284232],"genre_scores_gemma":[0.13982978,0.0001496225,0.8570968,0.00018664397,0.00006304423,0.00025936955,0.00007411493,0.000079534264,0.0022610698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989969,0.00021624372,0.000056244928,0.00025110727,0.00041156088,0.00006790382],"domain_scores_gemma":[0.99917644,0.00026351665,0.0001155685,0.00009765964,0.00031199865,0.000034887733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015930883,0.00070744375,0.0007222384,0.00078553753,0.00060508004,0.0008590459,0.002000699,0.0012125815,0.0015854639],"category_scores_gemma":[0.0031132144,0.0003860877,0.0005234296,0.0009538907,0.0008191141,0.0017021593,0.0019637295,0.0012789,0.00065851986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015041325,0.000091814865,0.00070263864,0.0001278912,0.00008173569,0.000097138545,0.0002507783,0.25616884,0.022011358,0.031160558,0.003203366,0.6859535],"study_design_scores_gemma":[0.00001443664,0.00003450224,0.0000702104,0.00000536476,0.0000067767655,0.000041416668,0.000009080898,0.99165547,0.0030516789,0.0033242088,0.0017759777,0.000010911709],"about_ca_topic_score_codex":0.0023380872,"about_ca_topic_score_gemma":0.0019416201,"teacher_disagreement_score":0.0023380872,"about_ca_system_score_codex":0.0008513396,"about_ca_system_score_gemma":0.0010990733,"threshold_uncertainty_score":0.008425117},"labels":[],"label_agreement":null},{"id":"W2137367101","doi":"10.1109/pacrim.2001.953581","title":"Modular architecture for statistical signal parameter measurement using multi-bit random-data representation","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Modular design; Representation (politics); Binary number; SIGNAL (programming language); Binary data; Algorithm; Instrumentation (computer programming); Theoretical computer science; Mathematics; Programming language; Arithmetic","score_opus":0.2580749106034233,"score_gpt":0.3406040162200499,"score_spread":0.08252910561662657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137367101","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007352864,0.000086612796,0.99058515,0.0000875782,0.000041161773,0.00004821652,0.000034660436,0.00082244334,0.00094127964],"genre_scores_gemma":[0.31032223,0.00016509896,0.6874249,0.00020834907,0.000083048726,0.00026715276,0.0001404362,0.00009427379,0.0012945166],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990388,0.0002671996,0.00007918542,0.00019988595,0.00034742395,0.00006750312],"domain_scores_gemma":[0.9988696,0.00033736188,0.00013705577,0.0004037258,0.00021500626,0.000037222148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012478099,0.0007745871,0.00067873945,0.0008828422,0.0003408434,0.0011192508,0.0015660743,0.0008907507,0.0024290576],"category_scores_gemma":[0.0024335342,0.00031058118,0.00052566925,0.00069249416,0.0007474892,0.0020247079,0.0008752292,0.0011799582,0.0009791562],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006864827,0.00024693753,0.0013135788,0.00052419526,0.00015635468,0.00035473713,0.00030838267,0.076774076,0.25042915,0.22073773,0.0025067893,0.44596162],"study_design_scores_gemma":[0.00007938441,0.0005579048,0.0009923652,0.00009117356,0.00012773753,0.0006105284,0.00003450754,0.73859763,0.18280095,0.06310079,0.012891705,0.00011540476],"about_ca_topic_score_codex":0.00028105485,"about_ca_topic_score_gemma":0.00048027778,"teacher_disagreement_score":0.0024290576,"about_ca_system_score_codex":0.00057081116,"about_ca_system_score_gemma":0.00047783085,"threshold_uncertainty_score":0.00812602},"labels":[],"label_agreement":null},{"id":"W2137599484","doi":"10.1109/icnn.1997.616113","title":"Modeling of neural networks in feedback systems using describing functions","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Computer science; Simple (philosophy); Control theory (sociology); Stochastic neural network; Extension (predicate logic); Transient (computer programming); Control engineering; Time delay neural network; Artificial intelligence; Control (management); Engineering","score_opus":0.1203130324886533,"score_gpt":0.2674095212788595,"score_spread":0.14709648879020618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137599484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004762351,0.00013927241,0.99289024,0.00007021052,0.000021751324,0.000029382201,0.000025117077,0.00015369018,0.0019080021],"genre_scores_gemma":[0.65370065,0.0018920152,0.33338895,0.00019045708,0.00007584462,0.00065197743,0.00024505612,0.00013602426,0.009719103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996094,0.00015362441,0.000025469319,0.000054789736,0.00012618229,0.000030503168],"domain_scores_gemma":[0.9995809,0.00022189107,0.000067200126,0.00003879717,0.00007842686,0.000012782996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008618909,0.00082412397,0.00053162116,0.0004429872,0.0003514691,0.0010106311,0.0012396383,0.0014599492,0.0011438875],"category_scores_gemma":[0.0018115926,0.00038473745,0.0007142999,0.00036317072,0.000737019,0.001686754,0.0005678951,0.0010360023,0.00043080546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017233691,0.000016876462,0.00018582893,0.00006087221,0.000015690766,0.00010104404,0.0000928301,0.8722746,0.0051229317,0.11315031,0.00025610934,0.008705745],"study_design_scores_gemma":[0.000002958137,0.000010292077,0.000023685585,0.0000069895973,0.000004185294,0.000019055076,0.000008425823,0.98324525,0.00096514693,0.014581952,0.0011267441,0.0000052663127],"about_ca_topic_score_codex":0.0025640056,"about_ca_topic_score_gemma":0.0012271601,"teacher_disagreement_score":0.0025640056,"about_ca_system_score_codex":0.00077518483,"about_ca_system_score_gemma":0.00063637283,"threshold_uncertainty_score":0.0056244135},"labels":[],"label_agreement":null},{"id":"W2137690529","doi":"10.1109/nebc.1995.513734","title":"Classification of myoelectric signal burst patterns using a dynamic neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Artificial neural network; Computer science; Waveform; Feed forward; Artificial intelligence; Transient (computer programming); Time delay neural network; Pattern recognition (psychology); SIGNAL (programming language); Feedforward neural network; Classifier (UML); Signal processing; Speech recognition; Engineering; Telecommunications; Control engineering","score_opus":0.036379490699243774,"score_gpt":0.25421668767661065,"score_spread":0.21783719697736686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137690529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22700524,0.0011144641,0.76632583,0.0005645675,0.000119389704,0.00011348654,0.00029994064,0.0008697784,0.0035872702],"genre_scores_gemma":[0.88173246,0.00049108686,0.1140198,0.00011032158,0.00007097916,0.00009926373,0.00043993376,0.000031888696,0.0030042478],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998167,0.000028858878,0.000014781087,0.00007831628,0.000034616885,0.000026839702],"domain_scores_gemma":[0.99973816,0.00011379645,0.000038265418,0.000021071777,0.00007130265,0.00001743417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003818643,0.00044141427,0.0004228542,0.0009238696,0.0002770363,0.0007022587,0.000577073,0.00073658046,0.000993468],"category_scores_gemma":[0.0012794117,0.00022645466,0.00034575732,0.00060285034,0.00028782248,0.0007217818,0.00034117614,0.0005242424,0.00028734212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005925968,0.00026323248,0.008549893,0.000115030656,0.0001055015,0.00028031776,0.00011948435,0.28206143,0.051159833,0.004012268,0.001828346,0.6509121],"study_design_scores_gemma":[0.000004385941,0.000029251107,0.00118917,0.000005311672,0.000009333395,0.000037277117,0.0000098411965,0.99525946,0.0020621868,0.0010659585,0.0003207954,0.0000070303427],"about_ca_topic_score_codex":0.0027393235,"about_ca_topic_score_gemma":0.0023704874,"teacher_disagreement_score":0.0027393235,"about_ca_system_score_codex":0.00053043495,"about_ca_system_score_gemma":0.000295877,"threshold_uncertainty_score":0.005446732},"labels":[],"label_agreement":null},{"id":"W2137714673","doi":"10.1109/ijcnn.2007.4371078","title":"A Sound Localization and Recognition System using Pulsed Neural Networks on FPGA","year":2007,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"","keywords":"Field-programmable gate array; Computer science; Noise (video); Software; Artificial neural network; Computer hardware; Embedded system; Artificial intelligence","score_opus":0.09953924064576908,"score_gpt":0.3243847837885544,"score_spread":0.22484554314278532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137714673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06622436,0.00035655632,0.9218531,0.00014225923,0.00024680042,0.00015590897,0.00014750061,0.0071221213,0.0037514106],"genre_scores_gemma":[0.5767748,0.00027305487,0.4167075,0.00017899324,0.0000701508,0.0002131659,0.0002493636,0.000086663524,0.005446376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998147,0.00003266829,0.000014879039,0.00004708196,0.000071111244,0.000019525402],"domain_scores_gemma":[0.99976546,0.00006533438,0.000020884176,0.000045690333,0.00008051302,0.000022111475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024200891,0.00037027625,0.0003848055,0.0003111594,0.00020921955,0.000474638,0.0009497056,0.00042710346,0.003966149],"category_scores_gemma":[0.00045085573,0.00021093032,0.00020744333,0.00026661382,0.00016408796,0.000620088,0.0003355444,0.00039269112,0.0009677555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011068524,0.00020298133,0.0025297184,0.0005130655,0.00017710357,0.0005935851,0.00011546054,0.02926328,0.39788595,0.004694295,0.005379306,0.5575385],"study_design_scores_gemma":[0.00032266477,0.0018220755,0.004750155,0.00009758618,0.0002294961,0.0017005418,0.000073333315,0.74660414,0.22343943,0.0028219405,0.01803005,0.000108635824],"about_ca_topic_score_codex":0.0010118233,"about_ca_topic_score_gemma":0.0011728795,"teacher_disagreement_score":0.003966149,"about_ca_system_score_codex":0.00022075091,"about_ca_system_score_gemma":0.00027706564,"threshold_uncertainty_score":0.013268113},"labels":[],"label_agreement":null},{"id":"W2137889427","doi":"10.1109/ijcnn.2000.861455","title":"A Neural Support Vector Network architecture with adaptive kernels","year":2000,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Support vector machine; Kernel (algebra); Artificial intelligence; Artificial neural network; Similarity (geometry); Computer science; Similarity measure; Pattern recognition (psychology); Kernel method; Measure (data warehouse); Machine learning; Least squares support vector machine; Relevance vector machine; Simple (philosophy); Discriminant; Mathematics; Data mining","score_opus":0.009667968102555895,"score_gpt":0.20861407407434737,"score_spread":0.19894610597179146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137889427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058573484,0.0010662109,0.93166536,0.0005209441,0.00021337277,0.00007625132,0.00010277484,0.0015226388,0.0062589175],"genre_scores_gemma":[0.7177172,0.0007002502,0.27005893,0.00023822881,0.000119031334,0.00014028812,0.00028084032,0.000051289495,0.010693943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977523,0.000040532017,0.000015261381,0.000059582577,0.000078544144,0.000030848132],"domain_scores_gemma":[0.9996307,0.00007380239,0.00002637535,0.000038700873,0.00020126915,0.000029125325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000530638,0.00046152622,0.0005545706,0.00030437583,0.00028845266,0.00080505386,0.0012571819,0.0011743993,0.002186111],"category_scores_gemma":[0.0014181243,0.00026141154,0.0003398711,0.0005155725,0.00027747353,0.001147012,0.0007331382,0.0007969391,0.0011707564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036250145,0.00019542772,0.0010516953,0.00015598207,0.00010148843,0.0002189218,0.00008481681,0.5220562,0.02386041,0.015167204,0.0046223653,0.43212292],"study_design_scores_gemma":[0.000009945479,0.000060108952,0.00011771242,0.000007702741,0.0000115981165,0.00002973141,0.0000034971395,0.9950068,0.0017996565,0.0020290634,0.0009172849,0.0000068459567],"about_ca_topic_score_codex":0.0025158022,"about_ca_topic_score_gemma":0.0024214182,"teacher_disagreement_score":0.0025158022,"about_ca_system_score_codex":0.0004175776,"about_ca_system_score_gemma":0.0005409131,"threshold_uncertainty_score":0.007313311},"labels":[],"label_agreement":null},{"id":"W2138441731","doi":"10.1109/ijcnn.1991.155363","title":"Neural-net method for dual subspace pattern recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Subspace topology; Hebbian theory; Computer science; Artificial neural network; Artificial intelligence; Dual (grammatical number); Pattern recognition (psychology); Set (abstract data type); Backpropagation; Net (polyhedron); Random subspace method; Layer (electronics); Mathematics","score_opus":0.06509666865989061,"score_gpt":0.2917495880686889,"score_spread":0.22665291940879828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138441731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002167309,0.00012724727,0.9948655,0.00006267156,0.00007515225,0.000030290676,0.00008767213,0.0011153768,0.0014688048],"genre_scores_gemma":[0.077285044,0.00021055734,0.9094474,0.0001254599,0.000055149503,0.00018848784,0.0004956317,0.00018415823,0.012008041],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962175,0.00006628408,0.000024662366,0.00007979869,0.0001771553,0.000030227886],"domain_scores_gemma":[0.9997087,0.000055962682,0.000022294687,0.00005955203,0.00013488287,0.000018593086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056855043,0.00062666886,0.0006709843,0.00089666375,0.00030614794,0.0008485448,0.001199481,0.00072654564,0.006092631],"category_scores_gemma":[0.0009498069,0.00025095866,0.0004200279,0.00084088533,0.0003924587,0.0010610365,0.0007632119,0.0009109999,0.0027603302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018870007,0.00007897218,0.0004770118,0.00014098453,0.00007351475,0.000117485644,0.000032158972,0.108877964,0.023705827,0.02825582,0.008350126,0.8297015],"study_design_scores_gemma":[0.000011982881,0.000022651164,0.00017431656,0.0000055449427,0.000010288106,0.00008834001,0.000005251695,0.9776718,0.0102636665,0.0057043894,0.0060300194,0.000011659108],"about_ca_topic_score_codex":0.0025844309,"about_ca_topic_score_gemma":0.0029988505,"teacher_disagreement_score":0.006092631,"about_ca_system_score_codex":0.00045432325,"about_ca_system_score_gemma":0.0007677616,"threshold_uncertainty_score":0.020381927},"labels":[],"label_agreement":null},{"id":"W2138727659","doi":"10.1109/tnn.2009.2023120","title":"BAM Learning of Nonlinearly Separable Tasks by Using an Asymmetrical Output Function and Reinforcement Learning","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; University of Ottawa","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Artificial neural network; Content-addressable memory; Function (biology); Property (philosophy)","score_opus":0.020679957306906246,"score_gpt":0.2564761758607362,"score_spread":0.23579621855382993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138727659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13626722,0.00016912715,0.85987025,0.000161371,0.00003292585,0.00003904929,0.000020844369,0.0003994462,0.0030397573],"genre_scores_gemma":[0.93327355,0.00010318303,0.06446471,0.00004378672,0.000013289604,0.000064470056,0.000027983919,0.000019177478,0.001989959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982125,0.00007004301,0.000013512588,0.000034150013,0.000040090235,0.000021106956],"domain_scores_gemma":[0.9992747,0.0003315016,0.000104357474,0.00013200402,0.00011177754,0.00004575169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097387534,0.0005012177,0.0005219128,0.00022647194,0.0001758983,0.00049927895,0.00082196295,0.0005373051,0.0012588486],"category_scores_gemma":[0.0029865354,0.00024384189,0.00029907667,0.00020762242,0.0006072651,0.001176264,0.0006653563,0.0006193308,0.00026036412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037803673,0.00018473247,0.0023134213,0.0001534249,0.00009613821,0.00019660039,0.00020284523,0.73591346,0.030300325,0.050149288,0.00078467483,0.17932706],"study_design_scores_gemma":[0.00001341897,0.000046128174,0.00012165679,0.0000039776864,0.000008701598,0.000025335718,0.0000060098223,0.98710674,0.0028814364,0.0095087765,0.00027225845,0.000005381784],"about_ca_topic_score_codex":0.0011054128,"about_ca_topic_score_gemma":0.00094786554,"teacher_disagreement_score":0.0012588486,"about_ca_system_score_codex":0.00039940132,"about_ca_system_score_gemma":0.00036014014,"threshold_uncertainty_score":0.0051504374},"labels":[],"label_agreement":null},{"id":"W2138776269","doi":"10.1109/ijcnn.2002.1007489","title":"Clustering unlabeled data with SOMs improves classification of labeled real-world data","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Labeled data; Cluster analysis; Artificial intelligence; Benchmark (surveying); Pattern recognition (psychology); Artificial neural network; Self-organizing map; Multilayer perceptron; Semi-supervised learning; Supervised learning; Cluster (spacecraft); Perceptron; Unsupervised learning; Data mining; Machine learning","score_opus":0.09779251755219591,"score_gpt":0.3197640324068891,"score_spread":0.22197151485469319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138776269","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26890242,0.0010158804,0.71449715,0.0006780763,0.00043984325,0.0001363754,0.00059205986,0.0046890983,0.009049147],"genre_scores_gemma":[0.649299,0.00048409618,0.34236145,0.00024213997,0.00020142205,0.00007810319,0.0018172956,0.00026576102,0.005250744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990859,0.00032827625,0.000040627787,0.0002001111,0.0002708515,0.00007430574],"domain_scores_gemma":[0.9965642,0.0014113501,0.00023363007,0.00090641016,0.00074700546,0.00013734352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001879155,0.0009771273,0.00072523585,0.0010158771,0.00050243945,0.0013195989,0.0007630407,0.0011474956,0.0014724334],"category_scores_gemma":[0.0077931914,0.00025827295,0.00058503036,0.0011581811,0.0007180082,0.0019095406,0.0009228345,0.0012458629,0.0011832849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013117203,0.0011836604,0.012422152,0.00035624285,0.00030281438,0.0002445803,0.00043952971,0.30259928,0.036426995,0.0062330784,0.011466682,0.6270133],"study_design_scores_gemma":[0.000022765727,0.0001604955,0.0015624781,0.00002190311,0.000031370448,0.00004621446,0.00011117049,0.97005254,0.017638417,0.008412274,0.0019160056,0.000024315921],"about_ca_topic_score_codex":0.0025176648,"about_ca_topic_score_gemma":0.0056138365,"teacher_disagreement_score":0.0025176648,"about_ca_system_score_codex":0.0004256307,"about_ca_system_score_gemma":0.00043071,"threshold_uncertainty_score":0.009938061},"labels":[],"label_agreement":null},{"id":"W2138976787","doi":"10.1109/rtcon.1991.673248","title":"A Computer Upgrade for the CAMAC-based Control System of the Chalk River","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Atomic Energy (Canada)","funders":"","keywords":"Upgrade; Computer Automated Measurement and Control; Computer science; Control system; Operating system; Engineering; Data acquisition; Electrical engineering","score_opus":0.01058379528847723,"score_gpt":0.21479639007733198,"score_spread":0.20421259478885476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138976787","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37529862,0.0010701969,0.4103232,0.0021651976,0.003235409,0.0014524192,0.002433383,0.07759803,0.12642351],"genre_scores_gemma":[0.89746606,0.0002001256,0.051544532,0.00036399576,0.00020018154,0.00024535996,0.0012465552,0.0006030972,0.048130095],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99964404,0.00005270617,0.000017918832,0.00008914466,0.00014827047,0.000047948528],"domain_scores_gemma":[0.99936014,0.00006293556,0.000013341889,0.00013830139,0.00034666163,0.00007864439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005022369,0.0005024715,0.00067411596,0.0007648585,0.0010235572,0.0007552642,0.0011442717,0.0007052641,0.033791732],"category_scores_gemma":[0.000855652,0.00028033496,0.00032188703,0.00051915366,0.00031940968,0.00058089953,0.0006981731,0.0008666629,0.004318332],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024125017,0.00048592998,0.01288775,0.0005605627,0.00013645012,0.0016837816,0.0006762509,0.027148567,0.15499231,0.005517178,0.06730107,0.7261978],"study_design_scores_gemma":[0.0012671485,0.0027475134,0.04290873,0.00022142255,0.0003975535,0.00199033,0.00040990493,0.44246948,0.118451156,0.0019951458,0.38686526,0.00027642417],"about_ca_topic_score_codex":0.010094498,"about_ca_topic_score_gemma":0.009954584,"teacher_disagreement_score":0.033791732,"about_ca_system_score_codex":0.000575721,"about_ca_system_score_gemma":0.001307406,"threshold_uncertainty_score":0.11304456},"labels":[],"label_agreement":null},{"id":"W2139201457","doi":"10.1109/ccece.2005.1557141","title":"A novel approach for finding the movement of an object in video sequences by an artificial neural network for 2.5D object modeling","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Motion estimation; Pixel; Motion vector; Object (grammar); Frame (networking); Segmentation; Artificial neural network; Block-matching algorithm; Reference frame; Motion compensation; Image segmentation; Motion (physics); Pattern recognition (psychology); Video tracking; Image (mathematics)","score_opus":0.06020318023079394,"score_gpt":0.28436840508015265,"score_spread":0.22416522484935872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139201457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039809668,0.00016242047,0.9949078,0.00004119805,0.000030980962,0.00002071908,0.000027055034,0.00034361484,0.0004851658],"genre_scores_gemma":[0.10994415,0.00045044292,0.8856969,0.000084313,0.000049854996,0.00012399757,0.00014912368,0.000044854285,0.0034564468],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997892,0.00003258298,0.00001601477,0.000081877595,0.00006547565,0.000014968537],"domain_scores_gemma":[0.999874,0.000039723407,0.000022180957,0.000017565131,0.000039373175,0.0000072256066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004037553,0.0006689085,0.00046370464,0.00075785926,0.00027659768,0.0005310724,0.00077387475,0.0009502681,0.0015134312],"category_scores_gemma":[0.00061132835,0.00034863717,0.0005854459,0.0006411322,0.00032093315,0.0009006934,0.00032772144,0.0005342405,0.0005050645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017926839,0.000087979046,0.0015036565,0.00021735585,0.00011083984,0.0002411379,0.00012854912,0.27885166,0.061480332,0.013017891,0.0017166074,0.64246476],"study_design_scores_gemma":[0.000004154697,0.000040593262,0.0003899998,0.00001053024,0.000015016386,0.00006155543,0.000007679354,0.9902919,0.005634242,0.0012926005,0.0022410932,0.000010633649],"about_ca_topic_score_codex":0.0052158907,"about_ca_topic_score_gemma":0.007230539,"teacher_disagreement_score":0.0052158907,"about_ca_system_score_codex":0.0006260334,"about_ca_system_score_gemma":0.00046069044,"threshold_uncertainty_score":0.010371029},"labels":[],"label_agreement":null},{"id":"W2139215385","doi":"10.1109/isic.1990.128502","title":"Artificial neural networks in visual perception","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial neural network; Artificial intelligence; Computer science; Perception; Object (grammar); Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Psychology","score_opus":0.027812496205837447,"score_gpt":0.2608149881887067,"score_spread":0.23300249198286924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139215385","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0112876445,0.19649734,0.6891989,0.010729358,0.0020325286,0.00007653255,0.0004576438,0.0011260147,0.088594064],"genre_scores_gemma":[0.53342897,0.14120452,0.2609552,0.002373093,0.002775508,0.00034575007,0.0008917518,0.00028214243,0.05774316],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996878,0.00008953991,0.000021339149,0.00007156439,0.000107424436,0.00002238327],"domain_scores_gemma":[0.9997466,0.00012493435,0.000020361776,0.000024608538,0.0000681879,0.000015320966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005131444,0.000583752,0.00056644576,0.00069992524,0.0002726148,0.0020660767,0.00091744936,0.0018450947,0.0038792067],"category_scores_gemma":[0.0014442634,0.00027756058,0.0003671465,0.0011687891,0.0013527592,0.0022455652,0.00067251595,0.0017914685,0.0010680702],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054006046,0.000034344375,0.0004956857,0.000668499,0.00009757172,0.00019930046,0.0001955838,0.08747878,0.003536732,0.6372694,0.016091127,0.25387907],"study_design_scores_gemma":[0.0000135685905,0.000021607615,0.0006217461,0.00020372994,0.0000171784,0.00011814443,0.00006179075,0.14775439,0.0010954671,0.79792166,0.052137103,0.00003360812],"about_ca_topic_score_codex":0.0031640565,"about_ca_topic_score_gemma":0.001925511,"teacher_disagreement_score":0.0038792067,"about_ca_system_score_codex":0.0009996974,"about_ca_system_score_gemma":0.0005358645,"threshold_uncertainty_score":0.012977302},"labels":[],"label_agreement":null},{"id":"W2139263136","doi":"10.1109/ijcnn.2006.247153","title":"Improving the Convergence of Backpropagation by Opposite Transfer Functions","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backpropagation; Computer science; Convergence (economics); Benchmark (surveying); Perceptron; Artificial neural network; Rate of convergence; Activation function; Artificial intelligence; Multilayer perceptron; Transfer function; Machine learning; Algorithm; Key (lock); Engineering","score_opus":0.024682205241403854,"score_gpt":0.22773344666735623,"score_spread":0.20305124142595238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139263136","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05065626,0.0003181325,0.9422725,0.00024646946,0.00020645127,0.00005981188,0.000019044817,0.00095436245,0.0052670105],"genre_scores_gemma":[0.5445768,0.00037871656,0.45030332,0.0002320449,0.00008901852,0.00016983788,0.00011392953,0.0003285922,0.0038077761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881124,0.0003186696,0.000088518114,0.00012565182,0.00054710836,0.00010881229],"domain_scores_gemma":[0.99604404,0.0020388456,0.00026905804,0.00040705633,0.0011348566,0.00010611005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032839521,0.0012544158,0.0009308086,0.00095875154,0.00044417966,0.0010899288,0.0013447842,0.0019798495,0.0020011265],"category_scores_gemma":[0.01368438,0.00040639486,0.0006816783,0.00063330587,0.00092038023,0.0017931338,0.0013920028,0.0018892887,0.00096177036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047427195,0.00028093238,0.001454493,0.00027969494,0.00013347827,0.00025782033,0.00013340102,0.7106795,0.028389333,0.021091558,0.0026710986,0.2341545],"study_design_scores_gemma":[0.000029935905,0.00008775524,0.00016415895,0.000010777286,0.000014240461,0.00007433945,0.000008604177,0.9863239,0.008053888,0.0041555883,0.00106525,0.00001145192],"about_ca_topic_score_codex":0.0011605285,"about_ca_topic_score_gemma":0.00081956456,"teacher_disagreement_score":0.0032839521,"about_ca_system_score_codex":0.00049418194,"about_ca_system_score_gemma":0.0008076918,"threshold_uncertainty_score":0.017367363},"labels":[],"label_agreement":null},{"id":"W2139389881","doi":"10.1109/icnn.1997.616182","title":"Incorporating state space constraints into a neural network","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Manifold (fluid mechanics); Computer science; Recurrent neural network; Artificial neural network; Backpropagation; Nonlinear dimensionality reduction; State space; Differentiable function; Artificial intelligence; Space (punctuation); Mathematical optimization; Topology (electrical circuits); Mathematics","score_opus":0.04064891687919103,"score_gpt":0.25925171624709886,"score_spread":0.21860279936790783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139389881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031172622,0.00017496065,0.96578425,0.00032500073,0.000026876925,0.00003065381,0.000041974894,0.00024562262,0.0021980032],"genre_scores_gemma":[0.8373759,0.0002910734,0.15842308,0.00014444262,0.00005930073,0.0001846963,0.000104391125,0.00010662951,0.00331048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993605,0.00021278106,0.00003535565,0.00015726133,0.00016583549,0.00006822321],"domain_scores_gemma":[0.99800223,0.0012462101,0.00022538376,0.00017115742,0.0002733948,0.00008172618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013875331,0.00084100396,0.00085819914,0.00037954247,0.0005173652,0.001057146,0.0011339772,0.001763182,0.002067967],"category_scores_gemma":[0.0074415193,0.00060982344,0.00040481458,0.00046614037,0.0013623993,0.0034564394,0.0018274046,0.0015773192,0.00029908863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025856114,0.000013813751,0.00017941916,0.00002280228,0.000010129958,0.000045398807,0.000029538918,0.97356707,0.0013053415,0.016067686,0.00015480815,0.0085781645],"study_design_scores_gemma":[0.000003180916,0.000015836731,0.000025131021,0.000003227867,0.0000019183353,0.0000038854723,0.000002298534,0.9925447,0.0003622693,0.0068198866,0.00021447243,0.0000031232557],"about_ca_topic_score_codex":0.0055466285,"about_ca_topic_score_gemma":0.0052969255,"teacher_disagreement_score":0.0055466285,"about_ca_system_score_codex":0.0008838111,"about_ca_system_score_gemma":0.0010302254,"threshold_uncertainty_score":0.011028707},"labels":[],"label_agreement":null},{"id":"W2139983816","doi":"10.1109/ccece.1997.614852","title":"Evolutionary artificial neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Encoding (memory); Artificial neural network; Fitness function; Generalization; Artificial intelligence; Pattern recognition (psychology); Chromosome; Genetic algorithm; Detector; Set (abstract data type); Feature (linguistics); Task (project management); Feature extraction; Machine learning; Mathematics; Engineering; Gene","score_opus":0.029256987307327193,"score_gpt":0.22609405608042465,"score_spread":0.19683706877309745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139983816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35109007,0.002406768,0.5960929,0.0012830673,0.00046923815,0.00031590374,0.00030078375,0.0011295021,0.046911843],"genre_scores_gemma":[0.7140994,0.0008793268,0.27405733,0.0004068165,0.000046329355,0.00026258797,0.00030817595,0.00010327471,0.009836819],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945575,0.00018117193,0.00003141392,0.00008958548,0.00019376984,0.00004833807],"domain_scores_gemma":[0.9984388,0.00092581555,0.00009931006,0.00019597681,0.0002928742,0.000047234018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013437235,0.0006260378,0.0006267516,0.00048245932,0.0004918597,0.0010546729,0.00096758036,0.0010763694,0.0027675114],"category_scores_gemma":[0.006129299,0.00021325507,0.00032869173,0.00082598964,0.00055008836,0.00097410235,0.00060985924,0.0009120292,0.00050229335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022843486,0.0003247668,0.00506017,0.00023418882,0.00020545429,0.000238926,0.00019186293,0.6495826,0.005918844,0.034252286,0.0034466248,0.30031583],"study_design_scores_gemma":[0.00005194049,0.00017761419,0.0010421567,0.000036161608,0.000048981376,0.00013488292,0.000037727557,0.9752172,0.0034844007,0.012564848,0.007186409,0.000017652323],"about_ca_topic_score_codex":0.0019840715,"about_ca_topic_score_gemma":0.0028801893,"teacher_disagreement_score":0.0027675114,"about_ca_system_score_codex":0.00057956064,"about_ca_system_score_gemma":0.0003761614,"threshold_uncertainty_score":0.009258211},"labels":[],"label_agreement":null},{"id":"W2140293891","doi":"10.1109/pacrim.1989.48414","title":"A neural network implementation of the median filter","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial neural network; Computer science; Contrast (vision); Constant (computer programming); Filter (signal processing); Implementation; Algorithm; Real-time computing; Artificial intelligence; Computer vision; Programming language","score_opus":0.01625565221468949,"score_gpt":0.26724466923298973,"score_spread":0.25098901701830023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140293891","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042486303,0.00011610429,0.9910568,0.00011408155,0.0000839147,0.00002528767,0.00007320652,0.0009573075,0.0033246789],"genre_scores_gemma":[0.2687283,0.00040304556,0.71454656,0.00017582058,0.000114819166,0.00017368454,0.0003035091,0.00010449451,0.015449796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983096,0.000023790451,0.000009664789,0.000047574256,0.00006321189,0.000024844216],"domain_scores_gemma":[0.999879,0.000025875184,0.000011297015,0.000017269775,0.00005728306,0.000009178777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002698692,0.0003292481,0.00035770683,0.00030096824,0.0003163983,0.0006457471,0.0008597833,0.000864991,0.00597459],"category_scores_gemma":[0.00067458063,0.00024746306,0.00038207765,0.00048058073,0.00025190582,0.00078744494,0.00044129038,0.0006593095,0.0013669468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002815313,0.00010407802,0.00070656446,0.00015511086,0.000107355496,0.00018820896,0.00005895845,0.413876,0.056895223,0.04752148,0.006035592,0.47406995],"study_design_scores_gemma":[0.000014457183,0.00004905002,0.00015776775,0.000007556994,0.000015590314,0.000060051276,0.000005698044,0.97892445,0.0107116,0.0045159617,0.0055272104,0.000010619775],"about_ca_topic_score_codex":0.0039040905,"about_ca_topic_score_gemma":0.0046287887,"teacher_disagreement_score":0.00597459,"about_ca_system_score_codex":0.00059579307,"about_ca_system_score_gemma":0.0006634175,"threshold_uncertainty_score":0.019986987},"labels":[],"label_agreement":null},{"id":"W2140587028","doi":"10.1017/s0140525x00503355","title":"Prototypes and portability in artificial neural network models","year":2000,"lang":"en","type":"article","venue":"Behavioral and Brain Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Software portability; Computer science; Artificial neural network; Stimulus (psychology); Coding (social sciences); Artificial intelligence; Deep neural networks; Machine learning; Psychology; Cognitive psychology; Programming language; Mathematics","score_opus":0.06636256343518356,"score_gpt":0.31335479876252914,"score_spread":0.2469922353273456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140587028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13990246,0.00058783893,0.8446162,0.0014683149,0.000097885175,0.00012922038,0.00020588048,0.0009030544,0.01208915],"genre_scores_gemma":[0.9523578,0.00042957635,0.043304056,0.000120849625,0.00010781647,0.00019969023,0.00013305865,0.0001424394,0.0032046225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972452,0.0015608703,0.00019281115,0.00052076497,0.0003031191,0.00017723677],"domain_scores_gemma":[0.9637104,0.028047781,0.0022712438,0.0043978705,0.0009912999,0.000581282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057744044,0.0013436339,0.0014315813,0.001945953,0.0008075744,0.0036071008,0.0028478086,0.002483029,0.006101534],"category_scores_gemma":[0.047342252,0.0012564462,0.001688306,0.0011125157,0.004395599,0.011530193,0.0040329047,0.003707716,0.0005397357],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026741394,0.00012555967,0.0020298152,0.00011543605,0.00014730093,0.00042213674,0.000301262,0.5728741,0.0009677507,0.39964157,0.0005979318,0.022509666],"study_design_scores_gemma":[0.000028777722,0.000040063907,0.00010951793,0.000015967418,0.000014230389,0.00006758023,0.000015457375,0.5891475,0.00026731085,0.410012,0.000265566,0.000016012815],"about_ca_topic_score_codex":0.0013191113,"about_ca_topic_score_gemma":0.0007504559,"teacher_disagreement_score":0.006101534,"about_ca_system_score_codex":0.0010636491,"about_ca_system_score_gemma":0.00052481267,"threshold_uncertainty_score":0.03053838},"labels":[],"label_agreement":null},{"id":"W2141070932","doi":"10.1109/tnn.2004.824415","title":"Scalable Closed-Boundary Analog Neural Networks","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto; Sharif University of Technology","keywords":"Computer science; Scalability; Quadratic equation; Artificial neural network; Boundary (topology); Classifier (UML); Activation function; Topology (electrical circuits); Artificial intelligence; Mathematics","score_opus":0.013883666511080792,"score_gpt":0.23672248705283175,"score_spread":0.22283882054175097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141070932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11710623,0.00041905508,0.87204134,0.00029361082,0.0000953623,0.00005750081,0.000057197656,0.001221063,0.0087086195],"genre_scores_gemma":[0.8995622,0.00013644039,0.09602217,0.00012235908,0.00003099815,0.000087533626,0.00009075689,0.00003637264,0.0039112177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999808,0.000034032066,0.0000095269725,0.00005342947,0.00007025282,0.000024809482],"domain_scores_gemma":[0.99949634,0.0002246727,0.00005211573,0.000092907045,0.00010432042,0.000029680095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042992405,0.00030281456,0.00042925696,0.00019226417,0.00029307985,0.0007255964,0.001379605,0.0007437872,0.0027508747],"category_scores_gemma":[0.0018117549,0.00021289913,0.0002000951,0.00027533897,0.000738608,0.0016736024,0.0010177774,0.000626187,0.00038169543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020505924,0.000119216194,0.0006091927,0.0001282484,0.000030586725,0.00020925088,0.00014129972,0.6934443,0.035718575,0.059137557,0.0024935058,0.20776314],"study_design_scores_gemma":[0.000008847849,0.000029293918,0.00007495897,0.000003009663,0.0000028886732,0.00002689097,0.0000099115405,0.98331213,0.0022166686,0.013654168,0.0006576889,0.0000035619294],"about_ca_topic_score_codex":0.0010018713,"about_ca_topic_score_gemma":0.0015004892,"teacher_disagreement_score":0.0027508747,"about_ca_system_score_codex":0.00057002006,"about_ca_system_score_gemma":0.00028412897,"threshold_uncertainty_score":0.0092025995},"labels":[],"label_agreement":null},{"id":"W2141201766","doi":"10.1109/ijcnn.1991.155597","title":"The Guelph Darwin Project: the evolution of neural networks by genetic algorithms","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Darwin (ADL); Artificial neural network; Darwinism; Computer science; Artificial intelligence; Backpropagation; Genetic algorithm; Algorithm; Feedforward neural network; Machine learning; Evolutionary algorithm; Code (set theory); Biology","score_opus":0.016138598895407752,"score_gpt":0.23128820338159892,"score_spread":0.21514960448619116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141201766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005784551,0.03157362,0.77768797,0.0104031,0.004808501,0.00020056289,0.0013255986,0.005186351,0.16302979],"genre_scores_gemma":[0.10602545,0.06280056,0.61171144,0.001938787,0.0028804047,0.0007193029,0.0042645894,0.005613922,0.20404556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995869,0.0001435736,0.000021892643,0.00006013955,0.00016406021,0.00002341708],"domain_scores_gemma":[0.99955386,0.00022454554,0.00001878722,0.000041965493,0.000106882464,0.00005389396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001044887,0.0005795736,0.0005354237,0.0007553768,0.00037575656,0.0014664661,0.0007797011,0.0010784437,0.015923636],"category_scores_gemma":[0.0023751594,0.00025588684,0.00039725853,0.0012341265,0.0009363269,0.0011605523,0.0007692043,0.0011006073,0.0037802195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066734465,0.000023483462,0.00030611496,0.0005252503,0.00006095012,0.00013635308,0.00016702521,0.030132763,0.0019977377,0.31085858,0.2149839,0.44074106],"study_design_scores_gemma":[0.000058883874,0.000059362228,0.00065468723,0.00021912802,0.000039893614,0.00022977889,0.00003636369,0.042721454,0.0034565986,0.15008399,0.80239177,0.00004808428],"about_ca_topic_score_codex":0.0033207608,"about_ca_topic_score_gemma":0.0018948151,"teacher_disagreement_score":0.015923636,"about_ca_system_score_codex":0.0009744161,"about_ca_system_score_gemma":0.00093418563,"threshold_uncertainty_score":0.053269923},"labels":[],"label_agreement":null},{"id":"W2141408493","doi":"10.1109/iccd.1992.276243","title":"Synthesis of multiple bus/functional unit architectures implementing neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Heuristics; Computer architecture; Artificial neural network; Parallelism (grammar); Very-large-scale integration; Parallel computing; Artificial intelligence; Embedded system; Operating system","score_opus":0.02415846675850632,"score_gpt":0.24351072841442759,"score_spread":0.21935226165592125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141408493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068760574,0.00033834568,0.91552776,0.00015735421,0.00010944814,0.00009476934,0.00012611457,0.0016389915,0.013246548],"genre_scores_gemma":[0.49086064,0.00025370755,0.50233746,0.000075336306,0.000026213493,0.0001709504,0.0002027275,0.00011339818,0.0059596123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985373,0.000029788955,0.000010181916,0.000027762493,0.000051482384,0.0000270927],"domain_scores_gemma":[0.99985147,0.00005901728,0.000017259106,0.000018298713,0.000046498466,0.0000074351365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022848425,0.00041529856,0.0002455567,0.00027684515,0.00028451197,0.0004264643,0.0005277679,0.0003382552,0.0035147537],"category_scores_gemma":[0.00048260036,0.0002540682,0.00032500422,0.00027131106,0.00021417305,0.00040124115,0.00021610844,0.00036941966,0.0005153873],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012765614,0.00007018192,0.00039254464,0.0003200738,0.000070183196,0.0002527,0.00009430204,0.6007412,0.10263396,0.04373878,0.0021512555,0.24940708],"study_design_scores_gemma":[0.00002894686,0.00009892007,0.00018726924,0.000023475734,0.000026021995,0.000058496127,0.000019242372,0.9442335,0.040444996,0.008959871,0.0059088976,0.000010400695],"about_ca_topic_score_codex":0.0020342246,"about_ca_topic_score_gemma":0.0046665375,"teacher_disagreement_score":0.0035147537,"about_ca_system_score_codex":0.0005848004,"about_ca_system_score_gemma":0.00062428816,"threshold_uncertainty_score":0.011758089},"labels":[],"label_agreement":null},{"id":"W2141449116","doi":"10.1109/icnn.1995.488979","title":"A self-organizing neural network for learning and generating sequences of target-directed movements in the context of a delta-lognormal synergy","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Artificial intelligence; Context (archaeology); Kinematics; Population; Trajectory; Topology (electrical circuits); Mathematics","score_opus":0.019032728430902573,"score_gpt":0.2327218615686749,"score_spread":0.21368913313777232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141449116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05362569,0.00031163514,0.94278747,0.00020591267,0.00003497337,0.000026484491,0.00002853779,0.0003739251,0.002605317],"genre_scores_gemma":[0.75648534,0.00024682432,0.23995548,0.000103002145,0.000023418954,0.00011137304,0.000052880623,0.000029435196,0.0029921697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999032,0.000026946926,0.000006642391,0.000026081068,0.00002616495,0.000010849016],"domain_scores_gemma":[0.99979216,0.0000985164,0.000020931431,0.000027705084,0.000042605378,0.000018141354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004192948,0.00021885199,0.00026415903,0.000196553,0.00025232116,0.0003872695,0.0006891923,0.00050567725,0.0008683996],"category_scores_gemma":[0.0009137165,0.00017493038,0.00024039565,0.00026809634,0.0005697417,0.0007748492,0.00045390704,0.0004964153,0.00017285845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013954946,0.000070691094,0.0013078135,0.00011064398,0.000081553575,0.00017412519,0.00017457861,0.7545208,0.024701122,0.06307797,0.0013763078,0.15426484],"study_design_scores_gemma":[0.0000050667977,0.000026583642,0.00014428067,0.0000031300983,0.0000062360436,0.000025215064,0.0000055724945,0.9895399,0.0011604168,0.008609094,0.0004702777,0.0000041802105],"about_ca_topic_score_codex":0.001423961,"about_ca_topic_score_gemma":0.0017953031,"teacher_disagreement_score":0.001423961,"about_ca_system_score_codex":0.00038780353,"about_ca_system_score_gemma":0.0004181323,"threshold_uncertainty_score":0.0029051304},"labels":[],"label_agreement":null},{"id":"W2141484660","doi":"10.1109/iembs.1990.691844","title":"A Study Of Backpropagation, Counterpropagation, And Adaptive Resonance Theory Neural Network Models","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Backpropagation; Binary number; Artificial neural network; Adaptive resonance theory; Computer science; Artificial intelligence; Pattern recognition (psychology); Noise (video); Set (abstract data type); Stability (learning theory); Mathematics; Machine learning; Image (mathematics); Arithmetic","score_opus":0.02125918663302416,"score_gpt":0.24104168980279206,"score_spread":0.21978250316976788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141484660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2553005,0.004085799,0.72322315,0.0013153873,0.00023134373,0.00010454972,0.000049441915,0.0003061637,0.015383721],"genre_scores_gemma":[0.9549831,0.0011190961,0.040344656,0.00012734326,0.00007569075,0.0000872286,0.000027192442,0.000043351534,0.0031923088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951506,0.00020029828,0.000021384043,0.00007822682,0.00014526084,0.00003986658],"domain_scores_gemma":[0.9959402,0.0031284385,0.000276616,0.00015798364,0.00040412965,0.00009261641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00172327,0.00079482654,0.00073098176,0.00058466825,0.0004032679,0.0010306971,0.001177396,0.0015922595,0.0009622758],"category_scores_gemma":[0.010954358,0.00044181902,0.0005448797,0.000347468,0.0010519716,0.0021391057,0.0006278908,0.0010881807,0.00013710707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011185094,0.000064732754,0.0011334012,0.00011766948,0.000071718394,0.00015149852,0.00016707309,0.92564607,0.002798784,0.04704952,0.000479783,0.022207908],"study_design_scores_gemma":[0.0000045613733,0.000023489065,0.00009040133,0.0000060484076,0.0000076266706,0.0000217629,0.000005430472,0.99447745,0.0003265857,0.0048516574,0.00018029944,0.000004796883],"about_ca_topic_score_codex":0.0037110257,"about_ca_topic_score_gemma":0.0015893391,"teacher_disagreement_score":0.0037110257,"about_ca_system_score_codex":0.0007842997,"about_ca_system_score_gemma":0.000665428,"threshold_uncertainty_score":0.009113669},"labels":[],"label_agreement":null},{"id":"W2141875137","doi":"10.1109/ccdc.2008.4597822","title":"A fuzzy predictive model with confidence interval estimation for alloy property assessment","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Property (philosophy); Confidence interval; Reliability (semiconductor); Fuzzy logic; Computer science; Prediction interval; Fuzzy set; Interval (graph theory); Data mining; Training set; Reliability engineering; Statistics; Artificial intelligence; Machine learning; Mathematics; Engineering; Power (physics)","score_opus":0.03775219409180205,"score_gpt":0.28359598959578813,"score_spread":0.24584379550398608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141875137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009430186,0.00044502917,0.9884311,0.0001069031,0.00003296418,0.00001757667,0.00006599318,0.0002904138,0.0011798877],"genre_scores_gemma":[0.85610604,0.00076019886,0.14021373,0.000113145514,0.0000871364,0.00018379399,0.0003278234,0.000064443724,0.0021436617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986064,0.00037729045,0.00007801443,0.00032097296,0.00051935896,0.000098051365],"domain_scores_gemma":[0.99705637,0.0019586221,0.0002526403,0.00017443197,0.0005177488,0.000040199127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00303458,0.00077037286,0.0012699038,0.0010927478,0.00043032222,0.0015134758,0.0023845693,0.0013380252,0.0012573897],"category_scores_gemma":[0.010653144,0.00047046976,0.0009723058,0.0014259471,0.0007199459,0.0015226535,0.00090650615,0.0019496193,0.00031074113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059056234,0.000019450848,0.00053399516,0.000048317197,0.000044759716,0.00006830485,0.00004930128,0.9571799,0.00072457903,0.009338403,0.00040766297,0.031526227],"study_design_scores_gemma":[0.0000020140392,0.000005962719,0.000052177842,0.0000031407271,0.0000064151536,0.00000838547,0.0000014659057,0.9979723,0.0001571542,0.0016891866,0.000096467535,0.000005291006],"about_ca_topic_score_codex":0.011807827,"about_ca_topic_score_gemma":0.0045053377,"teacher_disagreement_score":0.011807827,"about_ca_system_score_codex":0.00089519547,"about_ca_system_score_gemma":0.0008977357,"threshold_uncertainty_score":0.02347821},"labels":[],"label_agreement":null},{"id":"W2141878707","doi":"10.1109/icsmc.1993.390688","title":"A neurocontroller for robot manipulators","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Control theory (sociology); Robot manipulator; Robot; Property (philosophy); Stability (learning theory); Artificial neural network; Nonlinear system; Computer science; Feed forward; Artificial intelligence; Control engineering; Control (management); Engineering; Machine learning","score_opus":0.044675108638501965,"score_gpt":0.22767940154300265,"score_spread":0.18300429290450068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141878707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01685292,0.0013120918,0.9534096,0.00057619147,0.0004069865,0.00010956212,0.0000803346,0.004336478,0.022915702],"genre_scores_gemma":[0.6711811,0.0011034225,0.28586146,0.0005457922,0.00016219137,0.00049853325,0.00015260944,0.00011107454,0.04038391],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998659,0.000016346847,0.000007689209,0.000033754946,0.000065923174,0.000010313475],"domain_scores_gemma":[0.9999001,0.000021833543,0.0000109810135,0.000010328642,0.00004814044,0.000008659683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015769903,0.00039870705,0.00029268977,0.00026566104,0.00048198798,0.00056875806,0.0006877594,0.00095257873,0.0040912684],"category_scores_gemma":[0.00041285795,0.00020167686,0.0002539149,0.00017715902,0.00042700942,0.00039333256,0.0004384901,0.00085900247,0.0009023057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015171389,0.00012463622,0.00057466346,0.00030311817,0.00009112496,0.0006188182,0.00016815905,0.3714834,0.10639559,0.078106105,0.011453809,0.4305289],"study_design_scores_gemma":[0.000055484714,0.00019697883,0.0005655734,0.000043618493,0.000029793804,0.00027876926,0.000024643745,0.93286484,0.016489368,0.017464167,0.031957693,0.000029078079],"about_ca_topic_score_codex":0.0025496255,"about_ca_topic_score_gemma":0.003456799,"teacher_disagreement_score":0.0040912684,"about_ca_system_score_codex":0.00039825254,"about_ca_system_score_gemma":0.0006314351,"threshold_uncertainty_score":0.013686657},"labels":[],"label_agreement":null},{"id":"W2142058217","doi":"10.5539/mas.v2n3p57","title":"Logic Learning in Hopfield Networks","year":2008,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hebbian theory; Computer science; Leabra; Equivalence (formal languages); Artificial intelligence; Hopfield network; Artificial neural network; Theoretical computer science; Mathematics; Discrete mathematics; Generalization error; Wake-sleep algorithm","score_opus":0.024777901443939175,"score_gpt":0.2356040832882785,"score_spread":0.21082618184433932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142058217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32312915,0.0010023331,0.64913577,0.000869856,0.00009958554,0.00006041522,0.00010396025,0.00071348017,0.024885468],"genre_scores_gemma":[0.9666995,0.00031685244,0.029203802,0.00008068852,0.000022811133,0.000037368267,0.000038637136,0.000019906776,0.0035804377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980074,0.000058907168,0.0000127225585,0.000032205313,0.000072152274,0.000023319208],"domain_scores_gemma":[0.9993722,0.00039438365,0.00005318549,0.000046293306,0.00009949158,0.000034602286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064873026,0.0002259334,0.0002916869,0.00037838603,0.00041846724,0.0007940642,0.000730203,0.000656188,0.001832443],"category_scores_gemma":[0.0031679887,0.00022064484,0.00021993011,0.0003182604,0.00087926054,0.0012766661,0.00038817877,0.0005744264,0.00018572022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011138638,0.000047910155,0.0008834203,0.00007802958,0.000035377238,0.00010023602,0.000107263266,0.7883488,0.008186811,0.1459804,0.00071872125,0.055401582],"study_design_scores_gemma":[0.000013036314,0.000016775028,0.00015977553,0.0000056532285,0.0000042933934,0.000012470589,0.000008275101,0.9059593,0.0015985046,0.09183205,0.00038269866,0.0000072391795],"about_ca_topic_score_codex":0.0040016444,"about_ca_topic_score_gemma":0.0025393665,"teacher_disagreement_score":0.0040016444,"about_ca_system_score_codex":0.0010839216,"about_ca_system_score_gemma":0.0004778648,"threshold_uncertainty_score":0.007956684},"labels":[],"label_agreement":null},{"id":"W2142072633","doi":"10.1109/icnn.1995.488138","title":"Application of neural networks in circuit analysis","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial neural network; Signal processing; Network analysis; Circuit design; Electronic engineering; Artificial intelligence; Engineering; Electrical engineering; Digital signal processing; Computer hardware; Embedded system","score_opus":0.019387535896230726,"score_gpt":0.2280670868947832,"score_spread":0.20867955099855245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142072633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008725275,0.021703813,0.94155604,0.0011474361,0.0002873927,0.00003823054,0.00008900218,0.00044882548,0.02600403],"genre_scores_gemma":[0.48618162,0.04254861,0.4474892,0.0006526319,0.0007920393,0.00023320227,0.00025431442,0.00019242337,0.021656012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995783,0.00012828829,0.000024189836,0.000062449835,0.0001831254,0.000023684914],"domain_scores_gemma":[0.999708,0.00016604445,0.00001989648,0.000041224263,0.000054823682,0.000010001918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004879827,0.00078052736,0.00062898005,0.00094261824,0.00029094008,0.0010290081,0.00066858385,0.0010639613,0.0024061564],"category_scores_gemma":[0.0014378403,0.00036395786,0.0005446196,0.0010420316,0.00090639864,0.0010368555,0.0010781119,0.0011425235,0.00072749524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058076857,0.00003932942,0.0006792052,0.00051117165,0.00014449435,0.00018985024,0.000091079666,0.55851924,0.009906004,0.19274646,0.002655788,0.23445931],"study_design_scores_gemma":[0.000006736014,0.00002189536,0.00023658582,0.000080809084,0.000020663512,0.00008446273,0.00001461082,0.8323634,0.0029269666,0.14891319,0.015311157,0.000019394845],"about_ca_topic_score_codex":0.0016775964,"about_ca_topic_score_gemma":0.0015832229,"teacher_disagreement_score":0.0024061564,"about_ca_system_score_codex":0.0007711801,"about_ca_system_score_gemma":0.0005035489,"threshold_uncertainty_score":0.008049369},"labels":[],"label_agreement":null},{"id":"W2142100971","doi":"10.1109/72.822508","title":"A new supervised learning algorithm for multilayered and interconnected neural networks","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Backpropagation; Computer science; Algorithm; Time delay neural network; Types of artificial neural networks; Supervised learning; Wake-sleep algorithm; Artificial intelligence; Rprop; Generalization error","score_opus":0.014730562225411959,"score_gpt":0.23788358253301103,"score_spread":0.22315302030759906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142100971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011149443,0.0001174456,0.99791986,0.000037081296,0.000035641184,0.000029892428,0.000019434925,0.0002639413,0.00046175846],"genre_scores_gemma":[0.04377825,0.00023414589,0.95271784,0.00009391259,0.00010887352,0.00033754812,0.00014663057,0.00010185234,0.0024810168],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991025,0.0002012599,0.00006845382,0.00017557899,0.00040489854,0.00004725924],"domain_scores_gemma":[0.99903333,0.00036851296,0.00010761651,0.00010133198,0.00035550067,0.000033684537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012878261,0.00080618233,0.0010355588,0.00091120007,0.0005139403,0.0008248487,0.0016685734,0.0011307013,0.0024158303],"category_scores_gemma":[0.002962933,0.00052283145,0.0008445957,0.00090658694,0.0006907208,0.001832218,0.0011225005,0.0016590529,0.0010491232],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091102294,0.00007671787,0.0007015184,0.0002691623,0.00015336223,0.00011105026,0.00013688313,0.319354,0.0112090185,0.039566807,0.00545949,0.6228709],"study_design_scores_gemma":[0.0000131636525,0.000037666312,0.00011125283,0.000015579879,0.000010657648,0.000047352667,0.000005915142,0.9827267,0.0021971238,0.010564888,0.0042581065,0.000011618521],"about_ca_topic_score_codex":0.0013979198,"about_ca_topic_score_gemma":0.0019669815,"teacher_disagreement_score":0.0024158303,"about_ca_system_score_codex":0.0006386595,"about_ca_system_score_gemma":0.0010592095,"threshold_uncertainty_score":0.008081794},"labels":[],"label_agreement":null},{"id":"W2142315608","doi":"10.1109/glocom.1991.188370","title":"A neural network approach to the maximum flow problem","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Simplex algorithm; Artificial neural network; Computer science; Linear programming; Multi-commodity flow problem; Maximum flow problem; Flow network; Mathematical optimization; Flow (mathematics); Theoretical computer science; Algorithm; Artificial intelligence; Mathematics","score_opus":0.027111566706194266,"score_gpt":0.2123077210882353,"score_spread":0.18519615438204104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142315608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004070725,0.0013012743,0.97802246,0.0011353272,0.0000988602,0.000034258253,0.000102605394,0.00012411333,0.015110421],"genre_scores_gemma":[0.43821597,0.0065480554,0.51522654,0.00063681434,0.00081650366,0.000513864,0.00045800963,0.00015708282,0.037427194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995524,0.00016808134,0.000021481752,0.00008587679,0.00012661131,0.000045516223],"domain_scores_gemma":[0.9995925,0.0002550733,0.00003074092,0.000017464323,0.00008274007,0.000021532065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085595244,0.0006590855,0.00072031684,0.0009114471,0.0004492861,0.0014342136,0.0012941277,0.0019011762,0.0066004405],"category_scores_gemma":[0.0023510577,0.0004679229,0.0005500537,0.0013370464,0.0010253404,0.0023403072,0.0008850612,0.001744164,0.00063921395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028535473,0.000026005195,0.00012694913,0.000082662074,0.000022264643,0.000063317646,0.000030756666,0.82875764,0.0004630128,0.13339117,0.0021575384,0.034850203],"study_design_scores_gemma":[0.000005886076,0.0000056949516,0.000034487697,0.000009545648,0.0000031358447,0.000011910334,0.0000041002227,0.9434832,0.00011960002,0.054605104,0.0017130254,0.0000044519124],"about_ca_topic_score_codex":0.0069135944,"about_ca_topic_score_gemma":0.0058420934,"teacher_disagreement_score":0.0069135944,"about_ca_system_score_codex":0.0014657604,"about_ca_system_score_gemma":0.0013005399,"threshold_uncertainty_score":0.02208066},"labels":[],"label_agreement":null},{"id":"W2142343760","doi":"10.1109/iscas.2009.5118213","title":"Efficient hardware implementation of the hyperbolic tangent sigmoid function","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"CMC Microsystems","keywords":"Sigmoid function; Hyperbolic function; Activation function; Computer science; Tangent; Lookup table; Very-large-scale integration; Piecewise; Inverse trigonometric functions; Artificial neural network; Computer hardware; Mathematics; Embedded system; Artificial intelligence; Mathematical analysis","score_opus":0.011647112695612367,"score_gpt":0.2543881834724698,"score_spread":0.24274107077685741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142343760","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043267842,0.000674411,0.940908,0.00027574497,0.00019493033,0.00010782306,0.00014902558,0.0048724846,0.009549707],"genre_scores_gemma":[0.6226414,0.00065225956,0.36604062,0.00016123924,0.00005732901,0.00010019222,0.000245983,0.00013382993,0.009967079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979323,0.000025794026,0.000022216382,0.000028874278,0.00009273721,0.0000371926],"domain_scores_gemma":[0.99973553,0.00005640468,0.000024209521,0.000056100664,0.00011444611,0.00001337145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026873843,0.00040450788,0.0002869933,0.0003955173,0.0002707511,0.00077989354,0.0010760399,0.0003680091,0.0058881007],"category_scores_gemma":[0.00080095127,0.00022154373,0.00022001755,0.0005224322,0.00020116445,0.0007933446,0.00034756245,0.0004363903,0.0022204786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005104202,0.00012407705,0.00129256,0.00057695527,0.00008918706,0.00054323475,0.00019560332,0.0391412,0.28676486,0.02507215,0.009267229,0.6364225],"study_design_scores_gemma":[0.00012844699,0.00058616244,0.0018573409,0.00008307464,0.00010096786,0.001361476,0.00011127457,0.45362362,0.48419145,0.008622216,0.049260903,0.000073216564],"about_ca_topic_score_codex":0.0010215245,"about_ca_topic_score_gemma":0.0014914214,"teacher_disagreement_score":0.0058881007,"about_ca_system_score_codex":0.00036980462,"about_ca_system_score_gemma":0.00069380144,"threshold_uncertainty_score":0.019697666},"labels":[],"label_agreement":null},{"id":"W2142350753","doi":"10.1109/ijcnn.1993.714311","title":"Control relevant dynamics of recurrent neural networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Nonlinear system; Control theory (sociology); Artificial neural network; Computer science; Linearization; Adaptive control; Control (management); Recurrent neural network; Class (philosophy); Feedback linearization; Degree (music); Control engineering; Artificial intelligence; Engineering","score_opus":0.008466806941112574,"score_gpt":0.23277098572708926,"score_spread":0.2243041787859767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142350753","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19117425,0.0017175928,0.7610758,0.00065499265,0.00017761748,0.000041999454,0.0001412478,0.00028406372,0.044732384],"genre_scores_gemma":[0.9839325,0.00062571204,0.009423846,0.000051333373,0.000039085233,0.00003148191,0.000055537275,0.000026130454,0.0058144704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982435,0.000044095905,0.000010290558,0.000040991865,0.00005966665,0.000020638952],"domain_scores_gemma":[0.99971026,0.00009996252,0.00006827047,0.000030437022,0.000069642745,0.00002141694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037608107,0.00035982634,0.0003669191,0.00028480834,0.00025373994,0.0009090781,0.0004928269,0.00050359254,0.0023262415],"category_scores_gemma":[0.0013110496,0.00014595815,0.00027070878,0.00020978386,0.0006536028,0.0009146852,0.0005530698,0.00051144947,0.00024017846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077632736,0.00004882615,0.00031992205,0.00014212549,0.000036007816,0.00030434038,0.00022883824,0.34842333,0.026675547,0.5996878,0.0012219304,0.022833709],"study_design_scores_gemma":[0.0000071310237,0.000033687385,0.00019330994,0.0000095412,0.00000775316,0.00004470687,0.000025722133,0.9097322,0.001483493,0.087406814,0.001045193,0.0000103621505],"about_ca_topic_score_codex":0.0013460738,"about_ca_topic_score_gemma":0.0011009265,"teacher_disagreement_score":0.0023262415,"about_ca_system_score_codex":0.0004331052,"about_ca_system_score_gemma":0.00025976705,"threshold_uncertainty_score":0.0077821016},"labels":[],"label_agreement":null},{"id":"W2142963728","doi":"10.1093/bioinformatics/btt602","title":"Exploring high dimensional data with Butterfly: a novel classification algorithm based on discrete dynamical systems","year":2013,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University Health Network","funders":"Government of Ontario; Eli Lilly Canada; University Health Network; Natural Sciences and Engineering Research Council of Canada; Ottawa Hospital Research Institute; Ontario Institute for Cancer Research; Eli Lilly and Company","keywords":"Computer science; Cluster analysis; Representation (politics); Set (abstract data type); Univariate; Dynamical systems theory; Visualization; Algorithm; Artificial intelligence; External Data Representation; Pattern recognition (psychology); Data mining; Machine learning; Multivariate statistics","score_opus":0.09020328266180674,"score_gpt":0.2489412473830473,"score_spread":0.15873796472124055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142963728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067024855,0.000088917346,0.98890823,0.00022693934,0.000039222316,0.0000751431,0.00014675506,0.0030130139,0.0007993199],"genre_scores_gemma":[0.076206714,0.00009552614,0.91996723,0.00017273055,0.00004259001,0.00017706196,0.0005132246,0.00037018178,0.0024547728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994653,0.0000692611,0.000035854722,0.00016865082,0.00021428781,0.000046559653],"domain_scores_gemma":[0.99898,0.0004943057,0.000088950634,0.00013920372,0.00022984961,0.000067677625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008631286,0.00091585424,0.0008214657,0.0015185738,0.00075050205,0.0017245756,0.0019741238,0.0010854795,0.0070700287],"category_scores_gemma":[0.0035078146,0.0003817624,0.0008298228,0.0010854882,0.00093934895,0.002256695,0.0019620499,0.0012318745,0.001701405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003177168,0.00010739547,0.0038614045,0.00019045609,0.00010575063,0.0002943631,0.00045775005,0.14703412,0.015520646,0.034140516,0.018606033,0.7793639],"study_design_scores_gemma":[0.000031682426,0.000036513266,0.00032411478,0.000017571308,0.000009126837,0.00012419002,0.000040256662,0.9759975,0.00394263,0.014703274,0.0047542094,0.000018820332],"about_ca_topic_score_codex":0.006188132,"about_ca_topic_score_gemma":0.0059010605,"teacher_disagreement_score":0.0070700287,"about_ca_system_score_codex":0.00094344077,"about_ca_system_score_gemma":0.00096759835,"threshold_uncertainty_score":0.0236516},"labels":[],"label_agreement":null},{"id":"W2142993588","doi":"10.1109/iembs.2001.1019029","title":"Acceleration of learning in hybrid neural networks: A novel approach for the design of brain chaosmakers","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Computer science; Acceleration; Artificial intelligence; Chaotic; Conjugate gradient method; Types of artificial neural networks; Recurrent neural network; Deep learning; Algorithm; Physics","score_opus":0.04968666192479012,"score_gpt":0.26245467319308013,"score_spread":0.21276801126829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142993588","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029112197,0.00031158747,0.9662654,0.00012794026,0.000043489315,0.00004267045,0.000007114808,0.00013242541,0.0039571505],"genre_scores_gemma":[0.8571371,0.00025909816,0.13890338,0.00008890716,0.000053800348,0.0001568692,0.000017938042,0.0000406917,0.0033421507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986374,0.000051826548,0.000006179229,0.000019659848,0.000044376462,0.000014170657],"domain_scores_gemma":[0.99976,0.000112592046,0.000039610353,0.00002453903,0.000048224807,0.000015121028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005566714,0.0003961684,0.00035490567,0.00027539907,0.00017252154,0.00038218952,0.00066935143,0.0005853463,0.0010280923],"category_scores_gemma":[0.000904607,0.0002301683,0.00030659305,0.00017610572,0.00056485366,0.0005395596,0.00064416276,0.00044278603,0.00015668191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095366384,0.000048452825,0.00041548157,0.00007469605,0.000054283642,0.00006515018,0.000073370014,0.8822676,0.01457056,0.03354962,0.00054325355,0.06824222],"study_design_scores_gemma":[0.000009465887,0.000045775545,0.000047984166,0.000003219144,0.000004364408,0.000012185498,0.0000022282163,0.9946355,0.00093187415,0.0037757072,0.0005282091,0.000003473135],"about_ca_topic_score_codex":0.00040312533,"about_ca_topic_score_gemma":0.0005024789,"teacher_disagreement_score":0.0010280923,"about_ca_system_score_codex":0.0002913095,"about_ca_system_score_gemma":0.00020993032,"threshold_uncertainty_score":0.0034393072},"labels":[],"label_agreement":null},{"id":"W2143084910","doi":"10.1016/s0730-725x(99)00102-2","title":"Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis","year":2000,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":171,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics; National Research Council Canada","funders":"","keywords":"Principal component analysis; Pattern recognition (psychology); Cluster analysis; Exploratory analysis; Artificial intelligence; Exploratory data analysis; Computer science; Fuzzy clustering; Fuzzy logic; Data mining; Data science","score_opus":0.06357153850612737,"score_gpt":0.3963559520015357,"score_spread":0.3327844134954083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143084910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09876777,0.003946737,0.88911265,0.0016489292,0.0004857631,0.00082164357,0.0008918849,0.0012443104,0.0030802677],"genre_scores_gemma":[0.28229684,0.00225246,0.71066904,0.00031630558,0.00027070797,0.0011430866,0.0008831838,0.00082211394,0.001346299],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98550296,0.008569356,0.00084390515,0.0010756108,0.0036748839,0.0003333196],"domain_scores_gemma":[0.9099258,0.073979855,0.0026609986,0.0031668777,0.009184916,0.0010814891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025763577,0.001956597,0.0021953282,0.0053682304,0.0014321847,0.003019231,0.002084109,0.002153166,0.0035947158],"category_scores_gemma":[0.101844065,0.00064841495,0.0027440302,0.0026657043,0.0014180979,0.004354678,0.0022014242,0.002392167,0.0008122656],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00993156,0.00075736316,0.011542607,0.003172916,0.0032341785,0.00010874353,0.0022150541,0.029906813,0.0082885185,0.029787775,0.00697916,0.89407533],"study_design_scores_gemma":[0.0022810714,0.004615605,0.06657303,0.0014285576,0.0029192255,0.0011855436,0.0029305923,0.77729255,0.021745097,0.108674064,0.009371994,0.0009827148],"about_ca_topic_score_codex":0.0030978806,"about_ca_topic_score_gemma":0.005273937,"teacher_disagreement_score":0.025763577,"about_ca_system_score_codex":0.0011418468,"about_ca_system_score_gemma":0.002444218,"threshold_uncertainty_score":0.1362524},"labels":[],"label_agreement":null},{"id":"W2143351695","doi":"10.1109/isit.1991.695428","title":"A Study On Associating Schemes In Associative Memory Classifier","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Associative property; Classifier (UML); Content-addressable memory; Artificial intelligence; Bidirectional associative memory; Machine learning; Artificial neural network; Mathematics","score_opus":0.03925073321216522,"score_gpt":0.30861650615249697,"score_spread":0.26936577294033176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143351695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33990145,0.0039118105,0.6412822,0.00087346905,0.00027446315,0.00016729122,0.00013791388,0.00021175623,0.013239655],"genre_scores_gemma":[0.88862026,0.0019395407,0.10197456,0.00020398607,0.00025304148,0.00010339304,0.00017995389,0.000058160476,0.0066671493],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981464,0.00076584815,0.00015921,0.0003582618,0.00044463304,0.00012564483],"domain_scores_gemma":[0.9825659,0.011772872,0.0007027095,0.002726308,0.0019185642,0.00031368184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036331683,0.0003102033,0.00070569303,0.0009111888,0.0010418866,0.0017002814,0.0014904687,0.0010359056,0.00421995],"category_scores_gemma":[0.023183806,0.00029300526,0.000611664,0.0019058272,0.0012314357,0.0071040657,0.0010869561,0.0016056219,0.0004649657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004314587,0.00027804423,0.006585944,0.0003141305,0.00007939668,0.00012618111,0.00063053437,0.024669122,0.007525957,0.68736565,0.0024316907,0.26956204],"study_design_scores_gemma":[0.000053377975,0.0005015154,0.0026451747,0.00010932141,0.00016061346,0.0006057779,0.00027523714,0.56137383,0.009203162,0.4144518,0.010567459,0.000052694806],"about_ca_topic_score_codex":0.0010596192,"about_ca_topic_score_gemma":0.0007780085,"teacher_disagreement_score":0.00421995,"about_ca_system_score_codex":0.0008420544,"about_ca_system_score_gemma":0.00090362586,"threshold_uncertainty_score":0.019214272},"labels":[],"label_agreement":null},{"id":"W2143579868","doi":"10.1016/j.jprocont.2003.06.003","title":"The impact of compression on data-driven process analyses","year":2003,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Data compression; Process (computing); Data mining; Compression (physics); Benchmarking; Variance (accounting); Resource (disambiguation); Algorithm","score_opus":0.06775728886460919,"score_gpt":0.4151766104151633,"score_spread":0.3474193215505541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143579868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30206886,0.0042412737,0.68242675,0.0024474054,0.0005959368,0.00015255479,0.0007356255,0.0028781544,0.0044533513],"genre_scores_gemma":[0.90815806,0.001443265,0.08770004,0.00032102712,0.00040076455,0.00006443121,0.0006324695,0.00022725404,0.0010526074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740976,0.00101,0.00017675754,0.00025964415,0.0009919896,0.00015183492],"domain_scores_gemma":[0.9182774,0.07315588,0.0010994794,0.0043078614,0.0028884474,0.00027102797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004296676,0.0011201281,0.0010222602,0.0012563894,0.0006620015,0.001569661,0.00068484066,0.0017365606,0.001821164],"category_scores_gemma":[0.056597658,0.00046750956,0.0005626222,0.0014403398,0.001110204,0.00288853,0.0012474207,0.0014882606,0.0004056718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042641517,0.0002418171,0.0072033596,0.0003738221,0.00015163775,0.0006988601,0.00021284066,0.5356495,0.016385179,0.015138588,0.0022016314,0.4174787],"study_design_scores_gemma":[0.000033496613,0.00008758266,0.0010632467,0.000021366779,0.000031562606,0.00011978519,0.0000233071,0.9809187,0.010724171,0.0065482594,0.00041567258,0.000012836039],"about_ca_topic_score_codex":0.0030637488,"about_ca_topic_score_gemma":0.0017637233,"teacher_disagreement_score":0.004296676,"about_ca_system_score_codex":0.00058906,"about_ca_system_score_gemma":0.0009290569,"threshold_uncertainty_score":0.022723258},"labels":[],"label_agreement":null},{"id":"W2143831814","doi":"10.1109/icsmc.1994.399994","title":"Neural networks for modelling and control of discrete-time nonlinear systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Nonlinear system; Artificial neural network; Kalman filter; Computer science; Feed forward; Control theory (sociology); Discrete time and continuous time; Identification (biology); System identification; Extended Kalman filter; Feedforward neural network; Inverse; Control (management); Control system; Class (philosophy); Artificial intelligence; Control engineering; Data modeling; Mathematics; Engineering","score_opus":0.019618802686458464,"score_gpt":0.22052645273293653,"score_spread":0.20090765004647806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143831814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021624523,0.014917094,0.97474074,0.000608959,0.00035094592,0.000030690062,0.00014257139,0.00053014554,0.0065163295],"genre_scores_gemma":[0.38394797,0.054484498,0.51686484,0.00060345884,0.0013022219,0.0008048782,0.0014843785,0.0002993681,0.04020836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964166,0.00010008433,0.00003196015,0.000051630945,0.00015553889,0.000019181056],"domain_scores_gemma":[0.9997898,0.00010145136,0.000030393556,0.000026612319,0.00004353983,0.000008315778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005377526,0.0009445692,0.0007824099,0.00061998254,0.00027604733,0.0011661868,0.0008592223,0.0013235108,0.002876173],"category_scores_gemma":[0.0012115211,0.0003359176,0.0005431649,0.0010163336,0.0006110627,0.0012403588,0.0006243929,0.00188671,0.001180241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059685302,0.000026114445,0.0003394553,0.00069293537,0.00009868232,0.0002067558,0.00012364252,0.6026822,0.006264443,0.202176,0.0060277926,0.18130238],"study_design_scores_gemma":[0.000009583121,0.000019161116,0.00016130546,0.00007306941,0.000016056807,0.000045135526,0.000011930038,0.91856354,0.0010908387,0.0605345,0.019455526,0.000019377405],"about_ca_topic_score_codex":0.0055936263,"about_ca_topic_score_gemma":0.0042946874,"teacher_disagreement_score":0.0055936263,"about_ca_system_score_codex":0.00071064907,"about_ca_system_score_gemma":0.000583236,"threshold_uncertainty_score":0.011122167},"labels":[],"label_agreement":null},{"id":"W2143871051","doi":"10.1109/mwscas.1993.343336","title":"On pattern classification using linear-output neural network classifiers","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Subspace topology; Linear discriminant analysis; Artificial neural network; Discriminant; Value (mathematics); Artificial intelligence; Set (abstract data type); Mean squared error; Computer science; Mathematics; Pattern recognition (psychology); Algorithm; Machine learning; Statistics","score_opus":0.09408212244263889,"score_gpt":0.27594121855511833,"score_spread":0.18185909611247944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143871051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040176255,0.002849506,0.9893289,0.00037520166,0.00013156519,0.000033178712,0.000049093538,0.00025743202,0.0029574402],"genre_scores_gemma":[0.30184063,0.013243098,0.6681796,0.001067597,0.001715351,0.00044142889,0.00083541276,0.00020918966,0.012467674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986222,0.00048434865,0.000104461935,0.00025400412,0.00045955626,0.00007550729],"domain_scores_gemma":[0.9971517,0.0020819209,0.00013686677,0.0001948158,0.00040791332,0.000026847874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002018158,0.0010079271,0.0010108338,0.0016044214,0.00034934716,0.002107343,0.0011133098,0.0015178554,0.002619903],"category_scores_gemma":[0.009097959,0.00029032494,0.0004981494,0.0023312066,0.0016441388,0.002920345,0.001133819,0.00116079,0.0011526791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017874259,0.00008231789,0.0010837849,0.00042418906,0.00018293492,0.0001915767,0.00015865902,0.26975816,0.004677338,0.07944153,0.006640209,0.63718057],"study_design_scores_gemma":[0.000013032559,0.000048268255,0.00038764943,0.00007715172,0.000025626914,0.00007127336,0.000020854908,0.9209986,0.0023662539,0.07062892,0.0053410875,0.000021211707],"about_ca_topic_score_codex":0.0018547041,"about_ca_topic_score_gemma":0.0011122545,"teacher_disagreement_score":0.002619903,"about_ca_system_score_codex":0.000601877,"about_ca_system_score_gemma":0.00045959343,"threshold_uncertainty_score":0.010673165},"labels":[],"label_agreement":null},{"id":"W2144003766","doi":"10.1109/wescan.1995.493968","title":"Speech recognition techniques using RBF networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Computer science; Speech recognition; Artificial intelligence","score_opus":0.0653492027705878,"score_gpt":0.2590200195128538,"score_spread":0.193670816742266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144003766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023982073,0.00063918385,0.99533856,0.000065081076,0.00005475199,0.000014435597,0.000020539968,0.0005781645,0.00089101196],"genre_scores_gemma":[0.1734118,0.002417081,0.8162798,0.00016887388,0.00028549778,0.000113893366,0.00026782166,0.00018700265,0.00686825],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991918,0.00018083017,0.000053550957,0.00016302995,0.00036583503,0.000044933015],"domain_scores_gemma":[0.9992995,0.00028566545,0.000062801584,0.00008530912,0.00024963633,0.000016969378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009062238,0.0007840041,0.0008389126,0.0010532641,0.000281083,0.00088665227,0.0010103524,0.001041137,0.0029223755],"category_scores_gemma":[0.002683381,0.00036022018,0.00073652476,0.0010490197,0.00035031163,0.0011350537,0.00058612286,0.0011613478,0.0029789743],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013204412,0.00005719281,0.00026278826,0.00023790191,0.000108912376,0.00008574021,0.00010190704,0.09704098,0.036327794,0.009647077,0.002395669,0.85360205],"study_design_scores_gemma":[0.000021929221,0.000080930826,0.0004208351,0.000036758556,0.000042811036,0.00021665917,0.00002563925,0.96265894,0.020018574,0.0071288133,0.009316404,0.00003160438],"about_ca_topic_score_codex":0.0014274966,"about_ca_topic_score_gemma":0.0012319678,"teacher_disagreement_score":0.0029223755,"about_ca_system_score_codex":0.0003116487,"about_ca_system_score_gemma":0.00024566255,"threshold_uncertainty_score":0.009776354},"labels":[],"label_agreement":null},{"id":"W2144196357","doi":"10.1109/icnn.1995.488093","title":"Neural network approximation: application to peak switching overvoltage determination in power systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Overvoltage; Artificial neural network; Backpropagation; Emtp; Electric power system; Computer science; Transient (computer programming); Heuristics; Power (physics); Electronic engineering; Control theory (sociology); Artificial intelligence; Voltage; Engineering; Electrical engineering; Physics","score_opus":0.014562964200799527,"score_gpt":0.23347998187829114,"score_spread":0.2189170176774916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144196357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0360066,0.00057148817,0.95975864,0.00019583777,0.000043198066,0.000022675034,0.000035705514,0.0009925432,0.0023733156],"genre_scores_gemma":[0.9021656,0.00045345887,0.0950759,0.000052126255,0.00005012393,0.00004146908,0.000060280967,0.00007148514,0.0020295673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997743,0.00008770542,0.000013575113,0.00003212636,0.00006553634,0.000026741962],"domain_scores_gemma":[0.99929595,0.00049053837,0.000049904662,0.0000339664,0.000114572336,0.000015053851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005471295,0.00048119703,0.00056088984,0.00053229573,0.0002767211,0.0004937624,0.00045843393,0.00083654653,0.0012094445],"category_scores_gemma":[0.0027434235,0.0002319335,0.00024621314,0.0009881661,0.00029828274,0.0004849718,0.0003275837,0.00078822044,0.00017564271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059404334,0.000022055365,0.00065872894,0.000047593316,0.000016058953,0.000049850307,0.000033264296,0.92498446,0.0018258413,0.0021816404,0.0003835072,0.0697377],"study_design_scores_gemma":[0.0000016722253,0.000004782298,0.00008028615,0.0000020161735,0.0000014012371,0.0000066981697,0.0000022662132,0.9987783,0.00036028898,0.000664834,0.00009597106,0.0000015873842],"about_ca_topic_score_codex":0.006599112,"about_ca_topic_score_gemma":0.0031138624,"teacher_disagreement_score":0.006599112,"about_ca_system_score_codex":0.0005637224,"about_ca_system_score_gemma":0.00040618336,"threshold_uncertainty_score":0.0131213665},"labels":[],"label_agreement":null},{"id":"W2144227570","doi":"10.1109/pacrim.1995.519582","title":"MRI reconstruction from truncated data using a complex domain backpropagation neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Backpropagation; Extrapolation; Artificial neural network; Computer science; Frequency domain; Missing data; Artificial intelligence; Domain (mathematical analysis); Data mining; Pattern recognition (psychology); Algorithm; Machine learning; Computer vision; Mathematics","score_opus":0.12642376196162905,"score_gpt":0.27428177844796353,"score_spread":0.14785801648633448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144227570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010550978,0.00010616057,0.9887106,0.00009562679,0.000020367615,0.000010808475,0.000029316669,0.00022592772,0.0002501677],"genre_scores_gemma":[0.19337772,0.0005061699,0.8035465,0.00012549655,0.000057258974,0.000088779976,0.0002916722,0.00009338117,0.0019130189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998241,0.000046696186,0.000013451695,0.000029809271,0.00007444778,0.000011472218],"domain_scores_gemma":[0.99947923,0.0002535858,0.00005825263,0.00007716499,0.0001104859,0.000021338705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007961888,0.00057942804,0.00055902847,0.00037592242,0.000131806,0.00043064085,0.0007501174,0.0007318273,0.0009952111],"category_scores_gemma":[0.0021127437,0.00031766412,0.00051840354,0.00044065455,0.00035086664,0.0011468907,0.0006673294,0.0008895781,0.0003507125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027862756,0.00008585578,0.00087284076,0.00017109247,0.000099762714,0.00037814898,0.00010716991,0.6951622,0.06216323,0.008740736,0.0014047764,0.23053557],"study_design_scores_gemma":[0.000006172758,0.000017141187,0.00008527049,0.0000047775175,0.0000055426567,0.00004702774,0.0000035268486,0.9935354,0.0042262534,0.0016292422,0.00043257143,0.000007042408],"about_ca_topic_score_codex":0.0012749011,"about_ca_topic_score_gemma":0.0011208423,"teacher_disagreement_score":0.0012749011,"about_ca_system_score_codex":0.00024391145,"about_ca_system_score_gemma":0.00047598948,"threshold_uncertainty_score":0.004210651},"labels":[],"label_agreement":null},{"id":"W2145196268","doi":"10.1109/foci.2007.371529","title":"Opposite Transfer Functions and Backpropagation Through Time","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backpropagation; Computer science; Stability (learning theory); Artificial neural network; Process (computing); Artificial intelligence; Acceleration; Transfer of learning; Machine learning; Algorithm","score_opus":0.012283161607557077,"score_gpt":0.22861471498889957,"score_spread":0.2163315533813425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145196268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006480106,0.00047260005,0.9880084,0.00031019477,0.00018898422,0.000028696573,0.000016838181,0.0005826183,0.0039115855],"genre_scores_gemma":[0.38900077,0.0014348354,0.5978496,0.0004768702,0.00028461101,0.000172556,0.00012995793,0.0003819902,0.010268853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988424,0.00028120985,0.00007595502,0.0001932071,0.000514665,0.000092618626],"domain_scores_gemma":[0.9975708,0.0011990858,0.00024473428,0.00037280313,0.00053961854,0.000072886716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001853369,0.00091886,0.000536167,0.00094737986,0.0003636882,0.0013230935,0.0013427999,0.0017015445,0.0026627649],"category_scores_gemma":[0.010401176,0.00035259404,0.00061222573,0.0007233449,0.0015343473,0.0036589075,0.0013843088,0.0021658712,0.0014830423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004122636,0.00016403498,0.0010830867,0.00028279494,0.000095103445,0.000304201,0.00017063935,0.37216768,0.019104328,0.22156563,0.004638717,0.38001153],"study_design_scores_gemma":[0.000027652428,0.0000985635,0.00030378115,0.00002289314,0.00001978787,0.00028158736,0.00001691933,0.9221883,0.013575172,0.05759283,0.005839235,0.000033378965],"about_ca_topic_score_codex":0.0013612192,"about_ca_topic_score_gemma":0.0006430344,"teacher_disagreement_score":0.0026627649,"about_ca_system_score_codex":0.00057969766,"about_ca_system_score_gemma":0.00059862935,"threshold_uncertainty_score":0.009801626},"labels":[],"label_agreement":null},{"id":"W2145847807","doi":"10.1109/pesc.1996.548655","title":"Development of an artificial neural network based induction motor speed estimator","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial neural network; Stator; Estimator; Control theory (sociology); Induction motor; Computer science; Smoothing; Scheme (mathematics); Filter (signal processing); Control engineering; Artificial intelligence; Engineering; Mathematics","score_opus":0.05534859647188088,"score_gpt":0.2592212947456921,"score_spread":0.20387269827381121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145847807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004475849,0.00021143635,0.9929426,0.0000637068,0.000078061006,0.000037487316,0.000021789636,0.0006347405,0.0015344046],"genre_scores_gemma":[0.17308962,0.00055774365,0.81800854,0.0001082619,0.00007519572,0.00016994198,0.00017416046,0.00007240355,0.00774412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998134,0.000024553638,0.000013402673,0.000044527384,0.000090907095,0.0000132817195],"domain_scores_gemma":[0.9996543,0.00008391377,0.00003258492,0.0000233075,0.00019274057,0.000013287308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005493247,0.0003416201,0.0004586888,0.0004012,0.00022240619,0.0004380383,0.0006875222,0.0007248776,0.0014880785],"category_scores_gemma":[0.0011664773,0.0003929718,0.00024044747,0.00030756358,0.0001723923,0.0006603612,0.00036135313,0.00077369047,0.0010438702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013521047,0.00012790292,0.0018526706,0.00028113002,0.00009157109,0.000265143,0.00008417922,0.19188662,0.086141795,0.014613666,0.0029502478,0.7015699],"study_design_scores_gemma":[0.000013694783,0.00006412036,0.00059072,0.000023123495,0.000022015955,0.00008828473,0.0000051526154,0.9764498,0.014838474,0.0008102551,0.007079293,0.000015079152],"about_ca_topic_score_codex":0.0013053269,"about_ca_topic_score_gemma":0.0016042168,"teacher_disagreement_score":0.0014880785,"about_ca_system_score_codex":0.0002799581,"about_ca_system_score_gemma":0.00047094875,"threshold_uncertainty_score":0.00497818},"labels":[],"label_agreement":null},{"id":"W2146234362","doi":"10.1142/s146902680400129x","title":"EVALUATION OF SELECTING INTERVAL VALUES OF INPUT VARIABLES IN CONNECTIONIST NETWORKS","year":2004,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Connectionism; Variable (mathematics); Selection (genetic algorithm); Interval (graph theory); Artificial neural network; Feature selection; Entropy (arrow of time); Value (mathematics); Artificial intelligence; Principle of maximum entropy; Backpropagation; Data mining; Machine learning; Mathematics","score_opus":0.03888642290138364,"score_gpt":0.3370188435194664,"score_spread":0.29813242061808276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146234362","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7656278,0.0007968564,0.2292542,0.00023128354,0.00005076339,0.000224834,0.00010847912,0.00073391234,0.002971894],"genre_scores_gemma":[0.94584835,0.00012502306,0.05346706,0.000032589713,0.000010885674,0.00009208008,0.00010880086,0.00003377656,0.0002813872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825126,0.0007760472,0.00015682394,0.00019897171,0.0005077486,0.00010912821],"domain_scores_gemma":[0.98312426,0.013787737,0.00077264744,0.00062340155,0.0014932507,0.0001987373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052408846,0.0009769208,0.00058709265,0.0010294243,0.00033910648,0.0008028712,0.0008551792,0.0008856787,0.0006819454],"category_scores_gemma":[0.032029297,0.00025519784,0.0002609113,0.0006465566,0.00045702243,0.0013433531,0.00064851384,0.0005919075,0.000089632485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016471266,0.00033583742,0.027863564,0.00046384518,0.0002765688,0.00037358672,0.0004076434,0.60316026,0.02371833,0.0037967206,0.0008366063,0.33711994],"study_design_scores_gemma":[0.000054499997,0.00043860127,0.0049228645,0.0000389123,0.000085505206,0.0001102277,0.00008186905,0.9652074,0.026891496,0.0017882093,0.0003629221,0.000017513908],"about_ca_topic_score_codex":0.0012241948,"about_ca_topic_score_gemma":0.0012222887,"teacher_disagreement_score":0.0052408846,"about_ca_system_score_codex":0.000625538,"about_ca_system_score_gemma":0.00037929125,"threshold_uncertainty_score":0.027716815},"labels":[],"label_agreement":null},{"id":"W2146299581","doi":"10.1109/ccece.1996.548066","title":"VLSI implementation of a neural network classifier","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Classifier (UML); Very-large-scale integration; Artificial neural network; Field-programmable gate array; Artificial intelligence; Computer architecture; Machine learning; Pattern recognition (psychology); Computer engineering; Embedded system","score_opus":0.034719232803022006,"score_gpt":0.27770579624093833,"score_spread":0.24298656343791633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146299581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037457604,0.00071998464,0.9212698,0.0007543744,0.00063029985,0.0003001776,0.00031590383,0.005257658,0.033294193],"genre_scores_gemma":[0.48876682,0.0005407573,0.48569113,0.0006030582,0.00020649543,0.00034947827,0.00060822285,0.0000946948,0.023139374],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997856,0.000024925295,0.000014104512,0.000041755553,0.000096129166,0.000037475365],"domain_scores_gemma":[0.99977905,0.00003516737,0.000013476178,0.000024355033,0.00013380127,0.000014106586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021835467,0.0002543069,0.00029070303,0.0004497566,0.00040059196,0.00069364574,0.0009532243,0.0005581218,0.007707345],"category_scores_gemma":[0.0006444806,0.00018300048,0.0001940336,0.00040214832,0.0001637363,0.0004962284,0.00025203818,0.00037244696,0.00215933],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032569634,0.00019268587,0.0014586136,0.0003890903,0.000109604975,0.0005265511,0.00010243858,0.057760138,0.17438824,0.026515538,0.021451792,0.71677953],"study_design_scores_gemma":[0.00017403321,0.0010836421,0.0026079414,0.00010713258,0.00015039745,0.001138008,0.000059701157,0.7139633,0.1499669,0.007978709,0.1227078,0.00006244473],"about_ca_topic_score_codex":0.0028149087,"about_ca_topic_score_gemma":0.0037812898,"teacher_disagreement_score":0.007707345,"about_ca_system_score_codex":0.00062490284,"about_ca_system_score_gemma":0.0010399311,"threshold_uncertainty_score":0.025783658},"labels":[],"label_agreement":null},{"id":"W2146631111","doi":"10.1109/ccece.1997.608254","title":"Fuzzy neural networks for edge detection","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Fuzzy logic; Tracing; Pattern recognition (psychology); Edge detection; Fuzzy set; Enhanced Data Rates for GSM Evolution; Block (permutation group theory); Layer (electronics); Pixel; Image processing; Mathematics; Image (mathematics)","score_opus":0.026480286232453534,"score_gpt":0.23545531682877846,"score_spread":0.20897503059632494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146631111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040591476,0.0041719936,0.9867776,0.00023726141,0.0001433446,0.000046078374,0.000083995306,0.0004343063,0.004046335],"genre_scores_gemma":[0.23148434,0.0064654914,0.7492918,0.00023742348,0.0002568818,0.0002690249,0.00037511098,0.00008435124,0.011535469],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996263,0.00011054178,0.000021925704,0.000056625897,0.00016119082,0.000023401148],"domain_scores_gemma":[0.999582,0.00022713815,0.000036175603,0.00003190051,0.000113854985,0.000008915865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006690322,0.000617919,0.00078380766,0.0008334157,0.00035973627,0.0007180712,0.0008674973,0.0011135866,0.0031009864],"category_scores_gemma":[0.0020236273,0.00032481187,0.0004656199,0.0010478597,0.00040358695,0.000997609,0.00040006975,0.0012242643,0.00080926256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000148484,0.000056809782,0.0009498459,0.00052513136,0.00012680903,0.00020322551,0.00008488682,0.43520162,0.0123955095,0.057955015,0.006848162,0.48550445],"study_design_scores_gemma":[0.0000115808425,0.000025992193,0.00031703894,0.0000528175,0.00001773486,0.0000696876,0.000014002668,0.965012,0.0031556373,0.022468219,0.008835428,0.000019855754],"about_ca_topic_score_codex":0.0029121896,"about_ca_topic_score_gemma":0.0029357125,"teacher_disagreement_score":0.0031009864,"about_ca_system_score_codex":0.00061858364,"about_ca_system_score_gemma":0.0004444511,"threshold_uncertainty_score":0.010373831},"labels":[],"label_agreement":null},{"id":"W2146641079","doi":"10.1109/ijcnn.1992.287133","title":"A fuzzy neural networks technique with fast backpropagation learning","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Backpropagation; Artificial neural network; Computer science; Fuzzy logic; Artificial intelligence; Process (computing); Types of artificial neural networks; Neuro-fuzzy; Machine learning; Fuzzy control system; Time delay neural network","score_opus":0.008139421890468828,"score_gpt":0.21179977589448673,"score_spread":0.2036603540040179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146641079","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011524466,0.00053993217,0.9954723,0.000093024595,0.00014164361,0.000053107593,0.000024571165,0.00041234423,0.0021106137],"genre_scores_gemma":[0.06732907,0.0011173835,0.9220684,0.0001558168,0.00015898822,0.00019038728,0.000096591655,0.00009504382,0.008788264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994037,0.00008248157,0.000034654404,0.000106821884,0.00034377296,0.000028594259],"domain_scores_gemma":[0.9996878,0.00008892949,0.000030358591,0.000036150872,0.00014305532,0.000013695922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008729146,0.00092891086,0.0007247018,0.0011599278,0.0005982718,0.00085188245,0.0012246993,0.0015413301,0.002958685],"category_scores_gemma":[0.001915023,0.00039779817,0.0007792254,0.0010897482,0.00047388842,0.001399391,0.00080033403,0.001369023,0.0015621017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009179865,0.00006892706,0.00042494634,0.00032935035,0.00010718305,0.00021817787,0.00010115201,0.16738677,0.02836601,0.03176741,0.006344871,0.7647934],"study_design_scores_gemma":[0.000027355061,0.00007737001,0.00033684046,0.00006905222,0.0000393214,0.0003926386,0.0000122293795,0.93800586,0.015713599,0.011662587,0.03362153,0.000041696458],"about_ca_topic_score_codex":0.004337816,"about_ca_topic_score_gemma":0.004728786,"teacher_disagreement_score":0.004337816,"about_ca_system_score_codex":0.0005580173,"about_ca_system_score_gemma":0.00085834635,"threshold_uncertainty_score":0.0098977685},"labels":[],"label_agreement":null},{"id":"W2146794146","doi":"10.1109/icsmc.2004.1401139","title":"Guided construction of training data set for neural networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"University of Crete; University of Victoria","keywords":"Computer science; Artificial neural network; Training (meteorology); Training set; Set (abstract data type); Artificial intelligence","score_opus":0.1447549859030021,"score_gpt":0.3350814766672733,"score_spread":0.1903264907642712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146794146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03135214,0.00018469091,0.9642995,0.00021956112,0.00004757394,0.00026892085,0.0004009581,0.0014197314,0.0018068737],"genre_scores_gemma":[0.1664703,0.00016603993,0.8280053,0.00016432148,0.000028907205,0.0010699085,0.0025200308,0.00037623427,0.0011989276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99877125,0.00036792964,0.00011311506,0.0002920715,0.00034322136,0.00011235977],"domain_scores_gemma":[0.99597174,0.002018842,0.00018080558,0.00071141316,0.0010307637,0.000086393884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002142823,0.0012682242,0.001722226,0.001526313,0.0008646132,0.0011384316,0.0034277434,0.0018280614,0.004303822],"category_scores_gemma":[0.013578918,0.0012980984,0.0011100904,0.001033557,0.0009440162,0.002140904,0.0032043867,0.0036120536,0.0017120072],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002986274,0.00036276795,0.0034381195,0.00045575647,0.00014879578,0.0002845959,0.00040753445,0.545174,0.021193225,0.016962172,0.0060682744,0.40520614],"study_design_scores_gemma":[0.000020734067,0.000068529094,0.0003734487,0.000041245075,0.000019254012,0.00004701768,0.00005503685,0.9804067,0.009137596,0.007427492,0.0023888082,0.000014191333],"about_ca_topic_score_codex":0.0021089993,"about_ca_topic_score_gemma":0.0029185612,"teacher_disagreement_score":0.004303822,"about_ca_system_score_codex":0.00096479536,"about_ca_system_score_gemma":0.0010732961,"threshold_uncertainty_score":0.014397681},"labels":[],"label_agreement":null},{"id":"W2147075969","doi":"10.1109/iembs.1997.756633","title":"Memory of neuronal networks: the white noise approach","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Laguerre polynomials; Kernel (algebra); White noise; Measure (data warehouse); Additive white Gaussian noise; Noise (video); Computer science; Gaussian noise; Mathematics; Artificial intelligence; Mathematical analysis; Telecommunications; Discrete mathematics","score_opus":0.022556708386129783,"score_gpt":0.20569301011272048,"score_spread":0.1831363017265907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147075969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0727183,0.00089542835,0.9242508,0.00009436895,0.00006047941,0.00002887341,0.000057650883,0.0002129586,0.0016812779],"genre_scores_gemma":[0.92030734,0.0008320688,0.07568837,0.000070990194,0.00009474105,0.00010577911,0.00010565873,0.000073859104,0.0027210845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997539,0.000044131335,0.000019278246,0.00006440193,0.000080817335,0.00003751473],"domain_scores_gemma":[0.9990694,0.00043451463,0.00017426319,0.0001363152,0.00013681427,0.000048728787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007831133,0.0005081749,0.0004937742,0.0008200032,0.00022380163,0.00075365586,0.0008952116,0.0007309121,0.00073412847],"category_scores_gemma":[0.003950798,0.00017965263,0.00040536796,0.00047284883,0.0006943601,0.0025812504,0.00078661885,0.00075354485,0.0001825237],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044092463,0.00014849161,0.0031287475,0.00039694193,0.00022713949,0.00029680086,0.000340243,0.5340975,0.10353106,0.18540037,0.0007009442,0.17129092],"study_design_scores_gemma":[0.000009418597,0.00013696837,0.0012413812,0.00001833674,0.00002756903,0.00009569404,0.000019765896,0.9442887,0.011517484,0.041768525,0.00084466574,0.000031674197],"about_ca_topic_score_codex":0.00097164325,"about_ca_topic_score_gemma":0.0006121814,"teacher_disagreement_score":0.00097164325,"about_ca_system_score_codex":0.0004984242,"about_ca_system_score_gemma":0.00036940663,"threshold_uncertainty_score":0.004141569},"labels":[],"label_agreement":null},{"id":"W2147527458","doi":"10.1109/ijcnn.1991.155614","title":"Learning and generalization in logic trees","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Generalization; Binary tree; Computer science; Boolean function; Tree (set theory); Theoretical computer science; Implementation; Property (philosophy); Binary number; Boolean algebra; Artificial intelligence; Discrete mathematics; Programming language; Algorithm; Arithmetic; Mathematics; Combinatorics","score_opus":0.02500590866243785,"score_gpt":0.24193951981684786,"score_spread":0.21693361115441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147527458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054053076,0.005564178,0.8886052,0.0026681195,0.0004387114,0.000102246406,0.00043617704,0.0017576142,0.046374682],"genre_scores_gemma":[0.70861274,0.008175114,0.24520217,0.0014402476,0.0012226511,0.00033398115,0.0017614053,0.00044685946,0.0328048],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935025,0.00015848843,0.000055359087,0.00021575361,0.00014996971,0.00007011405],"domain_scores_gemma":[0.99809116,0.0013413309,0.00009500899,0.00023071568,0.00018601643,0.000055723245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010751376,0.0004785645,0.0007852146,0.0005589714,0.00044960677,0.0013687381,0.0008730336,0.0006098915,0.010955443],"category_scores_gemma":[0.00470581,0.00023531569,0.0013072902,0.0010861031,0.0015541613,0.002898787,0.0011152405,0.0015868442,0.0013805947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001198078,0.00006735738,0.0011861521,0.0005323745,0.000115919,0.00021339866,0.00030466376,0.17708172,0.0024048851,0.34266958,0.01755584,0.45774838],"study_design_scores_gemma":[0.00001724991,0.00005367664,0.00057062384,0.00007176764,0.000032885455,0.00011271001,0.00003172478,0.27893603,0.0010981924,0.7076384,0.0114178285,0.000018968749],"about_ca_topic_score_codex":0.0026466262,"about_ca_topic_score_gemma":0.0025348207,"teacher_disagreement_score":0.010955443,"about_ca_system_score_codex":0.0013530768,"about_ca_system_score_gemma":0.00058211136,"threshold_uncertainty_score":0.036649644},"labels":[],"label_agreement":null},{"id":"W2147546249","doi":"","title":"Incorporating complex cells into neural networks for pattern classification","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Benchmark (surveying); Computer science; Visual cortex; Artificial neural network; Conditional probability; Pattern recognition (psychology); Latent variable; Machine learning; Probability distribution; Image (mathematics); Basis (linear algebra); Algorithm; Mathematics; Statistics; Geography","score_opus":0.05499700654867274,"score_gpt":0.2881650422196446,"score_spread":0.23316803567097188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147546249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023031479,0.0007517619,0.97265065,0.00031451965,0.000051811057,0.00004913955,0.000097162134,0.001076298,0.0019772544],"genre_scores_gemma":[0.48985553,0.0011840824,0.502001,0.00025768726,0.00011947566,0.00022930851,0.00036127056,0.00026663137,0.0057250345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996753,0.00008577176,0.000022636514,0.00009220107,0.000089762885,0.000034343782],"domain_scores_gemma":[0.99927694,0.0004260672,0.00005873145,0.000110234534,0.000102302074,0.000025695874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092002604,0.0008091184,0.0006883033,0.00074943644,0.0002854793,0.001081691,0.0013185223,0.00084598147,0.0020657012],"category_scores_gemma":[0.0030318853,0.0005058031,0.00058340555,0.001110781,0.0006936653,0.0017535691,0.00096941664,0.0014900289,0.00081399694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048155172,0.000048518235,0.0005734726,0.000052217492,0.000050240207,0.000022607468,0.000045100445,0.82901025,0.004662406,0.015987843,0.0011886851,0.14831053],"study_design_scores_gemma":[0.0000019761424,0.0000058189667,0.000047747686,0.000002148944,0.0000027584538,0.00000300646,0.000001915038,0.99277765,0.00035195588,0.00651484,0.0002875756,0.000002545925],"about_ca_topic_score_codex":0.007271261,"about_ca_topic_score_gemma":0.012672165,"teacher_disagreement_score":0.007271261,"about_ca_system_score_codex":0.0012988168,"about_ca_system_score_gemma":0.00070754957,"threshold_uncertainty_score":0.014457881},"labels":[],"label_agreement":null},{"id":"W2147553903","doi":"10.1109/isic.1995.525045","title":"Dynamic recurrent neural networks for modeling flexible robot dynamics","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Backpropagation; Nonlinear system; Parameterized complexity; Recurrent neural network; Artificial neural network; Control theory (sociology); System dynamics; State space; MIMO; Set (abstract data type); Robot; Artificial intelligence; Algorithm; Mathematics","score_opus":0.038088481323133416,"score_gpt":0.2701684644539552,"score_spread":0.2320799831308218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147553903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011313291,0.0012649054,0.9847719,0.00017001833,0.00004673485,0.000012229848,0.00005750941,0.00040746963,0.0019559271],"genre_scores_gemma":[0.7576799,0.0031687645,0.23088586,0.00012660089,0.00013572516,0.00017437914,0.00041775205,0.000117047384,0.0072939955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999833,0.000044139622,0.000012553916,0.000034765006,0.0000629884,0.000012670443],"domain_scores_gemma":[0.9998016,0.00008829218,0.000038312763,0.000025844374,0.000040359122,0.0000056785984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040158958,0.0005524023,0.00045814842,0.00027153388,0.00014062539,0.00046644371,0.000616561,0.00071825355,0.00090326945],"category_scores_gemma":[0.001176199,0.00026533578,0.00035003724,0.0004669787,0.0002956768,0.0008886526,0.00035099962,0.0007501417,0.00029901278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030643918,0.000012234775,0.00022206786,0.000070687696,0.00003542215,0.00009228016,0.0000383526,0.91999435,0.006130295,0.024661986,0.0007736935,0.04793793],"study_design_scores_gemma":[0.000001279182,0.000005262156,0.00003798245,0.0000026438593,0.0000030881351,0.000009851959,0.0000016449632,0.9952415,0.00036241327,0.003887384,0.0004444297,0.000002601366],"about_ca_topic_score_codex":0.0027214563,"about_ca_topic_score_gemma":0.0025885776,"teacher_disagreement_score":0.0027214563,"about_ca_system_score_codex":0.00036636787,"about_ca_system_score_gemma":0.00027852284,"threshold_uncertainty_score":0.005411148},"labels":[],"label_agreement":null},{"id":"W2147569620","doi":"10.1109/ijcnn.2008.4634257","title":"Numerical condition of feedforward networks with opposite transfer functions","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Artificial neural network; Benchmark (surveying); Computer science; Transfer function; Feedforward neural network; Feed forward; Transfer of learning; Process (computing); Artificial intelligence; Machine learning; Algorithm; Control engineering; Engineering","score_opus":0.012365938682351193,"score_gpt":0.20653847055391858,"score_spread":0.19417253187156738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147569620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28389347,0.00022346964,0.7054812,0.00088882074,0.00011979528,0.00009063568,0.00006055092,0.00056250574,0.008679568],"genre_scores_gemma":[0.9531431,0.000114515984,0.045163635,0.00014741028,0.000033328593,0.00013059429,0.00005771728,0.0000749706,0.0011347067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982204,0.0005166141,0.00020197185,0.00029792995,0.00055032904,0.00021273746],"domain_scores_gemma":[0.97808903,0.014858708,0.002079451,0.002280474,0.0021590146,0.0005333682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050439686,0.0006351652,0.0006810455,0.00051656883,0.0006588277,0.0016121599,0.001243914,0.0019044925,0.004991033],"category_scores_gemma":[0.064196676,0.00052242295,0.00037379705,0.00032912163,0.0024989578,0.005131993,0.002160262,0.0023570803,0.0006544611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010689405,0.00024384675,0.007204715,0.00045415846,0.000070098045,0.0006548616,0.00043159784,0.62561405,0.05363781,0.21481515,0.0014567423,0.09434803],"study_design_scores_gemma":[0.00007887203,0.00020262244,0.0010162746,0.000039706312,0.000018217212,0.0001877366,0.000040725135,0.9327823,0.01773832,0.04735576,0.0005147489,0.000024681216],"about_ca_topic_score_codex":0.00064825616,"about_ca_topic_score_gemma":0.0005305887,"teacher_disagreement_score":0.0050439686,"about_ca_system_score_codex":0.0008204659,"about_ca_system_score_gemma":0.0009527344,"threshold_uncertainty_score":0.026675403},"labels":[],"label_agreement":null},{"id":"W2147819847","doi":"10.1109/ccece.1996.548088","title":"Chaotic simulated annealing in multilayer feedforward networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Attractor; Chaotic; Statistical physics; Simulated annealing; Parametric statistics; Gaussian; Feedforward neural network; Perturbation (astronomy); Feed forward; Computer science; Maxima and minima; Artificial neural network; Mathematics; Applied mathematics; Algorithm; Mathematical analysis; Physics; Artificial intelligence; Statistics","score_opus":0.02582512529038882,"score_gpt":0.23210854116210025,"score_spread":0.20628341587171142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147819847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11362128,0.00043546272,0.8815919,0.00013821138,0.00005180846,0.000053275733,0.000018725188,0.00045578906,0.0036335383],"genre_scores_gemma":[0.8172093,0.0002211002,0.18010883,0.000046901216,0.000017935205,0.00013639813,0.000028430723,0.000053011994,0.0021780722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999767,0.00008925568,0.000014011195,0.000032048036,0.000075328295,0.00002227176],"domain_scores_gemma":[0.99972516,0.0001583865,0.000035542518,0.000031559186,0.000037575595,0.000011839707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005783455,0.00029656437,0.00044999868,0.00021703601,0.00028758275,0.00034137754,0.0004203696,0.0005492349,0.0007227696],"category_scores_gemma":[0.0016972835,0.00030277157,0.00044059198,0.00020955157,0.00055729575,0.0004789193,0.00052075327,0.00039579318,0.000114126524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004597675,0.00001719081,0.00039626934,0.00003578621,0.000028954177,0.00004491273,0.00005611431,0.96652937,0.007366172,0.0064372667,0.00013287051,0.018909026],"study_design_scores_gemma":[0.000005950667,0.000017190703,0.000087454086,0.0000034626419,0.0000039155157,0.000008868311,0.00000249143,0.99633515,0.0015726738,0.0016114564,0.00034806115,0.0000032835637],"about_ca_topic_score_codex":0.0017719463,"about_ca_topic_score_gemma":0.0015999716,"teacher_disagreement_score":0.0017719463,"about_ca_system_score_codex":0.0006333689,"about_ca_system_score_gemma":0.0003248215,"threshold_uncertainty_score":0.0045954585},"labels":[],"label_agreement":null},{"id":"W2147906280","doi":"10.1109/icnn.1993.298717","title":"Dynamics and stability of multilayered recurrent neural networks","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Stability (learning theory); Artificial neural network; Dynamics (music); Nonlinear system; Computer science; Equilibrium point; Artificial intelligence; Applied mathematics; Mathematics; Machine learning; Physics","score_opus":0.06517075631685977,"score_gpt":0.29343027480704786,"score_spread":0.2282595184901881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147906280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33782953,0.0019705247,0.64092445,0.0010512271,0.00010028033,0.000044124543,0.0002884073,0.0005473844,0.01724415],"genre_scores_gemma":[0.98691154,0.0003967479,0.008234385,0.00004455567,0.000024680901,0.00004087824,0.000085401254,0.000032488504,0.0042292336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996445,0.00007367274,0.000023903172,0.0001078325,0.00009866036,0.000051347128],"domain_scores_gemma":[0.9991253,0.00029355055,0.00024279585,0.000049035327,0.00023799806,0.000051258092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009167735,0.00042140533,0.0005121629,0.00057114364,0.0004720996,0.0012464831,0.0006238052,0.00079089514,0.0020479253],"category_scores_gemma":[0.0038434092,0.00031171404,0.00050303387,0.00028067862,0.0010363256,0.0012949513,0.0010006975,0.0006544811,0.00038456215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015193949,0.000026103631,0.0023100418,0.00011301342,0.000089960056,0.0003202667,0.00029710712,0.83554584,0.01841616,0.120418735,0.0013409149,0.020969909],"study_design_scores_gemma":[0.0000050898625,0.000017199745,0.0003440794,0.000008702038,0.0000060200264,0.00003413454,0.000018214465,0.98085785,0.00063899107,0.017775442,0.00028446224,0.0000098765295],"about_ca_topic_score_codex":0.0042334767,"about_ca_topic_score_gemma":0.001661027,"teacher_disagreement_score":0.0042334767,"about_ca_system_score_codex":0.0008284541,"about_ca_system_score_gemma":0.00055450236,"threshold_uncertainty_score":0.008417666},"labels":[],"label_agreement":null},{"id":"W2148010302","doi":"10.1109/iecon.1996.565994","title":"Speed estimation of induction motor using artificial neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial neural network; Gravitational singularity; Control theory (sociology); Induction motor; Computer science; Dynamic equation; Control engineering; Artificial intelligence; Mathematics; Engineering; Nonlinear system; Physics; Mathematical analysis; Voltage","score_opus":0.06358428830933978,"score_gpt":0.27041156227304225,"score_spread":0.20682727396370248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148010302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022613786,0.00073616905,0.9739826,0.00012409544,0.00007041218,0.00002204964,0.000023388418,0.0006774189,0.0017501964],"genre_scores_gemma":[0.6641512,0.0011285567,0.33040518,0.00008309278,0.000100909805,0.000079300095,0.0001610665,0.00008259222,0.0038080728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963236,0.0000726502,0.000035173078,0.00007495715,0.00015810542,0.000026710233],"domain_scores_gemma":[0.99951017,0.00017436701,0.000086569366,0.000035019708,0.00018283617,0.000011002303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046784437,0.0006097581,0.0006097197,0.00086634903,0.00024586712,0.00055149506,0.0004365168,0.0006617438,0.0006563841],"category_scores_gemma":[0.0021710559,0.00029317406,0.00028423246,0.0005032099,0.00021231086,0.0007436628,0.00029278215,0.00057132274,0.0003395736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022255625,0.000063031024,0.0023528931,0.00020647909,0.00006966208,0.00012318887,0.000078305406,0.2989013,0.04013556,0.004519832,0.0014774025,0.6518498],"study_design_scores_gemma":[0.000010391193,0.000028232002,0.0009844736,0.000017531473,0.000014754773,0.000049189282,0.0000068140353,0.98694474,0.008538887,0.0015063768,0.0018833696,0.0000151333925],"about_ca_topic_score_codex":0.0018427242,"about_ca_topic_score_gemma":0.0014377327,"teacher_disagreement_score":0.0018427242,"about_ca_system_score_codex":0.0003923564,"about_ca_system_score_gemma":0.000255423,"threshold_uncertainty_score":0.0036640167},"labels":[],"label_agreement":null},{"id":"W2148623873","doi":"10.1109/ijcnn.2004.1380155","title":"Transferring domain rules in a constructive network: introducing RBCC","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Constructive; Pruning; Computer science; Domain (mathematical analysis); Artificial neural network; Simple (philosophy); Task (project management); Artificial intelligence; Algorithm; Theoretical computer science; Process (computing); Mathematics; Engineering; Programming language","score_opus":0.00828221898240674,"score_gpt":0.22560968575762091,"score_spread":0.21732746677521417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148623873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037703554,0.000101006946,0.990999,0.0001266778,0.00006854099,0.00007496841,0.000025368417,0.0006942844,0.0041397763],"genre_scores_gemma":[0.10129739,0.00025630876,0.89398766,0.00029773562,0.00010243021,0.00019141812,0.00011531143,0.00019383249,0.0035578853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858284,0.00039806784,0.000083846455,0.0002349389,0.00059772254,0.00010261724],"domain_scores_gemma":[0.99630046,0.0017336896,0.00024169667,0.0006314479,0.0009698369,0.00012287048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018667902,0.00079116545,0.0007621537,0.0014210674,0.00057748734,0.0016125775,0.002535326,0.0015081313,0.0038071894],"category_scores_gemma":[0.008360458,0.00058312307,0.0006636224,0.0012732453,0.002042819,0.0021342766,0.0019237853,0.0017113987,0.0017580539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009764723,0.00015660038,0.00079074997,0.00019894615,0.000080009275,0.00054327544,0.00029648034,0.26263732,0.025681412,0.23623712,0.004321682,0.46895877],"study_design_scores_gemma":[0.000012266137,0.000058041423,0.00008299894,0.000031972857,0.000024683088,0.0001935709,0.000023339944,0.9418585,0.011583751,0.037399698,0.008707236,0.00002399743],"about_ca_topic_score_codex":0.002395885,"about_ca_topic_score_gemma":0.0028185062,"teacher_disagreement_score":0.0038071894,"about_ca_system_score_codex":0.0009144882,"about_ca_system_score_gemma":0.0012953294,"threshold_uncertainty_score":0.0127363205},"labels":[],"label_agreement":null},{"id":"W2148652096","doi":"10.1109/icnn.1995.488134","title":"Universal approximation using dynamic recurrent neural networks: discrete-time version","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Discrete time and continuous time; Artificial neural network; Nonlinear system; Recurrent neural network; Trajectory; Interval (graph theory); Computer science; Control theory (sociology); State space; Set (abstract data type); Class (philosophy); Function approximation; Applied mathematics; State (computer science); Function (biology); Mathematics; Algorithm; Artificial intelligence; Control (management); Physics","score_opus":0.015597843999380336,"score_gpt":0.23175523680532295,"score_spread":0.21615739280594262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148652096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017533742,0.0006853897,0.9797095,0.000090270805,0.000054717973,0.000009964389,0.000024358767,0.00026859308,0.0016233736],"genre_scores_gemma":[0.8987917,0.001077098,0.0971996,0.00009484587,0.00007320529,0.00003509381,0.00014043778,0.000078158766,0.0025098615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995028,0.000107885,0.00003446936,0.00014777438,0.00015040484,0.000056754914],"domain_scores_gemma":[0.9988111,0.0006458381,0.00015207144,0.00020047509,0.00015881377,0.000031703523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010243069,0.0005118618,0.000715303,0.00031406258,0.00017088713,0.00076692394,0.0010132095,0.0007201257,0.0012389843],"category_scores_gemma":[0.004470455,0.00025788002,0.0004696171,0.00043307527,0.0006317974,0.001757581,0.0007229414,0.0010336727,0.0002891184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016288615,0.00005239206,0.0010466976,0.0002251433,0.00009541071,0.00028430708,0.00014708382,0.7355255,0.011941585,0.14189377,0.00102731,0.1075979],"study_design_scores_gemma":[0.0000017512352,0.000012174184,0.000042790773,0.0000035732526,0.0000045187376,0.000026225964,0.000002654747,0.99386,0.0006735071,0.005065742,0.00030363337,0.0000033653505],"about_ca_topic_score_codex":0.0020503202,"about_ca_topic_score_gemma":0.0014328859,"teacher_disagreement_score":0.0020503202,"about_ca_system_score_codex":0.0004944102,"about_ca_system_score_gemma":0.00028576684,"threshold_uncertainty_score":0.0054171085},"labels":[],"label_agreement":null},{"id":"W2149169205","doi":"10.1109/reconfig.2005.5","title":"A Handel-C Implementation of the Back-Propagation Algorithm on Field Programmable Gate Arrays","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Field-programmable gate array; Computer science; Flexibility (engineering); Computer architecture; Artificial neural network; Parallel computing; Computation; Implementation; Programmable logic device; Gate array; Embedded system; Algorithm; Artificial intelligence","score_opus":0.010005339039311237,"score_gpt":0.25468890527816807,"score_spread":0.24468356623885684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149169205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13275044,0.00026707875,0.82631093,0.00019101023,0.00017263264,0.00027988025,0.0003931185,0.012513385,0.027121548],"genre_scores_gemma":[0.4601976,0.0001719118,0.52716637,0.00010417158,0.000015478348,0.00014883885,0.00036850796,0.00019202562,0.011635189],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987626,0.000015142891,0.000007887039,0.000027578846,0.00004800712,0.000025045294],"domain_scores_gemma":[0.9997583,0.00008982343,0.000019925414,0.000040641098,0.000078922145,0.000012441354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016933728,0.00045336786,0.00023003908,0.00031717628,0.00026966858,0.00044962068,0.0007622775,0.0004359633,0.006928047],"category_scores_gemma":[0.0005815404,0.0001678855,0.0002369945,0.00028214458,0.00019106515,0.00031763536,0.00016394844,0.00036037937,0.00082469743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008420538,0.0003072203,0.0018686716,0.0007119694,0.00013300855,0.0010529337,0.0001748423,0.1965843,0.23484401,0.020814812,0.008012196,0.5346541],"study_design_scores_gemma":[0.0001829555,0.00058919186,0.0016840649,0.00006004812,0.00005416583,0.00070294115,0.000028741368,0.78349656,0.18999673,0.0022811792,0.020872485,0.00005093414],"about_ca_topic_score_codex":0.00590094,"about_ca_topic_score_gemma":0.0058526373,"teacher_disagreement_score":0.006928047,"about_ca_system_score_codex":0.00037468132,"about_ca_system_score_gemma":0.0007633246,"threshold_uncertainty_score":0.02317667},"labels":[],"label_agreement":null},{"id":"W2149172817","doi":"10.1109/mwscas.1996.594204","title":"A neural network approach to circuit extraction","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial neural network; Circuit extraction; Perceptron; Extraction (chemistry); Artificial intelligence; Pattern recognition (psychology); Feature extraction; Equivalent circuit; Engineering; Electrical engineering; Voltage","score_opus":0.0531733991994597,"score_gpt":0.24706641009292604,"score_spread":0.19389301089346633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149172817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001058589,0.0010181345,0.9908632,0.00027506522,0.0000832139,0.000021596737,0.00005838759,0.00017412807,0.006447786],"genre_scores_gemma":[0.1430384,0.006258707,0.82186043,0.0005087058,0.0004944908,0.00027458798,0.00036641333,0.00017786531,0.027020331],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997569,0.000048707567,0.0000137539255,0.00005569453,0.00010421365,0.000020831721],"domain_scores_gemma":[0.9997993,0.0000937541,0.000017446762,0.000025535572,0.000055435114,0.000008586495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003135731,0.0006361724,0.0006977341,0.0008660735,0.00046635125,0.0010994038,0.0012580563,0.0011754681,0.0047126445],"category_scores_gemma":[0.0010539362,0.00040137596,0.0006633063,0.001000178,0.0006944504,0.001956571,0.00083239,0.0015484012,0.0011438727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042849308,0.00003887783,0.00028656563,0.00038484266,0.000083745144,0.00023783895,0.00007922504,0.4547023,0.012914133,0.30330205,0.005768136,0.22215948],"study_design_scores_gemma":[0.000007111098,0.000018752231,0.0001222715,0.000033436256,0.000017276649,0.00010831756,0.000012858152,0.8434181,0.0028229258,0.13750418,0.015918054,0.000016619482],"about_ca_topic_score_codex":0.002057159,"about_ca_topic_score_gemma":0.0026397489,"teacher_disagreement_score":0.0047126445,"about_ca_system_score_codex":0.0007496,"about_ca_system_score_gemma":0.00065927365,"threshold_uncertainty_score":0.015765369},"labels":[],"label_agreement":null},{"id":"W2149278801","doi":"10.1109/isit.1995.535773","title":"Nonparametric classification using radial basis function nets and empirical risk minimization","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Nonparametric statistics; Radial basis function; Basis (linear algebra); Minification; Random variable; Multivariate random variable; Pattern recognition (psychology); Artificial intelligence; Empirical risk minimization; Mathematics; Function (biology); Convergence (economics); Computer science; Artificial neural network; Statistics; Mathematical optimization","score_opus":0.0787034492449981,"score_gpt":0.27952321974720923,"score_spread":0.20081977050221111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149278801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02257891,0.0008261211,0.97517735,0.00033867973,0.000027388187,0.000015853484,0.00002333614,0.00012840192,0.0008838773],"genre_scores_gemma":[0.7925044,0.0013654404,0.20161717,0.00024324423,0.00024709408,0.00018036082,0.00024769478,0.00015718768,0.0034374273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971253,0.0019077258,0.00010324768,0.00025345755,0.00050464924,0.00010551837],"domain_scores_gemma":[0.9807829,0.014908308,0.0014417376,0.00084017415,0.001781088,0.0002457145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008281316,0.0010651306,0.0015889693,0.001062192,0.0003698672,0.0012955589,0.0014064634,0.0019549718,0.0006286096],"category_scores_gemma":[0.028587699,0.0005152293,0.00070847094,0.0010022168,0.0022671302,0.0024947335,0.0015241606,0.0016464172,0.0002917114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015580768,0.00006300917,0.0019096457,0.00012766728,0.00009431001,0.000109076485,0.000099915196,0.8552235,0.0013930986,0.093305744,0.0010475083,0.04647079],"study_design_scores_gemma":[0.000002663285,0.0000110193405,0.00010445332,0.0000044085245,0.000002812716,0.000009934072,0.0000033067165,0.98777276,0.00013883115,0.011853486,0.00009279343,0.000003484438],"about_ca_topic_score_codex":0.0019509965,"about_ca_topic_score_gemma":0.0010713587,"teacher_disagreement_score":0.008281316,"about_ca_system_score_codex":0.0010336698,"about_ca_system_score_gemma":0.0005867374,"threshold_uncertainty_score":0.04379636},"labels":[],"label_agreement":null},{"id":"W2149375037","doi":"10.1109/tnn.2005.845142","title":"Decision Feedback Recurrent Neural Equalization With Fast Convergence Rate","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Recurrent neural network; Extended Kalman filter; Convergence (economics); Kalman filter; Control theory (sociology); Artificial neural network; Rate of convergence; Nonlinear system; Equalization (audio); Algorithm; Artificial intelligence; Channel (broadcasting); Decoding methods; Control (management)","score_opus":0.016166194613349737,"score_gpt":0.24887757157500331,"score_spread":0.2327113769616536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149375037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011554099,0.00045751658,0.9844518,0.000097927346,0.00008182431,0.000038153772,0.00002392021,0.0006659374,0.0026288242],"genre_scores_gemma":[0.6499111,0.00064510794,0.3395585,0.00019913692,0.000098987555,0.00013519588,0.00015404598,0.00008988148,0.009208042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993476,0.00015747931,0.000060136626,0.00013025523,0.00023719667,0.00006732388],"domain_scores_gemma":[0.9990169,0.00041566152,0.000095203104,0.00016028038,0.00029425946,0.000017724771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010893403,0.0006183028,0.00093954674,0.00025063992,0.00026579632,0.000632544,0.0007716108,0.0007885743,0.0017677904],"category_scores_gemma":[0.0031359477,0.00028396645,0.00038282163,0.0002950142,0.0003730283,0.0011387336,0.00059477065,0.0008548664,0.0008034877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052703207,0.00020460767,0.0009603913,0.0002806541,0.00017707387,0.00027936962,0.00012358588,0.35395834,0.046472073,0.026579184,0.003852311,0.56658536],"study_design_scores_gemma":[0.000034168886,0.00008791392,0.00018910125,0.0000104421515,0.000019846511,0.00010898242,0.000005858946,0.98385066,0.0119177,0.002368266,0.0013893748,0.000017739607],"about_ca_topic_score_codex":0.0017966594,"about_ca_topic_score_gemma":0.0029979255,"teacher_disagreement_score":0.0017966594,"about_ca_system_score_codex":0.0003263872,"about_ca_system_score_gemma":0.00057646696,"threshold_uncertainty_score":0.0059138536},"labels":[],"label_agreement":null},{"id":"W2149533665","doi":"10.1109/aim.2005.1511175","title":"Using adaptive neuro fuzzy inference system in developing an electrical arc furnace simulator","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Adaptive neuro fuzzy inference system; Cascade; Electric arc furnace; Control engineering; Engineering; Parametric statistics; Voltage regulator; Fuzzy control system; Computer science; Simulation; Fuzzy logic; Control theory (sociology); Voltage; Artificial intelligence; Control (management)","score_opus":0.051737585625812856,"score_gpt":0.29382112561061263,"score_spread":0.24208353998479978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149533665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04357942,0.00010409454,0.94968915,0.000099396784,0.00004124511,0.0001450072,0.00007414427,0.0013122804,0.0049552633],"genre_scores_gemma":[0.54925275,0.00021796014,0.44571668,0.00004286159,0.0000140121065,0.0002749298,0.00018907813,0.000097218326,0.004194494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985087,0.000037456477,0.000011659483,0.00002430323,0.000066399436,0.000009287421],"domain_scores_gemma":[0.9997634,0.00012304148,0.000015943226,0.00001570818,0.0000726695,0.000009146364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004895698,0.00031340693,0.00026164693,0.00023233036,0.00027866813,0.00044227915,0.0005220275,0.0005355135,0.0014455266],"category_scores_gemma":[0.0011612279,0.00023425148,0.0003303744,0.0001356599,0.0001708332,0.00054630404,0.0002755409,0.00056109595,0.00033087525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055940276,0.00004050451,0.0008879838,0.00010066811,0.000024900071,0.0001190277,0.00014431597,0.92803615,0.014653287,0.0033263862,0.00033150805,0.052279253],"study_design_scores_gemma":[0.0000052009136,0.00002052962,0.000097084674,0.000006325652,0.000005327033,0.000012772309,0.000011055114,0.99512756,0.003132738,0.0003454712,0.0012318202,0.000004055976],"about_ca_topic_score_codex":0.0057889563,"about_ca_topic_score_gemma":0.006438108,"teacher_disagreement_score":0.0057889563,"about_ca_system_score_codex":0.0003846432,"about_ca_system_score_gemma":0.000604116,"threshold_uncertainty_score":0.011510491},"labels":[],"label_agreement":null},{"id":"W2149614431","doi":"10.1007/s102080010030","title":"Best Choices for Regularization Parameters in Learning Theory: On the Bias—Variance Problem","year":2002,"lang":"en","type":"article","venue":"Mathematische Annalen","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":258,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematics; Regularization (linguistics); Variance (accounting); Statistics; Econometrics; Applied mathematics; Mathematical optimization; Artificial intelligence; Calculus (dental); Computer science","score_opus":0.08291733840487436,"score_gpt":0.2644609902347111,"score_spread":0.18154365182983673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149614431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023061004,0.0055323998,0.96117115,0.00631052,0.00017837151,0.000047887737,0.00009222287,0.0003378408,0.0032685671],"genre_scores_gemma":[0.48554218,0.005115408,0.49625582,0.0029590197,0.0011802505,0.00039900126,0.0003622047,0.001536376,0.00664972],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98981655,0.0073970193,0.0003440214,0.0009190712,0.0011564353,0.00036678312],"domain_scores_gemma":[0.95141274,0.041867264,0.0014005032,0.0023573257,0.0022096261,0.00075262104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023918701,0.0020864953,0.0030308578,0.0026496046,0.0016498514,0.0034474717,0.0033421859,0.007257114,0.0017305962],"category_scores_gemma":[0.08112113,0.0018371649,0.0013819555,0.0016366155,0.0052677738,0.007810921,0.005124811,0.0075860317,0.00063546863],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006624734,0.00021816145,0.001734158,0.0006733895,0.00041118922,0.0001261844,0.00035911571,0.20020223,0.0025715253,0.6476317,0.01369287,0.13171701],"study_design_scores_gemma":[0.00011122216,0.000050942144,0.00024085626,0.00018072191,0.00005188743,0.000046487647,0.00003838339,0.39289325,0.0007859449,0.6040152,0.001543304,0.00004183475],"about_ca_topic_score_codex":0.0016335469,"about_ca_topic_score_gemma":0.0015309543,"teacher_disagreement_score":0.023918701,"about_ca_system_score_codex":0.0017766943,"about_ca_system_score_gemma":0.0016153844,"threshold_uncertainty_score":0.12649572},"labels":[],"label_agreement":null},{"id":"W2149653171","doi":"10.1109/iscas.2003.1205061","title":"Three-layer bidirectional asymmetrical associative memory","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Bidirectional associative memory; Content-addressable memory; Computer science; Associative property; Layer (electronics); Artificial neural network; Feed forward; Content-addressable storage; Feedforward neural network; Recall; Recurrent neural network; Pattern recognition (psychology); Artificial intelligence; Mathematics; Psychology","score_opus":0.031304103625884296,"score_gpt":0.26454559057267574,"score_spread":0.23324148694679145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149653171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13062227,0.0007052525,0.8428096,0.00023729574,0.00026259985,0.000068840534,0.00023382234,0.0021148224,0.022945447],"genre_scores_gemma":[0.879778,0.00044083386,0.108949594,0.00016638917,0.00005052067,0.0000726076,0.00025135642,0.0000529651,0.010237678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998461,0.000025129351,0.000015126773,0.00003813915,0.000045032513,0.000030362355],"domain_scores_gemma":[0.99974316,0.00004190697,0.000035603447,0.00006972904,0.00008223457,0.000027358912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030982323,0.00033646007,0.00038271782,0.00020134501,0.000244415,0.0005920979,0.0010988158,0.0005218606,0.004374118],"category_scores_gemma":[0.00072391593,0.00014011517,0.00041698592,0.00024080744,0.00034400553,0.0010164987,0.0006200316,0.00039314094,0.0012320194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090980454,0.0002641784,0.0032257065,0.000572397,0.0002584916,0.001039463,0.0002849566,0.17623681,0.17168754,0.1357282,0.00599528,0.5037971],"study_design_scores_gemma":[0.00005699776,0.000342337,0.0011379077,0.000039578532,0.00014451265,0.00080995436,0.000047498135,0.89036095,0.04554901,0.048502814,0.012954852,0.00005345954],"about_ca_topic_score_codex":0.00071367295,"about_ca_topic_score_gemma":0.000951896,"teacher_disagreement_score":0.004374118,"about_ca_system_score_codex":0.00018942425,"about_ca_system_score_gemma":0.0002874065,"threshold_uncertainty_score":0.014632881},"labels":[],"label_agreement":null},{"id":"W2149849922","doi":"10.1109/icassp.1995.479740","title":"A fuzzy inference network for classification","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Adaptive neuro fuzzy inference system; Artificial intelligence; Computer science; Inference; Fuzzy logic; Artificial neural network; Fuzzy inference; Fuzzy classification; Machine learning; Fuzzy inference system; Fuzzy set; Data mining; Fuzzy control system; Pattern recognition (psychology)","score_opus":0.07780143586445706,"score_gpt":0.28511130495069154,"score_spread":0.20730986908623447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149849922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030374709,0.0011104012,0.98884845,0.00026247124,0.00023599592,0.00007386254,0.0001755662,0.0009708016,0.005285032],"genre_scores_gemma":[0.15979174,0.0016756398,0.82819104,0.0003704512,0.00023704329,0.00026768848,0.0006689892,0.00007899616,0.008718452],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993243,0.00010719628,0.00004849636,0.00020411014,0.00025028997,0.00006556703],"domain_scores_gemma":[0.999619,0.00012878249,0.00002825185,0.000048629612,0.00015312913,0.000022155642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009695168,0.0007336938,0.0008336627,0.0011535062,0.0009105409,0.001349737,0.001566759,0.0017196981,0.0041791233],"category_scores_gemma":[0.0020675657,0.00028981306,0.0006506841,0.0011058057,0.00051010156,0.0017282079,0.0006866372,0.0014275945,0.0019359243],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001838972,0.00012121524,0.0017622708,0.00035357743,0.00013782513,0.00034062992,0.000116731644,0.09273581,0.02188611,0.050190017,0.012648276,0.81952363],"study_design_scores_gemma":[0.000022143791,0.000088360284,0.0009309368,0.000089478955,0.00009145634,0.0004367838,0.000033559794,0.9220267,0.009988174,0.03114531,0.03509261,0.000054526143],"about_ca_topic_score_codex":0.003947416,"about_ca_topic_score_gemma":0.00445649,"teacher_disagreement_score":0.0041791233,"about_ca_system_score_codex":0.000682687,"about_ca_system_score_gemma":0.00090731017,"threshold_uncertainty_score":0.013980567},"labels":[],"label_agreement":null},{"id":"W2150035213","doi":"10.1109/72.991413","title":"Optimization-based learning with bounded error for feedforward neural networks","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Backpropagation; Artificial neural network; Robustness (evolution); Feedforward neural network; Feed forward; Rprop; Convergence (economics); Bounded function; Artificial intelligence; Algorithm; Time delay neural network; Types of artificial neural networks; Mathematics; Engineering","score_opus":0.021503306138241456,"score_gpt":0.23170742515867057,"score_spread":0.21020411902042913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150035213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014273928,0.00032305735,0.99746454,0.000049829643,0.00002157357,0.000015670026,0.000008212883,0.000114351664,0.0005754799],"genre_scores_gemma":[0.3737386,0.0015234628,0.6167143,0.0001671212,0.00015804092,0.00065155484,0.0001826053,0.00020812971,0.0066562113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992974,0.00023333568,0.000048725597,0.00013061862,0.00023608505,0.00005384217],"domain_scores_gemma":[0.9984249,0.0010911649,0.000133839,0.000064970714,0.00025051608,0.00003468957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016672593,0.0010053646,0.001191277,0.00048278,0.00041882542,0.0010697626,0.0011356139,0.0014342873,0.0020765243],"category_scores_gemma":[0.006026797,0.0005901372,0.00050331664,0.000744013,0.0010774734,0.0012440196,0.0012692672,0.0015378322,0.00056126283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039929662,0.000020413241,0.000107342785,0.00009412281,0.000025085907,0.000035606692,0.0000306713,0.933535,0.0012254704,0.01899227,0.00068019255,0.045213945],"study_design_scores_gemma":[0.0000035061828,0.000008919203,0.00001816783,0.00000497921,0.0000020571138,0.0000036489666,8.917889e-7,0.9953153,0.00023696682,0.0041611097,0.00024173855,0.0000027916299],"about_ca_topic_score_codex":0.004005696,"about_ca_topic_score_gemma":0.003075675,"teacher_disagreement_score":0.004005696,"about_ca_system_score_codex":0.0010239338,"about_ca_system_score_gemma":0.0011066899,"threshold_uncertainty_score":0.008817434},"labels":[],"label_agreement":null},{"id":"W2150073070","doi":"10.1109/icnn.1997.616183","title":"Incorporating State Space Constraints Into a Neural","year":2005,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; State space; State (computer science); Space (punctuation); Artificial neural network; Artificial intelligence; Mathematics; Algorithm; Operating system; Statistics","score_opus":0.0313352503733068,"score_gpt":0.2712921873444512,"score_spread":0.2399569369711444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150073070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015291202,0.00019697008,0.97439325,0.0004836288,0.000103282175,0.00008823297,0.0001950373,0.0005282264,0.008720254],"genre_scores_gemma":[0.8200694,0.00021690964,0.1676642,0.00045878405,0.000103728074,0.00045583217,0.00034764508,0.00020295939,0.010480517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947435,0.00012735391,0.000044198205,0.00011973484,0.00016628229,0.00006804268],"domain_scores_gemma":[0.99818605,0.0011467277,0.0001173936,0.00014188082,0.00034935764,0.000058548285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014088275,0.0008084066,0.0011849011,0.00057334686,0.0005764018,0.0015076582,0.0018138203,0.0025348351,0.009096953],"category_scores_gemma":[0.0053186542,0.0009287489,0.00050418,0.0006972901,0.00077390095,0.0027441632,0.0020090586,0.0021827,0.00097174733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039692062,0.000037140882,0.0001656006,0.00004913235,0.000018479413,0.000041247204,0.000017176642,0.9696614,0.0007201847,0.0075081545,0.0006218765,0.02111985],"study_design_scores_gemma":[0.0000041538165,0.000011157025,0.000023578665,0.0000049156106,0.0000033807412,0.000002712347,0.000001313563,0.9973584,0.0002034791,0.0021707872,0.00021405234,0.0000019436638],"about_ca_topic_score_codex":0.007669833,"about_ca_topic_score_gemma":0.013551508,"teacher_disagreement_score":0.009096953,"about_ca_system_score_codex":0.0007945055,"about_ca_system_score_gemma":0.0012757563,"threshold_uncertainty_score":0.030432343},"labels":[],"label_agreement":null},{"id":"W2150126637","doi":"10.1109/ijcnn.1998.687256","title":"A distance based network for sequence processing","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Guelph","funders":"","keywords":"Computer science; Artificial neural network; Simple (philosophy); Sequence (biology); Set (abstract data type); Artificial intelligence; Data mining; Programming language","score_opus":0.05461712876700436,"score_gpt":0.2709054210762917,"score_spread":0.2162882923092873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150126637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054862173,0.00058114406,0.9881764,0.00022927324,0.000109519024,0.000056461595,0.00013111322,0.0007237906,0.0045061074],"genre_scores_gemma":[0.20461722,0.0010873372,0.76781154,0.00036899364,0.00017737717,0.00024741236,0.0007607578,0.00018330556,0.02474602],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992093,0.00013369277,0.00005193963,0.0002456169,0.0003145208,0.00004494906],"domain_scores_gemma":[0.9994209,0.00021456927,0.0000383481,0.000102651655,0.00018899137,0.00003455029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083477807,0.00052782166,0.0005826242,0.00084661413,0.0005893798,0.0010810462,0.0018020287,0.0011862472,0.0063125994],"category_scores_gemma":[0.0028197675,0.0002588703,0.0005118342,0.0009586302,0.0007349107,0.0024412586,0.0011925804,0.0013680571,0.0022411186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029609303,0.00011699493,0.0005097539,0.0001906046,0.0000707601,0.000111145484,0.000103891456,0.22945896,0.01603321,0.13793978,0.007396032,0.60777277],"study_design_scores_gemma":[0.000014860444,0.00006778695,0.000164775,0.0000149505095,0.000016221878,0.000086163316,0.000009350116,0.94111353,0.0046950555,0.042935796,0.010859569,0.000021950334],"about_ca_topic_score_codex":0.0051746657,"about_ca_topic_score_gemma":0.005952536,"teacher_disagreement_score":0.0063125994,"about_ca_system_score_codex":0.0013147239,"about_ca_system_score_gemma":0.0010022207,"threshold_uncertainty_score":0.021117806},"labels":[],"label_agreement":null},{"id":"W2150156063","doi":"10.1109/icdsp.2002.1028345","title":"A possible genetic-algorithm based method for optimizing a class of ANN transfer functions","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TRIUMF","funders":"","keywords":"Genetic algorithm; Class (philosophy); Computer science; Artificial neural network; Scheme (mathematics); Backpropagation; Transfer function; Algorithm; Artificial intelligence; Machine learning; Mathematics; Engineering","score_opus":0.023491138626785476,"score_gpt":0.27649339601565603,"score_spread":0.25300225738887056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150156063","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050372337,0.00015879385,0.9914794,0.00011952156,0.000042008705,0.00003257208,0.000013907179,0.00028325943,0.0028332414],"genre_scores_gemma":[0.133466,0.00032496583,0.8606765,0.0001490557,0.00005170304,0.00017848278,0.00006230222,0.00011460149,0.0049763173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981016,0.000046363708,0.000008521189,0.000039810595,0.00008208063,0.000013064607],"domain_scores_gemma":[0.99980026,0.000084420586,0.000023418394,0.000024200932,0.000059070113,0.000008660692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006299489,0.00061732304,0.00044746386,0.00057087996,0.00037189331,0.0005594512,0.0007880815,0.0009167027,0.0020197232],"category_scores_gemma":[0.001334256,0.0002546653,0.00040124153,0.00050906744,0.00048164855,0.00055754546,0.00028446314,0.0007084727,0.0005710487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010124842,0.00016215042,0.001197829,0.0001881359,0.0001435965,0.00014889141,0.00011351383,0.5309977,0.029368239,0.05410871,0.0023516149,0.38111836],"study_design_scores_gemma":[0.000036529153,0.00007762459,0.0003825091,0.000029974277,0.000032729906,0.00015074479,0.00001433225,0.97208595,0.0081047835,0.011207328,0.007850169,0.000027341282],"about_ca_topic_score_codex":0.0015079144,"about_ca_topic_score_gemma":0.0026202532,"teacher_disagreement_score":0.0020197232,"about_ca_system_score_codex":0.00040587102,"about_ca_system_score_gemma":0.00052600354,"threshold_uncertainty_score":0.0067566633},"labels":[],"label_agreement":null},{"id":"W2150618910","doi":"10.1109/nrsc.1998.711509","title":"Hardware realization of analog CMOS current-mode minimum circuit","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Computer science; CMOS; Fuzzy logic; Electronic engineering; Very-large-scale integration; Software; Electronic circuit; Computer hardware; Electrical engineering; Embedded system; Engineering; Artificial intelligence","score_opus":0.04714734911484396,"score_gpt":0.27379240891757034,"score_spread":0.22664505980272637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150618910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066130616,0.0013741156,0.89739925,0.0007246793,0.00038196333,0.0002971215,0.00040500998,0.0043326677,0.028954552],"genre_scores_gemma":[0.7152843,0.00053664576,0.2761113,0.00043603455,0.00010719536,0.00017780303,0.00026801895,0.000042907286,0.007035785],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998317,0.0000151934655,0.000016438742,0.00003749368,0.00007585613,0.000023347546],"domain_scores_gemma":[0.9998925,0.000025541849,0.000012836097,0.00001143523,0.000048345082,0.000009229531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012166271,0.00025355592,0.0002280918,0.0003430998,0.00027511828,0.00054783083,0.001082986,0.00053295953,0.005684177],"category_scores_gemma":[0.00031972988,0.0001532327,0.00018735137,0.00025836236,0.00013433736,0.0006292966,0.00022613145,0.00035551537,0.00089228054],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047854256,0.00016837251,0.0015545302,0.0010357457,0.00006806123,0.0007248201,0.00037822127,0.01039328,0.51123565,0.043702178,0.009015573,0.42124492],"study_design_scores_gemma":[0.00047346114,0.0022973088,0.0050920197,0.0002803632,0.0002447499,0.0049281,0.00016901946,0.25877848,0.5710507,0.020021997,0.13651063,0.00015321186],"about_ca_topic_score_codex":0.00053275004,"about_ca_topic_score_gemma":0.0006865407,"teacher_disagreement_score":0.005684177,"about_ca_system_score_codex":0.0003373562,"about_ca_system_score_gemma":0.00030151304,"threshold_uncertainty_score":0.019015431},"labels":[],"label_agreement":null},{"id":"W2150861113","doi":"10.1109/ccece.1998.682773","title":"Java tools for research and education in artificial neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Java; Computer science; Artificial neural network; Artificial intelligence; Software engineering; Programming language","score_opus":0.17800312554178155,"score_gpt":0.3800292858058838,"score_spread":0.20202616026410225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150861113","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096532446,0.00070561195,0.8755724,0.00038757597,0.000192048,0.00019415344,0.0014002296,0.11221003,0.008372608],"genre_scores_gemma":[0.020190656,0.0024278555,0.90327775,0.0010459683,0.00024148032,0.001230189,0.009218428,0.043216392,0.019151233],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99758196,0.0004932144,0.00044300174,0.0003410394,0.00093978195,0.00020100457],"domain_scores_gemma":[0.99011093,0.0063031767,0.0004948791,0.0012477922,0.001458401,0.00038480182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034251984,0.002065191,0.0014341426,0.0024516387,0.0005997801,0.0030638522,0.0037574961,0.0016067236,0.037000857],"category_scores_gemma":[0.014347391,0.0016959453,0.0016406532,0.0023069782,0.000656989,0.0048862495,0.0022510965,0.0043435055,0.025551742],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041099658,0.000723355,0.001193195,0.0021584048,0.00015810084,0.00034992475,0.00034716452,0.0055289473,0.01463584,0.046066985,0.18473946,0.74368775],"study_design_scores_gemma":[0.0010785582,0.00027067823,0.0029701367,0.0011925839,0.0002156531,0.0017275512,0.00016419029,0.14841357,0.05027051,0.15736176,0.63601756,0.00031722392],"about_ca_topic_score_codex":0.0018176205,"about_ca_topic_score_gemma":0.0019472427,"teacher_disagreement_score":0.037000857,"about_ca_system_score_codex":0.0005229516,"about_ca_system_score_gemma":0.0015847145,"threshold_uncertainty_score":0.12378025},"labels":[],"label_agreement":null},{"id":"W2151103537","doi":"10.5430/air.v2n2p77","title":"An adaptive methodology to discretize and select features","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministerio de Economía y Competitividad","keywords":"Computer science; Feature (linguistics); Discretization; Artificial intelligence; Machine learning; Data mining; Feature selection; Pattern recognition (psychology); Mathematics","score_opus":0.33541271962095504,"score_gpt":0.4778933484378903,"score_spread":0.14248062881693524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151103537","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027271607,0.000045669403,0.9965599,0.000031063028,0.000025271096,0.0000338455,0.000019060022,0.0002000204,0.0003581103],"genre_scores_gemma":[0.1273428,0.00009715455,0.86979985,0.0001089981,0.00006384489,0.00028726188,0.0001630565,0.00016855734,0.0019684874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914765,0.00018104374,0.00006602186,0.00025494106,0.00027513568,0.00007518291],"domain_scores_gemma":[0.9983729,0.00086360396,0.00014257368,0.0002590303,0.0003169229,0.000044941513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011623573,0.0006996928,0.00081135664,0.0011836868,0.00047605892,0.0009963964,0.0016966832,0.00092401437,0.0027029945],"category_scores_gemma":[0.0054294816,0.00041482673,0.0007093577,0.0011623772,0.00068931095,0.001086025,0.0010848474,0.0015990189,0.0008055945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025989077,0.00019877167,0.0021561016,0.00022274708,0.00011108348,0.00016345351,0.0003546653,0.12511405,0.08266905,0.03366131,0.0041704387,0.75091857],"study_design_scores_gemma":[0.000035133075,0.00011608603,0.00068644003,0.000019839588,0.00002533339,0.00016029393,0.000047876263,0.9645648,0.012949394,0.013966572,0.007398907,0.000029258694],"about_ca_topic_score_codex":0.0013446048,"about_ca_topic_score_gemma":0.0019661046,"teacher_disagreement_score":0.0027029945,"about_ca_system_score_codex":0.00044123273,"about_ca_system_score_gemma":0.0005610238,"threshold_uncertainty_score":0.009042382},"labels":[],"label_agreement":null},{"id":"W2151221590","doi":"10.1109/ijcnn.1993.716804","title":"A generic neural model based on excitatory-inhibitory neural population","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Neurophysiology; Excitatory postsynaptic potential; Inhibitory postsynaptic potential; Artificial neural network; Computer science; Population; Biological neuron model; Artificial intelligence; Neuron; Neuroscience; Biological neural network; Neural system; Biological system; Machine learning; Biology","score_opus":0.021922992090292,"score_gpt":0.24543847049970458,"score_spread":0.22351547840941258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151221590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08354342,0.0014676427,0.87137645,0.000945031,0.00022014527,0.00010752428,0.00043523154,0.00035385005,0.041550707],"genre_scores_gemma":[0.9194304,0.0018224713,0.05774422,0.00030661852,0.00014264212,0.00035356797,0.00023860035,0.000051486313,0.019909965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997904,0.00005070006,0.000012619592,0.000067493434,0.000044234454,0.000034656074],"domain_scores_gemma":[0.9998865,0.000029163748,0.000026269974,0.000011393273,0.00003108126,0.00001561407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003575805,0.00047494392,0.0006470877,0.00037808786,0.00035961458,0.0007812916,0.001711594,0.0013624732,0.0017597852],"category_scores_gemma":[0.00065513473,0.00021990052,0.0006336977,0.0005552177,0.0007284054,0.0013205557,0.0006781595,0.0007568222,0.00043572675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006107478,0.000037017133,0.0007972725,0.00012532054,0.000058339407,0.00038389247,0.00018752582,0.80497724,0.009764773,0.17019278,0.0014325212,0.0119822845],"study_design_scores_gemma":[0.00001272282,0.00003688882,0.00022585702,0.000009682205,0.000018841594,0.000116867624,0.000018340368,0.9750132,0.00039377957,0.022669954,0.0014727411,0.0000111653835],"about_ca_topic_score_codex":0.001880728,"about_ca_topic_score_gemma":0.0013939575,"teacher_disagreement_score":0.001880728,"about_ca_system_score_codex":0.0005331208,"about_ca_system_score_gemma":0.0005544241,"threshold_uncertainty_score":0.005887091},"labels":[],"label_agreement":null},{"id":"W2151384915","doi":"10.1142/s0218001409007338","title":"K-MEANS CLUSTERING FOR PROBLEMS WITH PERIODIC ATTRIBUTES","year":2009,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Akademie Věd České Republiky","keywords":"Trigonometry; Computer science; Cluster analysis; Metric (unit); Simple (philosophy); Algorithm; Minification; Encoding (memory); Mathematics; Mathematical optimization; Artificial intelligence","score_opus":0.09189453764181123,"score_gpt":0.30478066952682376,"score_spread":0.21288613188501254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151384915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032044176,0.0004016339,0.9949837,0.00016597287,0.000044444423,0.000056792884,0.000069285845,0.00035816792,0.00071558816],"genre_scores_gemma":[0.059175313,0.0005365179,0.93721014,0.0000714819,0.000092435854,0.00031479634,0.00046951935,0.00019974449,0.0019301901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986193,0.00042528444,0.000117094285,0.00035305702,0.00041298635,0.00007218415],"domain_scores_gemma":[0.9974649,0.0013538301,0.00023656292,0.0003499426,0.00053919194,0.00005553676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00210559,0.001270153,0.0020531788,0.0017407667,0.0016671123,0.0014841435,0.0021671134,0.0018858433,0.002074165],"category_scores_gemma":[0.0083151655,0.00072237634,0.0012852632,0.00281899,0.0009463663,0.0015358478,0.0013991726,0.0020425625,0.0011331134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008911642,0.00005320451,0.00067901827,0.00050496944,0.000107794076,0.0001295719,0.00028790324,0.75308114,0.0033612691,0.032619014,0.008617348,0.20046967],"study_design_scores_gemma":[0.000008067367,0.000017772436,0.0003031215,0.00001710122,0.000008564626,0.00005182811,0.000050822997,0.9640466,0.001187752,0.029925015,0.0043591065,0.000024245937],"about_ca_topic_score_codex":0.007023664,"about_ca_topic_score_gemma":0.0072401254,"teacher_disagreement_score":0.007023664,"about_ca_system_score_codex":0.0011561684,"about_ca_system_score_gemma":0.0023338045,"threshold_uncertainty_score":0.013965547},"labels":[],"label_agreement":null},{"id":"W2151770284","doi":"10.1109/12.954505","title":"Stochastic neural computation. I. Computational elements","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":560,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Exponentiation; Computer science; Stochastic computing; Artificial neural network; Multiplication (music); Sigmoid function; Computation; Stochastic neural network; Noise (video); Binary number; Algorithm; Artificial intelligence; Time delay neural network; Arithmetic; Mathematics","score_opus":0.021107610935111924,"score_gpt":0.26267279027639334,"score_spread":0.2415651793412814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151770284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071884766,0.01399118,0.9317604,0.0014935998,0.00047937955,0.00006565487,0.00018697267,0.00044750536,0.04438691],"genre_scores_gemma":[0.5572103,0.02460973,0.37588844,0.0013845339,0.0013566078,0.0004642845,0.0004842463,0.00021514423,0.038386725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994906,0.00013388187,0.000033232274,0.00008078974,0.00022881269,0.00003263995],"domain_scores_gemma":[0.9996082,0.000190355,0.0000460209,0.00006177065,0.000077602126,0.0000160904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049238105,0.00052366336,0.00053174427,0.00049888145,0.00039043967,0.0015230139,0.00070108654,0.0011187429,0.0034247376],"category_scores_gemma":[0.0018835624,0.00030584654,0.0004980711,0.0011475415,0.0018848816,0.0017770216,0.00085056934,0.0014274822,0.0012563923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027559876,0.00001879139,0.00049234874,0.00025376966,0.000031470732,0.00005736253,0.00005702077,0.08782567,0.0027468055,0.84424573,0.0032312048,0.06101225],"study_design_scores_gemma":[0.000010493803,0.000052469215,0.00035409193,0.00011600364,0.000017396094,0.00017951804,0.000028712397,0.39736733,0.0023910278,0.54494673,0.0545103,0.000025986212],"about_ca_topic_score_codex":0.0012696087,"about_ca_topic_score_gemma":0.0010071929,"teacher_disagreement_score":0.0034247376,"about_ca_system_score_codex":0.0009537026,"about_ca_system_score_gemma":0.0006114379,"threshold_uncertainty_score":0.011456847},"labels":[],"label_agreement":null},{"id":"W2151930786","doi":"10.5539/mas.v8n1p164","title":"Extraction of Inherent Frequency Components of Multiway EEG Data Using Two-Stage Neural Canonical Correlation Analysis","year":2014,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Canonical correlation; Computer science; Electroencephalography; Artificial neural network; Pattern recognition (psychology); Nonlinear system; Correlation; Set (abstract data type); Data set; Process (computing); Artificial intelligence; Function (biology); Data mining; Mathematics","score_opus":0.0802915553029301,"score_gpt":0.33345913257930015,"score_spread":0.25316757727637007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151930786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012874065,0.00013640945,0.98583394,0.000040063216,0.00003901471,0.000062808664,0.00006203869,0.00037886333,0.00057274976],"genre_scores_gemma":[0.17012022,0.00039642974,0.826894,0.00003450216,0.000044067117,0.00018820724,0.0004756263,0.0001287278,0.0017182007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937683,0.00010875487,0.000045067347,0.00019096123,0.00021841991,0.000060013143],"domain_scores_gemma":[0.9994442,0.00015052847,0.000046052595,0.00007049142,0.00026373134,0.000024853442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007028171,0.00095608475,0.0006057529,0.0013304721,0.00044826814,0.0006957995,0.0005241193,0.0004064479,0.0016248287],"category_scores_gemma":[0.0021542942,0.00029184212,0.0009934603,0.001586811,0.0002998338,0.0008683308,0.00064643763,0.0006362655,0.00053605175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015929408,0.00013550361,0.0051979953,0.00025071733,0.00016187452,0.00031211416,0.00027569314,0.054648,0.06322786,0.009643751,0.0030710932,0.86291605],"study_design_scores_gemma":[0.00001243863,0.000113320006,0.008232472,0.000024906112,0.00006507563,0.00030912572,0.00009188545,0.9554031,0.025745103,0.00439395,0.0055511743,0.000057482026],"about_ca_topic_score_codex":0.004284305,"about_ca_topic_score_gemma":0.007033672,"teacher_disagreement_score":0.004284305,"about_ca_system_score_codex":0.0002652761,"about_ca_system_score_gemma":0.0009748399,"threshold_uncertainty_score":0.008518696},"labels":[],"label_agreement":null},{"id":"W2152120659","doi":"10.1109/ijcnn.2000.857821","title":"Target adaptation to improve the performance of least-squared classifiers","year":2000,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Classifier (UML); Explained sum of squares; Mean squared error; Separable space; Least-squares function approximation; Mathematics; Ordinary least squares; Computer science; Mathematical optimization; Artificial intelligence; Algorithm; Pattern recognition (psychology); Machine learning; Statistics","score_opus":0.013896535045708171,"score_gpt":0.22177135082112814,"score_spread":0.20787481577541997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152120659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049663745,0.00013532637,0.9931519,0.00007777604,0.000044948716,0.000041956428,0.000012969351,0.00078925455,0.000779407],"genre_scores_gemma":[0.21105985,0.00026037352,0.78094864,0.0003850483,0.00012991087,0.0003261287,0.00027333345,0.0006950081,0.005921789],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795973,0.00068405754,0.0001376256,0.00039626512,0.0006937625,0.0001284676],"domain_scores_gemma":[0.99396896,0.003170387,0.0002735794,0.0009062846,0.0015753334,0.00010541643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035410682,0.0015230414,0.0012045415,0.0005021361,0.00047605395,0.0009716742,0.0018526643,0.002248184,0.0036576218],"category_scores_gemma":[0.020896994,0.0006635168,0.0007646552,0.00089141633,0.0007540272,0.0020490014,0.0019256998,0.0030925425,0.004045055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057238556,0.0003297791,0.0013688108,0.00028966326,0.00013970099,0.0001405067,0.00031504483,0.39934957,0.060254123,0.014449281,0.007275779,0.51551545],"study_design_scores_gemma":[0.000013989817,0.000060378075,0.00028492545,0.0000094993975,0.000010430262,0.00004666564,0.000008801676,0.9852003,0.010278154,0.0027016632,0.0013739098,0.00001130826],"about_ca_topic_score_codex":0.0010185183,"about_ca_topic_score_gemma":0.001365201,"teacher_disagreement_score":0.0036576218,"about_ca_system_score_codex":0.00046769725,"about_ca_system_score_gemma":0.000665721,"threshold_uncertainty_score":0.018727183},"labels":[],"label_agreement":null},{"id":"W2152500150","doi":"10.1109/tnn.2002.804285","title":"Gaussian activation functions using Markov chains","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Markov chain; Gaussian; Stochastic computing; Artificial neural network; Signal processing; Nonlinear system; Offset (computer science); Stochastic resonance; Sigmoid function; Stochastic process; Algorithm; Markov process; Digital signal processing; Noise (video); Artificial intelligence; Mathematics; Machine learning; Computer hardware","score_opus":0.031213089371973087,"score_gpt":0.23627035196988516,"score_spread":0.20505726259791207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152500150","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006563693,0.0003015478,0.98777944,0.00036652898,0.000057490193,0.000055725868,0.00012899742,0.00036112586,0.004385479],"genre_scores_gemma":[0.634584,0.0021881508,0.3184781,0.0005102228,0.00024159785,0.0011867206,0.0008668275,0.00041534178,0.041529026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897975,0.0003940125,0.00005543438,0.00019093981,0.00023245184,0.00014747694],"domain_scores_gemma":[0.9956691,0.0031302727,0.00025983102,0.00031284447,0.00047900065,0.00014889709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026052326,0.00097587286,0.0012132493,0.0011159042,0.0007606297,0.0018605101,0.0018310603,0.0017407036,0.01178777],"category_scores_gemma":[0.00948695,0.0008144947,0.0013246029,0.0010000741,0.001597129,0.0028726754,0.0016383358,0.0022224288,0.002623127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006619372,0.000032874694,0.0004718921,0.000053997104,0.00003492418,0.00009951231,0.000082949075,0.64627194,0.00058783,0.33178747,0.0017299576,0.018780474],"study_design_scores_gemma":[0.000010932415,0.0000073285896,0.00004146417,0.000010771415,0.0000056454564,0.0000129057935,0.000004004332,0.9361507,0.0002016266,0.06281644,0.0007297269,0.000008443501],"about_ca_topic_score_codex":0.008930781,"about_ca_topic_score_gemma":0.010138161,"teacher_disagreement_score":0.01178777,"about_ca_system_score_codex":0.0019287934,"about_ca_system_score_gemma":0.0018486205,"threshold_uncertainty_score":0.039434016},"labels":[],"label_agreement":null},{"id":"W2153407169","doi":"10.1109/icsmc.1995.537945","title":"Dynamic feedback control of unknown nonlinear systems using dynamic neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Nonlinear system; Control theory (sociology); Artificial neural network; Scheme (mathematics); MIMO; Control engineering; Control (management); Robust control; Artificial intelligence; Engineering; Mathematics; Telecommunications","score_opus":0.01455760795513888,"score_gpt":0.2363992823254038,"score_spread":0.22184167437026492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153407169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008218292,0.0011240125,0.9883636,0.00008835518,0.00008536508,0.000017497936,0.000012703848,0.00019356459,0.001896546],"genre_scores_gemma":[0.86131954,0.002423451,0.13140462,0.0001519253,0.00015760172,0.00014257681,0.00008692229,0.00003615866,0.0042772517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998264,0.000028746646,0.00001526522,0.000047992868,0.00006219167,0.000019399935],"domain_scores_gemma":[0.99983144,0.00007154936,0.00003899268,0.000015765107,0.000035538535,0.0000066760217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040282778,0.0006097427,0.00040511877,0.0002548764,0.00025589953,0.0004617993,0.00060945045,0.00055397383,0.00085461873],"category_scores_gemma":[0.0008091126,0.00024698666,0.00030208935,0.00024046833,0.0005059753,0.00067199377,0.0005313405,0.00057514897,0.00017164534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071698065,0.000032220836,0.00028879254,0.0003186166,0.0000545666,0.0001273297,0.00009212028,0.79736626,0.02488539,0.025209071,0.00071565027,0.15083832],"study_design_scores_gemma":[0.000010516506,0.000042560914,0.00013631252,0.000015079408,0.000012557044,0.000029718853,0.0000064710907,0.9893607,0.0028820096,0.0051253783,0.0023694027,0.00000936016],"about_ca_topic_score_codex":0.0016506269,"about_ca_topic_score_gemma":0.001973173,"teacher_disagreement_score":0.0016506269,"about_ca_system_score_codex":0.00031956047,"about_ca_system_score_gemma":0.00026709115,"threshold_uncertainty_score":0.0032820702},"labels":[],"label_agreement":null},{"id":"W2154019474","doi":"10.1109/ccece.1998.685648","title":"On the optimal number of hidden nodes in a neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Computer science; Entropy (arrow of time); Stochastic neural network; Time delay neural network; Artificial intelligence","score_opus":0.025635692228276302,"score_gpt":0.248064000343145,"score_spread":0.2224283081148687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154019474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3077839,0.0047877883,0.6715367,0.0025887177,0.00019923688,0.0001365622,0.00013401694,0.0005977531,0.012235358],"genre_scores_gemma":[0.88839704,0.0011935076,0.10877179,0.00025465028,0.000094114635,0.00009539873,0.0000701725,0.00011947756,0.0010038813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971042,0.0015239028,0.00017379821,0.0004642885,0.0004991578,0.00023470358],"domain_scores_gemma":[0.98003656,0.016649595,0.0010513609,0.0006670605,0.0011950827,0.00040042624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059375386,0.0011973697,0.0011744463,0.0008711196,0.0008372176,0.0015417256,0.0010983889,0.0021103756,0.001456468],"category_scores_gemma":[0.04197564,0.0008131237,0.00035595844,0.0005581862,0.0020322227,0.005591107,0.0012706104,0.0016772179,0.00042573846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080173754,0.00017838448,0.004382687,0.00031679697,0.00011918344,0.00020817702,0.00015355603,0.8728493,0.007902353,0.04134718,0.0012782092,0.07046253],"study_design_scores_gemma":[0.00006393981,0.00013832405,0.00087486295,0.00012502413,0.00003821401,0.00007942669,0.000064729924,0.9563053,0.0047958544,0.03697153,0.00051679177,0.000025975478],"about_ca_topic_score_codex":0.0014144974,"about_ca_topic_score_gemma":0.0014427003,"teacher_disagreement_score":0.0059375386,"about_ca_system_score_codex":0.0010821105,"about_ca_system_score_gemma":0.001281538,"threshold_uncertainty_score":0.03140104},"labels":[],"label_agreement":null},{"id":"W2154250681","doi":"10.1016/s0893-6080(03)00088-1","title":"On the quality of ART1 text clustering","year":2003,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Cluster analysis; Computer science; Brown clustering; Document clustering; Quality (philosophy); Information retrieval; Adaptive resonance theory; Conceptual clustering; Fuzzy clustering; Artificial intelligence; Data mining; Artificial neural network; CURE data clustering algorithm","score_opus":0.04054464542946404,"score_gpt":0.2857822013628829,"score_spread":0.24523755593341887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154250681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23156968,0.04021937,0.64210314,0.017183142,0.0025316593,0.0003546505,0.01486776,0.010792366,0.040378336],"genre_scores_gemma":[0.82536054,0.0037171836,0.13138893,0.0015004519,0.0018689318,0.00018125081,0.018327074,0.0032024663,0.014453175],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.97809464,0.0092714485,0.001627672,0.0032184997,0.006774665,0.0010130501],"domain_scores_gemma":[0.8168342,0.1308653,0.0053875307,0.028342728,0.016438462,0.0021317953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02698499,0.0023518049,0.005306801,0.010036593,0.0042404956,0.009843199,0.006143397,0.008228456,0.014540177],"category_scores_gemma":[0.1441195,0.0013107575,0.0029325206,0.0093158465,0.0040772045,0.013822261,0.0062347706,0.003990841,0.005602692],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0067042094,0.00035799405,0.03271831,0.0025232278,0.0014066424,0.0003152223,0.0010899625,0.25328672,0.0049149813,0.06335034,0.09549279,0.53783965],"study_design_scores_gemma":[0.00021839913,0.00028902298,0.007816473,0.00020195595,0.00030730382,0.0004607958,0.0004258101,0.8561138,0.0055879103,0.120299175,0.008173696,0.000105625724],"about_ca_topic_score_codex":0.007992167,"about_ca_topic_score_gemma":0.009746559,"teacher_disagreement_score":0.02698499,"about_ca_system_score_codex":0.0045285285,"about_ca_system_score_gemma":0.0026140495,"threshold_uncertainty_score":0.142712},"labels":[],"label_agreement":null},{"id":"W2154338144","doi":"10.1109/72.914519","title":"STRIP - a strip-based neural-network growth algorithm for learning multiple-valued functions","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; University of Windsor","funders":"Purdue University","keywords":"Hyperplane; Artificial neural network; STRIPS; Algorithm; Computer science; Genetic algorithm; Artificial intelligence; Mathematics; Combinatorics; Machine learning","score_opus":0.019815359783414175,"score_gpt":0.24156098461776043,"score_spread":0.22174562483434626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154338144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02108208,0.00023778023,0.97593766,0.00015215583,0.00002983341,0.00003876547,0.00004789184,0.00065941905,0.0018143384],"genre_scores_gemma":[0.3232582,0.0002511078,0.67116207,0.00018167886,0.000033092678,0.000206979,0.0003333518,0.00018832646,0.004385224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997892,0.000067868874,0.000011743886,0.000041198407,0.00006780269,0.000022142514],"domain_scores_gemma":[0.99946266,0.00027609154,0.00004743353,0.0000603322,0.00012819236,0.000025299016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009327102,0.00056444283,0.00068605965,0.0004776942,0.00028253492,0.00050820375,0.001088062,0.0008359163,0.0023783182],"category_scores_gemma":[0.0013745079,0.00034904704,0.00038579412,0.0006290798,0.00060044165,0.0009976935,0.0007616259,0.0009111605,0.00068557466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016622797,0.00006179303,0.0007133715,0.00007885575,0.000041276875,0.000069633905,0.000082593164,0.7983766,0.01071206,0.013752924,0.0020879179,0.1738567],"study_design_scores_gemma":[0.000007997731,0.000029777837,0.00002700676,0.0000032361304,0.0000025356146,0.0000075887183,0.000004323414,0.99614716,0.0009822856,0.0023949838,0.00039092894,0.0000021854592],"about_ca_topic_score_codex":0.0015144445,"about_ca_topic_score_gemma":0.0016698899,"teacher_disagreement_score":0.0023783182,"about_ca_system_score_codex":0.0005145638,"about_ca_system_score_gemma":0.000526832,"threshold_uncertainty_score":0.007956266},"labels":[],"label_agreement":null},{"id":"W2154439441","doi":"10.5267/j.dsl.2013.11.001","title":"A novel method to extend SAW for decision-making problems with interval data","year":2014,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interval (graph theory); Interval data; Computer science; Mathematical optimization; Management science; Mathematics; Engineering; Data envelopment analysis; Combinatorics","score_opus":0.0588178214829408,"score_gpt":0.3727434082330295,"score_spread":0.3139255867500887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154439441","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017969656,0.00007237919,0.99740136,0.00004407958,0.000037290883,0.000030389732,0.000020370018,0.000053472588,0.0005437242],"genre_scores_gemma":[0.078041695,0.00024745698,0.9196656,0.00012034423,0.00012617128,0.00027552503,0.0001381519,0.00004889619,0.0013362134],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974993,0.00096787437,0.00020336486,0.00042543627,0.00080032105,0.000103736944],"domain_scores_gemma":[0.99579453,0.0030086103,0.00023482532,0.00027596112,0.0005880184,0.000098143355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038643237,0.00087438105,0.001187121,0.0015778465,0.00048217937,0.0010863174,0.0012897152,0.00085557246,0.0032489426],"category_scores_gemma":[0.008183474,0.00044915744,0.0020431294,0.001489267,0.00093534304,0.002288952,0.0016419331,0.002251241,0.000525879],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002510762,0.00020778157,0.0018081202,0.00064244954,0.0002575134,0.00033209068,0.00049789576,0.22055705,0.013980424,0.1612258,0.003088642,0.59715116],"study_design_scores_gemma":[0.000046830904,0.00019461902,0.0006074106,0.00005795195,0.000058710943,0.0002082695,0.000050316405,0.90320706,0.0032651536,0.08500497,0.0072551393,0.00004356175],"about_ca_topic_score_codex":0.0007399215,"about_ca_topic_score_gemma":0.00075657445,"teacher_disagreement_score":0.0038643237,"about_ca_system_score_codex":0.00047813813,"about_ca_system_score_gemma":0.0010118103,"threshold_uncertainty_score":0.020436704},"labels":[],"label_agreement":null},{"id":"W2154615866","doi":"","title":"Locally Weighted Full Covariance Gaussian Density Estimation","year":2004,"lang":"en","type":"preprint","venue":"Érudit documents and data repository (Érudit Consortium, University of Montreal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Estimator; Mathematics; Covariance; Gaussian; Density estimation; Covariance matrix; Dimension (graph theory); Convergence (economics); Applied mathematics; Combinatorics; Statistics; Physics","score_opus":0.0123768022811354,"score_gpt":0.22604811354805304,"score_spread":0.21367131126691763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154615866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016719097,0.00031929504,0.9810836,0.00011589128,0.00001893319,0.000025761614,0.00019002245,0.00071422796,0.0008131392],"genre_scores_gemma":[0.3994079,0.0007219271,0.58612365,0.00024342825,0.000107106134,0.0002503996,0.0017002898,0.00045579637,0.010989537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988462,0.00038275076,0.000053871263,0.00028801337,0.0003316946,0.00009744876],"domain_scores_gemma":[0.99689543,0.0014052772,0.00018942227,0.00077323464,0.000653076,0.0000835412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002467085,0.000579295,0.0014422436,0.0008848213,0.00042129247,0.0012662713,0.001811202,0.0012472556,0.00362141],"category_scores_gemma":[0.01138061,0.00057614915,0.0009385277,0.0011414373,0.0007027063,0.0021746496,0.001758989,0.001155363,0.001194362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004779658,0.00012661946,0.0063538286,0.00042531796,0.00033637352,0.00021629411,0.00039697342,0.5222422,0.018075204,0.07618721,0.008181252,0.36698082],"study_design_scores_gemma":[0.0000150235,0.000043247033,0.0017291646,0.000023431432,0.000029435772,0.00008218216,0.000026747723,0.97286916,0.0031494603,0.019675922,0.0023263227,0.000029925248],"about_ca_topic_score_codex":0.012999942,"about_ca_topic_score_gemma":0.015241234,"teacher_disagreement_score":0.012999942,"about_ca_system_score_codex":0.0008695654,"about_ca_system_score_gemma":0.0013012785,"threshold_uncertainty_score":0.025848567},"labels":[],"label_agreement":null},{"id":"W2154766171","doi":"10.1109/tcsii.2010.2067775","title":"Resistive-Type CVNS Distributed Neural Networks With Improved Noise-to-Signal Ratio","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial neural network; Resistive touchscreen; Computer science; Noise (video); Sensitivity (control systems); SIGNAL (programming language); Scaling; Type (biology); Artificial intelligence; Electronic engineering; Mathematics; Engineering","score_opus":0.01183932658984262,"score_gpt":0.22199474500344205,"score_spread":0.21015541841359942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154766171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02855595,0.0010086166,0.9627314,0.00018285988,0.0002400713,0.000054618406,0.00006507819,0.0008523906,0.0063090357],"genre_scores_gemma":[0.7455087,0.00081265136,0.24353778,0.00033317032,0.00016198757,0.0001101099,0.00014836986,0.00007587442,0.009311377],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996432,0.000070245784,0.00002805931,0.00009087654,0.00013990828,0.000027768032],"domain_scores_gemma":[0.9995234,0.00013367244,0.000046855956,0.000052794985,0.00022028499,0.00002301867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000554859,0.0005227572,0.00045505533,0.0003494381,0.00022424954,0.0008001434,0.0012299072,0.000733962,0.001090565],"category_scores_gemma":[0.0013817364,0.00016973786,0.00027830148,0.00048269003,0.0003620721,0.0008104499,0.00047703672,0.000701876,0.00033344183],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049247907,0.00012762695,0.00069812953,0.00043105328,0.000090800524,0.00024882608,0.00010961254,0.36154324,0.12537964,0.056407914,0.0051083555,0.4493624],"study_design_scores_gemma":[0.000033831016,0.00017326478,0.00026954556,0.000025672884,0.000038273905,0.00013536072,0.000009887806,0.9534406,0.029929051,0.009430724,0.0064889155,0.000024953519],"about_ca_topic_score_codex":0.0011138539,"about_ca_topic_score_gemma":0.0020791623,"teacher_disagreement_score":0.0012299072,"about_ca_system_score_codex":0.0006549715,"about_ca_system_score_gemma":0.00032514648,"threshold_uncertainty_score":0.004752159},"labels":[],"label_agreement":null},{"id":"W2154842400","doi":"10.1109/arith.1991.145534","title":"Arithmetic for digital neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; CMOS; Artificial neural network; Process (computing); Transistor; Binary number; Standard cell; Scale (ratio); Sample (material); Computer hardware; Arithmetic; Integrated circuit; Artificial intelligence; Electronic engineering; Mathematics; Electrical engineering; Voltage; Engineering; Programming language; Operating system","score_opus":0.023579384313387117,"score_gpt":0.22266414291740713,"score_spread":0.19908475860402003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154842400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003712522,0.004314687,0.9706641,0.0005493778,0.0003774429,0.00006913312,0.00022219014,0.001980092,0.018110488],"genre_scores_gemma":[0.12621577,0.0053999103,0.8491758,0.00058225094,0.00053454755,0.0005552444,0.00067656906,0.00028508308,0.016574727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99953496,0.00011383573,0.00005711048,0.00008884604,0.0001721726,0.000033154032],"domain_scores_gemma":[0.99952483,0.00024442497,0.00003456357,0.00007929347,0.00010230237,0.000014560947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000583239,0.0007151149,0.0005390787,0.00070212357,0.0005130356,0.0016246422,0.0012785243,0.00071190344,0.0109951645],"category_scores_gemma":[0.0028604087,0.00030420322,0.0004863335,0.0011780098,0.0008473743,0.0022175256,0.001053905,0.0016896294,0.003382174],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011866914,0.000027657514,0.00021622996,0.0005316536,0.000041953128,0.00010663232,0.00009565344,0.04134722,0.006247985,0.5489177,0.0128903575,0.38945824],"study_design_scores_gemma":[0.000054253072,0.00008027633,0.00021780888,0.0001881362,0.00003904182,0.00024678602,0.000044096378,0.31002408,0.0070109973,0.58311516,0.0989421,0.00003735611],"about_ca_topic_score_codex":0.0009879576,"about_ca_topic_score_gemma":0.0014880012,"teacher_disagreement_score":0.0109951645,"about_ca_system_score_codex":0.00087657245,"about_ca_system_score_gemma":0.0004806934,"threshold_uncertainty_score":0.036782563},"labels":[],"label_agreement":null},{"id":"W2155478003","doi":"10.1109/icsmc.2000.884396","title":"Neural-based control and stability of a nonlinear, non-autonomous, base-excited inverted pendulum system","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Inverted pendulum; Control theory (sociology); Nonlinear system; Controller (irrigation); Artificial neural network; Pendulum; Computer science; Stability (learning theory); Double inverted pendulum; Inverse dynamics; Lyapunov function; Control engineering; Engineering; Control (management); Artificial intelligence; Physics; Classical mechanics; Kinematics","score_opus":0.018754600939936375,"score_gpt":0.21173925702802826,"score_spread":0.1929846560880919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155478003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22700217,0.00038776785,0.7591291,0.00040161656,0.00010292075,0.000066415596,0.000042408035,0.00040152943,0.012466033],"genre_scores_gemma":[0.9799676,0.00008368558,0.017896,0.000019517482,0.000011008265,0.000037256745,0.000011748349,0.000005288557,0.0019678837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999268,0.000013931964,0.000004622132,0.000012487174,0.000029477096,0.000012622518],"domain_scores_gemma":[0.9998648,0.000048588347,0.00003139925,0.000008530542,0.00003709105,0.000009658611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029812413,0.00032510032,0.0002163018,0.00013123399,0.0002746853,0.00041046785,0.00039253035,0.00047510103,0.0008134391],"category_scores_gemma":[0.0007895789,0.00013889004,0.00016836933,0.00010648095,0.000441502,0.00032021888,0.0003787323,0.00036668236,0.00011987819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011820662,0.00005053694,0.00058963744,0.00008780944,0.000027527916,0.00018762825,0.00009672894,0.8983533,0.03413224,0.009136748,0.0003603141,0.056859322],"study_design_scores_gemma":[0.0000066103394,0.00004248944,0.00016005264,0.0000032189935,0.0000039740626,0.000010532934,0.0000029876514,0.99633956,0.0025612456,0.00062174816,0.00024385756,0.0000037424973],"about_ca_topic_score_codex":0.0031659924,"about_ca_topic_score_gemma":0.0028928348,"teacher_disagreement_score":0.0031659924,"about_ca_system_score_codex":0.0003548304,"about_ca_system_score_gemma":0.00031708376,"threshold_uncertainty_score":0.0062951446},"labels":[],"label_agreement":null},{"id":"W2155581961","doi":"10.1109/icip.2008.4711860","title":"Supervised image segmentation via ground truth decomposition","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pixel; Artificial intelligence; Ground truth; Segmentation; Computer science; Pattern recognition (psychology); Image segmentation; Computer vision; Perceptron; Artificial neural network","score_opus":0.018162961959170783,"score_gpt":0.2638342102018582,"score_spread":0.24567124824268743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155581961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008046106,0.000046528312,0.9903377,0.000029695311,0.00000814066,0.000016248396,0.00004267891,0.0012211631,0.00025167843],"genre_scores_gemma":[0.1426267,0.000074187694,0.8553648,0.00005162355,0.000027135447,0.00008445306,0.0007045912,0.0002767953,0.0007896889],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989183,0.00017505277,0.000049254093,0.00043882828,0.00032367074,0.00009492119],"domain_scores_gemma":[0.9988281,0.00037976765,0.00016763071,0.00031678632,0.00026294915,0.000044809698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094751525,0.0011583847,0.0013963996,0.0016934035,0.00042828236,0.0010119423,0.0011804642,0.001219594,0.0012641859],"category_scores_gemma":[0.0021857552,0.0006935304,0.0010977022,0.0011296716,0.0009940336,0.0016195232,0.0011557224,0.0011761754,0.0007753485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029943915,0.0001432202,0.0013988264,0.00017261328,0.00009649403,0.00012473199,0.00018483799,0.27516842,0.077551894,0.011869158,0.0027219416,0.6302685],"study_design_scores_gemma":[0.0000122443535,0.000049393715,0.00039329717,0.000010014435,0.000013883512,0.00006250665,0.00002229855,0.9638458,0.023594776,0.010641804,0.0013436883,0.000010341081],"about_ca_topic_score_codex":0.001239816,"about_ca_topic_score_gemma":0.0020775571,"teacher_disagreement_score":0.0016934035,"about_ca_system_score_codex":0.0007282485,"about_ca_system_score_gemma":0.0009358988,"threshold_uncertainty_score":0.0052838326},"labels":[],"label_agreement":null},{"id":"W2155625967","doi":"10.1109/icpr.1992.201751","title":"A structurally adaptive neural tree for the recognition of large character set","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University","keywords":"Character (mathematics); Computer science; Tree (set theory); Adaptation (eye); Artificial intelligence; Set (abstract data type); Artificial neural network; Parametric statistics; Character recognition; Pattern recognition (psychology); Machine learning; Mathematics; Biology; Image (mathematics); Neuroscience","score_opus":0.04427399205650867,"score_gpt":0.2703520017686727,"score_spread":0.22607800971216402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155625967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013380738,0.00061117986,0.98210484,0.000093856535,0.00007809252,0.000047571073,0.00015977309,0.0016856181,0.0018383625],"genre_scores_gemma":[0.17105438,0.00058768113,0.8224766,0.00015112314,0.0000804139,0.00014358382,0.000761435,0.00020070995,0.004544038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999694,0.000063021405,0.000020496575,0.000078079,0.00011514605,0.000029298926],"domain_scores_gemma":[0.99957913,0.00014624777,0.000031899533,0.000055115088,0.00016647205,0.000021104806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056774897,0.00032850532,0.00042482466,0.0008011723,0.0003833844,0.0004282046,0.0008606956,0.00065247784,0.0027664697],"category_scores_gemma":[0.0017659459,0.00019292747,0.00048157835,0.0013437197,0.00029416432,0.0011715674,0.00043415968,0.00067018566,0.0011663657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010596144,0.00008034152,0.0008724895,0.00014424078,0.000060005572,0.0001503047,0.00009446532,0.10675829,0.032408603,0.013637337,0.0073679592,0.8383201],"study_design_scores_gemma":[0.0000061656046,0.000053696007,0.0005966781,0.000012215642,0.000017987984,0.00011725972,0.000012929426,0.97957546,0.007375995,0.007377008,0.0048398795,0.000014742791],"about_ca_topic_score_codex":0.0031150512,"about_ca_topic_score_gemma":0.00475079,"teacher_disagreement_score":0.0031150512,"about_ca_system_score_codex":0.0003947788,"about_ca_system_score_gemma":0.00042765017,"threshold_uncertainty_score":0.009254754},"labels":[],"label_agreement":null},{"id":"W2155734726","doi":"10.1109/acssc.1997.679081","title":"Neural network integrated circuits with single-block mixed-signal arrays","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"CMC Microsystems","keywords":"Interconnection; Mixed-signal integrated circuit; Computer science; Multiplier (economics); Artificial neural network; Electronic circuit; Integrated circuit; Block (permutation group theory); Electronic engineering; Scaling; SIGNAL (programming language); Engineering; Electrical engineering; Telecommunications; Artificial intelligence; Mathematics","score_opus":0.028834087984927672,"score_gpt":0.20330738309907356,"score_spread":0.1744732951141459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155734726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067027494,0.0022464958,0.9106514,0.00041770516,0.000264179,0.00015247542,0.00012650497,0.0023324725,0.016781356],"genre_scores_gemma":[0.5359464,0.0009820316,0.45046073,0.0003768664,0.00013188383,0.00025406756,0.00018789145,0.00008195409,0.011578152],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997423,0.000048526457,0.00001832214,0.000042909687,0.00012242382,0.00002548303],"domain_scores_gemma":[0.99982494,0.000049515118,0.000031715044,0.000024202589,0.000059485727,0.000010208828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002088166,0.00035412595,0.00027314707,0.00021290459,0.00016954294,0.0005414026,0.0009924396,0.00068628165,0.00298289],"category_scores_gemma":[0.0005463077,0.00025238638,0.00023293766,0.00025058447,0.00018162996,0.00088742987,0.0003409066,0.00042109896,0.0011024253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047541148,0.0001893757,0.00074227026,0.00050981616,0.00016193002,0.0003279945,0.00011863108,0.04440472,0.552246,0.03107558,0.00396344,0.36578482],"study_design_scores_gemma":[0.00008072359,0.000859647,0.00071259827,0.000051290484,0.000111565576,0.00065552973,0.000020781377,0.63528526,0.31964108,0.007280271,0.035261713,0.000039514125],"about_ca_topic_score_codex":0.00026035839,"about_ca_topic_score_gemma":0.0008351273,"teacher_disagreement_score":0.00298289,"about_ca_system_score_codex":0.00034158913,"about_ca_system_score_gemma":0.00014916626,"threshold_uncertainty_score":0.009978771},"labels":[],"label_agreement":null},{"id":"W2155894447","doi":"10.48550/arxiv.1503.05671","title":"Optimizing Neural Networks with Kronecker-factored Approximate Curvature","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":292,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kronecker delta; Curvature; Stochastic gradient descent; Hessian matrix; Diagonal; Gradient descent; Artificial neural network; Mathematics; Matrix (chemical analysis); Invertible matrix; Applied mathematics; Rank (graph theory); Mathematical optimization; Algorithm; Computer science; Combinatorics; Geometry; Pure mathematics; Artificial intelligence; Physics","score_opus":0.06471831599879245,"score_gpt":0.1881355193432239,"score_spread":0.12341720334443144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155894447","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016901525,0.00018723382,0.9812228,0.0001486177,0.000032625878,0.00002181777,0.00003998084,0.00048880087,0.0009565179],"genre_scores_gemma":[0.5157895,0.000317256,0.47885436,0.00020695188,0.00006867534,0.00014557967,0.000322842,0.0002876159,0.0040071504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947625,0.00016609924,0.000031064596,0.00010422605,0.00016335266,0.000059000307],"domain_scores_gemma":[0.9988508,0.00047595773,0.00013458116,0.00020941989,0.00027110122,0.000058165726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012180895,0.0010375764,0.00091672497,0.0005756362,0.00037810512,0.00095054065,0.0011742276,0.0013072734,0.0014654937],"category_scores_gemma":[0.0060367426,0.00065286696,0.00065986865,0.0005840303,0.001003218,0.0018012064,0.0012578969,0.0012029416,0.0006059836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005361523,0.000023626872,0.000558262,0.000044665216,0.000034210032,0.000036406916,0.000042273175,0.94340104,0.0024976202,0.015726594,0.0010608559,0.03652083],"study_design_scores_gemma":[0.000002324568,0.000007928885,0.00003693914,0.0000022131549,0.000001378951,0.000006537411,0.0000019199654,0.9962315,0.0002588563,0.0032838692,0.00016388166,0.0000026278453],"about_ca_topic_score_codex":0.0071963873,"about_ca_topic_score_gemma":0.008855534,"teacher_disagreement_score":0.0071963873,"about_ca_system_score_codex":0.0012282027,"about_ca_system_score_gemma":0.0014845323,"threshold_uncertainty_score":0.014308989},"labels":[],"label_agreement":null},{"id":"W2156066310","doi":"10.1109/icnn.1995.488998","title":"Enhanced Hopfield network for morphologic image processing","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Hopfield network; Computer science; Artificial neural network; Flexibility (engineering); Artificial intelligence; Feature extraction; Pattern recognition (psychology); Simplicity; Image processing; Content-addressable memory; Filter (signal processing); Image (mathematics); Feature (linguistics); Associative property; Computer vision; Mathematics","score_opus":0.0294986180111601,"score_gpt":0.2595679097121899,"score_spread":0.2300692917010298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156066310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01269127,0.000726467,0.9793745,0.00013745927,0.00006700897,0.00003406209,0.000065494736,0.00047119617,0.0064326194],"genre_scores_gemma":[0.38203672,0.0012643083,0.6018954,0.00014698676,0.000081581005,0.000080482976,0.00017303425,0.00006676341,0.014254713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998939,0.000016054373,0.00000584472,0.000023984316,0.000050131734,0.000010030842],"domain_scores_gemma":[0.9999026,0.000029059383,0.000007784042,0.000014539499,0.000037885766,0.000008076721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023072814,0.00030625678,0.0002679029,0.00034074887,0.00022438748,0.00043447228,0.0005495973,0.00055427145,0.0039666384],"category_scores_gemma":[0.00061913254,0.00010441506,0.00027266913,0.0004258168,0.00031748245,0.00090934266,0.00033310155,0.00042035416,0.00074548705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002524504,0.00006620662,0.00059183926,0.00024471458,0.000061223065,0.0005943721,0.00014339497,0.16868763,0.17364837,0.12410076,0.0052210037,0.52638805],"study_design_scores_gemma":[0.000028791846,0.00008715129,0.00050153973,0.000021524935,0.000032377902,0.00046791887,0.000020482,0.9006953,0.03662289,0.039749734,0.02174199,0.000030279562],"about_ca_topic_score_codex":0.0014464326,"about_ca_topic_score_gemma":0.0022791212,"teacher_disagreement_score":0.0039666384,"about_ca_system_score_codex":0.00044135176,"about_ca_system_score_gemma":0.00038521824,"threshold_uncertainty_score":0.013269722},"labels":[],"label_agreement":null},{"id":"W2156211520","doi":"10.1109/ccece.1998.682758","title":"A systolic architecture for spectrometric data correction based on Kalman-spline and LMS filters","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Very-large-scale integration; Kalman filter; Computer science; Architecture; Systolic array; Spline (mechanical); Computer architecture; Computer engineering; Algorithm; Embedded system; Artificial intelligence; Engineering","score_opus":0.04650532271778424,"score_gpt":0.25974215151953933,"score_spread":0.2132368288017551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156211520","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0146555845,0.0003138428,0.9800679,0.00012305095,0.00016607056,0.000046589565,0.0000809026,0.00206048,0.0024857093],"genre_scores_gemma":[0.2540692,0.00049416744,0.7380256,0.00016841516,0.0002066526,0.00014322191,0.00029954623,0.000069142756,0.0065239873],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998186,0.000025201378,0.000013052526,0.000033501186,0.00008903218,0.000020603042],"domain_scores_gemma":[0.9997048,0.00008350439,0.000025519672,0.000033174467,0.00013450059,0.000018472861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042332447,0.00036000388,0.00029606913,0.0004501982,0.0003842954,0.0005674679,0.0010543851,0.00049502775,0.0031420519],"category_scores_gemma":[0.0007549292,0.00022730805,0.0002908394,0.00051789236,0.00024004065,0.0005097543,0.00030270402,0.00044644493,0.0014264114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060308,0.00016021443,0.0026059006,0.0003323718,0.00013209796,0.00032519893,0.00019153937,0.050171226,0.19618805,0.026432756,0.0072069415,0.7156507],"study_design_scores_gemma":[0.00030795054,0.0017207786,0.0029110247,0.000104187966,0.00029372395,0.0010481479,0.00008573497,0.72065884,0.19000898,0.01534533,0.06740615,0.00010922481],"about_ca_topic_score_codex":0.0012196271,"about_ca_topic_score_gemma":0.0025029297,"teacher_disagreement_score":0.0031420519,"about_ca_system_score_codex":0.00034566934,"about_ca_system_score_gemma":0.00074851007,"threshold_uncertainty_score":0.0105112195},"labels":[],"label_agreement":null},{"id":"W2156363271","doi":"10.1109/imtc.2005.1604519","title":"Data Fusion Architecture - An FPGA Implementation","year":2006,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Kalman filter; Sensor fusion; Covariance matrix; Computer science; Correlation coefficient; Fast Kalman filter; Weighting; Extended Kalman filter; Covariance intersection; Noise (video); Invariant extended Kalman filter; Filter (signal processing); Artificial intelligence; Control theory (sociology); Algorithm; Computer vision; Machine learning; Acoustics","score_opus":0.05943982273030714,"score_gpt":0.29395408881276014,"score_spread":0.23451426608245302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156363271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040271714,0.0015982246,0.91628915,0.00039704915,0.00039946698,0.00029063338,0.00053695263,0.021042403,0.019174378],"genre_scores_gemma":[0.6162644,0.00087967823,0.3586714,0.00047118665,0.00010504264,0.00045961278,0.0012705937,0.0003106161,0.021567436],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997578,0.00002594877,0.000018667664,0.000051027084,0.000104004255,0.000042449883],"domain_scores_gemma":[0.9998567,0.00002142079,0.0000106330735,0.00002513518,0.00007696735,0.000009125767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020478692,0.0006130081,0.00039640395,0.00064225565,0.00033264517,0.0006823702,0.0009821455,0.00048997666,0.009052034],"category_scores_gemma":[0.00038977884,0.00030489193,0.00027307082,0.00041619345,0.00014239423,0.0007705726,0.00034610395,0.0005118391,0.0026949253],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007689909,0.00020187219,0.0027223497,0.00078740035,0.00018397051,0.0006556922,0.0002580931,0.050070435,0.14124705,0.017454604,0.029307673,0.7563418],"study_design_scores_gemma":[0.0003321525,0.0013310843,0.0040296214,0.00021568872,0.00023551949,0.0021088729,0.00017329621,0.43766916,0.36584967,0.010597604,0.1773134,0.0001438317],"about_ca_topic_score_codex":0.0020448938,"about_ca_topic_score_gemma":0.0016775404,"teacher_disagreement_score":0.009052034,"about_ca_system_score_codex":0.00058898784,"about_ca_system_score_gemma":0.00046642084,"threshold_uncertainty_score":0.03028208},"labels":[],"label_agreement":null},{"id":"W2156451528","doi":"10.1109/icnn.1996.548897","title":"On MISE convergence rates of radial basis functions networks","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'96)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Alexander von Humboldt-Stiftung","keywords":"Convergence (economics); Radial basis function; Rate of convergence; Radial basis function network; Basis (linear algebra); Mathematics; Applied mathematics; Mean squared error; Computer science; Mathematical optimization; Algorithm; Artificial neural network; Artificial intelligence; Statistics; Telecommunications; Geometry","score_opus":0.043550514904178717,"score_gpt":0.2629600539197134,"score_spread":0.21940953901553467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156451528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03754129,0.0086248,0.9422122,0.001217336,0.00024713716,0.00008240494,0.00014117084,0.00032209314,0.009611511],"genre_scores_gemma":[0.7400079,0.0120939715,0.23226134,0.0009032997,0.0008044966,0.00060753414,0.0005727585,0.0009140434,0.011834716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9927021,0.0034888117,0.0004227826,0.0009060173,0.0018710523,0.00060910924],"domain_scores_gemma":[0.8870595,0.088499844,0.005104511,0.0042501055,0.013815533,0.0012703516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025813557,0.0025747176,0.0021677415,0.0033702222,0.0010336934,0.002626329,0.0028293878,0.0029151982,0.0036012768],"category_scores_gemma":[0.14019626,0.0010203269,0.0015047558,0.0019701116,0.0039861263,0.007531467,0.0040598735,0.005100341,0.0014868382],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053883967,0.000110577945,0.0056628603,0.0005961084,0.00023550456,0.0003758937,0.0006004421,0.5484269,0.0061622015,0.37339965,0.003214012,0.060676944],"study_design_scores_gemma":[0.000013249382,0.00008691548,0.0006719117,0.000116595395,0.000030674575,0.00011775028,0.00006104652,0.9256096,0.0034540065,0.06836079,0.0014319613,0.00004544442],"about_ca_topic_score_codex":0.002535888,"about_ca_topic_score_gemma":0.001321882,"teacher_disagreement_score":0.025813557,"about_ca_system_score_codex":0.0024253882,"about_ca_system_score_gemma":0.0011385531,"threshold_uncertainty_score":0.13651681},"labels":[],"label_agreement":null},{"id":"W2156581800","doi":"10.1109/icnn.1997.614125","title":"Evolutionary artificial neural networks for competitive learning","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Artificial neural network; Computer science; Encoding (memory); Artificial intelligence; Fitness function; Chromosome; Pattern recognition (psychology); Feature (linguistics); Detector; Set (abstract data type); Genetic algorithm; Task (project management); Feature extraction; Machine learning; Engineering; Biology; Gene","score_opus":0.05397032825021998,"score_gpt":0.26916563027206225,"score_spread":0.21519530202184228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156581800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013640739,0.0024879612,0.95686173,0.0008704521,0.00022031786,0.00011866976,0.0000605721,0.0005791067,0.025160419],"genre_scores_gemma":[0.46857658,0.0019024741,0.50971836,0.0005641322,0.00030200067,0.0007856363,0.0002093059,0.0002367963,0.017704716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992384,0.00030433148,0.000038013237,0.00009901194,0.00026859337,0.000051669314],"domain_scores_gemma":[0.9979208,0.0015281814,0.000117802876,0.00015200954,0.00021877975,0.00006251202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019566985,0.0010029303,0.0010778239,0.0006267808,0.00049478124,0.0013656613,0.001888902,0.001947074,0.0064455033],"category_scores_gemma":[0.008308644,0.0003346804,0.0004329867,0.0010293674,0.0011148572,0.0014456531,0.0012110589,0.0018238305,0.0010338412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080040765,0.00010258558,0.0005082616,0.00015684498,0.00008724378,0.00013068695,0.00010986908,0.6173122,0.0012756559,0.25470975,0.0042102146,0.12131668],"study_design_scores_gemma":[0.000019525902,0.000023100181,0.00006917419,0.000011386665,0.0000073027986,0.000025275578,0.0000075621274,0.9393258,0.00022738022,0.057379678,0.0028959312,0.00000782565],"about_ca_topic_score_codex":0.0028916989,"about_ca_topic_score_gemma":0.0028427287,"teacher_disagreement_score":0.0064455033,"about_ca_system_score_codex":0.0013185656,"about_ca_system_score_gemma":0.0007418084,"threshold_uncertainty_score":0.021562338},"labels":[],"label_agreement":null},{"id":"W2156780311","doi":"10.1142/s0129065701000850","title":"AN INVESTIGATION OF COMPETITIVE LEARNING FOR AUTONOMOUS CLUSTER IDENTIFICATION IN EMBEDDED SYSTEMS","year":2001,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Identification (biology); Pruning; Competitive learning; Artificial intelligence; Representation (politics); Machine learning; Cluster analysis; Raw data; Unsupervised learning; Data mining; Pattern recognition (psychology)","score_opus":0.024320039307381035,"score_gpt":0.2980562626027633,"score_spread":0.2737362232953823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156780311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113477066,0.0004433923,0.8808248,0.00043652309,0.00003987331,0.000115781215,0.000021132895,0.0001410845,0.004500445],"genre_scores_gemma":[0.8686198,0.00019148535,0.12980628,0.00009247898,0.000040835814,0.000072353636,0.000030475745,0.000025452684,0.0011209503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985306,0.0005327957,0.0000496493,0.00013886034,0.00063860917,0.00010940053],"domain_scores_gemma":[0.9902254,0.0068739024,0.0004876491,0.00047170185,0.0017073567,0.00023401184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004422277,0.000422693,0.00063260517,0.0007313629,0.000784139,0.0009892526,0.0015180563,0.0009715882,0.0010066153],"category_scores_gemma":[0.017889952,0.0002709414,0.00046095467,0.0007598809,0.0015139796,0.0013946779,0.00089370826,0.0007817865,0.00014595815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018103597,0.00016095424,0.0025376158,0.00015703139,0.00008301658,0.00018758651,0.0003217228,0.75189954,0.008670385,0.10929673,0.0008447002,0.12565964],"study_design_scores_gemma":[0.0000045206016,0.000049285918,0.00020042878,0.0000023793177,0.0000033102872,0.000022901226,0.000012934702,0.9930883,0.00092142617,0.0055063805,0.00018368049,0.0000045321494],"about_ca_topic_score_codex":0.0054476988,"about_ca_topic_score_gemma":0.0040159635,"teacher_disagreement_score":0.0054476988,"about_ca_system_score_codex":0.0012539123,"about_ca_system_score_gemma":0.0011717019,"threshold_uncertainty_score":0.023387492},"labels":[],"label_agreement":null},{"id":"W2156791867","doi":"10.1023/a:1021726007566","title":"The Minimum Number of Errors in the N-Parity and its Solution with an Incremental Neural Network","year":2002,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial neural network; Computational intelligence; Constructive; Feedforward neural network; Perceptron; Parity (physics); Activation function; Computer science; Multilayer perceptron; Algorithm; Artificial intelligence; Mathematics; Pattern recognition (psychology); Physics","score_opus":0.027141671516260596,"score_gpt":0.25935013613505564,"score_spread":0.23220846461879505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156791867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058901574,0.0005612463,0.9300717,0.0016145244,0.00027915387,0.0000629169,0.00012631046,0.00025419044,0.008128339],"genre_scores_gemma":[0.5566294,0.0003009596,0.43210778,0.00032701663,0.00023632651,0.0002085622,0.00020838346,0.00032696957,0.009654608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995338,0.00015848655,0.00002716795,0.00012687378,0.00008900215,0.00006476588],"domain_scores_gemma":[0.99586284,0.0034210458,0.00018666833,0.00014208566,0.00028767227,0.00009976802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013919246,0.00097335223,0.0013336601,0.00065149047,0.0006954286,0.0012586148,0.0018381078,0.004292965,0.0049372427],"category_scores_gemma":[0.011878996,0.00068313675,0.0006999109,0.00068835646,0.001540991,0.0024498901,0.0016888799,0.0019599875,0.00041953832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040328095,0.00007130663,0.0006238553,0.00035391722,0.00006797446,0.00019431645,0.00013531955,0.84835875,0.00237932,0.067851834,0.004413584,0.07514656],"study_design_scores_gemma":[0.00002599185,0.000034735265,0.00010219714,0.000017929871,0.000011664219,0.00003559957,0.000018804847,0.95932406,0.0007114718,0.039397232,0.0003099436,0.00001037359],"about_ca_topic_score_codex":0.0035559626,"about_ca_topic_score_gemma":0.0031323824,"teacher_disagreement_score":0.0049372427,"about_ca_system_score_codex":0.00068512757,"about_ca_system_score_gemma":0.0012573956,"threshold_uncertainty_score":0.016516685},"labels":[],"label_agreement":null},{"id":"W2156797674","doi":"10.1049/el:20057770","title":"Nonlinear noise cancellation for image with adaptive neuro-fuzzy inference systems","year":2005,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Adaptive neuro fuzzy inference system; Salt-and-pepper noise; Noise (video); Active noise control; Gaussian noise; Nonlinear system; Computer science; Median filter; Mean squared error; Neuro-fuzzy; Adaptive filter; Control theory (sociology); Artificial neural network; Least mean squares filter; Noise reduction; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Fuzzy control system; Mathematics; Algorithm; Fuzzy logic; Image (mathematics); Image processing; Statistics","score_opus":0.009795006305735297,"score_gpt":0.23226220557746458,"score_spread":0.22246719927172928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156797674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020254329,0.00090146466,0.9755519,0.00017966652,0.00009028713,0.000034818553,0.000016177804,0.00033717332,0.0026341982],"genre_scores_gemma":[0.70004314,0.0010258151,0.29118267,0.00012417466,0.00010389211,0.000117229436,0.00007749634,0.00003649002,0.0072890874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998173,0.000039119917,0.000010855566,0.000022710354,0.000096142765,0.000013818788],"domain_scores_gemma":[0.9998017,0.000095432115,0.000017625174,0.000010157877,0.0000713883,0.0000036970737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042716254,0.0003299463,0.00033002152,0.00018182125,0.00029440562,0.00038883582,0.00040256156,0.00057419157,0.0011515564],"category_scores_gemma":[0.0012014712,0.00014044355,0.00034224335,0.00021054855,0.00028169385,0.00036176195,0.00032978717,0.0005237405,0.0002698633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033051957,0.00008717877,0.0009309522,0.00046893876,0.000103348706,0.00032594116,0.00025335062,0.40667617,0.10362035,0.01688564,0.0025171356,0.4678005],"study_design_scores_gemma":[0.000010695385,0.000057959263,0.0002937127,0.000013070635,0.000017300807,0.000054808403,0.000010713821,0.9852888,0.010281459,0.0019570107,0.0020025584,0.0000118332255],"about_ca_topic_score_codex":0.0038298373,"about_ca_topic_score_gemma":0.004949261,"teacher_disagreement_score":0.0038298373,"about_ca_system_score_codex":0.00031073016,"about_ca_system_score_gemma":0.00035693034,"threshold_uncertainty_score":0.0076150894},"labels":[],"label_agreement":null},{"id":"W2156860687","doi":"10.1007/11867586_4","title":"Frozen-State Hierarchical Annealing","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Simulated annealing; Annealing (glass); Binary number; Computational complexity theory; Porous medium; Hierarchical database model; Algorithm; Data mining; Porosity; Materials science; Mathematics","score_opus":0.014056310435531084,"score_gpt":0.23990679398889078,"score_spread":0.2258504835533597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156860687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018206555,0.00063355785,0.93028235,0.00040738183,0.0003365829,0.0001271739,0.00022451648,0.0013392939,0.0484425],"genre_scores_gemma":[0.54151934,0.00045814412,0.41105685,0.0004791054,0.00010237669,0.00052495534,0.000876084,0.0011764874,0.043806624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996722,0.00012363764,0.000011915242,0.00007142358,0.00007181912,0.000049033915],"domain_scores_gemma":[0.9992403,0.00035506094,0.0000223244,0.00025539374,0.000080457066,0.000046602116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007510472,0.00048404117,0.0010747423,0.00044402276,0.00093095365,0.0012323986,0.002040979,0.0014027387,0.019028848],"category_scores_gemma":[0.0029592856,0.00068721594,0.0008204139,0.0006840729,0.001335642,0.0015550088,0.0017856244,0.0020803884,0.0023823301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016581836,0.00007336011,0.00021679822,0.00014436943,0.00005265705,0.00004698769,0.00015107958,0.4745696,0.003112383,0.4401034,0.0117096,0.0696539],"study_design_scores_gemma":[0.000024462417,0.000022496733,0.00007756604,0.000016335005,0.000011099495,0.0000116483225,0.000014551103,0.82369107,0.000863945,0.17185819,0.0033977835,0.000010969031],"about_ca_topic_score_codex":0.0019653076,"about_ca_topic_score_gemma":0.003872346,"teacher_disagreement_score":0.019028848,"about_ca_system_score_codex":0.0011266522,"about_ca_system_score_gemma":0.0009294678,"threshold_uncertainty_score":0.06365782},"labels":[],"label_agreement":null},{"id":"W2157093035","doi":"10.1109/ijcnn.2000.861541","title":"Knowledge-based cascade-correlation","year":2000,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Cascade; Computer science; Correlation; Artificial intelligence; Artificial neural network; Machine learning; Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.015132145042105704,"score_gpt":0.2517647359173725,"score_spread":0.23663259087526678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157093035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017591797,0.0012593787,0.95639026,0.00048764795,0.00036131218,0.00021642778,0.00083022943,0.0044600805,0.018402802],"genre_scores_gemma":[0.56368977,0.0011279632,0.38209,0.0005772369,0.00036878177,0.00031150377,0.0034625297,0.0005262829,0.04784594],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99893075,0.0001798972,0.000048519025,0.00040082383,0.00029564524,0.00014441478],"domain_scores_gemma":[0.9986517,0.000383193,0.00007125655,0.00038153454,0.000442015,0.00007023202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014734609,0.0013217555,0.0016304129,0.0014634869,0.000986043,0.0015870808,0.002815839,0.0016848744,0.019685186],"category_scores_gemma":[0.0038958942,0.0008131701,0.0010653274,0.0019478134,0.00059473806,0.0023461683,0.0016942638,0.0015258089,0.008508664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008418786,0.0004034998,0.002067473,0.00034708652,0.00041914906,0.00037142835,0.00013038082,0.18553913,0.0151207065,0.031635158,0.041424226,0.72169995],"study_design_scores_gemma":[0.000026037462,0.00006339185,0.000555744,0.000018869274,0.00006563895,0.000116221454,0.0000123801665,0.9796112,0.005671152,0.0095678605,0.0042665107,0.000024943809],"about_ca_topic_score_codex":0.01010718,"about_ca_topic_score_gemma":0.020599823,"teacher_disagreement_score":0.019685186,"about_ca_system_score_codex":0.0010442004,"about_ca_system_score_gemma":0.0020836222,"threshold_uncertainty_score":0.06585348},"labels":[],"label_agreement":null},{"id":"W2157444450","doi":"","title":"Stochastic Neighbor Embedding","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1461,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Embedding; Probabilistic logic; Dimensionality reduction; Gaussian; Artificial intelligence; Computer science; Pairwise comparison; Object (grammar); Pattern recognition (psychology); Curse of dimensionality; Probability distribution; Mathematics; Statistics","score_opus":0.025531290812627422,"score_gpt":0.24688826460294158,"score_spread":0.22135697379031416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157444450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054201065,0.0005355218,0.98880285,0.00024509724,0.00011074423,0.000056315406,0.00017824402,0.0003321505,0.0043190154],"genre_scores_gemma":[0.33665645,0.00175152,0.63606143,0.00043176126,0.00037817712,0.0003483828,0.0016185623,0.00027262868,0.0224812],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985511,0.00037612926,0.00007193623,0.00027483483,0.00062475057,0.000101363556],"domain_scores_gemma":[0.99897194,0.0003423657,0.00008987029,0.00024332089,0.0002825658,0.0000699208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001045099,0.0007205767,0.0012166307,0.0010138216,0.00072893867,0.0015799098,0.0020382602,0.0012560525,0.0043073175],"category_scores_gemma":[0.0035998127,0.0004185662,0.0008986694,0.0013572288,0.0008789029,0.0023827665,0.0025013136,0.0014285552,0.0017186465],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011534269,0.00013157079,0.0014508477,0.00026421662,0.00012091395,0.00015128983,0.00022705954,0.24481905,0.0045441324,0.45916316,0.0105945775,0.27841786],"study_design_scores_gemma":[0.000011109563,0.000050889266,0.00022931915,0.000019125033,0.000021334154,0.000119116004,0.00003165828,0.8752403,0.0016653447,0.10741103,0.015178393,0.000022277378],"about_ca_topic_score_codex":0.0023557865,"about_ca_topic_score_gemma":0.0033727444,"teacher_disagreement_score":0.0043073175,"about_ca_system_score_codex":0.0007385517,"about_ca_system_score_gemma":0.0009865565,"threshold_uncertainty_score":0.0144094825},"labels":[],"label_agreement":null},{"id":"W2157584804","doi":"10.1109/ccece.1996.548277","title":"ATM congestion control using a fuzzy neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Computer science; Asynchronous Transfer Mode; Fuzzy logic; Artificial neural network; Set (abstract data type); Asynchronous communication; Fuzzy set; Fuzzy control system; SIGNAL (programming language); Inference; Controller (irrigation); Artificial intelligence; Computer network","score_opus":0.03483824376740671,"score_gpt":0.23959980080946452,"score_spread":0.2047615570420578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157584804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06433085,0.0011878068,0.9231024,0.0006352571,0.00038467252,0.0000666045,0.00005480216,0.0009184297,0.009319193],"genre_scores_gemma":[0.92156416,0.0004897753,0.07311357,0.00014636354,0.00015172576,0.000067371126,0.000037880756,0.00001536438,0.004413712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998166,0.00003208412,0.000012298438,0.000047217265,0.00006588724,0.000025946014],"domain_scores_gemma":[0.9997477,0.00009075171,0.000028177721,0.000013453015,0.0001036512,0.000016240221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004881188,0.00037415963,0.00039051636,0.00035632428,0.00044936148,0.00070656516,0.0007671403,0.0008636486,0.0012725694],"category_scores_gemma":[0.0011366901,0.00019198113,0.00035932532,0.0002725189,0.00037640243,0.00081353827,0.00035997987,0.00064232486,0.00015252075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022371771,0.00013794433,0.00095858786,0.00008611886,0.00007711477,0.00014583906,0.00006409339,0.7878857,0.01420712,0.013076502,0.0017849578,0.1813523],"study_design_scores_gemma":[0.000010645511,0.000024317695,0.00009439889,0.0000040364857,0.0000124069675,0.00000974397,0.0000020987977,0.99731183,0.00089987833,0.0012039809,0.00042062975,0.000006030163],"about_ca_topic_score_codex":0.008497793,"about_ca_topic_score_gemma":0.0063441284,"teacher_disagreement_score":0.008497793,"about_ca_system_score_codex":0.00071398413,"about_ca_system_score_gemma":0.0005395213,"threshold_uncertainty_score":0.016896665},"labels":[],"label_agreement":null},{"id":"W2157789521","doi":"10.1111/j.1756-8765.2010.01127.x","title":"A Neural Model of Rule Generation in Inductive Reasoning","year":2011,"lang":"en","type":"article","venue":"Topics in Cognitive Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inductive reasoning; Computer science; Artificial intelligence; Set (abstract data type); Task (project management); Human intelligence; Raven's Progressive Matrices; Domain (mathematical analysis); Field (mathematics); Rule-based system; Machine learning; Cognition; Mathematics; Psychology","score_opus":0.1400770349804721,"score_gpt":0.32086985631037296,"score_spread":0.18079282132990085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157789521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05125891,0.00087399036,0.90606433,0.0025423283,0.0001768049,0.00010053654,0.00029097678,0.00067806244,0.038014002],"genre_scores_gemma":[0.7944682,0.0010261018,0.18248817,0.00048207497,0.00016509696,0.0003048253,0.0002906387,0.00012244034,0.020652382],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994597,0.00016891191,0.000029804403,0.00012671624,0.00015782972,0.00005702096],"domain_scores_gemma":[0.99871445,0.00078490854,0.000091688606,0.00015624656,0.00016407012,0.00008873637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011281859,0.0004571695,0.0006006723,0.00072726683,0.0006022278,0.0022444201,0.003035182,0.002067303,0.005334768],"category_scores_gemma":[0.0037619488,0.00053027394,0.0011701147,0.00079264975,0.0018692956,0.0034745105,0.0008706577,0.0018672512,0.0010689603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008395543,0.00007453184,0.0005140329,0.00009062738,0.000061014485,0.0002502898,0.00030314364,0.41357186,0.004584834,0.5523293,0.0014219037,0.02671457],"study_design_scores_gemma":[0.000023236411,0.000021942129,0.00009563513,0.000010030454,0.000010270318,0.00006758491,0.000014647937,0.74594253,0.0005286515,0.2520399,0.0012344756,0.000011053069],"about_ca_topic_score_codex":0.004313029,"about_ca_topic_score_gemma":0.003106559,"teacher_disagreement_score":0.005334768,"about_ca_system_score_codex":0.0013377584,"about_ca_system_score_gemma":0.0011860506,"threshold_uncertainty_score":0.017846584},"labels":[],"label_agreement":null},{"id":"W2157941220","doi":"10.1109/cifer.1995.495261","title":"Predicting the Canadian spot exchange rate with neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Thursday; Artificial neural network; Spot contract; Variance (accounting); Exchange rate; Computer science; Econometrics; Sample (material); Hot spot (computer programming); Statistics; Artificial intelligence; Economics; Financial economics; Mathematics; Finance; Accounting; Computer network","score_opus":0.022233728964805993,"score_gpt":0.20220457800110891,"score_spread":0.17997084903630292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157941220","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8936474,0.0029608295,0.055802587,0.003654773,0.0003884863,0.00020286304,0.0067474437,0.0016626086,0.034932997],"genre_scores_gemma":[0.96305084,0.0013201379,0.022078019,0.00015858197,0.00007146908,0.000048075148,0.003593918,0.00004144404,0.009637453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964297,0.00005771249,0.000016006108,0.0000587561,0.00014862495,0.000076021475],"domain_scores_gemma":[0.9993129,0.00014489844,0.00003830985,0.000025546648,0.00043495683,0.00004342992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001061755,0.001193439,0.00034417084,0.0013256802,0.0010994493,0.001032118,0.00055728055,0.00048256168,0.0020688314],"category_scores_gemma":[0.0035776023,0.00029088877,0.0005814498,0.0018847407,0.0002656856,0.000424883,0.00041483346,0.00070230255,0.00037695363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038668184,0.000081888036,0.028870799,0.00007830072,0.00013520535,0.00018826876,0.0000995045,0.8490894,0.0013592808,0.003192871,0.013396327,0.10312149],"study_design_scores_gemma":[0.000038939193,0.000028235625,0.012981564,0.000013579939,0.000039862876,0.000016995285,0.00007174421,0.9805133,0.0017020119,0.0012247692,0.003340305,0.000028687791],"about_ca_topic_score_codex":0.949593,"about_ca_topic_score_gemma":0.95546496,"teacher_disagreement_score":0.050406992,"about_ca_system_score_codex":0.0074729696,"about_ca_system_score_gemma":0.0076473355,"threshold_uncertainty_score":0.10140765},"labels":[],"label_agreement":null},{"id":"W2158135329","doi":"10.1109/hpcs.2005.6","title":"A Fully Parallel and Scalable Implementation of a Hopfield Neural Network on the SHARC-NET Supercomputer","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sheridan College","funders":"","keywords":"Supercomputer; Computer science; Scalability; Artificial neural network; Artificial intelligence; Network topology; Distributed computing; Parallel computing; Machine learning; Computer architecture; Operating system","score_opus":0.018021695479276515,"score_gpt":0.2612533285284901,"score_spread":0.24323163304921358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158135329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21687631,0.00031572976,0.71274585,0.00068401825,0.0003156527,0.00030130678,0.0006644791,0.018469516,0.04962716],"genre_scores_gemma":[0.49594685,0.00020905244,0.48656058,0.00021480425,0.000045561002,0.0002402613,0.0012013289,0.0004082417,0.015173229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998429,0.000024024755,0.00001024335,0.000025828811,0.00007152432,0.000025494297],"domain_scores_gemma":[0.99969506,0.00006606237,0.000015536938,0.000067333924,0.00011896638,0.00003716136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040647486,0.0002751078,0.0003069411,0.0002542153,0.00032825206,0.0006173533,0.0016194823,0.00035382496,0.0064391885],"category_scores_gemma":[0.00084037555,0.00020462726,0.00023979566,0.00047398734,0.00030239395,0.00072762946,0.0004246372,0.0005227014,0.0012177936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011855225,0.00043387734,0.0041275956,0.00028489478,0.0001540043,0.00073393015,0.0002554875,0.34430033,0.063613094,0.036270656,0.03553835,0.5131023],"study_design_scores_gemma":[0.00009398627,0.0001307096,0.0006679069,0.000014401075,0.000018721526,0.00007491796,0.00003555399,0.9680333,0.015834862,0.004724494,0.010349313,0.000021732121],"about_ca_topic_score_codex":0.0053523947,"about_ca_topic_score_gemma":0.0072291247,"teacher_disagreement_score":0.0064391885,"about_ca_system_score_codex":0.0005820634,"about_ca_system_score_gemma":0.0009788298,"threshold_uncertainty_score":0.021541178},"labels":[],"label_agreement":null},{"id":"W2158183364","doi":"10.1109/isuma.1993.366704","title":"A neural processor for coordinating multiple systems with dynamic uncertainties","year":2002,"lang":"en","type":"article","venue":"1993 (2nd) International Symposium on Uncertainty Modeling and Analysis","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Computer science; Population; Nonlinear system; Artificial intelligence; Variable (mathematics); Mathematics","score_opus":0.01857226935201695,"score_gpt":0.2470603259668957,"score_spread":0.22848805661487875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158183364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005411283,0.00016945899,0.98959166,0.00012528412,0.000063681124,0.000031217936,0.00002525932,0.00045882084,0.0041234116],"genre_scores_gemma":[0.49369192,0.00071473164,0.4916121,0.000248203,0.00014523242,0.00039303847,0.00014317688,0.000112343536,0.012939196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987113,0.000029780782,0.000008896238,0.000029588844,0.00004856567,0.0000120345085],"domain_scores_gemma":[0.99987614,0.000046439964,0.000013365822,0.000022672044,0.00003224465,0.0000090581725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026462763,0.00038810997,0.0003540549,0.00019987847,0.00027856082,0.0005785371,0.0008024609,0.0007386263,0.0029257678],"category_scores_gemma":[0.00073560193,0.00020600978,0.00028101573,0.00030585795,0.0003303599,0.00085002126,0.0006784581,0.00082298956,0.0005853926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012023014,0.00006512265,0.00043361165,0.00017477374,0.00006257405,0.00023836672,0.00011003275,0.7007408,0.016315944,0.08673295,0.004333822,0.19067177],"study_design_scores_gemma":[0.000013268296,0.000057885096,0.000047226735,0.000008536008,0.000013018205,0.000053586704,0.000007575499,0.9820649,0.0027302753,0.008288409,0.00670844,0.0000069503426],"about_ca_topic_score_codex":0.0012375383,"about_ca_topic_score_gemma":0.0015497856,"teacher_disagreement_score":0.0029257678,"about_ca_system_score_codex":0.00033004698,"about_ca_system_score_gemma":0.000565733,"threshold_uncertainty_score":0.009787619},"labels":[],"label_agreement":null},{"id":"W2158271278","doi":"10.1109/ijcnn.2005.1556039","title":"Comparing \"pattern discovery\" and back-propagation classifiers","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Classifier (UML); Artificial intelligence; A priori and a posteriori; Computer science; Backpropagation; Artificial neural network; Pattern recognition (psychology); Machine learning; Separable space; Random subspace method; Data mining; Mathematics","score_opus":0.049353381280723974,"score_gpt":0.25965346328819194,"score_spread":0.21030008200746797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158271278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20466122,0.001621962,0.7866253,0.0008376716,0.00027548315,0.00043901656,0.00030097782,0.0018688353,0.0033693952],"genre_scores_gemma":[0.58861566,0.0007817838,0.406563,0.0003395719,0.000102234015,0.00045456388,0.0008146583,0.00015192725,0.0021765716],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9924696,0.0024356793,0.0007757632,0.0011811296,0.0026889434,0.0004488266],"domain_scores_gemma":[0.95776844,0.033548534,0.00128236,0.0017091472,0.0053338422,0.00035765572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018222746,0.0015397407,0.0020092328,0.004507231,0.00080493995,0.0029003187,0.0023313863,0.002905025,0.00093237346],"category_scores_gemma":[0.044537943,0.00067715003,0.00121932,0.0036782695,0.0011471076,0.0047445963,0.0017648174,0.0022746138,0.000629117],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002263627,0.00044226117,0.018313292,0.00047868845,0.00069435174,0.0001902452,0.00026343725,0.23797913,0.0036042237,0.007630032,0.00224469,0.72589594],"study_design_scores_gemma":[0.00006577543,0.00022367046,0.0015886498,0.000022460918,0.000078979305,0.00007329108,0.00004811274,0.98956794,0.0036483442,0.0041447906,0.000516829,0.000021146936],"about_ca_topic_score_codex":0.0053045307,"about_ca_topic_score_gemma":0.00293283,"teacher_disagreement_score":0.018222746,"about_ca_system_score_codex":0.0018109021,"about_ca_system_score_gemma":0.0018779526,"threshold_uncertainty_score":0.09637225},"labels":[],"label_agreement":null},{"id":"W2158616110","doi":"10.1109/fuzzy.2000.838638","title":"Information granulation: percepts and their stability","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Alberta","funders":"","keywords":"Computer science; Judgement; Relevance (law); Stability (learning theory); Inference; Artificial intelligence; Human–computer interaction; Machine learning; Epistemology","score_opus":0.023830921182311338,"score_gpt":0.20494152567436438,"score_spread":0.18111060449205305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158616110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024617616,0.0073506352,0.95317954,0.0026443175,0.00029872468,0.00013644564,0.00038366704,0.00048308284,0.01090597],"genre_scores_gemma":[0.6643189,0.004836215,0.3218381,0.0013840569,0.0013551916,0.0006145782,0.0005666664,0.00030406014,0.0047821263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9936493,0.0019280484,0.00071863685,0.0014174037,0.00188173,0.00040481327],"domain_scores_gemma":[0.95121706,0.037316415,0.0036050016,0.0040945504,0.0030561879,0.0007108347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008022359,0.0009542101,0.0017928546,0.006009782,0.0015336331,0.008220692,0.0020601794,0.0029332235,0.0039648227],"category_scores_gemma":[0.057498,0.0009199007,0.002429269,0.0041021337,0.009259628,0.0151847135,0.0047352295,0.0033803277,0.0006722189],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012768195,0.000033023163,0.0014914647,0.00036903122,0.00013163617,0.00026356816,0.0009724797,0.016474448,0.001960755,0.91857886,0.0016013767,0.057995673],"study_design_scores_gemma":[0.000017195543,0.000042464504,0.0007337324,0.00006076741,0.000027633498,0.00014868373,0.000078950274,0.039178763,0.0008138456,0.95499974,0.0038551155,0.000043124957],"about_ca_topic_score_codex":0.0015360899,"about_ca_topic_score_gemma":0.0006154962,"teacher_disagreement_score":0.008220692,"about_ca_system_score_codex":0.0023219194,"about_ca_system_score_gemma":0.0010080036,"threshold_uncertainty_score":0.042426765},"labels":[],"label_agreement":null},{"id":"W2158623280","doi":"10.1162/089976600300015394","title":"Visualizing the Function Computed by a Feedforward Neural Network","year":2000,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Canada; University of British Columbia","funders":"","keywords":"Visualization; Artificial neural network; Feed forward; Feedforward neural network; Computer science; Function (biology); Probabilistic neural network; Artificial intelligence; Probabilistic logic; Activation function; Regression; Machine learning; Regression analysis; Data mining; Time delay neural network; Mathematics; Statistics; Control engineering; Engineering","score_opus":0.01474548070326481,"score_gpt":0.2558216911158163,"score_spread":0.2410762104125515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158623280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024518173,0.00023461555,0.9666954,0.0004036778,0.0001330412,0.000028120632,0.00037168528,0.0038386493,0.0037766919],"genre_scores_gemma":[0.3748443,0.00048148457,0.61757886,0.00013343173,0.00005398909,0.00017512612,0.0006113523,0.0010369297,0.005084538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997619,0.00008744093,0.000015255153,0.000045991557,0.00006708262,0.000022339462],"domain_scores_gemma":[0.99906164,0.00054875325,0.000053647127,0.000102472564,0.0001898069,0.000043698365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083155156,0.0011815582,0.0004649883,0.0009866345,0.00043203574,0.0012184004,0.0005158148,0.0006746025,0.007794205],"category_scores_gemma":[0.0030584575,0.00026880633,0.0005875506,0.0006148943,0.00041625593,0.0013970342,0.0007195117,0.0010439199,0.0009218174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038923486,0.00008616738,0.0023939894,0.0004154684,0.00007776116,0.00035266686,0.00047662065,0.6387547,0.037654497,0.069551565,0.013912462,0.23593484],"study_design_scores_gemma":[0.00001591431,0.00005193774,0.000554317,0.00004712656,0.000010845974,0.00006025705,0.00005128318,0.9557526,0.0073104394,0.031226164,0.004891579,0.000027453782],"about_ca_topic_score_codex":0.0043582935,"about_ca_topic_score_gemma":0.0022757885,"teacher_disagreement_score":0.007794205,"about_ca_system_score_codex":0.00075210223,"about_ca_system_score_gemma":0.00066489074,"threshold_uncertainty_score":0.02607423},"labels":[],"label_agreement":null},{"id":"W2158853651","doi":"10.14864/softscis.2008.0.76.0","title":"Optimizing Artificial Neural Networks through Layer Partition and Genetic Algorithms","year":2008,"lang":"en","type":"article","venue":"SCIS & ISIS SCIS & ISIS 2008","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Artificial neural network; Computer science; Genetic algorithm; Algorithm; Partition (number theory); Layer (electronics); Artificial intelligence; Machine learning; Mathematics; Combinatorics; Chemistry","score_opus":0.042778280660034404,"score_gpt":0.2644916838100463,"score_spread":0.22171340315001187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158853651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09133255,0.0010310895,0.8984439,0.00041483517,0.00017610211,0.00012305756,0.000099142024,0.0012692091,0.007110064],"genre_scores_gemma":[0.61012834,0.0003293221,0.38401133,0.00023735531,0.00007644516,0.0003871345,0.0002359755,0.0003604035,0.004233623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995363,0.00021783293,0.00002577476,0.00008201535,0.00007789965,0.00006024465],"domain_scores_gemma":[0.99816126,0.0013241854,0.00012596176,0.000094441995,0.0002451836,0.00004882403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001908295,0.0016444253,0.0017878932,0.0011101416,0.0007663446,0.0013774747,0.0019633567,0.0026458788,0.0031896026],"category_scores_gemma":[0.006142343,0.001418522,0.0010500192,0.001070822,0.0010302131,0.0020719573,0.001313919,0.002055615,0.00045739525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005119817,0.00002552526,0.00014770315,0.000020439398,0.00002853012,0.000014883783,0.000018087814,0.9823805,0.00033094967,0.0014481745,0.00037071708,0.0151633425],"study_design_scores_gemma":[0.000010786728,0.000011877029,0.000025567308,0.000004157524,0.0000087527205,0.0000023984808,0.000003866305,0.9986142,0.0001744987,0.0010807796,0.000061141,0.0000019633376],"about_ca_topic_score_codex":0.009348504,"about_ca_topic_score_gemma":0.010414431,"teacher_disagreement_score":0.009348504,"about_ca_system_score_codex":0.0017526262,"about_ca_system_score_gemma":0.0015770256,"threshold_uncertainty_score":0.018588185},"labels":[],"label_agreement":null},{"id":"W2158949039","doi":"10.1007/s00138-010-0306-8","title":"An image differencing method for interface level detection in separation cells","year":2011,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Interface (matter); Computer science; Noise (video); Confidence interval; Process (computing); Edge detection; Computation; Artificial intelligence; Algorithm; Image (mathematics); Statistics; Image processing; Mathematics","score_opus":0.03587988196720137,"score_gpt":0.3597751556565739,"score_spread":0.32389527368937254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158949039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011294934,0.00019499466,0.9868751,0.00007208325,0.00006431168,0.00003872638,0.000058117108,0.0006033762,0.0007984084],"genre_scores_gemma":[0.09405547,0.0001803424,0.9021203,0.00009381155,0.000041649033,0.00006732197,0.00017508614,0.00015473284,0.0031113115],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974865,0.000021185606,0.000013685461,0.000042907704,0.00015115141,0.000022445043],"domain_scores_gemma":[0.99953544,0.00014398647,0.000032641135,0.00009111667,0.00016671371,0.000030139527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042672397,0.00044844404,0.0006382424,0.0010183869,0.0004141702,0.00085467123,0.0016049608,0.0008944281,0.003766659],"category_scores_gemma":[0.0010504833,0.0004064921,0.00049900607,0.00086984306,0.00041757085,0.0008897154,0.00089747104,0.0010081265,0.0010436336],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032027173,0.00012342901,0.0009348726,0.00015991679,0.0000608803,0.00010093678,0.00010927052,0.013596541,0.29547274,0.01059594,0.0023510417,0.6761741],"study_design_scores_gemma":[0.000054360837,0.000101715676,0.0017350402,0.000014538014,0.000052539803,0.00032784813,0.000031802672,0.8062973,0.17955476,0.003944216,0.007836305,0.0000495706],"about_ca_topic_score_codex":0.0019257885,"about_ca_topic_score_gemma":0.003871371,"teacher_disagreement_score":0.003766659,"about_ca_system_score_codex":0.0005942318,"about_ca_system_score_gemma":0.0007359458,"threshold_uncertainty_score":0.01260072},"labels":[],"label_agreement":null},{"id":"W2159276570","doi":"10.5539/cis.v5n4p110","title":"Improved SOM-Based High-Dimensional Data Visualization Algorithm","year":2012,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Coordinate system; Viewpoints; Self-organizing map; Point (geometry); Visualization; Data mining; Set (abstract data type); Algorithm; Data set; Multidimensional data; Artificial intelligence; Artificial neural network; Mathematics","score_opus":0.021678020219377984,"score_gpt":0.2783615712266175,"score_spread":0.2566835510072395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159276570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00634602,0.00025455627,0.98960507,0.000117062,0.000100317295,0.0000344379,0.00013849359,0.0018037072,0.0016002982],"genre_scores_gemma":[0.13829625,0.00058165024,0.85531604,0.00010446003,0.00009092885,0.00022243442,0.0008822473,0.00034615147,0.004159734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913055,0.00015783387,0.00005964316,0.00014514501,0.0004266883,0.00008015492],"domain_scores_gemma":[0.99914265,0.00018184469,0.000046211222,0.00007306237,0.000507693,0.000048586367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089747144,0.00097226596,0.0012064013,0.0020462761,0.0005982644,0.0016115544,0.0017268849,0.0010995708,0.0038866394],"category_scores_gemma":[0.002230409,0.0004751631,0.001454986,0.0031217409,0.00037154427,0.0023145028,0.0012727767,0.0011218565,0.0014534573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003348031,0.00011972398,0.001941854,0.00032911616,0.00022916008,0.00026271777,0.00026005504,0.26173696,0.016938927,0.013171836,0.011348414,0.6933265],"study_design_scores_gemma":[0.000019817899,0.00002230523,0.00026118185,0.000012112117,0.000013574142,0.00008233427,0.000033096574,0.98878354,0.0037376683,0.0037979437,0.003218317,0.000018075663],"about_ca_topic_score_codex":0.0050357264,"about_ca_topic_score_gemma":0.0037425533,"teacher_disagreement_score":0.0050357264,"about_ca_system_score_codex":0.00063213956,"about_ca_system_score_gemma":0.0010112957,"threshold_uncertainty_score":0.013002098},"labels":[],"label_agreement":null},{"id":"W2159315530","doi":"10.1007/3-540-45153-6_31","title":"The Bottom-Up Freezing: An Approach to Neural Engineering","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Node (physics); Pruning; Artificial neural network; Generalization; Reduction (mathematics); Training (meteorology); Process (computing); Artificial intelligence; Layer (electronics); Algorithm; Machine learning; Mathematics; Materials science","score_opus":0.020903927110734372,"score_gpt":0.23625487459041905,"score_spread":0.21535094747968467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159315530","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016949638,0.0005320948,0.9863231,0.00031506276,0.0001278714,0.000022158205,0.000033373984,0.00019007738,0.010761286],"genre_scores_gemma":[0.31122336,0.004118135,0.6298323,0.0010077065,0.00067145616,0.00033701834,0.0002587581,0.00075381517,0.051797334],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99969447,0.00008149445,0.00001566488,0.000048393485,0.00012655988,0.000033483233],"domain_scores_gemma":[0.99958426,0.00019012782,0.000024914618,0.00009573939,0.000076839104,0.000028077893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007115402,0.00092979864,0.001040425,0.00064968556,0.0006964563,0.0015930338,0.0026956648,0.0010896018,0.0062832083],"category_scores_gemma":[0.0024844997,0.0005415662,0.0009401735,0.0008680881,0.0018108133,0.002620643,0.0031592278,0.0031313626,0.0011380597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037505157,0.00003110645,0.0001241649,0.00013025779,0.000034315854,0.00006502534,0.00008447969,0.14904596,0.0016697446,0.7389746,0.005507977,0.104294896],"study_design_scores_gemma":[0.000004538005,0.000016191445,0.000029108769,0.000020414102,0.0000107043925,0.000024783532,0.000011924338,0.40322694,0.00075300987,0.59144694,0.0044454797,0.000009928148],"about_ca_topic_score_codex":0.0022650633,"about_ca_topic_score_gemma":0.002374406,"teacher_disagreement_score":0.0062832083,"about_ca_system_score_codex":0.00063538196,"about_ca_system_score_gemma":0.0007874198,"threshold_uncertainty_score":0.0210194},"labels":[],"label_agreement":null},{"id":"W2159972742","doi":"10.1109/icnn.1993.298648","title":"Dynamic neural unit and function approximation","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Activation function; Artificial neural network; Trigonometric functions; Function approximation; Feed forward; Function (biology); Feedforward neural network; Nonlinear system; Robotics; Artificial intelligence; Control theory (sociology); Trigonometry; Task (project management); Algorithm; Control engineering; Mathematics; Control (management); Robot; Engineering; Physics; Mathematical analysis","score_opus":0.056372542734858457,"score_gpt":0.278444129817011,"score_spread":0.22207158708215255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159972742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072615733,0.0009462946,0.980645,0.00023856308,0.00009047953,0.000024414741,0.00008395886,0.00026424133,0.010445423],"genre_scores_gemma":[0.7524318,0.002871889,0.21993615,0.0002252041,0.0001439729,0.0003492646,0.00035832034,0.00016986867,0.023513509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998406,0.000043971104,0.0000073970905,0.00003209285,0.000057878355,0.00001798413],"domain_scores_gemma":[0.9998976,0.000039255516,0.00001291167,0.000013947509,0.000029009452,0.0000072990388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022323431,0.00041567624,0.00059780123,0.00028550188,0.00023774565,0.0007481424,0.0012607558,0.0010932919,0.0028015077],"category_scores_gemma":[0.0007408239,0.00020292272,0.00041236292,0.0005703603,0.00052637747,0.0011210691,0.00055584376,0.00091214565,0.00087053125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041210413,0.00001744235,0.00025348028,0.0001154391,0.000022115195,0.00014803476,0.000069628215,0.78872865,0.0059816563,0.16586596,0.0016681657,0.037088238],"study_design_scores_gemma":[0.0000019838365,0.000013251301,0.00004059928,0.000005661161,0.0000025304953,0.00003711188,0.000005352973,0.9828445,0.0005249643,0.014178788,0.0023395903,0.000005690673],"about_ca_topic_score_codex":0.004081046,"about_ca_topic_score_gemma":0.0019893404,"teacher_disagreement_score":0.004081046,"about_ca_system_score_codex":0.0005974535,"about_ca_system_score_gemma":0.00050317706,"threshold_uncertainty_score":0.009371996},"labels":[],"label_agreement":null},{"id":"W2160305096","doi":"10.1109/mwscas.1998.759553","title":"An algorithm for in-the-loop training based on activation function derivative approximation","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Backpropagation; Very-large-scale integration; Artificial neural network; Computer science; Algorithm; Activation function; Obstacle; Loop (graph theory); Artificial intelligence; Mathematics","score_opus":0.06076098240478691,"score_gpt":0.2688550982387101,"score_spread":0.2080941158339232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160305096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011971396,0.000037627946,0.99789727,0.00002909274,0.000028557964,0.000021112319,0.000004233083,0.0003331905,0.00045172617],"genre_scores_gemma":[0.09221658,0.000114507166,0.90459025,0.00008725031,0.000042606756,0.0001694994,0.00004000863,0.00013627169,0.0026030478],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996631,0.00006155774,0.000020204137,0.000067857465,0.00015368717,0.000033523047],"domain_scores_gemma":[0.99964035,0.00013526692,0.000032084372,0.000048331603,0.0001257024,0.000018327399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060663366,0.0007155113,0.00071086246,0.0004513715,0.0005066509,0.00057202455,0.0014374815,0.0011349061,0.0032481086],"category_scores_gemma":[0.0016444699,0.00031268052,0.00029428073,0.0004474043,0.0005853689,0.0009382564,0.0006196027,0.0014427623,0.0012806186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022935602,0.0001148613,0.0005475466,0.00014985252,0.000060299844,0.00011027284,0.00015577146,0.3437096,0.027082723,0.029250104,0.0038851372,0.59470445],"study_design_scores_gemma":[0.00002403231,0.000043662138,0.00009676527,0.000008260563,0.000011197815,0.00007864898,0.0000067536394,0.9860844,0.0070702354,0.0032245114,0.003341941,0.00000957514],"about_ca_topic_score_codex":0.0018494485,"about_ca_topic_score_gemma":0.0018371484,"teacher_disagreement_score":0.0032481086,"about_ca_system_score_codex":0.00055862824,"about_ca_system_score_gemma":0.00078826287,"threshold_uncertainty_score":0.010865986},"labels":[],"label_agreement":null},{"id":"W2160396196","doi":"10.1007/11759966_38","title":"Absolute Stability of Hopfield Neural Network","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Sigmoid function; Artificial neural network; Hopfield network; Stability (learning theory); Nonlinear system; Control theory (sociology); Computer science; Relation (database); Control (management); Mathematics; Artificial intelligence; Physics; Machine learning","score_opus":0.01888646881446816,"score_gpt":0.23654674372322293,"score_spread":0.21766027490875478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160396196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35021925,0.005072803,0.5563898,0.0012573862,0.0003287002,0.000045150548,0.00020592517,0.0004917083,0.08598928],"genre_scores_gemma":[0.9812641,0.00065190153,0.00749936,0.000040861578,0.0000492366,0.00003246786,0.000044924247,0.00003114359,0.010386094],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99985147,0.000034136196,0.0000071544346,0.00003490601,0.000051993782,0.000020370166],"domain_scores_gemma":[0.9997249,0.00010848133,0.00003971453,0.000020352229,0.00008245248,0.000024059107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041920447,0.000286891,0.0003817045,0.0005276112,0.0005449693,0.00086592615,0.0006490478,0.0004932052,0.0036681443],"category_scores_gemma":[0.001670286,0.0001756841,0.00029077008,0.00036576082,0.0008942875,0.0009970013,0.0005631196,0.0005988625,0.0003915859],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025371075,0.00003104836,0.00091845274,0.00023169265,0.000070262366,0.00015096902,0.00037241474,0.2964665,0.022561515,0.6024183,0.0038184747,0.072706714],"study_design_scores_gemma":[0.000015826996,0.000074875,0.0006342308,0.000024157813,0.000013787198,0.00008875826,0.000055613724,0.6962951,0.0025382529,0.29825047,0.001988527,0.000020416033],"about_ca_topic_score_codex":0.0024497432,"about_ca_topic_score_gemma":0.001238237,"teacher_disagreement_score":0.0036681443,"about_ca_system_score_codex":0.0007873346,"about_ca_system_score_gemma":0.0004772216,"threshold_uncertainty_score":0.012271225},"labels":[],"label_agreement":null},{"id":"W2160422165","doi":"10.1016/j.neunet.2009.08.007","title":"Clustering: A neural network approach","year":2009,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":325,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cluster analysis; Fuzzy clustering; Correlation clustering; Learning vector quantization; Artificial intelligence; Pattern recognition (psychology); CURE data clustering algorithm; Computer science; Consensus clustering; Canopy clustering algorithm; Data stream clustering; Single-linkage clustering; Conceptual clustering; Neural gas; Hierarchical clustering; Data mining; Competitive learning; Vector quantization; Unsupervised learning; Artificial neural network; Time delay neural network","score_opus":0.01879314230365507,"score_gpt":0.23987867848859035,"score_spread":0.2210855361849353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160422165","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033201282,0.0008815172,0.99235886,0.0002635455,0.00010012957,0.00006348201,0.00013526448,0.0004286092,0.0024485292],"genre_scores_gemma":[0.20222493,0.0018855643,0.77739686,0.00028657896,0.0004462962,0.00030512383,0.0009695895,0.00046038526,0.016024759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985569,0.00040202594,0.000086826956,0.00042935615,0.00043660527,0.000088298555],"domain_scores_gemma":[0.9986047,0.00058965816,0.000111508576,0.0002007453,0.00044651836,0.00004678553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014646504,0.0009788325,0.0018426715,0.0035802752,0.0013430339,0.0022008626,0.0032271545,0.0026086795,0.00491282],"category_scores_gemma":[0.00457376,0.0008055227,0.0013330964,0.0043040668,0.00093995326,0.0026362853,0.0016297316,0.0011455755,0.001558711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017896296,0.00013679612,0.0016930926,0.00047817396,0.00045870978,0.00012671629,0.00019927099,0.44404516,0.0037867513,0.10446324,0.0135021685,0.4309311],"study_design_scores_gemma":[0.000013556609,0.000025920895,0.00063168514,0.000033223787,0.000056278965,0.00009664888,0.000052829426,0.9323176,0.0014475387,0.060863554,0.0044330647,0.000027994902],"about_ca_topic_score_codex":0.0062346794,"about_ca_topic_score_gemma":0.0068942104,"teacher_disagreement_score":0.0062346794,"about_ca_system_score_codex":0.0013265124,"about_ca_system_score_gemma":0.0009257067,"threshold_uncertainty_score":0.016435027},"labels":[],"label_agreement":null},{"id":"W2160603239","doi":"10.1109/isimp.2001.925419","title":"Second-order recurrent neural network for word sequence learning","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Computer science; Artificial neural network; Backpropagation; Recurrent neural network; Artificial intelligence; Sequence (biology); Feed forward; Word (group theory); Word error rate; Feedforward neural network; Population; Genetic algorithm; Phone; Machine learning; Mathematics; Engineering","score_opus":0.05480359481568905,"score_gpt":0.27664986702915495,"score_spread":0.2218462722134659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160603239","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023111794,0.0017756169,0.9703333,0.00019199426,0.00016229608,0.00003373326,0.000107607855,0.0014383985,0.0028452626],"genre_scores_gemma":[0.51144713,0.0014349202,0.47614416,0.00020442434,0.0001419688,0.00015573438,0.0005269416,0.0001654232,0.009779172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973434,0.000073823416,0.000019039799,0.00005758669,0.00008563372,0.00002949808],"domain_scores_gemma":[0.9996555,0.00016345753,0.000033939366,0.000039139,0.000095554395,0.000012435021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005426195,0.00044971996,0.00054280495,0.0003954072,0.0001673788,0.00053311395,0.0009627632,0.0007449023,0.0019983344],"category_scores_gemma":[0.0017482003,0.00016450153,0.00042428114,0.00056557154,0.00027830518,0.0009011221,0.0002633942,0.00069857546,0.0007255988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001622287,0.000085528634,0.00091631117,0.0002428335,0.000118721546,0.00020280444,0.00011953567,0.51317316,0.02261881,0.03448372,0.003989123,0.4238871],"study_design_scores_gemma":[0.0000067100377,0.00003778295,0.00013552845,0.0000069149937,0.000013271375,0.000052108957,0.0000043393975,0.98985076,0.0029192646,0.005035827,0.0019282926,0.000009172855],"about_ca_topic_score_codex":0.004012404,"about_ca_topic_score_gemma":0.0050914832,"teacher_disagreement_score":0.004012404,"about_ca_system_score_codex":0.0005438663,"about_ca_system_score_gemma":0.0004681085,"threshold_uncertainty_score":0.007978082},"labels":[],"label_agreement":null},{"id":"W2160632603","doi":"10.1109/3477.826956","title":"Identification of a two-link flexible manipulator using adaptive time delay neural networks","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Concordia University","funders":"","keywords":"Nonlinear system; Control theory (sociology); Identifier; Computer science; Identification (biology); Artificial neural network; A priori and a posteriori; Link (geometry); MIMO; Nonlinear system identification; Input/output; System identification; Control (management); Artificial intelligence; Data modeling","score_opus":0.026029599531130324,"score_gpt":0.2514568415297994,"score_spread":0.2254272419986691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160632603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.123276345,0.0002483555,0.87400436,0.00012408866,0.000035126748,0.000045013225,0.000027964972,0.00017554667,0.0020632765],"genre_scores_gemma":[0.9459147,0.000110288966,0.051707108,0.00002785193,0.000011045142,0.000086754575,0.000029629204,0.000005851755,0.002106854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998666,0.00002597539,0.000008378704,0.00004460567,0.000036916572,0.000017459619],"domain_scores_gemma":[0.9997701,0.0000934763,0.000059748825,0.00002357281,0.000039906543,0.000013166786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003100321,0.00059059274,0.000303141,0.00029802806,0.00037742846,0.0003719192,0.00039833228,0.0008624101,0.00069131487],"category_scores_gemma":[0.00075880124,0.00022572582,0.00033323397,0.00027595946,0.00043762653,0.0005553087,0.00056949793,0.00049658096,0.00012047053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013624759,0.000036441525,0.0014600708,0.00010065955,0.000050438346,0.0003815117,0.00014376114,0.90537345,0.027578956,0.0047672046,0.00016359737,0.05980781],"study_design_scores_gemma":[0.000009622028,0.00006927404,0.0004897048,0.0000074058876,0.000006701326,0.000053730866,0.000015306929,0.9950694,0.0025919564,0.0012398975,0.00043761588,0.000009331061],"about_ca_topic_score_codex":0.0025973753,"about_ca_topic_score_gemma":0.0021979536,"teacher_disagreement_score":0.0025973753,"about_ca_system_score_codex":0.00029186718,"about_ca_system_score_gemma":0.00037501042,"threshold_uncertainty_score":0.0051645637},"labels":[],"label_agreement":null},{"id":"W2160680757","doi":"10.1109/tnn.2004.824261","title":"Dynamics of Projective Adaptive Resonance Theory Model: The Foundation of PART Algorithm","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Adaptive resonance theory; Computer science; Algorithm; Cluster analysis; Differential equation; Artificial neural network; Partial differential equation; Mathematics; Applied mathematics; Artificial intelligence; Mathematical analysis","score_opus":0.01739360236093799,"score_gpt":0.24168147907590098,"score_spread":0.224287876714963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160680757","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0156714,0.00009061631,0.9806646,0.00023623928,0.000028836537,0.0000211883,0.000020682437,0.00011331145,0.0031531693],"genre_scores_gemma":[0.8531462,0.00031448933,0.1414407,0.00018383386,0.00007836333,0.0001756894,0.00007224085,0.00008299122,0.004505404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997117,0.000102804195,0.000010960491,0.000059796235,0.000088594614,0.000026168022],"domain_scores_gemma":[0.99939847,0.00028318996,0.00007890841,0.00008554994,0.00011141387,0.000042567568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081385387,0.00038252815,0.0005920832,0.0003115789,0.00037434587,0.0007021249,0.0010081677,0.0007342317,0.0023652508],"category_scores_gemma":[0.003002096,0.00028398217,0.0004081112,0.00026706312,0.0013498032,0.0015947538,0.0013528777,0.0010968184,0.00034979588],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059154383,0.000020904607,0.00063268235,0.00006430591,0.00003831468,0.0001281508,0.00016507966,0.5969464,0.0071704364,0.3683294,0.0010893533,0.02535582],"study_design_scores_gemma":[0.0000041755197,0.00002030377,0.00006362589,0.0000027707706,0.0000024378178,0.000018350136,0.0000053839826,0.9691943,0.00033701552,0.030013729,0.00033215387,0.0000057315915],"about_ca_topic_score_codex":0.0010889933,"about_ca_topic_score_gemma":0.0005253592,"teacher_disagreement_score":0.0023652508,"about_ca_system_score_codex":0.00042421743,"about_ca_system_score_gemma":0.00051814516,"threshold_uncertainty_score":0.007912576},"labels":[],"label_agreement":null},{"id":"W2160707417","doi":"10.4224/8913277","title":"Discriminative vs. generative classifiers: an in-depth experimental comparison using cost curves","year":2005,"lang":"en","type":"article","venue":"NPARC","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Discriminative model; Artificial intelligence; Generative grammar; Pattern recognition (psychology); Generative model; Computer science; Mathematics; Machine learning","score_opus":0.09377635420905782,"score_gpt":0.3623842875101874,"score_spread":0.2686079333011296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160707417","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5780959,0.0078103137,0.38371015,0.0017447902,0.001199378,0.0025741782,0.0019686944,0.0030117126,0.019884817],"genre_scores_gemma":[0.7597356,0.002252818,0.22677349,0.0005553302,0.00030008273,0.001958089,0.002438714,0.00086144084,0.005124533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9821898,0.0075008743,0.0021905561,0.0022337013,0.005135669,0.0007494067],"domain_scores_gemma":[0.91229814,0.06555747,0.0029350212,0.0106381625,0.007799437,0.00077168125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016378593,0.0021807163,0.0018188087,0.0026279807,0.001059898,0.0025561615,0.002383603,0.0028586606,0.003933925],"category_scores_gemma":[0.09024751,0.0006703997,0.0011584532,0.0023734707,0.0014603671,0.0058585745,0.0023584901,0.002624546,0.0015382401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008138412,0.004352868,0.013075888,0.00330548,0.00087763753,0.00026259065,0.0007781387,0.19855967,0.033472978,0.014695417,0.011063743,0.71141726],"study_design_scores_gemma":[0.00066508405,0.010654257,0.017865848,0.00045464447,0.00052656396,0.0010232193,0.0012949348,0.83253026,0.09222502,0.025908677,0.016482428,0.00036898424],"about_ca_topic_score_codex":0.0021001927,"about_ca_topic_score_gemma":0.002300385,"teacher_disagreement_score":0.016378593,"about_ca_system_score_codex":0.0014939694,"about_ca_system_score_gemma":0.000888289,"threshold_uncertainty_score":0.08661932},"labels":[],"label_agreement":null},{"id":"W2160998010","doi":"10.1109/pacrim.2005.1517268","title":"An associative memory model for unsupervised sequence processing","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Associative property; Content-addressable memory; Sequence (biology); Set (abstract data type); Block (permutation group theory); Artificial intelligence; Content-addressable storage; Theoretical computer science; Information processing; Artificial neural network; Programming language; Mathematics; Cognitive psychology","score_opus":0.06132474811474135,"score_gpt":0.32628487234624454,"score_spread":0.26496012423150317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160998010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027514035,0.00017550302,0.9606801,0.00039508805,0.00007076033,0.00006201072,0.00024144928,0.0005456348,0.010315456],"genre_scores_gemma":[0.60414225,0.0005013281,0.3787265,0.00042033877,0.0001417777,0.00046967992,0.0005315564,0.00013424689,0.014932273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996933,0.000060468137,0.00002066232,0.00007310326,0.00011382737,0.000038749797],"domain_scores_gemma":[0.999411,0.00023668069,0.000061187624,0.0001347463,0.00011683516,0.00003947787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047891226,0.00035220484,0.00046172226,0.0004862264,0.00042082646,0.0010415011,0.0014153254,0.000810833,0.0057999934],"category_scores_gemma":[0.0018639882,0.00023196138,0.0006334583,0.00061650306,0.00089221593,0.0026320422,0.0007273552,0.00097100186,0.0011238562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010258411,0.000087321016,0.0005448458,0.00012698071,0.000056599347,0.00019635842,0.0001484756,0.13724847,0.013793634,0.78017855,0.0032988554,0.06421721],"study_design_scores_gemma":[0.000018667488,0.00007569206,0.00016626222,0.0000133540225,0.00002007525,0.00017617202,0.000018655563,0.6498356,0.0037463878,0.34064895,0.005260546,0.000019715178],"about_ca_topic_score_codex":0.0012216752,"about_ca_topic_score_gemma":0.001629367,"teacher_disagreement_score":0.0057999934,"about_ca_system_score_codex":0.00050068845,"about_ca_system_score_gemma":0.0010094268,"threshold_uncertainty_score":0.019402862},"labels":[],"label_agreement":null},{"id":"W2161010017","doi":"10.1109/coginf.2003.1225951","title":"Signal classification through multifractal analysis and complex domain neural networks","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multifractal system; Pattern recognition (psychology); Computer science; Artificial intelligence; Artificial neural network; Affine transformation; Probabilistic logic; Fractal; Probabilistic neural network; Self-organizing map; SIGNAL (programming language); Domain (mathematical analysis); Dimension (graph theory); Feature extraction; Contextual image classification; Feature (linguistics); Time delay neural network; Mathematics; Image (mathematics)","score_opus":0.03364316341323907,"score_gpt":0.2745696509863145,"score_spread":0.2409264875730754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161010017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026493521,0.0010892565,0.9695721,0.00032470044,0.00006156941,0.00004208603,0.00005618022,0.0005268257,0.0018336943],"genre_scores_gemma":[0.45445147,0.0010529696,0.54106873,0.00016598737,0.00015745312,0.000105948006,0.00018156267,0.00007085323,0.0027450912],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943656,0.00016747565,0.000035551817,0.00009956254,0.00021533275,0.00004563398],"domain_scores_gemma":[0.99914694,0.00047007733,0.000117702766,0.00007325266,0.00017589872,0.000016084232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010700495,0.00049831613,0.00039926163,0.0014560277,0.00035423116,0.0009804519,0.000443708,0.0008308879,0.00095399335],"category_scores_gemma":[0.0033240214,0.00018812988,0.0004355629,0.0011203726,0.0006716739,0.0014471345,0.00040621823,0.00065299356,0.00033790513],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001808756,0.000114096074,0.003157755,0.00016297873,0.000106341024,0.00013714882,0.00018011959,0.36197564,0.026173433,0.039268363,0.0020143092,0.5665289],"study_design_scores_gemma":[0.0000034734426,0.000016969369,0.00065266516,0.000010373027,0.00000796881,0.000034112585,0.000011966766,0.9872415,0.0026618007,0.008290291,0.0010591744,0.000009658219],"about_ca_topic_score_codex":0.003166786,"about_ca_topic_score_gemma":0.002815807,"teacher_disagreement_score":0.003166786,"about_ca_system_score_codex":0.0006453072,"about_ca_system_score_gemma":0.0003324277,"threshold_uncertainty_score":0.0062966943},"labels":[],"label_agreement":null},{"id":"W2161294075","doi":"10.1109/icnn.1993.298622","title":"Acceleration of back propagation through initial weight pre-training with delta rule","year":2002,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Backpropagation; Convergence (economics); Acceleration; Training (meteorology); Artificial neural network; Computer science; Artificial intelligence; Machine learning","score_opus":0.10744726402476547,"score_gpt":0.3119474789161292,"score_spread":0.20450021489136372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161294075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009527743,0.0001818784,0.98727846,0.000064251995,0.00007707321,0.000040422794,0.000019360068,0.0013604588,0.001450406],"genre_scores_gemma":[0.30376208,0.00048090704,0.6895042,0.00011768017,0.000065835746,0.00014381713,0.00020002366,0.00020110607,0.005524343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959475,0.0000680475,0.00003335916,0.00006204926,0.00020055319,0.000041285766],"domain_scores_gemma":[0.99917454,0.00027855104,0.000044701483,0.000106265434,0.00037242586,0.000023495651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007418154,0.0008077498,0.00061387103,0.0007354848,0.00026967216,0.000571721,0.0011665346,0.0005965025,0.0019665407],"category_scores_gemma":[0.002310641,0.00034581433,0.00037816478,0.00061140995,0.00036254444,0.0010462367,0.00049720303,0.0010365113,0.0008163105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016998847,0.000119077165,0.0009256964,0.00015687456,0.00005939683,0.00013842362,0.00008515274,0.19671601,0.024457306,0.0072286627,0.003075472,0.7668679],"study_design_scores_gemma":[0.000016811298,0.00005160049,0.00028302215,0.000009805868,0.000014958397,0.00007413066,0.0000056902404,0.98158216,0.01439598,0.0013976255,0.0021593631,0.000008811536],"about_ca_topic_score_codex":0.0044533364,"about_ca_topic_score_gemma":0.0039727567,"teacher_disagreement_score":0.0044533364,"about_ca_system_score_codex":0.00039981023,"about_ca_system_score_gemma":0.00080344675,"threshold_uncertainty_score":0.008854806},"labels":[],"label_agreement":null},{"id":"W2161388792","doi":"10.48550/arxiv.1402.1869","title":"On the Number of Linear Regions of Deep Neural Networks","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":572,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Rectifier (neural networks); Piecewise linear function; Computer science; Computation; Artificial neural network; Feedforward neural network; Feed forward; Deep learning; Activation function; Homogeneous space; Artificial intelligence; Algorithm; Mathematics; Stochastic neural network; Recurrent neural network","score_opus":0.05273342306533113,"score_gpt":0.19882131165962197,"score_spread":0.14608788859429084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161388792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33396277,0.003666758,0.6323458,0.0051825996,0.00013821054,0.00013980438,0.0013056722,0.0009966564,0.022261802],"genre_scores_gemma":[0.91454774,0.0014944194,0.075688355,0.0005096213,0.00025353418,0.00029112445,0.00087511464,0.00048352906,0.005856522],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979005,0.00068845926,0.00011412987,0.0004688893,0.0005373074,0.00029068164],"domain_scores_gemma":[0.9635846,0.030235961,0.002131129,0.0018944595,0.0010405738,0.0011132886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002759005,0.0011766002,0.0014705664,0.0013295067,0.00097079336,0.003654542,0.002385795,0.0018537571,0.005993531],"category_scores_gemma":[0.030049497,0.0010918379,0.0015973032,0.0010127699,0.0035635263,0.012220006,0.0037925148,0.0046440717,0.0006920999],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008894222,0.00015338048,0.0051492774,0.00059670804,0.00013656536,0.0003354726,0.00064020295,0.3290299,0.0067967423,0.6225513,0.004081494,0.029639557],"study_design_scores_gemma":[0.000039524948,0.00005820339,0.0005778075,0.000038038837,0.00002575585,0.00008455357,0.000048180067,0.47209117,0.0014197049,0.52452475,0.0010673539,0.000025000187],"about_ca_topic_score_codex":0.0016576989,"about_ca_topic_score_gemma":0.0013649084,"teacher_disagreement_score":0.005993531,"about_ca_system_score_codex":0.002248689,"about_ca_system_score_gemma":0.001171293,"threshold_uncertainty_score":0.020050406},"labels":[],"label_agreement":null},{"id":"W2161463231","doi":"10.5267/j.msl.2012.07.009","title":"Linear and nonlinear dynamic systems in financial time series prediction","year":2012,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Series (stratigraphy); Nonlinear system; Computer science; Finance; Time series; Econometrics; Mathematics; Economics; Machine learning; Physics","score_opus":0.005396198258662315,"score_gpt":0.21045793509508118,"score_spread":0.20506173683641887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161463231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29198205,0.012727711,0.67776865,0.0019219124,0.00038136065,0.00010525271,0.00020420202,0.00052260305,0.014386335],"genre_scores_gemma":[0.9665926,0.0027981831,0.027530484,0.000097597935,0.000105419385,0.000052070107,0.00008690281,0.000016812579,0.0027198757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994609,0.00026272883,0.000033685683,0.00007628837,0.00012985495,0.000036546287],"domain_scores_gemma":[0.9982206,0.0013719551,0.00016157629,0.00005434481,0.00016706783,0.00002441399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015117603,0.00044489745,0.0006156734,0.00043226295,0.00023525982,0.0011380668,0.00031563785,0.00085886504,0.00091058604],"category_scores_gemma":[0.00523202,0.00024317233,0.00032597955,0.00054456067,0.00046594357,0.001026902,0.0005654834,0.0007721702,0.00021041928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014712359,0.000049589824,0.0042628394,0.00018782001,0.00008826964,0.00013081424,0.000078287034,0.91777694,0.0020339827,0.012023748,0.0004416383,0.062778816],"study_design_scores_gemma":[0.000005928535,0.000045103876,0.001289594,0.0000140708535,0.000014073512,0.000018210565,0.000019184454,0.99331516,0.00045575158,0.0044087023,0.00040271657,0.00001164494],"about_ca_topic_score_codex":0.0070116366,"about_ca_topic_score_gemma":0.0040643415,"teacher_disagreement_score":0.0070116366,"about_ca_system_score_codex":0.0005840968,"about_ca_system_score_gemma":0.0005231884,"threshold_uncertainty_score":0.013941646},"labels":[],"label_agreement":null},{"id":"W2161892539","doi":"10.1109/ijcnn.1992.226870","title":"On the application of feed forward neural networks to channel equalization","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Quadrature amplitude modulation; QAM; Artificial neural network; Channel (broadcasting); Computer science; Equalization (audio); Feedforward neural network; Blind equalization; Binary number; Feed forward; Electronic engineering; Algorithm; Telecommunications; Artificial intelligence; Mathematics; Engineering; Bit error rate; Arithmetic","score_opus":0.019853315403135903,"score_gpt":0.2553413895625704,"score_spread":0.2354880741594345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161892539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014168361,0.0059163705,0.9624251,0.0012275957,0.0002904299,0.000022499675,0.000030415564,0.00025632107,0.015662933],"genre_scores_gemma":[0.7434123,0.017081127,0.20714988,0.00095105765,0.00088811724,0.000110320034,0.00010674022,0.00010205242,0.03019846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996567,0.00014577883,0.000015679592,0.000035781683,0.00011742425,0.00002863086],"domain_scores_gemma":[0.9988605,0.00088690844,0.000029859317,0.000042485743,0.00016956446,0.0000106563375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008039859,0.0005361657,0.0003972998,0.00046220247,0.00026863083,0.00084934634,0.00045044435,0.0010624308,0.0022397384],"category_scores_gemma":[0.004297845,0.00026504096,0.00028575322,0.00059637055,0.0009925356,0.0011638702,0.000634056,0.0010484138,0.00039777657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000113448725,0.00003383226,0.00040332804,0.00013230678,0.000069971364,0.00019658513,0.00007449599,0.7487971,0.003094099,0.088185176,0.0021066293,0.15679306],"study_design_scores_gemma":[0.000006375241,0.000028329716,0.00012978919,0.000026061647,0.000009030549,0.000039394345,0.000009191403,0.95443094,0.001399007,0.042005498,0.0019073675,0.000008986588],"about_ca_topic_score_codex":0.004378242,"about_ca_topic_score_gemma":0.0037108844,"teacher_disagreement_score":0.004378242,"about_ca_system_score_codex":0.00052715774,"about_ca_system_score_gemma":0.00027042473,"threshold_uncertainty_score":0.008705497},"labels":[],"label_agreement":null},{"id":"W2161948516","doi":"10.1109/ijcnn.2002.1007782","title":"Weighted combination of neural network ensembles","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Machine learning; Artificial neural network; Feature (linguistics); Pattern recognition (psychology); Task (project management); Ensemble learning; Statistical classification; Data mining; Engineering","score_opus":0.013278179466965806,"score_gpt":0.22920466101540016,"score_spread":0.21592648154843436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161948516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044930827,0.0006952664,0.94866323,0.00015857644,0.00017771359,0.00009641226,0.000060329912,0.000710004,0.004507641],"genre_scores_gemma":[0.66740656,0.0005580531,0.32400903,0.00016388124,0.00021376548,0.00021883516,0.00030847907,0.00013566176,0.006985667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824166,0.00058025046,0.00010988701,0.0003143487,0.00059948495,0.00015436947],"domain_scores_gemma":[0.9975171,0.0006681507,0.0001902561,0.0005044957,0.0010250318,0.0000949712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029664047,0.0010443118,0.0018109116,0.0011438826,0.00048387374,0.0011262852,0.0015399803,0.00092823093,0.001658505],"category_scores_gemma":[0.0062802434,0.00053967815,0.00088656944,0.001238913,0.00035269608,0.0020777578,0.0016097433,0.0010210882,0.0006082857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028085773,0.00012292527,0.0022709754,0.00013481414,0.0005369744,0.00012237704,0.00011798125,0.48132616,0.0084690135,0.009651903,0.0031928527,0.49377325],"study_design_scores_gemma":[0.0000081355765,0.00008166635,0.00041333796,0.0000107907745,0.0000707342,0.000050559258,0.000013785798,0.9889381,0.0028667382,0.0059995563,0.001532071,0.000014563482],"about_ca_topic_score_codex":0.0011082254,"about_ca_topic_score_gemma":0.0024353005,"teacher_disagreement_score":0.0029664047,"about_ca_system_score_codex":0.00054666423,"about_ca_system_score_gemma":0.0005309608,"threshold_uncertainty_score":0.015688002},"labels":[],"label_agreement":null},{"id":"W2162122461","doi":"10.1109/iscas.2009.5118324","title":"A new normalized minimum-error entropy algorithm with reduced computational complexity","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computation; Algorithm; Entropy (arrow of time); Computer science; Mathematics; Computational complexity theory","score_opus":0.027633217448968003,"score_gpt":0.27477582970099146,"score_spread":0.24714261225202344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162122461","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026486632,0.00013511171,0.99620014,0.00006122315,0.000059939903,0.000023150384,0.000020487329,0.0002734472,0.0005777129],"genre_scores_gemma":[0.061157294,0.00018342442,0.9352248,0.000088518944,0.000104958766,0.0001609677,0.00021080858,0.00016460258,0.0027046732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986369,0.00031111133,0.00012312633,0.0002718405,0.0005904965,0.00006658613],"domain_scores_gemma":[0.9978137,0.0008988869,0.00016786212,0.00025529167,0.0007846248,0.00007973195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014944673,0.0008584392,0.0014681416,0.0012615223,0.00057801045,0.0013491978,0.0021337657,0.0012275962,0.0027727594],"category_scores_gemma":[0.0069054305,0.00052897754,0.0006950056,0.0010375535,0.00077644206,0.0026698832,0.0015277838,0.001382114,0.0009559318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003283927,0.00012645032,0.0011323745,0.00021635316,0.0001347368,0.0001296395,0.0000941723,0.35455817,0.016875748,0.035112884,0.0050039105,0.5862871],"study_design_scores_gemma":[0.00002057065,0.000032246833,0.00016730258,0.000008840385,0.000008332525,0.00008031592,0.0000061665473,0.98873705,0.0043504634,0.0048774714,0.0016933008,0.000018046823],"about_ca_topic_score_codex":0.0015932551,"about_ca_topic_score_gemma":0.0017600055,"teacher_disagreement_score":0.0027727594,"about_ca_system_score_codex":0.0006525923,"about_ca_system_score_gemma":0.0015868095,"threshold_uncertainty_score":0.009275794},"labels":[],"label_agreement":null},{"id":"W2162418894","doi":"10.4028/www.scientific.net/amm.29-32.1627","title":"Gear Crack Assessment Using Correlation Dimension","year":2010,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Correlation dimension; Fractal dimension; Dimension (graph theory); Correlation; Vibration; Structural engineering; Monotonic function; Fractal; Correlation integral; Nonlinear system; Fault (geology); Mathematics; Engineering; Mathematical analysis; Physics; Geometry; Acoustics; Geology","score_opus":0.014198844652636239,"score_gpt":0.25786152769175485,"score_spread":0.24366268303911862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162418894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71642447,0.0015503119,0.27069113,0.00014698165,0.000060048635,0.0001349827,0.0010970066,0.0012020044,0.008693191],"genre_scores_gemma":[0.9677196,0.00026064587,0.031114323,0.000014629562,0.000015550337,0.00003885926,0.00026551026,0.000024158331,0.0005467268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994553,0.000101305675,0.00004226889,0.000094526564,0.00025789844,0.00004868995],"domain_scores_gemma":[0.9974718,0.001052012,0.00043472496,0.00021857834,0.0007035436,0.00011933846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007296208,0.00046543175,0.0005031636,0.005719547,0.00023294298,0.0007374275,0.0003070974,0.0005811274,0.001524668],"category_scores_gemma":[0.0042727157,0.000198557,0.00033722774,0.0021602383,0.0003910463,0.0012233049,0.0007166564,0.0003245528,0.00028896547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010875268,0.00029382255,0.20917298,0.0006592005,0.00049832696,0.000844642,0.0005125468,0.120816305,0.12822291,0.012740919,0.006259365,0.51889145],"study_design_scores_gemma":[0.0000354903,0.0003028156,0.17348549,0.000089113,0.00013877708,0.0006974832,0.0002742538,0.7762345,0.036196206,0.008365566,0.003943619,0.00023670254],"about_ca_topic_score_codex":0.0014643973,"about_ca_topic_score_gemma":0.001371411,"teacher_disagreement_score":0.005719547,"about_ca_system_score_codex":0.00039493313,"about_ca_system_score_gemma":0.00034937615,"threshold_uncertainty_score":0.0051004887},"labels":[],"label_agreement":null},{"id":"W2162560361","doi":"10.1109/tnn.2009.2030582","title":"Adachi-Like Chaotic Neural Networks Requiring <i>Linear</i>-Time Computations by Enforcing a Tree-Shaped Topology","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial neural network; Computation; Computer science; Chaotic; Topology (electrical circuits); Content-addressable memory; Tree (set theory); Network topology; Quadratic equation; Time complexity; Algorithm; Spanning tree; Tree structure; Mathematics; Artificial intelligence; Discrete mathematics; Binary tree; Combinatorics","score_opus":0.014394666311383895,"score_gpt":0.24755057400835606,"score_spread":0.23315590769697217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162560361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09421587,0.00020603182,0.89878535,0.00013494937,0.00006500668,0.000038119833,0.000051968706,0.00068216363,0.005820583],"genre_scores_gemma":[0.67953,0.00019517665,0.31614956,0.00009174707,0.00003538241,0.00010044659,0.00017028562,0.0000755076,0.0036518385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984646,0.000030242265,0.000011483683,0.000030119476,0.000062939056,0.00001874793],"domain_scores_gemma":[0.9995371,0.00016914294,0.00005642926,0.00010872233,0.00010139111,0.000027153794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029622557,0.0003437269,0.0003903171,0.00019324891,0.00031165255,0.0005412344,0.00062618044,0.0004807539,0.0012949919],"category_scores_gemma":[0.0016788194,0.00022372545,0.00024878525,0.00039192426,0.0004742513,0.0011959672,0.00042853534,0.0006852477,0.0003923091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030066428,0.00008033006,0.0018125625,0.0002849386,0.000068203975,0.00029662703,0.00021275585,0.56430614,0.093384475,0.102809004,0.002866951,0.23357737],"study_design_scores_gemma":[0.000008350721,0.000049952225,0.0003619421,0.000004620189,0.000009705092,0.00008636734,0.000013016135,0.9743251,0.008770887,0.014879866,0.0014791578,0.000011093398],"about_ca_topic_score_codex":0.0013068849,"about_ca_topic_score_gemma":0.0025442059,"teacher_disagreement_score":0.0013068849,"about_ca_system_score_codex":0.00041327788,"about_ca_system_score_gemma":0.0004262678,"threshold_uncertainty_score":0.004332185},"labels":[],"label_agreement":null},{"id":"W2162601441","doi":"10.1109/icnn.1997.614203","title":"Multiple categorization using fuzzy ART","year":2002,"lang":"en","type":"article","venue":"Proceedings of International Conference on Neural Networks (ICNN'97)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Nortel (Canada); Department of National Defence","funders":"","keywords":"Vigilance (psychology); Categorization; Adaptive resonance theory; Fuzzy logic; Computer science; Artificial intelligence; Property (philosophy); Control theory (sociology); Cognitive psychology; Psychology; Control (management)","score_opus":0.06816014477678572,"score_gpt":0.26609997077666747,"score_spread":0.19793982599988175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162601441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02137476,0.00081646757,0.96520406,0.00036172854,0.00013562033,0.000060467482,0.000058272308,0.00082151074,0.011167183],"genre_scores_gemma":[0.641932,0.0004673772,0.3468664,0.00023985653,0.00012342859,0.00011920425,0.00014612272,0.00011983275,0.009985738],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993049,0.00013209932,0.000047397327,0.00022363337,0.00022720579,0.00006479955],"domain_scores_gemma":[0.9993112,0.00030442825,0.000062940046,0.00015531054,0.0001126273,0.000053587497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009521903,0.0004837799,0.0010432587,0.0013733506,0.0008188016,0.0020394332,0.0017440311,0.0014626936,0.0059171687],"category_scores_gemma":[0.0030016305,0.00038079935,0.0017576747,0.0009020727,0.0011054629,0.0029322265,0.001720648,0.0011705445,0.0009668307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002059832,0.00009370497,0.0007722699,0.00027215155,0.00022230546,0.0003071388,0.0004580665,0.28417656,0.012088729,0.24449232,0.004275015,0.4526358],"study_design_scores_gemma":[0.000010839948,0.000025421567,0.00018363744,0.000017473136,0.000023928405,0.00008776662,0.000022164037,0.89346987,0.001286508,0.102356195,0.002492791,0.000023391587],"about_ca_topic_score_codex":0.003028209,"about_ca_topic_score_gemma":0.0028194028,"teacher_disagreement_score":0.0059171687,"about_ca_system_score_codex":0.0011914423,"about_ca_system_score_gemma":0.000560719,"threshold_uncertainty_score":0.019794881},"labels":[],"label_agreement":null},{"id":"W2162779423","doi":"10.1109/glocom.1989.64143","title":"A neural network implementation of an input access scheme in a high-speed packet switch","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial neural network; Computer network; Network packet; Integrated Services Digital Network; Throughput; Packet switching; Asynchronous Transfer Mode; LAN switching; Queue; Fast packet switching; Burst switching; Algorithm; Real-time computing; Processing delay; Transmission delay; Telecommunications; Artificial intelligence","score_opus":0.028106888238173235,"score_gpt":0.3262222298796397,"score_spread":0.29811534164146647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162779423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13187268,0.00026223858,0.84460187,0.0007144725,0.00021564285,0.000067865,0.00009871098,0.0022360624,0.019930461],"genre_scores_gemma":[0.86312556,0.000121310644,0.12631087,0.00014285411,0.000025787129,0.000078548976,0.00006341217,0.000026860054,0.010104642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998659,0.00003382326,0.0000064613305,0.000024468904,0.000043900225,0.000025445745],"domain_scores_gemma":[0.99981374,0.00006621397,0.000015048041,0.000019756824,0.000070654336,0.000014651811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024289938,0.00023907518,0.00018685308,0.00011816953,0.00034139934,0.0004938721,0.0008132743,0.0006970712,0.0037209168],"category_scores_gemma":[0.000585584,0.00012248628,0.00022061412,0.00020973345,0.0003347496,0.00052021747,0.00028852143,0.0006507241,0.00039471145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026788155,0.0002334636,0.0011172169,0.00009083987,0.0000649547,0.00025642547,0.000091617665,0.8039906,0.030132513,0.02067441,0.0025953439,0.14048482],"study_design_scores_gemma":[0.0000116597785,0.000052078536,0.0001223508,0.000003483974,0.000008019611,0.00001386754,0.0000051319785,0.9934035,0.0043855654,0.0013899044,0.0005997488,0.0000047248477],"about_ca_topic_score_codex":0.0052626044,"about_ca_topic_score_gemma":0.0061925612,"teacher_disagreement_score":0.0052626044,"about_ca_system_score_codex":0.00059579447,"about_ca_system_score_gemma":0.00045629262,"threshold_uncertainty_score":0.012447655},"labels":[],"label_agreement":null},{"id":"W2163063207","doi":"10.1109/ijcnn.2003.1223768","title":"\"Freecell\" neural network heuristics","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Heuristics; Heuristic; Computer science; Artificial neural network; Benchmark (surveying); Artificial intelligence; Domain (mathematical analysis); Domain knowledge; Machine learning; Hyper-heuristic; A priori and a posteriori; Mathematics; Robot","score_opus":0.013186538068970946,"score_gpt":0.22795364405688845,"score_spread":0.2147671059879175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163063207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058200132,0.00087798893,0.89346826,0.0008387111,0.00016621793,0.00074265304,0.00040482025,0.0012206584,0.044080615],"genre_scores_gemma":[0.61234146,0.00059876946,0.36644465,0.00081967335,0.000052513275,0.0010264454,0.00064451183,0.0002069136,0.017865026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993292,0.00023716288,0.000045547305,0.0001444754,0.00013287594,0.0001108302],"domain_scores_gemma":[0.9979663,0.0013088919,0.00015932738,0.00019451286,0.00029647877,0.00007460875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001416543,0.0013742239,0.000829875,0.0011273372,0.000597877,0.0015098592,0.0024754463,0.0019899218,0.00833605],"category_scores_gemma":[0.0057565556,0.0005438355,0.00069549517,0.0009638967,0.0011516227,0.0024600027,0.0012419039,0.0011513579,0.0010077404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017581135,0.000104377184,0.00068789546,0.0002281412,0.00007731469,0.00016259486,0.00016664098,0.80984175,0.0010081087,0.076110914,0.006431531,0.10500496],"study_design_scores_gemma":[0.000049966948,0.000065842556,0.00011542094,0.000040754734,0.000028029825,0.00003614966,0.000041020317,0.960004,0.0014802467,0.03441924,0.003702772,0.000016600457],"about_ca_topic_score_codex":0.0059310356,"about_ca_topic_score_gemma":0.011821105,"teacher_disagreement_score":0.00833605,"about_ca_system_score_codex":0.0020522908,"about_ca_system_score_gemma":0.0014547035,"threshold_uncertainty_score":0.027886808},"labels":[],"label_agreement":null},{"id":"W2163150635","doi":"10.1109/oceans.1994.363826","title":"Artificial neural network for additive noise filtering techniques","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Artificial neural network; Backpropagation; Computer science; Noise (video); Artificial intelligence; Filter (signal processing); Feedforward neural network; White noise; Feed forward; Time delay neural network; SIGNAL (programming language); Pattern recognition (psychology); Machine learning; Engineering; Control engineering","score_opus":0.0377757466440232,"score_gpt":0.25888099111993707,"score_spread":0.22110524447591387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163150635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016566004,0.008902457,0.9790359,0.00048466105,0.000452553,0.000034693396,0.00006115721,0.00050715136,0.0088647045],"genre_scores_gemma":[0.15626447,0.02083856,0.77990466,0.0007422509,0.0010769515,0.00040637038,0.00052226835,0.00020694807,0.04003754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948347,0.00011708858,0.000034635163,0.00008166574,0.00025846984,0.000024654815],"domain_scores_gemma":[0.9996997,0.0001380321,0.000025350524,0.000026672144,0.00010197552,0.000008257296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060359115,0.00067438197,0.0006282991,0.0006587041,0.00024323887,0.000861542,0.0008323039,0.0013521939,0.0035972581],"category_scores_gemma":[0.0017085611,0.00022396684,0.0004998727,0.0011886191,0.0004139987,0.0009153517,0.00052110304,0.0015391903,0.0019334337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111382615,0.00006901988,0.0005019376,0.00072612683,0.00016710389,0.00032072456,0.000101788835,0.24615297,0.014199671,0.15798296,0.010791353,0.5688749],"study_design_scores_gemma":[0.000014884286,0.00004953982,0.00032157265,0.00014158619,0.000040283387,0.00014941994,0.0000139499,0.89519686,0.0037185142,0.046197616,0.05412534,0.000030351],"about_ca_topic_score_codex":0.0024375198,"about_ca_topic_score_gemma":0.0019631353,"teacher_disagreement_score":0.0035972581,"about_ca_system_score_codex":0.0005159648,"about_ca_system_score_gemma":0.00047895353,"threshold_uncertainty_score":0.012034059},"labels":[],"label_agreement":null},{"id":"W2163805212","doi":"10.1109/ijcnn.1991.155319","title":"Continuous-time temporal back-propagation","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalization; Computer science; Backpropagation; Feed forward; Artificial intelligence; Extension (predicate logic); Artificial neural network; Presentation (obstetrics); Pattern recognition (psychology); Mathematics; Engineering; Programming language; Control engineering","score_opus":0.01721601280731222,"score_gpt":0.21061640479740412,"score_spread":0.1934003919900919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163805212","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034847076,0.00028695434,0.9893132,0.00014594768,0.00026034913,0.000027663997,0.00016681582,0.0010196259,0.005294848],"genre_scores_gemma":[0.3495483,0.0015012114,0.6105741,0.0003793073,0.00024146022,0.00020609703,0.0010538687,0.0005098976,0.03598577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99961555,0.0000403647,0.000022308774,0.00009630494,0.00018279198,0.000042667296],"domain_scores_gemma":[0.99915195,0.00024504153,0.00006212518,0.00017552741,0.00032810794,0.000037134585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008129931,0.00072445703,0.00048056996,0.00044236667,0.00034839677,0.0012321367,0.0017021941,0.0010147817,0.010660091],"category_scores_gemma":[0.0028606006,0.00035550655,0.0005579261,0.000973739,0.00055232673,0.0015338382,0.0010680732,0.001166843,0.0023465813],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028912746,0.00012287729,0.0007353005,0.00034117853,0.00011099503,0.00026613224,0.00011581958,0.45500264,0.02482528,0.08144298,0.012748899,0.42399886],"study_design_scores_gemma":[0.000007338273,0.000014183295,0.000095210926,0.000010739296,0.000011931874,0.000038731232,0.0000044961566,0.98389465,0.004854505,0.0053848843,0.0056754127,0.000007953619],"about_ca_topic_score_codex":0.0053914543,"about_ca_topic_score_gemma":0.00537783,"teacher_disagreement_score":0.010660091,"about_ca_system_score_codex":0.00065126695,"about_ca_system_score_gemma":0.00083331484,"threshold_uncertainty_score":0.03566152},"labels":[],"label_agreement":null},{"id":"W2164009665","doi":"10.1109/cccrv.2004.1301441","title":"A dynamic fuzzy classifier for detecting abnormalities in mammograms","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Segmentation; Mammography; Computer science; Artificial intelligence; Pixel; Digital mammography; Classifier (UML); Computer vision; Pattern recognition (psychology); Fuzzy logic; Image segmentation; Breast cancer; Medicine; Cancer","score_opus":0.018542050843332915,"score_gpt":0.26245768123653973,"score_spread":0.24391563039320682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164009665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048131865,0.00060927524,0.9467429,0.00021637598,0.00011957737,0.00011855217,0.0001786874,0.0009770709,0.0029057693],"genre_scores_gemma":[0.44559294,0.0005260962,0.5478059,0.00020980934,0.00012937542,0.00021208766,0.00039452736,0.00004716026,0.0050821197],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944407,0.000045549023,0.00004046167,0.000121406265,0.00028758563,0.000060849165],"domain_scores_gemma":[0.9995524,0.00016699344,0.000032083968,0.00003577567,0.0001859331,0.00002682603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007978893,0.00033118864,0.0006777498,0.0012817223,0.00056838786,0.0007079631,0.0010476146,0.0013330805,0.0012753997],"category_scores_gemma":[0.0024151087,0.00022060383,0.00048290173,0.00067675265,0.00033914397,0.00067717425,0.00031385248,0.00075121544,0.00056386343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029831077,0.00019144779,0.0032110065,0.00013240465,0.00007128071,0.00034724124,0.00009871876,0.10587191,0.05541454,0.007976315,0.0036016223,0.8227852],"study_design_scores_gemma":[0.0000149817,0.000082389925,0.0017880313,0.000021272586,0.000035338508,0.00026391743,0.000021084406,0.98056257,0.011674378,0.0024973787,0.0030102243,0.000028550256],"about_ca_topic_score_codex":0.006584626,"about_ca_topic_score_gemma":0.0071469196,"teacher_disagreement_score":0.006584626,"about_ca_system_score_codex":0.0008444501,"about_ca_system_score_gemma":0.0007518213,"threshold_uncertainty_score":0.013092577},"labels":[],"label_agreement":null},{"id":"W2164029659","doi":"10.1109/cca.2005.1507231","title":"Rowing stroke force estimation with EMG signals using artificial neural networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"University Research Committee, Emory University","keywords":"Rowing; Artificial neural network; Elbow; Multilayer perceptron; Computer science; Electromyography; Simulation; Artificial intelligence; Physical medicine and rehabilitation; Medicine","score_opus":0.02777421636131323,"score_gpt":0.2665182820093224,"score_spread":0.2387440656480092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164029659","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39025673,0.00092736597,0.60571605,0.00017945813,0.000065559245,0.000052003874,0.00008697158,0.0009362037,0.0017796217],"genre_scores_gemma":[0.9075774,0.000289991,0.090477765,0.000036094727,0.000020332138,0.000042452346,0.00009075304,0.000013987113,0.0014511904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997819,0.00006839944,0.000020859097,0.00004742398,0.00006361202,0.00001783282],"domain_scores_gemma":[0.9995585,0.00021867994,0.00006393648,0.000028915441,0.00012087691,0.000009019976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005190765,0.00048220673,0.00036070144,0.00033747588,0.00012710677,0.0003567398,0.00026535525,0.0005659871,0.00048287434],"category_scores_gemma":[0.0017328763,0.00024274935,0.00019112545,0.00031038406,0.00014642849,0.0005058824,0.00015798108,0.00029564323,0.00019762415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064953294,0.00021822107,0.008187635,0.00022306626,0.0001640384,0.00021005237,0.000105687366,0.3879833,0.10989051,0.0005337231,0.0007606146,0.4910736],"study_design_scores_gemma":[0.0000070154506,0.00008471023,0.00437816,0.000011428684,0.00001608173,0.000038339993,0.000011104729,0.982373,0.01270038,0.000171474,0.00019805622,0.00001018967],"about_ca_topic_score_codex":0.0022435423,"about_ca_topic_score_gemma":0.0026999998,"teacher_disagreement_score":0.0022435423,"about_ca_system_score_codex":0.00019939539,"about_ca_system_score_gemma":0.0001695562,"threshold_uncertainty_score":0.004460931},"labels":[],"label_agreement":null},{"id":"W2164122462","doi":"10.1162/089976600300015691","title":"Nonlinear Autoassociation Is Not Equivalent to PCA","year":2000,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":170,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Tel Aviv University; Office of Naval Research; Princeton University","keywords":"Nonlinear system; Principal component analysis; Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Backpropagation; Projection (relational algebra); Mathematics; Interpolation (computer graphics); Computer science; Linear interpolation; Algorithm; Image (mathematics)","score_opus":0.02768951998526132,"score_gpt":0.2925187329385902,"score_spread":0.2648292129533289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164122462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07534103,0.0009932612,0.8793022,0.0041171084,0.0004022589,0.000053302563,0.00026530045,0.0023981482,0.037127387],"genre_scores_gemma":[0.80382055,0.0014443111,0.1619528,0.0015003049,0.0002566413,0.00012171044,0.00043161496,0.0006454972,0.029826595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990489,0.00016304955,0.000060301823,0.00029104893,0.00033981504,0.000096959564],"domain_scores_gemma":[0.99709046,0.00093245547,0.00028320146,0.0010892925,0.0004994869,0.00010500704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010207128,0.00043119435,0.0005671565,0.00040342542,0.00043204727,0.0016612943,0.0008162262,0.0007022848,0.0074576424],"category_scores_gemma":[0.006073065,0.000405949,0.00050909974,0.00060604914,0.0028437574,0.003963806,0.0016328986,0.0015591185,0.0035598218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000387844,0.00008908773,0.0034404853,0.0004960491,0.00017973148,0.00028972534,0.000533015,0.07960328,0.045556266,0.4381025,0.014349878,0.41697216],"study_design_scores_gemma":[0.000043505825,0.0001951535,0.006603828,0.00008806926,0.00007324317,0.0011746758,0.00028888587,0.388578,0.029815959,0.5328865,0.040162724,0.00008946512],"about_ca_topic_score_codex":0.0014401061,"about_ca_topic_score_gemma":0.0012038156,"teacher_disagreement_score":0.0074576424,"about_ca_system_score_codex":0.00041698213,"about_ca_system_score_gemma":0.00058481627,"threshold_uncertainty_score":0.024948299},"labels":[],"label_agreement":null},{"id":"W2164523586","doi":"10.1109/ijcnn.2007.4371068","title":"Chosen Plaintext Attack against Neural Network-Based Symmetric Cipher","year":2007,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Plaintext; Cipher; Artificial neural network; Vulnerability (computing); Symmetric-key algorithm; Computer security; Theoretical computer science; Encryption; Public-key cryptography; Artificial intelligence","score_opus":0.0804572910384774,"score_gpt":0.3303453480910246,"score_spread":0.2498880570525472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164523586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34879622,0.0019967977,0.6237679,0.001272965,0.00025231633,0.0001153075,0.00020392142,0.00048683042,0.023107843],"genre_scores_gemma":[0.9781124,0.00084717,0.017823273,0.00006114009,0.000048096797,0.00003342675,0.000049769675,0.000024562913,0.0030002664],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987111,0.0003932039,0.00008240414,0.00013131926,0.0005470861,0.00013487235],"domain_scores_gemma":[0.99814093,0.0011045657,0.00026993424,0.0002761659,0.00017553083,0.000032806758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008788619,0.00050892675,0.0005926312,0.0008053017,0.00037367718,0.0012717921,0.00039987316,0.0010513954,0.001527987],"category_scores_gemma":[0.0032873016,0.00018616291,0.0006058834,0.0004261136,0.0013596604,0.0027132547,0.0008829933,0.0009317834,0.00040707435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067179016,0.00009033405,0.002056311,0.00037040928,0.00017816597,0.0012595849,0.00036910633,0.19788001,0.08421457,0.6559251,0.0018558637,0.05512883],"study_design_scores_gemma":[0.00003097887,0.00020485521,0.00079401606,0.0000524999,0.000040592542,0.0010219374,0.000052236806,0.8149512,0.045896437,0.13348019,0.0034329477,0.000042092186],"about_ca_topic_score_codex":0.00022535467,"about_ca_topic_score_gemma":0.00013057778,"teacher_disagreement_score":0.001527987,"about_ca_system_score_codex":0.00062165136,"about_ca_system_score_gemma":0.00041742998,"threshold_uncertainty_score":0.0051116347},"labels":[],"label_agreement":null},{"id":"W2164554122","doi":"10.1109/tnn.2009.2027319","title":"Granular Neural Networks and Their Development Through Context-Based Clustering and Adjustable Dimensionality of Receptive Fields","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Curse of dimensionality; Cluster analysis; Receptive field; Artificial neural network; Artificial intelligence; Context (archaeology); Pattern recognition (psychology)","score_opus":0.02072436511733904,"score_gpt":0.2366613044974035,"score_spread":0.21593693938006445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164554122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038094055,0.0005959489,0.9560187,0.0003363162,0.00005166095,0.000044883298,0.00004317908,0.0002545447,0.0045606485],"genre_scores_gemma":[0.70935124,0.00047525286,0.28757718,0.00012628497,0.000033505265,0.00010750639,0.00006658363,0.00006485837,0.0021976822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995566,0.00014336525,0.000027638105,0.00010031556,0.00012271208,0.00004930814],"domain_scores_gemma":[0.99915755,0.00041226685,0.00011484339,0.00012902563,0.00013183056,0.000054519787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015407188,0.00032598144,0.00049343164,0.0006745225,0.00039827934,0.0014508658,0.0008067007,0.0008887559,0.0010091845],"category_scores_gemma":[0.004179369,0.00042096007,0.000446081,0.0005249272,0.0011430368,0.0019791676,0.0012474105,0.0010649058,0.00018080056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009880117,0.000034253117,0.0015912486,0.00010966789,0.00004823696,0.00019312292,0.0002933088,0.72310144,0.009243814,0.18924795,0.0011207043,0.07491747],"study_design_scores_gemma":[0.0000068284303,0.000016349948,0.00029430003,0.000019447842,0.0000068716895,0.000047449354,0.000026786856,0.95703566,0.0013300229,0.03997689,0.0012273311,0.000012080439],"about_ca_topic_score_codex":0.0020046493,"about_ca_topic_score_gemma":0.0015504424,"teacher_disagreement_score":0.0020046493,"about_ca_system_score_codex":0.001026499,"about_ca_system_score_gemma":0.0005280567,"threshold_uncertainty_score":0.008148193},"labels":[],"label_agreement":null},{"id":"W2164700406","doi":"10.1146/annurev-statistics-010814-020120","title":"Learning Deep Generative Models","year":2015,"lang":"en","type":"article","venue":"Annual Review of Statistics and Its Application","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":448,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Office of Naval Research; Samsung; Canadian Institute for Advanced Research","keywords":"Deep belief network; Boltzmann machine; Artificial intelligence; Computer science; Deep learning; Generative grammar; Feature (linguistics); Generative model; Object (grammar); Machine learning; Perception; Restricted Boltzmann machine; Cognitive neuroscience of visual object recognition","score_opus":0.027068275005767077,"score_gpt":0.2962983746660544,"score_spread":0.2692300996602873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164700406","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006248454,0.0018437018,0.9855295,0.00073071476,0.000072464354,0.000021417032,0.0004345457,0.0006083038,0.004510877],"genre_scores_gemma":[0.6058874,0.009440622,0.35631847,0.0012783519,0.00057803775,0.0004128612,0.003408825,0.00064468296,0.02203071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994948,0.00018174804,0.000024385079,0.0001246323,0.00011996017,0.000054417345],"domain_scores_gemma":[0.9987569,0.00087668793,0.000095038544,0.00012948447,0.00009807176,0.000043773656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010025832,0.0009571096,0.0010738345,0.001159624,0.00036663096,0.001962033,0.0015725035,0.0015455635,0.0054209605],"category_scores_gemma":[0.004539987,0.00086928165,0.001334106,0.0010206078,0.001275919,0.0024431634,0.0015754424,0.0025176385,0.0016499903],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038856026,0.000034487293,0.0013484055,0.00017460858,0.00012383066,0.00013053879,0.00013631496,0.5602263,0.0015205903,0.3626562,0.0055813445,0.06802854],"study_design_scores_gemma":[0.0000066763155,0.000006873985,0.00012437634,0.00002290686,0.000013054401,0.00004214864,0.000010174721,0.82274264,0.0002664176,0.17398235,0.002772837,0.000009525571],"about_ca_topic_score_codex":0.0034788605,"about_ca_topic_score_gemma":0.0054541305,"teacher_disagreement_score":0.0054209605,"about_ca_system_score_codex":0.0010996672,"about_ca_system_score_gemma":0.0008311191,"threshold_uncertainty_score":0.018134892},"labels":[],"label_agreement":null},{"id":"W2164749336","doi":"10.1109/aim.2001.936485","title":"Neurocomputing approach for real time optimisation modelling of an industrial process","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Distillation; Artificial neural network; Radial basis function; Computer science; Fractionating column; Nonlinear system; Multivariable calculus; Variable (mathematics); Process (computing); Mathematics; Artificial intelligence; Control engineering; Engineering; Chemistry","score_opus":0.12194021903483511,"score_gpt":0.2666592208535378,"score_spread":0.14471900181870273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164749336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014359522,0.00038237937,0.9794372,0.00013955738,0.000038351427,0.00003966771,0.00005144282,0.000326621,0.0052252985],"genre_scores_gemma":[0.7282059,0.0010447798,0.25968084,0.00008545108,0.00007558391,0.0005349881,0.00017338822,0.00011226539,0.010086744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997762,0.000077407734,0.00001194151,0.000036164693,0.00007778878,0.000020459198],"domain_scores_gemma":[0.9998491,0.00007700677,0.000020577632,0.000012533184,0.000036148915,0.0000045857187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003923388,0.0010871404,0.0006549085,0.0004047646,0.00034259574,0.0008345605,0.00075278233,0.0010519085,0.0016094706],"category_scores_gemma":[0.00082081824,0.00032899177,0.00066994526,0.00065457344,0.0004098268,0.0005043939,0.00036784896,0.0008637297,0.0004598798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014715054,0.000009683999,0.00010548411,0.00004138822,0.000015533333,0.00003472534,0.00001959919,0.98370516,0.0009745726,0.0033192907,0.00013907792,0.011620773],"study_design_scores_gemma":[0.0000012974606,0.000010344628,0.000034241006,0.0000028404108,0.000003658998,0.0000061478977,0.00000322052,0.9979954,0.00031443185,0.0011013428,0.00052534457,0.0000018525152],"about_ca_topic_score_codex":0.0070223548,"about_ca_topic_score_gemma":0.0070652002,"teacher_disagreement_score":0.0070223548,"about_ca_system_score_codex":0.0008226227,"about_ca_system_score_gemma":0.00085752155,"threshold_uncertainty_score":0.0139629245},"labels":[],"label_agreement":null},{"id":"W2165313956","doi":"10.1016/s0031-3203(00)00181-3","title":"A class-modular feedforward neural network for handwriting recognition","year":2002,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Modular neural network; Modular design; Computer science; Neocognitron; Artificial neural network; Modularity (biology); Artificial intelligence; Numeral system; Pattern recognition (psychology); Feedforward neural network; Time delay neural network; Classifier (UML); Intelligent character recognition; Feed forward; Class (philosophy); Handwriting; Character recognition","score_opus":0.060974392022505194,"score_gpt":0.24935487591281505,"score_spread":0.18838048389030987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165313956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019165033,0.00067727367,0.9741741,0.00017145877,0.00023584015,0.00006143123,0.00019309533,0.0017744071,0.0035473304],"genre_scores_gemma":[0.38796085,0.0007322407,0.59306616,0.00029123717,0.0003090524,0.00019482955,0.0007150083,0.0002652229,0.01646546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998648,0.000016759783,0.000007295056,0.000049811697,0.000043798063,0.000017521137],"domain_scores_gemma":[0.9998123,0.00004273671,0.000014807688,0.000036174828,0.00007279631,0.000021252137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003069844,0.00050626183,0.00051602605,0.00030869365,0.0003510371,0.0004147619,0.0012105001,0.0006866594,0.0053072586],"category_scores_gemma":[0.0006371457,0.00020137554,0.00048104956,0.00046136652,0.00024306084,0.0007699241,0.00067492883,0.0006935387,0.0013957057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025981045,0.00009892791,0.00051197025,0.00019176012,0.00008207217,0.00012134757,0.000044342058,0.08421227,0.047424596,0.011175528,0.007981327,0.84789604],"study_design_scores_gemma":[0.000014777091,0.00006221073,0.0003825425,0.0000101648275,0.000028752633,0.00007857696,0.000005206054,0.9849888,0.006804029,0.0038454405,0.0037694902,0.000009872527],"about_ca_topic_score_codex":0.002535613,"about_ca_topic_score_gemma":0.005104027,"teacher_disagreement_score":0.0053072586,"about_ca_system_score_codex":0.00039365626,"about_ca_system_score_gemma":0.00049105723,"threshold_uncertainty_score":0.017754555},"labels":[],"label_agreement":null},{"id":"W2165866200","doi":"10.1109/tnn.2002.1031943","title":"Application of adaptive constructive neural networks to image compression","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Constructive; Computer science; Artificial neural network; Quantization (signal processing); Artificial intelligence; Feedforward neural network; Data compression; Image compression; JPEG; Feed forward; Generalization; Image (mathematics); Pattern recognition (psychology); Algorithm; Image processing; Mathematics; Engineering; Control engineering","score_opus":0.01646623996808614,"score_gpt":0.23656812362640053,"score_spread":0.2201018836583144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165866200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07182546,0.0011086409,0.9210451,0.00021841556,0.00010403178,0.0000516224,0.00001995196,0.0005018844,0.0051248698],"genre_scores_gemma":[0.8144294,0.0007912482,0.18225193,0.000141686,0.00007215966,0.000044474167,0.00003438973,0.000029388457,0.0022053444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997354,0.00009437172,0.000010152032,0.00002463616,0.00011762483,0.000017783896],"domain_scores_gemma":[0.9993717,0.00037037363,0.000044432858,0.000052286847,0.00014922919,0.000011900816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051786593,0.00036887632,0.0002667181,0.0004257342,0.00013827992,0.00026963462,0.00061313197,0.0004486204,0.00081984303],"category_scores_gemma":[0.0018668034,0.00011578191,0.00021324611,0.0004124848,0.00057538226,0.00033369556,0.00042592923,0.00044189612,0.0001448117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018793868,0.00008543913,0.00073877303,0.00018451792,0.00006899305,0.00037158685,0.00010488168,0.49335152,0.0723093,0.011744315,0.0009167781,0.41993594],"study_design_scores_gemma":[0.0000072228354,0.00010121674,0.00023325396,0.000009874131,0.0000116613965,0.00013421565,0.000008123394,0.97358334,0.0222932,0.0027908434,0.0008189308,0.000008122185],"about_ca_topic_score_codex":0.00073028373,"about_ca_topic_score_gemma":0.0008706892,"teacher_disagreement_score":0.00081984303,"about_ca_system_score_codex":0.00024425818,"about_ca_system_score_gemma":0.00017301296,"threshold_uncertainty_score":0.0027425885},"labels":[],"label_agreement":null},{"id":"W2165882272","doi":"10.1109/icassp.2008.4517923","title":"A study of using locality preserving projections for feature extraction in speech recognition","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Dimensionality reduction; Pattern recognition (psychology); Linear discriminant analysis; Feature extraction; Artificial intelligence; Computer science; Principal component analysis; Mel-frequency cepstrum; Nonlinear dimensionality reduction; Subspace topology; Speech recognition; Projection (relational algebra); Locality; Word error rate; Reduction (mathematics); Feature (linguistics); Isomap; Mathematics; Algorithm","score_opus":0.1292416143801121,"score_gpt":0.3428219675556988,"score_spread":0.2135803531755867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165882272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026991947,0.0008006083,0.9705113,0.0001482678,0.00003026785,0.000042166466,0.00002130491,0.000226109,0.0012280262],"genre_scores_gemma":[0.39371103,0.0015422646,0.6010983,0.00009632748,0.0001880322,0.00013738936,0.00013674036,0.00012351484,0.0029665236],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998939,0.0004626382,0.00005418058,0.00018613362,0.00032023466,0.000037802463],"domain_scores_gemma":[0.99692446,0.0022226935,0.0001611355,0.00029605348,0.00035924392,0.000036349986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001302403,0.00043556912,0.0006905313,0.00047689848,0.00040177046,0.00064245035,0.000502891,0.0005300612,0.0009336068],"category_scores_gemma":[0.0043061906,0.00035931403,0.0007011196,0.000842245,0.0010313006,0.0017934504,0.0005586807,0.0006774741,0.0004652215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003407832,0.0001857778,0.0016492414,0.00046980835,0.00015055423,0.0003706611,0.0005595807,0.094890684,0.09974753,0.038122315,0.0014124546,0.7621007],"study_design_scores_gemma":[0.000029062974,0.00063369505,0.0025690082,0.000036694117,0.00007183838,0.0007163742,0.00012942248,0.9004919,0.0701559,0.017611934,0.007476856,0.000077359706],"about_ca_topic_score_codex":0.0010358967,"about_ca_topic_score_gemma":0.00069441367,"teacher_disagreement_score":0.001302403,"about_ca_system_score_codex":0.00028906798,"about_ca_system_score_gemma":0.00035675833,"threshold_uncertainty_score":0.006887853},"labels":[],"label_agreement":null},{"id":"W2166199958","doi":"10.1109/ccece.2003.1226465","title":"A mixed-signal VLSI neural network with on-chip learning","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Very-large-scale integration; Artificial neural network; Node (physics); Chip; Routing (electronic design automation); Resistive touchscreen; Mixed-signal integrated circuit; Generality; SIGNAL (programming language); System on a chip; Integrated circuit design; Computer engineering; Electronic engineering; Integrated circuit; Embedded system; Artificial intelligence; Engineering","score_opus":0.018163919595292614,"score_gpt":0.2189489284240849,"score_spread":0.20078500882879227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166199958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054319687,0.0008359328,0.9342406,0.0003069857,0.00019508379,0.000096116644,0.00007346585,0.0011767414,0.008755344],"genre_scores_gemma":[0.5770929,0.0003452263,0.41338485,0.00033802344,0.0000723909,0.00017193322,0.00011902278,0.00003598795,0.008439666],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982244,0.0000374377,0.000008444206,0.000030311992,0.00008016928,0.00002118652],"domain_scores_gemma":[0.99987054,0.00003961148,0.000012799193,0.00001768436,0.000049074333,0.0000103153725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002463761,0.00033900916,0.00026457364,0.00018593957,0.00022894262,0.000485404,0.0011701349,0.0006120647,0.0020305598],"category_scores_gemma":[0.0004684348,0.00019721058,0.00021935419,0.00029228683,0.00023056431,0.00058894075,0.00039317057,0.00045321533,0.00046342958],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039559611,0.000265499,0.0015362534,0.00034906107,0.00014710105,0.00038197596,0.00009436111,0.18483041,0.14856546,0.025252951,0.0039717983,0.63420945],"study_design_scores_gemma":[0.000038206657,0.00040319635,0.0004561412,0.000020767113,0.000050917075,0.0002363283,0.000010003035,0.95794034,0.029154481,0.0034705198,0.008200975,0.000018148949],"about_ca_topic_score_codex":0.00094000116,"about_ca_topic_score_gemma":0.002653124,"teacher_disagreement_score":0.0020305598,"about_ca_system_score_codex":0.00033450013,"about_ca_system_score_gemma":0.00035082485,"threshold_uncertainty_score":0.0067928433},"labels":[],"label_agreement":null},{"id":"W2166696791","doi":"10.1109/isuma.2003.1236181","title":"Cubic neural unit for control applications","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Nonlinear system; Control theory (sociology); Artificial neural network; Controller (irrigation); Computer science; Quadratic equation; Control engineering; Control (management); Artificial intelligence; Engineering; Mathematics; Physics; Geometry","score_opus":0.018576242624513,"score_gpt":0.2649727441267244,"score_spread":0.2463965015022114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166696791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008614082,0.02932446,0.86661834,0.0018279593,0.0015409602,0.00010961192,0.00028362198,0.0015635162,0.09011748],"genre_scores_gemma":[0.484616,0.022033028,0.41275084,0.0015617232,0.0011825066,0.00047915202,0.00067738764,0.00023459595,0.0764647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998617,0.000024437131,0.000008533102,0.000022449809,0.000071345894,0.000011339346],"domain_scores_gemma":[0.99979156,0.000065873646,0.000015636639,0.000029403569,0.0000872219,0.000010372531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016461451,0.00030528544,0.0003217916,0.00031901774,0.00021430092,0.0005857423,0.00047648838,0.0008539159,0.0074856947],"category_scores_gemma":[0.00082624075,0.0001113282,0.00018380754,0.0009742299,0.00032200335,0.00048005785,0.0003891883,0.00092941464,0.00158146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012288247,0.000049856975,0.00031604085,0.0008505613,0.000027810016,0.00025230684,0.00010112067,0.090364374,0.016517395,0.23040117,0.032649454,0.6283471],"study_design_scores_gemma":[0.000029404919,0.00011137664,0.0004792258,0.00013661607,0.000022332779,0.00037790075,0.00003488472,0.7145673,0.005870724,0.11154643,0.16678572,0.000038001297],"about_ca_topic_score_codex":0.0024543141,"about_ca_topic_score_gemma":0.0023876103,"teacher_disagreement_score":0.0074856947,"about_ca_system_score_codex":0.00043046128,"about_ca_system_score_gemma":0.0005383634,"threshold_uncertainty_score":0.025042117},"labels":[],"label_agreement":null},{"id":"W2166734522","doi":"10.1007/0-387-28487-7_2","title":"On the Arithmetic Precision for Implementing Back-Propagation Networks on FPGA: A Case Study","year":2006,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Field-programmable gate array; Computer science; Floating point; Quantization (signal processing); Artificial neural network; Fixed-point arithmetic; Point (geometry); Single-precision floating-point format; Perceptron; Computer engineering; Fixed point; Multilayer perceptron; Parallel computing; Computer hardware; Algorithm; Artificial intelligence; Mathematics","score_opus":0.04634688288532828,"score_gpt":0.2833551298389025,"score_spread":0.2370082469535742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166734522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36534846,0.014106342,0.4891206,0.002850165,0.00037720054,0.00023145381,0.00026683166,0.0022775142,0.12542146],"genre_scores_gemma":[0.80214626,0.0032399066,0.17754397,0.00021875993,0.00007246199,0.00004778855,0.00012476873,0.00025035592,0.016355691],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992822,0.00019886479,0.00006429271,0.000076503704,0.00027456926,0.000103614286],"domain_scores_gemma":[0.9974148,0.0017798049,0.00011553584,0.0002758929,0.00038514924,0.000028810562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010765115,0.00079324597,0.00029531054,0.00058856927,0.00045078117,0.0021429139,0.0015738163,0.0011752913,0.0065006623],"category_scores_gemma":[0.0047629033,0.0003425448,0.0002681408,0.0013625264,0.00056748796,0.0025977115,0.0004985784,0.0012479008,0.00095297454],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080983917,0.00019334255,0.0038231718,0.0011125883,0.00006623063,0.0035245642,0.0008701088,0.106679484,0.03318935,0.11477993,0.008877967,0.7260734],"study_design_scores_gemma":[0.00026314863,0.0014637302,0.0035688758,0.0007888705,0.00035952483,0.008418689,0.0014249021,0.5754823,0.20081913,0.09553213,0.111756906,0.00012167736],"about_ca_topic_score_codex":0.0033628852,"about_ca_topic_score_gemma":0.0047821957,"teacher_disagreement_score":0.0065006623,"about_ca_system_score_codex":0.00085600343,"about_ca_system_score_gemma":0.0006291509,"threshold_uncertainty_score":0.021746874},"labels":[],"label_agreement":null},{"id":"W2166958963","doi":"10.1142/s0218488505003746","title":"FUZZY RULE EXTRACTION FROM A FEED FORWARD NEURAL NETWORK BY TRAINING A REPRESENTATIVE FUZZY NEURAL NETWORK USING GRADIENT DESCENT","year":2005,"lang":"en","type":"article","venue":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Artificial neural network; Gradient descent; Defuzzification; Fuzzy number; Fuzzy logic; Transformation (genetics); Measure (data warehouse); Membership function; Mathematics; Neuro-fuzzy; Artificial intelligence; Fuzzy classification; Computer science; Function (biology); Fuzzy set; Pattern recognition (psychology); Data mining; Fuzzy control system","score_opus":0.03643321490396093,"score_gpt":0.30625585886907913,"score_spread":0.2698226439651182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166958963","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025635205,0.00016109971,0.96964484,0.00007724932,0.00005133866,0.0002470277,0.00017450789,0.0016612904,0.0023474747],"genre_scores_gemma":[0.20587517,0.00018169961,0.78970957,0.00006448605,0.000018457811,0.000368766,0.0006002249,0.000097739816,0.0030838747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995697,0.00006404264,0.00004527975,0.000118214004,0.00016278411,0.00004000004],"domain_scores_gemma":[0.99900454,0.0003542082,0.00005310552,0.000119726275,0.00044483878,0.0000235697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010281125,0.0009758854,0.0011357431,0.0012631286,0.00056660426,0.0011135362,0.00087423075,0.0010542158,0.0040620314],"category_scores_gemma":[0.0034556314,0.0006786612,0.0010555263,0.0008711648,0.0003614475,0.00081716076,0.0004157093,0.0011887873,0.0013175305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021248878,0.00015117685,0.0012878857,0.00032616852,0.00011178188,0.00041896128,0.00022488258,0.4443861,0.026583578,0.003691111,0.0025212897,0.52008456],"study_design_scores_gemma":[0.000015797335,0.000073351126,0.0006118027,0.0000382694,0.000031997977,0.000081106075,0.000032304473,0.9839244,0.01163968,0.0021121046,0.0014248204,0.000014441667],"about_ca_topic_score_codex":0.006136085,"about_ca_topic_score_gemma":0.0068287267,"teacher_disagreement_score":0.006136085,"about_ca_system_score_codex":0.0007675842,"about_ca_system_score_gemma":0.0012678409,"threshold_uncertainty_score":0.013588846},"labels":[],"label_agreement":null},{"id":"W2167002058","doi":"10.1109/ijcnn.1998.682293","title":"Incorporating combined functional/structural knowledge in library of neural models","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.04015952160465652,"score_gpt":0.22204059459555983,"score_spread":0.1818810729909033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167002058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008456328,0.000106859676,0.9881512,0.000046387082,0.000011394235,0.000024755258,0.00010268004,0.00085021416,0.0022501645],"genre_scores_gemma":[0.3967539,0.00064989924,0.5939105,0.00011989416,0.000036687066,0.0002969464,0.0009113124,0.00017687114,0.0071439985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998067,0.00003927018,0.000011933198,0.000045121404,0.0000756433,0.000021360502],"domain_scores_gemma":[0.99953794,0.00019333443,0.000038146314,0.00012036346,0.000095746924,0.0000144494215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005002264,0.00068321615,0.0006111606,0.0008276871,0.00034557725,0.000903022,0.0016536171,0.0007748182,0.0026901595],"category_scores_gemma":[0.001900828,0.00050176797,0.00088326173,0.0010465871,0.000424195,0.0018817255,0.0007417426,0.0012885033,0.0010626029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044980025,0.00006810669,0.0005367054,0.00011327827,0.00006976702,0.000103589555,0.0000549483,0.7928942,0.0062166518,0.013710319,0.0010962002,0.18509121],"study_design_scores_gemma":[0.000002569777,0.000017576102,0.00009348321,0.000008817462,0.000019057765,0.000024253699,0.000004723421,0.9912697,0.002511538,0.005002548,0.0010389782,0.0000067585197],"about_ca_topic_score_codex":0.005453491,"about_ca_topic_score_gemma":0.01030085,"teacher_disagreement_score":0.005453491,"about_ca_system_score_codex":0.0006033631,"about_ca_system_score_gemma":0.00068891887,"threshold_uncertainty_score":0.010843515},"labels":[],"label_agreement":null},{"id":"W2167114805","doi":"10.1109/ijcnn.1991.155567","title":"Neural based methods for music composition","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Composition (language); Computer science; Artificial neural network; Representation (politics); Artificial intelligence; Cognitive science; Musical composition; Psychology; Music education; Linguistics","score_opus":0.07780605708261998,"score_gpt":0.33763878302350714,"score_spread":0.25983272594088713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167114805","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013428235,0.02625118,0.9262034,0.0022538723,0.0022295546,0.00010132307,0.00026306958,0.0015072646,0.039847437],"genre_scores_gemma":[0.08556343,0.032711565,0.75274056,0.0009834664,0.0030657728,0.0004153241,0.00087264844,0.0007699062,0.122877255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997253,0.0000702858,0.000018007386,0.000054294895,0.00011945976,0.000012675555],"domain_scores_gemma":[0.9997533,0.000113312046,0.000016693693,0.00003320571,0.00007069044,0.000012874544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058671227,0.0006147763,0.00066690246,0.0009962037,0.0003875936,0.0012400073,0.0010666086,0.0010691768,0.031592075],"category_scores_gemma":[0.0020631666,0.00022747283,0.0006187081,0.0011105615,0.0006872693,0.0012810901,0.0008429889,0.0016281566,0.008453282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004554525,0.000030956875,0.00028404174,0.00059948635,0.000097063355,0.0000855236,0.000055483066,0.06996121,0.001990743,0.1963032,0.058264755,0.672282],"study_design_scores_gemma":[0.000021233105,0.000026264208,0.00054691534,0.00020462576,0.00003954164,0.00019780158,0.00004399589,0.47641826,0.0012534333,0.2897508,0.23146072,0.00003630473],"about_ca_topic_score_codex":0.0017368583,"about_ca_topic_score_gemma":0.003043241,"teacher_disagreement_score":0.031592075,"about_ca_system_score_codex":0.0008130985,"about_ca_system_score_gemma":0.00041790179,"threshold_uncertainty_score":0.10568601},"labels":[],"label_agreement":null},{"id":"W2167125707","doi":"10.1109/cacsd.1996.555188","title":"Design of bang-bang controller based on a fuzzy-neuro approach","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bang–bang control; Control theory (sociology); Controller (irrigation); Fuzzy control system; Fuzzy logic; Neuro-fuzzy; Computer science; Control engineering; Control (management); Mathematics; Engineering; Optimal control; Artificial intelligence; Mathematical optimization","score_opus":0.04309939809757381,"score_gpt":0.22021069797514364,"score_spread":0.17711129987756985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167125707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007967865,0.0005668327,0.9815299,0.00007291904,0.0001108872,0.00015889436,0.000018568262,0.0005485178,0.009025596],"genre_scores_gemma":[0.57263005,0.0010280367,0.4112353,0.00026520432,0.00009087307,0.0006844642,0.00006176249,0.00005293729,0.013951387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979526,0.00002909747,0.000015290592,0.00003998854,0.000102052785,0.00001837736],"domain_scores_gemma":[0.99985564,0.00003674669,0.000014218164,0.00000970343,0.00007193342,0.0000116690835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003336781,0.0004890577,0.000534286,0.0003164212,0.0005092046,0.0006783618,0.00072159094,0.0007791789,0.0016220425],"category_scores_gemma":[0.00039451956,0.00026890554,0.0003509098,0.00020320732,0.00036107423,0.00033907496,0.00029500521,0.0005398626,0.00045719036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039937394,0.00024911895,0.0007874349,0.0009996606,0.0001542759,0.000612553,0.00046497627,0.30459964,0.1970395,0.05459733,0.0033885685,0.4367076],"study_design_scores_gemma":[0.000058440117,0.00033425743,0.00054429163,0.000054308737,0.000059253238,0.00028612648,0.000036314268,0.95679843,0.02404115,0.0050892625,0.01264748,0.00005064043],"about_ca_topic_score_codex":0.0024430556,"about_ca_topic_score_gemma":0.0025827053,"teacher_disagreement_score":0.0024430556,"about_ca_system_score_codex":0.0003160956,"about_ca_system_score_gemma":0.00076286873,"threshold_uncertainty_score":0.0054262877},"labels":[],"label_agreement":null},{"id":"W2167442946","doi":"10.1109/icci.2004.9","title":"Building Linux based neural network applications","year":2004,"lang":"en","type":"article","venue":"IEEE International Conference on Cognitive Informatics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kwantlen Polytechnic University","funders":"","keywords":"Artificial neural network; Computer science; Human multitasking; Process (computing); Retraining; Time delay neural network; Artificial intelligence; Machine learning; Software; Adaptive system; Recurrent neural network; Task (project management); Operating system; Engineering","score_opus":0.05533573260311505,"score_gpt":0.32562150940444406,"score_spread":0.27028577680132904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167442946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06769466,0.00041838005,0.8646451,0.00034012532,0.00018793861,0.00026264958,0.00037038396,0.055057522,0.01102322],"genre_scores_gemma":[0.49763745,0.00055608986,0.48082322,0.00022451214,0.00006917469,0.00041239322,0.0013736342,0.0022805042,0.016623018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960893,0.0000737466,0.000034197703,0.0000954992,0.0001295423,0.000058173802],"domain_scores_gemma":[0.9991015,0.00028908357,0.000050926374,0.00014037106,0.00035610856,0.00006198829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089285977,0.0007551037,0.00047656972,0.0007559061,0.00041091084,0.0008876256,0.0019239379,0.0006736231,0.008720374],"category_scores_gemma":[0.0038719885,0.0005036444,0.00046247663,0.0005223767,0.00036437254,0.0018539284,0.00131463,0.0008656163,0.0033199023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096999295,0.0003651043,0.0071277525,0.0004567244,0.00017240092,0.0010990762,0.00061431294,0.24569023,0.036923904,0.015133374,0.02602506,0.66542214],"study_design_scores_gemma":[0.000044071974,0.00012335608,0.0013489894,0.000034866192,0.00003664981,0.00016992514,0.000066918416,0.93901134,0.031667437,0.007283811,0.020181144,0.00003141037],"about_ca_topic_score_codex":0.0033217047,"about_ca_topic_score_gemma":0.003036263,"teacher_disagreement_score":0.008720374,"about_ca_system_score_codex":0.00067261944,"about_ca_system_score_gemma":0.00047855196,"threshold_uncertainty_score":0.02917254},"labels":[],"label_agreement":null},{"id":"W2167613311","doi":"10.1016/j.cogsys.2016.04.002","title":"Neural implementation of probabilistic models of cognition","year":2016,"lang":"en","type":"preprint","venue":"Cognitive Systems Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Bayes' theorem; Computer science; Artificial intelligence; Probabilistic logic; Artificial neural network; Bayesian probability; Machine learning; Bayesian inference; Inference; Matching (statistics); Bayesian network; Mathematics; Statistics","score_opus":0.1968756832622374,"score_gpt":0.43981464435565,"score_spread":0.2429389610934126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167613311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11309663,0.0007069553,0.8570538,0.0037493196,0.00027304806,0.000026672633,0.00034256163,0.00047194446,0.024279013],"genre_scores_gemma":[0.9598893,0.00041570453,0.035416048,0.00014786019,0.000088562134,0.000034229033,0.00013618675,0.000040951712,0.0038312024],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99964094,0.00013143389,0.000017888777,0.000076850054,0.000090788584,0.0000420731],"domain_scores_gemma":[0.998697,0.00071568234,0.00013287028,0.00018830255,0.00018479416,0.000081414066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082298374,0.00029504966,0.00037847876,0.00038181024,0.0002467014,0.0016575586,0.0012837241,0.0010332647,0.0043357033],"category_scores_gemma":[0.006904865,0.00038507432,0.00051948876,0.0004175832,0.00066658284,0.0029502092,0.00080461515,0.0013633475,0.00035932395],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011317827,0.000071013776,0.0014709404,0.00012679778,0.00011900217,0.00008500712,0.00018623727,0.23915863,0.0058020814,0.68164337,0.002365868,0.06885789],"study_design_scores_gemma":[0.000008338631,0.0000132400455,0.000588895,0.000008957536,0.000013106726,0.000033569064,0.000017634085,0.57986224,0.00053655717,0.41824362,0.0006625512,0.000011203958],"about_ca_topic_score_codex":0.002123059,"about_ca_topic_score_gemma":0.0021670195,"teacher_disagreement_score":0.0043357033,"about_ca_system_score_codex":0.0006554786,"about_ca_system_score_gemma":0.0005693709,"threshold_uncertainty_score":0.014504373},"labels":[],"label_agreement":null},{"id":"W2167784001","doi":"10.1184/r1/6475760","title":"Hierarchical Latent Dictionaries for Models of Brain Activation","year":2018,"lang":"en","type":"article","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Air Force Office of Scientific Research; National Institute of Environmental Health Sciences; Natural Sciences and Engineering Research Council of Canada; W. M. Keck Foundation; National Institutes of Health; National Science Foundation","keywords":"Computer science; Magnetoencephalography; Leverage (statistics); Artificial intelligence; Redundancy (engineering); Covariance; Hierarchical database model; Pattern recognition (psychology); Natural language processing; Speech recognition; Machine learning; Data mining; Mathematics; Electroencephalography; Statistics; Psychology","score_opus":0.07067759326496327,"score_gpt":0.28420444283609636,"score_spread":0.2135268495711331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167784001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062765065,0.0002708538,0.9922518,0.00018216063,0.000022345715,0.000017875958,0.00027672108,0.00022064403,0.0004812098],"genre_scores_gemma":[0.5952575,0.0012566532,0.3918663,0.00034727002,0.00020194455,0.00037422575,0.002758343,0.00032312557,0.007614645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940693,0.00022248468,0.000034813744,0.00018347523,0.00008613831,0.000066161665],"domain_scores_gemma":[0.9983279,0.001042842,0.00017594773,0.0002449207,0.00015095602,0.0000575088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010968664,0.0006424289,0.00089640747,0.00081097364,0.0003449117,0.0010412233,0.0014125259,0.001052194,0.0034150486],"category_scores_gemma":[0.0047766725,0.00070961594,0.00093948457,0.001019596,0.00090596516,0.0018932123,0.001168365,0.0024424538,0.0013497762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013915202,0.00007262429,0.0015341279,0.0001575024,0.00015469648,0.00006646367,0.00019429559,0.7598256,0.0040509994,0.10628702,0.0040935366,0.12342407],"study_design_scores_gemma":[0.0000050086815,0.000008840531,0.00017401269,0.000008006996,0.0000050475032,0.000008834447,0.0000063243365,0.9754538,0.00020351478,0.023697576,0.00042279297,0.0000063340913],"about_ca_topic_score_codex":0.0039136023,"about_ca_topic_score_gemma":0.007125894,"teacher_disagreement_score":0.0039136023,"about_ca_system_score_codex":0.000772165,"about_ca_system_score_gemma":0.00062689104,"threshold_uncertainty_score":0.011424482},"labels":[],"label_agreement":null},{"id":"W2167898087","doi":"10.1109/ccece.1999.808205","title":"A systolic architecture for channel equalization based on a piecewise linear fuzzy logic algorithm","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Piecewise linear function; CMOS; Algorithm; Very-large-scale integration; Fuzzy logic; Piecewise; Channel (broadcasting); Logic gate; Computer science; Mathematics; Electronic engineering; Engineering; Telecommunications; Artificial intelligence; Embedded system","score_opus":0.02821842237447044,"score_gpt":0.27280343934174595,"score_spread":0.2445850169672755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167898087","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012781708,0.00018636636,0.9832946,0.00007485083,0.000050645063,0.000027836768,0.000030476032,0.000990467,0.0025630386],"genre_scores_gemma":[0.33206823,0.00026706595,0.66132444,0.00013039254,0.000061984465,0.000069579306,0.00011362328,0.00004179708,0.005922934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999101,0.000015288848,0.000006437174,0.000017479555,0.000036383684,0.000014273488],"domain_scores_gemma":[0.9998673,0.000039283346,0.00000797192,0.000013398004,0.0000632663,0.000008703011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019945907,0.00019689288,0.00022334352,0.00026823004,0.00032372214,0.0003589518,0.0006052563,0.00039504777,0.0034797285],"category_scores_gemma":[0.0003579089,0.00011698492,0.00025030295,0.0003104894,0.00024332124,0.00036672168,0.00023267529,0.0005001489,0.0007400093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003256009,0.00011651929,0.00060411345,0.00016174771,0.00006096804,0.0002461229,0.0001243257,0.09621641,0.17534325,0.030398209,0.0038970201,0.6925057],"study_design_scores_gemma":[0.000073956704,0.00045286998,0.0007299762,0.000028946124,0.000083345796,0.00045231232,0.000039014612,0.8986787,0.07168074,0.012377966,0.015366677,0.000035413075],"about_ca_topic_score_codex":0.0016662906,"about_ca_topic_score_gemma":0.0024207318,"teacher_disagreement_score":0.0034797285,"about_ca_system_score_codex":0.00029272493,"about_ca_system_score_gemma":0.0005785209,"threshold_uncertainty_score":0.011640847},"labels":[],"label_agreement":null},{"id":"W2167934364","doi":"10.1109/ccece.2005.1557158","title":"Neural network methods for the localization and navigation of mobile robots","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Mobile robot; Principal component analysis; Support vector machine; Computer vision; Orientation (vector space); Robot; Component (thermodynamics); Pattern recognition (psychology)","score_opus":0.01575364653735407,"score_gpt":0.3189982018995799,"score_spread":0.30324455536222583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167934364","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016182108,0.0066502723,0.9890855,0.00026386548,0.00013639813,0.000021339836,0.000039747076,0.00017632991,0.0020084716],"genre_scores_gemma":[0.28671122,0.024197837,0.67194295,0.00031811564,0.0009989301,0.00057010626,0.0004437617,0.00012437026,0.014692651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999721,0.000085164604,0.0000200783,0.000045669265,0.000112148715,0.000016015803],"domain_scores_gemma":[0.99971217,0.00014578748,0.000034310546,0.000017945056,0.00008267402,0.0000070187048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005631362,0.0008641528,0.0007284151,0.0008407555,0.0003157399,0.00062388374,0.0008347299,0.0010343706,0.0019719116],"category_scores_gemma":[0.0014775462,0.0003049864,0.0005114019,0.0014424942,0.00051777513,0.0010489653,0.0005182795,0.0012578538,0.0007124097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052024294,0.000028293534,0.00050527626,0.0004986957,0.00010832149,0.000102622755,0.00007372618,0.5874944,0.0031142265,0.06651427,0.004125528,0.33738258],"study_design_scores_gemma":[0.0000089602545,0.000015398824,0.00017294608,0.000034010834,0.000014832846,0.000033109704,0.000011247597,0.9648133,0.00065459387,0.028118722,0.0061098123,0.000012985697],"about_ca_topic_score_codex":0.0041742544,"about_ca_topic_score_gemma":0.003458143,"teacher_disagreement_score":0.0041742544,"about_ca_system_score_codex":0.0005917337,"about_ca_system_score_gemma":0.00059703423,"threshold_uncertainty_score":0.008299947},"labels":[],"label_agreement":null},{"id":"W2168115495","doi":"10.1142/s0218001407005284","title":"DETERMINISTIC AND EVOLUTIONARY EXTRACTION OF DELTA-LOGNORMAL PARAMETERS: PERFORMANCE COMPARISON","year":2007,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Evolutionary algorithm; Log-normal distribution; Noise (video); Handwriting; Artificial intelligence; Algorithm; Kinematics; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.10015920341830806,"score_gpt":0.3437041253537559,"score_spread":0.24354492193544783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168115495","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5295544,0.0041086157,0.45514664,0.0005097242,0.00010829768,0.000099687844,0.00016849468,0.0026581462,0.007645961],"genre_scores_gemma":[0.79096425,0.0008990783,0.2051461,0.000087120054,0.000032410742,0.00006269118,0.00035388846,0.0001254616,0.0023288557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984529,0.00033258786,0.00013401508,0.00033517944,0.0006309254,0.00011428969],"domain_scores_gemma":[0.99447095,0.0032917606,0.0004703606,0.0005881669,0.001078897,0.000099851626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024728407,0.0006639602,0.00096893735,0.001870425,0.00043151982,0.0011329612,0.0011940884,0.0012760115,0.00097357016],"category_scores_gemma":[0.010029011,0.00029853254,0.00052047434,0.0015037869,0.00042826947,0.0011485624,0.00069108035,0.00042933755,0.00031709345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012040167,0.00027372327,0.009751387,0.0003100344,0.00023708155,0.00011290929,0.00017783958,0.36116195,0.013012045,0.0035648057,0.0006571551,0.60953707],"study_design_scores_gemma":[0.00005061237,0.00025475616,0.00663687,0.000030411287,0.00007660635,0.0002601812,0.00006206814,0.9738558,0.016031904,0.0012829506,0.0014131007,0.000044706812],"about_ca_topic_score_codex":0.004038074,"about_ca_topic_score_gemma":0.0029973513,"teacher_disagreement_score":0.004038074,"about_ca_system_score_codex":0.00074486533,"about_ca_system_score_gemma":0.00084284664,"threshold_uncertainty_score":0.0130777955},"labels":[],"label_agreement":null},{"id":"W2168148636","doi":"","title":"Incorporating Second-Order Functional Knowledge for Better Option Pricing","year":2000,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":273,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Generalization; Computer science; A priori and a posteriori; Class (philosophy); Task (project management); Function (biology); Order (exchange); Convex function; Artificial neural network; Machine learning; Artificial intelligence; Regular polygon; Mathematical optimization; Mathematics","score_opus":0.021756917981157455,"score_gpt":0.2499960200512804,"score_spread":0.22823910207012296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168148636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041899398,0.00040068815,0.9539743,0.0004704314,0.000049024587,0.000026940326,0.00003461886,0.00084573583,0.002298855],"genre_scores_gemma":[0.7320511,0.0005912946,0.2624489,0.0004043712,0.00012543632,0.00007718663,0.00021860172,0.00021003366,0.0038730702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994941,0.00016926102,0.00003574916,0.00010147361,0.00013814117,0.00006128831],"domain_scores_gemma":[0.9968342,0.0016072835,0.00025150285,0.00088431116,0.00034245933,0.00008023226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027452295,0.0009902281,0.0012254338,0.0005671997,0.00031150403,0.0015449735,0.0009338008,0.0021803093,0.003656572],"category_scores_gemma":[0.008553853,0.000562892,0.0008824836,0.000589206,0.00065358565,0.0049742367,0.0011397109,0.0030313204,0.0011302641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016894621,0.00038998513,0.002248135,0.00012714646,0.0001349474,0.000090445385,0.00008569635,0.7358449,0.01828142,0.027464459,0.0017985456,0.2133653],"study_design_scores_gemma":[0.0000094598,0.000028182876,0.00029425794,0.000007653382,0.000007686217,0.00001643474,0.0000033022022,0.9901528,0.0016922044,0.0072830906,0.00049714814,0.00000776209],"about_ca_topic_score_codex":0.0019615223,"about_ca_topic_score_gemma":0.0035936674,"teacher_disagreement_score":0.003656572,"about_ca_system_score_codex":0.0006246938,"about_ca_system_score_gemma":0.00095631444,"threshold_uncertainty_score":0.014518321},"labels":[],"label_agreement":null},{"id":"W2168268735","doi":"10.1109/wescan.1997.627106","title":"Web applets for interactive tutorials on artificial neural learning","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Java applet; Computer science; Java; Artificial neural network; Artificial intelligence; Perceptron; Machine learning; Operating system","score_opus":0.04498055281121229,"score_gpt":0.2819714557502905,"score_spread":0.23699090293907818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168268735","genre_codex":"other","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067759245,0.0028018823,0.3209389,0.0016486032,0.002459935,0.0016160443,0.013939997,0.31317115,0.33664754],"genre_scores_gemma":[0.028940968,0.0032738743,0.2621345,0.003773896,0.0014779993,0.0027492528,0.030220022,0.08870019,0.5787292],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99964523,0.000043206168,0.000027352344,0.0000658124,0.00016503222,0.000053302145],"domain_scores_gemma":[0.9974475,0.0012205733,0.00011230795,0.00020085179,0.0005456204,0.00047311897],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00066175486,0.0025285063,0.001106136,0.001474468,0.00037415448,0.0018354957,0.0016732424,0.0015936147,0.4163352],"category_scores_gemma":[0.0047165514,0.0007685364,0.001041257,0.0012378684,0.0002903601,0.0037883592,0.0022009024,0.0024293684,0.20571293],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029310313,0.0003347477,0.00045845043,0.0005387368,0.000033567398,0.0005368505,0.00013304579,0.00066933036,0.0088636065,0.006923108,0.8340322,0.1471833],"study_design_scores_gemma":[0.00016812907,0.00015163927,0.0028450359,0.00028280946,0.000027412625,0.0008934503,0.000063265354,0.0072880234,0.004580797,0.0115015255,0.9721319,0.000066057524],"about_ca_topic_score_codex":0.00031268052,"about_ca_topic_score_gemma":0.00085281,"teacher_disagreement_score":0.4163352,"about_ca_system_score_codex":0.00040555483,"about_ca_system_score_gemma":0.00025661368,"threshold_uncertainty_score":0.832527},"labels":[],"label_agreement":null},{"id":"W2168758828","doi":"10.1109/ccece.1997.614848","title":"A modified BPTT algorithm for trajectory learning in block-diagonal recurrent neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Diagonal; Computer science; Backpropagation; Algorithm; Block (permutation group theory); Artificial neural network; Trajectory; Recurrent neural network; Computation; Artificial intelligence; Mathematics","score_opus":0.03252021060205968,"score_gpt":0.2503932135784937,"score_spread":0.21787300297643403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168758828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014572113,0.000070006674,0.99739826,0.000030658553,0.000044349013,0.000027484717,0.000023939607,0.00046442408,0.00048368055],"genre_scores_gemma":[0.068000406,0.00018004203,0.92702997,0.00006996133,0.000057455945,0.0002288647,0.0002804676,0.00022697129,0.00392589],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955755,0.00008760918,0.000037651753,0.00008529486,0.00020243246,0.000029419572],"domain_scores_gemma":[0.9993875,0.00020201891,0.00004581554,0.00008001238,0.00025580433,0.000028762157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006961414,0.0006797758,0.000768569,0.00043147086,0.00036978116,0.00070618483,0.0017726092,0.0010849325,0.0039024334],"category_scores_gemma":[0.0027354925,0.00037059022,0.00043501682,0.0006898919,0.00038691267,0.0012278114,0.000671235,0.0011451833,0.0016068048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016152076,0.00008142372,0.00034065868,0.00015450227,0.00005876404,0.00012763888,0.00006838431,0.38347623,0.012774582,0.026080057,0.004727021,0.5719492],"study_design_scores_gemma":[0.000011496693,0.000020795023,0.000042280888,0.0000038647395,0.000004157517,0.000022536538,0.0000022939944,0.99451643,0.0012927603,0.0024312218,0.0016466102,0.000005538218],"about_ca_topic_score_codex":0.0068331067,"about_ca_topic_score_gemma":0.0058753644,"teacher_disagreement_score":0.0068331067,"about_ca_system_score_codex":0.0005765892,"about_ca_system_score_gemma":0.001115883,"threshold_uncertainty_score":0.01358664},"labels":[],"label_agreement":null},{"id":"W2168767705","doi":"10.1109/icpr.2000.906142","title":"Multivariate structural Bernoulli mixtures for recognition of handwritten numerals","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Numeral system; Multivariate statistics; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Bernoulli's principle; Natural language processing; Machine learning; Engineering","score_opus":0.04536426820903318,"score_gpt":0.270901924394404,"score_spread":0.22553765618537086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168767705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063472646,0.0006556172,0.9913258,0.0001501674,0.00004241123,0.00001909248,0.000054384847,0.00067409396,0.0007311661],"genre_scores_gemma":[0.27957425,0.0017442608,0.710429,0.00014831299,0.00023191328,0.00016070275,0.00046917915,0.00036974906,0.0068725334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936336,0.00027283898,0.00002853459,0.0001299492,0.00016338436,0.00004199408],"domain_scores_gemma":[0.99903107,0.0006501956,0.000097012584,0.00010669065,0.00008256846,0.000032509528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013196837,0.0007124725,0.00074659864,0.00061257713,0.00032946697,0.00084712374,0.00089104904,0.0011046792,0.002361253],"category_scores_gemma":[0.0055423956,0.0005946128,0.00069433625,0.0010375902,0.0007534419,0.0012997778,0.00094665255,0.0012779842,0.0011072989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002711548,0.000069173126,0.00074150565,0.0001766792,0.00009896071,0.00010297589,0.00013222618,0.578635,0.0233552,0.06720725,0.0034923765,0.3257176],"study_design_scores_gemma":[0.0000046712976,0.000011534018,0.00039515528,0.000009933846,0.000008463517,0.000048566977,0.0000045663173,0.9725415,0.0034818896,0.021916678,0.0015586043,0.00001830468],"about_ca_topic_score_codex":0.0018550343,"about_ca_topic_score_gemma":0.0023833457,"teacher_disagreement_score":0.002361253,"about_ca_system_score_codex":0.0005559108,"about_ca_system_score_gemma":0.00054688525,"threshold_uncertainty_score":0.007899225},"labels":[],"label_agreement":null},{"id":"W2169061308","doi":"10.1109/mwscas.1995.504510","title":"Design of equiripple linear phase FIR filters by feedback neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linear phase; Finite impulse response; Artificial neural network; Computer science; Very-large-scale integration; Iterative method; Control theory (sociology); Algorithm; Digital filter; Hopfield network; Mathematics; Filter (signal processing); Artificial intelligence","score_opus":0.04514728488767971,"score_gpt":0.2680290595510406,"score_spread":0.2228817746633609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169061308","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009235764,0.00021394715,0.98818845,0.000053900738,0.000036300407,0.00003588372,0.000014340894,0.00026222176,0.0019592512],"genre_scores_gemma":[0.47214875,0.0004131976,0.5223729,0.00021537484,0.000043760454,0.00019395608,0.00006429212,0.000052595544,0.004495208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997502,0.000047182166,0.000023028155,0.000057967023,0.00008923205,0.000032435375],"domain_scores_gemma":[0.9997061,0.00009855801,0.00005558908,0.000020892438,0.000104798455,0.000014191074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006564185,0.00048005584,0.0003247224,0.00028090752,0.00030436277,0.00051817606,0.0009295931,0.0008205168,0.0019760167],"category_scores_gemma":[0.0012638476,0.00027786984,0.00027023608,0.00026548406,0.0003667453,0.0005778595,0.00033007594,0.0004560587,0.00044855798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024093343,0.00011475544,0.0006280166,0.000267373,0.000065136795,0.00019385354,0.00021021342,0.47433114,0.08580599,0.022221768,0.00180409,0.41411665],"study_design_scores_gemma":[0.000030162071,0.0001721369,0.00020568128,0.000027758899,0.000025147456,0.00010306995,0.00001441539,0.97569805,0.015555947,0.0043687643,0.0037810185,0.000017846252],"about_ca_topic_score_codex":0.0015220265,"about_ca_topic_score_gemma":0.0031042444,"teacher_disagreement_score":0.0019760167,"about_ca_system_score_codex":0.0005035016,"about_ca_system_score_gemma":0.0004886895,"threshold_uncertainty_score":0.0066103935},"labels":[],"label_agreement":null},{"id":"W2169116170","doi":"10.1109/tnn.2004.839357","title":"Incremental Communication for Adaptive Resonance Theory Networks","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Adaptive resonance theory; Computer science; Convergence (economics); Artificial neural network; Implementation; Process (computing); Communications system; Bounded function; Telecommunications network; Artificial intelligence; Algorithm; Mathematics; Telecommunications","score_opus":0.02048169323534046,"score_gpt":0.25192284606960763,"score_spread":0.23144115283426717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169116170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034321465,0.0003054746,0.9591308,0.00019764609,0.000072671246,0.00003656182,0.000016207696,0.0003734634,0.005545739],"genre_scores_gemma":[0.83596474,0.00023841888,0.16018079,0.00015808409,0.00009355897,0.00011977198,0.00004080955,0.000047151843,0.003156527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968135,0.000104913655,0.000014422499,0.000039451206,0.00013647704,0.000023415298],"domain_scores_gemma":[0.99920964,0.0004744149,0.00005715514,0.000106270585,0.00013420852,0.000018202336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047846665,0.00032832185,0.00023668828,0.00027348273,0.0003375441,0.00045362161,0.00074029464,0.00050213566,0.0021939196],"category_scores_gemma":[0.0031851665,0.00014929746,0.00021083943,0.00026474395,0.00044865438,0.0010364564,0.0005455209,0.0006939919,0.00030558917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000363085,0.00007690824,0.0007706136,0.00022045764,0.000042532392,0.00041130406,0.00033196743,0.4871646,0.047677744,0.15991633,0.0026563702,0.30036804],"study_design_scores_gemma":[0.000023760907,0.00011102267,0.0001480488,0.000009593017,0.000010962415,0.00010004737,0.000017144988,0.965867,0.008311361,0.02254652,0.0028417332,0.000012745764],"about_ca_topic_score_codex":0.00068947126,"about_ca_topic_score_gemma":0.0009695667,"teacher_disagreement_score":0.0021939196,"about_ca_system_score_codex":0.00039903732,"about_ca_system_score_gemma":0.00035174348,"threshold_uncertainty_score":0.007339418},"labels":[],"label_agreement":null},{"id":"W2169459393","doi":"10.2307/3316032","title":"Flexible regression modeling","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Basis function; Affine transformation; Covariate; Computer science; Algorithm; Computation; Logistic regression; Basis (linear algebra); Regression analysis; Mathematics; Statistics","score_opus":0.04114403781486139,"score_gpt":0.25650985273179866,"score_spread":0.21536581491693726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169459393","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022677,0.00036320498,0.9950395,0.00035605006,0.000063583226,0.000022195214,0.00015165407,0.0002534912,0.0014824658],"genre_scores_gemma":[0.4012182,0.0021977022,0.5683899,0.0005698724,0.0005928538,0.0006460161,0.0016731774,0.0007281431,0.023984091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99197716,0.0043403953,0.00028697264,0.0015355479,0.0014426388,0.00041721913],"domain_scores_gemma":[0.9907816,0.0056659435,0.0008419497,0.0016023376,0.00083894894,0.000269235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0079628425,0.0017584171,0.0023778963,0.0026274333,0.0008824668,0.0028061143,0.003832489,0.0020637508,0.0065781074],"category_scores_gemma":[0.02277465,0.0011348298,0.003439117,0.0035628781,0.0020844273,0.0024000737,0.003464174,0.0036436873,0.0021210285],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008690687,0.00006808296,0.0026085912,0.00019365642,0.0005023904,0.00029223182,0.00021705395,0.54986405,0.0009568042,0.32360902,0.007069226,0.11453193],"study_design_scores_gemma":[0.000013065243,0.000019070483,0.00040740558,0.000037262744,0.000030556028,0.00005753089,0.000015812459,0.90147793,0.00019708683,0.09294399,0.004775792,0.000024495695],"about_ca_topic_score_codex":0.008741232,"about_ca_topic_score_gemma":0.0068646204,"teacher_disagreement_score":0.008741232,"about_ca_system_score_codex":0.0018038515,"about_ca_system_score_gemma":0.001617278,"threshold_uncertainty_score":0.042111993},"labels":[],"label_agreement":null},{"id":"W2169923338","doi":"10.1109/ijcnn.2009.5178798","title":"Improving gradient-based learning algorithms for large scale feedforward networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Benchmark (surveying); Computer science; Backpropagation; Convergence (economics); Artificial neural network; Scale (ratio); Feed forward; Algorithm; Feedforward neural network; Artificial intelligence; Machine learning; Rate of convergence; Simple (philosophy); Key (lock)","score_opus":0.012441878435917986,"score_gpt":0.2487592542064199,"score_spread":0.2363173757705019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169923338","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010780104,0.0009941778,0.9851423,0.00017918824,0.00011660557,0.000058562215,0.0000328044,0.0015138753,0.0011824011],"genre_scores_gemma":[0.11507542,0.00061982096,0.88049614,0.00017107303,0.00011943073,0.00015309229,0.00019082172,0.0004800924,0.0026941905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990773,0.00029830547,0.000061121886,0.00011615788,0.0003973575,0.00004977773],"domain_scores_gemma":[0.99604744,0.0020667827,0.0002575228,0.00041513826,0.0011315907,0.000081513615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002976621,0.0016048056,0.0011932509,0.0014016639,0.00053341873,0.0010033705,0.0016285349,0.0015788368,0.0020832175],"category_scores_gemma":[0.013552864,0.00059102156,0.0005701203,0.0013732904,0.0007071438,0.0024056449,0.0009923351,0.001995662,0.0014040819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015399964,0.00021905659,0.0012995729,0.00025842333,0.0001486906,0.00007844242,0.00009257085,0.5964992,0.0056911996,0.01223373,0.0057451976,0.37757987],"study_design_scores_gemma":[0.00002127432,0.000033568907,0.00019689431,0.000010295441,0.000009150591,0.000021472199,0.0000053202493,0.9924339,0.001762248,0.0045529883,0.0009440721,0.000008748592],"about_ca_topic_score_codex":0.0038979482,"about_ca_topic_score_gemma":0.004504863,"teacher_disagreement_score":0.0038979482,"about_ca_system_score_codex":0.00060961134,"about_ca_system_score_gemma":0.0010958168,"threshold_uncertainty_score":0.015742064},"labels":[],"label_agreement":null},{"id":"W2169934683","doi":"10.1109/ijcnn.1999.830877","title":"Finite element analysis based Hopfield neural network model for solving nonlinear electromagnetic field problems","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Finite element method; Correctness; Hopfield network; Artificial neural network; Nonlinear system; Computer science; Boundary value problem; Electromagnetic field; Electromagnetism; Energy functional; Mixed finite element method; Function (biology); Energy (signal processing); Applied mathematics; Mathematical optimization; Mathematics; Mathematical analysis; Algorithm; Artificial intelligence; Physics","score_opus":0.019328588576899088,"score_gpt":0.24586412278915118,"score_spread":0.2265355342122521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169934683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01430237,0.0001899604,0.98185915,0.0001032339,0.000019409028,0.000018625009,0.00002778219,0.00014496758,0.0033345197],"genre_scores_gemma":[0.6816267,0.00051453884,0.30975375,0.00007544241,0.00003721306,0.00023738886,0.0001401805,0.00006078096,0.0075539723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988925,0.000028592494,0.0000058243604,0.000014503133,0.00005416389,0.000007631922],"domain_scores_gemma":[0.99988675,0.000060077844,0.000009202286,0.0000074952018,0.00003053617,0.0000059288645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029099835,0.0003481003,0.00043194104,0.00028322724,0.00025188108,0.00035095797,0.0007564127,0.00070203125,0.0013554875],"category_scores_gemma":[0.00066998135,0.00016208892,0.00033913087,0.00028846256,0.00041798883,0.00066914735,0.00035203496,0.000492302,0.0002132933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022211312,0.000014227907,0.00017283008,0.000051622806,0.000015084007,0.000038461574,0.00003780225,0.9548319,0.0077262856,0.016422523,0.00028649363,0.020380529],"study_design_scores_gemma":[0.0000015342429,0.0000038049548,0.000021168102,0.0000016914055,0.0000016889456,0.000004417391,0.0000017668428,0.9974138,0.00044350573,0.001959372,0.00014553001,0.0000017184165],"about_ca_topic_score_codex":0.0047765644,"about_ca_topic_score_gemma":0.003944622,"teacher_disagreement_score":0.0047765644,"about_ca_system_score_codex":0.00042684216,"about_ca_system_score_gemma":0.00064706453,"threshold_uncertainty_score":0.009497523},"labels":[],"label_agreement":null},{"id":"W2170106370","doi":"10.1109/kes.1997.619407","title":"Inverse kinematic neuro-control of robotic systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Kinematics; Artificial neural network; Computer science; Inverse; Scheme (mathematics); Inverse dynamics; Adaptive control; Inverse kinematics; Robot; Control engineering; Control theory (sociology); Artificial intelligence; Control (management); Mathematics; Engineering; Physics; Classical mechanics","score_opus":0.022216474524139992,"score_gpt":0.20765482906798244,"score_spread":0.18543835454384244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170106370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0114767095,0.0038289567,0.9620161,0.0004604932,0.00023037542,0.00004197249,0.000050440143,0.00042372645,0.021471282],"genre_scores_gemma":[0.83882844,0.004762546,0.13880779,0.00018012337,0.00019439352,0.00020870799,0.00013149373,0.00005248357,0.016834037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997882,0.00004483549,0.00001596445,0.000035877663,0.00010119698,0.000013867443],"domain_scores_gemma":[0.9998516,0.00004816497,0.000022634245,0.000021614766,0.000050494316,0.0000054882066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030193877,0.00038528617,0.00036923218,0.00021110475,0.00021373118,0.00085870794,0.0004264141,0.0005298733,0.0018833992],"category_scores_gemma":[0.0008741153,0.00015497395,0.00018595517,0.00027119857,0.00058696984,0.00053069234,0.0005300506,0.0005404166,0.00042037078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006321647,0.00003453214,0.000487431,0.00037760034,0.000048496517,0.00019506521,0.00014845836,0.6170327,0.018481236,0.12687829,0.0024873328,0.23376557],"study_design_scores_gemma":[0.000016181126,0.000051221756,0.000380689,0.00004586504,0.00001378866,0.00009911121,0.000025804056,0.93405557,0.004375926,0.039068483,0.02185282,0.00001457419],"about_ca_topic_score_codex":0.0016691533,"about_ca_topic_score_gemma":0.0012710618,"teacher_disagreement_score":0.0018833992,"about_ca_system_score_codex":0.0003020479,"about_ca_system_score_gemma":0.00042991218,"threshold_uncertainty_score":0.0063005686},"labels":[],"label_agreement":null},{"id":"W2170593953","doi":"10.1109/iscas.1996.541638","title":"A unified synapse-neuron building block for hybrid VLSI neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Very-large-scale integration; Computer science; Robustness (evolution); Block (permutation group theory); Artificial neural network; Interconnection; Scalability; Modularity (biology); Synapse; Computer architecture; Topology (electrical circuits); Artificial intelligence; Embedded system; Engineering; Mathematics; Telecommunications; Electrical engineering","score_opus":0.02534232513163772,"score_gpt":0.23953631161026542,"score_spread":0.2141939864786277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170593953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039108068,0.00071754627,0.9534922,0.00008453402,0.0000595484,0.000054168268,0.000048001868,0.0012422019,0.0051937113],"genre_scores_gemma":[0.37291953,0.0004040618,0.6212412,0.0000949269,0.00004769866,0.00013809308,0.00012025066,0.00007781237,0.004956387],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999163,0.00001040198,0.000005932909,0.000015512123,0.00004118086,0.000010687775],"domain_scores_gemma":[0.9999349,0.000013088623,0.0000079437095,0.000015592208,0.000020586936,0.000007840419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016624901,0.00023260764,0.00025845526,0.00022061329,0.0002089014,0.00030419798,0.0008862701,0.00039477297,0.0020807092],"category_scores_gemma":[0.00019336246,0.00022199115,0.000253063,0.00022809724,0.0001839406,0.0006025198,0.00036082198,0.00044738132,0.0007954445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018611804,0.00009236006,0.00055775937,0.0003320806,0.00010751037,0.0003225802,0.00008451826,0.059000395,0.58327097,0.061684232,0.002472689,0.29188883],"study_design_scores_gemma":[0.00008334142,0.00092725485,0.0012737444,0.000053303727,0.00014009564,0.0011987337,0.000025512338,0.7006183,0.2442745,0.011750358,0.039603706,0.000051149138],"about_ca_topic_score_codex":0.00034734613,"about_ca_topic_score_gemma":0.001136465,"teacher_disagreement_score":0.0020807092,"about_ca_system_score_codex":0.00020309498,"about_ca_system_score_gemma":0.0002620064,"threshold_uncertainty_score":0.00696069},"labels":[],"label_agreement":null},{"id":"W2170960849","doi":"10.1109/ijcnn.2002.1007809","title":"Towards the on-line recognition of Arabic characters","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Arabic; Genetic programming; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Feature extraction; Perceptron; Multilayer perceptron; Heuristic; Line (geometry); Machine learning; Artificial neural network","score_opus":0.0693484188935595,"score_gpt":0.2693654823048989,"score_spread":0.20001706341133937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170960849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019106887,0.0006540688,0.9657666,0.0001852434,0.00017990178,0.00015319887,0.00015781567,0.007669893,0.0061263745],"genre_scores_gemma":[0.12904187,0.00083119393,0.8501079,0.0003676483,0.00014924665,0.00014823754,0.0010045329,0.0002611558,0.01808828],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994425,0.00009308633,0.000034712906,0.00013451609,0.0002337012,0.00006147974],"domain_scores_gemma":[0.9994161,0.000054513883,0.000047234822,0.000119181044,0.00030536982,0.00005770467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004955305,0.0007617732,0.0007337155,0.00090559834,0.00040634352,0.0013246104,0.0013398877,0.0014624777,0.0049114563],"category_scores_gemma":[0.0009321483,0.0002343605,0.0004897138,0.0007681205,0.00043745802,0.0010796377,0.000945925,0.000984997,0.009044359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022682786,0.00014937519,0.00081454066,0.00018638719,0.000040443672,0.00024742074,0.00018788522,0.004488893,0.1809362,0.0029216707,0.008295014,0.8015054],"study_design_scores_gemma":[0.0000818378,0.0009931268,0.00683016,0.00016547227,0.00019330897,0.002706338,0.0004565957,0.48482126,0.36059672,0.0074374103,0.13560486,0.00011283702],"about_ca_topic_score_codex":0.001480456,"about_ca_topic_score_gemma":0.0012159947,"teacher_disagreement_score":0.0049114563,"about_ca_system_score_codex":0.00023022007,"about_ca_system_score_gemma":0.0004197399,"threshold_uncertainty_score":0.016430438},"labels":[],"label_agreement":null},{"id":"W2171036529","doi":"10.1109/nafips.2007.383808","title":"A Novel Granular Neural Network Architecture","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Benchmark (surveying); Architecture; Perceptron; Java; Artificial intelligence; Decision tree; Artificial neural network; Connection (principal bundle); Layer (electronics); Machine learning; Data mining; Programming language; Mathematics","score_opus":0.013462260876392842,"score_gpt":0.23987835254559536,"score_spread":0.2264160916692025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171036529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027406106,0.0005620748,0.9611989,0.00046222185,0.00028166219,0.000057664638,0.0003541187,0.002567849,0.0071093957],"genre_scores_gemma":[0.6347528,0.0006244057,0.3563898,0.00045607888,0.00013920089,0.00015943793,0.00072321505,0.00019800746,0.0065569906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995838,0.0000614335,0.000032394586,0.0001084505,0.00015333299,0.00006058521],"domain_scores_gemma":[0.9995782,0.00011186119,0.000042607477,0.00008958459,0.00013340823,0.000044282882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062547,0.00049650285,0.0007625874,0.0006247718,0.0003775848,0.0012628098,0.0015033481,0.0009457506,0.003804353],"category_scores_gemma":[0.002171683,0.00035542913,0.00050411007,0.0010135116,0.00053641055,0.0022389893,0.0011975185,0.0010804974,0.0009996293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042741877,0.00012868221,0.002654895,0.00029916642,0.00017919848,0.00035538245,0.00014058301,0.5317556,0.017187418,0.06633432,0.011188507,0.36934876],"study_design_scores_gemma":[0.000021096901,0.000034758235,0.00038581097,0.000019776337,0.000026830898,0.000066822395,0.000010007012,0.96912766,0.0019366696,0.024918722,0.0034366187,0.00001517838],"about_ca_topic_score_codex":0.004034079,"about_ca_topic_score_gemma":0.0041145263,"teacher_disagreement_score":0.004034079,"about_ca_system_score_codex":0.0007288102,"about_ca_system_score_gemma":0.0006335344,"threshold_uncertainty_score":0.012726843},"labels":[],"label_agreement":null},{"id":"W2171639644","doi":"10.1109/ijcnn.2001.939093","title":"A new paradigm for classification tasks the 'race to the attractor' neural network model","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Attractor; Class (philosophy); Categorization; Computer science; Set (abstract data type); Artificial neural network; Race (biology); Nonlinear system; Key (lock); Artificial intelligence; Pattern recognition (psychology); Mathematics; Physics","score_opus":0.08982705554144871,"score_gpt":0.2825131277331835,"score_spread":0.19268607219173478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171639644","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047291615,0.0005212901,0.9903824,0.00079446594,0.00015448917,0.00006701881,0.00010494674,0.00020908266,0.00303713],"genre_scores_gemma":[0.28189948,0.0017187477,0.6970482,0.0010361288,0.000630761,0.0010699736,0.00046391564,0.00015719669,0.015975675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905056,0.000282431,0.00006396703,0.0002500833,0.0002881528,0.000064836764],"domain_scores_gemma":[0.9991078,0.000424311,0.000078965466,0.00015721376,0.00017904378,0.00005273163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001701678,0.0009451709,0.0010621789,0.00088036846,0.00047737898,0.0017128257,0.0029520555,0.0018696924,0.0027521546],"category_scores_gemma":[0.0034763007,0.00031466494,0.000998294,0.0008970799,0.0014509334,0.0042152996,0.0015630651,0.002511387,0.0010231321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016222066,0.00013249842,0.0013005947,0.00038372277,0.00018964487,0.00023682414,0.0004148465,0.23490332,0.013731162,0.52783614,0.009096389,0.21161267],"study_design_scores_gemma":[0.000022406613,0.00006609434,0.00015951821,0.000020755622,0.000018109315,0.000101609585,0.000023082604,0.8002392,0.0011715447,0.18940485,0.008750761,0.000022115493],"about_ca_topic_score_codex":0.0015526192,"about_ca_topic_score_gemma":0.0015725931,"teacher_disagreement_score":0.0029520555,"about_ca_system_score_codex":0.0008555602,"about_ca_system_score_gemma":0.0008344591,"threshold_uncertainty_score":0.009206891},"labels":[],"label_agreement":null},{"id":"W2177403092","doi":"10.1109/nafips-wconsc.2015.7284117","title":"Fuzzy neural networks: Theory and applications","year":2015,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Fuzzy logic; Neuro-fuzzy; Fuzzy set; Fuzzy control system","score_opus":0.019257774702265456,"score_gpt":0.2530779636292023,"score_spread":0.23382018892693687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2177403092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008189829,0.12927537,0.7952586,0.006849652,0.0020853523,0.00009826089,0.0004193518,0.00040902232,0.0574146],"genre_scores_gemma":[0.45434362,0.16134393,0.33301237,0.001529254,0.0035763802,0.0004923828,0.0006916778,0.00015197392,0.044858407],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99949145,0.0001302172,0.000043875716,0.00007355013,0.00023217534,0.000028705654],"domain_scores_gemma":[0.9993857,0.0003314025,0.000054930388,0.00004609015,0.00015844827,0.000023416498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008880888,0.00055247126,0.0007905135,0.0012800345,0.0003661661,0.001604248,0.0007334247,0.0017666364,0.0049437736],"category_scores_gemma":[0.0028606525,0.00024414994,0.00042575388,0.0018104066,0.00087156537,0.001342601,0.0006686864,0.0015176176,0.0011602113],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079974736,0.00004635235,0.0010818068,0.0013974344,0.00013410425,0.00027183903,0.00021701396,0.056297146,0.0029616079,0.3466456,0.025186688,0.56568044],"study_design_scores_gemma":[0.000019209525,0.0000746677,0.0012858546,0.00082207617,0.00005969964,0.0004976543,0.00013427822,0.28661624,0.0022341905,0.5950838,0.11311278,0.000059494803],"about_ca_topic_score_codex":0.0016594527,"about_ca_topic_score_gemma":0.0011592082,"teacher_disagreement_score":0.0049437736,"about_ca_system_score_codex":0.0008782294,"about_ca_system_score_gemma":0.0005684735,"threshold_uncertainty_score":0.01653862},"labels":[],"label_agreement":null},{"id":"W2179498001","doi":"10.1016/j.patrec.2015.11.021","title":"Fast adaptive algorithms for optimal feature extraction from Gaussian data","year":2015,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Computer science; Algorithm; Gaussian; Pattern recognition (psychology); Feature (linguistics); Artificial intelligence; Feature extraction","score_opus":0.13133847775882232,"score_gpt":0.3132313624639037,"score_spread":0.1818928847050814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179498001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019385112,0.00016632516,0.9972742,0.000035290996,0.000018567915,0.000014220839,0.000024255723,0.00042420824,0.00010438472],"genre_scores_gemma":[0.047176357,0.0002601658,0.950861,0.000060805774,0.000043906326,0.00016340613,0.00023861567,0.00016607124,0.0010296917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990403,0.00018540342,0.000105328036,0.00018508825,0.000381726,0.00010220881],"domain_scores_gemma":[0.9978162,0.0012574937,0.0001437162,0.00027167177,0.00046036838,0.000050605056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014073953,0.0011746679,0.0012705686,0.0015672185,0.0005310676,0.0009580618,0.0017965194,0.001227131,0.0026384487],"category_scores_gemma":[0.006571318,0.00097588403,0.0010517937,0.0023055738,0.00083696353,0.001913244,0.0022283103,0.0021168292,0.0016100146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026773682,0.00006273291,0.00041424154,0.000121273304,0.00006051536,0.00005167581,0.00009939204,0.06892596,0.015369439,0.011526007,0.0034541783,0.89964676],"study_design_scores_gemma":[0.00003392935,0.000038260707,0.0004949648,0.000013736952,0.000015814965,0.00005843956,0.000021042775,0.9769507,0.0062047434,0.014229799,0.0019132771,0.000025285766],"about_ca_topic_score_codex":0.004873699,"about_ca_topic_score_gemma":0.005403488,"teacher_disagreement_score":0.004873699,"about_ca_system_score_codex":0.00071498705,"about_ca_system_score_gemma":0.001165456,"threshold_uncertainty_score":0.009690642},"labels":[],"label_agreement":null},{"id":"W2182988819","doi":"10.32920/ryerson.14649354.v1","title":"Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Chaotic; Residual; Artificial neural network; Computer science; Series (stratigraphy); Time series; Embedding; Phase space; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Machine learning","score_opus":0.049014708877945734,"score_gpt":0.2585273144339447,"score_spread":0.209512605555999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182988819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13460317,0.00046403368,0.8621197,0.00005153177,0.000099096185,0.0010235019,0.00005634303,0.00028199662,0.0013007509],"genre_scores_gemma":[0.4873596,0.00043013535,0.50944626,0.000034229484,0.000019569065,0.0022104,0.00006301569,0.000022051288,0.000414759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99626356,0.0018216104,0.00035937518,0.00045480474,0.00091850705,0.00018214392],"domain_scores_gemma":[0.99676526,0.0019302876,0.00036954015,0.00034288774,0.0005340101,0.00005802535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008875763,0.0012155998,0.0015735907,0.0011152852,0.00046605527,0.0008692482,0.0010468154,0.0008712549,0.00072752923],"category_scores_gemma":[0.0067080907,0.00051412726,0.0016102539,0.0010067506,0.000531245,0.0007874618,0.0005522502,0.0009786143,0.00007680463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012035279,0.0010965386,0.0058343993,0.0014795432,0.00048876967,0.00012809527,0.00043798806,0.64058065,0.119340725,0.01019134,0.00031937353,0.21889904],"study_design_scores_gemma":[0.000114854025,0.0026008554,0.0029023937,0.000055549106,0.0002482634,0.000035862256,0.00007196518,0.9218667,0.06842422,0.0024190675,0.0011804753,0.00007970622],"about_ca_topic_score_codex":0.0013880731,"about_ca_topic_score_gemma":0.0014269272,"teacher_disagreement_score":0.008875763,"about_ca_system_score_codex":0.000806404,"about_ca_system_score_gemma":0.0010010836,"threshold_uncertainty_score":0.04694003},"labels":[],"label_agreement":null},{"id":"W2183529325","doi":"","title":"Soft-clustering and improved stability for adaptive resonance theory neural networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Adaptive resonance theory; Cluster analysis; Stability (learning theory); Computer science; Artificial intelligence; Artificial neural network; Plasticity; Quality (philosophy); Machine learning; Physics","score_opus":0.032173173739497546,"score_gpt":0.2368987868888994,"score_spread":0.20472561314940185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2183529325","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048118062,0.0003206089,0.9471256,0.0003309136,0.00004239525,0.000032764478,0.00001901944,0.00023516007,0.003775573],"genre_scores_gemma":[0.8714005,0.00030324524,0.121606745,0.00015681153,0.00007438541,0.00012674386,0.000059350485,0.00011536825,0.00615691],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951804,0.00015892737,0.000028823939,0.000107305656,0.00014108747,0.000045825323],"domain_scores_gemma":[0.9983924,0.0009397475,0.00015966226,0.00017852306,0.0002513563,0.00007818062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012919682,0.0006280644,0.00057999603,0.00083441334,0.0006713467,0.00079222006,0.0009806103,0.0010559517,0.0022024235],"category_scores_gemma":[0.005268424,0.0002949487,0.0005511895,0.00034804008,0.0013381987,0.0013432563,0.0014213996,0.0010546112,0.00046749643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013245313,0.00004771561,0.00056454324,0.00010669444,0.000051913044,0.00010967867,0.00026422847,0.80708045,0.025954185,0.11248788,0.0012095362,0.05199071],"study_design_scores_gemma":[0.0000057423026,0.000020591942,0.00007691548,0.0000038505186,0.0000030029223,0.000016052996,0.000010935954,0.97671086,0.0012487853,0.021660995,0.00023389906,0.000008339741],"about_ca_topic_score_codex":0.0011162922,"about_ca_topic_score_gemma":0.0010098077,"teacher_disagreement_score":0.0022024235,"about_ca_system_score_codex":0.00082510343,"about_ca_system_score_gemma":0.00042865545,"threshold_uncertainty_score":0.0073678493},"labels":[],"label_agreement":null},{"id":"W2184927432","doi":"","title":"MEASURING DEPENDENCE VIA MUTUAL INFORMATION","year":2011,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University","keywords":"Mutual information; Computer science; Information retrieval; Data science; Artificial intelligence","score_opus":0.011142148880183368,"score_gpt":0.1758898782541602,"score_spread":0.16474772937397683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184927432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07070463,0.0040491642,0.8958697,0.00090133655,0.00010057912,0.00017833764,0.0010956984,0.0006535914,0.026446942],"genre_scores_gemma":[0.8730883,0.002952394,0.118703105,0.0003581416,0.00026972938,0.0003107173,0.0013375777,0.00016106309,0.0028190552],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931063,0.002588563,0.00041742306,0.0011132453,0.0024893067,0.0002850722],"domain_scores_gemma":[0.9784374,0.015168156,0.0024062886,0.0020491132,0.0015836661,0.00035525436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043105907,0.0012087143,0.0014376724,0.0057162507,0.0006422146,0.002248626,0.0013304505,0.0011906368,0.0040669786],"category_scores_gemma":[0.028632741,0.00058458885,0.0008647207,0.0045815166,0.0023771906,0.0064238557,0.0036343874,0.0017008358,0.0009674932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043357318,0.00031540368,0.034002833,0.0014117878,0.0011848074,0.0004290484,0.0012710987,0.097122945,0.01670964,0.48960945,0.007884946,0.34962445],"study_design_scores_gemma":[0.00003280381,0.00034979454,0.025395395,0.00022662645,0.00031058572,0.0007737518,0.00031046843,0.32013008,0.01155804,0.6292722,0.011381022,0.00025923373],"about_ca_topic_score_codex":0.0007972528,"about_ca_topic_score_gemma":0.0006559861,"teacher_disagreement_score":0.0057162507,"about_ca_system_score_codex":0.0009467468,"about_ca_system_score_gemma":0.0008005439,"threshold_uncertainty_score":0.02279687},"labels":[],"label_agreement":null},{"id":"W2187039664","doi":"","title":"EFFICIENT OPTIMIZATION ALGORITHMS FOR LEARNING","year":2003,"lang":"en","type":"article","venue":"TSpace","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Computer science; Algorithm; Artificial intelligence","score_opus":0.026511479970579423,"score_gpt":0.3107368891639483,"score_spread":0.2842254091933689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187039664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009229743,0.0010931576,0.9948072,0.0002504257,0.000055312874,0.000042535095,0.00004196548,0.0002291232,0.0025573964],"genre_scores_gemma":[0.09282402,0.0036281866,0.8941727,0.0003195925,0.00036420935,0.000791861,0.00046478558,0.00038897168,0.007045635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99817944,0.0006897365,0.00012371667,0.00030168658,0.00059404224,0.00011139105],"domain_scores_gemma":[0.9971506,0.0020086586,0.00015061161,0.00036803112,0.00027487395,0.000047295514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030396339,0.002021605,0.0017889232,0.0013071421,0.0008122389,0.0021752114,0.0020505718,0.0021414256,0.006361246],"category_scores_gemma":[0.012223765,0.00084458326,0.00087687775,0.002400511,0.0020084775,0.0031363023,0.0029996904,0.0032234692,0.0025893373],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050450795,0.00005898183,0.00024285511,0.000374332,0.000075739226,0.000032621418,0.00008930724,0.47954276,0.0007069519,0.3428516,0.0074182767,0.16855612],"study_design_scores_gemma":[0.000025311134,0.000021093676,0.000059211787,0.00003731354,0.00001039624,0.000021204243,0.000015550386,0.80036205,0.00035381448,0.19289568,0.0061902557,0.000008166127],"about_ca_topic_score_codex":0.0020625463,"about_ca_topic_score_gemma":0.0026005348,"teacher_disagreement_score":0.006361246,"about_ca_system_score_codex":0.0015730192,"about_ca_system_score_gemma":0.0018869765,"threshold_uncertainty_score":0.021280468},"labels":[],"label_agreement":null},{"id":"W2188755879","doi":"","title":"FOURIER NEURAL NETWORKS: AN APPROACH WITH SINUSOIDAL ACTIVATION FUNCTIONS 1","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial neural network; Perceptron; Activation function; Connection (principal bundle); Computer science; Fourier series; Fourier transform; Biological system; Algorithm; Topology (electrical circuits); Mathematics; Artificial intelligence; Mathematical analysis; Geometry; Combinatorics; Biology","score_opus":0.016832854552154924,"score_gpt":0.21699539479612315,"score_spread":0.20016254024396823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188755879","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027542019,0.0018169197,0.9895173,0.00045868967,0.00016653263,0.000016286356,0.000027855778,0.00010145983,0.0051407465],"genre_scores_gemma":[0.2220699,0.006671473,0.74825925,0.0004203871,0.0009420527,0.00018017752,0.00013991844,0.00017174536,0.021145076],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965143,0.00015385152,0.000018768744,0.000055183034,0.000098411365,0.000022427883],"domain_scores_gemma":[0.99963355,0.0002068223,0.000031386087,0.000036740068,0.00007550351,0.000016014172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089149276,0.0007010825,0.00052700826,0.0006794374,0.00026813493,0.0011507225,0.0010455537,0.0015791247,0.0030814502],"category_scores_gemma":[0.0025394608,0.00040223153,0.00052184146,0.0008037063,0.0008979158,0.0020737485,0.001092853,0.0014069818,0.0009614817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090598194,0.00004215563,0.0005230404,0.00023542006,0.000062593026,0.00021900973,0.0001416468,0.22610754,0.006210829,0.5888349,0.0037692217,0.17376307],"study_design_scores_gemma":[0.000009073875,0.000030297464,0.00012138719,0.00004077634,0.000015553213,0.00012544292,0.00002234843,0.82253134,0.0012400766,0.16439168,0.011454952,0.000017124124],"about_ca_topic_score_codex":0.0010594588,"about_ca_topic_score_gemma":0.00077857217,"teacher_disagreement_score":0.0030814502,"about_ca_system_score_codex":0.0005379746,"about_ca_system_score_gemma":0.00037998415,"threshold_uncertainty_score":0.0103084445},"labels":[],"label_agreement":null},{"id":"W2189473194","doi":"","title":"Specia li ssue on Advances i nW eb Intelligence","year":2012,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data science; Library science; Computer science; History","score_opus":0.026768976038508627,"score_gpt":0.2891761647997191,"score_spread":0.26240718876121044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189473194","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015185274,0.27055123,0.007142329,0.08710394,0.16420883,0.00013406192,0.0016229284,0.00057204464,0.4671461],"genre_scores_gemma":[0.018564854,0.29005852,0.008191391,0.031714816,0.14092736,0.00016483339,0.003287072,0.0006772478,0.5064138],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984034,0.00018815279,0.00012305516,0.00029973968,0.00077293604,0.00021281501],"domain_scores_gemma":[0.99635065,0.0006159762,0.0002409142,0.00038894618,0.0016482946,0.0007552426],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017317317,0.00083601824,0.0005264623,0.004437391,0.001013169,0.0058451565,0.0011369323,0.0023474144,0.072616346],"category_scores_gemma":[0.0049314317,0.000268794,0.0005275883,0.0046242126,0.0010201926,0.005653994,0.0023783105,0.003168228,0.056995336],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003295403,0.000036365636,0.00028054824,0.00053085585,0.00001054626,0.000058266283,0.0000596915,0.00013293576,0.000535678,0.018888308,0.8549094,0.12452441],"study_design_scores_gemma":[0.0000018498105,0.000006230976,0.00015218697,0.00012343307,0.0000034299326,0.000054458338,0.000020654388,0.00003998162,0.00011869061,0.0022186923,0.99725693,0.000003398732],"about_ca_topic_score_codex":0.0016937259,"about_ca_topic_score_gemma":0.0038050744,"teacher_disagreement_score":0.92738366,"about_ca_system_score_codex":0.002372045,"about_ca_system_score_gemma":0.0020479409,"threshold_uncertainty_score":0.24292588},"labels":[],"label_agreement":null},{"id":"W2195130835","doi":"","title":"BAM Learning in High Level of Connection Sparseness.","year":2014,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Curse of dimensionality; Computer science; Artificial intelligence; Recall; Convergence (economics); Artificial neural network; Associative property; Machine learning; Connection (principal bundle); Content-addressable memory; Associative learning; Mathematics; Cognitive psychology; Psychology","score_opus":0.1606850996455916,"score_gpt":0.36657954996822034,"score_spread":0.20589445032262874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2195130835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15443207,0.00066786143,0.83738446,0.00084311573,0.00009066302,0.000050025297,0.00017451422,0.00040998938,0.005947244],"genre_scores_gemma":[0.93621874,0.0004127096,0.059828795,0.00020655003,0.00006186156,0.00008451079,0.00017704064,0.000038902836,0.0029710035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995209,0.0001798403,0.000027730553,0.00010344758,0.000105114625,0.00006296075],"domain_scores_gemma":[0.9945258,0.0035236054,0.0005995681,0.00067509426,0.00047008335,0.00020593042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014881039,0.00037320209,0.0008708473,0.00041971312,0.00038721634,0.0010314039,0.0010752805,0.0010218584,0.0022893974],"category_scores_gemma":[0.012727116,0.00043734672,0.0005023135,0.00042368117,0.001257025,0.0028749807,0.0013882285,0.0014556304,0.00033370766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034526127,0.00014016987,0.0048211827,0.00039240107,0.00015881966,0.00041423767,0.0004419679,0.61472785,0.018654086,0.29150057,0.0031263707,0.065277025],"study_design_scores_gemma":[0.00001321833,0.000054684162,0.00050312496,0.000015265801,0.000015450552,0.00010147655,0.000031494605,0.86546403,0.0016976435,0.13133815,0.0007547148,0.000010780022],"about_ca_topic_score_codex":0.0011767794,"about_ca_topic_score_gemma":0.0015305913,"teacher_disagreement_score":0.0022893974,"about_ca_system_score_codex":0.00073018076,"about_ca_system_score_gemma":0.0004945476,"threshold_uncertainty_score":0.007869959},"labels":[],"label_agreement":null},{"id":"W2204573956","doi":"10.1080/18756891.2015.1129591","title":"Archimedean-Compensatory Fuzzy Logic Systems","year":2015,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fuzzy logic; Mathematics; Algebra over a field; Computer science; Artificial intelligence; Pure mathematics","score_opus":0.06622067931955804,"score_gpt":0.32835018305344366,"score_spread":0.2621295037338856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204573956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056810282,0.0006612391,0.90935695,0.00069383194,0.00024705468,0.0001033979,0.000114803486,0.00031037288,0.03170212],"genre_scores_gemma":[0.7958566,0.00043961869,0.18969274,0.0003563903,0.00013473033,0.0001747962,0.00013055722,0.000025875552,0.013188768],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99862385,0.00035182672,0.00012094207,0.00028348906,0.00050824205,0.00011174778],"domain_scores_gemma":[0.9991334,0.00020950452,0.00013413826,0.000102857084,0.0003334814,0.0000866626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017031003,0.00062683667,0.00060767704,0.0010148484,0.0009441589,0.0017690093,0.0011624254,0.0010583395,0.002626521],"category_scores_gemma":[0.0023407193,0.00018320841,0.00073970796,0.00088080385,0.001549368,0.0019818745,0.0015175664,0.0011588598,0.00042356545],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065713255,0.00003529002,0.00035423128,0.00008951937,0.000041988627,0.00035357533,0.00018413924,0.025248721,0.0069156415,0.9403139,0.0009024373,0.02549484],"study_design_scores_gemma":[0.000040920975,0.0001283228,0.00033299768,0.00003276692,0.000038992664,0.00050156674,0.00012860839,0.3640396,0.006501865,0.61762154,0.010585461,0.000047356752],"about_ca_topic_score_codex":0.00093898975,"about_ca_topic_score_gemma":0.001063254,"teacher_disagreement_score":0.002626521,"about_ca_system_score_codex":0.001245928,"about_ca_system_score_gemma":0.0011329348,"threshold_uncertainty_score":0.009039879},"labels":[],"label_agreement":null},{"id":"W2204630551","doi":"10.1177/147470490500300119","title":"Book Review: A Major Feat of Intelligent Synthesis","year":2005,"lang":"en","type":"article","venue":"Evolutionary Psychology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Psychology; Cognitive science; History","score_opus":0.01667264818860233,"score_gpt":0.30489911047757945,"score_spread":0.28822646228897714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204630551","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016015114,0.31382057,0.021245353,0.14737102,0.16222478,0.00010892776,0.0005967909,0.000820081,0.352211],"genre_scores_gemma":[0.011082685,0.07552847,0.00889834,0.027345443,0.054735485,0.00008142386,0.0003826444,0.0006307861,0.82131475],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99934083,0.00009932247,0.000026915774,0.00010187632,0.00039523558,0.00003569922],"domain_scores_gemma":[0.9974184,0.0010776807,0.00009783689,0.0001477407,0.0009901571,0.00026813816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010075547,0.00055779953,0.00061738776,0.001444009,0.0007168046,0.003468231,0.0007746222,0.0013505323,0.057580303],"category_scores_gemma":[0.0047695874,0.00024655188,0.00038717408,0.0013155418,0.0008892463,0.0023907414,0.0008858821,0.0023873704,0.03449572],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012418502,0.0000107248125,0.00006055577,0.00015923462,0.00000984349,0.000020013595,0.00002293415,0.00019077401,0.00017582688,0.015883576,0.869925,0.11352908],"study_design_scores_gemma":[0.0000038257717,0.0000080126065,0.00006964824,0.000071619244,0.000005754963,0.000051513238,0.000010695183,0.00013170508,0.000092159695,0.0051990068,0.994352,0.0000040841965],"about_ca_topic_score_codex":0.0009548213,"about_ca_topic_score_gemma":0.0029030389,"teacher_disagreement_score":0.057580303,"about_ca_system_score_codex":0.0014968758,"about_ca_system_score_gemma":0.0018204708,"threshold_uncertainty_score":0.19262534},"labels":[],"label_agreement":null},{"id":"W2214733097","doi":"10.1023/b:mach.0000019803.57720.7b","title":"Classification Using Φ-Machines and Constructive Function Approximation","year":2004,"lang":"en","type":"article","venue":"Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Cleveland Clinic Foundation; Cleveland Clinic; National Science Foundation","keywords":"Clef; Computer science; Support vector machine; Artificial intelligence; Decision tree; Machine learning; Classifier (UML); Constructive; Artificial neural network; Linear classifier","score_opus":0.020357497995076457,"score_gpt":0.251239282618204,"score_spread":0.23088178462312753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2214733097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002597087,0.00040705007,0.9955511,0.00019666298,0.000045197383,0.0000137590105,0.000010549177,0.000111322166,0.0010672195],"genre_scores_gemma":[0.26462516,0.0011789277,0.7295685,0.00029036764,0.00045130655,0.00022942649,0.000176236,0.00015557639,0.0033246113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643886,0.0022298603,0.00014435432,0.00036722695,0.00068122544,0.00013855069],"domain_scores_gemma":[0.98583686,0.011114678,0.00066589273,0.0010935256,0.0011258185,0.00016321329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050503765,0.0010087865,0.0019444384,0.0026335744,0.00091731735,0.002589309,0.0026329884,0.002412258,0.0026437466],"category_scores_gemma":[0.024643555,0.0008052725,0.0019927192,0.002228785,0.0036939671,0.0037030808,0.0027815215,0.0027578531,0.0010604439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009767932,0.000050758365,0.0008105491,0.0002218995,0.00007526373,0.00009545408,0.00015207824,0.20137313,0.0015676961,0.6148785,0.0027280534,0.17794901],"study_design_scores_gemma":[0.000010726688,0.000022201959,0.000109760964,0.00002624784,0.000015334823,0.000059985203,0.000020782314,0.68150526,0.00073159987,0.3158835,0.001604125,0.000010461441],"about_ca_topic_score_codex":0.00077119586,"about_ca_topic_score_gemma":0.00073365046,"teacher_disagreement_score":0.0050503765,"about_ca_system_score_codex":0.0014766635,"about_ca_system_score_gemma":0.00082984735,"threshold_uncertainty_score":0.026709259},"labels":[],"label_agreement":null},{"id":"W2215574653","doi":"","title":"CONTINUOUS MISSING PARTICIPANT DATA IN RANDOMIZED CONTROLLED TRIALS","year":2015,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Missing data; Randomized controlled trial; Psychology; Medicine; Statistics; Mathematics; Internal medicine","score_opus":0.08038496187836154,"score_gpt":0.3027176385456857,"score_spread":0.22233267666732415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2215574653","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":"methods","prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021796238,0.29212478,0.5797278,0.04718327,0.0080327345,0.03194779,0.0067420024,0.0011625069,0.011282849],"genre_scores_gemma":[0.31699812,0.055755258,0.48407084,0.02365039,0.0035366449,0.11127937,0.0031355254,0.00041665306,0.0011572428],"study_design_codex":"systematic_review","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.061462738,0.7699587,0.12472521,0.012249794,0.030781312,0.0008223071],"domain_scores_gemma":[0.019853255,0.8680915,0.077419676,0.020685254,0.013202464,0.0007478928],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.68428594,0.0026728674,0.007995957,0.010382223,0.0029491407,0.010954519,0.008203798,0.010753997,0.007045955],"category_scores_gemma":[0.9074521,0.0033949306,0.012354757,0.013340222,0.01587824,0.011452067,0.00798373,0.011086815,0.0015166844],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006248325,0.00037364935,0.03741558,0.41906995,0.019810783,0.000715629,0.016360085,0.012914054,0.0006894225,0.12397553,0.026790539,0.33563653],"study_design_scores_gemma":[0.007088499,0.0038948746,0.02041894,0.46311712,0.0133101875,0.0026276694,0.0028813828,0.025560094,0.0034859465,0.3538133,0.10277039,0.0010316257],"about_ca_topic_score_codex":0.0017448273,"about_ca_topic_score_gemma":0.0012620252,"teacher_disagreement_score":0.31571406,"about_ca_system_score_codex":0.010261399,"about_ca_system_score_gemma":0.025760895,"threshold_uncertainty_score":0.3893317},"labels":[],"label_agreement":null},{"id":"W2215823012","doi":"10.5555/1999416.1999457","title":"Accelerating the computation of parallel trajectories of gradient descent with the Cell-BE multiprocessor environment","year":2010,"lang":"en","type":"article","venue":"Summer Computer Simulation Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Stochastic gradient descent; Multiprocessing; Gradient descent; Computation; Parallel computing; Series (stratigraphy); Function (biology); Artificial neural network; Tracing; Algorithm; Parallel algorithm; Ray tracing (physics); Function approximation; Artificial intelligence","score_opus":0.04443369841855124,"score_gpt":0.2592793533070587,"score_spread":0.21484565488850746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2215823012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08374322,0.00012698238,0.90927494,0.00018516877,0.000057202655,0.000050289396,0.00008393689,0.0028383436,0.003639917],"genre_scores_gemma":[0.5234409,0.000100625926,0.47306195,0.000053102347,0.000011797749,0.00020083073,0.00023505681,0.00024584596,0.0026499294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980944,0.00005366133,0.000010046278,0.000031338575,0.00006108832,0.00003443636],"domain_scores_gemma":[0.9994653,0.00020281474,0.000034108303,0.00014254729,0.00011565947,0.00003960827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037122561,0.0005064365,0.00054796634,0.0002896117,0.00040611098,0.0005894804,0.00093886897,0.00060886407,0.0029075216],"category_scores_gemma":[0.0019808996,0.00029814287,0.00029641797,0.0004638446,0.0003459624,0.00075761665,0.0006518976,0.00075963984,0.00067178276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003718458,0.00012727098,0.0025482154,0.00008062233,0.000055782162,0.00022826408,0.00016376883,0.88029265,0.016024096,0.018502872,0.0029304714,0.07867415],"study_design_scores_gemma":[0.000016751615,0.000018845238,0.0001156661,0.0000018896914,0.000003434379,0.000014536385,0.0000067913797,0.99341875,0.003579412,0.0019014066,0.00091890915,0.0000035047556],"about_ca_topic_score_codex":0.004969658,"about_ca_topic_score_gemma":0.006429241,"teacher_disagreement_score":0.004969658,"about_ca_system_score_codex":0.0004892537,"about_ca_system_score_gemma":0.0008284604,"threshold_uncertainty_score":0.009881437},"labels":[],"label_agreement":null},{"id":"W2218318129","doi":"","title":"Contractive Auto-Encoders: Explicit Invariance During Feature Extraction","year":2011,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1136,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Encoder; Jacobian matrix and determinant; Autoencoder; Algorithm; Matrix norm; Pattern recognition (psychology); Feature extraction; Range (aeronautics); Representation (politics); Term (time); Artificial intelligence; Mathematics; Mathematical optimization; Artificial neural network; Eigenvalues and eigenvectors; Applied mathematics","score_opus":0.03152472788549156,"score_gpt":0.24440166149488252,"score_spread":0.21287693360939097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2218318129","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018703928,0.00009745976,0.9795924,0.00006121917,0.000019120558,0.000024764946,0.00003260436,0.000642761,0.00082577503],"genre_scores_gemma":[0.50412595,0.00019672982,0.49091002,0.00016020107,0.00004370683,0.00013845896,0.00024618645,0.0002940783,0.0038846089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999433,0.0001494949,0.000039326133,0.00014263474,0.00016525782,0.00007034262],"domain_scores_gemma":[0.99845886,0.0007288996,0.00014406201,0.00046891122,0.0001598237,0.00003945365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014624811,0.0007574185,0.00059825584,0.0003250148,0.00025810458,0.0006465772,0.0011155211,0.00073367637,0.0016289908],"category_scores_gemma":[0.005146147,0.0006000975,0.00058757997,0.00038541088,0.00079278956,0.0013177332,0.0012258798,0.0014608804,0.00081845245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018907433,0.00013572356,0.0018575703,0.00012567252,0.00009112709,0.00015322924,0.00019270605,0.4224046,0.07129134,0.02579818,0.0019255497,0.4758352],"study_design_scores_gemma":[0.00000595747,0.000049261707,0.00035942436,0.000008294011,0.000009806905,0.000057547146,0.000011119993,0.9723914,0.020765204,0.0050708763,0.0012604102,0.000010588533],"about_ca_topic_score_codex":0.001829166,"about_ca_topic_score_gemma":0.0033611166,"teacher_disagreement_score":0.001829166,"about_ca_system_score_codex":0.00042942865,"about_ca_system_score_gemma":0.0007271819,"threshold_uncertainty_score":0.007734418},"labels":[],"label_agreement":null},{"id":"W2221432182","doi":"10.4236/ojs.2015.57068","title":"Statistical Classification Using the Maximum Function","year":2015,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Linear discriminant analysis; Cluster analysis; Discriminant function analysis; Mathematics; Extension (predicate logic); Function (biology); Pattern recognition (psychology); Statistical hypothesis testing; Computer science; Artificial intelligence; Statistics; Algorithm","score_opus":0.1598425714878807,"score_gpt":0.35332995444597515,"score_spread":0.19348738295809445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2221432182","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030237809,0.00069090945,0.9938112,0.0002330349,0.000040070274,0.000020483429,0.0000807921,0.0003409493,0.0017587361],"genre_scores_gemma":[0.25501388,0.002134652,0.7371047,0.00041435217,0.0006907486,0.0004647905,0.0005669473,0.00039725206,0.0032126608],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967933,0.0015028416,0.00019367506,0.0005484868,0.00077118777,0.0001904952],"domain_scores_gemma":[0.9947385,0.0037488423,0.00038562246,0.0006224353,0.00038501946,0.00011959375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004996874,0.0010937101,0.0016805521,0.004333501,0.0010996405,0.0033057781,0.0017697293,0.0021068237,0.0034213413],"category_scores_gemma":[0.01521102,0.00051202724,0.0015424158,0.0034354583,0.0031439594,0.004663635,0.0028744107,0.0025297885,0.0021867028],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021018511,0.00006479297,0.0020294664,0.00040310057,0.00013414434,0.00016845635,0.0002909814,0.10292239,0.005474414,0.5292936,0.0072568557,0.35175163],"study_design_scores_gemma":[0.000014561497,0.000059172824,0.0009128505,0.00008816641,0.000026784252,0.00021134054,0.000035128855,0.5596225,0.0032021583,0.4257288,0.010050265,0.0000482797],"about_ca_topic_score_codex":0.00060597435,"about_ca_topic_score_gemma":0.00029849695,"teacher_disagreement_score":0.004996874,"about_ca_system_score_codex":0.0010489179,"about_ca_system_score_gemma":0.0010613969,"threshold_uncertainty_score":0.026426375},"labels":[],"label_agreement":null},{"id":"W2222323524","doi":"10.1007/978-4-431-65955-6_19","title":"Nonlinear Generalized Canonical Correlation Analysis by Neural Network Models","year":2002,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Canonical correlation; Transformation (genetics); Nonlinear system; Integrator; Set (abstract data type); Computer science; Multivariate statistics; Artificial neural network; Correlation; Canonical analysis; Algorithm; Mathematics; Data mining; Theoretical computer science; Artificial intelligence; Machine learning; Geometry; Physics","score_opus":0.02753410110429462,"score_gpt":0.23388257747938926,"score_spread":0.20634847637509462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2222323524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037958599,0.0037422013,0.97490644,0.00036967627,0.00034547114,0.000016123713,0.00010366309,0.00026431918,0.01645619],"genre_scores_gemma":[0.3396671,0.015003413,0.5347852,0.0005586453,0.0013783658,0.00021883207,0.00088022114,0.0010313105,0.10647698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962175,0.00014649327,0.000014154924,0.00007718335,0.00011842045,0.00002196633],"domain_scores_gemma":[0.99954563,0.00019357781,0.00004262462,0.0000835934,0.00011681123,0.000017785198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064545945,0.00085198035,0.00094667595,0.0007246808,0.00034498997,0.0011426329,0.0007974181,0.0007266322,0.0037631926],"category_scores_gemma":[0.0021152957,0.00046994173,0.00072595116,0.0015295917,0.001164479,0.0015530495,0.000884305,0.0011679344,0.001316991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022792225,0.000013386788,0.0001948971,0.00015472734,0.00005763709,0.00006079538,0.000051965006,0.11107066,0.0015352457,0.7902106,0.013986214,0.08264114],"study_design_scores_gemma":[0.0000035348885,0.0000075831904,0.00018802518,0.000021829353,0.000014821758,0.000063873835,0.000010980592,0.5534889,0.00075652986,0.43281856,0.012601042,0.00002437657],"about_ca_topic_score_codex":0.002525337,"about_ca_topic_score_gemma":0.0029615331,"teacher_disagreement_score":0.0037631926,"about_ca_system_score_codex":0.0007390167,"about_ca_system_score_gemma":0.0006924737,"threshold_uncertainty_score":0.012589157},"labels":[],"label_agreement":null},{"id":"W2226312161","doi":"10.71781/26042","title":"Développement d'un système d'enregistrement des neurones péri et intracardiaques et études préliminaires","year":2007,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Art","score_opus":0.1024742439273392,"score_gpt":0.38770049899350084,"score_spread":0.2852262550661616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2226312161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13239938,0.0016369757,0.85357976,0.00044103563,0.00032759094,0.00042600703,0.00032942588,0.005977116,0.004882779],"genre_scores_gemma":[0.3826339,0.0009530013,0.60036165,0.0002598962,0.000117725474,0.00059311395,0.00045164436,0.00032555332,0.014303541],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952435,0.00006866125,0.000024450886,0.00018799648,0.00013547274,0.00005915076],"domain_scores_gemma":[0.9989598,0.00045061452,0.000046650704,0.00013527516,0.0003400405,0.000067683155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001643023,0.0007519328,0.0008513052,0.00055863964,0.0006743759,0.0018446248,0.0012505042,0.0014524112,0.004301381],"category_scores_gemma":[0.003259638,0.0005627319,0.0006455468,0.0005216659,0.0005274344,0.0015299442,0.00084897946,0.0007931602,0.0015564783],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068083976,0.00015137748,0.004342109,0.00034033434,0.00015789148,0.00039113712,0.00092384533,0.015691612,0.42968524,0.0045228386,0.0024833244,0.5406294],"study_design_scores_gemma":[0.00032004135,0.0012362615,0.028839832,0.0001687829,0.00044220997,0.0015464931,0.00050572405,0.42973194,0.47846603,0.0058111334,0.052773006,0.00015853345],"about_ca_topic_score_codex":0.012600376,"about_ca_topic_score_gemma":0.0098920325,"teacher_disagreement_score":0.012600376,"about_ca_system_score_codex":0.00086972694,"about_ca_system_score_gemma":0.0013300951,"threshold_uncertainty_score":0.025054038},"labels":[],"label_agreement":null},{"id":"W222640773","doi":"","title":"Les réseaux de neurones artificiels et leurs applications en imagerie et en vision par ordinateur","year":2003,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Humanities; Art","score_opus":0.034475814971397116,"score_gpt":0.2864565693351731,"score_spread":0.251980754363776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W222640773","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43526483,0.016766584,0.51468205,0.0023804838,0.0009353642,0.000073766765,0.00016561647,0.0011818024,0.028549425],"genre_scores_gemma":[0.87827325,0.007662761,0.06847858,0.0002349715,0.00030637643,0.000061318024,0.00014377979,0.00023352768,0.04460562],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999569,0.00009711732,0.000018329098,0.000079426856,0.00019988697,0.000036224777],"domain_scores_gemma":[0.99919254,0.0003808423,0.000057173518,0.00007406519,0.00024460553,0.00005077466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084925495,0.00076462055,0.00047906107,0.00056405395,0.0005295151,0.0015776386,0.00058564794,0.0014140536,0.0034632196],"category_scores_gemma":[0.0033275099,0.00032119945,0.0004800638,0.00065683125,0.0010512222,0.0013520889,0.00037974966,0.001133718,0.0009808196],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006068749,0.00011948029,0.001747662,0.00061746343,0.00008703193,0.0008292793,0.0007578925,0.14478885,0.4393675,0.0898216,0.0034880484,0.3177683],"study_design_scores_gemma":[0.00009300988,0.00050425355,0.0063412753,0.00009288748,0.00007588372,0.001777744,0.0005395023,0.6215343,0.29005235,0.04653474,0.032351714,0.000102244834],"about_ca_topic_score_codex":0.0054394635,"about_ca_topic_score_gemma":0.0031342623,"teacher_disagreement_score":0.0054394635,"about_ca_system_score_codex":0.0008957774,"about_ca_system_score_gemma":0.00028483086,"threshold_uncertainty_score":0.011585593},"labels":[],"label_agreement":null},{"id":"W2232680801","doi":"10.6084/m9.figshare.1187113.v4","title":"Lab 3: Techniques &amp; data: field sample training with animals (pans, pitfalls, sweeps, and distances) - Distance Based Dataset","year":2014,"lang":"en","type":"article","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Training (meteorology); Sample (material); Field (mathematics); Geography; Computer science; Mathematics; Meteorology; Physics","score_opus":0.07970254693977186,"score_gpt":0.2948278131189302,"score_spread":0.21512526617915834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2232680801","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049151145,0.000042532836,0.0027508603,0.000123907,0.000080187354,0.00020212423,0.9858043,0.0038466887,0.0022343548],"genre_scores_gemma":[0.008371164,0.000033460587,0.009631196,0.00012501657,0.00002042919,0.001118212,0.97744364,0.00034837783,0.0029084429],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99908197,0.00011595543,0.00010465555,0.00034402226,0.00022026352,0.00013311178],"domain_scores_gemma":[0.99736077,0.0003992725,0.00021239699,0.00072901166,0.0010891078,0.00020949413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011967157,0.0013390426,0.00087770744,0.0013424117,0.0005594936,0.00078983285,0.0018810754,0.0010531839,0.049711462],"category_scores_gemma":[0.003902704,0.00040095893,0.0007402287,0.0015280967,0.0003436263,0.0009700171,0.0013317195,0.0012213722,0.067961544],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031253838,0.0003254671,0.013571614,0.0006094775,0.000049048755,0.00008649569,0.00013081651,0.0017653679,0.0019063237,0.0004010744,0.9422766,0.038565177],"study_design_scores_gemma":[0.00054146844,0.0003429561,0.08084597,0.00038364527,0.000072459,0.00023212521,0.0008670586,0.013006655,0.006887479,0.0028623743,0.8938036,0.00015414934],"about_ca_topic_score_codex":0.011143824,"about_ca_topic_score_gemma":0.026038202,"teacher_disagreement_score":0.049711462,"about_ca_system_score_codex":0.00075379765,"about_ca_system_score_gemma":0.0011153696,"threshold_uncertainty_score":0.16630143},"labels":[],"label_agreement":null},{"id":"W2235065339","doi":"10.1007/978-0-387-39940-9_2930","title":"Knowledge-based Systems","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.022464314573431107,"score_gpt":0.2506218538700801,"score_spread":0.228157539296649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2235065339","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027934245,0.020819383,0.29088905,0.0046792384,0.001356554,0.00030499318,0.0017508565,0.0042858636,0.6731207],"genre_scores_gemma":[0.090124875,0.03480564,0.27428946,0.002640913,0.0013045832,0.00041068892,0.009043631,0.0010588347,0.5863214],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992693,0.000092152026,0.00006338283,0.0001829173,0.0003447858,0.000047491198],"domain_scores_gemma":[0.9992316,0.00023576105,0.000026274383,0.000290157,0.00017037356,0.00004581119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006429879,0.0008916176,0.0007846551,0.0012907855,0.00085688353,0.005868918,0.0022800812,0.0014742503,0.05888067],"category_scores_gemma":[0.0022791808,0.0004934947,0.0006071245,0.0023409575,0.0011659925,0.0062009306,0.0024744575,0.0013955855,0.035771254],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000372133,0.000079314435,0.00021775745,0.0005857795,0.00005268115,0.00015101068,0.0002760616,0.002128324,0.002025644,0.31453148,0.1299251,0.54998964],"study_design_scores_gemma":[0.000012517451,0.00002023437,0.00022221424,0.00022079427,0.000024912188,0.0002888816,0.00007432683,0.0051223,0.0017706729,0.21974094,0.7724856,0.000016665845],"about_ca_topic_score_codex":0.0014052009,"about_ca_topic_score_gemma":0.0017606459,"teacher_disagreement_score":0.05888067,"about_ca_system_score_codex":0.000984159,"about_ca_system_score_gemma":0.0011912589,"threshold_uncertainty_score":0.19697547},"labels":[],"label_agreement":null},{"id":"W2239461856","doi":"10.5539/mas.v10n1p154","title":"Mean Field Theory in Doing Logic Programming Using Hopfield Network","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Sains Malaysia","keywords":"Maxima and minima; Artificial neural network; Computer science; NetLogo; Field (mathematics); Hopfield network; Boltzmann machine; Artificial intelligence; Logic programming; Representation (politics); Theoretical computer science; Algorithm; Mathematics","score_opus":0.048105190781374665,"score_gpt":0.28597479379494123,"score_spread":0.23786960301356658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2239461856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01078849,0.00075769384,0.9779229,0.0005419039,0.000057824192,0.000028212668,0.00003482162,0.00014356243,0.009724631],"genre_scores_gemma":[0.59347475,0.002067474,0.38687786,0.00035393456,0.00017430916,0.00024567402,0.0001220534,0.00010680914,0.016577186],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998085,0.000057267443,0.000011670502,0.000040841143,0.000062435385,0.000019307194],"domain_scores_gemma":[0.9997918,0.00012145406,0.000020619984,0.000015960413,0.000034087316,0.00001612282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052794395,0.00030233111,0.00044492885,0.0004985045,0.0005139599,0.0010980055,0.0007543223,0.0007250541,0.0028770976],"category_scores_gemma":[0.0010271554,0.00022287341,0.0006456966,0.0005511191,0.0010726444,0.0015466874,0.00063805754,0.00097466767,0.00039886712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004540955,0.000037879745,0.00068964605,0.00015378343,0.000039374354,0.0001476373,0.00017610805,0.39396766,0.0032647252,0.54088765,0.0015836519,0.059006434],"study_design_scores_gemma":[0.000009023742,0.000017689428,0.000090883215,0.000014847445,0.000009380872,0.000031325057,0.000011345487,0.7548394,0.00061058044,0.24183358,0.0025207507,0.0000111807185],"about_ca_topic_score_codex":0.0028739145,"about_ca_topic_score_gemma":0.0023254966,"teacher_disagreement_score":0.0028770976,"about_ca_system_score_codex":0.0008498552,"about_ca_system_score_gemma":0.0008426576,"threshold_uncertainty_score":0.009624839},"labels":[],"label_agreement":null},{"id":"W2243397763","doi":"","title":"Caractérisation pratique des systèmes quantiques et mémoires quantiques auto-correctrices 2D","year":2013,"lang":"fr","type":"article","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science","score_opus":0.021898872064098865,"score_gpt":0.23552141138822363,"score_spread":0.21362253932412478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2243397763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02990903,0.0013748142,0.92091185,0.0021869764,0.00071012747,0.000112933,0.000397542,0.002201201,0.042195536],"genre_scores_gemma":[0.5809081,0.0017694718,0.33860898,0.001091242,0.00023916659,0.00033168058,0.0006710437,0.0009947419,0.07538562],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99857473,0.00024464904,0.000080762286,0.0003142072,0.0006702389,0.00011553129],"domain_scores_gemma":[0.99788,0.0008999591,0.00012984878,0.0004699285,0.0005624367,0.000057848654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017319863,0.0007132361,0.0008502905,0.0006465002,0.0012390681,0.0033684082,0.0016316451,0.0014153537,0.02576769],"category_scores_gemma":[0.0060532656,0.00067909074,0.0008234567,0.00051426253,0.0018097846,0.0038563197,0.0021444927,0.0018734701,0.0028472955],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002929417,0.00006739057,0.0016759377,0.0008240106,0.000086309,0.0003267457,0.001177763,0.1292387,0.040862266,0.6919956,0.008993618,0.12445872],"study_design_scores_gemma":[0.00009739863,0.00012299619,0.0009665786,0.00016307189,0.00006000178,0.00048592777,0.0004618689,0.6269224,0.036875553,0.24339455,0.09034367,0.00010587757],"about_ca_topic_score_codex":0.006297056,"about_ca_topic_score_gemma":0.003963917,"teacher_disagreement_score":0.02576769,"about_ca_system_score_codex":0.0018591533,"about_ca_system_score_gemma":0.0014668215,"threshold_uncertainty_score":0.08620149},"labels":[],"label_agreement":null},{"id":"W2244411073","doi":"10.1007/978-3-319-09274-4_29","title":"System Factorial Technology Applied to Artificial Neural Network Information Processing","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Categorization; Computer science; Information processing; Factorial; Artificial neural network; Recall; Artificial intelligence; Architecture; Serial memory processing; Cognition; Task (project management); Process (computing); Parallel processing; Cognitive architecture; Machine learning; Pattern recognition (psychology); Parallel computing; Mathematics; Programming language; Psychology; Engineering; Cognitive psychology","score_opus":0.011777703639591571,"score_gpt":0.22514326148419767,"score_spread":0.2133655578446061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2244411073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050146487,0.00058644597,0.989962,0.00012586823,0.00015328119,0.000052371197,0.00007131869,0.0004346118,0.0035995864],"genre_scores_gemma":[0.20484583,0.0008725023,0.78483975,0.0001076595,0.0001880588,0.00029806414,0.00023948094,0.0002205209,0.008388186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887234,0.0005182814,0.00007657567,0.00021182152,0.0002628329,0.00005815799],"domain_scores_gemma":[0.99850816,0.00094970356,0.00006308451,0.00015669025,0.00029531142,0.000026990776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017010731,0.00053229445,0.00083334505,0.0007090816,0.0005465457,0.0013916848,0.0007031364,0.000673669,0.011008607],"category_scores_gemma":[0.005163708,0.0004467527,0.0008806784,0.0014318144,0.00082754315,0.0013324509,0.0006976966,0.0012026563,0.0015929003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037649382,0.00013492069,0.0009866232,0.0007125831,0.00018729382,0.00015881693,0.0002454265,0.11215938,0.03279323,0.40728733,0.0057833754,0.43917453],"study_design_scores_gemma":[0.00002885438,0.00019878217,0.00089667854,0.000031522206,0.000059947117,0.0000871532,0.000028900286,0.8440891,0.011018922,0.13204047,0.0114802,0.000039445917],"about_ca_topic_score_codex":0.0018646838,"about_ca_topic_score_gemma":0.0020015722,"teacher_disagreement_score":0.011008607,"about_ca_system_score_codex":0.0009108342,"about_ca_system_score_gemma":0.00075802545,"threshold_uncertainty_score":0.036827505},"labels":[],"label_agreement":null},{"id":"W2246796389","doi":"","title":"Programmable Triangular Neighborhood Functions of Kohonen Self-Organizing Maps Realized in CMOS Technology","year":2010,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Self-organizing map; Computer science; Asynchronous communication; Function (biology); Gaussian; Transistor; Artificial neural network; Digital electronics; CMOS; Computer hardware; Topology (electrical circuits); Electronic circuit; Electronic engineering; Artificial intelligence; Electrical engineering; Engineering; Telecommunications; Voltage","score_opus":0.005508936546801232,"score_gpt":0.2324075497704667,"score_spread":0.22689861322366547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2246796389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20146471,0.00064544036,0.7800009,0.00016048932,0.00014772933,0.000072002054,0.0001165082,0.001338426,0.01605377],"genre_scores_gemma":[0.86579436,0.000153464,0.13093472,0.00005060556,0.000011601079,0.00004062414,0.000050400962,0.000036368776,0.0029278786],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999343,0.000010474488,0.0000036841466,0.000011521366,0.000028162454,0.000011884248],"domain_scores_gemma":[0.99993753,0.00002085793,0.000007269098,0.000008887026,0.00001898267,0.0000064766004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009347578,0.00013713622,0.00014867564,0.00014336113,0.00016262876,0.0003317543,0.0005519536,0.00027033503,0.0014044798],"category_scores_gemma":[0.0002722997,0.000074937714,0.00015878711,0.00017093032,0.00017594679,0.00036257226,0.0002024554,0.0001708912,0.00026087125],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005619994,0.00013193038,0.0014153668,0.0004922174,0.00006272968,0.00068560976,0.000309744,0.1095891,0.4273386,0.07577572,0.0033449838,0.3802921],"study_design_scores_gemma":[0.000034493667,0.00030003404,0.0010410509,0.000027776005,0.00003195078,0.0004942345,0.00007278614,0.7990848,0.1679285,0.019451683,0.011498493,0.00003437102],"about_ca_topic_score_codex":0.0005804484,"about_ca_topic_score_gemma":0.00095767435,"teacher_disagreement_score":0.0014044798,"about_ca_system_score_codex":0.00023992854,"about_ca_system_score_gemma":0.0001503181,"threshold_uncertainty_score":0.004698515},"labels":[],"label_agreement":null},{"id":"W2248324223","doi":"","title":"A New Fast Training Algorithm of the WTM Kohonen Neural Network Implemented for Classification of Biomedical Signals","year":2009,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Computer science; Algorithm; Quantization (signal processing); Self-organizing map; Nonlinear system; Process (computing); Artificial intelligence; Convergence (economics); Maxima and minima; Mathematics","score_opus":0.02798134546479424,"score_gpt":0.2908150869729686,"score_spread":0.26283374150817435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2248324223","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003365598,0.0001877299,0.99482924,0.00004829945,0.00007910468,0.000040579886,0.000026284884,0.0005560587,0.0008671134],"genre_scores_gemma":[0.098949276,0.00034117702,0.89457816,0.000096965785,0.00004804936,0.00023661868,0.00017714215,0.00014606795,0.0054265964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970835,0.00003410469,0.000020640988,0.00006850499,0.00014030971,0.000028131504],"domain_scores_gemma":[0.99975127,0.000058903748,0.000022007405,0.00003639744,0.00012196355,0.000009458013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005123929,0.000528722,0.0005218372,0.00044671271,0.00035280554,0.0005711597,0.0011371071,0.0007946308,0.0027432928],"category_scores_gemma":[0.0011508228,0.00039233323,0.00044483342,0.00062057783,0.00028640154,0.0011509336,0.00046994243,0.00091196486,0.0009877924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011995249,0.000057917903,0.00075750443,0.00023986782,0.0000838523,0.00010406884,0.00010652712,0.10221029,0.04829414,0.011649719,0.003283572,0.83309263],"study_design_scores_gemma":[0.000018763807,0.000058786205,0.00044626748,0.000017577546,0.000018038938,0.00011476022,0.000012287664,0.9754504,0.01446976,0.002503642,0.006870255,0.000019531193],"about_ca_topic_score_codex":0.0028510373,"about_ca_topic_score_gemma":0.004423877,"teacher_disagreement_score":0.0028510373,"about_ca_system_score_codex":0.00043109612,"about_ca_system_score_gemma":0.00075444666,"threshold_uncertainty_score":0.009177268},"labels":[],"label_agreement":null},{"id":"W2248371515","doi":"","title":"CMOS implementation of Conscience Mechanism in Kohonen\"s Neural Network","year":2008,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Self-organizing map; CMOS; Artificial neural network; Conscience; Mechanism (biology); Computer science; Artificial intelligence; Electronic engineering; Engineering; Political science; Philosophy; Epistemology; Law","score_opus":0.016058869544181023,"score_gpt":0.2697528522578753,"score_spread":0.2536939827136943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2248371515","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1375107,0.0022066592,0.7593879,0.0012557791,0.0012439286,0.00013820111,0.00031559786,0.003446741,0.094494544],"genre_scores_gemma":[0.9332391,0.0002622748,0.057981506,0.0001375809,0.000036899244,0.000039354258,0.00006821825,0.000032337368,0.008202609],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992895,0.000007926138,0.0000048989086,0.000014281653,0.000028902416,0.000015099372],"domain_scores_gemma":[0.99993396,0.00001085499,0.0000041225853,0.000012520413,0.000031168285,0.0000073445103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102906364,0.0001451212,0.00016212356,0.00014783339,0.00037777168,0.0006007353,0.0007828167,0.0003743887,0.006055019],"category_scores_gemma":[0.00031742555,0.000078031,0.00014652793,0.0001590829,0.00019606652,0.00058442843,0.00029229184,0.00030627375,0.0006790089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005207641,0.00024129022,0.0017764448,0.0005609008,0.00009822729,0.0005273576,0.00037997987,0.061329477,0.18236966,0.24032944,0.012783137,0.49908328],"study_design_scores_gemma":[0.00011062672,0.00032208415,0.0020983086,0.00008925779,0.000057423207,0.00044734022,0.00012990796,0.7862999,0.110088296,0.07047395,0.029823327,0.000059568156],"about_ca_topic_score_codex":0.001665655,"about_ca_topic_score_gemma":0.0030589304,"teacher_disagreement_score":0.006055019,"about_ca_system_score_codex":0.00030206487,"about_ca_system_score_gemma":0.00036479224,"threshold_uncertainty_score":0.020256102},"labels":[],"label_agreement":null},{"id":"W2249255386","doi":"","title":"Hardware Implementation Issues of the Neighborhood Mechanism in Kohonen Self Organized Feature Maps","year":2009,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Self-organizing map; Artificial neural network; Quantization (signal processing); Computer science; Realization (probability); RADIUS; Minification; Dissipation; Reduction (mathematics); Artificial intelligence; Range (aeronautics); Power (physics); Chip; Feature (linguistics); Algorithm; Mathematics; Engineering; Telecommunications; Statistics; Physics","score_opus":0.00684054560019458,"score_gpt":0.26248988832740405,"score_spread":0.25564934272720946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2249255386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08338915,0.0008301437,0.90886986,0.00035013843,0.00012388443,0.0000546123,0.00003580046,0.0009786298,0.005367832],"genre_scores_gemma":[0.74118936,0.00028236918,0.2556889,0.000060114646,0.000019646546,0.00006275615,0.00003252264,0.00005212819,0.0026122376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986625,0.000025493044,0.000009635505,0.000019442974,0.000063116175,0.000015978232],"domain_scores_gemma":[0.9997149,0.00009135552,0.000016188244,0.00008540541,0.00008081457,0.000011313887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004004723,0.0001285703,0.00028791965,0.00015671285,0.00032773425,0.00052884803,0.0013319269,0.00043325158,0.001956853],"category_scores_gemma":[0.0014110471,0.0001902636,0.00015322714,0.00019263354,0.00026540004,0.0011772459,0.0004217608,0.00034306967,0.00039612103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003956105,0.00013716271,0.0018380368,0.00038026794,0.00008302956,0.0002617181,0.00042928313,0.13971704,0.09385451,0.12832588,0.0027650783,0.63181233],"study_design_scores_gemma":[0.000046293964,0.00025306674,0.0010580934,0.00004983541,0.000027868671,0.00037137297,0.00010133338,0.8739885,0.07801352,0.03419768,0.011852899,0.000039584826],"about_ca_topic_score_codex":0.001099696,"about_ca_topic_score_gemma":0.0018107203,"teacher_disagreement_score":0.001956853,"about_ca_system_score_codex":0.00023563046,"about_ca_system_score_gemma":0.00027828634,"threshold_uncertainty_score":0.0065463185},"labels":[],"label_agreement":null},{"id":"W2252807606","doi":"10.1002/9780470168967.ch14","title":"Granular Models and Human‐Centric Computing","year":2007,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Granular computing; Cluster analysis; Computer science; Fuzzy clustering; Representation (politics); Fuzzy logic; Artificial intelligence; Context (archaeology); Cluster (spacecraft); Data mining; Machine learning; Rough set; Geography","score_opus":0.02466708321776437,"score_gpt":0.2713033275672555,"score_spread":0.24663624434949113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2252807606","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017497683,0.004445183,0.9103171,0.005076519,0.00046747446,0.0000872422,0.00021640974,0.0004039767,0.061488487],"genre_scores_gemma":[0.7543541,0.004999801,0.2193001,0.0005490191,0.000471931,0.00023536036,0.00026260756,0.00015248808,0.019674594],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994312,0.00022602115,0.000027554663,0.00010024254,0.00015510566,0.000059873313],"domain_scores_gemma":[0.9993104,0.00032944843,0.000046292163,0.00017083835,0.00008627846,0.000056712448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092988677,0.0005562609,0.000862029,0.00067089027,0.00059759646,0.0033082583,0.0011683111,0.0012079104,0.004797332],"category_scores_gemma":[0.0022457724,0.00033709884,0.00080603216,0.0013391867,0.0037574768,0.0040342133,0.0014423295,0.002163281,0.00046446867],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060672264,0.000004680527,0.00005167289,0.000029887358,0.000011206284,0.000017667997,0.00009106489,0.024996735,0.00012565013,0.96887344,0.0008106932,0.0049811746],"study_design_scores_gemma":[0.000004131478,0.00000500215,0.00008558126,0.000016534123,0.0000040754508,0.000013511615,0.000043810123,0.055977605,0.000117541334,0.93719476,0.00653056,0.0000069384287],"about_ca_topic_score_codex":0.0047909296,"about_ca_topic_score_gemma":0.0033183629,"teacher_disagreement_score":0.004797332,"about_ca_system_score_codex":0.0021725604,"about_ca_system_score_gemma":0.0010253222,"threshold_uncertainty_score":0.01604867},"labels":[],"label_agreement":null},{"id":"W2255466643","doi":"10.1007/978-3-642-24797-2_4","title":"Long Short-Term Memory","year":2012,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2210,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Recurrent neural network; Term (time); Context (archaeology); Computer science; Range (aeronautics); Artificial intelligence; Artificial neural network; Algorithm; History; Engineering","score_opus":0.13452606323349378,"score_gpt":0.3677383313228128,"score_spread":0.23321226808931902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255466643","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007695724,0.2314809,0.03844207,0.007051306,0.0031040276,0.000060985418,0.00074369856,0.00042022293,0.7110011],"genre_scores_gemma":[0.059001137,0.10610086,0.012673131,0.0021874884,0.0021779973,0.00009914211,0.0011756078,0.00020306007,0.8163816],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99991,0.000010699629,0.000007090351,0.000019908453,0.000043044278,0.000009302402],"domain_scores_gemma":[0.99973696,0.00011270484,0.0000170068,0.000041344996,0.00007179947,0.000020226007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002971528,0.00059273705,0.0004127696,0.0007031179,0.0004435942,0.0019574799,0.0009057037,0.00074190594,0.034200966],"category_scores_gemma":[0.0008355209,0.0001584944,0.00022137987,0.0012933926,0.0009379297,0.0033283164,0.00062568975,0.0011233258,0.013217012],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003511504,0.00003447279,0.00023262111,0.000647117,0.000026239524,0.00007977522,0.0002531924,0.0005203082,0.002224868,0.3039419,0.11320526,0.5787991],"study_design_scores_gemma":[0.000007048848,0.000036731068,0.000743434,0.00038372359,0.000022839573,0.00027441606,0.000109958426,0.0005360837,0.0016897459,0.27989572,0.71628475,0.00001547388],"about_ca_topic_score_codex":0.00074955,"about_ca_topic_score_gemma":0.0017905777,"teacher_disagreement_score":0.034200966,"about_ca_system_score_codex":0.0007349601,"about_ca_system_score_gemma":0.0007698239,"threshold_uncertainty_score":0.11441362},"labels":[],"label_agreement":null},{"id":"W2260772580","doi":"10.1002/0471427950.refs","title":"References and Bibliography","year":2003,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Bibliography; File Transfer Protocol; Citation; Library science; Computer science; World Wide Web; The Internet","score_opus":0.01605672917693366,"score_gpt":0.23886452559164204,"score_spread":0.22280779641470838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2260772580","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006820477,0.02874172,0.009948785,0.0025578227,0.007234631,0.00021079803,0.016798938,0.0028712773,0.9309539],"genre_scores_gemma":[0.0023008527,0.024211917,0.006534058,0.0009624376,0.0013637408,0.00013542634,0.020221762,0.0015812482,0.9426887],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993013,0.00005699567,0.000047316316,0.00009955798,0.0004479319,0.00004688157],"domain_scores_gemma":[0.9986719,0.00029043565,0.000052976116,0.00020069332,0.0006684918,0.000115579125],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005349633,0.0011850812,0.0012792216,0.010326832,0.001152302,0.003132527,0.0016015116,0.00088924804,0.4367426],"category_scores_gemma":[0.0038933835,0.0003334027,0.00067240663,0.016516794,0.00043271563,0.0027881067,0.001427646,0.0011228711,0.42527637],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011003715,0.000028091388,0.00006308332,0.0002767876,0.0000034726318,0.000022757304,0.000014719573,0.00022107837,0.00011986831,0.0019587607,0.8551503,0.14213005],"study_design_scores_gemma":[0.0000028872248,0.000004394945,0.00018017754,0.00021557088,0.000009427797,0.000040889267,0.000017066002,0.00016119512,0.00019764208,0.0021846783,0.9969795,0.000006538615],"about_ca_topic_score_codex":0.010112248,"about_ca_topic_score_gemma":0.02052067,"teacher_disagreement_score":0.5632574,"about_ca_system_score_codex":0.0012079444,"about_ca_system_score_gemma":0.0019415019,"threshold_uncertainty_score":0.8034183},"labels":[],"label_agreement":null},{"id":"W2264780458","doi":"10.5539/cis.v9n1p136","title":"Self-Organizing Map Learning with Momentum","year":2016,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Self-organizing map; Computer science; Momentum (technical analysis); Artificial neural network; Artificial intelligence; Heuristic; Function (biology); Character (mathematics); Mode (computer interface); Mathematics; Geometry","score_opus":0.005315034960735382,"score_gpt":0.1961635551499104,"score_spread":0.19084852018917503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2264780458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017871352,0.00080887205,0.9701057,0.00058863254,0.00029813778,0.00012535971,0.000096128824,0.0021282425,0.007977534],"genre_scores_gemma":[0.6819803,0.0005798683,0.29821658,0.0006471293,0.00031143895,0.00044242793,0.00051666866,0.00035732694,0.016948208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994234,0.0001509058,0.00003374699,0.0001291529,0.00018149083,0.00008137071],"domain_scores_gemma":[0.9989796,0.0004404647,0.000069040005,0.00009770673,0.00035349,0.000059600607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011921495,0.00086327136,0.0011535402,0.0006737502,0.00063952507,0.0014066389,0.0014888169,0.0015099276,0.0046993843],"category_scores_gemma":[0.004337215,0.00056302454,0.00070035196,0.00087723084,0.0006516746,0.0014841909,0.00130068,0.0012182443,0.0015396908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016749073,0.00014111385,0.0011872086,0.00015304066,0.00010386027,0.00014335438,0.000111448346,0.6239274,0.002297119,0.014376447,0.012367764,0.3450237],"study_design_scores_gemma":[0.000009137933,0.000013816815,0.000050734816,0.0000046396053,0.000003428366,0.000010035855,0.0000045420206,0.996332,0.00027203164,0.002602192,0.00069380965,0.000003706693],"about_ca_topic_score_codex":0.004132671,"about_ca_topic_score_gemma":0.002863796,"teacher_disagreement_score":0.0046993843,"about_ca_system_score_codex":0.00070566643,"about_ca_system_score_gemma":0.0012769039,"threshold_uncertainty_score":0.015720963},"labels":[],"label_agreement":null},{"id":"W2268310503","doi":"10.5539/ijsp.v5n2p35","title":"Transmetric Density Estimation","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Mathematics; Kernel density estimation; Kernel (algebra); Mean squared error; Convergence (economics); Metric (unit); Generalization; Variable kernel density estimation; Applied mathematics; Type (biology); Density estimation; Mathematical optimization; Statistics; Kernel method; Discrete mathematics; Computer science; Mathematical analysis; Artificial intelligence","score_opus":0.016150572865106842,"score_gpt":0.27109305951228274,"score_spread":0.2549424866471759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2268310503","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026841634,0.00042903793,0.99122506,0.00044502187,0.00013608627,0.000059783153,0.00022813537,0.00037779703,0.004414924],"genre_scores_gemma":[0.20114838,0.001988205,0.7665244,0.0013800983,0.00083952385,0.0004501559,0.0022129207,0.0010901672,0.024366174],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99512535,0.002411055,0.0002801778,0.0007836281,0.0011942817,0.00020551388],"domain_scores_gemma":[0.9809507,0.008663213,0.001343612,0.0047949282,0.0037365896,0.0005110124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00880189,0.0015090548,0.0016696778,0.00412067,0.0011292202,0.0035505348,0.0032906123,0.0035893726,0.016529122],"category_scores_gemma":[0.061967976,0.0009053275,0.0019519764,0.0035656234,0.0024982924,0.0070007415,0.0060794265,0.0050605675,0.0058600884],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016082378,0.000095453564,0.0025960742,0.00031258343,0.00015213281,0.0001986115,0.0002333338,0.06908972,0.0020863465,0.6951353,0.02032187,0.2096177],"study_design_scores_gemma":[0.000025811725,0.00009534718,0.0008702884,0.00010009753,0.00004686486,0.0005501273,0.000098054246,0.55613303,0.0020985915,0.41980705,0.020113977,0.000060703886],"about_ca_topic_score_codex":0.0025050032,"about_ca_topic_score_gemma":0.0023073119,"teacher_disagreement_score":0.016529122,"about_ca_system_score_codex":0.001830752,"about_ca_system_score_gemma":0.0018028892,"threshold_uncertainty_score":0.055295408},"labels":[],"label_agreement":null},{"id":"W2270613357","doi":"10.1007/978-3-7908-1859-8_6","title":"Discretization and Fuzzification of Numerical Attributes in Attribute-Based Learning","year":2000,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Discretization; Discretization of continuous features; Computer science; Artificial intelligence; Fuzzy set; Algorithm; Extension (predicate logic); Machine learning; Fuzzy logic; Mathematics; Mathematical analysis","score_opus":0.04364831736640252,"score_gpt":0.28971936279509175,"score_spread":0.24607104542868924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270613357","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015779419,0.00080788234,0.9814392,0.00021025023,0.000042302887,0.00006705508,0.00013688958,0.00040892317,0.0011079948],"genre_scores_gemma":[0.21761362,0.0009400961,0.7798174,0.00016115146,0.000060782753,0.00016958993,0.00048574293,0.00008940938,0.00066231197],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99218494,0.0027349193,0.00078762224,0.0011969168,0.0028801095,0.00021552919],"domain_scores_gemma":[0.97394437,0.020122781,0.00097143865,0.0033767507,0.0014284133,0.00015621845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007983183,0.00066389504,0.0011670692,0.0020687603,0.00091963564,0.0030808055,0.0024701275,0.0010548668,0.0017105499],"category_scores_gemma":[0.03674716,0.00051140436,0.0013160369,0.0026362725,0.0029916356,0.0043628938,0.0026888177,0.0038924408,0.0004813255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003576433,0.00015656347,0.007701472,0.00067305175,0.00014873117,0.00016795352,0.0013875407,0.23460504,0.008182571,0.19497839,0.0022091554,0.54943186],"study_design_scores_gemma":[0.00004081318,0.00014477367,0.0024854033,0.00029933776,0.00006387443,0.00036809783,0.00025834073,0.7021085,0.021980764,0.26047024,0.011704748,0.00007504459],"about_ca_topic_score_codex":0.0018923429,"about_ca_topic_score_gemma":0.0016696554,"teacher_disagreement_score":0.007983183,"about_ca_system_score_codex":0.0016219498,"about_ca_system_score_gemma":0.0012811158,"threshold_uncertainty_score":0.04221958},"labels":[],"label_agreement":null},{"id":"W2271601362","doi":"10.48550/arxiv.1410.5884","title":"Mean-Field Networks","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Discriminative model; Inference; Mean field theory; Field (mathematics); Computer science; Artificial intelligence; Algorithm; Mathematics","score_opus":0.04974298785328049,"score_gpt":0.18294367774109282,"score_spread":0.13320068988781233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2271601362","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068557025,0.0013544066,0.985835,0.00065681624,0.000100404264,0.000047496553,0.0003354889,0.00059236237,0.0042223446],"genre_scores_gemma":[0.45682272,0.003946095,0.5124072,0.0012218172,0.00056550297,0.00053556956,0.0022387821,0.00046226653,0.021800116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988512,0.0004383099,0.000050029663,0.00037247385,0.0002025251,0.000085424195],"domain_scores_gemma":[0.99714893,0.0019861776,0.00019787258,0.0002772701,0.0003040643,0.00008559181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020021333,0.001312382,0.0016632052,0.0016734335,0.0009367244,0.0017069246,0.003040884,0.00268567,0.00778837],"category_scores_gemma":[0.0106211575,0.00089428155,0.0014137196,0.0019186412,0.0015878653,0.0038844487,0.0014801408,0.0026023458,0.0020319384],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012703023,0.00006446843,0.0013278419,0.00022238438,0.00016359922,0.00009854047,0.00012721014,0.62387073,0.0012081016,0.21553043,0.009917094,0.14734252],"study_design_scores_gemma":[0.000013844951,0.000018625835,0.0001330667,0.000031108004,0.000019459134,0.00004161421,0.000009469372,0.829214,0.00032309056,0.16677926,0.0034023693,0.0000140441325],"about_ca_topic_score_codex":0.004520921,"about_ca_topic_score_gemma":0.0059031304,"teacher_disagreement_score":0.00778837,"about_ca_system_score_codex":0.0014959452,"about_ca_system_score_gemma":0.0009789827,"threshold_uncertainty_score":0.02605468},"labels":[],"label_agreement":null},{"id":"W2277196398","doi":"10.1017/cbo9781139058544.007","title":"Embodied and Embedded: The Dynamics of Extracting Perceptual Visual Invariants","year":2008,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université du Québec en Outaouais","funders":"","keywords":"Embodied cognition; Perception; Dynamics (music); Computer science; Cognitive science; Communication; Artificial intelligence; Computer vision; Psychology; Neuroscience","score_opus":0.02482283256786011,"score_gpt":0.21628336224844918,"score_spread":0.19146052968058908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2277196398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39323622,0.0030040846,0.54238313,0.0021208937,0.00016915407,0.000057670502,0.00027432127,0.0005347123,0.058219817],"genre_scores_gemma":[0.9090906,0.0013299844,0.08122607,0.00012668465,0.000050363757,0.00003507233,0.00016854638,0.0001720878,0.0078005474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997985,0.000032644803,0.000008258411,0.000073914445,0.00006321399,0.000023614633],"domain_scores_gemma":[0.9992335,0.00042307578,0.00012466579,0.000118553304,0.000059134672,0.00004110179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048393782,0.00028751066,0.0003223446,0.0006086775,0.00041136253,0.0027556915,0.0009287051,0.0007279727,0.0038579958],"category_scores_gemma":[0.0056554633,0.00052434613,0.00030872927,0.00074997905,0.0022684594,0.0071398946,0.0015050597,0.0012382977,0.00038704177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001379526,0.00006205493,0.002840467,0.00024451935,0.000084314255,0.00038529004,0.003956153,0.059910964,0.037859377,0.6612818,0.0030745915,0.23016241],"study_design_scores_gemma":[0.000017657418,0.000048945305,0.0070478246,0.000061838095,0.000023262823,0.00018539217,0.0006663119,0.26891613,0.005865203,0.71104395,0.006065789,0.000057745136],"about_ca_topic_score_codex":0.0012149521,"about_ca_topic_score_gemma":0.0008388242,"teacher_disagreement_score":0.0038579958,"about_ca_system_score_codex":0.0006449042,"about_ca_system_score_gemma":0.0003315159,"threshold_uncertainty_score":0.012906313},"labels":[],"label_agreement":null},{"id":"W2278730326","doi":"10.1109/tnnls.2015.2434847","title":"Analog Programmable Distance Calculation Circuit for Winner Takes All Neural Network Realized in the CMOS Technology","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"CMOS; Realization (probability); Computer science; Artificial neural network; Dissipation; Euclidean distance; Power (physics); Voltage; Chip; Electrical engineering; Topology (electrical circuits); Electronic circuit; Electronic engineering; Computer hardware; Engineering; Mathematics; Physics; Artificial intelligence","score_opus":0.03548457495893528,"score_gpt":0.26428790103867256,"score_spread":0.22880332607973727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278730326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058390215,0.0011972674,0.8973858,0.0005271859,0.00053370453,0.0003028873,0.00033391244,0.006024972,0.035304114],"genre_scores_gemma":[0.8060325,0.0004146193,0.18063061,0.00047891043,0.00009660339,0.00021783928,0.0001913205,0.00010437192,0.011833251],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996375,0.000045583565,0.000025186386,0.00008857545,0.00016231935,0.00004094584],"domain_scores_gemma":[0.99983823,0.00003515309,0.00002298827,0.000021717871,0.00007077726,0.000011020765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021069695,0.00038198134,0.00022735096,0.00035685275,0.00035027426,0.0006874878,0.0014826081,0.00042895405,0.0062364875],"category_scores_gemma":[0.0004721399,0.00011945587,0.0002001428,0.00037262897,0.00022899783,0.0007060651,0.00034801138,0.0004506441,0.0006980138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046786005,0.00019843633,0.0014928558,0.0009494454,0.00011478156,0.0007376969,0.0003355218,0.026602393,0.2898532,0.049277753,0.015243975,0.6147261],"study_design_scores_gemma":[0.0001993026,0.0015243611,0.0023109454,0.00012537539,0.00017788737,0.0023647489,0.00013395323,0.49149203,0.39860135,0.016006459,0.08693659,0.00012703426],"about_ca_topic_score_codex":0.0010830521,"about_ca_topic_score_gemma":0.0016715768,"teacher_disagreement_score":0.0062364875,"about_ca_system_score_codex":0.0006177644,"about_ca_system_score_gemma":0.00048381882,"threshold_uncertainty_score":0.020863175},"labels":[],"label_agreement":null},{"id":"W2283598653","doi":"10.1017/s0025557200004381","title":"Pattern theory: tbe stocbastic analysis of real-world signals","year":2012,"lang":"en","type":"article","venue":"The Mathematical Gazette","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Sociology; Art history; Humanities; Art; Philosophy","score_opus":0.031773724231951773,"score_gpt":0.29276597833636886,"score_spread":0.26099225410441707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283598653","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017452578,0.3540974,0.5898757,0.016339594,0.004411682,0.000049324364,0.00057027827,0.00057606306,0.03233471],"genre_scores_gemma":[0.14912431,0.4598102,0.27759564,0.008508133,0.02747772,0.00045177573,0.0019360178,0.00093006116,0.07416617],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982401,0.00048396253,0.00013811991,0.00043117278,0.00062906794,0.00007756919],"domain_scores_gemma":[0.99799865,0.0011788692,0.00011002404,0.00030103995,0.0003199228,0.00009149209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025460941,0.0015158909,0.0017936016,0.0031352069,0.00074076117,0.0051414277,0.001822241,0.0025144264,0.010973469],"category_scores_gemma":[0.0047877883,0.00095763686,0.0011470376,0.0033733677,0.005906957,0.008426353,0.0022758273,0.004157622,0.006312525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049149603,0.00003742394,0.00051856093,0.0018221777,0.00017846609,0.0001404049,0.00022902018,0.0041807913,0.002280277,0.6299498,0.10444361,0.2561704],"study_design_scores_gemma":[0.00001063323,0.00004092813,0.00060282584,0.0003645857,0.000033054712,0.00045847672,0.00005663103,0.01015709,0.0012192873,0.7423536,0.24465133,0.00005153866],"about_ca_topic_score_codex":0.0011271401,"about_ca_topic_score_gemma":0.0010393071,"teacher_disagreement_score":0.010973469,"about_ca_system_score_codex":0.0016768315,"about_ca_system_score_gemma":0.0010186989,"threshold_uncertainty_score":0.036709964},"labels":[],"label_agreement":null},{"id":"W2283653960","doi":"10.1016/j.plrev.2016.01.004","title":"Boltzmann equation modelling of Learning Dynamics","year":2016,"lang":"en","type":"review","venue":"Physics of Life Reviews","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dynamics (music); Boltzmann equation; Computer science; Statistical physics; Artificial intelligence; Psychology; Physics; Thermodynamics","score_opus":0.19470743238062077,"score_gpt":0.3479047392447639,"score_spread":0.15319730686414312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283653960","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040504197,0.68003386,0.28132343,0.0060599507,0.0018897376,0.00004982222,0.00028376761,0.000370832,0.025938217],"genre_scores_gemma":[0.08307426,0.8485029,0.044195823,0.0008882883,0.0014313753,0.00013618192,0.00055132806,0.00010026585,0.021119725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977833,0.00006619717,0.000020302583,0.000031407195,0.00008538801,0.000018519275],"domain_scores_gemma":[0.9997191,0.00015189401,0.000025616304,0.000025391275,0.00006605949,0.000011999024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060406135,0.0007348925,0.0014031102,0.0006402514,0.00014712787,0.0010201149,0.0018832361,0.0012573901,0.0027995743],"category_scores_gemma":[0.0015198056,0.0003337166,0.0007077968,0.0011251565,0.0008514127,0.002026871,0.00072843384,0.0016973902,0.0015986649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042381256,0.000050920888,0.0003506652,0.004151013,0.00017515461,0.00010337518,0.00008216149,0.08728448,0.0022303937,0.38470334,0.022956846,0.49786925],"study_design_scores_gemma":[0.000026514106,0.000040674062,0.0005591407,0.0014593508,0.00012646566,0.0004432224,0.000034066223,0.20536105,0.002365876,0.45336103,0.33614978,0.000072900555],"about_ca_topic_score_codex":0.002945891,"about_ca_topic_score_gemma":0.002081033,"teacher_disagreement_score":0.002945891,"about_ca_system_score_codex":0.0009518489,"about_ca_system_score_gemma":0.0012740381,"threshold_uncertainty_score":0.009365499},"labels":[],"label_agreement":null},{"id":"W2284875084","doi":"10.12943/anr.2013.00008","title":"Using Function Approximation to Determine Neural Network Accuracy","year":2013,"lang":"en","type":"article","venue":"AECL Nuclear Review","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Atomic Energy (Canada)","funders":"","keywords":"Artificial neural network; Computer science; Outlier; Function approximation; Activation function; Range (aeronautics); Algorithm; Artificial intelligence; Feedforward neural network; Probabilistic neural network; Function (biology); Time delay neural network; Engineering","score_opus":0.05983260690616459,"score_gpt":0.2897313741670672,"score_spread":0.22989876726090258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284875084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03143082,0.00455145,0.9587719,0.00044833776,0.00017318052,0.000060174603,0.000120590295,0.0009141886,0.0035293316],"genre_scores_gemma":[0.5955011,0.005134394,0.3956959,0.0002612751,0.00016121243,0.00024786528,0.00042890263,0.0003163516,0.0022529059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974827,0.0007599104,0.00028967127,0.00043513745,0.0008907124,0.00014190665],"domain_scores_gemma":[0.9834926,0.011363128,0.00092903245,0.0012120265,0.0029116708,0.00009150963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007209372,0.0012103636,0.0014664682,0.002323051,0.00043373488,0.001782461,0.0017454065,0.002243566,0.0010270415],"category_scores_gemma":[0.040631108,0.0006440759,0.0007241547,0.001232978,0.0010713189,0.0028452207,0.0009914204,0.0020443227,0.0005720783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013023478,0.00003273089,0.0064174556,0.0003339527,0.00023487178,0.0000830071,0.00015956214,0.77599484,0.0037682336,0.015071515,0.0012623325,0.19651137],"study_design_scores_gemma":[0.000008328252,0.000039471317,0.0015021523,0.0000799913,0.000026943782,0.00006043291,0.00002063465,0.9836473,0.0042714397,0.0090954965,0.0012243858,0.000023401797],"about_ca_topic_score_codex":0.0066950675,"about_ca_topic_score_gemma":0.0036749367,"teacher_disagreement_score":0.007209372,"about_ca_system_score_codex":0.0016813515,"about_ca_system_score_gemma":0.00087773794,"threshold_uncertainty_score":0.038127303},"labels":[],"label_agreement":null},{"id":"W2287662383","doi":"10.1007/978-0-387-36795-8_4","title":"Concepts of Learning, Classification, and Regression","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Regression; Computer science; Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.05410684579872431,"score_gpt":0.3135651357855611,"score_spread":0.2594582899868368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2287662383","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022114555,0.19660181,0.59542924,0.005168592,0.004358012,0.000170155,0.0011765971,0.0019835338,0.19290064],"genre_scores_gemma":[0.04567454,0.1920895,0.48803368,0.0032762745,0.0062395954,0.0008445277,0.0024874988,0.00091660547,0.2604378],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993844,0.00009357382,0.00004515795,0.0001384416,0.00030970774,0.000028616947],"domain_scores_gemma":[0.99925035,0.0004882827,0.000039517472,0.000094022995,0.00009760243,0.00003022481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091835856,0.0020532804,0.0017141078,0.0019878915,0.0004693139,0.0035649964,0.0022588829,0.0011809348,0.016835125],"category_scores_gemma":[0.0022513126,0.00059870846,0.0007520926,0.0030463082,0.0023828435,0.0050106696,0.0010381448,0.002921931,0.008574936],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022386113,0.00004172962,0.0002120773,0.0011322239,0.000044942382,0.000057485748,0.00026283605,0.0037247483,0.0008415089,0.4269933,0.11654193,0.45012483],"study_design_scores_gemma":[0.000007216409,0.000024295598,0.00034354147,0.00037762107,0.00002234856,0.0003355414,0.00008412994,0.013207513,0.00067507185,0.60459155,0.38030002,0.000031072825],"about_ca_topic_score_codex":0.0010266966,"about_ca_topic_score_gemma":0.001746752,"teacher_disagreement_score":0.016835125,"about_ca_system_score_codex":0.00087524584,"about_ca_system_score_gemma":0.0010817498,"threshold_uncertainty_score":0.056319118},"labels":[],"label_agreement":null},{"id":"W2292678148","doi":"10.1109/nemo.2015.7415056","title":"Parallel back-propagation neural network training technique using CUDA on multiple GPUs","year":2015,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"CUDA; Computer science; Kernel (algebra); Parallel computing; Artificial neural network; Graphics; Backpropagation; Graphics processing unit; Coprocessor; Computational science; General-purpose computing on graphics processing units; Algorithm; Artificial intelligence; Computer graphics (images); Mathematics","score_opus":0.10690230102151799,"score_gpt":0.2922494455424687,"score_spread":0.1853471445209507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292678148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01431559,0.00010185174,0.9762023,0.00018788168,0.00008323535,0.000052651656,0.00006322677,0.004455429,0.0045378916],"genre_scores_gemma":[0.22406343,0.00015544871,0.76731247,0.00008571906,0.000025867454,0.00025078113,0.00022335566,0.0003488094,0.007534111],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996754,0.000052541352,0.000022349162,0.00006165519,0.00014914961,0.000038781567],"domain_scores_gemma":[0.9993544,0.00012996599,0.000045021876,0.00012364915,0.00031527618,0.000031655254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004979631,0.0007310981,0.0005880772,0.0004841912,0.0006395427,0.000803881,0.0015546957,0.0005756225,0.0040162783],"category_scores_gemma":[0.0016142196,0.0004070763,0.00040088999,0.0008460407,0.00035967713,0.0009723039,0.0005889943,0.0012603032,0.0009720613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030578286,0.00013591358,0.0021338034,0.00021305308,0.00013677412,0.00039484986,0.00025489012,0.5237099,0.0370272,0.018758552,0.01486802,0.4020612],"study_design_scores_gemma":[0.0000116890615,0.000014314021,0.00011778611,0.000003957718,0.000004977639,0.000028179194,0.0000077068935,0.99057215,0.005886316,0.0010192086,0.0023283162,0.000005375258],"about_ca_topic_score_codex":0.01501094,"about_ca_topic_score_gemma":0.013539904,"teacher_disagreement_score":0.01501094,"about_ca_system_score_codex":0.00080215966,"about_ca_system_score_gemma":0.0014429003,"threshold_uncertainty_score":0.029847145},"labels":[],"label_agreement":null},{"id":"W2293294498","doi":"","title":"Implementation Issues of Kohonen Self-Organizing Map Realized on FPGA","year":2012,"lang":"en","type":"article","venue":"The European Symposium on Artificial Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Self-organizing map; Field-programmable gate array; Quantization (signal processing); Computer science; Process (computing); Reduction (mathematics); Field (mathematics); Algorithm; Lookup table; Artificial neural network; Artificial intelligence; Topology (electrical circuits); Pattern recognition (psychology); Computer hardware; Mathematics","score_opus":0.025383053802632338,"score_gpt":0.28251192593378477,"score_spread":0.25712887213115243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293294498","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52863246,0.0011283434,0.45099977,0.00043308036,0.00020041768,0.00015713534,0.00016696274,0.0022835948,0.015998205],"genre_scores_gemma":[0.9049642,0.00020419329,0.09250548,0.000038407892,0.000010805193,0.0000531132,0.00008072116,0.00003239177,0.0021107537],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998399,0.00003942173,0.000010880884,0.000021081822,0.000060604954,0.000028132039],"domain_scores_gemma":[0.99977416,0.00009019707,0.000015237311,0.00004223635,0.00006870783,0.0000094852385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021561714,0.00021481543,0.00014406975,0.00014299783,0.00013072118,0.0004856958,0.0006484687,0.00028980197,0.0022893525],"category_scores_gemma":[0.00072185544,0.00011228559,0.00013177909,0.00015200271,0.00010500456,0.00044212845,0.00013379953,0.0002349319,0.00033168093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001113996,0.0002435465,0.0037218716,0.0013431119,0.00015284737,0.00080245704,0.00043856184,0.12728992,0.33556318,0.021797458,0.002854148,0.5046789],"study_design_scores_gemma":[0.000143872,0.0013555358,0.004733434,0.00014716265,0.000089944064,0.00087377,0.00026905836,0.61524355,0.34962285,0.0072818194,0.02018673,0.000052266594],"about_ca_topic_score_codex":0.00066589395,"about_ca_topic_score_gemma":0.0007733653,"teacher_disagreement_score":0.0022893525,"about_ca_system_score_codex":0.00022157667,"about_ca_system_score_gemma":0.0002380464,"threshold_uncertainty_score":0.0076586604},"labels":[],"label_agreement":null},{"id":"W2294999365","doi":"10.1109/radar.1990.201117","title":"Radar vision","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Radar; Computer science; Artificial intelligence; Radar engineering details; Radar lock-on; Radar imaging; Man-portable radar; Remote sensing; Computer vision; Real-time computing; Telecommunications; Geography","score_opus":0.01757747843089031,"score_gpt":0.23443463754524932,"score_spread":0.21685715911435902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294999365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003349844,0.019050088,0.6960873,0.0033233664,0.0032794992,0.000131813,0.000912917,0.004993493,0.26887164],"genre_scores_gemma":[0.23534714,0.0367262,0.4897751,0.0069779092,0.0041360687,0.00039997682,0.005306069,0.0014761594,0.21985543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928683,0.000102144186,0.00003400641,0.00022125893,0.00028454675,0.00007129405],"domain_scores_gemma":[0.99951816,0.000096583986,0.000038277325,0.00012454337,0.00017039155,0.00005210206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044238038,0.0010499841,0.00083976676,0.0015572387,0.0007469132,0.0042464877,0.0015238589,0.0021535472,0.032569192],"category_scores_gemma":[0.0018469873,0.00041468398,0.0006787883,0.0010631406,0.001255635,0.0037669113,0.0021271533,0.0018300533,0.0223908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011923648,0.000039837836,0.00055457593,0.00051513914,0.000070609305,0.00025293906,0.0001565698,0.006350567,0.010634997,0.40530485,0.1235405,0.45246023],"study_design_scores_gemma":[0.000030544452,0.00007754675,0.0005336981,0.00018249826,0.000034061606,0.001431759,0.0001267533,0.030480022,0.0055382675,0.22472364,0.7367778,0.0000634176],"about_ca_topic_score_codex":0.000998088,"about_ca_topic_score_gemma":0.00077836,"teacher_disagreement_score":0.032569192,"about_ca_system_score_codex":0.00084816193,"about_ca_system_score_gemma":0.00066343014,"threshold_uncertainty_score":0.10895485},"labels":[],"label_agreement":null},{"id":"W2296073425","doi":"10.1145/1553374.1553380","title":"Curriculum learning","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4978,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Canadian Institute for Advanced Research","keywords":"Curriculum; Computer science; Generalization; Context (archaeology); Maxima and minima; Process (computing); Convergence (economics); Artificial intelligence; Set (abstract data type); Artificial neural network; Machine learning; Quality (philosophy); Mathematics; Pedagogy; Psychology","score_opus":0.006241040853153789,"score_gpt":0.23294534053960214,"score_spread":0.22670429968644834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296073425","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037834004,0.0010046939,0.91334826,0.0017743672,0.0001929117,0.00030255786,0.0003043942,0.00089877937,0.044339936],"genre_scores_gemma":[0.53916395,0.0014907499,0.42673212,0.0010134344,0.00016248593,0.0007080933,0.00101063,0.0002131007,0.029505476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993192,0.0002453922,0.00003194963,0.00024190637,0.00011628777,0.000045307403],"domain_scores_gemma":[0.9981987,0.00090922596,0.00013913306,0.00036591955,0.00025004905,0.00013703923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011873449,0.0006452374,0.00037233141,0.00044966507,0.00048199508,0.0012242707,0.0012606449,0.00087766774,0.013016894],"category_scores_gemma":[0.009047899,0.00031152452,0.00058642094,0.00035821766,0.0010036955,0.002670729,0.001618864,0.0014706034,0.0021471758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015000938,0.00029412104,0.003538262,0.00043047877,0.000099916164,0.000071768154,0.00045497937,0.056325458,0.0055510257,0.31421977,0.015318583,0.60354567],"study_design_scores_gemma":[0.00010366862,0.00032926348,0.0020885863,0.00017998274,0.000053673535,0.0001823812,0.00020210278,0.27696565,0.006041185,0.65508074,0.05872879,0.000044030807],"about_ca_topic_score_codex":0.0006980534,"about_ca_topic_score_gemma":0.0010843694,"teacher_disagreement_score":0.013016894,"about_ca_system_score_codex":0.0006779297,"about_ca_system_score_gemma":0.0009806458,"threshold_uncertainty_score":0.0435459},"labels":[],"label_agreement":null},{"id":"W2297954512","doi":"10.58079/ouf6","title":"Mandelbrot, fractals and counterexamples in applied probability","year":2010,"lang":"en","type":"article","venue":"OpenEdition (OpenEdition)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mandelbrot set; Yesterday; Counterexample; Fractal; Mathematics; Event (particle physics); Series (stratigraphy); Mathematical economics; Calculus (dental); Discrete mathematics; Physics; Mathematical analysis; Geology","score_opus":0.017174331524147223,"score_gpt":0.23963562031097702,"score_spread":0.2224612887868298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2297954512","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22875355,0.1534367,0.342859,0.13963118,0.00422756,0.00006716476,0.000418221,0.00041370664,0.13019288],"genre_scores_gemma":[0.9411888,0.012920689,0.030652013,0.002271505,0.0036475598,0.00013354515,0.00011595359,0.00007606467,0.008993802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99804544,0.0011452596,0.000093275725,0.00022439405,0.0004015404,0.00009003321],"domain_scores_gemma":[0.9743308,0.022994539,0.0008417253,0.0007683525,0.00070423615,0.00036038188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004883424,0.00033106076,0.0010063809,0.0029213044,0.0019933453,0.0031089417,0.00070334144,0.002209364,0.0036134594],"category_scores_gemma":[0.031521972,0.00038335574,0.0006398372,0.0018508178,0.006330904,0.0053862687,0.0018488043,0.004128381,0.0002773225],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019281291,0.000007424475,0.0003361212,0.00004394719,0.000015448602,0.000056413548,0.00011261237,0.0018450479,0.00006068028,0.98857355,0.0029948954,0.0059345327],"study_design_scores_gemma":[0.0000122173515,0.000007543492,0.00045800765,0.000039769933,0.0000049365576,0.000056438184,0.000036939364,0.009204635,0.000071369315,0.98560995,0.004489028,0.0000093035615],"about_ca_topic_score_codex":0.0030044701,"about_ca_topic_score_gemma":0.002523285,"teacher_disagreement_score":0.004883424,"about_ca_system_score_codex":0.002202998,"about_ca_system_score_gemma":0.00074228144,"threshold_uncertainty_score":0.025826335},"labels":[],"label_agreement":null},{"id":"W2313948521","doi":"10.1142/9789814602228_0005","title":"COMPLEX DATA CLUSTERING: FROM NEURAL NETWORK ARCHITECTURE TO THEORY AND APPLICATIONS OF NONLINEAR DYNAMICS OF PATTERN RECOGNITION","year":2014,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Cluster analysis; Artificial neural network; Artificial intelligence; Dynamics (music); Nonlinear system; Architecture; Pattern recognition (psychology); Data mining","score_opus":0.03153028425785613,"score_gpt":0.2659093497737418,"score_spread":0.23437906551588567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313948521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010646004,0.0043876297,0.98198813,0.00097236934,0.00011340542,0.000024497147,0.000086589826,0.00017656037,0.0016047981],"genre_scores_gemma":[0.45127693,0.009274677,0.5344037,0.00033247427,0.00054237293,0.0001380603,0.00027872357,0.00015983402,0.0035932397],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995172,0.00014454946,0.0000362614,0.00013145196,0.00014604763,0.000024549056],"domain_scores_gemma":[0.9979194,0.0011363629,0.00020356812,0.00028065837,0.00038570215,0.00007434614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013406377,0.00057866424,0.00083191576,0.0015331417,0.00044911995,0.0018475105,0.0011855955,0.001038409,0.0013628957],"category_scores_gemma":[0.0067476546,0.00045694117,0.0004776077,0.002257409,0.001887456,0.0031014937,0.0012988539,0.0011765615,0.0003356321],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000120984245,0.00005596424,0.0037740124,0.0005128121,0.00015018535,0.00007174563,0.00034103377,0.3301502,0.0049424856,0.2897071,0.004823833,0.36534974],"study_design_scores_gemma":[0.0000074581567,0.000020461199,0.0009850436,0.00004407257,0.00001785277,0.000051201736,0.000050215807,0.8228583,0.0012064235,0.17148626,0.0032510376,0.000021735681],"about_ca_topic_score_codex":0.0044307224,"about_ca_topic_score_gemma":0.0035010371,"teacher_disagreement_score":0.0044307224,"about_ca_system_score_codex":0.0012251777,"about_ca_system_score_gemma":0.001063166,"threshold_uncertainty_score":0.0088893175},"labels":[],"label_agreement":null},{"id":"W2314140600","doi":"10.5023/jappstat.33.243","title":"Functional Regression Models via Regularized Radial Basis Function Networks","year":2004,"lang":"en","type":"article","venue":"Ouyou toukeigaku","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Functional data analysis; Functional principal component analysis; Smoothing; Regularization (linguistics); Mathematics; Basis function; Regression; Regression analysis; Reproducing kernel Hilbert space; Computer science; Artificial intelligence; Kernel method; Machine learning; Mathematical optimization; Statistics; Support vector machine; Hilbert space","score_opus":0.018737294258237686,"score_gpt":0.21554519480231155,"score_spread":0.19680790054407385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2314140600","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055121407,0.0005338457,0.9920752,0.00025645396,0.000025133366,0.000029971428,0.00006628641,0.00027028544,0.001230735],"genre_scores_gemma":[0.50581086,0.0019854775,0.4755765,0.00040775133,0.00020016881,0.0008275818,0.0006836147,0.00044031558,0.014067682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99814284,0.0010450449,0.000064462576,0.00030164042,0.0003310835,0.00011496968],"domain_scores_gemma":[0.99691427,0.0018702517,0.0003680748,0.00013877988,0.00062622706,0.00008243501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041105733,0.0016265096,0.0017668988,0.0015079518,0.0005004403,0.0013640301,0.0025017133,0.0028891372,0.0025018991],"category_scores_gemma":[0.00903644,0.0010951171,0.0015444085,0.0011786288,0.0011371776,0.0018057602,0.0017388783,0.0020519472,0.0011089419],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025324014,0.000015101441,0.00021412577,0.000034557026,0.00003413482,0.000029884975,0.000039547875,0.9699157,0.00027165376,0.016343048,0.0005644565,0.012512404],"study_design_scores_gemma":[0.000001308196,0.000003488526,0.000018782961,0.0000031331992,0.0000020378905,0.00000360119,0.0000012854321,0.9965179,0.000026283917,0.0032636796,0.00015567991,0.0000027915075],"about_ca_topic_score_codex":0.012684599,"about_ca_topic_score_gemma":0.009215019,"teacher_disagreement_score":0.012684599,"about_ca_system_score_codex":0.0016388671,"about_ca_system_score_gemma":0.0013413989,"threshold_uncertainty_score":0.025221527},"labels":[],"label_agreement":null},{"id":"W2316752057","doi":"10.11120/msor.2003.03010051","title":"Calculator Statistics and Calculator CalculusTitles: Calculator Statistics and Calculator Calculus Authors: Barrie Galpin and Alan Graham","year":2003,"lang":"en","type":"article","venue":"MSOR Connections","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Calculator; Computer science; Statistics; Calculus (dental); Mathematics; Medicine; Operating system","score_opus":0.01089395324445932,"score_gpt":0.24498611718274016,"score_spread":0.23409216393828083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316752057","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002893161,0.0050910194,0.9279982,0.0089196535,0.0016999805,0.000058922542,0.0004146618,0.0071474854,0.045776997],"genre_scores_gemma":[0.19657956,0.0075858855,0.6417517,0.0043770876,0.007901588,0.00047579946,0.0013092575,0.011148127,0.12887102],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947771,0.0014675941,0.00039056328,0.0007888632,0.002335571,0.00024036932],"domain_scores_gemma":[0.98527724,0.008561231,0.00070424145,0.0019329122,0.0029836057,0.00054079964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004165107,0.0010329689,0.0014771122,0.0041177985,0.0024072863,0.00983833,0.0019330481,0.0022412296,0.029306356],"category_scores_gemma":[0.040854834,0.0011591463,0.000921219,0.006299587,0.0066672005,0.016907746,0.003459576,0.005917665,0.012433577],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023589277,0.000011043142,0.00017549944,0.00004015887,0.0000067945666,0.00001947951,0.00011073932,0.0011159978,0.00014219996,0.91225755,0.032538153,0.053558685],"study_design_scores_gemma":[0.000010264637,0.000007340438,0.00013056846,0.000046192083,0.00000927968,0.00007835172,0.000036686935,0.0134273525,0.00093021366,0.88782114,0.0974649,0.000037747217],"about_ca_topic_score_codex":0.003325426,"about_ca_topic_score_gemma":0.003758737,"teacher_disagreement_score":0.029306356,"about_ca_system_score_codex":0.002665125,"about_ca_system_score_gemma":0.0033349998,"threshold_uncertainty_score":0.09803951},"labels":[],"label_agreement":null},{"id":"W2320408901","doi":"10.1037/a0032174","title":"Differentiating models of associative learning: Reorientation, superconditioning, and the role of inhibition.","year":2013,"lang":"en","type":"article","venue":"Journal of Experimental Psychology Animal Behavior Processes","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Associative property; Associative learning; TRACE (psycholinguistics); Animal learning; Animal behavior; Artificial neural network; Cognitive science; Artificial intelligence; Psychology; Miller; Mathematical model; Cognitive psychology; Lateral inhibition; Computer science; Neuroscience; Ecology; Mathematics; Pure mathematics; Philosophy","score_opus":0.015725500765155376,"score_gpt":0.3008045206317727,"score_spread":0.2850790198666173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320408901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15908246,0.0034190204,0.79831135,0.0056648245,0.0002449558,0.000044746277,0.00021602804,0.00034144468,0.032675065],"genre_scores_gemma":[0.9564259,0.0009864139,0.037792485,0.00033150436,0.000092755494,0.00005324707,0.000089149864,0.00006377478,0.00416463],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948585,0.0002504133,0.000029304367,0.00006972805,0.00010489855,0.000059826412],"domain_scores_gemma":[0.9960323,0.002277492,0.0005069825,0.0005677829,0.0003169718,0.00029839142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019002263,0.00056777603,0.00060338696,0.0004748751,0.00033577977,0.0011517,0.0021794941,0.00081621757,0.002612957],"category_scores_gemma":[0.0060484307,0.00032569095,0.0008195802,0.00038339302,0.0023266664,0.004430184,0.0015840146,0.0019788437,0.00037622664],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018690579,0.00007982621,0.0021724387,0.00008676737,0.000042607197,0.00014963846,0.00038476635,0.1073238,0.001996767,0.85562503,0.0018572641,0.030094197],"study_design_scores_gemma":[0.000014275373,0.000022334772,0.00025559796,0.000010383941,0.0000105187755,0.000054708282,0.00002562776,0.33228508,0.00053489,0.66592276,0.00085188966,0.0000119716115],"about_ca_topic_score_codex":0.0023530314,"about_ca_topic_score_gemma":0.0021904474,"teacher_disagreement_score":0.002612957,"about_ca_system_score_codex":0.001064053,"about_ca_system_score_gemma":0.0007705648,"threshold_uncertainty_score":0.010049462},"labels":[],"label_agreement":null},{"id":"W2320531917","doi":"10.1097/wnr.0b013e32835fa73f","title":"A neural computational model for animal’s time-to-collision estimation","year":2013,"lang":"en","type":"article","venue":"Neuroreport","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Chinese Academy of Sciences; National Natural Science Foundation of China; McMaster University","keywords":"Collision; Neuroscience; Computer science; Estimation; Animal model; Artificial neural network; Artificial intelligence; Biology; Engineering; Computer security","score_opus":0.02012477355626323,"score_gpt":0.2656073219490419,"score_spread":0.24548254839277867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320531917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028630609,0.0004500176,0.96408516,0.00043565233,0.00009406874,0.0000338263,0.00013059104,0.0002220179,0.0059181233],"genre_scores_gemma":[0.8306506,0.0008628872,0.1570274,0.0002836908,0.0001102576,0.0003130086,0.00026540842,0.0000882266,0.0103985015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975485,0.00003781866,0.000016198017,0.00007206616,0.00007594346,0.000043115077],"domain_scores_gemma":[0.999605,0.00017005601,0.000056269342,0.00003555469,0.00010255567,0.000030565116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042414223,0.00050550065,0.0006463721,0.00054227305,0.00047556634,0.00089960586,0.002783804,0.0011077404,0.002581561],"category_scores_gemma":[0.0017452079,0.00036696685,0.000765634,0.0008243517,0.0006348632,0.001985133,0.0007222459,0.0010824956,0.00042951992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007354957,0.000037022925,0.000927349,0.00006315783,0.000038180093,0.00009437713,0.00006526511,0.9089978,0.0031318723,0.05844509,0.0009043262,0.027222015],"study_design_scores_gemma":[0.000002765516,0.000009720488,0.00009884318,0.0000024126737,0.0000059819463,0.00001969532,0.0000025170466,0.99247473,0.0002042046,0.0069672754,0.00020706892,0.0000047120147],"about_ca_topic_score_codex":0.007204012,"about_ca_topic_score_gemma":0.0055398964,"teacher_disagreement_score":0.007204012,"about_ca_system_score_codex":0.0010294341,"about_ca_system_score_gemma":0.00093567505,"threshold_uncertainty_score":0.014324188},"labels":[],"label_agreement":null},{"id":"W2322850345","doi":"10.3166/jesa.43.871-887","title":"Réseau de neurones doublement récurrent à base de fonctions radiales : RR2FR Application au pronostic prévisionnel","year":2009,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Base (topology); Anatomy; Biology; Mathematics","score_opus":0.03176464147816348,"score_gpt":0.28639661203415184,"score_spread":0.25463197055598835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322850345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11643526,0.0010309959,0.8781537,0.00020778965,0.00014399443,0.000043986405,0.00005756117,0.0013412934,0.0025853491],"genre_scores_gemma":[0.7710107,0.00036445548,0.2242805,0.000076643744,0.00007108296,0.000049772916,0.00007929582,0.00011940061,0.0039481274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997285,0.00006430363,0.000016782142,0.000069107184,0.000085635176,0.000035625886],"domain_scores_gemma":[0.99933976,0.00021452771,0.00006453341,0.00007082271,0.00027233918,0.000037948812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009868474,0.0006781498,0.0005976497,0.00040264602,0.0002404374,0.00054185744,0.0008109814,0.0008784931,0.0017501052],"category_scores_gemma":[0.0026389994,0.00023418695,0.000482688,0.00029540938,0.00028946262,0.0006680399,0.00039510053,0.00050349656,0.00076337415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063900294,0.00013876341,0.0022445484,0.00020975061,0.00011550115,0.00043690644,0.00023082714,0.18773475,0.08568545,0.0055119605,0.0024495812,0.71460295],"study_design_scores_gemma":[0.000017551958,0.00015586334,0.0006930477,0.000015765616,0.000032367614,0.00017592605,0.000016974795,0.9784695,0.01800051,0.00092461146,0.001479051,0.00001885733],"about_ca_topic_score_codex":0.0029242146,"about_ca_topic_score_gemma":0.0026281558,"teacher_disagreement_score":0.0029242146,"about_ca_system_score_codex":0.00034088417,"about_ca_system_score_gemma":0.0002687269,"threshold_uncertainty_score":0.005854726},"labels":[],"label_agreement":null},{"id":"W2324833697","doi":"10.3166/jesa.42.1279-1309","title":"Identification des systèmes non linéaires par des modèles de type NARMA","year":2008,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics","score_opus":0.04544483895678254,"score_gpt":0.2762218345262905,"score_spread":0.23077699556950798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324833697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020155905,0.00025812292,0.97782284,0.00010210463,0.00006975705,0.000033974768,0.000057966787,0.00030757213,0.0011918681],"genre_scores_gemma":[0.71836287,0.0009545779,0.260634,0.00022558535,0.00019132714,0.0004420046,0.00044017137,0.00014171684,0.018607786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866176,0.00050595397,0.000059188715,0.00033512758,0.0003510903,0.00008694124],"domain_scores_gemma":[0.99865305,0.0007210066,0.00023615028,0.00012206775,0.00023496123,0.00003263892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001115707,0.0011862213,0.0015152112,0.0005060724,0.0005398621,0.0016123096,0.0007861134,0.0016004575,0.001928786],"category_scores_gemma":[0.004475417,0.0006580223,0.00090894423,0.0005439037,0.0010292949,0.0015474868,0.00075917155,0.0020061648,0.0009212365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033694314,0.00010011063,0.002236554,0.00034249068,0.00026554608,0.0006102951,0.00045353544,0.7880694,0.039668187,0.04617248,0.0015946309,0.12014986],"study_design_scores_gemma":[0.000017243854,0.0000479512,0.0005848754,0.000010145253,0.000010973788,0.0000896584,0.00002343921,0.9899261,0.0024443488,0.0047732433,0.0020515115,0.000020585374],"about_ca_topic_score_codex":0.0057592336,"about_ca_topic_score_gemma":0.0051722364,"teacher_disagreement_score":0.0057592336,"about_ca_system_score_codex":0.0007340036,"about_ca_system_score_gemma":0.0007161652,"threshold_uncertainty_score":0.011451423},"labels":[],"label_agreement":null},{"id":"W2325435355","doi":"10.1190/1.3513119","title":"Neural network regression analysis and post‐stack inversion‐ A comparison","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"ARC Resources (Canada)","funders":"","keywords":"Reservoir modeling; Inversion (geology); Artificial neural network; Seismic inversion; Geology; Regression analysis; Regression; Seismology; Linear regression; Computer science; Artificial intelligence; Statistics; Mathematics; Machine learning; Geotechnical engineering; Geometry; Tectonics","score_opus":0.013106041114561338,"score_gpt":0.26685393888532144,"score_spread":0.2537478977707601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2325435355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114215165,0.0006511064,0.87093085,0.0005854858,0.0001287113,0.00007236993,0.0003247312,0.0034267243,0.009664889],"genre_scores_gemma":[0.790618,0.0006685713,0.19943903,0.0001350329,0.00010084766,0.00012319862,0.0007806874,0.0006203403,0.00751428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988133,0.00032808338,0.000054917404,0.00020779266,0.0005137138,0.00008235781],"domain_scores_gemma":[0.9975292,0.0010531817,0.00013781701,0.00028450825,0.00096418784,0.00003108669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002261326,0.00086276187,0.000670857,0.0008105696,0.00024875606,0.0009258528,0.0012738014,0.0007305235,0.0033677537],"category_scores_gemma":[0.008080622,0.00028657445,0.0005847678,0.0007952493,0.0003162858,0.00188459,0.00061874907,0.00097454747,0.0010363666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055840577,0.00017201158,0.0050415723,0.00019704303,0.0003448236,0.00010151713,0.00007847321,0.5982878,0.011682213,0.0074508195,0.0022739863,0.3738113],"study_design_scores_gemma":[0.0000074056443,0.000030537263,0.0013766068,0.000004613824,0.000018694882,0.000020461646,0.000009352849,0.9937366,0.0032147677,0.00084026146,0.0007306795,0.000009986809],"about_ca_topic_score_codex":0.010933125,"about_ca_topic_score_gemma":0.011694834,"teacher_disagreement_score":0.010933125,"about_ca_system_score_codex":0.0006685392,"about_ca_system_score_gemma":0.0010783251,"threshold_uncertainty_score":0.021739006},"labels":[],"label_agreement":null},{"id":"W2327494763","doi":"10.1142/9789812772435_0012","title":"LEVERAGING LATENT INFORMATION IN NMR SPECTRA FOR ROBUST PREDICTIVE MODELS","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Metabolomics Innovation Centre; Chenomx (Canada); University of Calgary; University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.023994629201658298,"score_gpt":0.21404716963415124,"score_spread":0.19005254043249295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327494763","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043150436,0.0003344291,0.9545612,0.00041363426,0.000020895659,0.000030262418,0.00027358634,0.0008271435,0.00038841393],"genre_scores_gemma":[0.83236825,0.0007448708,0.16432841,0.00019335243,0.00011502058,0.00017056735,0.0012378226,0.00015645708,0.0006852056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986695,0.0007166069,0.0000715179,0.00026273468,0.00019909933,0.00008051562],"domain_scores_gemma":[0.98915803,0.008847277,0.0007833899,0.0007983618,0.0003354296,0.000077455144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004646349,0.0015737803,0.0012822896,0.001623273,0.00044856398,0.0016231061,0.001330837,0.0011113712,0.0011457829],"category_scores_gemma":[0.016097972,0.0007511599,0.0013285897,0.0015920207,0.0011608812,0.0026855844,0.0011491522,0.0022807992,0.00046818613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008302207,0.000049219583,0.0009732748,0.00006246845,0.00008403403,0.000038463346,0.000041669504,0.95638144,0.0016298525,0.006963245,0.0003653549,0.033327967],"study_design_scores_gemma":[0.0000027495455,0.000008108383,0.00009036161,0.000004070554,0.0000057942416,0.0000047887065,0.0000029983398,0.99406517,0.00030726328,0.0054449933,0.0000587236,0.000005034148],"about_ca_topic_score_codex":0.0027869614,"about_ca_topic_score_gemma":0.003147258,"teacher_disagreement_score":0.004646349,"about_ca_system_score_codex":0.0008007078,"about_ca_system_score_gemma":0.0008911879,"threshold_uncertainty_score":0.024572492},"labels":[],"label_agreement":null},{"id":"W2331468521","doi":"10.1115/imece2010-37044","title":"Period Analysis Based on CNN and Periodicity Ratio Method","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Nonlinear system; Period (music); Prime (order theory); Trajectory; Computer science; Artificial neural network; Mathematics; Differential (mechanical device); Applied mathematics; Differential equation; Test data; Algorithm; Mathematical analysis; Control theory (sociology); Artificial intelligence; Physics; Combinatorics","score_opus":0.00980393739735187,"score_gpt":0.2753884353699817,"score_spread":0.26558449797262984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331468521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2588899,0.000668933,0.7295347,0.0001547596,0.00017335344,0.00012018211,0.00034511767,0.001070719,0.009042351],"genre_scores_gemma":[0.816852,0.00033181524,0.18064925,0.000036887668,0.000049954677,0.00009437462,0.0002362314,0.00010868337,0.0016407941],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996209,0.000079593265,0.00002813092,0.00007842365,0.00015036466,0.00004258996],"domain_scores_gemma":[0.99943835,0.00018011447,0.00010600559,0.00008942056,0.00015986823,0.00002621681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060159276,0.00030219884,0.0002828489,0.0014479058,0.0002406447,0.000379018,0.00041650698,0.00026858933,0.0016151053],"category_scores_gemma":[0.001919496,0.00017445034,0.00033963608,0.0006784995,0.00033736794,0.000865687,0.00037591794,0.000286531,0.00019523228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007045646,0.000100729885,0.021972686,0.00045278354,0.0001795242,0.0006630804,0.00028821925,0.13296013,0.23778541,0.040250495,0.0029417772,0.56170064],"study_design_scores_gemma":[0.000015352576,0.000092746246,0.008996522,0.000018736142,0.000028998853,0.0002964149,0.000043866417,0.9528912,0.02986658,0.0059405877,0.0017740614,0.00003486975],"about_ca_topic_score_codex":0.0014930313,"about_ca_topic_score_gemma":0.0008643397,"teacher_disagreement_score":0.0016151053,"about_ca_system_score_codex":0.000385979,"about_ca_system_score_gemma":0.00036073843,"threshold_uncertainty_score":0.005403042},"labels":[],"label_agreement":null},{"id":"W2333398907","doi":"10.1142/9789812811684_0038","title":"INTERPRETING KEY COMPARISON DATA FOR THE MRA DATABASE","year":2001,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Key (lock); Database; Computer security","score_opus":0.10764612583146571,"score_gpt":0.3583728281429113,"score_spread":0.2507267023114456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333398907","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13871406,0.0045934264,0.046292644,0.00887449,0.0049251304,0.001550257,0.69631934,0.021348948,0.07738164],"genre_scores_gemma":[0.32518274,0.0017190609,0.06363627,0.0012626431,0.0008316605,0.0008854238,0.58001137,0.005332423,0.021138398],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98995507,0.002010736,0.0022020785,0.0013499018,0.0038545115,0.00062771596],"domain_scores_gemma":[0.9283283,0.024072291,0.004249734,0.021428186,0.020701645,0.0012198332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008864083,0.00087585027,0.0011824195,0.008618819,0.0012283418,0.0050442317,0.0024161579,0.0015053392,0.051983025],"category_scores_gemma":[0.0916842,0.00046825845,0.00090482814,0.007762655,0.00070817734,0.006332188,0.0019412994,0.0013448776,0.034283116],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008286072,0.0005140236,0.026632138,0.0021857047,0.00037729047,0.0009425141,0.000542571,0.0029857124,0.019449,0.014708117,0.68999594,0.23338091],"study_design_scores_gemma":[0.0016263861,0.0015307901,0.06149351,0.00085712044,0.0006390156,0.0029668973,0.002604041,0.024966426,0.10012832,0.0338329,0.7689128,0.00044178378],"about_ca_topic_score_codex":0.005676781,"about_ca_topic_score_gemma":0.005677074,"teacher_disagreement_score":0.051983025,"about_ca_system_score_codex":0.0015319276,"about_ca_system_score_gemma":0.0031089701,"threshold_uncertainty_score":0.17390054},"labels":[],"label_agreement":null},{"id":"W2333630783","doi":"10.1142/9789812774118_0033","title":"STABLE NEURAL ARCHITECTURE OF DYNAMIC NEURAL UNITS WITH ADAPTIVE TIME DELAYS","year":2006,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial neural network; Architecture; Artificial intelligence","score_opus":0.019019750053308488,"score_gpt":0.2258104722236982,"score_spread":0.2067907221703897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333630783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07730283,0.0005161251,0.9156062,0.00017976855,0.00017209857,0.00002478894,0.000041613748,0.0001959033,0.005960639],"genre_scores_gemma":[0.95802534,0.00025369323,0.03693799,0.00004063515,0.000031349246,0.000047833215,0.00004545737,0.000022447217,0.004595235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999136,0.000019309959,0.0000052703863,0.000023462673,0.000024613822,0.000013851113],"domain_scores_gemma":[0.999814,0.000047203004,0.00002125959,0.000020335145,0.00008210428,0.000015219793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033941,0.0003461838,0.00029227705,0.00020225397,0.00026506395,0.0005622695,0.0008106588,0.00061387697,0.0014390862],"category_scores_gemma":[0.00096705783,0.00022322175,0.0002441462,0.00028757864,0.0004010914,0.00062799046,0.00042297857,0.00050182635,0.0002744612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018494682,0.000035172616,0.0005306611,0.00007421811,0.00004645549,0.00013252655,0.00009339493,0.8324792,0.028383,0.06775156,0.00083196635,0.06945697],"study_design_scores_gemma":[0.0000061914034,0.000024633326,0.00007055013,0.0000025505633,0.0000049458768,0.000012365689,0.0000032418163,0.994134,0.0009327745,0.0044924095,0.00031259636,0.000003733002],"about_ca_topic_score_codex":0.0019977505,"about_ca_topic_score_gemma":0.0021808904,"teacher_disagreement_score":0.0019977505,"about_ca_system_score_codex":0.0004759434,"about_ca_system_score_gemma":0.0004245749,"threshold_uncertainty_score":0.004814267},"labels":[],"label_agreement":null},{"id":"W2334151882","doi":"10.1142/9789812775894_0013","title":"EFFICIENT COUPLING OF ITERATIVE MODELS","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"","keywords":"Iterative method; Coupling (piping); Computer science; Materials science; Algorithm","score_opus":0.024279091096378373,"score_gpt":0.2677771276835416,"score_spread":0.24349803658716324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2334151882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011083666,0.00009109831,0.9839093,0.000089749876,0.00003379028,0.000034917135,0.000027384958,0.00071352284,0.004016608],"genre_scores_gemma":[0.59813803,0.0001901569,0.3885164,0.00015466593,0.000073692936,0.00032912116,0.0003781781,0.0010318175,0.011187873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983431,0.000723449,0.000088214765,0.00026520254,0.00042010646,0.00015996132],"domain_scores_gemma":[0.9961451,0.002056174,0.0002021489,0.0008506421,0.0005818084,0.00016418578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021447295,0.0012860683,0.0014456043,0.00083544524,0.0008297339,0.0016171148,0.0027913698,0.0020120621,0.0069119283],"category_scores_gemma":[0.011315839,0.0011990629,0.001224726,0.0008008242,0.0009184329,0.002767759,0.004054444,0.002220842,0.0024134906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019269393,0.00013188607,0.00066459854,0.00012353221,0.0001156976,0.00009629447,0.00027133737,0.7873476,0.0061687445,0.07044584,0.0030048091,0.1314369],"study_design_scores_gemma":[0.0000059839967,0.000013372012,0.000035049157,0.000004144934,0.0000055869186,0.000011449639,0.000007769595,0.9869413,0.0007121077,0.011825484,0.0004324984,0.0000052424666],"about_ca_topic_score_codex":0.00387966,"about_ca_topic_score_gemma":0.004852492,"teacher_disagreement_score":0.0069119283,"about_ca_system_score_codex":0.0009927847,"about_ca_system_score_gemma":0.0014584945,"threshold_uncertainty_score":0.023122728},"labels":[],"label_agreement":null},{"id":"W2339032990","doi":"10.14288/1.0104835","title":"A special purpose analog computer for statistical system identification","year":2011,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Identification (biology); Computer science; Statistical analysis; Mathematics; Statistics","score_opus":0.01647195919253246,"score_gpt":0.18174834345074362,"score_spread":0.16527638425821115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339032990","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046201954,0.0028679771,0.90591174,0.00045738035,0.0007674032,0.0004301545,0.00065064384,0.025341177,0.05895329],"genre_scores_gemma":[0.16514412,0.0024318895,0.7264985,0.001511532,0.0011839007,0.0014664372,0.0017452979,0.0012225766,0.09879581],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991191,0.0002466987,0.00005129949,0.0001514256,0.0003771955,0.000054248987],"domain_scores_gemma":[0.9988651,0.0003176,0.000046417525,0.000441528,0.00027910445,0.000050163373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007890738,0.00092947483,0.00078074535,0.001151602,0.0005949623,0.0013125339,0.0017048507,0.0010365923,0.067060344],"category_scores_gemma":[0.0025167884,0.0003521214,0.00046810493,0.001392951,0.0005151417,0.0012458421,0.0010022436,0.0018153744,0.021986635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044203651,0.00018349156,0.0010598421,0.00084724755,0.00011783465,0.00048991246,0.00021605112,0.0048032724,0.045318358,0.10247092,0.09205012,0.7520009],"study_design_scores_gemma":[0.00020352686,0.0005557329,0.0014824392,0.0002366595,0.00013869163,0.0022616403,0.0000561943,0.09822438,0.035906594,0.027384227,0.8334608,0.00008910792],"about_ca_topic_score_codex":0.00049743033,"about_ca_topic_score_gemma":0.0007630948,"teacher_disagreement_score":0.067060344,"about_ca_system_score_codex":0.0004714691,"about_ca_system_score_gemma":0.0006215095,"threshold_uncertainty_score":0.22433925},"labels":[],"label_agreement":null},{"id":"W2339949171","doi":"10.1007/978-0-387-31701-4_3","title":"Modeling Electrochemical Phenomena via Markov Chains and Processes","year":2007,"lang":"en","type":"book-chapter","venue":"Modern aspects of electrochemistry","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov chain; Examples of Markov chains; Markov model; Markov process; Variable-order Markov model; Balance equation; Computer science; Markov renewal process; Markov property; Statistical physics; Mathematics; Statistics; Machine learning; Physics","score_opus":0.013070722214143215,"score_gpt":0.22577659575402984,"score_spread":0.21270587353988663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339949171","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028777607,0.0009158583,0.96217597,0.00061926385,0.000101190046,0.000035807905,0.00021045755,0.00033953282,0.006824306],"genre_scores_gemma":[0.85648715,0.003301247,0.12290634,0.0002413494,0.00034005643,0.00028451282,0.0005136511,0.0001784165,0.015747296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958533,0.00015464053,0.00002113801,0.00007924234,0.00010406984,0.00005573518],"domain_scores_gemma":[0.9976169,0.0019192117,0.0001599697,0.00011758228,0.00012593679,0.00006048336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001102379,0.0008612547,0.0013102326,0.0008129284,0.0005407293,0.0016614557,0.001719293,0.001815917,0.003509545],"category_scores_gemma":[0.00402501,0.00080706814,0.0011411482,0.00091784017,0.00177233,0.0028365895,0.0010322817,0.001959109,0.0005410727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002445863,0.00003087308,0.00036485886,0.000031124157,0.000023258177,0.000039620205,0.00004586514,0.839256,0.000348053,0.15360577,0.00056547724,0.0056646937],"study_design_scores_gemma":[0.000004192531,0.0000027979008,0.000030301015,0.0000030373185,0.0000034765194,0.000006663878,0.000002547275,0.94268256,0.00008227571,0.056949623,0.00022841748,0.000004201276],"about_ca_topic_score_codex":0.0075465413,"about_ca_topic_score_gemma":0.006467609,"teacher_disagreement_score":0.0075465413,"about_ca_system_score_codex":0.0012481392,"about_ca_system_score_gemma":0.0010019657,"threshold_uncertainty_score":0.015005231},"labels":[],"label_agreement":null},{"id":"W2345668077","doi":"10.48550/arxiv.1605.00064","title":"Higher Order Recurrent Neural Networks","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Recurrent neural network; Computer science; Treebank; Dependency (UML); Long short term memory; Artificial intelligence; Sequence (biology); Language model; Artificial neural network; Task (project management); Speech recognition; Natural language processing; Machine learning","score_opus":0.06515286175752022,"score_gpt":0.19611216866909464,"score_spread":0.13095930691157442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345668077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061902527,0.0022856204,0.928573,0.0005198492,0.00017971352,0.00004100509,0.0005883785,0.0016703365,0.004239593],"genre_scores_gemma":[0.83290875,0.0014578226,0.15320385,0.00041870517,0.00016592967,0.000102598024,0.0016140281,0.00022832875,0.009900099],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993587,0.0001503529,0.000051025985,0.00019986132,0.00015417302,0.00008591412],"domain_scores_gemma":[0.99828184,0.0009442049,0.00020619112,0.00023891072,0.0002870335,0.00004176278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010337229,0.00096791046,0.0009714113,0.00069087883,0.0003193978,0.0011151067,0.0014565949,0.0010375911,0.0029096636],"category_scores_gemma":[0.0046736505,0.0004529126,0.00077433256,0.0008625802,0.00057351193,0.0025671097,0.00073348515,0.0015300383,0.0009858214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017959435,0.00011348229,0.0023583842,0.00027553047,0.00019673408,0.0003016303,0.00016478729,0.79874176,0.009823171,0.037207425,0.003965131,0.14667243],"study_design_scores_gemma":[0.0000038186145,0.000019604995,0.00018214039,0.000006312918,0.000011602731,0.000029277371,0.0000047860717,0.9903644,0.0009809558,0.007739749,0.00065148243,0.0000059780677],"about_ca_topic_score_codex":0.004450437,"about_ca_topic_score_gemma":0.007294635,"teacher_disagreement_score":0.004450437,"about_ca_system_score_codex":0.0008748285,"about_ca_system_score_gemma":0.0005318543,"threshold_uncertainty_score":0.009733796},"labels":[],"label_agreement":null},{"id":"W2351133820","doi":"","title":"Control of Permanent Magnet Synchronous Motor Chaotic Phenomena Based on Passive Network Theory","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Control theory (sociology); Chaotic; Permanent magnet synchronous motor; Magnet; Synchronous motor; Permanent magnet synchronous generator; Controller (irrigation); Computer science; State (computer science); Control (management); Control engineering; Engineering; Electrical engineering; Artificial intelligence; Algorithm","score_opus":0.005539296570949376,"score_gpt":0.20062108693166447,"score_spread":0.1950817903607151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2351133820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066174485,0.00044748787,0.92176306,0.00023207306,0.000117555755,0.000054122258,0.000047118567,0.00032208895,0.010841964],"genre_scores_gemma":[0.98616785,0.00025138663,0.010778162,0.00002294049,0.00002924922,0.00007781181,0.00002263736,0.000010552279,0.0026394601],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999324,0.000013448226,0.0000037302873,0.000018194687,0.000024524124,0.000007661816],"domain_scores_gemma":[0.99992085,0.000028988194,0.000017441329,0.0000045609377,0.000022661063,0.0000055406094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014319895,0.00032130143,0.00029270622,0.00014976013,0.0002289044,0.0003401656,0.0003576816,0.00020987488,0.0008783069],"category_scores_gemma":[0.0003626046,0.00011724109,0.00018585662,0.0001343083,0.00030709038,0.00039550939,0.00027361783,0.000239186,0.0000804338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029292994,0.00005643955,0.00052582845,0.00034997406,0.000062432446,0.00021116334,0.00019298098,0.7648254,0.066445954,0.079615146,0.0017752381,0.08564655],"study_design_scores_gemma":[0.000024296005,0.000056079225,0.00018195678,0.000005487429,0.00001062382,0.000019367639,0.0000050119615,0.99115855,0.0025780334,0.005125641,0.00082861626,0.0000063590005],"about_ca_topic_score_codex":0.001302684,"about_ca_topic_score_gemma":0.0009813855,"teacher_disagreement_score":0.001302684,"about_ca_system_score_codex":0.00027799173,"about_ca_system_score_gemma":0.00022407791,"threshold_uncertainty_score":0.002938211},"labels":[],"label_agreement":null},{"id":"W2351167323","doi":"","title":"The Data Communication Between Host Computer and Client Computer in Electric Neurodiagnostic Instrument","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Host (biology); Sampling (signal processing); Protocol (science); SIGNAL (programming language); Operating system; Computer hardware; Real-time computing; Telecommunications","score_opus":0.032353067213425915,"score_gpt":0.2506090146255621,"score_spread":0.21825594741213622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2351167323","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07859741,0.0028088733,0.8374104,0.0014738631,0.0010550271,0.00081825425,0.0007527932,0.022290545,0.05479281],"genre_scores_gemma":[0.66526264,0.0026805184,0.2622139,0.0011269797,0.0010343287,0.0014325754,0.001712444,0.0015520577,0.06298456],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978794,0.00042995208,0.00016987808,0.0005053366,0.0008036416,0.0002117979],"domain_scores_gemma":[0.998228,0.00045886825,0.00011610264,0.0003781487,0.00071570487,0.00010319265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090241095,0.000673547,0.00064153195,0.0009946228,0.0007034145,0.0021942223,0.001683207,0.0008383903,0.01043482],"category_scores_gemma":[0.0032013694,0.00035667553,0.00020129204,0.0009217747,0.00039970642,0.002038239,0.0011994581,0.0009688599,0.005549825],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015415567,0.0003107241,0.012032265,0.00091846404,0.000112460206,0.001833261,0.0021761889,0.0026009628,0.1249334,0.025080737,0.056123786,0.7723362],"study_design_scores_gemma":[0.00041923567,0.0011650941,0.014884071,0.0005621615,0.00034693847,0.009969011,0.0009998133,0.12181249,0.47792187,0.0146525055,0.35684636,0.00042044104],"about_ca_topic_score_codex":0.0012139278,"about_ca_topic_score_gemma":0.0005536706,"teacher_disagreement_score":0.01043482,"about_ca_system_score_codex":0.00057273655,"about_ca_system_score_gemma":0.00075069483,"threshold_uncertainty_score":0.034907877},"labels":[],"label_agreement":null},{"id":"W2364004482","doi":"","title":"Tandem system based on competing information","year":2011,"lang":"en","type":"article","venue":"Journal of Tsinghua University(Science and Technology)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Tandem; Computer science; Decoding methods; Perceptron; Artificial intelligence; Multilayer perceptron; Selection (genetic algorithm); Pattern recognition (psychology); Artificial neural network; Machine learning; Algorithm; Engineering","score_opus":0.009660123543860684,"score_gpt":0.1756202056033418,"score_spread":0.16596008205948112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2364004482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1343527,0.0010361329,0.8418848,0.00024018424,0.00044445533,0.00017971786,0.0003118277,0.004556023,0.016994191],"genre_scores_gemma":[0.77850175,0.00040026256,0.20334782,0.00038006995,0.00023076932,0.00017383107,0.00074359245,0.00024428344,0.01597753],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991912,0.00011551104,0.000054669756,0.00028023004,0.00027377927,0.00008463058],"domain_scores_gemma":[0.99920374,0.00018458825,0.00005551459,0.00016869385,0.00032064898,0.00006682691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057241315,0.0008858695,0.00080281304,0.00068847684,0.00065649924,0.00077446754,0.001152979,0.0010962407,0.0060798153],"category_scores_gemma":[0.0015219012,0.00034631236,0.00059326214,0.00089277513,0.000437035,0.0019781704,0.001306636,0.00090151845,0.0030627716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011367371,0.0002832134,0.0024564776,0.000367249,0.00016919656,0.0005372555,0.00027914182,0.031621244,0.2339518,0.009073258,0.007725626,0.7123989],"study_design_scores_gemma":[0.000084512576,0.0010277141,0.0028004197,0.00003540854,0.00027273712,0.0013169268,0.000094293544,0.8517755,0.12458265,0.0074349586,0.010446462,0.00012846726],"about_ca_topic_score_codex":0.0014030534,"about_ca_topic_score_gemma":0.0019183599,"teacher_disagreement_score":0.0060798153,"about_ca_system_score_codex":0.00034869663,"about_ca_system_score_gemma":0.0008122199,"threshold_uncertainty_score":0.020339012},"labels":[],"label_agreement":null},{"id":"W2369213853","doi":"","title":"Analysis of the Recognition Performances of Synergetic Neural Network","year":2000,"lang":"en","type":"article","venue":"Dianzi xuebao","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Character recognition; Time delay neural network; Neocognitron; Hopfield network; Image (mathematics)","score_opus":0.012026218812967266,"score_gpt":0.21554948491888137,"score_spread":0.20352326610591412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2369213853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5523074,0.0021838478,0.42872423,0.00034763664,0.00010061741,0.000059616686,0.00012866291,0.0012061466,0.014941819],"genre_scores_gemma":[0.9814186,0.00037152538,0.016455717,0.000021363585,0.000021363483,0.000028786557,0.000098910634,0.00002725581,0.0015563581],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944586,0.00011037352,0.000030314448,0.00010435714,0.00024663587,0.000062475745],"domain_scores_gemma":[0.9988458,0.00061543816,0.00006433895,0.00009426733,0.00035397615,0.00002616666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011773058,0.00037817075,0.00051188184,0.00074914977,0.00023525803,0.0004986642,0.00031981128,0.00044312407,0.0020964325],"category_scores_gemma":[0.0038561423,0.00015965414,0.00033090173,0.0004810654,0.00025076492,0.00092033774,0.00026292627,0.00029919957,0.0003650723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010635623,0.00013781704,0.006558035,0.0003026912,0.00019340706,0.00024006418,0.0001437888,0.39131248,0.068590045,0.0063980133,0.0015069188,0.52355325],"study_design_scores_gemma":[0.0000074422715,0.00009098713,0.0031302255,0.000005062634,0.000023386958,0.00010794607,0.000015398453,0.98340607,0.011879778,0.0009929709,0.0003277135,0.000013138301],"about_ca_topic_score_codex":0.0014861213,"about_ca_topic_score_gemma":0.0006634813,"teacher_disagreement_score":0.0020964325,"about_ca_system_score_codex":0.0004299718,"about_ca_system_score_gemma":0.0002947373,"threshold_uncertainty_score":0.007013321},"labels":[],"label_agreement":null},{"id":"W2370390198","doi":"","title":"Method of sample consensus based on Skinner operant conditioning","year":2015,"lang":"en","type":"article","venue":"Kongzhi yu juece","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"RANSAC; Operant conditioning; Sample (material); Set (abstract data type); Artificial intelligence; Sampling (signal processing); Computer science; Psychology; Machine learning; Statistics; Mathematics; Computer vision; Social psychology; Reinforcement","score_opus":0.04762437579885611,"score_gpt":0.31583841210889435,"score_spread":0.26821403631003826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2370390198","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023370057,0.000069180314,0.99671566,0.00003815752,0.00003525159,0.00004074588,0.000011005285,0.00023888102,0.00051404815],"genre_scores_gemma":[0.21516746,0.00031930898,0.77928776,0.00017615578,0.00012101053,0.00032129066,0.0001937357,0.0002751475,0.004138114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977214,0.0005512218,0.00010054237,0.00056697463,0.0009140952,0.00014588871],"domain_scores_gemma":[0.99803656,0.0005225107,0.00020204503,0.00025960297,0.00086547656,0.000113936294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023087908,0.0010910466,0.0016332826,0.0010655685,0.0008378537,0.0008875507,0.0018661337,0.0013935687,0.0037993148],"category_scores_gemma":[0.004535757,0.00044059512,0.0010665734,0.0007539924,0.0014790476,0.0018916532,0.0019070302,0.0018544535,0.0007041211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048124228,0.00012190192,0.0013543483,0.00039038216,0.00017983456,0.00023581549,0.00040988455,0.2953152,0.05629677,0.03970411,0.0047658603,0.60074466],"study_design_scores_gemma":[0.000025940368,0.000109551016,0.00029779793,0.0000139083895,0.000018206183,0.00013462236,0.000031241307,0.979001,0.014670692,0.0033096753,0.0023398213,0.00004763703],"about_ca_topic_score_codex":0.003081352,"about_ca_topic_score_gemma":0.001916057,"teacher_disagreement_score":0.0037993148,"about_ca_system_score_codex":0.0007191467,"about_ca_system_score_gemma":0.0018342746,"threshold_uncertainty_score":0.012709975},"labels":[],"label_agreement":null},{"id":"W2371468626","doi":"","title":"An Experimental Study of Perceiving Mandarin Tones for English Speakers Without Chinese Learning Experience","year":2012,"lang":"en","type":"article","venue":"Journal of Hechi University","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Mandarin Chinese; Tone (literature); Coda; Psychology; Perception; Audiology; Task (project management); Linguistics; Speech recognition; Computer science; Acoustics; Medicine; Physics; Engineering; Philosophy","score_opus":0.015332676103805091,"score_gpt":0.28772646043479866,"score_spread":0.2723937843309936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2371468626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9997137,0.0000125346305,0.000082387414,0.0000043404084,0.0000024675398,0.000011054886,0.000010185405,0.0000014680996,0.00016168915],"genre_scores_gemma":[0.9978083,0.0000628642,0.00085138326,0.000029592664,0.0000071665836,0.000049108843,0.000074750904,0.0000044297544,0.0011124635],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99970514,0.0000747699,0.000028592218,0.000086505286,0.00005426098,0.00005068626],"domain_scores_gemma":[0.99874395,0.00077272276,0.00010414371,0.0001049432,0.00009758256,0.00017659305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006222474,0.00053186546,0.00049538695,0.00019872311,0.00043568952,0.00040788428,0.00029748076,0.00038154132,0.0030838242],"category_scores_gemma":[0.002785842,0.00027758494,0.00024811958,0.00011568391,0.00044037594,0.00044016066,0.00041942214,0.0004828962,0.000300321],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009963676,0.007219421,0.063255176,0.00041782486,0.00011218856,0.0018280401,0.016783828,0.00020266001,0.8775486,0.00033485066,0.00023905456,0.02209477],"study_design_scores_gemma":[0.0008025683,0.054131966,0.76473707,0.00003564717,0.00035613513,0.003050297,0.012785329,0.0028759898,0.15817715,0.0005106647,0.0024004083,0.00013676481],"about_ca_topic_score_codex":0.0013865464,"about_ca_topic_score_gemma":0.0021788017,"teacher_disagreement_score":0.0030838242,"about_ca_system_score_codex":0.0001558453,"about_ca_system_score_gemma":0.00029083883,"threshold_uncertainty_score":0.0103164315},"labels":[],"label_agreement":null},{"id":"W2378736769","doi":"","title":"Application of Direct Digital Synthesis Technology In An Intelligent Metal Detector","year":2005,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sine; Detector; Computer science; Phase (matter); SIGNAL (programming language); Chip; Direct digital synthesizer; Electronic circuit; Function (biology); Electronic engineering; Phase-locked loop; Electrical engineering; Telecommunications; Physics; Mathematics","score_opus":0.008293469423913104,"score_gpt":0.2392632182777425,"score_spread":0.2309697488538294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2378736769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07963092,0.00071358366,0.8933772,0.00029858772,0.00022267972,0.00014038905,0.000078070785,0.0019014329,0.023637226],"genre_scores_gemma":[0.5887663,0.00028037612,0.4036606,0.0002133343,0.00006872636,0.000082188584,0.00006929479,0.000055842276,0.006803297],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999696,0.00004673876,0.000017842378,0.00007953922,0.00013179384,0.000028069413],"domain_scores_gemma":[0.9997744,0.000073874966,0.0000338815,0.000035333072,0.00006999806,0.000012544146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031937863,0.00029457465,0.00017677178,0.0003971052,0.00021390365,0.00056666933,0.00042579792,0.0002970304,0.0020278108],"category_scores_gemma":[0.00059059163,0.0002047789,0.00016231397,0.00025148608,0.00034308323,0.00037639542,0.00026245168,0.00020706234,0.00057916495],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002656417,0.000078402016,0.0009747964,0.00025924272,0.000025730415,0.00016947291,0.00019753489,0.0069006374,0.71431553,0.023858488,0.0015225393,0.25143203],"study_design_scores_gemma":[0.00014486258,0.001270106,0.0015066226,0.000049211107,0.0001082903,0.0011973523,0.00005641263,0.083698966,0.8324553,0.00662277,0.07283799,0.000052173655],"about_ca_topic_score_codex":0.00029695086,"about_ca_topic_score_gemma":0.00048691517,"teacher_disagreement_score":0.0020278108,"about_ca_system_score_codex":0.00042272807,"about_ca_system_score_gemma":0.00038620626,"threshold_uncertainty_score":0.006783724},"labels":[],"label_agreement":null},{"id":"W2384770035","doi":"","title":"Empirical Analysis on Dynamic Evolution of RMB Exchange Rate Formation Mechanism","year":2015,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Renminbi; Liberian dollar; Exchange rate; Economics; Currency; Exchange-rate flexibility; International economics; Monetary economics; Emerging markets; U.S. Dollar Index; Us dollar; Effective exchange rate; Exchange-rate regime; Macroeconomics; Finance","score_opus":0.03923191749197996,"score_gpt":0.29907338441648884,"score_spread":0.2598414669245089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2384770035","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99227494,0.0006850307,0.003084735,0.00041258638,0.00001993689,0.000014514926,0.000795811,0.00010024167,0.0026121824],"genre_scores_gemma":[0.99695706,0.00024886028,0.0005596631,0.00002891118,0.000021137412,0.000007690433,0.0011967716,0.000014255784,0.0009656794],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993963,0.00017107805,0.00004574373,0.00017541804,0.00011790204,0.00009352979],"domain_scores_gemma":[0.9961312,0.0015894244,0.0012588116,0.0003499606,0.0004261724,0.00024439459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002114227,0.0003209053,0.00051142095,0.001387378,0.00029064648,0.0013565065,0.0007211473,0.00073046924,0.0023646543],"category_scores_gemma":[0.008775109,0.00031745428,0.00051877776,0.0014988177,0.00027601578,0.0011451385,0.00055056304,0.0011934902,0.00070331834],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003857909,0.0002002799,0.90115833,0.00012729764,0.0003297417,0.0015974904,0.0005845988,0.034788575,0.0028347115,0.010236578,0.0049416167,0.042815015],"study_design_scores_gemma":[0.000035565085,0.00017866518,0.75553846,0.000048821195,0.00015397422,0.0007488558,0.0005540226,0.23279724,0.001706342,0.0027630434,0.0054072463,0.000067773566],"about_ca_topic_score_codex":0.006941498,"about_ca_topic_score_gemma":0.003910358,"teacher_disagreement_score":0.006941498,"about_ca_system_score_codex":0.00041991923,"about_ca_system_score_gemma":0.00029863816,"threshold_uncertainty_score":0.013802171},"labels":[],"label_agreement":null},{"id":"W2393514044","doi":"","title":"An AQM Algorithm Based on Single-Neuron Predictive PID","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Active queue management; Computer science; PID controller; Queue; Control theory (sociology); Model predictive control; Algorithm; Controller (irrigation); Real-time computing; Network congestion; Control (management); Artificial intelligence; Control engineering; Computer network; Network packet","score_opus":0.015184804542230843,"score_gpt":0.23018610200351816,"score_spread":0.21500129746128732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2393514044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017612634,0.0004935328,0.9762512,0.00021525311,0.0003042604,0.00009631866,0.00002910196,0.0009165263,0.0040812897],"genre_scores_gemma":[0.73483646,0.0004678041,0.25757217,0.00020211362,0.00013671155,0.00029589626,0.00008524641,0.0000399774,0.00636361],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997806,0.00002983283,0.00001413854,0.00006084046,0.000092978946,0.000021625438],"domain_scores_gemma":[0.99977726,0.000052623687,0.000021982303,0.000016239494,0.00011552612,0.000016500357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044264083,0.00041151198,0.0006625758,0.00035010165,0.00044988288,0.0006505473,0.0013469735,0.0006818228,0.0020903442],"category_scores_gemma":[0.0009621291,0.00022301116,0.0002432331,0.00047461037,0.00039751368,0.00057837367,0.00051429047,0.0009023882,0.00039554143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024028742,0.00013592692,0.0013594723,0.00031597444,0.00007787172,0.0001588053,0.00018906387,0.30437198,0.024193686,0.019632602,0.00464324,0.64468116],"study_design_scores_gemma":[0.000057638925,0.000073203286,0.00019545449,0.000008417073,0.000014858003,0.000053167263,0.000010230539,0.993665,0.0020288727,0.0016975616,0.0021855664,0.000010061581],"about_ca_topic_score_codex":0.0034405307,"about_ca_topic_score_gemma":0.0027451122,"teacher_disagreement_score":0.0034405307,"about_ca_system_score_codex":0.00040453335,"about_ca_system_score_gemma":0.0011017659,"threshold_uncertainty_score":0.0069928765},"labels":[],"label_agreement":null},{"id":"W2395088231","doi":"","title":"An Optimized Learning Algorithm Based on Linear Filters Suitable for Hardware implemented Self-Organizing Maps.","year":2014,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Algorithm; Quantization (signal processing); Computer science; Self-organizing map; Finite impulse response; Process (computing); Artificial intelligence; Artificial neural network","score_opus":0.013145446461570317,"score_gpt":0.26399542228752854,"score_spread":0.2508499758259582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395088231","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064027696,0.0001750096,0.9913283,0.000043107328,0.000051653267,0.00003864698,0.000018703151,0.00056217005,0.0013796192],"genre_scores_gemma":[0.22082016,0.0001445623,0.77474195,0.00008658957,0.000035361372,0.00023365008,0.000096016265,0.00008881188,0.0037529685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984205,0.000029010607,0.000009023618,0.000037683847,0.000064773565,0.000017419077],"domain_scores_gemma":[0.99982905,0.00005830315,0.000018808689,0.000017427092,0.00006828678,0.000008095297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002837965,0.00038380624,0.00028148445,0.0002764013,0.00019567506,0.00046329905,0.0006641941,0.00065150287,0.0025068924],"category_scores_gemma":[0.0006591097,0.00017923822,0.00027104863,0.000302719,0.00020779391,0.00045279882,0.00025183137,0.00046872825,0.0008653323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024194694,0.00015211469,0.00074640877,0.00028726883,0.000097631586,0.000095002935,0.000085974316,0.19521803,0.073373966,0.016661404,0.0047223056,0.70831794],"study_design_scores_gemma":[0.00002287306,0.000085661544,0.00027479144,0.000010551828,0.00001486669,0.00006793177,0.000008863982,0.98138994,0.012294146,0.0016035576,0.0042187013,0.000008056628],"about_ca_topic_score_codex":0.0013005164,"about_ca_topic_score_gemma":0.0020370928,"teacher_disagreement_score":0.0025068924,"about_ca_system_score_codex":0.00041348103,"about_ca_system_score_gemma":0.0005365773,"threshold_uncertainty_score":0.0083863735},"labels":[],"label_agreement":null},{"id":"W2396912282","doi":"","title":"Neural-network Modelling of Bayesian Learning and Inference.","year":2013,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Artificial intelligence; Machine learning; Computer science; Bayesian inference; Artificial neural network; Inference; Bayes' theorem; Bayesian probability; Bayesian statistics; Cognitive science; Psychology","score_opus":0.01709716146153614,"score_gpt":0.21191275769000836,"score_spread":0.19481559622847222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396912282","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023410644,0.0004359505,0.98919266,0.0007470702,0.00006353161,0.00003396084,0.00013191615,0.00015371843,0.006900114],"genre_scores_gemma":[0.42586884,0.001994179,0.55581117,0.00053801027,0.00031055437,0.0005272458,0.00047251914,0.00014471216,0.014332718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857557,0.00074116763,0.00007522819,0.00020572003,0.00031964225,0.00008270211],"domain_scores_gemma":[0.99692386,0.0020874846,0.00026496776,0.00031605607,0.00029048277,0.0001171133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003238605,0.0008683661,0.00081078004,0.0010138762,0.00056347495,0.0022938978,0.0031710153,0.0023667375,0.006356408],"category_scores_gemma":[0.014904865,0.00067306927,0.0009834222,0.0011621563,0.0019570887,0.0040174494,0.0016647445,0.0028642947,0.0016830862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028418437,0.000024045677,0.00048413523,0.00010202076,0.000064350614,0.00009974806,0.00009543121,0.36260942,0.00070954376,0.6112126,0.001651064,0.022919236],"study_design_scores_gemma":[0.0000061119213,0.0000074473596,0.00008406077,0.00001950787,0.000010809248,0.00003590035,0.0000060921643,0.73739856,0.00021173948,0.26017603,0.0020356476,0.000008048179],"about_ca_topic_score_codex":0.0052388636,"about_ca_topic_score_gemma":0.0061966274,"teacher_disagreement_score":0.006356408,"about_ca_system_score_codex":0.0017374188,"about_ca_system_score_gemma":0.0015493,"threshold_uncertainty_score":0.021264315},"labels":[],"label_agreement":null},{"id":"W2398265630","doi":"","title":"Nengo and the Neural Engineering Framework: From Spikes to Cognition","year":2012,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cognitive architecture; Cognition; Cognitive model; Cognitive science; Artificial intelligence; Variety (cybernetics); Psychology","score_opus":0.011743752660977111,"score_gpt":0.20498184215762377,"score_spread":0.19323808949664667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398265630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008567996,0.006305456,0.9328798,0.004571928,0.0005163043,0.000030084917,0.00028731674,0.0007530747,0.046088014],"genre_scores_gemma":[0.3295389,0.012642664,0.5908413,0.0012279165,0.00075946626,0.000325684,0.0005745776,0.0006268262,0.06346268],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999052,0.000028945893,0.000004193045,0.000019954587,0.000028883902,0.000012865516],"domain_scores_gemma":[0.99986506,0.000076660464,0.000008380872,0.00001489579,0.00002108549,0.00001390302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038969915,0.00045846976,0.00038696302,0.00052733236,0.00032774548,0.0013511877,0.0009904541,0.0009853401,0.0072418423],"category_scores_gemma":[0.001430741,0.0002213288,0.00041782888,0.00048103166,0.0009991385,0.0025108005,0.0008433352,0.0012649051,0.0010883933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023716091,0.000009515553,0.00023481663,0.00012011425,0.000011800742,0.00009898314,0.00015611685,0.017428763,0.0014246365,0.88690037,0.012648305,0.080942914],"study_design_scores_gemma":[0.000004006965,0.0000062224663,0.00025119513,0.00004988326,0.0000054414695,0.00010107513,0.00003282027,0.08704462,0.00074486,0.8755463,0.036201473,0.00001221485],"about_ca_topic_score_codex":0.0018079154,"about_ca_topic_score_gemma":0.0022205631,"teacher_disagreement_score":0.0072418423,"about_ca_system_score_codex":0.0006668253,"about_ca_system_score_gemma":0.0005786888,"threshold_uncertainty_score":0.024226367},"labels":[],"label_agreement":null},{"id":"W2400065095","doi":"","title":"Natural Gradient Revisited","year":2013,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Hessian matrix; Stochastic gradient descent; Gradient descent; Diagonal; Robustness (evolution); Generalization; Computer science; Metric (unit); Subspace topology; Mathematics; Algorithm; Mathematical optimization; Applied mathematics; Artificial intelligence; Artificial neural network; Mathematical analysis","score_opus":0.008321171765689783,"score_gpt":0.21735135065533143,"score_spread":0.20903017888964165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400065095","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03971022,0.003824745,0.9261082,0.0018475516,0.0009848174,0.00019597165,0.0007761976,0.0025401558,0.024012098],"genre_scores_gemma":[0.52286977,0.0011333307,0.4635918,0.0013961893,0.00039084474,0.00029773015,0.0013225871,0.0014004908,0.0075973007],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99674463,0.0014938475,0.00015373631,0.00055470096,0.00087012956,0.00018295104],"domain_scores_gemma":[0.9918881,0.003903134,0.00044816468,0.0016182917,0.0017997609,0.0003425386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065223696,0.001587284,0.0012149521,0.0022028543,0.0009678312,0.0021380843,0.002029224,0.0022000063,0.008193004],"category_scores_gemma":[0.034730095,0.00042041246,0.0010727467,0.0013933458,0.0022313963,0.005395399,0.0025865764,0.002700566,0.0014682395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006658299,0.00039893846,0.0059764637,0.00062952604,0.00030287154,0.0001728488,0.00024761702,0.35009384,0.0023609672,0.36363316,0.0354022,0.24011572],"study_design_scores_gemma":[0.00008031449,0.00031158628,0.0009410787,0.00009682466,0.00004312821,0.00023713031,0.000057159843,0.8416166,0.0034576368,0.13759346,0.015524088,0.000041005216],"about_ca_topic_score_codex":0.0060400884,"about_ca_topic_score_gemma":0.007096042,"teacher_disagreement_score":0.008193004,"about_ca_system_score_codex":0.0017136852,"about_ca_system_score_gemma":0.0023350103,"threshold_uncertainty_score":0.034494042},"labels":[],"label_agreement":null},{"id":"W2400704637","doi":"","title":"Pyschoacoustic Feature Based Perceptual Segmentation","year":2010,"lang":"en","type":"article","venue":"The Journal of the Abraham Lincoln Association","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Feature (linguistics); Perception; Pattern recognition (psychology); Computer vision; Psychology; Neuroscience","score_opus":0.004933143212449914,"score_gpt":0.22700909781589962,"score_spread":0.2220759546034497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400704637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15222037,0.00095171324,0.8164429,0.00020412654,0.00022642639,0.00013283819,0.0003541254,0.0022958938,0.027171643],"genre_scores_gemma":[0.7591025,0.00049779285,0.22942509,0.00013740221,0.00007367323,0.000052318283,0.00055854826,0.0003862352,0.009766403],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998392,0.000023543096,0.0000066001585,0.000046248024,0.00005303303,0.000031383388],"domain_scores_gemma":[0.9998,0.00006868765,0.000010928704,0.00003538861,0.00006499471,0.000019942556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025531685,0.00040348864,0.00035806658,0.0006976391,0.000281313,0.0009335264,0.00052250613,0.0005782047,0.008432053],"category_scores_gemma":[0.0008031371,0.00019748362,0.00031508013,0.00056339416,0.00043479854,0.0012536526,0.00075315294,0.000523066,0.0013595193],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010415337,0.000120074365,0.0007509581,0.00020820882,0.000036694655,0.00016215659,0.000110727764,0.010369098,0.37133172,0.006396782,0.0021937077,0.60727835],"study_design_scores_gemma":[0.00010846397,0.0005733625,0.022132538,0.000078431825,0.00014032694,0.0011559828,0.00022834662,0.67868626,0.26266652,0.01893302,0.015206406,0.000090442],"about_ca_topic_score_codex":0.0014902515,"about_ca_topic_score_gemma":0.0028222492,"teacher_disagreement_score":0.008432053,"about_ca_system_score_codex":0.00026074843,"about_ca_system_score_gemma":0.000285992,"threshold_uncertainty_score":0.028208077},"labels":[],"label_agreement":null},{"id":"W2401002434","doi":"","title":"Learning Dictionary Via Wavelet Sparse Principal Component Analysis.","year":2015,"lang":"en","type":"article","venue":"International Conference on Pattern Recognition Applications and Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Principal component analysis; Wavelet; Computer science; Artificial intelligence; Pattern recognition (psychology); Dictionary learning; Component (thermodynamics); Sparse PCA; Sparse approximation; Speech recognition; Physics","score_opus":0.11724250453842144,"score_gpt":0.3808358604053482,"score_spread":0.2635933558669268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401002434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061167977,0.00029889247,0.9918664,0.000118350246,0.000114026974,0.000028099495,0.00017220352,0.00051671773,0.00076854817],"genre_scores_gemma":[0.1753909,0.0012855867,0.81403613,0.00022398322,0.0002045021,0.00019573575,0.0028933745,0.00037307476,0.005396727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995796,0.00009955513,0.000028895292,0.000093057024,0.00014591195,0.000053027707],"domain_scores_gemma":[0.99906856,0.00026913307,0.00007283122,0.00022462689,0.00030701127,0.000057814363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006152798,0.0008821149,0.0011534836,0.00095253345,0.00038790097,0.0009282531,0.0011011143,0.0010078301,0.002920699],"category_scores_gemma":[0.0047818027,0.0005765977,0.0007782407,0.0017450865,0.0005827845,0.0015826313,0.0013743757,0.001976848,0.002765817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032997824,0.00016237862,0.0011851182,0.00026404578,0.0001494184,0.00012619868,0.00013256952,0.08578687,0.023550153,0.021264512,0.028710652,0.8383381],"study_design_scores_gemma":[0.00003267987,0.000069717775,0.00046313455,0.000023316657,0.000035399782,0.00010325358,0.00005884463,0.9751515,0.004407085,0.01432585,0.0053110337,0.00001822812],"about_ca_topic_score_codex":0.0024172359,"about_ca_topic_score_gemma":0.003160763,"teacher_disagreement_score":0.002920699,"about_ca_system_score_codex":0.00021673368,"about_ca_system_score_gemma":0.000951536,"threshold_uncertainty_score":0.009770691},"labels":[],"label_agreement":null},{"id":"W2401353505","doi":"","title":"Fisherman learning algorithm of the SOM realized in the CMOS technology","year":2011,"lang":"en","type":"article","venue":"The European Symposium on Artificial Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Realization (probability); Computer science; Adaptation (eye); Process (computing); Software; Self-organizing map; Algorithm; Transistor; Artificial intelligence; Artificial neural network; Mathematics; Engineering; Electrical engineering; Programming language","score_opus":0.024481091988584294,"score_gpt":0.22536596113501228,"score_spread":0.200884869146428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401353505","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020538958,0.00021408891,0.9696618,0.00013199539,0.000058182108,0.000023770737,0.000052668376,0.00032512893,0.008993422],"genre_scores_gemma":[0.41291937,0.0004173327,0.5765751,0.000117504016,0.00003628196,0.00010105782,0.00016393607,0.0000622033,0.009607155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991333,0.000015415659,0.0000043479126,0.0000198415,0.000034589804,0.000012576256],"domain_scores_gemma":[0.999946,0.000012168561,0.0000055714545,0.000009454444,0.000023147786,0.0000036426427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017138319,0.00020694174,0.00019969068,0.00018052754,0.00019872672,0.00039373152,0.00050074427,0.00041953012,0.001952544],"category_scores_gemma":[0.00036458223,0.00012780719,0.00032122672,0.00021536875,0.00025751514,0.0004973388,0.00024556255,0.00038995687,0.00052778446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013779773,0.000046866284,0.0012515013,0.00023622837,0.00005886259,0.0002003978,0.0001735205,0.27583474,0.081614025,0.29129982,0.0043017813,0.3448445],"study_design_scores_gemma":[0.0000147417395,0.000052461655,0.00041315323,0.000015522875,0.0000098803675,0.00015677146,0.00001982345,0.9405559,0.017213758,0.03400306,0.0075301593,0.00001470728],"about_ca_topic_score_codex":0.0012014025,"about_ca_topic_score_gemma":0.00096903933,"teacher_disagreement_score":0.001952544,"about_ca_system_score_codex":0.00036082836,"about_ca_system_score_gemma":0.00049824535,"threshold_uncertainty_score":0.006531954},"labels":[],"label_agreement":null},{"id":"W2401360348","doi":"10.4171/owr/2014/50","title":"Probability, Trees and Algorithms","year":2015,"lang":"en","type":"article","venue":"Oberwolfach Reports","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Algorithm","score_opus":0.06618142620406302,"score_gpt":0.2656332345902907,"score_spread":0.19945180838622767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401360348","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009106411,0.42551082,0.24643362,0.07925558,0.0073225535,0.00009260521,0.0009259431,0.000682104,0.23067033],"genre_scores_gemma":[0.49790207,0.28598064,0.09102466,0.016332604,0.033725042,0.0005826215,0.0012911909,0.0006859648,0.072475255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99715084,0.0011601403,0.00014657165,0.0005334483,0.0008390378,0.00017008463],"domain_scores_gemma":[0.99273866,0.0058240937,0.00026322532,0.0005090231,0.00046084347,0.0002041473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028621969,0.0010504783,0.0017725712,0.002949173,0.0017062959,0.007525179,0.0012311426,0.0027021708,0.012124327],"category_scores_gemma":[0.010927357,0.00055933977,0.000949726,0.004860131,0.008419245,0.010269887,0.0022593797,0.007338622,0.0023441457],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066859016,0.0000074623044,0.00012728495,0.0001252386,0.000013631695,0.000029194907,0.00012638781,0.0010830929,0.000060237195,0.9720795,0.011500072,0.01484121],"study_design_scores_gemma":[0.0000030476676,0.0000043937507,0.00009786913,0.000040778123,0.0000030517774,0.000041084233,0.00003151203,0.0009428687,0.000018652643,0.97207314,0.026739538,0.0000039996853],"about_ca_topic_score_codex":0.0024616201,"about_ca_topic_score_gemma":0.0012235245,"teacher_disagreement_score":0.012124327,"about_ca_system_score_codex":0.0038776298,"about_ca_system_score_gemma":0.0016716445,"threshold_uncertainty_score":0.040559947},"labels":[],"label_agreement":null},{"id":"W2402952899","doi":"","title":"Pattern Recognition System Combining KNN rules and New Feature Weighting algorithm","year":2005,"lang":"en","type":"article","venue":"Journal of the Institute of Electronics Engineers of Korea","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Weighting; Overfitting; Pattern recognition (psychology); Feature (linguistics); Computer science; Artificial intelligence; k-nearest neighbors algorithm; Feature vector; Class (philosophy); Numeral system; Algorithm; Artificial neural network","score_opus":0.010195244993446982,"score_gpt":0.20622807676627497,"score_spread":0.196032831772828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402952899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075732023,0.00020766984,0.9895559,0.00005144888,0.000070340786,0.00006820063,0.000026684022,0.0010981666,0.0013483489],"genre_scores_gemma":[0.19645251,0.00034606588,0.79826075,0.000189929,0.00010427816,0.00024389621,0.00022504566,0.00012835352,0.004049163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982868,0.00018240248,0.00018509793,0.0004283988,0.000831057,0.0000863828],"domain_scores_gemma":[0.99902654,0.00020977498,0.000091832786,0.00015732036,0.00048234375,0.000032275657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011436826,0.00062313967,0.0014643513,0.0012473114,0.00039614915,0.0009380981,0.0017418159,0.0008463625,0.001342041],"category_scores_gemma":[0.0028306663,0.0004049435,0.00060996355,0.0011345482,0.00049157446,0.0015488754,0.0007251958,0.0006535106,0.0009809966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012700557,0.00008834882,0.0014053523,0.00018253758,0.00016200198,0.00020125855,0.00008901008,0.06167813,0.0375773,0.004535466,0.0024618593,0.8914917],"study_design_scores_gemma":[0.000050342707,0.00016156978,0.0019045216,0.000034829827,0.000104925515,0.0005855602,0.000028278237,0.9597494,0.020132879,0.0062416554,0.010944935,0.0000610959],"about_ca_topic_score_codex":0.0030005306,"about_ca_topic_score_gemma":0.002881407,"teacher_disagreement_score":0.0030005306,"about_ca_system_score_codex":0.000487413,"about_ca_system_score_gemma":0.00058643916,"threshold_uncertainty_score":0.006048441},"labels":[],"label_agreement":null},{"id":"W2403054493","doi":"10.1371/journal.pone.0180234","title":"A neural model of hierarchical reinforcement learning","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Air Force Office of Scientific Research; Ontario Innovation Trust","keywords":"Reinforcement learning; Artificial intelligence; Computer science; Leverage (statistics); Transfer of learning; Artificial neural network; Reinforcement; Cognitive science; Machine learning; Psychology","score_opus":0.08724812942187102,"score_gpt":0.2616324953613466,"score_spread":0.17438436593947557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403054493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06833897,0.0003700999,0.90558356,0.0011185086,0.00006876856,0.000046911966,0.0002448718,0.00042497122,0.023803405],"genre_scores_gemma":[0.9318518,0.00026178025,0.057868704,0.00015165453,0.000033284992,0.0001292395,0.000097683194,0.000037090256,0.009568793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984694,0.000042827825,0.000006262155,0.000038922764,0.000037855993,0.000027073505],"domain_scores_gemma":[0.999777,0.000094429655,0.000035634712,0.000023851911,0.000037991824,0.000031017928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033655035,0.00032159104,0.0004233529,0.0002244703,0.00026433737,0.000647112,0.0012584794,0.0008624233,0.004023445],"category_scores_gemma":[0.00108342,0.00023322478,0.0005041444,0.00026576692,0.0007346873,0.001100612,0.0005639744,0.00090130593,0.00036704433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023585317,0.000018119934,0.000350381,0.000025526604,0.000016400778,0.000059868962,0.000060435905,0.89612854,0.0019466641,0.093951255,0.00053685694,0.00688243],"study_design_scores_gemma":[0.000006417946,0.000009595856,0.00007210446,0.0000023987466,0.0000027267586,0.000011475955,0.000003128654,0.98144513,0.00010076786,0.017993342,0.00034988756,0.0000029940643],"about_ca_topic_score_codex":0.008586951,"about_ca_topic_score_gemma":0.005909228,"teacher_disagreement_score":0.008586951,"about_ca_system_score_codex":0.0010104174,"about_ca_system_score_gemma":0.00093778485,"threshold_uncertainty_score":0.01707393},"labels":[],"label_agreement":null},{"id":"W2403074607","doi":"10.3233/ica-2003-10101","title":"A decade of ICAE","year":2003,"lang":"en","type":"article","venue":"Integrated Computer-Aided Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ames Research Center; University of Illinois at Urbana-Champaign; RWTH Aachen University; Universidad de Granada; Université de Genève; Università degli Studi di Pavia; Queen's University; Old Dominion University; University of Wollongong; Griffith University; University of Nevada, Reno; University of Alberta; Uniwersytet Warszawski; University of Pennsylvania; Oregon State University; University of Hull; Carnegie Mellon University; Iowa State University; DePaul University; University of Ottawa; Università degli Studi di Genova; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; National Aeronautics and Space Administration; University of Nebraska-Lincoln; Clemson University; Université Laval; Sheffield Hallam University; University of Pittsburgh; Seoul National University; Georgia Institute of Technology","keywords":"Geography","score_opus":0.008660536572333713,"score_gpt":0.20380719641493114,"score_spread":0.19514665984259744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403074607","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009262834,0.1597749,0.21025315,0.07207316,0.041450754,0.0001643122,0.0008044064,0.0017927607,0.50442374],"genre_scores_gemma":[0.16257636,0.15854816,0.12392556,0.032711167,0.044899315,0.00036435877,0.0016491988,0.0011644713,0.47416145],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998417,0.0004238101,0.00007587345,0.00031460187,0.0005712328,0.00019758985],"domain_scores_gemma":[0.9959661,0.0012614123,0.00013321357,0.00085519924,0.0014070756,0.00037703698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044458783,0.0012663992,0.0010646726,0.0024440724,0.0013437074,0.0074133542,0.0013706373,0.0025267126,0.028562445],"category_scores_gemma":[0.009264752,0.00046473526,0.0005973274,0.0028161376,0.0036481044,0.0067468877,0.004286398,0.004476316,0.013849261],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001937102,0.00013771406,0.0005892705,0.00033028863,0.00004922861,0.00016502585,0.0002984074,0.0021408338,0.001483444,0.3237299,0.19243072,0.47845152],"study_design_scores_gemma":[0.000014292069,0.000054658718,0.0003548688,0.00021540173,0.0000152947,0.00024059953,0.00014511705,0.0027149234,0.0017494536,0.05203808,0.9424262,0.000031066797],"about_ca_topic_score_codex":0.0017235883,"about_ca_topic_score_gemma":0.0019257849,"teacher_disagreement_score":0.028562445,"about_ca_system_score_codex":0.0018321257,"about_ca_system_score_gemma":0.0037337986,"threshold_uncertainty_score":0.095550954},"labels":[],"label_agreement":null},{"id":"W2403402995","doi":"","title":"Canonical form of the universal circuits matrix of feedback functions.","year":2009,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Matrix (chemical analysis); Electronic circuit; Pure mathematics; Algebra over a field; Canonical form; Arithmetic; Electrical engineering; Engineering","score_opus":0.009725310863182264,"score_gpt":0.23028933948091806,"score_spread":0.2205640286177358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403402995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10644539,0.0016738527,0.6094672,0.002469035,0.001318388,0.00024785983,0.0026831825,0.0012717507,0.27442324],"genre_scores_gemma":[0.8533184,0.00097410794,0.062246308,0.0010021094,0.0007802293,0.0003188295,0.0010783615,0.0005741025,0.07970755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99927753,0.00013476826,0.00003061279,0.000208279,0.00020089625,0.00014792675],"domain_scores_gemma":[0.99927205,0.00013323034,0.000095399024,0.00010040289,0.00026721074,0.0001317142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004167149,0.00087469054,0.0006977009,0.001590773,0.00092349155,0.0026737042,0.0012180123,0.0010773102,0.033333935],"category_scores_gemma":[0.0024604427,0.00035519354,0.0005518813,0.0007293493,0.0016254134,0.0028844583,0.0012223829,0.0017212804,0.0049646636],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021389056,0.000016467577,0.00008900836,0.000034645633,0.000008938618,0.000052520496,0.00005039732,0.0009995018,0.0013070295,0.9846309,0.004503501,0.008285818],"study_design_scores_gemma":[0.00001727515,0.000026733627,0.00022164699,0.000020995274,0.000010227138,0.00021296543,0.00006765256,0.013589605,0.001302552,0.9713675,0.013139091,0.000023815344],"about_ca_topic_score_codex":0.0010598333,"about_ca_topic_score_gemma":0.0018814943,"teacher_disagreement_score":0.033333935,"about_ca_system_score_codex":0.0009443194,"about_ca_system_score_gemma":0.00079545926,"threshold_uncertainty_score":0.11151308},"labels":[],"label_agreement":null},{"id":"W2403573955","doi":"","title":"Knowing When to Abandon Unproductive Learning","year":2012,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Psychology; Cognitive science; Machine learning; Operations research; Mathematics","score_opus":0.016031340305596206,"score_gpt":0.22117740947389464,"score_spread":0.20514606916829842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403573955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4900153,0.00040419935,0.486233,0.0012287366,0.0001593547,0.00024657295,0.00018857073,0.0021199093,0.0194044],"genre_scores_gemma":[0.9053271,0.00023745967,0.088196486,0.0002351899,0.000019714269,0.00011195491,0.00015477694,0.00018643508,0.005530889],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986331,0.00033183958,0.00011071097,0.00034223602,0.00041909105,0.00016303858],"domain_scores_gemma":[0.9903286,0.004277564,0.0014492647,0.0023806468,0.0010065222,0.0005573446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030132171,0.00070400897,0.0005699166,0.00030449833,0.00040667833,0.0017698945,0.0016467363,0.0012381378,0.0032297955],"category_scores_gemma":[0.021325419,0.00029853312,0.00039717412,0.00021274814,0.0015226158,0.0033146779,0.0012836575,0.0015599204,0.0007862396],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010528723,0.0007458638,0.061709516,0.00057472655,0.00026532603,0.0014482347,0.0025986338,0.19114046,0.11075877,0.11895036,0.005671582,0.5050837],"study_design_scores_gemma":[0.0000988215,0.0010510009,0.014308208,0.00022212861,0.00013776137,0.0008581657,0.00069335965,0.6645937,0.09901495,0.20393397,0.0149249295,0.00016301319],"about_ca_topic_score_codex":0.0012060715,"about_ca_topic_score_gemma":0.0021542185,"teacher_disagreement_score":0.0032297955,"about_ca_system_score_codex":0.00048498844,"about_ca_system_score_gemma":0.0012356614,"threshold_uncertainty_score":0.01593566},"labels":[],"label_agreement":null},{"id":"W2403614030","doi":"","title":"Bidirectional Associative Memory for Short-term Memory Learning","year":2014,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Connectionism; Bidirectional associative memory; Computer science; Associative property; Artificial neural network; Content-addressable memory; Associative learning; Memory model; Artificial intelligence; Categorization; Experimental psychology; Cognitive science; Cognition; Term (time); Cognitive psychology; Psychology; Neuroscience","score_opus":0.01627766771510216,"score_gpt":0.22994306478257612,"score_spread":0.21366539706747395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403614030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100639954,0.0031473795,0.8737609,0.0008871829,0.0006518267,0.00014087111,0.00039612298,0.0009880735,0.019387651],"genre_scores_gemma":[0.84259355,0.0018994157,0.14256628,0.0002737814,0.00021686578,0.00030137203,0.0004392016,0.000104752,0.011604752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998098,0.000045475175,0.000014182047,0.000061427774,0.00004384965,0.00002529412],"domain_scores_gemma":[0.9995338,0.00018349187,0.00005332045,0.00010556514,0.00008835857,0.0000355056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004592967,0.00045213566,0.00043452426,0.00028063127,0.00024212108,0.0007153903,0.0012768266,0.0006635136,0.008571648],"category_scores_gemma":[0.0019174297,0.00014577984,0.0004985128,0.00040919497,0.0005605817,0.0019024732,0.00061319745,0.0010215401,0.0013183992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000518469,0.00036928305,0.0025364882,0.0007651919,0.00019870912,0.00030943687,0.00023065723,0.10922788,0.05057897,0.3318293,0.0067541385,0.49668145],"study_design_scores_gemma":[0.000054552358,0.0001995743,0.0016319839,0.000055664284,0.0000908688,0.0002952103,0.000042405998,0.63336194,0.011264825,0.34096056,0.011998009,0.000044390614],"about_ca_topic_score_codex":0.0010056166,"about_ca_topic_score_gemma":0.0012407055,"teacher_disagreement_score":0.008571648,"about_ca_system_score_codex":0.00045047593,"about_ca_system_score_gemma":0.00044729048,"threshold_uncertainty_score":0.02867502},"labels":[],"label_agreement":null},{"id":"W2408843399","doi":"10.1109/acpr.2015.7486450","title":"IAPR keynote lecture III: Methods of achieving perfect recognition scores","year":2015,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Skeletonization; Computer science; Field (mathematics); Artificial intelligence; Heading (navigation); Machine learning; Pattern recognition (psychology); Engineering; Mathematics","score_opus":0.07420126630503213,"score_gpt":0.3429857208928709,"score_spread":0.26878445458783873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408843399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023867392,0.0036507482,0.96935475,0.0037492823,0.0026426166,0.00009549925,0.00024826717,0.0016304593,0.016241664],"genre_scores_gemma":[0.1038496,0.005236128,0.82622755,0.0018570899,0.005823107,0.00064031733,0.0010109406,0.0021916777,0.053163614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9871132,0.003973366,0.00080262014,0.003003264,0.004430951,0.0006766388],"domain_scores_gemma":[0.98388,0.0076941084,0.0006549179,0.0038652322,0.0034098062,0.0004959349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014969338,0.0028626607,0.0029282616,0.003410135,0.0018823525,0.008713485,0.0045022806,0.0032918404,0.019336231],"category_scores_gemma":[0.05675894,0.0017624603,0.00251849,0.002347775,0.0060249264,0.009834711,0.005824268,0.008341374,0.0151804695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028108218,0.00008041439,0.001237499,0.00049080234,0.00022505205,0.00010255196,0.00040304018,0.021547161,0.002916544,0.35060474,0.12563816,0.49647304],"study_design_scores_gemma":[0.000075897275,0.00026830405,0.0011731765,0.00039713774,0.0001723865,0.0006048471,0.00023139115,0.14824332,0.011477286,0.7059322,0.13121548,0.00020864536],"about_ca_topic_score_codex":0.0015905382,"about_ca_topic_score_gemma":0.0009861619,"teacher_disagreement_score":0.019336231,"about_ca_system_score_codex":0.00295702,"about_ca_system_score_gemma":0.0018612054,"threshold_uncertainty_score":0.07916635},"labels":[],"label_agreement":null},{"id":"W2409622689","doi":"10.3233/978-1-60750-934-9-431","title":"Cluster Analysis of Wisconsin Breast Cancer Dataset Using Self-Organizing Maps","year":2002,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Benchmarking; Self-organizing map; Computer science; Cluster (spacecraft); Complement (music); Data set; Data mining; Set (abstract data type); Multidimensional data; Artificial intelligence; Cluster analysis","score_opus":0.0569692730226226,"score_gpt":0.34761635672887475,"score_spread":0.29064708370625214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409622689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7519014,0.00133166,0.048092157,0.0015652066,0.00023417555,0.00083474943,0.18725547,0.003373133,0.005412071],"genre_scores_gemma":[0.57587314,0.0007044693,0.120613694,0.00010011063,0.000080780395,0.0012111717,0.29807866,0.00013103853,0.0032069008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991609,0.00025018485,0.00008493801,0.00012860126,0.00028378575,0.0000915707],"domain_scores_gemma":[0.99803334,0.00074730447,0.00018152919,0.00032954122,0.0006157673,0.00009247037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013039744,0.0006559217,0.0005356848,0.0043437593,0.0006789526,0.0008429495,0.0006610385,0.0004485618,0.0011662651],"category_scores_gemma":[0.004868568,0.00012650099,0.0006220615,0.005289775,0.0002579164,0.00042960144,0.00068379194,0.00047425987,0.00047984807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026215173,0.0011344347,0.15211163,0.0018112998,0.0009180692,0.001729921,0.002153664,0.08388359,0.017151803,0.008141854,0.23216829,0.49617392],"study_design_scores_gemma":[0.00035105995,0.00062817516,0.40204135,0.00033782958,0.00046179182,0.0013940537,0.004975363,0.3342267,0.045927063,0.012495852,0.19678292,0.00037781143],"about_ca_topic_score_codex":0.02903145,"about_ca_topic_score_gemma":0.033227038,"teacher_disagreement_score":0.02903145,"about_ca_system_score_codex":0.0011481494,"about_ca_system_score_gemma":0.0014595051,"threshold_uncertainty_score":0.057724953},"labels":[],"label_agreement":null},{"id":"W2415002020","doi":"10.1007/978-4-431-66996-8_73","title":"Knowledge-based Cascade-correlation: Varying the Size and Shape of Relevant Prior Knowledge","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Cascade; Relevance (law); Computer science; Correlation; Artificial intelligence; Artificial neural network; Machine learning; Space (punctuation); Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.025147259358980534,"score_gpt":0.25186306807626246,"score_spread":0.22671580871728192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2415002020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07570708,0.0016100354,0.9109485,0.00039767797,0.00013551698,0.00013159706,0.00020543834,0.001412976,0.009451115],"genre_scores_gemma":[0.747748,0.0009639642,0.24586527,0.00026032145,0.00013956454,0.00013180301,0.00038044198,0.00030970978,0.004200819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991683,0.00024279032,0.000037639995,0.00025806105,0.00020414504,0.00008904745],"domain_scores_gemma":[0.99312913,0.0047298474,0.00028636787,0.00087450544,0.0007352288,0.00024499296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032185854,0.00089564425,0.0014470587,0.0009775329,0.0007245986,0.0013437171,0.0026435053,0.0016969928,0.005518609],"category_scores_gemma":[0.01411171,0.0007809195,0.00067625294,0.001440991,0.0010359467,0.004519125,0.0019785226,0.0016996777,0.00082279154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017489382,0.0007298987,0.0025046235,0.0004332707,0.00026422285,0.0003977055,0.0004093285,0.37526223,0.02186971,0.04191242,0.012492998,0.5419747],"study_design_scores_gemma":[0.00004628259,0.00008633649,0.0005152745,0.000022650662,0.00007604381,0.00008790383,0.00001864307,0.9722908,0.0069455146,0.019149689,0.00073675945,0.000024048857],"about_ca_topic_score_codex":0.0034368853,"about_ca_topic_score_gemma":0.0064967433,"teacher_disagreement_score":0.005518609,"about_ca_system_score_codex":0.0011049144,"about_ca_system_score_gemma":0.0015760672,"threshold_uncertainty_score":0.018461585},"labels":[],"label_agreement":null},{"id":"W2419187779","doi":"10.58079/ouom","title":"Regression tree using Gini's index","year":2013,"lang":"en","type":"article","venue":"OpenEdition (OpenEdition)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; AXA Research Fund","keywords":"Mathematics; Statistics; Regression; Cart; Tree (set theory); Regression analysis; Index (typography); Set (abstract data type); Combinatorics; Computer science; Geography","score_opus":0.025201539844429733,"score_gpt":0.26136084642819074,"score_spread":0.236159306583761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2419187779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12444173,0.0024004357,0.8133413,0.0020423569,0.00059776136,0.0005714654,0.026869418,0.01114718,0.018588372],"genre_scores_gemma":[0.5196548,0.00091712433,0.4382904,0.00025786413,0.0001985491,0.0007212025,0.028271096,0.0024869922,0.009202035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979442,0.0010221188,0.00008056095,0.00045169936,0.00035036306,0.00015109415],"domain_scores_gemma":[0.9924345,0.0050436393,0.0003609525,0.00073260505,0.0012589358,0.00016946624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006464329,0.0010753348,0.0016059146,0.005324621,0.00079230237,0.0021475437,0.0014397455,0.0009848849,0.0074640694],"category_scores_gemma":[0.02983086,0.0004676634,0.0014274783,0.0062174434,0.00039004534,0.0017186563,0.0007775649,0.0020466861,0.0031951638],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039614204,0.00018544604,0.031181807,0.000382164,0.0006427448,0.00024111253,0.00026647424,0.55681753,0.0007842547,0.02610746,0.11824095,0.26475394],"study_design_scores_gemma":[0.000045428358,0.00007391386,0.0067507224,0.000056385194,0.00007498389,0.00008660704,0.000057752066,0.94638956,0.0006123828,0.033388007,0.0124212885,0.000042956595],"about_ca_topic_score_codex":0.012662737,"about_ca_topic_score_gemma":0.013776155,"teacher_disagreement_score":0.012662737,"about_ca_system_score_codex":0.0016107691,"about_ca_system_score_gemma":0.001428788,"threshold_uncertainty_score":0.03418708},"labels":[],"label_agreement":null},{"id":"W2419867842","doi":"10.1016/j.neucom.2015.08.072","title":"Fuzzy associative memories: A design through fuzzy clustering","year":2015,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Recruitment Program of Global Experts; Science and Technology Development Fund; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Associative property; Fuzzy clustering; Fuzzy logic; Computer science; Cluster analysis; Content-addressable memory; Artificial intelligence; Fuzzy set; Pattern recognition (psychology); Data mining; Mathematics; Artificial neural network","score_opus":0.08530206572733709,"score_gpt":0.2940947966301618,"score_spread":0.2087927309028247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2419867842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021762358,0.00021535829,0.97185946,0.00011478438,0.000084711144,0.00010388306,0.000040277162,0.0005152071,0.005303895],"genre_scores_gemma":[0.5817356,0.0002451607,0.41177866,0.00016255613,0.000058169557,0.00021465454,0.00007832244,0.0000717151,0.005655144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999689,0.00004534981,0.000029165745,0.000090036585,0.000109692504,0.00003680407],"domain_scores_gemma":[0.9993787,0.00009956531,0.00005360318,0.00011301182,0.00031891724,0.00003615928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005864162,0.00038944013,0.0005116701,0.0006075896,0.0007224339,0.0010056194,0.0017373825,0.00087512954,0.004016564],"category_scores_gemma":[0.0016888084,0.00030825313,0.00039668992,0.00062788604,0.00048909266,0.0011823322,0.00064839795,0.00059269415,0.00090151164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005776547,0.00029991748,0.000940495,0.00036473523,0.0001893655,0.0001496007,0.00029005003,0.19475941,0.06211143,0.083287016,0.0052215476,0.65180874],"study_design_scores_gemma":[0.000056642602,0.00033753662,0.00047075193,0.000041639963,0.00011921707,0.0002795611,0.00008141574,0.90366894,0.060729325,0.025752299,0.0084206015,0.000042033116],"about_ca_topic_score_codex":0.0010514841,"about_ca_topic_score_gemma":0.002541881,"teacher_disagreement_score":0.004016564,"about_ca_system_score_codex":0.00057999964,"about_ca_system_score_gemma":0.0006061046,"threshold_uncertainty_score":0.013436794},"labels":[],"label_agreement":null},{"id":"W2460984000","doi":"10.1006/jaer.2001.0705","title":"AE—Automation and Emerging Technologies","year":2001,"lang":"en","type":"article","venue":"Journal of Agricultural Engineering Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian International Grains Institute","funders":"","keywords":"Artificial intelligence; Classifier (UML); Quadratic classifier; Computer science; Artificial neural network; Pattern recognition (psychology); Machine learning; Linear discriminant analysis; Bayesian probability; Fuzzy logic; Margin classifier; Random subspace method; Data mining","score_opus":0.023165819484456503,"score_gpt":0.29249661220349304,"score_spread":0.26933079271903654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460984000","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057281762,0.16014771,0.50867414,0.016575657,0.0036293508,0.00008761332,0.00023823038,0.0006171658,0.25274834],"genre_scores_gemma":[0.75306344,0.08448659,0.0733127,0.001052966,0.003551841,0.00007824864,0.0002917035,0.000077064455,0.084085464],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996325,0.00009448284,0.000029096283,0.000054956738,0.00015473545,0.0000343659],"domain_scores_gemma":[0.99907136,0.0004047313,0.00009559077,0.00010949381,0.00026433435,0.000054527303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065943453,0.00036813453,0.00039339965,0.0009866181,0.00043211874,0.0030539234,0.0006032323,0.0011891491,0.009170268],"category_scores_gemma":[0.0017290941,0.0001763916,0.00024522073,0.0009637333,0.0015362062,0.0048909076,0.00080423115,0.0013106002,0.0016045047],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005613474,0.000056771634,0.0018105919,0.0003388145,0.000036295784,0.00017979604,0.00013673231,0.00828401,0.0052514346,0.6771531,0.008647078,0.2980493],"study_design_scores_gemma":[0.000018029834,0.00017731552,0.0037724401,0.00027595472,0.000035382804,0.0013506208,0.00045736096,0.05124963,0.0045884396,0.6933157,0.24472338,0.000035705165],"about_ca_topic_score_codex":0.00054381345,"about_ca_topic_score_gemma":0.0005174029,"teacher_disagreement_score":0.009170268,"about_ca_system_score_codex":0.00062084524,"about_ca_system_score_gemma":0.00041688536,"threshold_uncertainty_score":0.030677617},"labels":[],"label_agreement":null},{"id":"W24640360","doi":"10.1007/978-3-319-06483-3_6","title":"Analysis of Feature Maps Selection in Supervised Learning Using Convolutional Neural Networks","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Kernel (algebra); Field (mathematics); Deep learning; Receptive field; Feature selection; Object (grammar); Machine learning; Mathematics","score_opus":0.016290564706748452,"score_gpt":0.24313048378361501,"score_spread":0.22683991907686657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W24640360","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21835716,0.0015014947,0.7760154,0.0003161726,0.00005898548,0.00006770113,0.00023576939,0.0011131356,0.0023341759],"genre_scores_gemma":[0.88261175,0.0003411661,0.11232218,0.000060477316,0.00006778152,0.00006971613,0.0007712616,0.00021437943,0.0035413196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992035,0.00025702358,0.000042314176,0.00014783515,0.0002630851,0.0000862518],"domain_scores_gemma":[0.99312395,0.005271328,0.00027532154,0.00041941486,0.00081687345,0.00009311729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021614167,0.00054404733,0.0008042932,0.0008515977,0.00039788027,0.0008712241,0.001130444,0.0006248956,0.0018908782],"category_scores_gemma":[0.007273794,0.00028995454,0.0005724045,0.00083487824,0.00043373567,0.0011929838,0.00060185685,0.00082120515,0.00027696582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072962174,0.00030168038,0.009957045,0.0002219694,0.00020638644,0.00018380968,0.00010411437,0.4920915,0.011484427,0.0139949275,0.005839296,0.46488535],"study_design_scores_gemma":[0.000003673921,0.000017518805,0.00094453536,0.0000038325925,0.000008856856,0.000014971045,0.000006242104,0.99552274,0.0013196634,0.0020235723,0.00013135296,0.0000030374015],"about_ca_topic_score_codex":0.0038145883,"about_ca_topic_score_gemma":0.0049730875,"teacher_disagreement_score":0.0038145883,"about_ca_system_score_codex":0.0008139303,"about_ca_system_score_gemma":0.0009468158,"threshold_uncertainty_score":0.0114308},"labels":[],"label_agreement":null},{"id":"W2464465375","doi":"10.1109/tnnls.2016.2580570","title":"Improving Neural-Network Classifiers Using Nearest Neighbor Partitioning","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Government of Shandong Province; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Artificial neural network; Centroid; Artificial intelligence; Pattern recognition (psychology); Computer science; k-nearest neighbors algorithm; Classifier (UML); Large margin nearest neighbor; Decision boundary; Partition (number theory); Nearest-neighbor chain algorithm; Nearest neighbor search; Machine learning; Data mining; Mathematics; Cluster analysis","score_opus":0.020434277099213485,"score_gpt":0.2315582978179146,"score_spread":0.21112402071870112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2464465375","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026623264,0.0013202305,0.96782637,0.00015442532,0.00014015495,0.00010091353,0.00005035205,0.0014694033,0.0023149822],"genre_scores_gemma":[0.3741756,0.00066926086,0.62040424,0.0002917691,0.00022330756,0.00021253542,0.00056334684,0.0003156946,0.0031442395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759907,0.00054980343,0.00016166581,0.00051659724,0.0009993848,0.00017348875],"domain_scores_gemma":[0.9955568,0.001984215,0.0003319731,0.00042330346,0.0016081535,0.000095647374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024209553,0.0015227963,0.0022353192,0.003032949,0.0010805401,0.0016015245,0.0020968595,0.0020381769,0.0014613257],"category_scores_gemma":[0.010906709,0.00065208715,0.0009872171,0.0019239299,0.00059057167,0.0030090157,0.0015760545,0.0017962379,0.0013835232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020333144,0.00015292391,0.0016702937,0.00012472433,0.00012801214,0.0000977285,0.00012969377,0.36945215,0.007970392,0.0044274125,0.0034185683,0.6122248],"study_design_scores_gemma":[0.0000065254976,0.000022798564,0.0001989726,0.000007744048,0.000012875858,0.000025949324,0.000014589187,0.9945952,0.0025393153,0.0019121501,0.0006542734,0.000009522327],"about_ca_topic_score_codex":0.007287125,"about_ca_topic_score_gemma":0.0063915993,"teacher_disagreement_score":0.007287125,"about_ca_system_score_codex":0.0013525384,"about_ca_system_score_gemma":0.0008253763,"threshold_uncertainty_score":0.014489412},"labels":[],"label_agreement":null},{"id":"W2477215729","doi":"10.4018/978-1-59140-037-0.ch004","title":"A Biologically Inspired Neural Network Approach to Real-Time Map Building and Path Planning","year":2003,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Motion planning; Computer science; Artificial neural network; Path (computing); Robot; Spiking neural network; Artificial intelligence; Real-time computing","score_opus":0.025337866110705556,"score_gpt":0.24605257217600277,"score_spread":0.2207147060652972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2477215729","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020210485,0.005371941,0.9732308,0.0003185618,0.00021843813,0.000022165606,0.000052828414,0.00039214236,0.01837223],"genre_scores_gemma":[0.13220593,0.01281268,0.80160403,0.00028640404,0.00027133987,0.00025025159,0.00028534813,0.00018009703,0.05210389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999249,0.00001313556,0.0000042605648,0.000020144313,0.000030692252,0.0000069114785],"domain_scores_gemma":[0.999946,0.000022758897,0.000005482829,0.000007527646,0.000013907394,0.000004227527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016054505,0.00072295044,0.0004064979,0.00042207632,0.00022295566,0.0007800894,0.0013369381,0.001091294,0.0042398274],"category_scores_gemma":[0.0002901008,0.00030396055,0.0004990625,0.00088703167,0.0005178385,0.0009668671,0.00053914293,0.001077766,0.0011605815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041479627,0.000055904886,0.00023643172,0.0006081648,0.000084908716,0.00034036604,0.00014610773,0.40614587,0.022166917,0.18561386,0.010463558,0.37409645],"study_design_scores_gemma":[0.000011382491,0.000053472515,0.00024864078,0.000084480795,0.00003439487,0.0003831909,0.000030034724,0.8204154,0.0058365986,0.10479163,0.068080164,0.000030635634],"about_ca_topic_score_codex":0.0012447811,"about_ca_topic_score_gemma":0.002028801,"teacher_disagreement_score":0.0042398274,"about_ca_system_score_codex":0.00060817826,"about_ca_system_score_gemma":0.0004214437,"threshold_uncertainty_score":0.0141836405},"labels":[],"label_agreement":null},{"id":"W2481938535","doi":"","title":"Alternative Digit Sets for Nonadjacent Representations","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Integer (computer science); Representation (politics); Set (abstract data type); Numerical digit; Arithmetic; Mathematics; Discrete mathematics; Computer science; Property (philosophy); Combinatorics; Programming language","score_opus":0.03835181953870176,"score_gpt":0.32564201642996765,"score_spread":0.2872901968912659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2481938535","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53770787,0.0007075093,0.43299043,0.00055526814,0.0002882753,0.0001315863,0.0004736648,0.0010987445,0.02604668],"genre_scores_gemma":[0.82729024,0.00012597044,0.1651944,0.00016247803,0.000055399432,0.00007652452,0.00045311725,0.000074163036,0.0065676714],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984586,0.00027119127,0.00021362594,0.00035727298,0.0005301713,0.00016915941],"domain_scores_gemma":[0.99626875,0.0016179889,0.00039308378,0.0010249056,0.00050503487,0.00019021201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000977729,0.00043114505,0.000619927,0.0015198506,0.0011724003,0.0026235576,0.0011325474,0.0008540733,0.008975354],"category_scores_gemma":[0.007436133,0.00031725733,0.00059829315,0.0012386539,0.001372253,0.003049603,0.0020206913,0.0010062717,0.0012906067],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001621816,0.00025399812,0.00597437,0.00033524653,0.00005529972,0.0014894938,0.00078683783,0.02648021,0.030753296,0.64474857,0.0038475613,0.2836534],"study_design_scores_gemma":[0.00012902604,0.00043618047,0.001331071,0.00017343469,0.00006728651,0.0020527767,0.00046815936,0.17191477,0.061028454,0.73173255,0.03054463,0.00012175952],"about_ca_topic_score_codex":0.00020823462,"about_ca_topic_score_gemma":0.00038566193,"teacher_disagreement_score":0.008975354,"about_ca_system_score_codex":0.00074053596,"about_ca_system_score_gemma":0.0005951027,"threshold_uncertainty_score":0.030025542},"labels":[],"label_agreement":null},{"id":"W2482285656","doi":"10.7939/r3-sh28-7e77","title":"Design and development of dynamic collaborative frameworks using concepts of knowledge-based networks","year":2008,"lang":"en","type":"article","venue":"University of Alberta Library","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Consistency (knowledge bases); Cluster analysis; Data mining; Artificial intelligence; Fuzzy logic; Machine learning; Identification (biology)","score_opus":0.015595725569057566,"score_gpt":0.21847061558869188,"score_spread":0.20287489001963432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2482285656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036982142,0.00016463536,0.9923413,0.0003800707,0.000019377863,0.00009290062,0.000019513316,0.00009619017,0.003187815],"genre_scores_gemma":[0.1525983,0.000500819,0.84323424,0.00011833878,0.000038532507,0.00049633643,0.00009071393,0.000058074693,0.0028646537],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967687,0.0012359993,0.00020694532,0.0006317594,0.0009275301,0.00022906957],"domain_scores_gemma":[0.9970343,0.0012186286,0.0003000695,0.00064052135,0.0005288773,0.0002774607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051887305,0.000858516,0.00087146944,0.0015382702,0.0016448935,0.0041705137,0.0040521934,0.0019445329,0.0027929551],"category_scores_gemma":[0.008278196,0.00074709905,0.0015958257,0.0014102759,0.0035313761,0.006102945,0.0046340246,0.0026255434,0.00056500174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023344166,0.000068884474,0.0004951138,0.00015508944,0.000056886656,0.00018871477,0.0006709017,0.25400573,0.0019638462,0.687413,0.0008261692,0.054132245],"study_design_scores_gemma":[0.000025734389,0.000057668385,0.0001455098,0.00008732323,0.00002249166,0.00008933226,0.00021946151,0.675887,0.0013564616,0.30571118,0.01636776,0.000030100611],"about_ca_topic_score_codex":0.0044566314,"about_ca_topic_score_gemma":0.0048243133,"teacher_disagreement_score":0.0051887305,"about_ca_system_score_codex":0.0026315127,"about_ca_system_score_gemma":0.0030965055,"threshold_uncertainty_score":0.027440965},"labels":[],"label_agreement":null},{"id":"W2482610410","doi":"10.1017/s0317167100118645","title":"Books Reviews - Functional Electromyography: Provocative Maneuvers in Electrodiagnosis. 2011. By Lome M. Fishman, Allen N. Wilkins. Published by Springer, New York. 171 pages. C$190 approx.","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Electromyography; Electrodiagnosis; Approx; Medicine; Neuroscience; Physical medicine and rehabilitation; Psychology; Computer science; Internal medicine","score_opus":0.052466356137120404,"score_gpt":0.23303726809282044,"score_spread":0.18057091195570002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2482610410","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006000573,0.8055883,0.0041219313,0.017632151,0.028845385,0.00011793223,0.0015907998,0.0009163647,0.14058714],"genre_scores_gemma":[0.0029591364,0.52542394,0.0042495294,0.006742154,0.014954422,0.00012638012,0.0014785271,0.00054677366,0.4435191],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99929655,0.00006055053,0.00005940566,0.00009554851,0.00045024915,0.00003764935],"domain_scores_gemma":[0.9982565,0.0005672868,0.00017187567,0.00005174664,0.0006795449,0.00027315586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078071706,0.0027351636,0.0021156934,0.0041159354,0.00040957527,0.002156956,0.0012072168,0.0018893902,0.17618574],"category_scores_gemma":[0.0023395736,0.0009778836,0.0011697437,0.0027922436,0.000836062,0.003690829,0.0014378505,0.0031093513,0.1560311],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006357591,0.000025041512,0.00007573256,0.000716987,0.000016767868,0.00007519963,0.000019833607,0.00007861719,0.0005283671,0.00078495,0.8334017,0.16421318],"study_design_scores_gemma":[0.000017296888,0.000050465464,0.00064559054,0.00081402133,0.000019686695,0.0012518644,0.000033865726,0.00009511538,0.0002324116,0.0011681888,0.99565315,0.000018304296],"about_ca_topic_score_codex":0.0021481314,"about_ca_topic_score_gemma":0.0052981367,"teacher_disagreement_score":0.17618574,"about_ca_system_score_codex":0.00089554815,"about_ca_system_score_gemma":0.0007842634,"threshold_uncertainty_score":0.58940005},"labels":[],"label_agreement":null},{"id":"W2482994569","doi":"","title":"A new approximating model for the time invariant nonlinear operators with fading memory","year":2009,"lang":"en","type":"article","venue":"Computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Bank Note Company (Canada)","funders":"","keywords":"Mathematics; Nonlinear system; Invariant (physics); Operator theory; Operator (biology); Polynomial; Set (abstract data type); Algebra over a field; Representation (politics); Applied mathematics; Discrete mathematics; Pure mathematics; Computer science; Mathematical analysis","score_opus":0.03532033562343716,"score_gpt":0.28173244622704896,"score_spread":0.2464121106036118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2482994569","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04684394,0.0002487776,0.9431987,0.00042681737,0.00010017695,0.000033537577,0.00007054973,0.000090297486,0.008987258],"genre_scores_gemma":[0.8395788,0.000614798,0.14277132,0.00026825705,0.00022778538,0.000120458084,0.00012515509,0.000065973356,0.016227394],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998186,0.000043924498,0.000009218803,0.000033809538,0.00006421144,0.000030289508],"domain_scores_gemma":[0.9997725,0.00007348022,0.000042789856,0.000033423217,0.000041656564,0.000036100155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047139564,0.00042008876,0.00041668274,0.00045409254,0.00036506116,0.00088630174,0.0011661674,0.00079177856,0.002341931],"category_scores_gemma":[0.0010779769,0.00016655285,0.00054036477,0.00037117553,0.0010413225,0.0017233151,0.0009589813,0.0011362009,0.00026096555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001928029,0.000025362178,0.00019887241,0.000037902762,0.000010838857,0.000118856944,0.00012840744,0.035808567,0.005588467,0.9512412,0.0004735772,0.006348644],"study_design_scores_gemma":[0.000010737478,0.00004388294,0.00012588744,0.000011928704,0.000009668068,0.00014359286,0.000051817617,0.77667975,0.0012382753,0.21893986,0.0027293079,0.000015316278],"about_ca_topic_score_codex":0.0009059639,"about_ca_topic_score_gemma":0.000594069,"teacher_disagreement_score":0.002341931,"about_ca_system_score_codex":0.00052707933,"about_ca_system_score_gemma":0.00048768605,"threshold_uncertainty_score":0.007834494},"labels":[],"label_agreement":null},{"id":"W2484435351","doi":"10.1017/cbo9780511735127.017","title":"The <i>R</i>-transform","year":2006,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Waterloo","funders":"","keywords":"Geology; Computer science","score_opus":0.013424555303713063,"score_gpt":0.1819040603445296,"score_spread":0.16847950504081655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2484435351","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032717483,0.009504447,0.40520456,0.0031832953,0.0042201015,0.000053204363,0.00080500275,0.001757602,0.5720001],"genre_scores_gemma":[0.09326762,0.015462387,0.32982543,0.0029783163,0.0023762912,0.00020120887,0.0016462906,0.0040266844,0.5502157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99967706,0.000048167076,0.000016318436,0.00008010322,0.00014955764,0.00002876824],"domain_scores_gemma":[0.99982315,0.00004737192,0.000012098798,0.000054694316,0.000049301627,0.000013347624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041111218,0.00070780993,0.0005133501,0.00097549264,0.0004463862,0.0018628752,0.0008640293,0.00083737634,0.039889466],"category_scores_gemma":[0.0012261302,0.00023331397,0.0007438148,0.0011128554,0.0014456463,0.0021941639,0.0012179468,0.0022644675,0.027598422],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048231086,0.000014421288,0.000056457477,0.00022952691,0.0000111733425,0.00009966483,0.0000807115,0.0014527773,0.004764591,0.65237254,0.095353544,0.24551645],"study_design_scores_gemma":[0.000013399098,0.00004564759,0.00020637363,0.00014151115,0.000013721114,0.0009247252,0.000049758437,0.010279958,0.0107079735,0.38870966,0.5888755,0.00003169929],"about_ca_topic_score_codex":0.0004269229,"about_ca_topic_score_gemma":0.00045841926,"teacher_disagreement_score":0.039889466,"about_ca_system_score_codex":0.00054697675,"about_ca_system_score_gemma":0.000518077,"threshold_uncertainty_score":0.13344353},"labels":[],"label_agreement":null},{"id":"W2485985592","doi":"10.4018/978-1-59904-717-1.ch006","title":"Solving the Sensory Information Bottleneck to Central Processing in Adaptive Systems","year":2008,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Bottleneck; Information bottleneck method; Computer science; Sensory system; Information processing; Stimulus (psychology); Distributed computing; Artificial intelligence; Neuroscience; Psychology; Embedded system; Cluster analysis","score_opus":0.02240893097441493,"score_gpt":0.22835547377032375,"score_spread":0.20594654279590882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2485985592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022972649,0.08523091,0.6947219,0.015384448,0.001583609,0.00008128908,0.00013304043,0.0011706749,0.17872156],"genre_scores_gemma":[0.519352,0.06956681,0.24833663,0.004162613,0.004474746,0.0005312491,0.0002887595,0.00071282464,0.15257432],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997675,0.000066216715,0.0000130104745,0.000069026486,0.000056945144,0.000027314527],"domain_scores_gemma":[0.99947494,0.00036647302,0.000024830799,0.000060614962,0.00004307613,0.000030134686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006420182,0.00090085564,0.0006807522,0.00048342563,0.0007438042,0.0026428327,0.001291345,0.0019130046,0.010491349],"category_scores_gemma":[0.001728124,0.00057740614,0.00050536694,0.0009078316,0.00486979,0.006245057,0.0019972478,0.003084151,0.002269583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005127696,0.000026616091,0.00014754284,0.0007354204,0.000032404558,0.00017843868,0.00058412424,0.012989388,0.002012368,0.83452326,0.014057407,0.13466164],"study_design_scores_gemma":[0.000012118798,0.000022793934,0.0001535831,0.00011919433,0.000010826979,0.0001192894,0.00008175739,0.020184552,0.0008815457,0.9386898,0.039710607,0.000013922124],"about_ca_topic_score_codex":0.000877169,"about_ca_topic_score_gemma":0.00088298786,"teacher_disagreement_score":0.010491349,"about_ca_system_score_codex":0.00150285,"about_ca_system_score_gemma":0.0007255158,"threshold_uncertainty_score":0.035097063},"labels":[],"label_agreement":null},{"id":"W2488948576","doi":"10.1017/cbo9781107447615.013","title":"Encoding and decoding with stochastic neuron models","year":2014,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Ion channel; Neuroscience; Decoding methods; Neuron; Biological neuron model; Computer science; Hodgkin–Huxley model; Sodium channel; Mechanism (biology); Biological system; Artificial intelligence; Physics; Chemistry; Biology; Algorithm; Sodium","score_opus":0.02567322843493863,"score_gpt":0.18689038028127047,"score_spread":0.16121715184633184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2488948576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070602703,0.0007583223,0.9775168,0.00067380845,0.00017614472,0.000023927812,0.00026434055,0.00058709644,0.012939435],"genre_scores_gemma":[0.47923982,0.003990786,0.47505993,0.00066099927,0.00047330576,0.000277624,0.0012126239,0.00058221666,0.038502645],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995734,0.00014938577,0.000036121513,0.000063447726,0.00013682207,0.000040844396],"domain_scores_gemma":[0.9992254,0.00050413824,0.00003664998,0.00011141381,0.000101556005,0.000020734351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063877855,0.0005483593,0.00076649146,0.00037326984,0.00025214633,0.0013367719,0.0008994421,0.0008913964,0.004939522],"category_scores_gemma":[0.003049458,0.00033084842,0.0007477468,0.00070520694,0.00069199246,0.0017836917,0.0008921415,0.0014431552,0.0013422085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047680976,0.0000146248085,0.00016511745,0.00012170168,0.000038163227,0.00008223133,0.00011649706,0.42111018,0.0015584622,0.5126617,0.0050408985,0.059042722],"study_design_scores_gemma":[0.0000045542565,0.0000065079057,0.000022731294,0.000013895188,0.0000049646655,0.000025594849,0.000007189073,0.7714065,0.00043335467,0.22503008,0.0030386576,0.000005985823],"about_ca_topic_score_codex":0.0021704321,"about_ca_topic_score_gemma":0.002449735,"teacher_disagreement_score":0.004939522,"about_ca_system_score_codex":0.0008534127,"about_ca_system_score_gemma":0.00066549523,"threshold_uncertainty_score":0.016524374},"labels":[],"label_agreement":null},{"id":"W2491483370","doi":"10.1093/acprof:oso/9780198525202.003.0020","title":"Processing Emotions Induced by Music","year":2003,"lang":"en","type":"book-chapter","venue":"Oxford University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Psychology; Cognitive psychology; Communication","score_opus":0.03539261270449324,"score_gpt":0.20710387965543628,"score_spread":0.17171126695094305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2491483370","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1769911,0.03795964,0.15646626,0.0040693306,0.00373819,0.00014692341,0.0014116953,0.0017238142,0.61749303],"genre_scores_gemma":[0.6407929,0.013494912,0.05138655,0.00095357874,0.0008088897,0.0001257942,0.0015739572,0.00052246416,0.290341],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999349,0.00001032525,0.000003752823,0.00001564425,0.000026893968,0.000008499428],"domain_scores_gemma":[0.9999443,0.000020633162,0.000006508874,0.0000074596355,0.0000126935165,0.000008364153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016293359,0.00046751986,0.00024800582,0.00019932733,0.00016863234,0.0015684315,0.00031331312,0.00039122286,0.016603336],"category_scores_gemma":[0.00067094364,0.0001618267,0.0002393281,0.00025130034,0.00026774334,0.001022077,0.0004623087,0.0005098935,0.0027531977],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038805229,0.000057246343,0.0016815827,0.00040200126,0.00008934843,0.00022143323,0.00090167107,0.001539102,0.10158784,0.02505441,0.034735266,0.8333419],"study_design_scores_gemma":[0.00017014492,0.00063648185,0.13127078,0.0009107371,0.00035959136,0.0014667619,0.0031074707,0.045745827,0.08191366,0.29411185,0.44013357,0.00017311992],"about_ca_topic_score_codex":0.0006835844,"about_ca_topic_score_gemma":0.0013273155,"teacher_disagreement_score":0.016603336,"about_ca_system_score_codex":0.00021429868,"about_ca_system_score_gemma":0.00012475302,"threshold_uncertainty_score":0.05554366},"labels":[],"label_agreement":null},{"id":"W2491772294","doi":"10.1007/978-3-319-05380-6","title":"Capturing Connectivity and Causality in Complex Industrial Processes","year":2014,"lang":"en","type":"book","venue":"SpringerBriefs in applied sciences and technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Causality (physics); Computer science; Physics","score_opus":0.042895773937474055,"score_gpt":0.2547188559698643,"score_spread":0.21182308203239025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2491772294","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11983062,0.001880393,0.8701536,0.00047717532,0.000112714406,0.00002083687,0.00016278698,0.0004333948,0.0069284765],"genre_scores_gemma":[0.91668785,0.0027215048,0.07531292,0.00006469484,0.00015018092,0.000041711253,0.00022755716,0.000094104675,0.00469944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9999143,0.000024860034,0.000005102166,0.000026126267,0.000020837664,0.000008765728],"domain_scores_gemma":[0.99927706,0.0005705174,0.000057259,0.00003949381,0.000035023495,0.000020569627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024247778,0.0005969091,0.00033319427,0.0005901997,0.0002514511,0.00069735956,0.00054832373,0.0007429651,0.0019411432],"category_scores_gemma":[0.0019710595,0.00036078622,0.00036994283,0.00062072487,0.00062059914,0.0016041916,0.00069717417,0.00079441996,0.00017538098],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007392724,0.000040304472,0.0016710387,0.00012536722,0.000049109865,0.00019242131,0.00012154808,0.82786703,0.006387356,0.054957196,0.00150401,0.107010715],"study_design_scores_gemma":[0.000004202248,0.000018348732,0.0007998984,0.00000837588,0.000010173239,0.000043949043,0.000017508883,0.93163735,0.0004591582,0.06629662,0.0006959377,0.000008486151],"about_ca_topic_score_codex":0.003162073,"about_ca_topic_score_gemma":0.0036834246,"teacher_disagreement_score":0.003162073,"about_ca_system_score_codex":0.00033403139,"about_ca_system_score_gemma":0.0002131327,"threshold_uncertainty_score":0.0064937472},"labels":[],"label_agreement":null},{"id":"W2491942265","doi":"10.4018/978-1-59904-897-0.ch017","title":"Fundamental Theory of Artificial Higher Order Neural Networks","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Order (exchange); Artificial intelligence; Computer science; Mathematics; Economics","score_opus":0.022725826112072935,"score_gpt":0.24707060691573884,"score_spread":0.2243447808036659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2491942265","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075129657,0.01691095,0.8445356,0.0024425013,0.00087592175,0.000053920525,0.00028452097,0.00030260722,0.12708111],"genre_scores_gemma":[0.43237913,0.042559996,0.41927877,0.00227683,0.0024618881,0.00067577494,0.0007657851,0.00028996696,0.099311896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99962413,0.00009710731,0.000024795432,0.00006305727,0.00015777306,0.00003311629],"domain_scores_gemma":[0.9995752,0.00022027518,0.000031277435,0.000048644084,0.000102592334,0.000022069753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006939859,0.0005926264,0.00058877736,0.0007424997,0.00047350526,0.0016616945,0.0010626181,0.0012584464,0.0061961105],"category_scores_gemma":[0.0014197512,0.00030737653,0.0006473227,0.0007638701,0.001561593,0.0023531164,0.000783113,0.0022058822,0.0019040377],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057308202,0.000009231182,0.00007167157,0.00014838748,0.000016045436,0.00003612862,0.00006303737,0.014319362,0.0008607211,0.9604485,0.0028272443,0.021193793],"study_design_scores_gemma":[0.0000036830656,0.000012586255,0.00010415159,0.000056492685,0.0000061513733,0.00007551198,0.000013178676,0.047765084,0.0003992611,0.92731065,0.02424236,0.000010815433],"about_ca_topic_score_codex":0.00075761415,"about_ca_topic_score_gemma":0.0006876424,"teacher_disagreement_score":0.0061961105,"about_ca_system_score_codex":0.00094296184,"about_ca_system_score_gemma":0.00066676555,"threshold_uncertainty_score":0.020728052},"labels":[],"label_agreement":null},{"id":"W2492514055","doi":"10.4018/978-1-4666-8823-0.ch005","title":"On Simulation Performance of Feedforward and NARX Networks Under Different Numerical Training Algorithms","year":2015,"lang":"en","type":"book-chapter","venue":"Advances in systems analysis, software engineering, and high performance computing book series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Nonlinear autoregressive exogenous model; Conjugate gradient method; Broyden–Fletcher–Goldfarb–Shanno algorithm; Backpropagation; Algorithm; Computer science; Artificial neural network; Nonlinear conjugate gradient method; Rprop; Autoregressive model; Levenberg–Marquardt algorithm; Feedforward neural network; Nonlinear system; Moving average; Autoregressive–moving-average model; Feed forward; Artificial intelligence; Gradient descent; Mathematics; Recurrent neural network; Engineering; Econometrics; Types of artificial neural networks; Control engineering","score_opus":0.013497912856124021,"score_gpt":0.23121835876652327,"score_spread":0.21772044591039924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2492514055","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66661817,0.004748958,0.28956014,0.00079942227,0.0005019596,0.00019088922,0.0013979624,0.0027568787,0.033425596],"genre_scores_gemma":[0.963919,0.0008303911,0.031536374,0.00005184839,0.000018413879,0.00015130336,0.0006940328,0.000068767324,0.0027298208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996729,0.00011374487,0.00003181812,0.000050309478,0.0000980126,0.00003330217],"domain_scores_gemma":[0.99620146,0.0027527204,0.00015333312,0.00017061249,0.00068926805,0.000032620334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012597452,0.00085119443,0.0006381025,0.00058315066,0.00046276656,0.0006533085,0.00066198414,0.00091875973,0.002626269],"category_scores_gemma":[0.005486711,0.00021693669,0.00050582166,0.0007424895,0.00034498126,0.0008079527,0.0004057929,0.00081687735,0.00037755672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012003265,0.00005015501,0.0015884781,0.00015192704,0.000024765319,0.000046496123,0.000050090814,0.97691464,0.0010969932,0.0015323302,0.0005566495,0.017867364],"study_design_scores_gemma":[0.0000039677593,0.000033735447,0.00032303066,0.000012045561,0.0000054688962,0.0000075144494,0.00001221634,0.99809295,0.0010602248,0.00030985608,0.00013483797,0.0000041668063],"about_ca_topic_score_codex":0.01383286,"about_ca_topic_score_gemma":0.0070839645,"teacher_disagreement_score":0.01383286,"about_ca_system_score_codex":0.0006383872,"about_ca_system_score_gemma":0.0004945212,"threshold_uncertainty_score":0.027504683},"labels":[],"label_agreement":null},{"id":"W2492935393","doi":"10.4018/978-1-60566-902-1.ch027","title":"Foundation and Classification of Nonconventional Neural Units and Paradigm of Nonsynaptic Neural Interaction","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Notation; Computer science; Nonlinear system; Artificial intelligence; Function (biology); Representation (politics); Mathematics; Arithmetic; Physics; Biology","score_opus":0.03597466977907894,"score_gpt":0.2714739669106903,"score_spread":0.23549929713161138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2492935393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012117516,0.03354719,0.7134005,0.0017359875,0.002347409,0.0001400251,0.00043136362,0.00051230146,0.23576777],"genre_scores_gemma":[0.29786253,0.050530713,0.42297515,0.0017961506,0.002460012,0.0008688181,0.001392822,0.00062499533,0.22148885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99971455,0.00004651245,0.000033112017,0.00007669346,0.00010669484,0.000022438158],"domain_scores_gemma":[0.9997352,0.00010569986,0.000026784763,0.00003864432,0.00007989364,0.000013812911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040312708,0.00083017396,0.0005693975,0.0010403721,0.0004691938,0.0018286722,0.0012793415,0.0008857505,0.0085627865],"category_scores_gemma":[0.0009299979,0.00027503906,0.0006692701,0.0011670804,0.0016631895,0.004129938,0.00065603165,0.0017373296,0.0032203866],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009079865,0.000007666588,0.00009142186,0.00025656918,0.000010446605,0.000081034064,0.00017044997,0.0017837189,0.002046553,0.9423761,0.004159137,0.049007922],"study_design_scores_gemma":[0.0000051164943,0.000046756526,0.00060531334,0.00019345287,0.00002129597,0.00059200334,0.00011142402,0.029918373,0.0022833964,0.79024595,0.17595045,0.000026510314],"about_ca_topic_score_codex":0.0007623164,"about_ca_topic_score_gemma":0.0006230504,"teacher_disagreement_score":0.0085627865,"about_ca_system_score_codex":0.00096537825,"about_ca_system_score_gemma":0.0007283561,"threshold_uncertainty_score":0.028645337},"labels":[],"label_agreement":null},{"id":"W2495444474","doi":"10.4018/978-1-59140-836-9.ch005","title":"Modelling of Some Aspects of Skilled Locomotor Behaviour Using Artificial Neural Networks","year":2006,"lang":"en","type":"book-chapter","venue":"Computational intelligence and its applications (COMIA) book series/Computational intelligence and its applications series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Computer science; Neurophysiology; Action (physics); Artificial intelligence; Control (management); Motor control; Cognitive science; Neuroscience; Psychology","score_opus":0.05888902485881426,"score_gpt":0.2788740835796702,"score_spread":0.21998505872085594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2495444474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03416828,0.003946456,0.89524436,0.0006411863,0.00014132644,0.000060281633,0.00028260183,0.0005149967,0.06500047],"genre_scores_gemma":[0.6382075,0.010157423,0.28254494,0.00019240624,0.0001667995,0.00036721252,0.00077571376,0.00017389949,0.06741407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999404,0.000014068688,0.0000042864945,0.000013467265,0.000022323109,0.000005476245],"domain_scores_gemma":[0.9999107,0.00004731967,0.00001182572,0.000009509867,0.000016309583,0.0000043736727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014364452,0.0005143424,0.00034131636,0.000275345,0.00023040321,0.00070904876,0.0007829689,0.0008123345,0.0029166497],"category_scores_gemma":[0.00043095992,0.00034418268,0.0005307351,0.00040722475,0.00042937975,0.0007306074,0.0002696848,0.00065256865,0.0006637122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013604753,0.000014917665,0.00022565777,0.00009443615,0.000016350867,0.000098355806,0.00007185502,0.93688846,0.0028708642,0.038698353,0.0009052389,0.020101903],"study_design_scores_gemma":[0.0000020434438,0.0000074640075,0.00021798824,0.0000152568955,0.0000043288633,0.000035182053,0.000006584483,0.9802207,0.00046408526,0.0150422435,0.0039790184,0.000005087167],"about_ca_topic_score_codex":0.0042571756,"about_ca_topic_score_gemma":0.0055753365,"teacher_disagreement_score":0.0042571756,"about_ca_system_score_codex":0.00051826594,"about_ca_system_score_gemma":0.00034315456,"threshold_uncertainty_score":0.009757221},"labels":[],"label_agreement":null},{"id":"W2496270902","doi":"10.4018/978-1-4666-2175-6.ch014","title":"Symbolic Function Network","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Flexibility (engineering); Computer science; Function (biology); Basis (linear algebra); Point (geometry); Artificial intelligence; Theoretical computer science; Mathematics","score_opus":0.01877499619204677,"score_gpt":0.22869710906457136,"score_spread":0.2099221128725246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2496270902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007951088,0.012129877,0.825966,0.002216638,0.00082624203,0.00006794274,0.00075730326,0.0010365532,0.1490484],"genre_scores_gemma":[0.3854686,0.030085193,0.35044873,0.0011870742,0.0008501686,0.00038602878,0.0033798881,0.00079902174,0.22739531],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986017,0.000028520039,0.0000066708612,0.00003166795,0.000056488425,0.00001644766],"domain_scores_gemma":[0.9998741,0.00005706914,0.000008490689,0.000017482795,0.000033067317,0.00000986411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023880205,0.00067567575,0.000568922,0.0006331366,0.00048244925,0.0012893351,0.0010435425,0.00095169607,0.019593662],"category_scores_gemma":[0.0009602847,0.00019750038,0.0005769278,0.0009041913,0.0006852866,0.0022082732,0.00084723195,0.0010494326,0.0043822266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042952346,0.000031120082,0.00034169614,0.00026156363,0.000032027998,0.00015538036,0.00009676036,0.12617095,0.0023380152,0.6301112,0.018761013,0.22165729],"study_design_scores_gemma":[0.000009321858,0.0000347897,0.0001889647,0.00010552905,0.00001824985,0.00026241428,0.00003958857,0.4886309,0.0015813796,0.34498468,0.16411735,0.000026878552],"about_ca_topic_score_codex":0.0024583018,"about_ca_topic_score_gemma":0.0027441815,"teacher_disagreement_score":0.019593662,"about_ca_system_score_codex":0.0011218821,"about_ca_system_score_gemma":0.0006200298,"threshold_uncertainty_score":0.06554729},"labels":[],"label_agreement":null},{"id":"W2496649632","doi":"10.2495/dne-v11-n3-168-177","title":"A computational experience for automatic feature selection on big data frameworks","year":2016,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature selection; Data mining; Field (mathematics); Computer science; Variable (mathematics); Selection (genetic algorithm); Feature (linguistics); Set (abstract data type); Generator (circuit theory); Antecedent (behavioral psychology); Artificial intelligence; Big data; Linear discriminant analysis; Machine learning; Rough set; Mathematics","score_opus":0.026231847648405585,"score_gpt":0.3017007837665052,"score_spread":0.27546893611809964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2496649632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045587025,0.00035286785,0.98942125,0.00073526404,0.000041059142,0.000041597683,0.000036590467,0.0003188978,0.00449381],"genre_scores_gemma":[0.10777245,0.00053842826,0.88925743,0.00019940113,0.000113568654,0.00018901456,0.00011917895,0.00011271364,0.0016978539],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956131,0.0023277455,0.00030955405,0.00039196867,0.0011986243,0.00015897655],"domain_scores_gemma":[0.99275243,0.0051161586,0.00017913133,0.0011261944,0.00066616555,0.0001598769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004694538,0.0007051522,0.000655626,0.0013661862,0.0009513095,0.0031656884,0.0017690843,0.00080136163,0.004736823],"category_scores_gemma":[0.014689859,0.00048735767,0.0009057343,0.0018874733,0.0030325642,0.005231089,0.0025223584,0.0017700006,0.0008496766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014160528,0.00010405255,0.0008862358,0.00035073073,0.00004871174,0.00028297448,0.0008184909,0.07047386,0.0048054876,0.71894836,0.0067590806,0.19638042],"study_design_scores_gemma":[0.000057580033,0.00013153943,0.00061620114,0.00013387209,0.000026149126,0.00026718955,0.00028664284,0.5865359,0.0054326872,0.36085713,0.045596574,0.000058519254],"about_ca_topic_score_codex":0.0017054839,"about_ca_topic_score_gemma":0.0013367675,"teacher_disagreement_score":0.004736823,"about_ca_system_score_codex":0.0011646255,"about_ca_system_score_gemma":0.0009968104,"threshold_uncertainty_score":0.024827361},"labels":[],"label_agreement":null},{"id":"W2498769696","doi":"10.4018/978-1-61520-711-4.ch017","title":"Higher Order Neural Networks","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Order (exchange); Computer science; Artificial intelligence; Mathematics; Economics","score_opus":0.016071977155697442,"score_gpt":0.2389943673101256,"score_spread":0.22292239015442816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2498769696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063383407,0.01943156,0.86417466,0.0017027943,0.0012872559,0.000056033703,0.00043051064,0.00091525307,0.105663605],"genre_scores_gemma":[0.28657016,0.04150405,0.49874184,0.0016465487,0.0017078826,0.00029591503,0.0016517657,0.00064809574,0.16723374],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99968433,0.00006255721,0.000022660564,0.000075633325,0.00012481557,0.000030092748],"domain_scores_gemma":[0.99967086,0.00017036525,0.000018562121,0.000051999214,0.00007153009,0.000016672271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004807083,0.00070130907,0.0005612347,0.00054444675,0.00039090076,0.0017001454,0.0010190994,0.0011460623,0.015156337],"category_scores_gemma":[0.0016787067,0.0003059734,0.00055235333,0.00078736164,0.00072055747,0.0025263857,0.00088739564,0.0018103024,0.004738347],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029985502,0.000028333849,0.00029174483,0.00042626343,0.000054383585,0.00012722022,0.00013972342,0.05150022,0.0033724124,0.70708257,0.018880498,0.2180666],"study_design_scores_gemma":[0.0000087459575,0.000033963057,0.00031849995,0.0001515489,0.000027247279,0.00026793082,0.00003440882,0.20819713,0.0026950047,0.6543993,0.13383427,0.000032013322],"about_ca_topic_score_codex":0.0009190948,"about_ca_topic_score_gemma":0.0014753522,"teacher_disagreement_score":0.015156337,"about_ca_system_score_codex":0.0007239842,"about_ca_system_score_gemma":0.00046892307,"threshold_uncertainty_score":0.05070299},"labels":[],"label_agreement":null},{"id":"W2498806647","doi":"10.4018/978-1-4666-2175-6.ch006","title":"Fundamentals of Higher Order Neural Networks for Modeling and Simulation","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Computer science; Nonlinear system; Order (exchange); System dynamics; Artificial intelligence; Algorithm; Physics","score_opus":0.037778922021687575,"score_gpt":0.28528513986656057,"score_spread":0.24750621784487298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2498806647","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014045071,0.016860958,0.9114849,0.0013477906,0.00082373235,0.00006909219,0.00037882634,0.001052196,0.06657798],"genre_scores_gemma":[0.124352776,0.05849723,0.6953095,0.00093998807,0.0015750069,0.00078656635,0.0015662248,0.0008236426,0.1161491],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963963,0.00007402975,0.000030202664,0.000058204336,0.00017475321,0.000023296936],"domain_scores_gemma":[0.999655,0.00017882779,0.000020457852,0.000048890844,0.00008318421,0.000013644121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055490923,0.0013234686,0.0008071662,0.000744523,0.00038519618,0.0019073752,0.0014259177,0.0016693282,0.014325769],"category_scores_gemma":[0.0012327089,0.00058596843,0.00086206995,0.0010478566,0.00089909206,0.0020414644,0.00087828346,0.0027560191,0.0061306204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023039118,0.000034753713,0.00026406557,0.000851834,0.00006634232,0.00016322208,0.00018022503,0.1455782,0.003865822,0.61794937,0.022119386,0.2089038],"study_design_scores_gemma":[0.000009787883,0.000028499373,0.00022651076,0.00029338483,0.000021420068,0.00018042092,0.00003208965,0.37889597,0.0019749303,0.41101307,0.2072889,0.000034970686],"about_ca_topic_score_codex":0.001864454,"about_ca_topic_score_gemma":0.0016229046,"teacher_disagreement_score":0.014325769,"about_ca_system_score_codex":0.00096588477,"about_ca_system_score_gemma":0.000786521,"threshold_uncertainty_score":0.04792446},"labels":[],"label_agreement":null},{"id":"W2501466418","doi":"10.1007/978-1-4939-1969-7","title":"Analysis and Modeling of Coordinated Multi-neuronal Activity","year":2014,"lang":"en","type":"book","venue":"Springer series in computational neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Spike (software development); Neuroscience; Hippocampus; Psychology; Computer science; Cognitive science","score_opus":0.030533204405657474,"score_gpt":0.27277737944575475,"score_spread":0.24224417504009726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2501466418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008244569,0.0016046645,0.9858636,0.00018470085,0.00006263254,0.000010774927,0.0000953267,0.00023964034,0.0036940998],"genre_scores_gemma":[0.7973483,0.0063250116,0.18119378,0.00011998861,0.00031065205,0.0001646475,0.00052052847,0.00040317274,0.013614032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988854,0.000025580362,0.000006592413,0.000028732207,0.000038630977,0.000011912753],"domain_scores_gemma":[0.99975497,0.00013588846,0.000030371259,0.00003021436,0.000033406726,0.000015205925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029695398,0.0006108298,0.00077962084,0.00035924587,0.00018403323,0.0009151947,0.0011144504,0.00078684674,0.0017349924],"category_scores_gemma":[0.0009664288,0.00041988379,0.0008057116,0.0005425259,0.0004719529,0.00081392366,0.0005888265,0.0007338368,0.0004339431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000150349415,0.000011192109,0.00022851846,0.000058849077,0.000044223543,0.000052575288,0.00002630358,0.952904,0.002439977,0.018406363,0.001138874,0.024674142],"study_design_scores_gemma":[6.517258e-7,0.000002043033,0.00006349013,0.0000021669962,0.0000022911977,0.000012747348,0.0000019411884,0.9915039,0.00011448688,0.007993594,0.00030051667,0.0000021143526],"about_ca_topic_score_codex":0.0037206798,"about_ca_topic_score_gemma":0.0033539091,"teacher_disagreement_score":0.0037206798,"about_ca_system_score_codex":0.0004230471,"about_ca_system_score_gemma":0.0004057395,"threshold_uncertainty_score":0.0073980093},"labels":[],"label_agreement":null},{"id":"W2502373558","doi":"10.1007/978-1-4939-2736-4","title":"Referent control of action and perception","year":2015,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":147,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Referent; Perception; Action (physics); Context (archaeology); Control (management); Field (mathematics); Cognitive psychology; Psychology; Cognitive science; Computer science; Artificial intelligence; Geography; Mathematics; Neuroscience; Philosophy; Linguistics; Physics","score_opus":0.04773562715385616,"score_gpt":0.27790568237731594,"score_spread":0.2301700552234598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2502373558","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018073444,0.08729271,0.3288687,0.002334415,0.0043663364,0.00006531117,0.0003767986,0.001069578,0.57381886],"genre_scores_gemma":[0.059086073,0.061084013,0.076134406,0.0010846051,0.0022994166,0.00025830523,0.0006654982,0.00056755147,0.79882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99980766,0.000016803875,0.000008762354,0.00003720431,0.00011978745,0.000009746423],"domain_scores_gemma":[0.9998933,0.000037326092,0.000008073843,0.000025101843,0.000027505897,0.00000861262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026107504,0.00083240255,0.00062129094,0.00063670316,0.0003271371,0.0018208753,0.0010376865,0.00081097253,0.023368867],"category_scores_gemma":[0.00052569556,0.00036749037,0.00034395733,0.00083948387,0.0014709249,0.0022906163,0.00083401747,0.0013027987,0.010268272],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024679448,0.000023155138,0.000054010205,0.00039929303,0.00001910147,0.00004356678,0.0001016145,0.0032708242,0.0034977875,0.47640035,0.06963058,0.44653502],"study_design_scores_gemma":[0.000007778992,0.00002388642,0.00026416304,0.00021712123,0.000012743263,0.0001217561,0.00003602084,0.007973158,0.0016613575,0.42653745,0.5631243,0.000020199193],"about_ca_topic_score_codex":0.0008103923,"about_ca_topic_score_gemma":0.0013790249,"teacher_disagreement_score":0.023368867,"about_ca_system_score_codex":0.0006474652,"about_ca_system_score_gemma":0.00058213354,"threshold_uncertainty_score":0.07817662},"labels":[],"label_agreement":null},{"id":"W2507380695","doi":"10.1038/nmeth.3968","title":"Model selection and overfitting","year":2016,"lang":"en","type":"article","venue":"Nature Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":700,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Overfitting; Selection (genetic algorithm); Computational biology; Model selection; Computer science; Biology; Artificial intelligence; Machine learning; Evolutionary biology; Artificial neural network","score_opus":0.02306701447789608,"score_gpt":0.37223968741076074,"score_spread":0.3491726729328647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507380695","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029724287,0.0018746719,0.96035725,0.0016321563,0.0004755153,0.00017897159,0.00037525524,0.0014911221,0.0038907728],"genre_scores_gemma":[0.66067207,0.0012437749,0.3081189,0.0032723946,0.00056812004,0.00084282114,0.002595431,0.002166601,0.020519959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9844348,0.010620554,0.00086688885,0.00198719,0.0016978331,0.00039265052],"domain_scores_gemma":[0.96366096,0.025792116,0.0010507124,0.0071357745,0.0020554715,0.00030500212],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02412157,0.0017260859,0.0027046134,0.0023451855,0.0018541578,0.0027118966,0.0026984941,0.0026532197,0.005281439],"category_scores_gemma":[0.07866598,0.0014094858,0.002774842,0.0022657434,0.001408743,0.0032173693,0.0026607888,0.004806481,0.0015372606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006477063,0.00033262238,0.020942073,0.0007827488,0.0026884666,0.0013581914,0.0007263873,0.52525026,0.0031712835,0.09291188,0.031924885,0.3192635],"study_design_scores_gemma":[0.000047376656,0.00006190594,0.0023633935,0.000117500465,0.00023997154,0.00033596344,0.00007829683,0.88752073,0.0023306056,0.09996157,0.0069013466,0.000041330575],"about_ca_topic_score_codex":0.0033023125,"about_ca_topic_score_gemma":0.0056118616,"teacher_disagreement_score":0.9758784,"about_ca_system_score_codex":0.0012287169,"about_ca_system_score_gemma":0.0013362577,"threshold_uncertainty_score":0.12756854},"labels":[],"label_agreement":null},{"id":"W2509356178","doi":"10.1177/0956797618762400","title":"Statistical Learning Creates Novel Object Associations via Transitive Relations","year":2018,"lang":"en","type":"article","venue":"Psychological Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transitive relation; Categorical variable; Object (grammar); Inference; Psychology; Hierarchy; Sequence (biology); Statistical inference; Base (topology); Cognition; Artificial intelligence; Cognitive psychology; Computer science; Mathematics; Combinatorics; Machine learning; Statistics; Neuroscience","score_opus":0.03482277344684411,"score_gpt":0.3489444966895745,"score_spread":0.3141217232427304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509356178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6488855,0.00014870368,0.33889368,0.00052800594,0.00008206107,0.000069873306,0.0001545504,0.0009987288,0.010238839],"genre_scores_gemma":[0.9472176,0.00009834925,0.051409535,0.00008180256,0.000027350141,0.000027462527,0.00017849029,0.000075348544,0.0008841018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987563,0.00031687837,0.00007133949,0.0004662261,0.00030942072,0.00007990818],"domain_scores_gemma":[0.9936274,0.00306388,0.0011117602,0.001650165,0.00032816277,0.00021863083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014248766,0.00034009994,0.0003230796,0.0004492696,0.00038080462,0.0013755058,0.001079721,0.00047536378,0.0022197054],"category_scores_gemma":[0.013229767,0.0005096739,0.0006634339,0.00035802324,0.0018957623,0.0042330283,0.0015588002,0.0014407522,0.00033454926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074489263,0.00048340377,0.028532874,0.0006053341,0.0002506615,0.0012090668,0.004466727,0.033623584,0.29886457,0.28915063,0.0021915683,0.33987665],"study_design_scores_gemma":[0.00007851114,0.00050291047,0.019342486,0.000056482513,0.00015016852,0.0011321924,0.0006525064,0.28802335,0.09224032,0.5901641,0.0075326506,0.00012436544],"about_ca_topic_score_codex":0.0005152971,"about_ca_topic_score_gemma":0.000647368,"teacher_disagreement_score":0.0022197054,"about_ca_system_score_codex":0.00039223692,"about_ca_system_score_gemma":0.0004772202,"threshold_uncertainty_score":0.007535577},"labels":[],"label_agreement":null},{"id":"W2512118283","doi":"10.1186/s12868-016-0283-6","title":"25th Annual Computational Neuroscience Meeting: CNS-2016","year":2016,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University Health Network","funders":"RIKEN Brain Science Institute; Centre of Excellence for Integrative Brain Function, Australian Research Council; National Research Foundation; National Eye Institute; Danmarks Grundforskningsfond; National Institute of Mental Health; Office of Naval Research; RIKEN; Institute for Information and Communications Technology Promotion; Bundesministerium für Bildung und Forschung; Natural Sciences and Engineering Research Council of Canada; School of Medicine, Indiana University; Ministry of Science, ICT and Future Planning; Fundamental Research Funds for the Central Universities; National Institutes of Health; Board of the Swiss Federal Institutes of Technology; Agentura Pro Zdravotnický Výzkum České Republiky; Council for Science, Technology and Innovation; Korea Advanced Institute of Science and Technology; Narodowym Centrum Nauki; České Vysoké Učení Technické v Praze; Indiana University Health; National Natural Science Foundation of China; National Research Foundation of Korea; Hungarian Scientific Research Fund; Korea Health Industry Development Institute; Engineering and Physical Sciences Research Council; Deutsche Forschungsgemeinschaft; National Supercomputing Center, Korea Institute of Science and Technology Information; U.S. Department of Veterans Affairs; Grantová Agentura České Republiky; Office of Naval Research Global; Japan Society for the Promotion of Science; Daegu Gyeongbuk Institute of Science and Technology; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Japan Science and Technology Agency; Howard Hughes Medical Institute; Cabinet Office, Government of Japan; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; POSCO TJ Park Foundation; Hong Kong Baptist University; École Polytechnique Fédérale de Lausanne; National Institute of General Medical Sciences; East Carolina University; McKnight Foundation; Biotechnology and Biological Sciences Research Council; Wellcome Trust; Washington Research Foundation; National Research Council of Science and Technology; National Science Foundation","keywords":"Neuroscience; Cognitive science; Psychology; Computer science","score_opus":0.03146660882879103,"score_gpt":0.2697243209918304,"score_spread":0.23825771216303937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512118283","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016286096,0.043772984,0.10582227,0.15126556,0.23388799,0.001652187,0.029098187,0.011747233,0.4064675],"genre_scores_gemma":[0.044814095,0.023444317,0.041071262,0.005962027,0.028662793,0.0011059395,0.040433694,0.0046107275,0.80989516],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990119,0.0001830286,0.00005426303,0.0001998535,0.00038522825,0.00016562591],"domain_scores_gemma":[0.9965649,0.00017637709,0.00009522482,0.00022987349,0.0014525321,0.0014809702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033274717,0.0019035764,0.0010272929,0.0016757868,0.0014155067,0.0033371812,0.0019755878,0.0019324106,0.1728069],"category_scores_gemma":[0.0039148107,0.000507872,0.0007748883,0.0006879485,0.00073281757,0.002437332,0.0041337106,0.002567046,0.11042628],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016459693,0.00003711296,0.00020130849,0.00010429454,0.000017550745,0.000035014415,0.000018444542,0.00048659963,0.0010772015,0.0015880292,0.9534113,0.042858716],"study_design_scores_gemma":[0.000056343706,0.00008665719,0.0012194163,0.00022662575,0.000025827216,0.000115617164,0.00005345775,0.00483876,0.0017811027,0.009783737,0.98177886,0.000033636246],"about_ca_topic_score_codex":0.0035409215,"about_ca_topic_score_gemma":0.01634679,"teacher_disagreement_score":0.1728069,"about_ca_system_score_codex":0.0018313559,"about_ca_system_score_gemma":0.0038841313,"threshold_uncertainty_score":0.5780967},"labels":[],"label_agreement":null},{"id":"W2512221741","doi":"10.1145/2967413.2967432","title":"A Formal Algebra Implementation for Distributed Image and Video Stream Processing","year":2016,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Scalability; Semantics (computer science); Data stream mining; Stream processing; Theoretical computer science; Algebra over a field; Programming language; Distributed computing; Database; Data mining; Mathematics","score_opus":0.013406542975535443,"score_gpt":0.29018130161650807,"score_spread":0.2767747586409726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512221741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014600236,0.00006571886,0.99101174,0.00022151704,0.00007841116,0.00010498529,0.00023182509,0.0035035405,0.0033223154],"genre_scores_gemma":[0.116583996,0.00033093538,0.8722015,0.0003737003,0.00020125369,0.0006215282,0.0011240701,0.000842133,0.0077208783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998259,0.00036616268,0.00019484511,0.00026929402,0.0007343538,0.00017636344],"domain_scores_gemma":[0.9985524,0.00048158338,0.00008638502,0.00037945595,0.00034052087,0.00015953592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028827786,0.00062128797,0.00073619693,0.00093796675,0.0013454285,0.0036223133,0.0029527324,0.0011851348,0.017681107],"category_scores_gemma":[0.00457414,0.0006853509,0.0021337597,0.0010171841,0.0019147396,0.0048417943,0.002883275,0.0024885957,0.004634685],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022062802,0.00024560362,0.0004588414,0.00022738028,0.000060901,0.00038355586,0.00031462213,0.02993557,0.007433085,0.8820474,0.01163571,0.06703664],"study_design_scores_gemma":[0.00021215357,0.000109875255,0.00010743855,0.00008864979,0.00006152596,0.00033725327,0.00012346599,0.36960834,0.0100795,0.56246805,0.056744494,0.00005919377],"about_ca_topic_score_codex":0.0035106377,"about_ca_topic_score_gemma":0.004176903,"teacher_disagreement_score":0.017681107,"about_ca_system_score_codex":0.0013136332,"about_ca_system_score_gemma":0.0026373484,"threshold_uncertainty_score":0.059149146},"labels":[],"label_agreement":null},{"id":"W2512885195","doi":"10.1109/isit.2016.7541444","title":"The rates of convergence of neural network estimates of hierarchical interaction regression models","year":2016,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Curse of dimensionality; Artificial neural network; Regression; Convergence (economics); Smoothness; Computer science; Regression analysis; Function (biology); Rate of convergence; Artificial intelligence; Class (philosophy); Mathematics; Machine learning; Statistics","score_opus":0.032294793050237726,"score_gpt":0.30307885950149255,"score_spread":0.2707840664512548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512885195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016391842,0.00185601,0.9771631,0.0009811161,0.00009285871,0.000072642615,0.00014319501,0.00051804824,0.0027811904],"genre_scores_gemma":[0.491585,0.0036490923,0.49039784,0.0007911542,0.00029531683,0.0008949413,0.0012775494,0.0013949813,0.009714246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918234,0.0049465657,0.0004743584,0.0010666352,0.0013139823,0.00037509808],"domain_scores_gemma":[0.89236313,0.093314335,0.0039078626,0.0047286474,0.0050542285,0.00063168904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02362086,0.0021513852,0.0014973497,0.0023126912,0.000873535,0.0019900505,0.003496299,0.0028024497,0.0026915655],"category_scores_gemma":[0.13033465,0.0015751543,0.0017330783,0.0011842817,0.002637652,0.0048764283,0.0053029787,0.006086795,0.0010993441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004041483,0.000116479656,0.008143333,0.00067589094,0.00037068734,0.00024080911,0.0004033259,0.747171,0.004148664,0.14392872,0.0034792696,0.09091766],"study_design_scores_gemma":[0.0000101838405,0.000032263684,0.0007569807,0.00006463678,0.000020410449,0.000049117763,0.000026502305,0.97121865,0.001591094,0.025322637,0.00087791955,0.000029523164],"about_ca_topic_score_codex":0.0055325953,"about_ca_topic_score_gemma":0.0043996535,"teacher_disagreement_score":0.02362086,"about_ca_system_score_codex":0.002539033,"about_ca_system_score_gemma":0.0017136779,"threshold_uncertainty_score":0.12492055},"labels":[],"label_agreement":null},{"id":"W2513428348","doi":"10.7210/jrsj.22.727","title":"","year":2004,"lang":"","type":"article","venue":"Journal of the Robotics Society of Japan","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Computer science","score_opus":0.021273618616243852,"score_gpt":0.24853741198903606,"score_spread":0.2272637933727922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2513428348","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010625897,0.007361053,0.012657882,0.011113231,0.014333345,0.00010674538,0.00252641,0.0015736748,0.9397018],"genre_scores_gemma":[0.035522968,0.0041995826,0.003208697,0.00069147826,0.0011848151,0.000032757653,0.002481998,0.00022827595,0.95244944],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997861,0.000013739277,0.000013861226,0.000044320615,0.00011791787,0.000024152538],"domain_scores_gemma":[0.9990331,0.000047314177,0.000034985605,0.00014752448,0.0005650477,0.00017193344],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00049714145,0.00045739656,0.00034284947,0.0010315714,0.00075122406,0.0020978984,0.0005338783,0.0006236275,0.33325863],"category_scores_gemma":[0.0011774095,0.00017140165,0.00018398937,0.0007685328,0.00066532526,0.0014003682,0.0009024841,0.00064503757,0.1857887],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001654088,0.00012486092,0.0015824572,0.00024050938,0.000018951096,0.00009302098,0.00009425599,0.00042556462,0.0055050626,0.038192444,0.5937502,0.3598073],"study_design_scores_gemma":[0.000008913862,0.000022711387,0.0015416719,0.000045835146,0.000013932029,0.00013040629,0.000066819484,0.00038447927,0.0019883402,0.005713608,0.9900781,0.0000052155897],"about_ca_topic_score_codex":0.0026308605,"about_ca_topic_score_gemma":0.0037955139,"teacher_disagreement_score":0.6667414,"about_ca_system_score_codex":0.0007940722,"about_ca_system_score_gemma":0.0016405081,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2518873850","doi":"10.2316/journal.201.2016.3.201-0001","title":"PREFACE: SPECIAL ISSUE ON SOFT COMPUTING AND APPLICATIONS","year":2016,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Soft computing; Computer science; Artificial intelligence; Artificial neural network","score_opus":0.012123134056054807,"score_gpt":0.24477229890507668,"score_spread":0.23264916484902187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518873850","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00072486844,0.036923435,0.0043471246,0.032251786,0.8611381,0.0002342115,0.0018994297,0.0006006809,0.06188025],"genre_scores_gemma":[0.0044356277,0.039962057,0.0024019743,0.00853194,0.64327216,0.00021529179,0.0040312638,0.0012735324,0.29587615],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842,0.0001716643,0.00017538044,0.00026595395,0.00083755865,0.00012939367],"domain_scores_gemma":[0.988086,0.002213049,0.00051964435,0.0008005259,0.006101657,0.0022790413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002408346,0.0021878371,0.0026237408,0.004828899,0.001834225,0.006828817,0.0018728604,0.002762468,0.20954068],"category_scores_gemma":[0.009338824,0.00059575844,0.0013229997,0.0041154,0.0008200461,0.004308821,0.0020710307,0.0051892847,0.13626839],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022364095,0.00002420101,0.0000536476,0.00026862996,0.000007822861,0.000026702704,0.000013087565,0.000087184766,0.00021276315,0.000830171,0.9609186,0.037534878],"study_design_scores_gemma":[0.000012107747,0.000061814586,0.0005590179,0.00031991836,0.000012188764,0.00012727478,0.000029734747,0.00030815203,0.00021010695,0.0032236497,0.99512225,0.000013803146],"about_ca_topic_score_codex":0.0009490525,"about_ca_topic_score_gemma":0.0014874849,"teacher_disagreement_score":0.20954068,"about_ca_system_score_codex":0.0013594488,"about_ca_system_score_gemma":0.002898179,"threshold_uncertainty_score":0.7009834},"labels":[],"label_agreement":null},{"id":"W2519235992","doi":"10.1016/j.jesp.2015.11.004","title":"Replications with MTurkers who are naïve versus experienced with academic studies: A comment on Connors, Khamitov, Moroz, Campbell, and Henderson (2015)","year":2016,"lang":"en","type":"article","venue":"Journal of Experimental Social Psychology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Python (programming language); Computer science; Artificial intelligence; Human intelligence; Artificial neural network; Cognition; Tree (set theory); Bayesian probability; Machine learning; Psychology; Cognitive science; Mathematics","score_opus":0.08002235431030248,"score_gpt":0.4121358960628001,"score_spread":0.33211354175249763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519235992","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050518624,0.0021773393,0.0022294794,0.9231758,0.011354572,0.00027716288,0.0008793379,0.00010865433,0.009279063],"genre_scores_gemma":[0.23568204,0.0017189411,0.0037992515,0.7318712,0.0075547476,0.0016225496,0.0004125315,0.00013963162,0.017199093],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9696969,0.014080856,0.0030740183,0.004099045,0.0074013188,0.0016478752],"domain_scores_gemma":[0.68154806,0.23435192,0.014545701,0.020091183,0.042728085,0.00673511],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05630259,0.00075439527,0.0011356967,0.0011420241,0.009487766,0.004225301,0.007423063,0.019793065,0.007255287],"category_scores_gemma":[0.32437497,0.0007912363,0.0017677423,0.0015877064,0.006651004,0.006223067,0.0042114523,0.015267121,0.0034105545],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023974904,0.0006488602,0.04620656,0.0006540441,0.00024263955,0.0022019553,0.08095786,0.00015970797,0.0020762782,0.023670157,0.7860815,0.054702923],"study_design_scores_gemma":[0.0010951842,0.0013962513,0.17411871,0.0034933158,0.00076850347,0.0024844077,0.17860122,0.0011688545,0.009947883,0.041258,0.5850731,0.0005945498],"about_ca_topic_score_codex":0.06282317,"about_ca_topic_score_gemma":0.078223184,"teacher_disagreement_score":0.9436974,"about_ca_system_score_codex":0.0032820124,"about_ca_system_score_gemma":0.0052850405,"threshold_uncertainty_score":0.29776013},"labels":[],"label_agreement":null},{"id":"W2524428287","doi":"10.48550/arxiv.1609.07061","title":"Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1425,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Training (meteorology); Deep neural networks; Machine learning; Pattern recognition (psychology)","score_opus":0.054722642138298944,"score_gpt":0.19502700136157647,"score_spread":0.14030435922327752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2524428287","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019825079,0.00031355867,0.97004324,0.00020480306,0.00014227699,0.00009758124,0.0005237136,0.0066912514,0.0021585426],"genre_scores_gemma":[0.3506191,0.00036792347,0.6402027,0.00031284778,0.00007077825,0.00044489288,0.0014900012,0.000714371,0.0057774037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955565,0.00007026476,0.000031521053,0.00013193152,0.00015533135,0.0000551928],"domain_scores_gemma":[0.9993691,0.0002185972,0.00006900089,0.00017093656,0.00014747813,0.000024983196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075247104,0.0010279263,0.0005910153,0.00053412287,0.0003319264,0.00092721626,0.0024159634,0.0007536203,0.005973061],"category_scores_gemma":[0.0041356175,0.00071385456,0.00041151218,0.00079131394,0.0007351061,0.0021461598,0.0011291843,0.0016957943,0.0013584527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003159906,0.0001561106,0.001793216,0.0003438812,0.00013479975,0.00015913692,0.00015604854,0.5456729,0.028619085,0.02756546,0.014603797,0.3804796],"study_design_scores_gemma":[0.000023686958,0.00005144036,0.00019734474,0.000015436082,0.000011716617,0.00003211619,0.000012416944,0.97999567,0.008774011,0.008435422,0.0024407466,0.000010062456],"about_ca_topic_score_codex":0.0077708527,"about_ca_topic_score_gemma":0.013316116,"teacher_disagreement_score":0.0077708527,"about_ca_system_score_codex":0.0011145343,"about_ca_system_score_gemma":0.0009738457,"threshold_uncertainty_score":0.01998192},"labels":[],"label_agreement":null},{"id":"W2528048931","doi":"10.1371/journal.pone.0080126.t001","title":"Brain regions with significant differences in beta band ERD between the deviant and standard syllable conditions in experiment 1 (/Ne/−/ne/) and experiment 2 (/Nu/−/nu/).","year":2015,"lang":"en","type":"paratext","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"BETA (programming language); Syllable; Physics; Audiology; Medicine; Speech recognition; Computer science","score_opus":0.06591260159085019,"score_gpt":0.2991712092147287,"score_spread":0.23325860762387848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528048931","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9829457,0.0002652384,0.0020450405,0.00014711806,0.00041729055,0.00015965616,0.0049728956,0.00023135697,0.008815795],"genre_scores_gemma":[0.9786549,0.0001482579,0.004325574,0.00038418808,0.00003258167,0.0007330103,0.0043425346,0.00035874135,0.011020276],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959856,0.00003090935,0.000039583756,0.00018644828,0.0000881785,0.000056388453],"domain_scores_gemma":[0.9993031,0.00029207795,0.00013384603,0.00011880241,0.000050286933,0.00010178605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006363607,0.00089718297,0.0010465836,0.00035913117,0.0003728491,0.0006655656,0.00052920927,0.0012063668,0.016494486],"category_scores_gemma":[0.0025012314,0.00045720174,0.0003072274,0.00015052062,0.0009916935,0.000906058,0.00088617596,0.0011118271,0.0015327849],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008521424,0.00086958316,0.008426373,0.00044695183,0.00016823967,0.0006611394,0.00090077246,0.00023948331,0.9574644,0.0010825373,0.005382882,0.01583609],"study_design_scores_gemma":[0.0013852419,0.0038727722,0.89545405,0.0002026528,0.00040642676,0.0038081275,0.0013971634,0.0014755593,0.0734031,0.0048050787,0.013638425,0.00015131572],"about_ca_topic_score_codex":0.0017111396,"about_ca_topic_score_gemma":0.003557252,"teacher_disagreement_score":0.016494486,"about_ca_system_score_codex":0.00039853988,"about_ca_system_score_gemma":0.00029715817,"threshold_uncertainty_score":0.055179536},"labels":[],"label_agreement":null},{"id":"W2529194139","doi":"10.48550/arxiv.1610.01644","title":"Understanding intermediate layers using linear classifier probes","year":2016,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Intuition; Artificial intelligence; Classifier (UML); Artificial neural network; Monotonic function; Linear model; Machine learning; Linear classifier; Pattern recognition (psychology); Mathematics","score_opus":0.28588046257615984,"score_gpt":0.2130667029440658,"score_spread":0.07281375963209405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529194139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.111002415,0.00053172256,0.88070947,0.0021525032,0.00006146396,0.00008309223,0.000311326,0.0013276265,0.0038203655],"genre_scores_gemma":[0.89267015,0.00038852586,0.103104055,0.0004597888,0.000061553204,0.00022685088,0.00037691818,0.00031884908,0.002393375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888235,0.0003678688,0.00006839501,0.00032975327,0.00019215784,0.00015942832],"domain_scores_gemma":[0.98923206,0.006988576,0.0012545052,0.0014051717,0.0007553509,0.00036432556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024466815,0.001344544,0.0009275666,0.0010740407,0.000829236,0.0034382755,0.0025634044,0.002525546,0.004607566],"category_scores_gemma":[0.022717081,0.0012661266,0.0011570978,0.00072453124,0.0028062728,0.015545735,0.004235236,0.0056220246,0.00065722945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009031092,0.00023016844,0.01718202,0.00035644937,0.00014906882,0.0004763058,0.0015016012,0.5767321,0.02095449,0.27334604,0.0043560555,0.10381255],"study_design_scores_gemma":[0.000013026499,0.000042915784,0.000535347,0.000023744009,0.000017270098,0.000027564134,0.000058213533,0.8444824,0.0026632599,0.15137586,0.000743813,0.000016474403],"about_ca_topic_score_codex":0.0041543515,"about_ca_topic_score_gemma":0.0034996385,"teacher_disagreement_score":0.004607566,"about_ca_system_score_codex":0.0023618932,"about_ca_system_score_gemma":0.0010623984,"threshold_uncertainty_score":0.017136812},"labels":[],"label_agreement":null},{"id":"W2531447897","doi":"","title":"Coding of speech signals using fractal prediction","year":2002,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Speech recognition; Computer science; Speech coding; Algorithm; Fractal; Inverse filter; Linear prediction; Linear predictive coding; Coding (social sciences); Artificial intelligence; Mathematics; Inverse; Statistics","score_opus":0.041027932036341086,"score_gpt":0.24668415551404693,"score_spread":0.20565622347770585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531447897","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049524013,0.0017760763,0.94383764,0.0001856042,0.000099545075,0.000050383816,0.000035502828,0.00045187393,0.0040392787],"genre_scores_gemma":[0.52045196,0.0025936586,0.47120947,0.00011002573,0.00016915824,0.00008423874,0.00015609409,0.000087246524,0.005138212],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998311,0.000045851964,0.000011135027,0.000021191618,0.00007853977,0.00001220882],"domain_scores_gemma":[0.999156,0.0005277485,0.000051356554,0.00009492839,0.00015737377,0.000012611657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044030944,0.00033531975,0.00034490792,0.0004528016,0.00021867051,0.00033557834,0.0002696786,0.0003581518,0.0010023688],"category_scores_gemma":[0.0015181163,0.00011307577,0.00029322435,0.0005128747,0.00073907804,0.0005374203,0.00035365278,0.00043276697,0.00038007236],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000493391,0.000054658856,0.0010156764,0.00038418252,0.000043247914,0.00044127935,0.00038252637,0.15370414,0.18871765,0.07693995,0.0016639438,0.57615936],"study_design_scores_gemma":[0.000042891697,0.00022693191,0.0008498236,0.00005517086,0.000034128803,0.000591023,0.000035127912,0.88084817,0.07962056,0.023673791,0.01397208,0.000050250605],"about_ca_topic_score_codex":0.0011522652,"about_ca_topic_score_gemma":0.0011081933,"teacher_disagreement_score":0.0011522652,"about_ca_system_score_codex":0.00029226116,"about_ca_system_score_gemma":0.00022747544,"threshold_uncertainty_score":0.003353238},"labels":[],"label_agreement":null},{"id":"W2533588912","doi":"10.71781/17797","title":"Identification des corrélats neuronaux associés à la clôture perceptive des objets : une étude en potentiels évoqués","year":2015,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Humanities; Art","score_opus":0.012905149912649697,"score_gpt":0.21866030933839012,"score_spread":0.2057551594257404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2533588912","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85453147,0.0083313715,0.12503962,0.00083904236,0.00021098883,0.000115397655,0.0006010083,0.0003855491,0.009945566],"genre_scores_gemma":[0.9668463,0.0024651787,0.02521652,0.0001521727,0.000058902755,0.00006049607,0.00019460883,0.0000703635,0.0049353866],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968505,0.00004162262,0.000019216146,0.000096970514,0.00012085129,0.000036388195],"domain_scores_gemma":[0.9986299,0.0006356706,0.00020105443,0.00013644822,0.0003140724,0.00008281758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008673663,0.0005253447,0.00043402502,0.0007130261,0.0004502454,0.0012540801,0.0005391743,0.0009433006,0.003986564],"category_scores_gemma":[0.0036825186,0.00033235925,0.0005138247,0.00068032736,0.00070786156,0.0014706488,0.00081076927,0.0006984154,0.000502346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011010846,0.0000919731,0.037568655,0.0009634305,0.00027239037,0.0020885402,0.00278469,0.0037465412,0.6868579,0.0061412104,0.0011299223,0.2572536],"study_design_scores_gemma":[0.00010755753,0.0015396808,0.55437106,0.0005471272,0.00069717853,0.008602888,0.004771872,0.052558646,0.33318442,0.021738848,0.021576468,0.0003041452],"about_ca_topic_score_codex":0.0026202868,"about_ca_topic_score_gemma":0.003003112,"teacher_disagreement_score":0.003986564,"about_ca_system_score_codex":0.0005326107,"about_ca_system_score_gemma":0.00046841917,"threshold_uncertainty_score":0.01333642},"labels":[],"label_agreement":null},{"id":"W2536004288","doi":"10.5539/ijsp.v5n6p1","title":"Time Series Chaos Detection and Assessment via Scale Dependent Lyapunov Exponent","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lyapunov exponent; Computer science; Nonlinear system; Noise (video); Dynamical systems theory; Wavelet; Series (stratigraphy); CHAOS (operating system); Chaos theory; Range (aeronautics); Scale (ratio); Synchronization of chaos; Sample (material); Statistical physics; Control theory (sociology); Applied mathematics; Mathematics; Artificial intelligence; Chaotic; Physics; Control (management); Engineering","score_opus":0.010967374715838954,"score_gpt":0.26424962977939237,"score_spread":0.2532822550635534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2536004288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31780723,0.0007115667,0.6775284,0.00016108161,0.000057387017,0.00011731236,0.0002073786,0.00061155926,0.0027980604],"genre_scores_gemma":[0.9339006,0.00025299087,0.064974986,0.000011589831,0.000028277902,0.000055723955,0.00012509775,0.000034658904,0.00061612745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962974,0.00010619954,0.000031520263,0.00006833048,0.00013808733,0.000025989355],"domain_scores_gemma":[0.9985077,0.0007797233,0.00032067628,0.000087246495,0.00024281937,0.00006187647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009735809,0.00051612564,0.00040360968,0.002910004,0.00021244105,0.00077556545,0.0003145093,0.00038619776,0.0007461516],"category_scores_gemma":[0.00462666,0.00014577177,0.00036226667,0.0010510235,0.00040566773,0.0009149623,0.0006668316,0.00055288244,0.00023662804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009182279,0.00033889132,0.05744848,0.0007623615,0.00032038006,0.0011418766,0.0010709882,0.25687125,0.16643485,0.047105223,0.0024865817,0.46510082],"study_design_scores_gemma":[0.000010911925,0.00016803721,0.014314656,0.000028111173,0.000035032266,0.0002513405,0.00009317682,0.9630472,0.0135664735,0.0074816043,0.0009460033,0.000057567726],"about_ca_topic_score_codex":0.00044989982,"about_ca_topic_score_gemma":0.00027032447,"teacher_disagreement_score":0.002910004,"about_ca_system_score_codex":0.00025487808,"about_ca_system_score_gemma":0.00025666336,"threshold_uncertainty_score":0.005148828},"labels":[],"label_agreement":null},{"id":"W2538116701","doi":"10.1109/cans.2008.8","title":"Modeling of Social Transitions Using Intelligent Systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Rough set; Intelligent decision support system; Feature (linguistics); Set (abstract data type); Inference; Order (exchange); Linkage (software); Intelligent agent","score_opus":0.09064995362052038,"score_gpt":0.2825756521505424,"score_spread":0.19192569853002206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2538116701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13662794,0.0005964056,0.8239336,0.003060827,0.00020855945,0.0001420632,0.0004945501,0.0004629133,0.03447321],"genre_scores_gemma":[0.9352124,0.0006270301,0.05328712,0.00017787672,0.00015399909,0.0002956214,0.00024777453,0.000055027125,0.009943265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950564,0.00021623049,0.000022435459,0.0001133819,0.00007915928,0.00006326349],"domain_scores_gemma":[0.99923563,0.00039144358,0.00012958806,0.00007566927,0.00007636121,0.00009133011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007238228,0.000591641,0.0005984865,0.0009906853,0.00083520095,0.0019090754,0.0015737864,0.0013749885,0.003553909],"category_scores_gemma":[0.002661448,0.00037219923,0.00078636245,0.0006508007,0.0015907071,0.0028234385,0.0012938239,0.0011899167,0.0004280695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027227883,0.00005422797,0.001987284,0.00003435591,0.00004486611,0.00017144028,0.00031170054,0.74306864,0.00044628693,0.2469362,0.00091001636,0.0060077184],"study_design_scores_gemma":[0.000007893344,0.000011669077,0.00026170842,0.00000662331,0.0000074504105,0.000013908267,0.00006308508,0.9297127,0.000063436564,0.06851447,0.0013274336,0.000009530376],"about_ca_topic_score_codex":0.009141319,"about_ca_topic_score_gemma":0.0073294607,"teacher_disagreement_score":0.009141319,"about_ca_system_score_codex":0.001650307,"about_ca_system_score_gemma":0.0009901337,"threshold_uncertainty_score":0.018176258},"labels":[],"label_agreement":null},{"id":"W2538734477","doi":"","title":"Using Akaike's Criterion and Bordered Matrices for Initial Variable Selection for Econometric Model Evaluated with Least Squares Method","year":2015,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Akaike information criterion; Statistics; Mathematics; Least-squares function approximation; Selection (genetic algorithm); Econometrics; Bayesian information criterion; Variable (mathematics); Econometric model; Economics; Computer science; Mathematical analysis; Artificial intelligence","score_opus":0.11088446461438993,"score_gpt":0.36365292417874195,"score_spread":0.252768459564352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2538734477","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003880969,0.00015621417,0.9950852,0.000051020113,0.00002421121,0.00003828266,0.00004956715,0.00020892765,0.0005055956],"genre_scores_gemma":[0.13283552,0.0005449091,0.86289465,0.0000838779,0.000059610993,0.00042465318,0.00064280425,0.00031845132,0.002195498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953696,0.0029357045,0.00034035725,0.00050970376,0.000669053,0.00017559691],"domain_scores_gemma":[0.9860643,0.010780422,0.00044498176,0.0006071004,0.0019339396,0.00016916473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006999181,0.0015178872,0.0019313901,0.0025321806,0.0013689473,0.0023954907,0.0015461486,0.0018335816,0.0053037307],"category_scores_gemma":[0.03035156,0.0009248366,0.00193721,0.0020434444,0.0010656812,0.0027371165,0.0015765144,0.003143516,0.0021603936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048794373,0.000348642,0.00538777,0.0009472026,0.00056037516,0.00049424736,0.00043199665,0.46624467,0.009318407,0.11462858,0.007024042,0.39412618],"study_design_scores_gemma":[0.00004446397,0.0001275399,0.0013382885,0.00009125772,0.00007279074,0.00007815192,0.000056435896,0.9542293,0.0033312952,0.03836301,0.00220301,0.00006430926],"about_ca_topic_score_codex":0.0058106417,"about_ca_topic_score_gemma":0.005788374,"teacher_disagreement_score":0.006999181,"about_ca_system_score_codex":0.00058070826,"about_ca_system_score_gemma":0.0031912054,"threshold_uncertainty_score":0.037015617},"labels":[],"label_agreement":null},{"id":"W2542026628","doi":"","title":"Corporeal concepts and kinetic semantics","year":2014,"lang":"en","type":"article","venue":"PhaenEx","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Semantics (computer science); Computer science; Epistemology; Philosophy; Programming language","score_opus":0.012047421140938567,"score_gpt":0.2586650695000732,"score_spread":0.2466176483591346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542026628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08937054,0.0037478146,0.762246,0.002464035,0.00052643486,0.00005538232,0.00079116266,0.00044790853,0.14035065],"genre_scores_gemma":[0.9066405,0.0017967005,0.064218104,0.0003382194,0.0003538632,0.00009393581,0.0006343211,0.00020304536,0.025721293],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99964297,0.00009953857,0.000031086525,0.00012346283,0.000070020644,0.00003288666],"domain_scores_gemma":[0.99946123,0.00021795933,0.000072395356,0.00011903308,0.00007704315,0.000052247964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066990533,0.0005726044,0.00046198885,0.0015645302,0.0009909954,0.0028794885,0.0008941252,0.000881665,0.012721461],"category_scores_gemma":[0.002376908,0.00029945327,0.00082005595,0.0013117234,0.0048366,0.011134675,0.0019851623,0.001659443,0.00090483675],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007919033,0.0000031218797,0.00005983688,0.000026930142,0.000004675363,0.000018859044,0.00009822475,0.0006848455,0.0001307818,0.99415886,0.00022607048,0.004579936],"study_design_scores_gemma":[0.0000025773122,0.000004702194,0.00008572835,0.000010170027,0.0000033395931,0.000030210515,0.00004072378,0.0033995537,0.0001245208,0.9942332,0.0020614555,0.0000037734217],"about_ca_topic_score_codex":0.00073728315,"about_ca_topic_score_gemma":0.00058297446,"teacher_disagreement_score":0.012721461,"about_ca_system_score_codex":0.0007049758,"about_ca_system_score_gemma":0.00036619013,"threshold_uncertainty_score":0.042557478},"labels":[],"label_agreement":null},{"id":"W2542218231","doi":"10.1109/iembs.2004.1403497","title":"Radial basis functions - simulated annealing classification of mammographic calcifications","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Radial basis function; Simulated annealing; Computer science; Linear discriminant analysis; Mammography; Discriminant; Artificial neural network; Machine learning; Breast cancer; Medicine","score_opus":0.02904586508383166,"score_gpt":0.26319497537461145,"score_spread":0.23414911029077978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542218231","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6377141,0.00037279652,0.36010638,0.00011304823,0.00002702291,0.00010828551,0.000046351648,0.0004936698,0.0010183777],"genre_scores_gemma":[0.84965914,0.00007790962,0.14943396,0.000025426354,0.000009549606,0.00009125147,0.00008300756,0.000037805392,0.0005820386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900997,0.00052096555,0.00005495379,0.00014865652,0.0002137494,0.000051698014],"domain_scores_gemma":[0.99715084,0.0017290649,0.00025117662,0.00021766109,0.0005985615,0.000052769217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029015378,0.0005757188,0.0010252319,0.00063818705,0.00025668417,0.00043795162,0.00049177976,0.00077677914,0.00031906943],"category_scores_gemma":[0.0075295423,0.00040115355,0.0006968693,0.00034727546,0.0004901259,0.00045764106,0.0002963363,0.00046793337,0.00018601902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010388122,0.00022277777,0.010187878,0.000155227,0.00020681137,0.000089094836,0.00021826645,0.83065814,0.04619583,0.0017110785,0.00033287436,0.10898328],"study_design_scores_gemma":[0.000012815096,0.00010924385,0.002390981,0.0000037118664,0.000019011399,0.000032350985,0.000010732154,0.9859568,0.010941652,0.00039775888,0.00011528099,0.00000969022],"about_ca_topic_score_codex":0.0019648147,"about_ca_topic_score_gemma":0.0028363531,"teacher_disagreement_score":0.0029015378,"about_ca_system_score_codex":0.00059011276,"about_ca_system_score_gemma":0.00046608126,"threshold_uncertainty_score":0.015344977},"labels":[],"label_agreement":null},{"id":"W2543457963","doi":"10.1109/icit.2000.854103","title":"System identification: from frequency response to soft computing","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Soft computing; Identification (biology); Computer science; Work (physics); Data science; Artificial intelligence; Artificial neural network; Engineering; Mechanical engineering","score_opus":0.015011748669201342,"score_gpt":0.2550360057804016,"score_spread":0.24002425711120023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2543457963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043572905,0.008611594,0.97678906,0.0013403305,0.00028992098,0.000046655103,0.00005850154,0.00037196607,0.008134709],"genre_scores_gemma":[0.48320478,0.040761985,0.45522803,0.0017161042,0.002399628,0.0003277264,0.00023327321,0.0002673376,0.01586107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988758,0.00036986766,0.00006022747,0.00015553244,0.0004844361,0.000054101718],"domain_scores_gemma":[0.9980459,0.0013907012,0.00014252849,0.00016258447,0.00022568615,0.00003251999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011866422,0.001017706,0.0011217181,0.0015800566,0.00031948948,0.002576429,0.0009961132,0.0015297637,0.0034290517],"category_scores_gemma":[0.005979525,0.00041416546,0.0005241163,0.0015360914,0.0014706362,0.0027872305,0.0013665467,0.0017724524,0.001521281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015374145,0.00011672421,0.001465021,0.0013195561,0.00015479219,0.0002780062,0.00039363178,0.15804143,0.009639061,0.17515194,0.0044691167,0.6488169],"study_design_scores_gemma":[0.000024145453,0.00016092094,0.0014482002,0.0005030092,0.000048102705,0.00052364473,0.00029014167,0.6265167,0.007144469,0.33875814,0.02448178,0.00010072298],"about_ca_topic_score_codex":0.0007190374,"about_ca_topic_score_gemma":0.00042842852,"teacher_disagreement_score":0.0034290517,"about_ca_system_score_codex":0.00050569535,"about_ca_system_score_gemma":0.0004516588,"threshold_uncertainty_score":0.011471331},"labels":[],"label_agreement":null},{"id":"W2545227881","doi":"10.1109/icit.2000.854134","title":"Perspectives on computational perception and cognition under uncertainty","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Cognition; Computer science; Perception; Sophistication; Adaptability; Artificial intelligence; Robustness (evolution); Information processing; Fuzzy logic; Cognitive science; Machine learning; Cognitive psychology; Psychology","score_opus":0.0171104974872933,"score_gpt":0.2674877691972409,"score_spread":0.25037727170994756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545227881","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018031111,0.072961874,0.38588214,0.1251997,0.00216043,0.00005620063,0.00029980787,0.00018536422,0.39522335],"genre_scores_gemma":[0.8474408,0.054672424,0.068195395,0.009876995,0.007359726,0.00022879457,0.00026537877,0.00012736594,0.011833177],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974954,0.0011289619,0.00008679134,0.00032178723,0.0007436869,0.00022336494],"domain_scores_gemma":[0.99580586,0.0030171182,0.00021509606,0.00038356122,0.0003793036,0.00019905355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032661217,0.0009128707,0.0008263888,0.0018092257,0.0016825021,0.005773772,0.0017526068,0.0029519168,0.0039771837],"category_scores_gemma":[0.00466439,0.0002953201,0.001386977,0.0013981548,0.015210576,0.010459493,0.00269963,0.0049716504,0.00058072153],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036196736,0.00000239228,0.000023579636,0.000032010706,0.0000046748387,0.000017755816,0.00013532133,0.00089059974,0.000033178778,0.99624914,0.0005667963,0.002040879],"study_design_scores_gemma":[0.000001624124,0.000003055446,0.000027598484,0.000019719517,0.0000019681781,0.000016853972,0.000052397452,0.00087459735,0.00001609101,0.9934628,0.0055200635,0.0000032185947],"about_ca_topic_score_codex":0.0015070682,"about_ca_topic_score_gemma":0.0008942641,"teacher_disagreement_score":0.005773772,"about_ca_system_score_codex":0.0029564574,"about_ca_system_score_gemma":0.0012114604,"threshold_uncertainty_score":0.021450758},"labels":[],"label_agreement":null},{"id":"W2546346848","doi":"10.1109/nnsp.1992.253667","title":"Chaotic signal emulation using a recurrent time delay neural network","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SIGNAL (programming language); Emulation; Chaotic; Artificial neural network; Computer science; Fidelity; Artificial intelligence; Feedforward neural network; Speech recognition; Pattern recognition (psychology); Telecommunications","score_opus":0.029100573178976685,"score_gpt":0.2588421207237998,"score_spread":0.22974154754482312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546346848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065823585,0.00015109911,0.9294462,0.0001452164,0.000053827927,0.000033594377,0.000030531784,0.0008116167,0.0035041836],"genre_scores_gemma":[0.77642727,0.00014360438,0.21867494,0.00004706534,0.00002697711,0.000052143605,0.00006711411,0.00005941457,0.004501533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998865,0.00003403243,0.000007796124,0.00002094389,0.00004212777,0.000008702067],"domain_scores_gemma":[0.99980646,0.000084677034,0.000025031915,0.000029012648,0.000046678888,0.0000081462895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035620763,0.00034730576,0.00024936895,0.00019785154,0.00013978356,0.0003187268,0.0005173553,0.0004537654,0.0010646436],"category_scores_gemma":[0.0009703102,0.00015146278,0.00034340523,0.00017766737,0.00023495543,0.0005468146,0.00030477103,0.00038623495,0.0002542844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014302187,0.00004684395,0.0005343228,0.00004965618,0.000056681292,0.00013553312,0.0000863494,0.8670151,0.03470856,0.011916967,0.00056627585,0.08474067],"study_design_scores_gemma":[0.0000034425143,0.00001822944,0.000041225576,0.0000016202292,0.0000036306003,0.000012798386,0.0000016354662,0.9952023,0.0038459094,0.000508355,0.0003582215,0.000002709616],"about_ca_topic_score_codex":0.0016204509,"about_ca_topic_score_gemma":0.0014100502,"teacher_disagreement_score":0.0016204509,"about_ca_system_score_codex":0.0003466523,"about_ca_system_score_gemma":0.0001614577,"threshold_uncertainty_score":0.003561616},"labels":[],"label_agreement":null},{"id":"W2552378275","doi":"10.1109/allerton.2014.7028496","title":"Sparse binary matrices as efficient associative memories","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Eesti Teadusagentuur","keywords":"Computer science; Erasure; Content-addressable memory; Associative property; Theoretical computer science; Binary number; Content-addressable storage; Algorithm; Bidirectional associative memory; Linear algebra; Decoding methods; Code (set theory); Formalism (music); Artificial neural network; Artificial intelligence; Arithmetic; Mathematics","score_opus":0.01789279929318083,"score_gpt":0.2712701907481536,"score_spread":0.2533773914549728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552378275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33429933,0.0024516182,0.6274336,0.0011274546,0.00025748234,0.000103122526,0.00022479653,0.0009671721,0.033135455],"genre_scores_gemma":[0.9282018,0.0010229575,0.06089346,0.00020865108,0.00010251685,0.00009187273,0.00012276684,0.000061490085,0.009294467],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974173,0.00009671224,0.000011338083,0.000023750836,0.00009906644,0.000027435532],"domain_scores_gemma":[0.99947995,0.0002453472,0.000076811884,0.00006595928,0.00009078202,0.00004118123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043043852,0.00027002388,0.00035355234,0.00044756263,0.00025916344,0.0008985872,0.00071497826,0.00075813197,0.0025833382],"category_scores_gemma":[0.001994036,0.00020845354,0.00020806394,0.0005323671,0.00073594233,0.0014242525,0.0008417303,0.000750433,0.00060004636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119392775,0.000074590214,0.00029996224,0.00013474854,0.000024107092,0.00013373717,0.00012809073,0.25477383,0.0121164,0.7019216,0.0023360539,0.027937554],"study_design_scores_gemma":[0.000017011826,0.000040809016,0.00009082735,0.000011181959,0.0000053497424,0.000050333383,0.0000174492,0.8165094,0.0025470003,0.17935166,0.0013493627,0.000009637961],"about_ca_topic_score_codex":0.00052265084,"about_ca_topic_score_gemma":0.00072037964,"teacher_disagreement_score":0.0025833382,"about_ca_system_score_codex":0.00041257034,"about_ca_system_score_gemma":0.00033676653,"threshold_uncertainty_score":0.008642077},"labels":[],"label_agreement":null},{"id":"W2555967602","doi":"","title":"Randomized primitives for linear algebra and applications","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Linear algebra; Algebra over a field; Mathematics; Computer science; Pure mathematics; Geometry","score_opus":0.013180022528061587,"score_gpt":0.2797758238676584,"score_spread":0.2665958013395968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555967602","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012488157,0.0041372804,0.92808545,0.0044591003,0.0009041366,0.00013258806,0.0008901096,0.0014857439,0.04741746],"genre_scores_gemma":[0.53524727,0.0071575237,0.37811562,0.0021183703,0.0039113592,0.001379606,0.0026307388,0.001439096,0.068000354],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968748,0.0011190444,0.00016403725,0.00052898965,0.0010275966,0.0002856046],"domain_scores_gemma":[0.9968066,0.0015925724,0.00021040719,0.00088348,0.0003403453,0.00016652883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018355743,0.0012320887,0.0016858419,0.0012317084,0.0016097181,0.003714804,0.0020620006,0.001138591,0.020396098],"category_scores_gemma":[0.008538176,0.0006982653,0.0014003816,0.002852332,0.0029273457,0.0059176013,0.0028339806,0.007215084,0.0056689563],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005273423,0.00004596255,0.000056825393,0.00008233181,0.000013882063,0.000010534602,0.000042955788,0.0033661062,0.00048329,0.95684093,0.008140747,0.030863754],"study_design_scores_gemma":[0.00002211502,0.000011741906,0.000040652634,0.000018396084,0.0000047710096,0.000011017925,0.000010175504,0.0137380855,0.00042270983,0.9804919,0.005219165,0.00000934965],"about_ca_topic_score_codex":0.0010463418,"about_ca_topic_score_gemma":0.0013388105,"teacher_disagreement_score":0.020396098,"about_ca_system_score_codex":0.0022101726,"about_ca_system_score_gemma":0.00199396,"threshold_uncertainty_score":0.0682317},"labels":[],"label_agreement":null},{"id":"W2556045183","doi":"10.30684/etj.28.11.4","title":"ANN Modified Design Model to Adjust Field Current of D.C. Motor","year":2010,"lang":"en","type":"article","venue":"Engineering and Technology Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Artificial neural network; Controller (irrigation); MATLAB; Computer science; Control engineering; DC motor; Control theory (sociology); Field (mathematics); Feed forward; Constant (computer programming); Control (management); Artificial intelligence; Engineering; Mathematics","score_opus":0.015834586185687987,"score_gpt":0.23797476332996015,"score_spread":0.22214017714427217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556045183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011198263,0.00037563342,0.97716045,0.00008023372,0.00008153104,0.00008740216,0.00007377386,0.0007172416,0.010225538],"genre_scores_gemma":[0.812578,0.0007931556,0.16145372,0.00011102938,0.00006590039,0.00056829856,0.00037785893,0.00011654924,0.023935504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980336,0.000031367777,0.000011532751,0.000052217918,0.00008773935,0.000013807846],"domain_scores_gemma":[0.9998461,0.000030608866,0.000022346223,0.000014833574,0.000082267514,0.0000039460697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028411867,0.0006210012,0.00042373323,0.00027474551,0.00019226655,0.0004859475,0.0007044624,0.0006326392,0.0028767802],"category_scores_gemma":[0.0004799297,0.00027649433,0.00044442018,0.00020299954,0.00016544267,0.00036654342,0.0002044293,0.0004956486,0.00084358355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008890263,0.000047325102,0.000600183,0.00028097993,0.00004787104,0.00009270022,0.000063554624,0.87823755,0.027092047,0.0028729914,0.0015785886,0.08899726],"study_design_scores_gemma":[0.0000069627367,0.000060796912,0.00026051962,0.0000114530285,0.000016590293,0.000050645882,0.0000036039655,0.992307,0.003909474,0.00033803147,0.0030285479,0.0000063507355],"about_ca_topic_score_codex":0.0027866939,"about_ca_topic_score_gemma":0.0033028147,"teacher_disagreement_score":0.0028767802,"about_ca_system_score_codex":0.00044709825,"about_ca_system_score_gemma":0.00040734975,"threshold_uncertainty_score":0.009623826},"labels":[],"label_agreement":null},{"id":"W2557283755","doi":"","title":"Deep Learning","year":2016,"lang":"de","type":"book","venue":"MIT Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8960,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Algorithmic learning theory; Machine learning; Computational learning theory; Active learning (machine learning)","score_opus":0.028014025410041426,"score_gpt":0.24530076040058876,"score_spread":0.21728673499054735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557283755","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004950566,0.019237332,0.4909744,0.006034677,0.0036264462,0.00024840244,0.0073131137,0.013593138,0.45402193],"genre_scores_gemma":[0.07045927,0.02955513,0.22971745,0.004321409,0.0012156612,0.00040444318,0.028382247,0.0036810543,0.63226336],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99931765,0.0000593229,0.00003296537,0.00014726985,0.0003747453,0.00006801716],"domain_scores_gemma":[0.9995968,0.000084820174,0.00002031838,0.00009809041,0.00016085237,0.00003914133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053384114,0.0012065466,0.00081081875,0.0010299718,0.00069670024,0.0031871444,0.0022265944,0.0017126967,0.08776107],"category_scores_gemma":[0.0021202918,0.00048880366,0.00088200613,0.0014609847,0.0005428181,0.0036193924,0.0020868976,0.0022575196,0.06904547],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005747348,0.000052046908,0.00043019158,0.00038011806,0.000049116,0.00012248727,0.0000681123,0.010427171,0.0023628995,0.05475608,0.33580822,0.5954861],"study_design_scores_gemma":[0.00001474267,0.00003665395,0.00041530188,0.00024008703,0.00002166625,0.00031123028,0.00005263807,0.03343462,0.0031970923,0.0691313,0.8931115,0.000033167275],"about_ca_topic_score_codex":0.0019606124,"about_ca_topic_score_gemma":0.003238505,"teacher_disagreement_score":0.08776107,"about_ca_system_score_codex":0.00096977135,"about_ca_system_score_gemma":0.0010805365,"threshold_uncertainty_score":0.29359007},"labels":[],"label_agreement":null},{"id":"W2559705111","doi":"10.71781/17827","title":"Étude des mécanismes de localisation auditive et de leur plasticité dans le cortex auditif humain","year":2016,"lang":"fr","type":"dissertation","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Auditory cortex; Neuroscience; Biology","score_opus":0.014789811445647397,"score_gpt":0.24550584000256526,"score_spread":0.23071602855691786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559705111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9752505,0.0025968594,0.018492665,0.00013932721,0.00003993017,0.000026380558,0.00020859974,0.000094789175,0.0031510016],"genre_scores_gemma":[0.98368615,0.0009496457,0.008934285,0.000055470817,0.000011393574,0.00005022394,0.00011078415,0.000022748034,0.006179238],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997806,0.000022639071,0.000010070151,0.000073703224,0.00007079154,0.000042229174],"domain_scores_gemma":[0.9994654,0.00015156176,0.00012075171,0.00004672005,0.00015207793,0.000063559084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048054662,0.00036745574,0.0003201969,0.00057707465,0.00042678826,0.00076956867,0.0003373954,0.0004783793,0.0037304424],"category_scores_gemma":[0.0011691365,0.00040654966,0.00033440258,0.00041484626,0.0006642577,0.00081493915,0.000627508,0.00039651565,0.0004168456],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032786338,0.000029794124,0.02152139,0.0002560406,0.00010478164,0.0007263174,0.0009038847,0.0009205774,0.9199662,0.0016069508,0.00020060629,0.053435534],"study_design_scores_gemma":[0.000043375756,0.0006478782,0.7874677,0.0001328666,0.00021886076,0.0024563768,0.0016820622,0.008069741,0.18944268,0.0029157372,0.0068437806,0.00007897132],"about_ca_topic_score_codex":0.008933048,"about_ca_topic_score_gemma":0.014092823,"teacher_disagreement_score":0.008933048,"about_ca_system_score_codex":0.000526783,"about_ca_system_score_gemma":0.0010509589,"threshold_uncertainty_score":0.017762065},"labels":[],"label_agreement":null},{"id":"W2559793074","doi":"10.1109/epec.2016.7771702","title":"Comparison of artificial intelligence techniques for energy consumption estimation","year":2016,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mean squared error; Multilayer perceptron; Radial basis function; Support vector machine; Mean absolute error; Approximation error; Artificial neural network; Energy consumption; Perceptron; Computer science; Artificial intelligence; Mean absolute percentage error; Root mean square; Function (biology); Pattern recognition (psychology); Statistics; Mathematics; Algorithm; Engineering","score_opus":0.08291016527995292,"score_gpt":0.3691861956408504,"score_spread":0.2862760303608975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2559793074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24139772,0.021455117,0.71161515,0.0009811964,0.00028681656,0.0002188171,0.0004926295,0.001744741,0.021807777],"genre_scores_gemma":[0.71723175,0.007826091,0.27210197,0.000118046424,0.00010361402,0.00014602389,0.000533549,0.00012813977,0.0018107083],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99567187,0.0018862489,0.00034084183,0.00032504718,0.0016773432,0.00009858904],"domain_scores_gemma":[0.9919613,0.0058426885,0.00032443335,0.00047267976,0.0013604424,0.000038411865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042681512,0.00085136073,0.00077382574,0.0034403692,0.00022465487,0.0010986992,0.00075273245,0.00067583576,0.00085844897],"category_scores_gemma":[0.014996156,0.00023964663,0.0009134045,0.003887657,0.00028627814,0.0011446674,0.0005187713,0.000670027,0.00023787646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005520328,0.00019215145,0.011202165,0.00081446586,0.00089918874,0.0000864393,0.00019319219,0.29536963,0.0025048528,0.007287397,0.0010229447,0.6798755],"study_design_scores_gemma":[0.000065009124,0.0004982472,0.020616679,0.0002502455,0.0003192546,0.00016725853,0.0002872022,0.9536489,0.008036772,0.0069389185,0.0090863565,0.00008514881],"about_ca_topic_score_codex":0.007243872,"about_ca_topic_score_gemma":0.0039977883,"teacher_disagreement_score":0.007243872,"about_ca_system_score_codex":0.0008301056,"about_ca_system_score_gemma":0.00051481894,"threshold_uncertainty_score":0.022572458},"labels":[],"label_agreement":null},{"id":"W2560171240","doi":"10.1109/access.2016.2637381","title":"Neo-Fuzzy Integrated Adaptive Decayed Brain Emotional Learning Network for Online Time Series Prediction","year":2016,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Adaptive neuro fuzzy inference system; Artificial intelligence; Machine learning; Fuzzy logic; Time series; Artificial neural network; Neuro-fuzzy; Multilayer perceptron; Perceptron; Fuzzy control system","score_opus":0.026465985713928785,"score_gpt":0.2794480380081844,"score_spread":0.25298205229425563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560171240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083884,0.0006852555,0.90913534,0.00028828016,0.000097079384,0.00006718422,0.000064901375,0.0004191884,0.0053587644],"genre_scores_gemma":[0.9083956,0.00028347358,0.086395636,0.00010142701,0.000023022812,0.000102310965,0.00010559008,0.00001700047,0.0045759506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981016,0.000047258134,0.000013757716,0.00004717276,0.000049236285,0.00003244762],"domain_scores_gemma":[0.9996911,0.000120903795,0.000035572983,0.000020298992,0.00011703744,0.000015173549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007647881,0.00045765168,0.0003854098,0.00030088014,0.00033392885,0.00062614196,0.00079995743,0.00074563525,0.0016397607],"category_scores_gemma":[0.0012109153,0.0001823539,0.00044628582,0.00030481664,0.00038303866,0.00063034886,0.00071690197,0.0008183869,0.00019992422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019275701,0.000098893484,0.0019500081,0.000080796606,0.000072661525,0.00013952496,0.000092444294,0.85570997,0.0060045207,0.009074021,0.0013488781,0.12523554],"study_design_scores_gemma":[0.0000020622074,0.000019433925,0.00011716793,0.000002299629,0.000004585652,0.00000957552,0.0000039827123,0.9983158,0.00039726577,0.00093654374,0.00018855785,0.0000027755593],"about_ca_topic_score_codex":0.004001171,"about_ca_topic_score_gemma":0.0035850008,"teacher_disagreement_score":0.004001171,"about_ca_system_score_codex":0.00064586004,"about_ca_system_score_gemma":0.0004285664,"threshold_uncertainty_score":0.00795579},"labels":[],"label_agreement":null},{"id":"W2560378075","doi":"10.1016/j.jocs.2016.11.006","title":"Recent advances in parallel techniques for scientific computing","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Scalability; Cluster analysis; Parallel computing; Pipeline (software); Kernel (algebra); Scale (ratio); Benchmark (surveying); Artificial neural network; Implementation; Artificial intelligence; Mathematics","score_opus":0.024172417896376294,"score_gpt":0.32487627625100995,"score_spread":0.30070385835463365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560378075","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011791276,0.5097326,0.3933377,0.011372282,0.0060941055,0.00020267667,0.00039110906,0.0029731186,0.06410515],"genre_scores_gemma":[0.111781165,0.39092684,0.46141368,0.0025788369,0.011202881,0.0003986104,0.0010273235,0.0011843636,0.019486263],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876463,0.00028189734,0.00010750645,0.00022875794,0.0005203327,0.000096956755],"domain_scores_gemma":[0.99559706,0.0018767877,0.00020064444,0.00086576206,0.0012809596,0.00017878631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017166319,0.0010928295,0.001553738,0.0015107192,0.00072203635,0.0023223215,0.0021712335,0.001016123,0.013645408],"category_scores_gemma":[0.0065582106,0.00066896656,0.00080375845,0.0046878904,0.0011908851,0.0040381486,0.0018352037,0.0031610075,0.0058517023],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004034048,0.00019646305,0.0008520077,0.0041399654,0.00019951958,0.00012535011,0.00013058385,0.014286464,0.009061555,0.123949245,0.03818122,0.80847424],"study_design_scores_gemma":[0.00019575981,0.00026680727,0.0013243713,0.0007519127,0.0002865106,0.0008075455,0.00011257258,0.124288134,0.014540589,0.2913424,0.56599283,0.00009050293],"about_ca_topic_score_codex":0.0010116816,"about_ca_topic_score_gemma":0.001204264,"teacher_disagreement_score":0.013645408,"about_ca_system_score_codex":0.0010446586,"about_ca_system_score_gemma":0.0015732461,"threshold_uncertainty_score":0.045648456},"labels":[],"label_agreement":null},{"id":"W2563226131","doi":"10.1007/978-3-642-40104-6","title":"Algorithms and Data Structures","year":2013,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Carleton University","funders":"","keywords":"Computer science","score_opus":0.027527702090889446,"score_gpt":0.28041354703068283,"score_spread":0.2528858449397934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563226131","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015975998,0.011950937,0.9150719,0.0016931307,0.0011275272,0.00012454057,0.0012368299,0.003541403,0.06365607],"genre_scores_gemma":[0.036411688,0.016421797,0.80832106,0.0012599344,0.0019084007,0.0006922417,0.0067511466,0.0026677607,0.12556607],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99849856,0.00023853092,0.00014420575,0.00038360586,0.00066895096,0.00006607543],"domain_scores_gemma":[0.99828774,0.00057305786,0.00005078469,0.0008116551,0.00023665109,0.000040024395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010587197,0.0014661652,0.001665859,0.0020490296,0.00092580944,0.0046095327,0.0020625049,0.0013461449,0.035101894],"category_scores_gemma":[0.0047855144,0.00096684083,0.0012295352,0.004966723,0.0015615722,0.005589191,0.002578313,0.0032375015,0.033371985],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039670067,0.00004613479,0.0001688798,0.00046979243,0.000044134784,0.000039864317,0.000095648225,0.004674176,0.0014259383,0.28696612,0.083461866,0.6225678],"study_design_scores_gemma":[0.000020296318,0.00002975038,0.00019621661,0.00015790119,0.000029687915,0.00024516694,0.00003949292,0.024025276,0.0025836872,0.6963982,0.2762526,0.000021833284],"about_ca_topic_score_codex":0.0005564351,"about_ca_topic_score_gemma":0.0005251855,"teacher_disagreement_score":0.035101894,"about_ca_system_score_codex":0.0009278131,"about_ca_system_score_gemma":0.0012668896,"threshold_uncertainty_score":0.11742753},"labels":[],"label_agreement":null},{"id":"W2574408544","doi":"","title":"Boosted Decision Tree for Q-Matrix Refinement.","year":2016,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Decision tree; Computer science; Matrix (chemical analysis); Algorithm; Artificial intelligence; Materials science","score_opus":0.01303706787855303,"score_gpt":0.25728989244226363,"score_spread":0.2442528245637106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574408544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006331083,0.0011553859,0.9870739,0.00038554627,0.00015400995,0.00009412689,0.00035695938,0.0018759909,0.002572907],"genre_scores_gemma":[0.15687086,0.0003743361,0.83397776,0.0005392547,0.00017055607,0.00021973891,0.0017413666,0.00042589215,0.0056801597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988342,0.00048778288,0.000070307724,0.00017443614,0.00031274933,0.000120490426],"domain_scores_gemma":[0.9960657,0.0025866036,0.00012789879,0.0003208101,0.00072993024,0.0001691526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025670826,0.0008368782,0.0016198825,0.0009387652,0.0006155429,0.0008064282,0.002558127,0.0018007649,0.012228488],"category_scores_gemma":[0.009748226,0.0005950794,0.0009864813,0.0011159058,0.0006293332,0.0015575027,0.0014080355,0.002682886,0.00426441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005602951,0.00030068532,0.0012294062,0.00045827337,0.00018319748,0.00017643184,0.0001204775,0.31867528,0.0039259177,0.037939247,0.044138864,0.59229195],"study_design_scores_gemma":[0.000053069925,0.00004113782,0.00009824915,0.000019307627,0.000017288585,0.000027119686,0.000012342416,0.9761949,0.0008713184,0.020346146,0.0023131454,0.0000059208833],"about_ca_topic_score_codex":0.007524338,"about_ca_topic_score_gemma":0.016646968,"teacher_disagreement_score":0.012228488,"about_ca_system_score_codex":0.0006912513,"about_ca_system_score_gemma":0.0020606793,"threshold_uncertainty_score":0.040908396},"labels":[],"label_agreement":null},{"id":"W2580547472","doi":"10.18280/mmep.010101","title":"EFFECT OF EXTERNAL FIELD ON THE VARIATION OF MAGNETIC MEMORY SIGNALS","year":2014,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Variation (astronomy); Field (mathematics); Computer science; Physics; Mathematics; Astrophysics","score_opus":0.011615874891945021,"score_gpt":0.2014874748515678,"score_spread":0.18987159995962277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580547472","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948095,0.0003075805,0.0031104502,0.00003133514,0.000028426975,0.000011915026,0.00006019093,0.000072164046,0.0015683552],"genre_scores_gemma":[0.99882954,0.00008687469,0.0005144939,0.000021856786,0.0000088657225,0.0000064806068,0.000041484407,0.0000118662965,0.00047866712],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982846,0.000028920658,0.0000109541,0.000048225676,0.0000430013,0.000040339466],"domain_scores_gemma":[0.9990396,0.00048103486,0.000098341945,0.000075226846,0.00021403888,0.000091697126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022166078,0.00028804567,0.00017013533,0.0001994996,0.000119994635,0.0001977574,0.000188425,0.00027542666,0.0019134004],"category_scores_gemma":[0.0014921942,0.00010416872,0.00008571206,0.00012320974,0.0002441341,0.0002601465,0.00020596164,0.0002050636,0.00015807677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006478485,0.0000553701,0.0018796072,0.00011771096,0.000012532268,0.0001855028,0.00010106592,0.0007526568,0.9839755,0.000099409124,0.00008537585,0.012087255],"study_design_scores_gemma":[0.000039633287,0.0011548584,0.033992957,0.000030528714,0.00005523188,0.0002834159,0.00009881395,0.006144911,0.9566552,0.000080369966,0.0014480832,0.00001599132],"about_ca_topic_score_codex":0.00027856906,"about_ca_topic_score_gemma":0.00033781785,"teacher_disagreement_score":0.0019134004,"about_ca_system_score_codex":0.00010331037,"about_ca_system_score_gemma":0.000078561105,"threshold_uncertainty_score":0.006400943},"labels":[],"label_agreement":null},{"id":"W2582160025","doi":"10.47839/ijc.18.1.1270","title":"A DEEP CONVOLUTIONAL AUTO-ENCODER WITH POOLING – UNPOOLING LAYERS IN CAFFE","year":2019,"lang":"en","type":"preprint","venue":"International Journal of Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Pohang University of Science and Technology","keywords":"MNIST database; Pooling; Deep learning; Computer science; Artificial intelligence; Convolutional neural network; Encoder; Pattern recognition (psychology); Dimensionality reduction; Perceptron; Autoencoder; Cluster analysis; Artificial neural network","score_opus":0.0139874653528637,"score_gpt":0.2764441205441974,"score_spread":0.2624566551913337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2582160025","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019545823,0.001106247,0.9513115,0.00024314165,0.00022430232,0.00026794418,0.0014217774,0.020807806,0.0050713895],"genre_scores_gemma":[0.25917232,0.0010406695,0.72409433,0.00028360897,0.000071473056,0.0005172157,0.0051975204,0.0014113854,0.008211584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995453,0.00007740433,0.00002347959,0.00012644644,0.00013630901,0.000091113565],"domain_scores_gemma":[0.99963367,0.000078784244,0.00002449226,0.00012262737,0.0001019613,0.000038313636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095165847,0.0016416361,0.00083222304,0.0007421991,0.00048726294,0.00091431173,0.002510982,0.0010955824,0.0039898404],"category_scores_gemma":[0.001809564,0.0007230262,0.0014679136,0.0008704255,0.00047593683,0.0017621422,0.0011435411,0.0018517872,0.001651412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007821954,0.0004326022,0.0023569155,0.00072900084,0.0006766517,0.0006763304,0.00022027253,0.48717257,0.05072982,0.022994412,0.05164938,0.38157985],"study_design_scores_gemma":[0.000056489665,0.00014192444,0.0006219502,0.00003544671,0.00009978024,0.00019146893,0.000011881101,0.9495492,0.030219557,0.0040224134,0.015007245,0.000042654097],"about_ca_topic_score_codex":0.017642133,"about_ca_topic_score_gemma":0.020124115,"teacher_disagreement_score":0.017642133,"about_ca_system_score_codex":0.0010467621,"about_ca_system_score_gemma":0.001343733,"threshold_uncertainty_score":0.035078883},"labels":[],"label_agreement":null},{"id":"W2584401907","doi":"10.1007/978-3-642-24477-3_1","title":"On the Expressive Power of Deep Architectures","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":200,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Expressive power; Deep neural networks; Electronic circuit; Computer architecture; Theoretical computer science; Electrical engineering","score_opus":0.01627985766662682,"score_gpt":0.23056476091546363,"score_spread":0.2142849032488368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584401907","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025547707,0.0139071,0.8664644,0.0066764774,0.0004931057,0.000031577893,0.000543342,0.0011475737,0.08518885],"genre_scores_gemma":[0.69629526,0.01591841,0.24403363,0.0028146768,0.0017097248,0.00020434456,0.0009205232,0.0009658996,0.037137512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99906856,0.00027678814,0.0000782376,0.00013369217,0.00034467576,0.000098054385],"domain_scores_gemma":[0.9947305,0.004116452,0.00013401115,0.00072776614,0.00020749938,0.00008374967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016792679,0.0009705936,0.0008543021,0.000728168,0.0006431527,0.0031249926,0.0016244728,0.0012808479,0.0069908476],"category_scores_gemma":[0.008553319,0.0010025067,0.00077167625,0.0010929169,0.0036721113,0.010052786,0.003223866,0.0058284136,0.0012282904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009626856,0.000021538348,0.00020902169,0.0001852308,0.00003551324,0.00008050707,0.00013213744,0.026407162,0.0027526289,0.8872958,0.004804249,0.07797991],"study_design_scores_gemma":[0.000006866838,0.000008121997,0.0000502156,0.000035314988,0.00000810639,0.000029964696,0.0000114197255,0.045369104,0.00067571644,0.9500756,0.0037229662,0.000006564235],"about_ca_topic_score_codex":0.0013924814,"about_ca_topic_score_gemma":0.0021348777,"teacher_disagreement_score":0.0069908476,"about_ca_system_score_codex":0.0009688215,"about_ca_system_score_gemma":0.00043742094,"threshold_uncertainty_score":0.023386717},"labels":[],"label_agreement":null},{"id":"W2585154133","doi":"10.4018/978-1-5225-0063-6.ch014","title":"Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights","year":2016,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Ant colony optimization algorithms; Artificial neural network; Adaptation (eye); Computer science; Benchmark (surveying); Generalization; Set (abstract data type); Artificial intelligence; Exponential function; Algorithm; Mathematics; Psychology","score_opus":0.019431980923416046,"score_gpt":0.2381524463239891,"score_spread":0.21872046540057305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585154133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14314742,0.00096373115,0.8460749,0.0004680498,0.00020612338,0.00011870662,0.00015812425,0.0020745068,0.006788482],"genre_scores_gemma":[0.54147094,0.00038917325,0.45043305,0.00017064747,0.00005206425,0.00016061835,0.0004734732,0.0002571638,0.006592761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949265,0.00018500577,0.000030712563,0.000109830886,0.00015190002,0.000029834137],"domain_scores_gemma":[0.99804246,0.0011539023,0.00013478204,0.00027692245,0.00034069238,0.0000511039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011724287,0.00093855994,0.0006444452,0.00037759455,0.00031273713,0.0005561941,0.0010556937,0.00092394225,0.0011909554],"category_scores_gemma":[0.00536819,0.00034115725,0.0004971405,0.00059428875,0.00046430444,0.00071063376,0.000687744,0.0014125811,0.0003696171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005822319,0.000044865043,0.0007276067,0.00006037044,0.00003594643,0.000074489624,0.000046636334,0.92063075,0.0029814723,0.0019914173,0.0018025585,0.07154559],"study_design_scores_gemma":[0.0000025277532,0.000012466397,0.00008557573,0.0000018010969,0.000001975166,0.000005590678,0.0000023271457,0.99864966,0.0005633227,0.0004967417,0.00017675667,0.0000013423553],"about_ca_topic_score_codex":0.005780148,"about_ca_topic_score_gemma":0.007154807,"teacher_disagreement_score":0.005780148,"about_ca_system_score_codex":0.00063232996,"about_ca_system_score_gemma":0.0006792099,"threshold_uncertainty_score":0.011493027},"labels":[],"label_agreement":null},{"id":"W2586713367","doi":"10.3389/fpsyg.2018.00369","title":"Interaction in Spoken Word Recognition Models: Feedback Helps","year":2018,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill Genome Centre; McGill University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Deafness and Other Communication Disorders; National Institutes of Health; National Science Foundation","keywords":"Computer science; TRACE (psycholinguistics); Context (archaeology); Perspective (graphical); Perception; Set (abstract data type); Noise (video); Cognition; Word (group theory); Human–computer interaction; Action (physics); Corrective feedback; Speech recognition; Artificial intelligence; Psychology","score_opus":0.046274625166925015,"score_gpt":0.3139142910401487,"score_spread":0.26763966587322363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586713367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20994581,0.0013042677,0.7670705,0.003769493,0.00023804932,0.00006990491,0.00015646622,0.001244802,0.016200734],"genre_scores_gemma":[0.96077573,0.00040744562,0.035293307,0.00026153302,0.000054060016,0.00009480746,0.000073125135,0.00010925017,0.002930654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928325,0.00031251027,0.00003991131,0.00017512073,0.00013410069,0.000055147033],"domain_scores_gemma":[0.9959545,0.002973135,0.00023801961,0.0003786545,0.00027101103,0.0001845565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014947612,0.00079841947,0.00062593934,0.00031465507,0.00047440885,0.0016323661,0.0017536408,0.0018281735,0.0055653653],"category_scores_gemma":[0.010929052,0.0003769017,0.00080285704,0.00023284684,0.0017116495,0.0049924063,0.0017140582,0.001708483,0.00088486896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012853361,0.00029571474,0.004231133,0.00071630796,0.00019386859,0.00072783284,0.003588727,0.55465585,0.041298717,0.27606222,0.0031968679,0.11374745],"study_design_scores_gemma":[0.00007704959,0.0002658506,0.00064001203,0.000037159953,0.000060076705,0.00020165162,0.00014161266,0.8083497,0.00748033,0.17950703,0.0031771988,0.00006231103],"about_ca_topic_score_codex":0.0023238899,"about_ca_topic_score_gemma":0.001275229,"teacher_disagreement_score":0.0055653653,"about_ca_system_score_codex":0.00076264195,"about_ca_system_score_gemma":0.0005516005,"threshold_uncertainty_score":0.018618047},"labels":[],"label_agreement":null},{"id":"W2587137911","doi":"10.1016/j.amc.2017.01.043","title":"Efficient methods of initializing neuron weights in self-organizing networks implemented in hardware","year":2017,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Initialization; Computer science; Artificial neural network; Network topology; Process (computing); Self-organizing map; Key (lock); Software; Focus (optics); Artificial intelligence","score_opus":0.032354034647655065,"score_gpt":0.33049244116337095,"score_spread":0.2981384065157159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587137911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014068427,0.00008765301,0.98428935,0.00005011193,0.00003301121,0.000031068772,0.000012643146,0.00023146013,0.0011961863],"genre_scores_gemma":[0.29398763,0.00014346681,0.703114,0.00004469447,0.000026025758,0.00018295359,0.00004898808,0.00014965526,0.0023026452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998067,0.00005046124,0.000013586434,0.00002095916,0.00008626798,0.000021996266],"domain_scores_gemma":[0.9992362,0.00040171115,0.000064108586,0.00010367298,0.0001695841,0.000024655228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005373806,0.0004397534,0.00036370743,0.00033392152,0.000380509,0.00051040313,0.0010586637,0.0006431331,0.0022152143],"category_scores_gemma":[0.0029003986,0.00037793812,0.0003030699,0.00043012202,0.0004718501,0.0007134577,0.0007784717,0.0008094588,0.00049966056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015912554,0.000057492947,0.0006175044,0.00021467356,0.000044236393,0.00006540827,0.00020939492,0.7082074,0.01748816,0.035896808,0.0012689631,0.23577073],"study_design_scores_gemma":[0.000015302941,0.000016215776,0.00007632929,0.000007860117,0.0000059746512,0.0000135309165,0.000011974244,0.98914343,0.0032632686,0.006879481,0.0005616143,0.000004966915],"about_ca_topic_score_codex":0.0020665072,"about_ca_topic_score_gemma":0.0047862483,"teacher_disagreement_score":0.0022152143,"about_ca_system_score_codex":0.00053765083,"about_ca_system_score_gemma":0.00073487114,"threshold_uncertainty_score":0.007410586},"labels":[],"label_agreement":null},{"id":"W2588113330","doi":"10.1109/ssci.2016.7850276","title":"Ant Colony Optimization applied to the regularization process of a High Order neural Network","year":2016,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Ant colony optimization algorithms; Regularization (linguistics); Computer science; Artificial neural network; Support vector machine; Artificial intelligence; Exponential function; Data mining; Machine learning; Algorithm; Pattern recognition (psychology); Mathematics","score_opus":0.007533466812802465,"score_gpt":0.22077925557196565,"score_spread":0.2132457887591632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588113330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1321411,0.0002659776,0.85925364,0.00063277007,0.0001098478,0.00014894303,0.000079449615,0.00080624496,0.0065620714],"genre_scores_gemma":[0.6684789,0.00011508473,0.32640928,0.00019495489,0.000048691847,0.00023078368,0.0001479868,0.000154667,0.0042195767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993861,0.00023909153,0.00003246852,0.00011165117,0.00017429565,0.000056334975],"domain_scores_gemma":[0.9970204,0.001808267,0.00029184212,0.00023600334,0.0005264317,0.00011704067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002175317,0.0010175592,0.0009484049,0.0005630758,0.00057779567,0.00074268907,0.0013826789,0.0012722968,0.0009628054],"category_scores_gemma":[0.0067526707,0.00041566175,0.0006469896,0.00051538285,0.0010251445,0.0007906913,0.0008997696,0.0015286942,0.00023764932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024962876,0.000021606329,0.00040125,0.000020690726,0.00001697711,0.000037671038,0.000028077653,0.9852337,0.0010167195,0.002643005,0.00055490626,0.010000399],"study_design_scores_gemma":[0.0000032128432,0.0000063596713,0.000029117184,0.000001136561,0.0000013346828,0.0000027987744,0.0000016585828,0.99919015,0.00017905478,0.0005137761,0.000070432376,0.0000010601727],"about_ca_topic_score_codex":0.007021683,"about_ca_topic_score_gemma":0.007099268,"teacher_disagreement_score":0.007021683,"about_ca_system_score_codex":0.0012361022,"about_ca_system_score_gemma":0.0013960992,"threshold_uncertainty_score":0.013961673},"labels":[],"label_agreement":null},{"id":"W2588389710","doi":"10.1371/journal.pone.0172431","title":"Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Perceptron; Computer science; Artificial intelligence; Matching (statistics); Artificial neural network; Conditional independence; Conditional probability; Pattern recognition (psychology); Set (abstract data type); Machine learning; Mathematics; Statistics","score_opus":0.054472015878043005,"score_gpt":0.26967516814082676,"score_spread":0.21520315226278375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588389710","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93898296,0.00018365639,0.05691204,0.0006849857,0.00005994024,0.000072633455,0.00013124003,0.00046264473,0.0025100447],"genre_scores_gemma":[0.99435776,0.00004299559,0.004964392,0.000080816186,0.000016122149,0.000034669752,0.00006721477,0.00003901703,0.00039701842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685526,0.001509583,0.00021579371,0.0005480011,0.00038137342,0.0004899959],"domain_scores_gemma":[0.9157815,0.070497975,0.0044936584,0.0047323536,0.0015919001,0.0029025872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073300544,0.0010824786,0.0010339982,0.00072732504,0.0006110648,0.0010373228,0.001355348,0.0016080796,0.0030948336],"category_scores_gemma":[0.070987344,0.0010259901,0.0012905969,0.0003573966,0.0020461776,0.0033477542,0.0020051568,0.0037428099,0.00026740963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0062537706,0.0017224442,0.025849272,0.0002878663,0.00051425723,0.0005691959,0.00032900096,0.8619618,0.03126985,0.020279896,0.0011774963,0.049785092],"study_design_scores_gemma":[0.000088216824,0.0003978118,0.0055802227,0.000014733333,0.0000541314,0.000055352713,0.00001947714,0.97520304,0.003948259,0.014511591,0.000083124534,0.000044050383],"about_ca_topic_score_codex":0.0048200823,"about_ca_topic_score_gemma":0.0038986485,"teacher_disagreement_score":0.0073300544,"about_ca_system_score_codex":0.0012319161,"about_ca_system_score_gemma":0.0012814477,"threshold_uncertainty_score":0.03876555},"labels":[],"label_agreement":null},{"id":"W2588729329","doi":"10.1037/cep0000115","title":"The brain’s representations may be compatible with convolution-based memory models.","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolution (computer science); Computer science; Noise (video); Range (aeronautics); Representation (politics); Artificial intelligence; Theoretical computer science; Pattern recognition (psychology); Algorithm; Artificial neural network; Image (mathematics)","score_opus":0.06947232026206018,"score_gpt":0.33348623381280196,"score_spread":0.2640139135507418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588729329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1517836,0.0060860543,0.6266194,0.02587895,0.0010707942,0.00019915057,0.0031325843,0.0018586701,0.18337074],"genre_scores_gemma":[0.84238195,0.0035343138,0.13747011,0.0020130144,0.00021318896,0.00016695885,0.0016900868,0.00018840667,0.012342021],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994641,0.00011185952,0.000041537336,0.00021330154,0.000121707555,0.000047425136],"domain_scores_gemma":[0.99815685,0.0007128322,0.00025063302,0.0005848833,0.00020826279,0.00008662513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010044608,0.0005412782,0.0005413236,0.0006781281,0.00042007718,0.004258679,0.0016172489,0.0019200561,0.011374244],"category_scores_gemma":[0.010707048,0.0003335903,0.000907407,0.0011869757,0.0026609635,0.009943063,0.0012653429,0.0020746477,0.0019913807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016712821,0.00007365113,0.0029810905,0.0005984395,0.00023414673,0.0002892209,0.00082160364,0.011110028,0.009335128,0.84207755,0.009589772,0.122722305],"study_design_scores_gemma":[0.000020680052,0.000086734355,0.0057512703,0.00012033469,0.00006625756,0.00047278215,0.0002689542,0.02867119,0.0028815663,0.9445109,0.017099332,0.000050139817],"about_ca_topic_score_codex":0.0030091258,"about_ca_topic_score_gemma":0.0021082077,"teacher_disagreement_score":0.011374244,"about_ca_system_score_codex":0.001226684,"about_ca_system_score_gemma":0.0009023362,"threshold_uncertainty_score":0.038050592},"labels":[],"label_agreement":null},{"id":"W2590718101","doi":"10.1109/icci-cc.2016.7862080","title":"Development and application of an algorithm for extracting multiple linear regression equations from artificial neural networks for nonlinear regression problems","year":2016,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"University of California, Irvine","keywords":"Artificial neural network; Sigmoid function; Nonlinear system; Algorithm; Fidelity; Computer science; Artificial intelligence; Node (physics); Regression; Mathematics; Engineering; Statistics","score_opus":0.045039702998960957,"score_gpt":0.30232066676186736,"score_spread":0.25728096376290643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590718101","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045438512,0.00003173322,0.9990928,0.000014818778,0.000008167614,0.000021678474,0.000008930456,0.00020589706,0.00016148905],"genre_scores_gemma":[0.007644573,0.000077455916,0.9915469,0.000016907885,0.000010509063,0.00011216415,0.00007386603,0.000071883565,0.0004457095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988385,0.00032724673,0.00012814823,0.0002512322,0.00041340882,0.000041491305],"domain_scores_gemma":[0.9974503,0.0013452793,0.00016432379,0.00021818251,0.0007874194,0.000034519675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023723939,0.0010529135,0.0011443289,0.0013080261,0.0004677019,0.0010246504,0.0012536979,0.0010503132,0.0026150555],"category_scores_gemma":[0.008099662,0.0007640094,0.0012679802,0.0013282348,0.00045790506,0.0011146808,0.0010606151,0.0018968513,0.002025845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059566923,0.000087056214,0.0010017101,0.000283802,0.00012997516,0.00016416053,0.00014148581,0.2740534,0.016253496,0.014258584,0.0022235527,0.6913432],"study_design_scores_gemma":[0.000010296657,0.00002592248,0.00019604793,0.000020588946,0.000012130495,0.000067951725,0.000012729403,0.98693407,0.005228157,0.0034292978,0.0040511335,0.00001162243],"about_ca_topic_score_codex":0.0021642128,"about_ca_topic_score_gemma":0.0023379282,"teacher_disagreement_score":0.0026150555,"about_ca_system_score_codex":0.0005163306,"about_ca_system_score_gemma":0.0014760175,"threshold_uncertainty_score":0.012546599},"labels":[],"label_agreement":null},{"id":"W2593088764","doi":"","title":"Stochastic Resonance in Human Cognition: ACT-R Versus Game Theory, Associative Neural Networks, Recursive Neural Networks, Q-Learning, and Humans","year":2005,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Randomness; Artificial neural network; Noise (video); Associative property; Cognition; Computer science; Associative learning; Artificial intelligence; Cognitive science; Game theory; Content-addressable memory; Psychology; Algorithm; Mathematical economics; Cognitive psychology; Mathematics; Neuroscience; Statistics","score_opus":0.013914520105315329,"score_gpt":0.24144657360218816,"score_spread":0.22753205349687283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593088764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4985388,0.0050275223,0.44920036,0.016445108,0.00022254988,0.000110950474,0.00008642495,0.00029032608,0.030077958],"genre_scores_gemma":[0.96747285,0.0007774918,0.029870367,0.0004880616,0.000054886248,0.00006562311,0.000017209233,0.000023095272,0.0012304458],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984901,0.0011123032,0.000037961196,0.000115328425,0.00019763259,0.000046748497],"domain_scores_gemma":[0.9897425,0.008632284,0.0006104005,0.00039183293,0.00038068352,0.00024235684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033857634,0.0003928935,0.00042958773,0.00052057777,0.00034780128,0.0024025135,0.0005967109,0.0010052483,0.0015355049],"category_scores_gemma":[0.011979782,0.00020762718,0.0004004864,0.00044230893,0.0041562514,0.0026384764,0.00074087933,0.0011862213,0.00018347467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003465038,0.00022814889,0.0116890725,0.00033961708,0.00015429569,0.00018665187,0.0028187074,0.24865538,0.0057055913,0.6652867,0.0025858192,0.062003538],"study_design_scores_gemma":[0.000048884744,0.00018169294,0.0041865166,0.000091785114,0.00003167592,0.00008908114,0.00057815324,0.34691083,0.0010401569,0.6432548,0.003523268,0.00006322344],"about_ca_topic_score_codex":0.0025834192,"about_ca_topic_score_gemma":0.0017026853,"teacher_disagreement_score":0.0033857634,"about_ca_system_score_codex":0.0010718226,"about_ca_system_score_gemma":0.00060921116,"threshold_uncertainty_score":0.017905891},"labels":[],"label_agreement":null},{"id":"W2593639632","doi":"","title":"Learning the Structure of a Mathematical Group","year":2007,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Group (periodic table); Set (abstract data type); Identity (music); Associative property; Closure (psychology); German; Artificial neural network; Artificial intelligence; Computer science; Mathematics; Pure mathematics; Linguistics; Philosophy","score_opus":0.009912177364811809,"score_gpt":0.21490332789226432,"score_spread":0.2049911505274525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593639632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57861006,0.00013265117,0.40613016,0.001311784,0.00006790166,0.000110033056,0.00016374142,0.0007434198,0.012730264],"genre_scores_gemma":[0.8605231,0.00015460663,0.13588992,0.00017983583,0.000019272886,0.00005785559,0.00035999488,0.000045686906,0.0027696772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995023,0.00018743811,0.000021873302,0.00017575164,0.00008802994,0.000024475032],"domain_scores_gemma":[0.99807703,0.00095031527,0.00021294864,0.00049467717,0.00015657423,0.00010845972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009225528,0.0003542874,0.0002555967,0.0002936619,0.0002849974,0.0010850173,0.0007492543,0.0007487511,0.0030018687],"category_scores_gemma":[0.008642331,0.00027689003,0.00044198733,0.00014668661,0.0013416455,0.0045532687,0.0012072817,0.0016510448,0.00049213285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061566633,0.00046651755,0.016892357,0.00045989957,0.00014183206,0.00027084938,0.0029343562,0.15272921,0.08402978,0.25959262,0.0066720196,0.4751949],"study_design_scores_gemma":[0.00004598548,0.0003680802,0.003696602,0.000045426503,0.000030886265,0.00014156637,0.00030606025,0.5180949,0.020394044,0.4494304,0.0074046035,0.000041477753],"about_ca_topic_score_codex":0.0011133151,"about_ca_topic_score_gemma":0.0009620012,"teacher_disagreement_score":0.0030018687,"about_ca_system_score_codex":0.0006501163,"about_ca_system_score_gemma":0.00051497575,"threshold_uncertainty_score":0.01004225},"labels":[],"label_agreement":null},{"id":"W2593971914","doi":"10.1109/acssc.2016.7869646","title":"Precise digital implementations of hyperbolic tanh and sigmoid function","year":2016,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"CMC Microsystems","keywords":"Hyperbolic function; Sigmoid function; Exponential function; Activation function; Computer science; Hyperbolic coordinates; Transfer function; Field-programmable gate array; Implementation; Artificial neural network; Mathematics; Hyperbolic manifold; Artificial intelligence; Mathematical analysis; Engineering; Hyperbolic equilibrium point; Embedded system","score_opus":0.008507170868124792,"score_gpt":0.21824307470474913,"score_spread":0.20973590383662433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593971914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02815652,0.001531598,0.9533379,0.00024905198,0.0002465858,0.00009603513,0.00017817643,0.0021098412,0.014094263],"genre_scores_gemma":[0.72663385,0.0013761385,0.2605571,0.0001890076,0.0000761507,0.00010667976,0.00025696968,0.00012573988,0.010678324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995795,0.000056983758,0.0000465323,0.00006155399,0.00020520992,0.000050220606],"domain_scores_gemma":[0.99963963,0.00007276368,0.00004021436,0.000108959975,0.00012609371,0.000012380038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003879542,0.0005072496,0.0002634216,0.0004753123,0.00020850523,0.0010732281,0.00091816473,0.00054449734,0.0044799596],"category_scores_gemma":[0.0013140161,0.0002624189,0.00025284183,0.00048071422,0.0003315883,0.0011305538,0.00043882275,0.0005897996,0.0018004624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005553035,0.00007809167,0.0014669795,0.000810728,0.00008159887,0.00042140295,0.000258639,0.042595744,0.16263981,0.09285986,0.0066226134,0.69160926],"study_design_scores_gemma":[0.00017679518,0.000929173,0.002705142,0.00021051886,0.00012388939,0.0028816974,0.00016510545,0.3578015,0.44848296,0.03344966,0.15293467,0.00013900395],"about_ca_topic_score_codex":0.0004820711,"about_ca_topic_score_gemma":0.0005382546,"teacher_disagreement_score":0.0044799596,"about_ca_system_score_codex":0.00036198116,"about_ca_system_score_gemma":0.00040477348,"threshold_uncertainty_score":0.014986992},"labels":[],"label_agreement":null},{"id":"W2597133720","doi":"10.1016/j.eswa.2017.02.048","title":"A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Classifier (UML); Multiclass classification; Artificial neural network; Artificial intelligence; Constructive; Algorithm; Machine learning; Process (computing); Support vector machine","score_opus":0.15719325117653854,"score_gpt":0.35676153569637986,"score_spread":0.19956828451984132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597133720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002737649,0.000072710616,0.99557626,0.000050302817,0.000030213785,0.000087898996,0.000017487464,0.0003408497,0.0010867435],"genre_scores_gemma":[0.0878072,0.00007270153,0.9089072,0.00012721024,0.000047723956,0.00044996216,0.00015493797,0.00012053469,0.002312618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999081,0.00022706045,0.0000689908,0.00022841593,0.00030518245,0.00008944563],"domain_scores_gemma":[0.9982869,0.0007602344,0.000121689474,0.000076771285,0.00066562823,0.00008870135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020260273,0.0012710316,0.0017797303,0.0018930004,0.0012809997,0.0014617773,0.001816824,0.002448952,0.0064572305],"category_scores_gemma":[0.0040524015,0.00092748617,0.0011825929,0.0012677604,0.0006694366,0.0012357114,0.001423458,0.0020963417,0.0021794694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030457307,0.00023076734,0.0011583068,0.00022828957,0.00009470306,0.00013613644,0.00024121937,0.34400848,0.008760286,0.015807534,0.0071182726,0.62191135],"study_design_scores_gemma":[0.000020116318,0.00005844891,0.0001377102,0.000016830976,0.000012404087,0.0000293762,0.000013345122,0.9952667,0.0011175151,0.002166222,0.0011516552,0.0000097640495],"about_ca_topic_score_codex":0.0049428353,"about_ca_topic_score_gemma":0.0049591735,"teacher_disagreement_score":0.0064572305,"about_ca_system_score_codex":0.0009579742,"about_ca_system_score_gemma":0.0018602046,"threshold_uncertainty_score":0.021601617},"labels":[],"label_agreement":null},{"id":"W2599312750","doi":"10.4018/ijcini.2017040104","title":"Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction","year":2017,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Sigmoid function; Computer science; Artificial neural network; Algorithm; Fidelity; Nonlinear system; Artificial intelligence","score_opus":0.035699535361626074,"score_gpt":0.3503715311612569,"score_spread":0.31467199579963084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599312750","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008050312,0.00006545797,0.99848443,0.000037588343,0.000007796129,0.000024143324,0.000011703754,0.00029465652,0.00026931323],"genre_scores_gemma":[0.014486463,0.0001029161,0.9844283,0.00005146863,0.000014373528,0.000079023586,0.00009060884,0.0000670284,0.0006798548],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863213,0.000457115,0.00011528434,0.00030162948,0.00044294522,0.00005085718],"domain_scores_gemma":[0.9951396,0.0026014573,0.00027816795,0.0005118756,0.0014052914,0.00006354306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030169708,0.00087830605,0.0010420698,0.0013794183,0.00041198215,0.0017027986,0.0017966056,0.0011560974,0.003143426],"category_scores_gemma":[0.01102842,0.0007252393,0.0010824265,0.0012054725,0.0007070232,0.0019120186,0.0011175585,0.002091363,0.002469458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008683682,0.000088452296,0.001113284,0.00024585106,0.000110608926,0.00014430485,0.000195642,0.23513761,0.011534518,0.015505078,0.002289248,0.73354864],"study_design_scores_gemma":[0.00000799908,0.000029399298,0.000113697264,0.000023286208,0.000010041669,0.000057739217,0.00001604541,0.988509,0.0028456936,0.0057381857,0.0026401533,0.000008717916],"about_ca_topic_score_codex":0.0029088296,"about_ca_topic_score_gemma":0.0029608815,"teacher_disagreement_score":0.003143426,"about_ca_system_score_codex":0.00058903336,"about_ca_system_score_gemma":0.0012827214,"threshold_uncertainty_score":0.015955448},"labels":[],"label_agreement":null},{"id":"W2600111037","doi":"10.1016/j.laa.2017.03.014","title":"Two finite-time convergent Zhang neural network models for time-varying complex matrix Drazin inverse","year":2017,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Science and Technology Commission of Shanghai Municipality; Fudan University","keywords":"Drazin inverse; Mathematics; Inverse; Matrix (chemical analysis); Convergence (economics); Zhàng; Moore–Penrose pseudoinverse; Function (biology); Artificial neural network; Applied mathematics; Pure mathematics; Geometry; Computer science; Artificial intelligence","score_opus":0.03959575472427649,"score_gpt":0.2991370148100499,"score_spread":0.2595412600857734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600111037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071316935,0.00063063804,0.9157963,0.0005728414,0.00012985166,0.000036219866,0.00009135769,0.00019781214,0.011228072],"genre_scores_gemma":[0.9283894,0.00036322296,0.050403915,0.00015474814,0.00004936905,0.00007529597,0.00010798875,0.000064659274,0.020391429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976736,0.000074844116,0.000009455757,0.00006448399,0.000051037794,0.000032923104],"domain_scores_gemma":[0.9994918,0.00019193669,0.00008470328,0.000042403877,0.00013478188,0.00005439399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084984023,0.0007767743,0.00097619777,0.00029382377,0.00035733494,0.0011700735,0.0016633706,0.0015560064,0.0023057219],"category_scores_gemma":[0.001835424,0.00036723542,0.0006787087,0.00035684902,0.0009831819,0.0013291086,0.0010642766,0.0012258674,0.00031934635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016903009,0.00006014011,0.00061130803,0.000114904884,0.0000680659,0.00015608325,0.00016943549,0.8135803,0.005798519,0.16415383,0.00092950236,0.014188874],"study_design_scores_gemma":[0.000004778293,0.000013059606,0.00004602974,0.0000026756852,0.0000059063204,0.000012047549,0.0000050215335,0.99489707,0.00023049137,0.004609902,0.00016582002,0.000007129582],"about_ca_topic_score_codex":0.004548391,"about_ca_topic_score_gemma":0.004337834,"teacher_disagreement_score":0.004548391,"about_ca_system_score_codex":0.0009017006,"about_ca_system_score_gemma":0.00068999646,"threshold_uncertainty_score":0.009043872},"labels":[],"label_agreement":null},{"id":"W2600339276","doi":"10.1109/icassp.2017.7952956","title":"A distributed constrained-form support vector machine","year":2017,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Classifier (UML); Distributed algorithm; Context (archaeology); Nonlinear system; Distributed learning; Projection (relational algebra); Distributed Computing Environment; Distributed database; Machine learning; Distributed computing; Data mining; Algorithm","score_opus":0.018976626692414452,"score_gpt":0.2690721011793402,"score_spread":0.25009547448692576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600339276","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072150556,0.00010881014,0.9905087,0.0002593528,0.000065680986,0.00003983343,0.000051695188,0.00043236572,0.0013185638],"genre_scores_gemma":[0.5905987,0.00021137203,0.399861,0.00037988625,0.00019825004,0.00025090162,0.00041393674,0.000105666215,0.0079801865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990702,0.00024255892,0.000040166662,0.00026830906,0.00028928576,0.0000895371],"domain_scores_gemma":[0.99892396,0.00028730932,0.00008104009,0.0001996043,0.00041798875,0.00009002226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001051712,0.00051790325,0.0011104172,0.0003492496,0.00047516514,0.00092008035,0.0018555985,0.0014152823,0.0036914237],"category_scores_gemma":[0.0032806154,0.0003317932,0.0004010874,0.0006806964,0.0005841183,0.0017182889,0.001677895,0.0010674146,0.0011536309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033286656,0.00017477649,0.0008545146,0.000107808184,0.00006502134,0.00017418677,0.000081011705,0.6277971,0.008564514,0.022476709,0.009990064,0.32938147],"study_design_scores_gemma":[0.00001512185,0.00003593024,0.000058094327,0.0000023900175,0.0000022592135,0.00002543742,0.0000048092193,0.9947271,0.0005049073,0.0036756,0.00094442285,0.0000039928423],"about_ca_topic_score_codex":0.0016206638,"about_ca_topic_score_gemma":0.0014007057,"teacher_disagreement_score":0.0036914237,"about_ca_system_score_codex":0.0004582487,"about_ca_system_score_gemma":0.0012294123,"threshold_uncertainty_score":0.012349069},"labels":[],"label_agreement":null},{"id":"W2600440520","doi":"10.1371/journal.pone.0174621","title":"Phase diagrams and dynamics of a computationally efficient map-based neuron model","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Aperiodic graph; Parameter space; Dynamical systems theory; Bifurcation; Statistical physics; Biological neuron model; Phase space; Computer science; Physics; Phase diagram; Perceptron; Algorithm; Biological system; Phase (matter); Artificial intelligence; Artificial neural network; Mathematics; Biology; Geometry; Combinatorics; Nonlinear system","score_opus":0.049083986661488235,"score_gpt":0.2657808809288281,"score_spread":0.2166968942673399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600440520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40621534,0.00048321934,0.56977385,0.0007439415,0.00007325129,0.000055000513,0.00028544915,0.00032299495,0.022047007],"genre_scores_gemma":[0.9739622,0.00016674296,0.022646084,0.00004438207,0.000016784934,0.00006099026,0.00006957155,0.000041322033,0.00299178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991906,0.000023101205,0.000003723644,0.000010281272,0.000030866224,0.0000129701375],"domain_scores_gemma":[0.99979717,0.00009294546,0.000025811649,0.000022213417,0.000032331678,0.000029581182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024010522,0.0002077135,0.00039680718,0.00033302436,0.0002933899,0.0006430427,0.0006087239,0.0007470816,0.0021824278],"category_scores_gemma":[0.0009292729,0.00018407429,0.00031195307,0.0002428137,0.0005815147,0.0009865509,0.0005284124,0.00050972996,0.00026450047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047542562,0.00003618631,0.0004650255,0.000046849873,0.000023913106,0.00014845104,0.00005697799,0.8594082,0.008143843,0.12709056,0.00056795194,0.003964486],"study_design_scores_gemma":[0.0000064402707,0.0000058335563,0.00006125008,0.0000017555782,0.0000022867355,0.000013782945,0.000003445907,0.98760533,0.0002875906,0.011850437,0.00015938813,0.0000024756037],"about_ca_topic_score_codex":0.0015270414,"about_ca_topic_score_gemma":0.0010049337,"teacher_disagreement_score":0.0021824278,"about_ca_system_score_codex":0.0004526361,"about_ca_system_score_gemma":0.00046890788,"threshold_uncertainty_score":0.0073009133},"labels":[],"label_agreement":null},{"id":"W2602524466","doi":"","title":"A Novel System for Speech Recognition","year":2013,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Perceptron; Speech recognition; Artificial intelligence; Fuzzy logic; Pronunciation; Time delay neural network; Pattern recognition (psychology); Multilayer perceptron; Neuro-fuzzy; Machine learning; Fuzzy control system","score_opus":0.08769498942881333,"score_gpt":0.31157353231422413,"score_spread":0.22387854288541081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602524466","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01539151,0.00097174325,0.9549656,0.00043077563,0.0011139911,0.00055829936,0.00051814195,0.013112474,0.012937463],"genre_scores_gemma":[0.19752824,0.00086686725,0.7484786,0.001142019,0.00047654356,0.00087213615,0.0012836043,0.0002588824,0.049093146],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991411,0.000082613784,0.000054447446,0.00030672713,0.00035383762,0.00006128783],"domain_scores_gemma":[0.99966574,0.00006534768,0.000025546044,0.00006276746,0.00014764915,0.000033033764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000717791,0.00069774047,0.00080948666,0.00055020663,0.0005695492,0.0012134723,0.0015356283,0.0016212658,0.011926855],"category_scores_gemma":[0.0011055683,0.0003114093,0.0003922515,0.0004978971,0.00035505864,0.0014120344,0.0011527824,0.0011068106,0.008772842],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047102154,0.00021460163,0.0007427869,0.00053142407,0.00010661205,0.00048564302,0.00021968991,0.0045772707,0.25853142,0.01180067,0.022335257,0.69998366],"study_design_scores_gemma":[0.0001846206,0.0011649324,0.0034157927,0.00015426526,0.00023496033,0.0034942357,0.00010378841,0.45771396,0.24473448,0.008721451,0.27989966,0.00017791589],"about_ca_topic_score_codex":0.00090206537,"about_ca_topic_score_gemma":0.0015202631,"teacher_disagreement_score":0.011926855,"about_ca_system_score_codex":0.00047585135,"about_ca_system_score_gemma":0.0006120851,"threshold_uncertainty_score":0.03989935},"labels":[],"label_agreement":null},{"id":"W2607253359","doi":"10.2316/journal.201.2017.2.201-2798","title":"NEURAL PREDICTIVE OBSERVER FOR SENSORLESS-CONTROLLED INDUCTION MOTOR DRIVE","year":2017,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Induction motor; Control theory (sociology); Computer science; Observer (physics); Control engineering; Artificial intelligence; Engineering; Physics; Control (management); Voltage; Electrical engineering","score_opus":0.03312851823953285,"score_gpt":0.27011985983545855,"score_spread":0.23699134159592572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607253359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017545749,0.0004714335,0.9779353,0.00019816306,0.00024493196,0.000038299706,0.000036785706,0.00052578066,0.0030034604],"genre_scores_gemma":[0.9513797,0.00041891853,0.04202519,0.000094358606,0.0000936435,0.000103468235,0.00010045073,0.000027922293,0.0057563134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998198,0.000028769276,0.000013134468,0.000032864944,0.000088728666,0.000016822261],"domain_scores_gemma":[0.9996618,0.0001012984,0.000053913092,0.000034319874,0.00013870206,0.000009974194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043648062,0.00036819332,0.000614166,0.00017499027,0.00018935418,0.0006176676,0.0007515451,0.0007281687,0.0016211972],"category_scores_gemma":[0.0013898225,0.0002001472,0.00018112306,0.00021252554,0.00031022902,0.00056257285,0.0003594699,0.000929834,0.00038657754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006525767,0.00017154509,0.0010829552,0.00050007214,0.00006893158,0.0002861012,0.00022442552,0.64924425,0.038742084,0.017536044,0.0047609014,0.28673014],"study_design_scores_gemma":[0.000022126129,0.000063668915,0.00028119702,0.000009419652,0.00000764614,0.000012734365,0.0000042886177,0.99556255,0.0022128492,0.00090794713,0.00091055373,0.0000050536987],"about_ca_topic_score_codex":0.0039728493,"about_ca_topic_score_gemma":0.0042172237,"teacher_disagreement_score":0.0039728493,"about_ca_system_score_codex":0.00043353435,"about_ca_system_score_gemma":0.00044344814,"threshold_uncertainty_score":0.007899463},"labels":[],"label_agreement":null},{"id":"W2607499963","doi":"10.1101/125849","title":"From neural network to psychophysics of time: Exploring emergent properties of RNNs using novel Hamiltonian formalism","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Recurrent neural network; Hamiltonian (control theory); Artificial neural network; Computer science; Formalism (music); Applied mathematics; Artificial intelligence; Mathematics; Mathematical optimization","score_opus":0.06717636261798206,"score_gpt":0.24956882114999224,"score_spread":0.1823924585320102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607499963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23875423,0.00038213588,0.75325155,0.00049131725,0.00004264943,0.000024153043,0.00006552036,0.00012004103,0.006868424],"genre_scores_gemma":[0.96162283,0.00017507332,0.03624486,0.00007677904,0.000021943484,0.000032573324,0.000023248862,0.00003277621,0.0017699687],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999317,0.00003402686,0.0000023057287,0.000013289424,0.000011736871,0.000007021451],"domain_scores_gemma":[0.9997981,0.00011258689,0.000030407435,0.000017170847,0.000019624393,0.000022028387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035538882,0.00024095994,0.0002113527,0.0002481442,0.00018809276,0.00046668013,0.00039920965,0.00039257095,0.0012268663],"category_scores_gemma":[0.0007360479,0.00012644009,0.0003158531,0.000118226795,0.0009146133,0.0008259081,0.00048835005,0.00045748704,0.00007843243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038266167,0.000042968943,0.0011427453,0.00009997841,0.00005234701,0.00022125764,0.00025215803,0.45437783,0.034754477,0.49846566,0.0005025121,0.010049795],"study_design_scores_gemma":[0.0000031794377,0.000010566044,0.00022120615,0.0000043244186,0.0000021990666,0.0000145017475,0.00001756555,0.9083523,0.0006319887,0.09051531,0.00022139671,0.000005414816],"about_ca_topic_score_codex":0.00093289244,"about_ca_topic_score_gemma":0.0007241412,"teacher_disagreement_score":0.0012268663,"about_ca_system_score_codex":0.0003979277,"about_ca_system_score_gemma":0.00025272637,"threshold_uncertainty_score":0.0041043162},"labels":[],"label_agreement":null},{"id":"W2607635013","doi":"10.1016/j.cub.2017.03.068","title":"Feedback Synthesizes Neural Codes for Motion","year":2017,"lang":"en","type":"article","venue":"Current Biology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Canadian Institutes of Health Research","keywords":"Biology; Motion (physics); Computational biology; Neuroscience; Artificial intelligence; Computer science","score_opus":0.06823915092730222,"score_gpt":0.34854831889454635,"score_spread":0.28030916796724414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607635013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19662756,0.00076909317,0.7674019,0.0016252954,0.0017907558,0.00008894616,0.00050512125,0.0027450582,0.028446192],"genre_scores_gemma":[0.94689083,0.00019216961,0.04327711,0.00024592373,0.00014010254,0.000043132706,0.00023575785,0.00023363397,0.008741349],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988663,0.00001386225,0.000005600663,0.00003914973,0.00003696702,0.00001774098],"domain_scores_gemma":[0.999471,0.0002493484,0.00004162681,0.000063293046,0.00011709015,0.000057621834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002137591,0.00044001636,0.00029161407,0.00034959352,0.00030431637,0.0005084162,0.00053484755,0.00075992546,0.005370645],"category_scores_gemma":[0.00267819,0.00027085026,0.00033460028,0.0002514403,0.00033271054,0.0007163095,0.0007146727,0.0006732572,0.0007056917],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005567496,0.00020334992,0.0020135234,0.0003558855,0.00010785197,0.00042104072,0.00030468238,0.1384758,0.34303984,0.09858986,0.009952653,0.40597874],"study_design_scores_gemma":[0.00004939847,0.00011388708,0.0015027197,0.00003347859,0.0000314778,0.000116105955,0.00004584813,0.9168461,0.042733096,0.03286569,0.005625792,0.000036480393],"about_ca_topic_score_codex":0.0022131493,"about_ca_topic_score_gemma":0.003285126,"teacher_disagreement_score":0.005370645,"about_ca_system_score_codex":0.00044461663,"about_ca_system_score_gemma":0.00038358071,"threshold_uncertainty_score":0.017966628},"labels":[],"label_agreement":null},{"id":"W2609762885","doi":"10.1090/amsip/025/03","title":"A maximum entropy criterion for feature extraction","year":2002,"lang":"en","type":"book-chapter","venue":"AMS/IP studies in advanced mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Pattern recognition (psychology); Principle of maximum entropy; Statistics; Artificial intelligence; Computer science","score_opus":0.06859183985075433,"score_gpt":0.3451677014996903,"score_spread":0.27657586164893594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609762885","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015493371,0.00037668357,0.99707437,0.00007141306,0.00003097789,0.000017945838,0.00007533822,0.0001380401,0.00066590606],"genre_scores_gemma":[0.116412744,0.0010719672,0.8743968,0.00016270546,0.00041792382,0.00021694548,0.0009765974,0.00032070078,0.0060235807],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99875355,0.00034727258,0.00012034886,0.00020786369,0.0005041614,0.0000668316],"domain_scores_gemma":[0.9980211,0.0011723298,0.00009209319,0.00023463348,0.0004248054,0.000055012657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00212602,0.00081436196,0.001797166,0.0016923349,0.00065146876,0.001732392,0.0012402737,0.0013306197,0.0029650284],"category_scores_gemma":[0.0055572116,0.00052916567,0.0010970582,0.0021022153,0.0011350223,0.0024855505,0.001636416,0.0014539757,0.0018458272],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002669993,0.00009161783,0.0006123089,0.00045187617,0.00012573159,0.00025057734,0.00010833563,0.1301073,0.039397437,0.09845035,0.01038679,0.7197507],"study_design_scores_gemma":[0.000015853879,0.00008734947,0.0010635733,0.00004447025,0.000035691643,0.00023241245,0.000023068396,0.8958515,0.010010561,0.08686707,0.0057223677,0.00004621397],"about_ca_topic_score_codex":0.0010323683,"about_ca_topic_score_gemma":0.0010116035,"teacher_disagreement_score":0.0029650284,"about_ca_system_score_codex":0.00057581457,"about_ca_system_score_gemma":0.0007992766,"threshold_uncertainty_score":0.011243641},"labels":[],"label_agreement":null},{"id":"W2610162325","doi":"","title":"Proc. Advances in Neural Information Processing Systems (NIPS '06), Vancouver, Canada","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Remote sensing; Geography","score_opus":0.0036428164553425285,"score_gpt":0.19026140772184374,"score_spread":0.1866185912665012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610162325","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05253205,0.15663253,0.45328823,0.048258845,0.03973769,0.00050015503,0.01865048,0.011424071,0.218976],"genre_scores_gemma":[0.11143225,0.06491529,0.117516436,0.003213589,0.0034995752,0.00022493595,0.018253684,0.0018353863,0.6791089],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993467,0.00012539432,0.000038309285,0.00010724675,0.0002759034,0.000106363645],"domain_scores_gemma":[0.9977767,0.00034583517,0.000034416855,0.00022632911,0.0013147942,0.00030197008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019094552,0.0011216978,0.0015243937,0.0009688059,0.00088637986,0.0029663257,0.0012590092,0.0012431927,0.052358165],"category_scores_gemma":[0.0029410603,0.0004215036,0.00041543538,0.0011553213,0.0009544284,0.0015065222,0.0010504469,0.002355845,0.032630946],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032475265,0.00014842828,0.0017101873,0.00027136362,0.00009474502,0.00015550865,0.00013827796,0.0034276205,0.0027263938,0.0026409382,0.64027905,0.34808278],"study_design_scores_gemma":[0.00010265248,0.00015794669,0.010618946,0.00035641965,0.00023831,0.00053200964,0.00081944116,0.062314156,0.008910419,0.016526116,0.8993492,0.00007436245],"about_ca_topic_score_codex":0.09613633,"about_ca_topic_score_gemma":0.255273,"teacher_disagreement_score":0.09613633,"about_ca_system_score_codex":0.002032269,"about_ca_system_score_gemma":0.003951393,"threshold_uncertainty_score":0.19115353},"labels":[],"label_agreement":null},{"id":"W2611442203","doi":"10.5555/1416354.1416382","title":"Toward a theory of validation of hybrid minmax FuzzyNeuro systems","year":2008,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Fuzzy inference system; Computer science; Generalization; Minimax; Hybrid system; Perspective (graphical); Inference; Value (mathematics); Fuzzy logic; Artificial intelligence; Machine learning; Fuzzy control system; Mathematics; Mathematical optimization; Adaptive neuro fuzzy inference system","score_opus":0.021914327862476175,"score_gpt":0.2238328995306152,"score_spread":0.201918571668139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611442203","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010443482,0.00021510944,0.98547053,0.00035226336,0.000029006585,0.000033154214,0.000036260135,0.00011355335,0.0033066089],"genre_scores_gemma":[0.66149366,0.0005263874,0.33336326,0.00047087294,0.0001254405,0.00029650974,0.00017142396,0.00009048807,0.0034619102],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99503136,0.0023321568,0.00032213778,0.00086991966,0.0011910958,0.00025320478],"domain_scores_gemma":[0.9736144,0.020059481,0.0012025794,0.0021021154,0.002766625,0.00025484437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0126656415,0.0007432504,0.000897111,0.0012860304,0.0008280582,0.00267055,0.0022406625,0.001958204,0.0028303002],"category_scores_gemma":[0.03371486,0.0005798148,0.0015634998,0.00066457683,0.00507559,0.0040304638,0.002799758,0.003246101,0.00033273367],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000795378,0.000033440407,0.0011918838,0.00013322438,0.00005986881,0.00014600361,0.00048691782,0.18798845,0.0017022515,0.781075,0.000562743,0.026540682],"study_design_scores_gemma":[0.000011006301,0.00005872174,0.00018770664,0.000064012325,0.0000115213115,0.000043301006,0.00004257403,0.62462205,0.00113564,0.3724929,0.0013110625,0.000019442854],"about_ca_topic_score_codex":0.0030519278,"about_ca_topic_score_gemma":0.001940359,"teacher_disagreement_score":0.0126656415,"about_ca_system_score_codex":0.0024592853,"about_ca_system_score_gemma":0.0017014756,"threshold_uncertainty_score":0.066983104},"labels":[],"label_agreement":null},{"id":"W2613066522","doi":"10.12700/aph.13.4.2016.4.3","title":"Breast Tumor Computer-aided Diagnosis using Self-Validating Cerebellar Model Neural Networks","year":2016,"lang":"en","type":"article","venue":"Acta Polytechnica Hungarica","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; Xiamen University; National Science Council; National Natural Science Foundation of China","keywords":"Artificial neural network; Computer science; Artificial intelligence","score_opus":0.01850795242244857,"score_gpt":0.24325450183742567,"score_spread":0.2247465494149771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613066522","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19291845,0.00050683063,0.80124164,0.00015230637,0.00007928649,0.00010258155,0.00005415373,0.0018513883,0.003093374],"genre_scores_gemma":[0.96316814,0.00008426095,0.03573296,0.000031982887,0.000007924401,0.00005600349,0.00005119973,0.000014576372,0.00085293344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973565,0.00007934471,0.000020063624,0.0000630399,0.000074610216,0.000027337528],"domain_scores_gemma":[0.99896955,0.00048345138,0.00012891501,0.00009475006,0.0003011274,0.000022076392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008862578,0.00038882968,0.00038060718,0.00039182042,0.00029017855,0.0006402239,0.00062168017,0.0006044138,0.000719218],"category_scores_gemma":[0.002708257,0.00019917228,0.0003453309,0.00021137887,0.00029801897,0.00043273583,0.00046024754,0.00038755962,0.00017776378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032837983,0.00015703423,0.0044762865,0.000093729104,0.00007817644,0.00016195986,0.00011811295,0.7031793,0.013103031,0.0015407265,0.0006697862,0.27609333],"study_design_scores_gemma":[0.0000052844334,0.00003663397,0.00044455726,0.0000041271155,0.000008391636,0.000018733943,0.00000412087,0.99761707,0.0015499904,0.00019918467,0.00010750824,0.000004490781],"about_ca_topic_score_codex":0.0070357136,"about_ca_topic_score_gemma":0.0055297106,"teacher_disagreement_score":0.0070357136,"about_ca_system_score_codex":0.00048922346,"about_ca_system_score_gemma":0.0005503957,"threshold_uncertainty_score":0.013989508},"labels":[],"label_agreement":null},{"id":"W2614136060","doi":"10.5539/ibr.v10n6p137","title":"Designing Optimal Knowledge Base for Neural Expert Systems","year":2017,"lang":"en","type":"article","venue":"International Business Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Knowledge base; Expert system; Artificial neural network; Generalization; Set (abstract data type); Artificial intelligence; Machine learning; Legal expert system; Plan (archaeology); Domain (mathematical analysis); Domain knowledge; Subject-matter expert; Base (topology); Mathematics","score_opus":0.20065647414776105,"score_gpt":0.44491861300657265,"score_spread":0.2442621388588116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614136060","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010580876,0.000193116,0.9856651,0.00014129638,0.000025307883,0.00015517721,0.000090351066,0.00046311147,0.0026856505],"genre_scores_gemma":[0.2375783,0.0004773068,0.7587864,0.0002043583,0.000052895586,0.00066881726,0.0005437277,0.000083783096,0.0016044842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986632,0.0002945969,0.00014000591,0.00028732803,0.00048684943,0.00012805074],"domain_scores_gemma":[0.99777335,0.00075979147,0.00015579534,0.00021839348,0.0010385559,0.000054103217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022109465,0.00049848144,0.0010245636,0.0018360924,0.0007263747,0.0016221922,0.0018908116,0.0011067954,0.0026098806],"category_scores_gemma":[0.007456859,0.00066130655,0.00045108612,0.0010078907,0.0005810205,0.0025535563,0.0015239626,0.0011876164,0.0009152898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015942752,0.00022241984,0.0012656976,0.00029409732,0.00009141376,0.000290275,0.0002858802,0.52036107,0.0106553435,0.039803497,0.0043317373,0.42223915],"study_design_scores_gemma":[0.000022467015,0.00004298216,0.00025134173,0.000057889312,0.00003724542,0.000052891603,0.000066817265,0.9677862,0.0070435903,0.019746328,0.0048766057,0.000015615595],"about_ca_topic_score_codex":0.0035471593,"about_ca_topic_score_gemma":0.0039162165,"teacher_disagreement_score":0.0035471593,"about_ca_system_score_codex":0.0011621054,"about_ca_system_score_gemma":0.001633943,"threshold_uncertainty_score":0.011692762},"labels":[],"label_agreement":null},{"id":"W2615678818","doi":"10.14288/1.0343288","title":"Dimension reduction using Independent Component Analysis with an application in business psychology","year":2017,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Dimensionality reduction; Component (thermodynamics); Dimension (graph theory); Reduction (mathematics); Component analysis; Sufficient dimension reduction; Econometrics; Computer science; Psychology; Social psychology; Mathematics; Economics; Artificial intelligence; Thermodynamics; Combinatorics; Physics","score_opus":0.01682598601182611,"score_gpt":0.2318153368555892,"score_spread":0.21498935084376308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615678818","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021491207,0.0020232184,0.97168046,0.0010626263,0.00012533147,0.00014694927,0.0002946938,0.0006932597,0.0024821886],"genre_scores_gemma":[0.10554601,0.0019657146,0.88996774,0.000111642694,0.0001416244,0.0003391831,0.0005672688,0.0000845332,0.001276319],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986743,0.00064798654,0.00006951433,0.0001587758,0.00039760044,0.000051817642],"domain_scores_gemma":[0.99696106,0.0019834877,0.00015856873,0.0003114001,0.00053733325,0.00004817127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020522606,0.00086333835,0.00081772904,0.002532312,0.00080155785,0.001282071,0.0006584756,0.00086207775,0.0014392047],"category_scores_gemma":[0.006245765,0.00028766118,0.0011065054,0.004610769,0.0007594636,0.0010363932,0.0010927254,0.0014848178,0.0005728935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021046009,0.00028558713,0.0030554303,0.00045239506,0.00024601194,0.0002833047,0.0003106118,0.13637888,0.013356228,0.05455053,0.010131912,0.78073853],"study_design_scores_gemma":[0.000038777864,0.0001102068,0.0046614613,0.000070584625,0.00006155479,0.00024631852,0.00010796364,0.9158267,0.009223086,0.0562001,0.013372108,0.00008120531],"about_ca_topic_score_codex":0.0029486164,"about_ca_topic_score_gemma":0.0032642689,"teacher_disagreement_score":0.0029486164,"about_ca_system_score_codex":0.0006901934,"about_ca_system_score_gemma":0.0011727695,"threshold_uncertainty_score":0.010853529},"labels":[],"label_agreement":null},{"id":"W2617317093","doi":"10.71781/10395","title":"Structured prediction and generative modeling using neural networks","year":2016,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Samsung; Compute Canada; Nuance Foundation; Canadian Institute for Advanced Research","keywords":"Generative grammar; Artificial neural network; Artificial intelligence; Computer science; Machine learning","score_opus":0.04542608088271574,"score_gpt":0.31289965638451106,"score_spread":0.2674735755017953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617317093","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019288508,0.00038864627,0.9752578,0.00038110753,0.00006794035,0.000040390412,0.00025807216,0.0010736969,0.0032437579],"genre_scores_gemma":[0.6325523,0.0010285109,0.3462539,0.0003087041,0.00013326309,0.0002945279,0.001256735,0.0005642384,0.017607749],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991316,0.00027404397,0.000042525815,0.0002523461,0.00021896418,0.00008055864],"domain_scores_gemma":[0.99776876,0.0016644931,0.00009782385,0.00019805695,0.0002227855,0.00004809696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013656737,0.00093137455,0.0008094886,0.0008100773,0.0005418207,0.0020506128,0.001631444,0.0015437484,0.0052224244],"category_scores_gemma":[0.005874674,0.00075085147,0.001744022,0.00091373257,0.0010055763,0.0021805626,0.00145855,0.0018463033,0.001174811],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009747778,0.000036301848,0.0014684425,0.000106440246,0.000070123344,0.00019315646,0.0002366117,0.90509737,0.0030374494,0.03166357,0.0010951745,0.056897987],"study_design_scores_gemma":[0.0000020407258,0.000005254342,0.00009193896,0.0000065401814,0.0000043155383,0.000015076706,0.000007750454,0.99232227,0.00045982105,0.0066702827,0.00041026712,0.0000044659837],"about_ca_topic_score_codex":0.0153499255,"about_ca_topic_score_gemma":0.015586332,"teacher_disagreement_score":0.0153499255,"about_ca_system_score_codex":0.0014302833,"about_ca_system_score_gemma":0.0009775008,"threshold_uncertainty_score":0.030521154},"labels":[],"label_agreement":null},{"id":"W2617985161","doi":"10.5539/mer.v7n1p31","title":"A Model of Early Motivational States and Their Uses","year":2017,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ohio State University","keywords":"Psychology; Perception; Cognitive psychology; Computer science; Neuroscience","score_opus":0.10673723903531411,"score_gpt":0.3314522191126528,"score_spread":0.2247149800773387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617985161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061248697,0.0018084812,0.7899089,0.0033634058,0.00020830406,0.00017890507,0.0010232824,0.0005796582,0.14168039],"genre_scores_gemma":[0.751632,0.0019814735,0.1564579,0.00044887484,0.00015428058,0.0008505071,0.0006669957,0.00014668624,0.08766139],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997217,0.000077922276,0.000018235298,0.0000675199,0.000058693524,0.00005594233],"domain_scores_gemma":[0.99951863,0.00016250265,0.0000602825,0.000045505305,0.000120559416,0.00009245798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005128009,0.0005826981,0.00038883468,0.0007217323,0.00068147236,0.0016139146,0.0018856047,0.0012483192,0.012456679],"category_scores_gemma":[0.0013212495,0.00036194545,0.0008496759,0.00049664604,0.0011949206,0.0020855714,0.0008944371,0.0015257514,0.0020894343],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033877343,0.000039261802,0.00083264295,0.00006989168,0.000016621862,0.00022770444,0.00033988696,0.036889642,0.0018459245,0.94745624,0.0013988778,0.010849448],"study_design_scores_gemma":[0.000058433052,0.00013008947,0.0011450232,0.0000697891,0.000037117876,0.000329161,0.00015460752,0.39134517,0.000777261,0.58239794,0.023511238,0.000044193985],"about_ca_topic_score_codex":0.0047134636,"about_ca_topic_score_gemma":0.0024254736,"teacher_disagreement_score":0.012456679,"about_ca_system_score_codex":0.0013338812,"about_ca_system_score_gemma":0.0013518834,"threshold_uncertainty_score":0.041671753},"labels":[],"label_agreement":null},{"id":"W2618387153","doi":"10.20982/tqmp.13.2.p105","title":"Spike neural models (part I): The Hodgkin-Huxley model","year":2017,"lang":"en","type":"article","venue":"The Quantitative Methods for Psychology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Spike (software development); Hodgkin–Huxley model; Neuroscience; Computer science; Artificial intelligence; Biology","score_opus":0.36090419747832636,"score_gpt":0.5470005151064314,"score_spread":0.18609631762810508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618387153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02775509,0.021149492,0.81560653,0.0025335944,0.00094151904,0.00012780985,0.0011617697,0.0010929159,0.12963116],"genre_scores_gemma":[0.7457558,0.031235598,0.110123,0.0014216696,0.0010638572,0.00041042973,0.0011667968,0.00033780452,0.108485036],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999051,0.000024650026,0.000004872187,0.00001872773,0.000034186047,0.000012363432],"domain_scores_gemma":[0.99992263,0.000030399971,0.000009542887,0.000009140749,0.000021203017,0.000007032787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023708149,0.00036178128,0.0004425495,0.00026593576,0.00020637919,0.00087683514,0.0007953224,0.00095603376,0.0059547205],"category_scores_gemma":[0.0006492603,0.0002185315,0.0004552534,0.0005223627,0.0005441062,0.0011974861,0.0005493977,0.00077020365,0.0020548855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058383655,0.000040539646,0.0007863453,0.00044163616,0.000061307626,0.00015953176,0.00023097286,0.2866202,0.0054810895,0.59206617,0.021816475,0.092237405],"study_design_scores_gemma":[0.00001567462,0.000058649046,0.00068691815,0.00009115782,0.000028900397,0.00012584867,0.000045010736,0.6424401,0.0011540371,0.3112911,0.04403505,0.000027657834],"about_ca_topic_score_codex":0.0024730489,"about_ca_topic_score_gemma":0.0012523369,"teacher_disagreement_score":0.0059547205,"about_ca_system_score_codex":0.00054404675,"about_ca_system_score_gemma":0.0003989072,"threshold_uncertainty_score":0.019920528},"labels":[],"label_agreement":null},{"id":"W2619631212","doi":"10.1007/978-3-319-59063-9_10","title":"Convergence and Rates of Convergence of Recursive Radial Basis Functions Networks in Function Learning and Classification","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Radial basis function; Convergence (economics); Computer science; Radial basis function network; Basis (linear algebra); Function (biology); Basis function; Artificial intelligence; Applied mathematics; Mathematical optimization; Algorithm; Artificial neural network; Mathematics; Mathematical analysis","score_opus":0.022969933640991667,"score_gpt":0.2593289651635659,"score_spread":0.23635903152257423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619631212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044641882,0.020668183,0.9194969,0.0016993317,0.0005507042,0.00016383361,0.0002897808,0.0007712513,0.011718095],"genre_scores_gemma":[0.48624706,0.01896765,0.46590775,0.000732507,0.0012181788,0.00091264956,0.001217071,0.0021844073,0.022612592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939997,0.0033326868,0.0003709984,0.0007256156,0.0011969433,0.0003741318],"domain_scores_gemma":[0.8970223,0.08587222,0.002200689,0.003246857,0.010727711,0.0009302964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02330307,0.0022898384,0.002106982,0.00281406,0.0010255919,0.0036512893,0.004423346,0.0039096503,0.0045568966],"category_scores_gemma":[0.12551728,0.00145825,0.0018068942,0.0025313075,0.0037299676,0.007030752,0.004386254,0.006203329,0.0017045748],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000628637,0.00016571216,0.0032054372,0.001039601,0.00021176846,0.00014009807,0.00080487516,0.49876785,0.002443155,0.310866,0.009409795,0.17231707],"study_design_scores_gemma":[0.000018402474,0.000058488804,0.0005993084,0.00015548196,0.000046540925,0.0000794456,0.00006697318,0.9372719,0.0012867218,0.05897324,0.0014054283,0.000038050057],"about_ca_topic_score_codex":0.005486115,"about_ca_topic_score_gemma":0.0032285876,"teacher_disagreement_score":0.02330307,"about_ca_system_score_codex":0.0036632698,"about_ca_system_score_gemma":0.0021258339,"threshold_uncertainty_score":0.123239934},"labels":[],"label_agreement":null},{"id":"W2621405637","doi":"10.1007/978-1-4939-7131-2_100208","title":"Convolutional Neural Network","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence","score_opus":0.024433298191102928,"score_gpt":0.23039178261513085,"score_spread":0.20595848442402792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621405637","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056423675,0.036239706,0.45315832,0.002780601,0.0034576256,0.00010585647,0.001719142,0.0046799853,0.4922164],"genre_scores_gemma":[0.04829877,0.027505051,0.109380946,0.000953764,0.0009138574,0.000121194484,0.0037214956,0.0009635933,0.8081414],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99989176,0.0000070846786,0.000004191869,0.000027830883,0.000056186,0.000012963291],"domain_scores_gemma":[0.99986804,0.000032336593,0.0000068266095,0.000026309328,0.000056515328,0.000010021431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015778538,0.0007916382,0.00037741207,0.00060182274,0.0002789555,0.00086360984,0.0007502861,0.0007624733,0.034031395],"category_scores_gemma":[0.00061517156,0.00027921252,0.00027185483,0.00089769764,0.0003349351,0.0015055098,0.00086287374,0.0011249434,0.021541888],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023194583,0.000027218624,0.0001156127,0.00030946083,0.000022925253,0.000044480985,0.000024722665,0.01171347,0.0044887057,0.05678532,0.14853108,0.77791375],"study_design_scores_gemma":[0.0000066235016,0.000029485114,0.00056874065,0.00024245527,0.000037801085,0.00023195888,0.000019987952,0.06396547,0.010580643,0.08634137,0.83793914,0.000036243837],"about_ca_topic_score_codex":0.0024766016,"about_ca_topic_score_gemma":0.004583168,"teacher_disagreement_score":0.034031395,"about_ca_system_score_codex":0.00069542124,"about_ca_system_score_gemma":0.0006913491,"threshold_uncertainty_score":0.11384636},"labels":[],"label_agreement":null},{"id":"W2621768163","doi":"10.1007/11801603_90","title":"GA-Based Polynomial Neural Networks Architecture and Its Application to Multi-variable Software Process","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Computer science; Genetic algorithm; Process (computing); Polynomial; Variable (mathematics); Software; Data mining; Artificial intelligence; Algorithm; Machine learning; Mathematics","score_opus":0.010519681647606749,"score_gpt":0.23954455682628967,"score_spread":0.22902487517868292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621768163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039728906,0.00054519094,0.95498085,0.00017139333,0.000079490244,0.00003169614,0.000027945616,0.0006500575,0.0037845613],"genre_scores_gemma":[0.7444047,0.00039165968,0.2503349,0.00004838646,0.000029838335,0.000069097296,0.000041040625,0.00007220454,0.0046082153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987614,0.00004238605,0.000005312,0.000028960236,0.00003161652,0.00001557735],"domain_scores_gemma":[0.99954706,0.00025374163,0.000028522694,0.000035646262,0.00012216026,0.00001295341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043999328,0.00038805796,0.00049720897,0.00038015516,0.00035692268,0.00051295536,0.00086734234,0.0010586018,0.0017757525],"category_scores_gemma":[0.0015347359,0.00026364197,0.00049637246,0.0006946589,0.0004034458,0.0004963006,0.00036317194,0.00084187184,0.00028769328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053038548,0.000025580099,0.00023611149,0.00003620638,0.000019769333,0.000035825982,0.000031787502,0.93114686,0.0028051569,0.007975252,0.00040946298,0.05722497],"study_design_scores_gemma":[0.0000015148465,0.0000055063006,0.000027962797,0.0000010180019,0.000002458276,0.0000035210423,0.000001052861,0.9990294,0.00024308416,0.00059612276,0.00008695383,0.000001428259],"about_ca_topic_score_codex":0.013117295,"about_ca_topic_score_gemma":0.0100762695,"teacher_disagreement_score":0.013117295,"about_ca_system_score_codex":0.0007472934,"about_ca_system_score_gemma":0.00067755027,"threshold_uncertainty_score":0.02608186},"labels":[],"label_agreement":null},{"id":"W2623263179","doi":"","title":"Testing the Equality of Learning Rates Using a Linear Hypothesis","year":2003,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Curvature; Mathematics; Function (biology); Power function; Scaling; Linear model; Statistical hypothesis testing; Noise (video); Power (physics); Test (biology); Applied mathematics; Statistics; Econometrics; Artificial intelligence; Computer science; Mathematical analysis","score_opus":0.06398610123051654,"score_gpt":0.2561197025026501,"score_spread":0.19213360127213355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623263179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4125328,0.0012559051,0.5545115,0.0039694104,0.0007692181,0.0014903137,0.0014875364,0.00069655495,0.023286838],"genre_scores_gemma":[0.9325923,0.0002752047,0.05975309,0.001661445,0.0005760696,0.0021943396,0.0013837845,0.00015124591,0.0014126248],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.81382835,0.111640744,0.011288245,0.035847753,0.02202615,0.005368811],"domain_scores_gemma":[0.26034087,0.6885141,0.01538715,0.023568891,0.009470518,0.0027184887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.11763679,0.0025720801,0.003973328,0.0038440188,0.0022505235,0.00649726,0.0065865763,0.0064990027,0.016170887],"category_scores_gemma":[0.45607206,0.0012608021,0.004755827,0.002626038,0.012256742,0.013098076,0.005461877,0.006602789,0.0039395327],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0331923,0.00568827,0.31514025,0.0037609665,0.013768739,0.0035695273,0.0074094916,0.052826654,0.01508963,0.2056306,0.00986304,0.33406055],"study_design_scores_gemma":[0.004279986,0.01642759,0.111308776,0.0010481668,0.001717942,0.003551434,0.0031941608,0.40142846,0.021788163,0.4223279,0.012244785,0.0006825965],"about_ca_topic_score_codex":0.0013146658,"about_ca_topic_score_gemma":0.00033868823,"teacher_disagreement_score":0.11763679,"about_ca_system_score_codex":0.0024219435,"about_ca_system_score_gemma":0.0033865601,"threshold_uncertainty_score":0.6221303},"labels":[],"label_agreement":null},{"id":"W2624664693","doi":"","title":"A Neural Model of Rule Generation in Inductive Reasoning","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Raven's Progressive Matrices; Inductive reasoning; Computer science; Artificial intelligence; Human intelligence; Set (abstract data type); Deductive reasoning; Process (computing); Task (project management); Frame (networking); Rule-based system; Machine learning; Cognition; Cognitive science; Psychology; Programming language","score_opus":0.03085595880326708,"score_gpt":0.260176209446219,"score_spread":0.22932025064295192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624664693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043919235,0.0007738756,0.9156055,0.00245304,0.00017133448,0.00009992108,0.00029173397,0.00069081126,0.035994567],"genre_scores_gemma":[0.77830833,0.0009791584,0.19797261,0.00052308256,0.00016931808,0.00032963423,0.0003170923,0.00013180282,0.021268938],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994149,0.00017553242,0.000032681954,0.00014060605,0.00017516705,0.00006121792],"domain_scores_gemma":[0.9987123,0.00076364796,0.00009298027,0.00016940097,0.00017120407,0.000090429414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011620163,0.0004889728,0.0006349219,0.0007566329,0.00061560416,0.0022842796,0.003121112,0.0021781973,0.005438589],"category_scores_gemma":[0.003860106,0.0005637027,0.0012411394,0.0008295331,0.0019578694,0.0035354882,0.0009262511,0.0020212845,0.0011050786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008452844,0.00007438169,0.0004889293,0.00009207048,0.0000621583,0.0002542121,0.00030476562,0.41093522,0.0046816994,0.5548409,0.0014867218,0.026694367],"study_design_scores_gemma":[0.00002504421,0.000022792878,0.00009356691,0.000010430804,0.00001049032,0.00006881948,0.00001445173,0.74442136,0.0005852828,0.25339377,0.0013423781,0.000011574787],"about_ca_topic_score_codex":0.0043803854,"about_ca_topic_score_gemma":0.0030467254,"teacher_disagreement_score":0.005438589,"about_ca_system_score_codex":0.001359494,"about_ca_system_score_gemma":0.0012328693,"threshold_uncertainty_score":0.0181939},"labels":[],"label_agreement":null},{"id":"W2625152968","doi":"","title":"Vigilance Procedure Generalization for Recurrent Associative Memories","year":2006,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Vigilance (psychology); Computer science; Artificial neural network; Associative property; Artificial intelligence; Psychology; Cognitive science; Cognitive psychology; Mathematics","score_opus":0.011652160567417767,"score_gpt":0.22159509301543423,"score_spread":0.20994293244801646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625152968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21430102,0.00046195093,0.7742205,0.00040356643,0.00009209873,0.00018964593,0.00028949007,0.0014109918,0.008630659],"genre_scores_gemma":[0.9183743,0.00027673662,0.07241057,0.0001818981,0.00014162807,0.0002539892,0.0006062807,0.00031514486,0.0074394443],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977823,0.0006235484,0.00019374744,0.00066976197,0.0003767674,0.00035383456],"domain_scores_gemma":[0.983471,0.010065531,0.0015772115,0.0028650414,0.0012592076,0.0007619692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042034453,0.0005192034,0.0010488669,0.0013939159,0.0005861396,0.0012629168,0.0021871575,0.001194239,0.008034089],"category_scores_gemma":[0.027461011,0.00036567036,0.0023926538,0.0007428638,0.0015126509,0.0041250614,0.0024133252,0.0023008697,0.00092260813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077156135,0.00042174212,0.007414809,0.00026070658,0.00034392058,0.001089991,0.0007341253,0.11179474,0.014263926,0.6553302,0.0050298637,0.20254433],"study_design_scores_gemma":[0.000032925018,0.00018962532,0.002238835,0.00002239103,0.000036565758,0.00024163524,0.00004376256,0.52476436,0.0025398156,0.46893463,0.00090684753,0.0000485989],"about_ca_topic_score_codex":0.0015725445,"about_ca_topic_score_gemma":0.0012914853,"teacher_disagreement_score":0.008034089,"about_ca_system_score_codex":0.0013945092,"about_ca_system_score_gemma":0.0008357394,"threshold_uncertainty_score":0.026876688},"labels":[],"label_agreement":null},{"id":"W2626667510","doi":"10.17713/ajs.v34i4.423","title":"The Problem of Classification when the Data are Non-precise","year":2016,"lang":"en","type":"article","venue":"Austrian Journal of Statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Function (biology); Notation; Point (geometry); Computer science; Data mining; Data set; Scatter plot; Set (abstract data type); Statistical inference; Algorithm; Mathematics; Artificial intelligence; Statistics; Geometry","score_opus":0.08209308720337506,"score_gpt":0.2944085268775096,"score_spread":0.21231543967413458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626667510","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038325932,0.009712958,0.9282877,0.0152789075,0.0012208237,0.00021759376,0.0020788948,0.000370355,0.0045068637],"genre_scores_gemma":[0.5285598,0.012411957,0.43289843,0.0044933422,0.009134068,0.0014012132,0.0058173463,0.00018563066,0.005098251],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9715047,0.009467073,0.0035374805,0.00867384,0.0059647155,0.0008522429],"domain_scores_gemma":[0.88910794,0.092230536,0.0072385496,0.0073186457,0.0035503127,0.0005539669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02284228,0.0016594337,0.0048178504,0.0042352984,0.001976581,0.0076571233,0.0050647203,0.009425995,0.002051823],"category_scores_gemma":[0.08369999,0.0012176463,0.0019877572,0.0075229458,0.009359798,0.018194785,0.0036621005,0.0071708634,0.0009274251],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053761655,0.00030558006,0.017214486,0.0034419762,0.0006797853,0.0022178353,0.0023720777,0.09011731,0.001986121,0.4323034,0.029092897,0.4197309],"study_design_scores_gemma":[0.000045848978,0.0000891995,0.0032190606,0.00036374864,0.0000766384,0.0008300796,0.00046098672,0.1831316,0.00086189253,0.7975307,0.013281183,0.00010909333],"about_ca_topic_score_codex":0.0017388512,"about_ca_topic_score_gemma":0.0006953292,"teacher_disagreement_score":0.02284228,"about_ca_system_score_codex":0.0023539953,"about_ca_system_score_gemma":0.0012012807,"threshold_uncertainty_score":0.12080294},"labels":[],"label_agreement":null},{"id":"W2682984199","doi":"10.1299/jsmermd.2013._1p1-b03_1","title":"1P1-B03 Adaptive Behavior Generation using Attractor Dynamics of Recurrent Neural Network(Neurorobotics &amp; Cognitive Robotics)","year":2013,"lang":"en","type":"article","venue":"The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Artificial intelligence; Variance (accounting); Computer science; Gradient descent; Object (grammar); Artificial neural network; Humanoid robot; Attractor; Robotics; Function (biology); Robot; Machine learning; Mathematics","score_opus":0.06615076980717809,"score_gpt":0.28046276280344673,"score_spread":0.21431199299626863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2682984199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04459674,0.0005270195,0.936214,0.0002853741,0.00011816604,0.000051672865,0.00012751085,0.0014318676,0.016647564],"genre_scores_gemma":[0.8612151,0.00039451165,0.12483234,0.0000821777,0.000040710253,0.000109716195,0.00021862955,0.000098777105,0.013007928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993134,0.00001376147,0.0000033840558,0.000020707903,0.000023067629,0.000007691869],"domain_scores_gemma":[0.99992895,0.000016859738,0.000010636459,0.000011417274,0.000026513648,0.0000057384477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017726784,0.00024798242,0.00018557275,0.00012921875,0.00012796963,0.00033265215,0.00042793812,0.0003929639,0.0026083284],"category_scores_gemma":[0.00042257636,0.00012399683,0.0002775929,0.00014116925,0.00020597088,0.0003391679,0.00024576165,0.00032380858,0.00066756696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012505654,0.00008108781,0.0010655179,0.00015290541,0.00006163043,0.00032524613,0.00009838161,0.6721148,0.086992376,0.048686035,0.0054271696,0.18486986],"study_design_scores_gemma":[0.00000567083,0.000033551216,0.00015247906,0.0000042238753,0.0000051587717,0.00003500412,0.0000023968519,0.993544,0.0026645635,0.0018607951,0.0016878642,0.000004257674],"about_ca_topic_score_codex":0.0027818615,"about_ca_topic_score_gemma":0.0025741837,"teacher_disagreement_score":0.0027818615,"about_ca_system_score_codex":0.0003083369,"about_ca_system_score_gemma":0.00035278365,"threshold_uncertainty_score":0.008725703},"labels":[],"label_agreement":null},{"id":"W2711563245","doi":"10.1080/24748668.2001.11868253","title":"Artificial Neural Networks in Sports: New Concepts and Approaches","year":2001,"lang":"en","type":"article","venue":"International Journal of Performance Analysis in Sport","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Self-organizing map; Adaptation (eye); Context (archaeology); Artificial intelligence; Control (management); Machine learning; Feature (linguistics)","score_opus":0.027821417081355746,"score_gpt":0.2850683032418554,"score_spread":0.2572468861604996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2711563245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052486733,0.17444411,0.76216745,0.007844706,0.0024744305,0.00014292041,0.00031640573,0.0004080393,0.04695324],"genre_scores_gemma":[0.2520352,0.26493746,0.42854714,0.002954552,0.007679371,0.00073511025,0.0008898797,0.00030524426,0.041916095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999276,0.0002518613,0.000057496425,0.00013928322,0.00023411417,0.000041325933],"domain_scores_gemma":[0.99931943,0.00034740937,0.000057762183,0.00005975909,0.00017924968,0.00003637905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00133317,0.00093323644,0.000991958,0.0015643843,0.00036574792,0.0025113237,0.002244357,0.0022666901,0.003410407],"category_scores_gemma":[0.0023194288,0.0004908471,0.00072887755,0.0019181023,0.0016067466,0.0031514347,0.0016910374,0.0024179665,0.0011600477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063095365,0.00009830653,0.0016715433,0.0014887432,0.00022495574,0.00026383874,0.0003223034,0.1371868,0.0012575038,0.48815927,0.0146232145,0.35464048],"study_design_scores_gemma":[0.00002037078,0.00006955722,0.00082749245,0.000768824,0.00005325047,0.0002127043,0.00023257706,0.2626753,0.0007038168,0.59485817,0.13951564,0.00006230082],"about_ca_topic_score_codex":0.0028115404,"about_ca_topic_score_gemma":0.0016620117,"teacher_disagreement_score":0.003410407,"about_ca_system_score_codex":0.0011541701,"about_ca_system_score_gemma":0.00082765985,"threshold_uncertainty_score":0.011408925},"labels":[],"label_agreement":null},{"id":"W2726032611","doi":"10.1093/geroni/igx004.2053","title":"GAINING INSIGHT TO THE UNDERLYING NEURAL MECHANISMS BY WHICH EXERCISE IMPROVES MOBILITY","year":2017,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Functional magnetic resonance imaging; Aerobic exercise; Physical medicine and rehabilitation; Cognition; Medicine; Randomized controlled trial; Psychology; Physical therapy; Neuroscience; Internal medicine","score_opus":0.04800876267737729,"score_gpt":0.31550476956409435,"score_spread":0.26749600688671704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2726032611","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9778154,0.013671791,0.0031854277,0.0015258234,0.000060345294,0.00008749194,0.000320329,0.000030868072,0.003302417],"genre_scores_gemma":[0.980752,0.014749101,0.0025664242,0.00040670965,0.00011326104,0.00010893122,0.00021681996,0.000003609662,0.0010831042],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99994564,0.000010792863,0.000004803731,0.000014498197,0.00001005261,0.000014321186],"domain_scores_gemma":[0.9999001,0.000033964185,0.000030388444,0.000007878837,0.000015445446,0.00001213685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022444119,0.00026630086,0.0002456321,0.00029600452,0.000104954015,0.0002587995,0.00016603482,0.00028996478,0.0013319072],"category_scores_gemma":[0.0006437586,0.00011217867,0.0002358712,0.00011853779,0.00016345069,0.00042726105,0.00022524985,0.00033013386,0.00014838332],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029950615,0.0031893102,0.16116346,0.0031617817,0.0009005996,0.0005457567,0.0010475217,0.0021097972,0.19538139,0.0029765824,0.0025379576,0.62399083],"study_design_scores_gemma":[0.00013442824,0.0037748388,0.9779482,0.0005192038,0.000283227,0.0003067357,0.0004801188,0.00238445,0.0060694963,0.0038092448,0.0042694774,0.000020609263],"about_ca_topic_score_codex":0.00086845836,"about_ca_topic_score_gemma":0.002312161,"teacher_disagreement_score":0.0013319072,"about_ca_system_score_codex":0.00014828672,"about_ca_system_score_gemma":0.0002202623,"threshold_uncertainty_score":0.004455626},"labels":[],"label_agreement":null},{"id":"W2729697425","doi":"10.1145/3071178.3071209","title":"Searching for nonlinear relationships in fMRI data with symbolic regression","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Functional magnetic resonance imaging; Nonlinear system; Computer science; Artificial intelligence; Linear model; Machine learning; Cognitive science; Neuroscience; Psychology; Physics","score_opus":0.079053460547292,"score_gpt":0.30572559281929995,"score_spread":0.22667213227200794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729697425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061960045,0.00043268965,0.9334336,0.0010535021,0.000028116625,0.000048919494,0.0003411337,0.0014601118,0.0012417829],"genre_scores_gemma":[0.6751553,0.0008035383,0.3187121,0.00025376465,0.00011885059,0.00019687683,0.0014991441,0.0003069601,0.0029534767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864715,0.0006205026,0.00008229292,0.0002863021,0.00026193372,0.000101829886],"domain_scores_gemma":[0.99052536,0.0077257655,0.0007551089,0.0005670494,0.00031173325,0.0001149208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029752264,0.0011034986,0.0011608503,0.0033130886,0.0006355912,0.0015133461,0.0012252523,0.0013493373,0.0046032313],"category_scores_gemma":[0.0230039,0.0006998629,0.0012046426,0.003309002,0.0012349738,0.0020104195,0.0019357088,0.002225689,0.0017242702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033731546,0.00022522788,0.015654396,0.00038629764,0.00034952257,0.0010074679,0.0004678742,0.5034742,0.012590034,0.028684977,0.0048541245,0.43196854],"study_design_scores_gemma":[0.000008124211,0.000025337547,0.0010016368,0.000012176592,0.000011556867,0.00007292938,0.00003910059,0.97942376,0.00089389505,0.018013809,0.00048339088,0.000014180256],"about_ca_topic_score_codex":0.0027740125,"about_ca_topic_score_gemma":0.0036737989,"teacher_disagreement_score":0.0046032313,"about_ca_system_score_codex":0.00055415573,"about_ca_system_score_gemma":0.0013487622,"threshold_uncertainty_score":0.015734673},"labels":[],"label_agreement":null},{"id":"W2732825834","doi":"","title":"ANGELS project D.3.7. Multi-agents demonstrator","year":2012,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robotiq (Canada)","funders":"","keywords":"Computer science; Programming language","score_opus":0.03833602843672777,"score_gpt":0.2704922787155674,"score_spread":0.23215625027883963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732825834","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026222289,0.0032419376,0.6497584,0.004402921,0.003092508,0.0014004676,0.020073848,0.07547737,0.21633026],"genre_scores_gemma":[0.26612276,0.0016452839,0.42121884,0.0007162098,0.0006108289,0.002213466,0.05618283,0.012745047,0.23854463],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99806195,0.0007053717,0.000059429673,0.00031064884,0.00065165834,0.00021083216],"domain_scores_gemma":[0.99780375,0.00040001937,0.00009604077,0.00057427405,0.00057080796,0.000555127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042416155,0.0010948813,0.0009721493,0.0007372378,0.0005260349,0.0014849614,0.0023577935,0.0015116603,0.07617334],"category_scores_gemma":[0.0039050705,0.00028075516,0.0007248337,0.0006359133,0.0005533933,0.0013825342,0.0024977364,0.0012967137,0.037006788],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028569459,0.0008302923,0.0014519639,0.0013627809,0.00022764517,0.0008720649,0.00077533256,0.032628115,0.02595494,0.15795544,0.30535415,0.46973035],"study_design_scores_gemma":[0.0009706745,0.0008135846,0.00084288954,0.000111088026,0.000044821423,0.0005222786,0.00016594965,0.09303117,0.015016684,0.03990941,0.8485262,0.000045140234],"about_ca_topic_score_codex":0.0015157603,"about_ca_topic_score_gemma":0.0010751018,"teacher_disagreement_score":0.07617334,"about_ca_system_score_codex":0.0007877951,"about_ca_system_score_gemma":0.0013768191,"threshold_uncertainty_score":0.25482517},"labels":[],"label_agreement":null},{"id":"W2734837825","doi":"10.1109/ijcnn.2017.7966354","title":"Sensitivity and similarity regularization in dynamic selection of ensembles of neural networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid","keywords":"Computer science; Sensitivity (control systems); Artificial intelligence; Selection (genetic algorithm); Regularization (linguistics); Machine learning; Similarity (geometry); Artificial neural network; Data mining; Pattern recognition (psychology); Engineering","score_opus":0.011160260732756179,"score_gpt":0.25155110967185595,"score_spread":0.2403908489390998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734837825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15548724,0.00058519264,0.84145325,0.00020988783,0.000039672406,0.00008687948,0.000038595357,0.00023979112,0.0018594803],"genre_scores_gemma":[0.89052504,0.00025509426,0.10743333,0.00012026437,0.00007827304,0.00014257185,0.00016071097,0.000074797514,0.0012099473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967945,0.0014614478,0.00017571803,0.0004946938,0.00088922563,0.00018441101],"domain_scores_gemma":[0.99222505,0.005487624,0.00045522364,0.00073522324,0.0008944973,0.00020242004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007301679,0.0008772037,0.0012306519,0.0015617053,0.0008045611,0.0010362555,0.0011431152,0.0010648018,0.00065519655],"category_scores_gemma":[0.016581401,0.0005573191,0.0008915174,0.0009707618,0.0010659061,0.0014269203,0.0019469663,0.0010781777,0.00018815104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012020927,0.00010341777,0.0014533601,0.000034601788,0.000115276714,0.00006337763,0.00010982549,0.9185511,0.0049073645,0.0047650184,0.00036629903,0.069410175],"study_design_scores_gemma":[0.000004602379,0.00004669583,0.00045925975,0.0000045509464,0.000010809677,0.000018593386,0.000009591743,0.9958121,0.001195936,0.002284262,0.00014590423,0.000007632996],"about_ca_topic_score_codex":0.0017885043,"about_ca_topic_score_gemma":0.0018607954,"teacher_disagreement_score":0.007301679,"about_ca_system_score_codex":0.0009171099,"about_ca_system_score_gemma":0.00070339075,"threshold_uncertainty_score":0.038615406},"labels":[],"label_agreement":null},{"id":"W2735234465","doi":"10.1109/ijcnn.2017.7966138","title":"State initialization for recurrent neural network modeling of time-series data","year":2017,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Recurrent neural network; Artificial neural network; Computer science; Time series; Context (archaeology); Artificial intelligence; Echo state network; Time delay neural network; Series (stratigraphy); State (computer science); Network model; Machine learning; Algorithm","score_opus":0.10313240330174081,"score_gpt":0.33081722478594994,"score_spread":0.22768482148420913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735234465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018439401,0.00017871115,0.9791454,0.000075577234,0.00005251328,0.000030067595,0.0001028571,0.0008396353,0.0011357411],"genre_scores_gemma":[0.77814806,0.00026855693,0.21771066,0.00007606072,0.00003306463,0.00019919348,0.0005702161,0.00022289049,0.0027713892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997123,0.0000844229,0.000027995684,0.00008175348,0.00006703497,0.000026474907],"domain_scores_gemma":[0.9993394,0.0002927238,0.00009856151,0.00008764537,0.00016426614,0.000017431452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092183356,0.00073381444,0.0005287452,0.0004328691,0.00028406718,0.00065593387,0.00096119044,0.00082700513,0.0017159897],"category_scores_gemma":[0.0038485425,0.0004400761,0.0005334558,0.0003524472,0.00045626436,0.0013478576,0.0005948296,0.0015719591,0.00060563796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006894214,0.000028897719,0.0008556973,0.000056805417,0.00003449065,0.00006735473,0.00006468601,0.94837517,0.004748865,0.008898413,0.0007705416,0.036030147],"study_design_scores_gemma":[0.0000011844792,0.0000066409593,0.00007726497,0.0000033032836,0.0000020949205,0.0000046632795,0.0000015693191,0.99788755,0.00094365334,0.0008800046,0.00018890237,0.000003101852],"about_ca_topic_score_codex":0.0051970617,"about_ca_topic_score_gemma":0.0059494306,"teacher_disagreement_score":0.0051970617,"about_ca_system_score_codex":0.000731518,"about_ca_system_score_gemma":0.0005682178,"threshold_uncertainty_score":0.010333598},"labels":[],"label_agreement":null},{"id":"W2739482557","doi":"10.46867/ijcp.2017.30.01.01","title":"How and Why Does Category Learning Cause Categorical Perception?","year":2017,"lang":"en","type":"article","venue":"International Journal of Comparative Psychology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université du Québec à Montréal","funders":"","keywords":"Categorization; Categorical variable; Pairwise comparison; Psychology; Categorical perception; Similarity (geometry); Perception; Cognitive psychology; Concept learning; Correlation; Mathematics; Covariant transformation; Pattern recognition (psychology); Artificial intelligence; Developmental psychology; Statistics; Computer science","score_opus":0.06460958149908144,"score_gpt":0.3977375350205846,"score_spread":0.33312795352150315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739482557","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.770381,0.0036690724,0.18915585,0.017114276,0.001210709,0.00019991134,0.00060271216,0.001588378,0.016078208],"genre_scores_gemma":[0.9841474,0.00050217577,0.012374688,0.0012535462,0.00017748571,0.000049714265,0.0001414929,0.00010231931,0.0012510424],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981096,0.00041130162,0.0000705788,0.0006777461,0.00053965714,0.0001911386],"domain_scores_gemma":[0.9886932,0.005934829,0.0015068868,0.0017348106,0.0013907752,0.00073954096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019157286,0.00042193694,0.00070753176,0.0005494323,0.00029266212,0.0012540801,0.000982488,0.0022376804,0.0043002227],"category_scores_gemma":[0.025637299,0.00047863065,0.00062672707,0.00037278922,0.0034345181,0.0029419735,0.0009681322,0.0017874704,0.00071582024],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025804515,0.0009602368,0.1301783,0.0022076047,0.00084523816,0.0017787422,0.0032200536,0.01487566,0.24073906,0.07590611,0.014979226,0.51172924],"study_design_scores_gemma":[0.00036476768,0.0024650954,0.23819558,0.00027907256,0.00034535804,0.0057845805,0.0017066157,0.060485337,0.08823745,0.5856266,0.016075011,0.000434597],"about_ca_topic_score_codex":0.0011968196,"about_ca_topic_score_gemma":0.0006424803,"teacher_disagreement_score":0.0043002227,"about_ca_system_score_codex":0.0005165531,"about_ca_system_score_gemma":0.0005113817,"threshold_uncertainty_score":0.0143857},"labels":[],"label_agreement":null},{"id":"W2749845920","doi":"10.1109/fuzz-ieee.2017.8015687","title":"Inductive learning of classifiers via complex fuzzy sets and logic","year":2017,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fuzzy logic; Computer science; Artificial intelligence; Series (stratigraphy); Architecture; Machine learning; Fuzzy set; Pattern recognition (psychology)","score_opus":0.07340432433184095,"score_gpt":0.31071238748655006,"score_spread":0.2373080631547091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749845920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012731802,0.00015125661,0.9854855,0.0001693984,0.000026363234,0.000027957492,0.0000258191,0.00017061063,0.0012112202],"genre_scores_gemma":[0.53826994,0.00040712074,0.45882592,0.00021035233,0.000099113124,0.00018640938,0.00011658906,0.00003242805,0.0018520594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988273,0.00038903364,0.00009634581,0.00023848204,0.0003818397,0.00006699281],"domain_scores_gemma":[0.9957163,0.0028826115,0.0003390079,0.0003825875,0.0006180959,0.00006143761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028827363,0.00051383424,0.0005889382,0.0012421487,0.00040816274,0.001414164,0.0011252901,0.00070714176,0.0016087291],"category_scores_gemma":[0.009655177,0.00033681886,0.00058016385,0.0007577304,0.001280143,0.002215388,0.001123748,0.0013194097,0.00040402252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014459902,0.00013806316,0.0029631378,0.0001627576,0.00013631459,0.00014064905,0.00033406456,0.45189872,0.012383224,0.10439142,0.0018408798,0.42546612],"study_design_scores_gemma":[0.000007263672,0.00003405316,0.00027768646,0.000015832067,0.000011796582,0.000024592942,0.000014651432,0.95502007,0.0023873835,0.04122167,0.0009738621,0.000011181803],"about_ca_topic_score_codex":0.0014733311,"about_ca_topic_score_gemma":0.0015286177,"teacher_disagreement_score":0.0028827363,"about_ca_system_score_codex":0.00092369755,"about_ca_system_score_gemma":0.0005618856,"threshold_uncertainty_score":0.015245557},"labels":[],"label_agreement":null},{"id":"W2752214278","doi":"10.1109/wsom.2017.8020027","title":"Empirical evaluation of gradient methods for matrix learning vector quantization","year":2017,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Stochastic gradient descent; Gradient descent; Benchmark (surveying); Learning vector quantization; Quantization (signal processing); Artificial intelligence; Machine learning; Vector quantization; Algorithm; Artificial neural network","score_opus":0.18337038868387948,"score_gpt":0.5156207325397966,"score_spread":0.3322503438559171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752214278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11073755,0.014715481,0.85995984,0.0019735405,0.0006531353,0.00041976428,0.0009762157,0.0039917994,0.0065726712],"genre_scores_gemma":[0.5842491,0.0021784662,0.40655187,0.0005103719,0.00022988515,0.00040415998,0.0029293702,0.0007839046,0.0021629257],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9914145,0.0050676996,0.0005057797,0.00074866187,0.0020360888,0.00022724582],"domain_scores_gemma":[0.97243845,0.018909192,0.0008817122,0.0029420974,0.004410483,0.00041812443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015612553,0.0018384762,0.0017473343,0.0020405438,0.0007436777,0.0018134044,0.002426936,0.002420475,0.00201032],"category_scores_gemma":[0.069356754,0.0004194565,0.00065938174,0.0019791676,0.001858572,0.0033663777,0.0022606538,0.002327829,0.0007187261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076731463,0.00028871154,0.0037989467,0.0007038646,0.00033196632,0.000063747066,0.00016980134,0.68596727,0.0016850844,0.02631258,0.010941864,0.26896885],"study_design_scores_gemma":[0.000044699085,0.00013948635,0.0004355251,0.000048506234,0.000012271164,0.000030951815,0.000024981184,0.9881025,0.0012407575,0.009074767,0.0008278236,0.00001764424],"about_ca_topic_score_codex":0.0075630476,"about_ca_topic_score_gemma":0.0063857543,"teacher_disagreement_score":0.015612553,"about_ca_system_score_codex":0.0019434356,"about_ca_system_score_gemma":0.001965648,"threshold_uncertainty_score":0.08256811},"labels":[],"label_agreement":null},{"id":"W2752283293","doi":"10.1167/17.10.503","title":"Learning induced illusions: Statistical regularities create false memories","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Object (grammar); Statistical learning; Sequence (biology); Illusion; Random sequence; Artificial intelligence; False alarm; Computer science; Pattern recognition (psychology); Psychology; Communication; Mathematics; Cognitive psychology","score_opus":0.026804744980568316,"score_gpt":0.32345448635170304,"score_spread":0.2966497413711347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752283293","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9527135,0.0003976768,0.0429844,0.0007341994,0.000070540365,0.000060695165,0.000099665405,0.0004046069,0.0025346917],"genre_scores_gemma":[0.99323434,0.00009979197,0.0057314723,0.0003822992,0.00003173974,0.000023031791,0.00008434672,0.000037330024,0.00037570373],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99855787,0.0003099936,0.00015334986,0.00025417074,0.00057998946,0.00014475998],"domain_scores_gemma":[0.9800583,0.010475285,0.0040306714,0.00427369,0.0006598773,0.00050219306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015410336,0.00046374885,0.00038371628,0.00048199005,0.00024344515,0.0007815016,0.0009162704,0.0012858424,0.0019371167],"category_scores_gemma":[0.018608974,0.000490382,0.00043646086,0.00020313972,0.001465274,0.0018740864,0.0012359446,0.0020071273,0.00025617293],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005102734,0.0008357312,0.103361756,0.0005676616,0.00038692149,0.005667496,0.0023365326,0.00722397,0.7167254,0.013120186,0.0019065078,0.14276497],"study_design_scores_gemma":[0.00047126407,0.0044980664,0.2582919,0.00024076206,0.0003363477,0.020543726,0.00073467963,0.05272409,0.5833769,0.07515744,0.0033328529,0.00029205094],"about_ca_topic_score_codex":0.00025402775,"about_ca_topic_score_gemma":0.00022132267,"teacher_disagreement_score":0.0019371167,"about_ca_system_score_codex":0.00040840192,"about_ca_system_score_gemma":0.00023544049,"threshold_uncertainty_score":0.008149862},"labels":[],"label_agreement":null},{"id":"W2752596661","doi":"10.1167/17.10.411","title":"Linking normative models for natural tasks and subunit models of neural response","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Quadratic equation; Receptive field; Gaussian; Algorithm; Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.03981803272043804,"score_gpt":0.32568483010986,"score_spread":0.28586679738942194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752596661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04463323,0.00016707365,0.9432816,0.0021174564,0.000050707844,0.00004431286,0.00014868814,0.00020354525,0.009353421],"genre_scores_gemma":[0.8577622,0.0003487074,0.1367535,0.0004484389,0.00011286635,0.00036469786,0.00029402247,0.00022228633,0.0036932877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978275,0.0009872727,0.00012342267,0.00038393764,0.00048972527,0.00018813215],"domain_scores_gemma":[0.98762184,0.007537601,0.0011017616,0.0022220335,0.0011651464,0.00035163708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046260348,0.0006297757,0.000770353,0.0008386984,0.00059315894,0.0021423898,0.0027584694,0.0014892018,0.0037456073],"category_scores_gemma":[0.028435478,0.0006234186,0.0009886416,0.0004969754,0.0030132686,0.005256682,0.0017112341,0.0027636294,0.0007929138],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043380653,0.000047973692,0.0016069704,0.000056575045,0.000028461096,0.00009604054,0.00026000012,0.123730704,0.0011437179,0.8626098,0.0009286707,0.009447573],"study_design_scores_gemma":[0.000006578228,0.0000110108085,0.0004126105,0.0000069245766,0.000003864943,0.00004384496,0.000024740413,0.457712,0.00021266406,0.5410209,0.0005336199,0.000011166075],"about_ca_topic_score_codex":0.0024138407,"about_ca_topic_score_gemma":0.0026557194,"teacher_disagreement_score":0.0046260348,"about_ca_system_score_codex":0.0018730583,"about_ca_system_score_gemma":0.0011809865,"threshold_uncertainty_score":0.024465084},"labels":[],"label_agreement":null},{"id":"W2753113298","doi":"10.1167/17.10.56","title":"Limits to Attentional Selection of Features","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Interference (communication); Perception; Population; Orientation (vector space); Correlation; Selection (genetic algorithm); Artificial intelligence; Psychology; Mathematics; Computer vision; Computer science; Cognitive psychology; Communication; Telecommunications; Geometry; Medicine","score_opus":0.02058315071286738,"score_gpt":0.3263769404684207,"score_spread":0.3057937897555533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753113298","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8257346,0.0056339125,0.08202475,0.0022507533,0.00018684144,0.0001221996,0.00018459071,0.00073114625,0.08313117],"genre_scores_gemma":[0.9878907,0.0007314512,0.008417253,0.0006929984,0.00012887176,0.00015209253,0.00012254159,0.00015525996,0.0017088543],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962365,0.00046913934,0.00022881258,0.001183986,0.0014855696,0.00039596172],"domain_scores_gemma":[0.98368526,0.009530876,0.0012317019,0.0032847216,0.0012601584,0.0010072631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025182348,0.00042047098,0.0007882381,0.00075051497,0.00064279116,0.0029239783,0.0017237762,0.0013024067,0.00435369],"category_scores_gemma":[0.024830226,0.00060320063,0.0005634677,0.00046669765,0.0014870188,0.0042037745,0.0041901413,0.001374061,0.0010333571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001470482,0.00035814114,0.034196507,0.0011188064,0.00044584565,0.0007010371,0.0038244957,0.0058333836,0.48368204,0.094666995,0.0059669246,0.36773536],"study_design_scores_gemma":[0.0005112392,0.0015218164,0.39418373,0.0006638997,0.0004588579,0.00503163,0.0024613359,0.086828366,0.0924534,0.3767696,0.03871072,0.00040536842],"about_ca_topic_score_codex":0.0021487754,"about_ca_topic_score_gemma":0.0012418227,"teacher_disagreement_score":0.00435369,"about_ca_system_score_codex":0.0009716654,"about_ca_system_score_gemma":0.0007195892,"threshold_uncertainty_score":0.014564574},"labels":[],"label_agreement":null},{"id":"W275425313","doi":"10.1007/0-306-47318-6_2","title":"Fuzzy Logic and Neural Networks","year":2001,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Neuro-fuzzy; Artificial intelligence; Fuzzy logic; Computer science; Artificial neural network; Fuzzy electronics; Adaptive neuro fuzzy inference system; Perceptron; Machine learning; Fuzzy control system","score_opus":0.025651992925622286,"score_gpt":0.23264057856910705,"score_spread":0.20698858564348477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W275425313","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021227926,0.20011419,0.16307032,0.004521999,0.0045564594,0.00006538886,0.00022805003,0.00041775475,0.6249031],"genre_scores_gemma":[0.06184657,0.13601209,0.06771563,0.0018459007,0.0044403784,0.00019528145,0.00039919486,0.00020887241,0.7273361],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997842,0.000035469544,0.000009695133,0.000030694577,0.00012638429,0.000013715673],"domain_scores_gemma":[0.9998012,0.000099862504,0.000011069315,0.000020995214,0.000059251393,0.000007658648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025308106,0.0008826775,0.0009563108,0.001262975,0.00049636303,0.0014709642,0.00072281226,0.001221004,0.021754116],"category_scores_gemma":[0.0010289699,0.00030989,0.00029332054,0.0018943712,0.0009855955,0.002190194,0.0005052981,0.0013728484,0.0062447526],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001866572,0.000042181553,0.00009081036,0.00067772856,0.000020555803,0.00008704095,0.0001457994,0.0066021667,0.00085780345,0.4232166,0.11736578,0.45087495],"study_design_scores_gemma":[0.0000065973622,0.000020205322,0.0002479278,0.00040865215,0.000015851596,0.00019594008,0.00007243813,0.010068361,0.00055169204,0.46831393,0.5200792,0.000019213076],"about_ca_topic_score_codex":0.002093617,"about_ca_topic_score_gemma":0.0033554237,"teacher_disagreement_score":0.021754116,"about_ca_system_score_codex":0.000988247,"about_ca_system_score_gemma":0.00062235387,"threshold_uncertainty_score":0.07277477},"labels":[],"label_agreement":null},{"id":"W2755341342","doi":"10.1109/smc.2019.8914200","title":"Classifying Unordered Feature Sets with Convolutional Deep Averaging Networks","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Spurious relationship; Concatenation (mathematics); Feature (linguistics); Invariant (physics); Computer science; Permutation (music); Convolutional neural network; Pattern recognition (psychology); Artificial intelligence; Equivariant map; Deep learning; Set (abstract data type); Theoretical computer science; Mathematics; Machine learning; Combinatorics","score_opus":0.01593654779609431,"score_gpt":0.23932798742759995,"score_spread":0.22339143963150565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755341342","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21219292,0.0003226001,0.7835424,0.00027347892,0.00007875893,0.00007524136,0.0006013055,0.0012662819,0.0016469496],"genre_scores_gemma":[0.78250057,0.00018877198,0.21340697,0.00015098603,0.00008483729,0.00010992191,0.0019084421,0.00007475763,0.0015747533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888116,0.00022456396,0.00012527603,0.0003017257,0.00032876094,0.00013852281],"domain_scores_gemma":[0.99801147,0.00075699517,0.00030358063,0.0005156856,0.00031684653,0.00009533703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011812431,0.0008628655,0.0009171632,0.002087103,0.00048685833,0.0013285252,0.0011275128,0.0007640612,0.0010071967],"category_scores_gemma":[0.0041912617,0.0003685373,0.0008155968,0.0020204142,0.0007304229,0.0031681315,0.0015600711,0.0014473931,0.00037301247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049290614,0.0003451884,0.012283114,0.00016198115,0.00019130472,0.00031790513,0.00030340865,0.22098796,0.03614878,0.032459233,0.006241608,0.6900666],"study_design_scores_gemma":[0.000007758784,0.000046712405,0.0016447346,0.000009568811,0.0000145459035,0.000042653297,0.00004468207,0.9504935,0.008091245,0.038519897,0.0010654796,0.000019219206],"about_ca_topic_score_codex":0.002915423,"about_ca_topic_score_gemma":0.0040855915,"teacher_disagreement_score":0.002915423,"about_ca_system_score_codex":0.00097578176,"about_ca_system_score_gemma":0.00059206685,"threshold_uncertainty_score":0.0070798397},"labels":[],"label_agreement":null},{"id":"W2757818414","doi":"10.1109/mwscas.2017.8053207","title":"A neural network architecture using high resolution multiplying digital to analog converters","year":2017,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Delta-sigma modulation; Artificial neural network; Converters; Electronic engineering; Filter (signal processing); Multiplication (music); Analog multiplier; Digital signal processing; Analog signal; Computer hardware; Artificial intelligence; Engineering; Electrical engineering; Mathematics; Telecommunications; Voltage; Bandwidth (computing)","score_opus":0.029359910960328996,"score_gpt":0.26827394132363613,"score_spread":0.23891403036330713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757818414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04249952,0.00085090887,0.9447118,0.00035984558,0.0002087325,0.00007201258,0.0000654699,0.0013058566,0.009925881],"genre_scores_gemma":[0.49576274,0.0007303505,0.49120373,0.00029792532,0.00009906373,0.00010976634,0.00012549835,0.000034337983,0.011636602],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999912,0.000013013745,0.0000054912507,0.000024183262,0.0000353438,0.000009927101],"domain_scores_gemma":[0.99989057,0.000028364611,0.000010717067,0.000012509272,0.000049057537,0.000008754943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020380924,0.0002579961,0.00017558495,0.00019512154,0.00023124878,0.0004714927,0.0008220878,0.00066230755,0.0026737626],"category_scores_gemma":[0.00038337146,0.0001827215,0.00016589636,0.00028337308,0.00021693422,0.00077575236,0.00023516476,0.0005001151,0.00072469213],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023631347,0.00019450058,0.0011731938,0.00036259825,0.00010403124,0.0002671416,0.000097934506,0.11211881,0.28345755,0.031060822,0.0039828303,0.56694424],"study_design_scores_gemma":[0.000054470212,0.00039554635,0.00097409677,0.000047869053,0.000079257086,0.0003474687,0.000019698156,0.88568586,0.086094424,0.0062095,0.020056328,0.00003558368],"about_ca_topic_score_codex":0.0013955322,"about_ca_topic_score_gemma":0.0029893024,"teacher_disagreement_score":0.0026737626,"about_ca_system_score_codex":0.0003358928,"about_ca_system_score_gemma":0.00036446532,"threshold_uncertainty_score":0.00894469},"labels":[],"label_agreement":null},{"id":"W2765480867","doi":"","title":"Workshop on Deep Learning and the Brain","year":2014,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGovern Institute for Brain Research, Massachusetts Institute of Technology; Hebrew University of Jerusalem; University of California, San Diego; Harvard University; University of Waterloo; Massachusetts Institute of Technology","keywords":"Deep learning; Artificial intelligence; Cognitive science; Computer science; Object (grammar); Psychology","score_opus":0.009801659701304598,"score_gpt":0.2139607483846899,"score_spread":0.2041590886833853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765480867","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0121232895,0.18253873,0.31873554,0.22455585,0.11458567,0.00037792072,0.008000517,0.002918165,0.13616434],"genre_scores_gemma":[0.15311185,0.11680213,0.14946665,0.03220123,0.04640286,0.00092328095,0.013904656,0.0022307662,0.48495644],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987612,0.0003131577,0.00007060251,0.00031346607,0.00034381443,0.00019773546],"domain_scores_gemma":[0.99678206,0.0011899738,0.000063592495,0.00028702788,0.0009958826,0.00068146817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004040669,0.0013738987,0.0010265578,0.0012484756,0.0005701703,0.0035251,0.001994968,0.0026480262,0.02449494],"category_scores_gemma":[0.004732453,0.00049884367,0.0012461762,0.0012026094,0.001235439,0.004249368,0.0029099956,0.005933864,0.009130416],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001751104,0.00008808254,0.00028797708,0.00026914006,0.00011417157,0.00011211737,0.000158408,0.0039543337,0.0009956878,0.04933242,0.7639182,0.18059443],"study_design_scores_gemma":[0.00006897313,0.00008417834,0.000694482,0.00047976305,0.00005352426,0.00016810953,0.00010435153,0.012112635,0.001198502,0.06728326,0.91770357,0.000048630212],"about_ca_topic_score_codex":0.0049334187,"about_ca_topic_score_gemma":0.0047773346,"teacher_disagreement_score":0.02449494,"about_ca_system_score_codex":0.0020162084,"about_ca_system_score_gemma":0.002910812,"threshold_uncertainty_score":0.08194375},"labels":[],"label_agreement":null},{"id":"W2766091780","doi":"10.5539/mas.v11n11p85","title":"A Generalization Concept of Measure by Using Binary Operations","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generalization; Measure (data warehouse); Binary number; Computer science; Algebraic number; Function (biology); Domain (mathematical analysis); Binary function; Mathematics; Algebra over a field; Data mining; Arithmetic; Pure mathematics; Mathematical analysis","score_opus":0.03855221277626298,"score_gpt":0.28536290015139487,"score_spread":0.2468106873751319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766091780","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020111227,0.005631388,0.9381008,0.0017962969,0.0015324898,0.000075058146,0.00018466245,0.0002459382,0.03232211],"genre_scores_gemma":[0.49030256,0.006974145,0.4839245,0.002451523,0.0033511685,0.00046189353,0.00036770987,0.0002283095,0.011938312],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970188,0.0007123784,0.00031362666,0.00096895796,0.0008001271,0.00018609023],"domain_scores_gemma":[0.9980876,0.00066050474,0.00026846983,0.00041605343,0.000412707,0.00015471806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026468579,0.00079913647,0.0009304202,0.0026294948,0.0011568696,0.003210585,0.0010895255,0.0010997211,0.002553668],"category_scores_gemma":[0.0039153965,0.0002900986,0.0016701381,0.002096997,0.0047519943,0.009729202,0.0029128222,0.0028253198,0.0007023323],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017431143,0.000011700406,0.00014336109,0.00007830781,0.000018305316,0.0000698158,0.00017062454,0.00075889565,0.0017849908,0.9779559,0.00089601584,0.018094638],"study_design_scores_gemma":[0.000015552634,0.00010089613,0.00044001566,0.00008558935,0.00003470846,0.00081925216,0.00013765482,0.013263332,0.0023877264,0.9169754,0.065677375,0.000062469095],"about_ca_topic_score_codex":0.00057712855,"about_ca_topic_score_gemma":0.0003159332,"teacher_disagreement_score":0.003210585,"about_ca_system_score_codex":0.0010575489,"about_ca_system_score_gemma":0.0009072134,"threshold_uncertainty_score":0.013998091},"labels":[],"label_agreement":null},{"id":"W2766196653","doi":"10.1142/11784","title":"Generalization with Deep Learning","year":2020,"lang":"en","type":"book","venue":"WORLD SCIENTIFIC eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":182,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Office; Air Force Office of Scientific Research; Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Generalization; Maxima and minima; Computer science; Deep learning; Artificial intelligence; Deep neural networks; Theoretical computer science; Machine learning; Mathematics","score_opus":0.015278631879373884,"score_gpt":0.21069738049754166,"score_spread":0.19541874861816777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766196653","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009298476,0.0065090964,0.9274663,0.00482308,0.00055458007,0.00006402992,0.00036385842,0.0006217937,0.05029877],"genre_scores_gemma":[0.6368071,0.012430855,0.30737367,0.0055386084,0.0027248976,0.0006110622,0.0011868377,0.0009008651,0.032426096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99718034,0.0007256671,0.00014925156,0.00065607025,0.001086498,0.0002020703],"domain_scores_gemma":[0.9950222,0.0028327163,0.00029067256,0.0012397742,0.00046509493,0.00014951814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004352353,0.0013995463,0.001282827,0.0018400834,0.0008695662,0.0026449503,0.0025136003,0.001626038,0.0074576847],"category_scores_gemma":[0.01499234,0.00069079257,0.0017903041,0.0014967424,0.0034926313,0.00841291,0.0054322556,0.0061517525,0.0014060275],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003397996,0.000020833111,0.00037808873,0.00022852205,0.00005841219,0.000049470924,0.0000764886,0.05663451,0.0009055267,0.8755525,0.00955737,0.056504183],"study_design_scores_gemma":[0.0000052159244,0.000022915558,0.00017205594,0.000056774723,0.000013586525,0.00006308101,0.000014730308,0.10620859,0.0004958728,0.88324493,0.0096911425,0.000011023846],"about_ca_topic_score_codex":0.0018146727,"about_ca_topic_score_gemma":0.0013835792,"teacher_disagreement_score":0.0074576847,"about_ca_system_score_codex":0.0030873045,"about_ca_system_score_gemma":0.0012361177,"threshold_uncertainty_score":0.024948418},"labels":[],"label_agreement":null},{"id":"W2766348178","doi":"10.5539/jmr.v9n6p82","title":"Dialectical Logic K-Model: A Mathematical Model for Machine","year":2017,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Axiom; Variable (mathematics); Truth table; Second-order logic; Algorithm; Theoretical computer science; Computer science; Higher-order logic; Description logic; Mathematical analysis","score_opus":0.3373000820088045,"score_gpt":0.4882597769478628,"score_spread":0.1509596949390583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766348178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059094634,0.0021754084,0.931585,0.005147748,0.00043690472,0.0001093377,0.00040857896,0.00045318156,0.053774413],"genre_scores_gemma":[0.36656857,0.0033534546,0.5913296,0.002669472,0.0006053914,0.00086513,0.00074206333,0.00031558136,0.033550743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99904877,0.00032048495,0.00008761804,0.00022425535,0.00024529867,0.00007353467],"domain_scores_gemma":[0.9993968,0.00021021388,0.000058071062,0.00011371573,0.0001676617,0.00005366451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011622793,0.0005771297,0.0005858076,0.001362627,0.0013758392,0.003178388,0.0013188884,0.001508203,0.0053472035],"category_scores_gemma":[0.0016850599,0.0002785874,0.0011612866,0.0012296934,0.00383654,0.006855193,0.0018382592,0.0030155526,0.0021733483],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004012,0.0000043656883,0.00005149783,0.000028911258,0.0000038669996,0.00002613888,0.00015787187,0.0008140269,0.00017508802,0.9940414,0.0013440676,0.0033486893],"study_design_scores_gemma":[0.0000073788183,0.000012022,0.00005093618,0.000022404301,0.000004649894,0.000084546184,0.000057359008,0.010068673,0.00024248323,0.9651631,0.024274286,0.000012179742],"about_ca_topic_score_codex":0.0020160472,"about_ca_topic_score_gemma":0.0011458724,"teacher_disagreement_score":0.0053472035,"about_ca_system_score_codex":0.002114555,"about_ca_system_score_gemma":0.0014908591,"threshold_uncertainty_score":0.017888129},"labels":[],"label_agreement":null},{"id":"W2766455950","doi":"10.1016/b978-008044612-7/50090-1","title":"ADAPTIVE CATEGORIZATION AND NEURAL NETWORKS1*The work reported in this chapter represents a decade of research in the Laboratoire d'Études en Intelligence Naturelle et Artificielle. The authors are in debt to Jean Bégin and Sylvain Chartier for some of the innovations reported.","year":2005,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Hebbian theory; Artificial neural network; Connectionism; Computer science; Categorization; Artificial intelligence; Subspace topology; Leabra; Eigenvalues and eigenvectors; Machine learning","score_opus":0.06942994443195379,"score_gpt":0.33059848986629375,"score_spread":0.26116854543434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766455950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016534103,0.22589251,0.47386315,0.008361345,0.004597589,0.00010950343,0.0005165716,0.0010841254,0.26904112],"genre_scores_gemma":[0.29739642,0.13573354,0.30472437,0.0017344481,0.0034194926,0.00030293028,0.0017180175,0.00042062163,0.25455016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998337,0.00004492399,0.000007061725,0.00004088808,0.000057408924,0.000015994376],"domain_scores_gemma":[0.9997322,0.00015862932,0.000019660487,0.00003248652,0.000046665627,0.000010348084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035449077,0.0006667369,0.0003927145,0.00059595495,0.0004063809,0.0014975393,0.0007523926,0.00096254545,0.0063669756],"category_scores_gemma":[0.0012025109,0.000213786,0.00027785634,0.0012874354,0.00072779995,0.0017155797,0.00045666602,0.0011250125,0.0026533057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038254704,0.00005427904,0.0006296903,0.000670203,0.000062449355,0.00009819156,0.0001838516,0.014928688,0.0027391445,0.32323414,0.07193141,0.58542967],"study_design_scores_gemma":[0.000008693675,0.0000370529,0.001688475,0.00022277395,0.000020980782,0.00034566052,0.0001078426,0.052837912,0.002753,0.68678826,0.25515425,0.000035059507],"about_ca_topic_score_codex":0.0011533612,"about_ca_topic_score_gemma":0.0022052452,"teacher_disagreement_score":0.0063669756,"about_ca_system_score_codex":0.00083253277,"about_ca_system_score_gemma":0.00034577513,"threshold_uncertainty_score":0.0212996},"labels":[],"label_agreement":null},{"id":"W2766687888","doi":"","title":"Computational network models of neocortical seizures","year":2007,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Deafness and Other Communication Disorders; Canadian Institutes of Health Research","keywords":"Neuroscience; Epileptogenesis; Epilepsy; Neocortex; Neuroplasticity; Homeostatic plasticity; Psychology; Computational model; Biological neural network; Synaptic plasticity; Biology; Computer science; Metaplasticity; Artificial intelligence; Receptor","score_opus":0.017018121500355844,"score_gpt":0.22843166942766163,"score_spread":0.2114135479273058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766687888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2435945,0.002239076,0.6757196,0.004540973,0.00027151417,0.000110538014,0.0016271798,0.0007110433,0.07118562],"genre_scores_gemma":[0.9469154,0.0012690589,0.0319279,0.00016568493,0.00006992003,0.0002940982,0.0006247665,0.000096511445,0.01863672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985373,0.0000657913,0.0000075952958,0.000021759175,0.00003122397,0.000019884072],"domain_scores_gemma":[0.99940133,0.00039372942,0.00006709342,0.000030481815,0.00006249585,0.000044808316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036859702,0.000519574,0.0005103574,0.00052105315,0.00035135887,0.0009244688,0.00082091836,0.00084746315,0.0031318646],"category_scores_gemma":[0.0021689353,0.00032281328,0.00053078466,0.00048221776,0.0007006183,0.0011104245,0.0009252751,0.00074727484,0.0003545002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017823797,0.000009860345,0.0002681254,0.000015624566,0.000012623584,0.000032194523,0.000033048833,0.9418743,0.00021109227,0.055286232,0.0005265347,0.0017124257],"study_design_scores_gemma":[0.0000037446541,0.000002125824,0.00003689467,0.0000018456415,0.0000013672558,0.0000032174871,0.000004345951,0.98366946,0.000017586519,0.01587085,0.00038712862,0.0000013808499],"about_ca_topic_score_codex":0.0076044155,"about_ca_topic_score_gemma":0.0069459695,"teacher_disagreement_score":0.0076044155,"about_ca_system_score_codex":0.00097209465,"about_ca_system_score_gemma":0.0006413962,"threshold_uncertainty_score":0.0151203275},"labels":[],"label_agreement":null},{"id":"W2767695874","doi":"","title":"A Compositional Neural-network Solution to Prime-number Testing","year":2006,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principle of compositionality; Connectionism; Computer science; Representation (politics); Expression (computer science); Artificial neural network; Inference; Cognitive science; Artificial intelligence; Meaning (existential); Natural language processing; Programming language; Psychology","score_opus":0.01589889783134963,"score_gpt":0.21765173080661213,"score_spread":0.2017528329752625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767695874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016123595,0.000057491343,0.978001,0.00035494153,0.000032552074,0.000057328678,0.00005487814,0.0008265587,0.004491625],"genre_scores_gemma":[0.38027498,0.00007928201,0.6124559,0.00019369146,0.0000696159,0.00016826953,0.00021504807,0.0001473957,0.0063959095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993363,0.00015164512,0.0000346155,0.000252912,0.00015312423,0.000071406794],"domain_scores_gemma":[0.9985964,0.00069786125,0.000091984635,0.0002281743,0.00033139664,0.000054168402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014952993,0.0007649686,0.0007289526,0.0006461725,0.00071876985,0.0010067262,0.0024297556,0.0014257873,0.007616177],"category_scores_gemma":[0.0067847953,0.0004940392,0.0008897387,0.0006593905,0.0014897181,0.0026733482,0.0015881525,0.0019620694,0.0008820969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029499517,0.00016014934,0.0015522355,0.00024030147,0.0001024551,0.00044097347,0.00039154038,0.3623997,0.009961373,0.23386544,0.0053924704,0.38519844],"study_design_scores_gemma":[0.000016826503,0.000022331562,0.00009580986,0.000007870555,0.000013225499,0.00004152575,0.000017201632,0.9057232,0.0024700984,0.09069345,0.0008917245,0.0000068637078],"about_ca_topic_score_codex":0.0036318898,"about_ca_topic_score_gemma":0.005117277,"teacher_disagreement_score":0.007616177,"about_ca_system_score_codex":0.00097464345,"about_ca_system_score_gemma":0.0017896693,"threshold_uncertainty_score":0.02547872},"labels":[],"label_agreement":null},{"id":"W2768149499","doi":"10.1109/icci-cc.2017.8109768","title":"A cognitive approach for attribute selection in internet dataset","year":2017,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; The Internet; Machine learning; Data mining; Artificial intelligence; Feature selection; Data pre-processing; Domain knowledge; Data science; World Wide Web","score_opus":0.07145867058437748,"score_gpt":0.33304684878178153,"score_spread":0.26158817819740404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768149499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16582511,0.0014145031,0.8164885,0.0019030954,0.00023359108,0.00077255035,0.007288276,0.0022428627,0.0038315093],"genre_scores_gemma":[0.67381114,0.0005216328,0.3096168,0.00043374486,0.00026249667,0.0007505684,0.012935517,0.00007081596,0.0015972211],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959622,0.001071146,0.00044332255,0.0009914278,0.0011829761,0.0003489302],"domain_scores_gemma":[0.99520594,0.0024772112,0.00028308626,0.0007153744,0.0011260314,0.00019241981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055884114,0.00093756657,0.0012186557,0.00680444,0.0014025044,0.0027592382,0.0026912394,0.0015491054,0.0012753614],"category_scores_gemma":[0.01086279,0.0002599774,0.0024448994,0.0059350766,0.000728863,0.0019512511,0.001694288,0.0021837605,0.00042503106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008227419,0.0015267992,0.047921088,0.0005711922,0.00091682974,0.0008827869,0.0007871913,0.18930046,0.005048728,0.02298434,0.023158668,0.7060791],"study_design_scores_gemma":[0.000036286478,0.00009339997,0.0070288754,0.000037697824,0.00008356851,0.00020229032,0.00025965006,0.96941197,0.001695162,0.017442625,0.0036653476,0.00004313512],"about_ca_topic_score_codex":0.007262874,"about_ca_topic_score_gemma":0.007339136,"teacher_disagreement_score":0.007262874,"about_ca_system_score_codex":0.00176513,"about_ca_system_score_gemma":0.0016018585,"threshold_uncertainty_score":0.029554725},"labels":[],"label_agreement":null},{"id":"W2769490618","doi":"10.1162/neco_a_01160","title":"Equivalence of Equilibrium Propagation and Recurrent Backpropagation","year":2018,"lang":"en","type":"preprint","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"Samsung; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Backpropagation; Artificial neural network; Recurrent neural network; Equivalence (formal languages); Computer science; Equilibrium point; Propagation of uncertainty; Fixed point; Algorithm; Computation; Mathematics; Artificial intelligence; Discrete mathematics; Mathematical analysis","score_opus":0.03689075785267298,"score_gpt":0.297257892274312,"score_spread":0.260367134421639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769490618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022093855,0.00028239976,0.96913344,0.00025550846,0.00008737823,0.00004657115,0.00006456139,0.00064843294,0.0073878462],"genre_scores_gemma":[0.749301,0.00060382334,0.23831078,0.00038740854,0.00019747831,0.00024420832,0.00030547797,0.00052419223,0.010125528],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981652,0.00036455272,0.000118111646,0.00052004994,0.00064788543,0.0001841753],"domain_scores_gemma":[0.9957586,0.001962699,0.00036068502,0.00060184125,0.0011629096,0.00015314267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020454738,0.0008801336,0.0008168428,0.00092413154,0.0004968487,0.0018445826,0.0021962402,0.001690117,0.0028783644],"category_scores_gemma":[0.013929429,0.00052486267,0.0006487404,0.00055954984,0.0020931938,0.0037538817,0.0020141744,0.0020725913,0.00082077837],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025486053,0.000113564805,0.0013126149,0.00014757049,0.00009336652,0.0002265115,0.00036104006,0.31112903,0.0123176575,0.51564014,0.0019847094,0.15641895],"study_design_scores_gemma":[0.000017260643,0.00006783242,0.0003160645,0.000016653801,0.000013808607,0.0000807094,0.000019061747,0.8650875,0.003777498,0.12891579,0.0016674709,0.000020390833],"about_ca_topic_score_codex":0.0039194096,"about_ca_topic_score_gemma":0.0024080032,"teacher_disagreement_score":0.0039194096,"about_ca_system_score_codex":0.0010925308,"about_ca_system_score_gemma":0.0012041883,"threshold_uncertainty_score":0.010817587},"labels":[],"label_agreement":null},{"id":"W2769930465","doi":"10.1121/1.5014864","title":"Computational modeling of human isolated auditory word recognition using DIANA","year":2017,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Lexical decision task; Speech recognition; Computer science; Perception; Categorical variable; Word recognition; Word (group theory); Computational model; SIGNAL (programming language); Cognition; Natural language processing; Artificial intelligence; Psychology; Linguistics; Machine learning","score_opus":0.04513691588199681,"score_gpt":0.2972733532035829,"score_spread":0.2521364373215861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769930465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30808815,0.00022483013,0.68161905,0.0005181457,0.00009309645,0.00011401891,0.00032502902,0.00093409454,0.00808354],"genre_scores_gemma":[0.91964155,0.00011609225,0.07521851,0.00014970792,0.0000204689,0.00023758826,0.00015862558,0.00009604287,0.0043613575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998826,0.000031199634,0.0000067213387,0.000040928626,0.000022335811,0.000016260297],"domain_scores_gemma":[0.9994906,0.00035483978,0.00002794075,0.000044113316,0.00005354327,0.000028884539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004237581,0.00040430162,0.00047203954,0.0002675124,0.00033728482,0.00083235226,0.0015592433,0.00073609524,0.002777868],"category_scores_gemma":[0.0012780481,0.0003579671,0.00063058955,0.00020370861,0.00045937859,0.00081669725,0.0007154049,0.00076349813,0.00043644418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014225431,0.000063166764,0.0011933156,0.00004800161,0.00003966481,0.0001246123,0.00014888389,0.97006726,0.006781217,0.010990953,0.00045335782,0.0099473605],"study_design_scores_gemma":[0.0000023853802,0.000007944779,0.00007374153,5.0553984e-7,0.0000017757046,0.00000879604,0.0000035599053,0.99812096,0.00028764072,0.0014020181,0.00008834304,0.000002265221],"about_ca_topic_score_codex":0.0063707083,"about_ca_topic_score_gemma":0.006579644,"teacher_disagreement_score":0.0063707083,"about_ca_system_score_codex":0.00070163596,"about_ca_system_score_gemma":0.00071334856,"threshold_uncertainty_score":0.012667298},"labels":[],"label_agreement":null},{"id":"W2771380128","doi":"10.1109/sielmen.2017.8123295","title":"SYSEG — Symbolic state equation generation","year":2017,"lang":"en","type":"article","venue":"2017 International Conference on Electromechanical and Power Systems (SIELMEN)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Northern Alberta Institute of Technology","funders":"","keywords":"Symbolic data analysis; Linear circuit; Electronic circuit; Resistor; Computer science; Software; Symbolic trajectory evaluation; Linear equation; Unitary state; Analogue electronics; State (computer science); Nonlinear system; Mathematics; Equivalent circuit; Theoretical computer science; Algorithm; Voltage; Mathematical analysis; Law; Electrical engineering; Engineering","score_opus":0.08155379582314622,"score_gpt":0.3083675654666526,"score_spread":0.2268137696435064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771380128","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030581155,0.00009694162,0.9663059,0.00017115336,0.00011561989,0.00009644128,0.0013526323,0.020679995,0.008123224],"genre_scores_gemma":[0.15579455,0.0005763768,0.8072762,0.00040413838,0.00012361039,0.0010095855,0.008118761,0.0078661805,0.01883065],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999537,0.00011490825,0.000035277182,0.00007709184,0.00019036393,0.00004534317],"domain_scores_gemma":[0.9993248,0.0003116979,0.000036048805,0.00012940142,0.00017895327,0.000019080304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006781701,0.0008741324,0.000505578,0.00074943714,0.00034998768,0.0012327636,0.0011074935,0.0005221432,0.031674888],"category_scores_gemma":[0.0029554458,0.00036274132,0.0007919925,0.0006056351,0.0004664495,0.0012327313,0.0010861715,0.0012213105,0.0065473486],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040961165,0.00013460904,0.0027414411,0.0010333121,0.00012518413,0.0006957059,0.00084834424,0.079985164,0.019663267,0.37841454,0.086867094,0.42908174],"study_design_scores_gemma":[0.00017094341,0.00009062335,0.00059092685,0.00015733062,0.000060491202,0.0004520312,0.0000965506,0.5851006,0.037547506,0.12327414,0.25239614,0.00006265292],"about_ca_topic_score_codex":0.0010614223,"about_ca_topic_score_gemma":0.0008696785,"teacher_disagreement_score":0.031674888,"about_ca_system_score_codex":0.00043122872,"about_ca_system_score_gemma":0.0008938633,"threshold_uncertainty_score":0.10596305},"labels":[],"label_agreement":null},{"id":"W2771430832","doi":"10.1016/j.knosys.2017.11.034","title":"Bidirectional and multidirectional associative memories as models in linkage analysis in data analytics: Conceptual and algorithmic developments","year":2017,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dynamic Systems Analysis (Canada)","funders":"","keywords":"Associative property; Computer science; Cluster analysis; Fuzzy logic; Content-addressable memory; Linkage (software); Recall; Basis (linear algebra); Artificial intelligence; Theoretical computer science; Mathematics; Artificial neural network; Psychology; Cognitive psychology","score_opus":0.08120881429835433,"score_gpt":0.3212446773541432,"score_spread":0.24003586305578886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771430832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038210556,0.0028948411,0.954439,0.0017872865,0.000059459682,0.000036437905,0.00013986872,0.00011812392,0.002314422],"genre_scores_gemma":[0.7071246,0.0052616755,0.28189617,0.00044496203,0.00033811905,0.0002622207,0.00021803369,0.00006594872,0.00438825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755365,0.0012118998,0.00021633736,0.00046042944,0.00043933393,0.0001185033],"domain_scores_gemma":[0.9797911,0.015535201,0.00126336,0.0021844085,0.0008653055,0.0003607632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004979663,0.00058919704,0.0012340688,0.0019854465,0.00070321874,0.0051132175,0.0029370652,0.0019161325,0.0023774563],"category_scores_gemma":[0.021497432,0.0008304387,0.0013162225,0.003866569,0.0035053154,0.01216044,0.004276279,0.0032085257,0.00037433303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092788105,0.00011338741,0.0021767726,0.00021608247,0.00017997345,0.00007979764,0.00047378763,0.10417244,0.00049823686,0.8131164,0.00079089147,0.07808948],"study_design_scores_gemma":[0.000006510623,0.000019133886,0.0001815217,0.000035839723,0.00003775788,0.000045632303,0.00007155545,0.26982337,0.000236216,0.72878724,0.0007349911,0.000020266909],"about_ca_topic_score_codex":0.0024767998,"about_ca_topic_score_gemma":0.004309641,"teacher_disagreement_score":0.0051132175,"about_ca_system_score_codex":0.0010643982,"about_ca_system_score_gemma":0.0014952837,"threshold_uncertainty_score":0.026335299},"labels":[],"label_agreement":null},{"id":"W2772031001","doi":"10.15353/vsnl.v3i1.162","title":"Design space exploration of Convolutional Neural Networks based on Evolutionary Algorithms","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Hyperparameter; MNIST database; Convolutional neural network; Computer science; Genetic algorithm; Artificial intelligence; Traverse; Pattern recognition (psychology); Design space exploration; Digit recognition; Machine learning; Algorithm; Space (punctuation); Artificial neural network","score_opus":0.03235407918901075,"score_gpt":0.29562278268781067,"score_spread":0.26326870349879994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772031001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043357942,0.00093162357,0.9504148,0.0001849128,0.0000397436,0.00007375961,0.0000281046,0.00024127575,0.004727914],"genre_scores_gemma":[0.61579347,0.00081870897,0.37896934,0.00016384941,0.00003766492,0.00042022482,0.00011821659,0.00010548439,0.0035731308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996351,0.0001499205,0.000018367127,0.000048571346,0.00010241528,0.00004563452],"domain_scores_gemma":[0.99950325,0.00031096814,0.000046617053,0.00004023166,0.00008016098,0.000018713492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011274731,0.0009769915,0.0006882666,0.0008787811,0.00035733767,0.000653325,0.00085651537,0.0008994095,0.0014440061],"category_scores_gemma":[0.0021109479,0.0005080013,0.00089157175,0.00052233133,0.00079023634,0.00058280834,0.00089773315,0.0008297251,0.00016500344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000183803,0.000026819213,0.00058264955,0.00004812501,0.000036381927,0.00004754143,0.00004354367,0.9513436,0.001549639,0.010170387,0.00028086078,0.035852104],"study_design_scores_gemma":[0.0000063475986,0.00002373797,0.000065446075,0.0000085463535,0.000006192862,0.000014520683,0.000007578708,0.9951747,0.0004031591,0.0037155706,0.0005714341,0.0000027956976],"about_ca_topic_score_codex":0.0023930555,"about_ca_topic_score_gemma":0.0022484537,"teacher_disagreement_score":0.0023930555,"about_ca_system_score_codex":0.0007817627,"about_ca_system_score_gemma":0.00077488687,"threshold_uncertainty_score":0.00596267},"labels":[],"label_agreement":null},{"id":"W2772747611","doi":"10.1038/nmeth.4526","title":"Machine learning: a primer","year":2017,"lang":"es","type":"article","venue":"Nature Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":241,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"National Institute of Mental Health","keywords":"Primer (cosmetics); Computational biology; Computer science; Biology; Artificial intelligence; Chemistry","score_opus":0.034654921834320285,"score_gpt":0.4239045969834016,"score_spread":0.3892496751490813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772747611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005160238,0.4010341,0.4646968,0.067630745,0.014867107,0.000110152476,0.0004049575,0.0008835891,0.049856644],"genre_scores_gemma":[0.024063978,0.43907562,0.333988,0.056300163,0.072620146,0.001504815,0.0009835997,0.0013813233,0.070082314],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974184,0.0012930633,0.0002228834,0.00031224883,0.00068486336,0.00006840898],"domain_scores_gemma":[0.9899595,0.007952446,0.0002959393,0.0006816415,0.00086521317,0.0002451407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050006206,0.001511394,0.0017394173,0.00347678,0.00075169024,0.00478225,0.003446513,0.0051307753,0.008953317],"category_scores_gemma":[0.008336027,0.001109428,0.0012692587,0.0028618565,0.0052806325,0.009917541,0.0030254722,0.010245004,0.0090816505],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028335371,0.00013030517,0.00038007624,0.001752179,0.000064252294,0.000102369035,0.0001793356,0.0027255684,0.00046907828,0.46394756,0.2601031,0.27011785],"study_design_scores_gemma":[0.000013285674,0.000030654406,0.0003230356,0.0010220289,0.0000146112325,0.0002957807,0.000057488414,0.0044018435,0.00032054493,0.43435717,0.55913347,0.000030192396],"about_ca_topic_score_codex":0.0006556229,"about_ca_topic_score_gemma":0.000878841,"teacher_disagreement_score":0.008953317,"about_ca_system_score_codex":0.0014075065,"about_ca_system_score_gemma":0.0016813539,"threshold_uncertainty_score":0.02995181},"labels":[],"label_agreement":null},{"id":"W2774043055","doi":"10.15353/vsnl.v3i1.170","title":"Equivalence of histogram equalization, histogram matching and the Nyul algorithm for intensity standardization in MRI","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University; Vector Institute","funders":"","keywords":"Histogram matching; Histogram equalization; Histogram; Adaptive histogram equalization; Preprocessor; Matching (statistics); Equivalence (formal languages); Mathematics; Artificial intelligence; Algorithm; Computer science; Pattern recognition (psychology); Image (mathematics); Statistics; Discrete mathematics","score_opus":0.012984926226158901,"score_gpt":0.3070395583726457,"score_spread":0.2940546321464868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774043055","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005740998,0.0002678437,0.9915343,0.00009632383,0.00005968022,0.000049192186,0.000029848577,0.00034389054,0.0018778607],"genre_scores_gemma":[0.13203807,0.0006290762,0.8625471,0.00012281779,0.00013521402,0.00015779684,0.00018024884,0.00037109008,0.003818564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828255,0.0004912261,0.000101843776,0.00033456564,0.00066886045,0.00012101101],"domain_scores_gemma":[0.99818856,0.0007550024,0.0001276432,0.00047926386,0.00038690938,0.00006249101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002193501,0.0005404495,0.0007969866,0.0015670358,0.0006070981,0.0017186718,0.0012352084,0.0009858133,0.0025086424],"category_scores_gemma":[0.008442377,0.00046411483,0.00056875317,0.00149529,0.0016921543,0.002956362,0.0015966219,0.0013378789,0.0010374982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005359483,0.000117613316,0.0014355686,0.000218914,0.00008491939,0.000115043615,0.00026985962,0.047349103,0.042245448,0.16619986,0.002862925,0.73856485],"study_design_scores_gemma":[0.00008792289,0.0002647514,0.005240354,0.00006709943,0.000053243977,0.0006596066,0.00013988029,0.76106465,0.07005303,0.14149898,0.020739773,0.00013078652],"about_ca_topic_score_codex":0.001833026,"about_ca_topic_score_gemma":0.0016714153,"teacher_disagreement_score":0.0025086424,"about_ca_system_score_codex":0.0007005976,"about_ca_system_score_gemma":0.0011892386,"threshold_uncertainty_score":0.011600435},"labels":[],"label_agreement":null},{"id":"W2774687037","doi":"10.1007/978-3-319-67988-4_18","title":"RBF-Based High Dimensional Model Representation Method Using Proportional Sampling Strategy","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Radial basis function; Hierarchical RBF; Benchmark (surveying); Sampling (signal processing); Mathematics; Computer science; Mathematical optimization; Applied mathematics; Algorithm; Artificial intelligence; Artificial neural network","score_opus":0.1893509136228182,"score_gpt":0.38580816342996893,"score_spread":0.19645724980715074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774687037","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010168668,0.00016739925,0.9976907,0.000024410896,0.000030019302,0.000008946212,0.0000145704225,0.0001655767,0.0008814842],"genre_scores_gemma":[0.18729085,0.0011222307,0.799525,0.00013617442,0.00014296758,0.00020120882,0.00039697427,0.00023980864,0.010944741],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969757,0.000083133746,0.00001624368,0.00005592754,0.00012826054,0.000018913375],"domain_scores_gemma":[0.9997434,0.00008368412,0.000016200644,0.000040193994,0.000103556195,0.000012905394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058585504,0.0005828733,0.0010151187,0.0003912717,0.00031227444,0.0007904462,0.0011324617,0.0007442644,0.0043016523],"category_scores_gemma":[0.0011651524,0.00031338423,0.0008657521,0.0006739348,0.0002809517,0.001127182,0.0007267711,0.0014305292,0.0015869886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013845247,0.00010684787,0.0003209895,0.00038698092,0.00010941435,0.00009976542,0.000100274985,0.28619942,0.020664701,0.056288306,0.008701438,0.62688345],"study_design_scores_gemma":[0.000002954513,0.00001417178,0.00005067212,0.000004819103,0.000008301657,0.000040853687,0.0000039485794,0.99478865,0.0010447728,0.0027988649,0.0012353935,0.0000065805984],"about_ca_topic_score_codex":0.0023461683,"about_ca_topic_score_gemma":0.0017538404,"teacher_disagreement_score":0.0043016523,"about_ca_system_score_codex":0.00029515123,"about_ca_system_score_gemma":0.00044815004,"threshold_uncertainty_score":0.014390409},"labels":[],"label_agreement":null},{"id":"W2775406975","doi":"10.1007/978-3-319-68913-5_7","title":"Generalised Pattern Search","year":2017,"lang":"en","type":"book-chapter","venue":"Springer series in operations research/Springer series in operations research and financial engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Polytechnique Montréal","funders":"","keywords":"Global Positioning System; Flexibility (engineering); Computer science; Algorithm; Order (exchange); Mathematics; Statistics","score_opus":0.060984378602192954,"score_gpt":0.3249829659634055,"score_spread":0.2639985873612125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775406975","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017920107,0.008034492,0.9038094,0.0014824562,0.00079030334,0.00016981109,0.0010358548,0.0016343185,0.06512327],"genre_scores_gemma":[0.40737846,0.004726289,0.43854985,0.0011282073,0.00090940413,0.00044569495,0.0040612966,0.0010465292,0.14175422],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99906653,0.0002855787,0.000061631275,0.00024395759,0.00026947228,0.00007290208],"domain_scores_gemma":[0.9986374,0.0005117415,0.000073275514,0.00050182146,0.00022366844,0.000051998395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080971763,0.0008472989,0.0016489272,0.0015718979,0.00049740047,0.0020673287,0.0015228522,0.0018107893,0.028895129],"category_scores_gemma":[0.0060403007,0.0005208801,0.0010706903,0.0024882264,0.0012220045,0.00308355,0.0024906513,0.0015973297,0.0082452595],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002395451,0.00007767633,0.00058523816,0.0005487901,0.00015544062,0.0001560292,0.00007945697,0.07090159,0.0034785054,0.26401663,0.03826912,0.62149197],"study_design_scores_gemma":[0.00006488239,0.00010115296,0.0005088102,0.00008295213,0.00005582815,0.00031463394,0.000036235753,0.37108475,0.0016880159,0.59732586,0.028708376,0.00002848061],"about_ca_topic_score_codex":0.000906646,"about_ca_topic_score_gemma":0.0012771605,"teacher_disagreement_score":0.028895129,"about_ca_system_score_codex":0.00052448513,"about_ca_system_score_gemma":0.000681513,"threshold_uncertainty_score":0.09666389},"labels":[],"label_agreement":null},{"id":"W2781492191","doi":"","title":"A self-organizing neural network with balanced excitatory and inhibitory input","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Cluster analysis; Dimensionality reduction; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Hebbian theory; Unsupervised learning; Principal component analysis; Initialization; Machine learning; Data mining","score_opus":0.004664526986383273,"score_gpt":0.1821361156006318,"score_spread":0.17747158861424853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781492191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113018036,0.0005133712,0.87814784,0.00034538086,0.000092784845,0.00012677384,0.00016779549,0.0010992838,0.006488843],"genre_scores_gemma":[0.835257,0.00033016372,0.15741709,0.00016450704,0.000042014733,0.00023477444,0.0002589286,0.00004907478,0.0062464457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997781,0.0000405125,0.000010516338,0.00007738434,0.00006246268,0.00003109681],"domain_scores_gemma":[0.9997826,0.0000668321,0.000031064046,0.000028269575,0.00007452865,0.000016680116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044089239,0.0005504518,0.00046117927,0.00033205742,0.0003681319,0.0008024651,0.0012575177,0.0006588795,0.0007748555],"category_scores_gemma":[0.00088567915,0.00027904753,0.00045522477,0.0005280136,0.00046609284,0.0009328342,0.00048483614,0.0005990478,0.00031310378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017487528,0.00013906379,0.0015691177,0.00012944691,0.000104605795,0.00013453573,0.00015431806,0.79367846,0.023071175,0.009444198,0.0022238689,0.16917637],"study_design_scores_gemma":[0.0000035199196,0.00002794668,0.00014250992,0.0000031447632,0.0000052663668,0.000012472166,0.000007700626,0.9966176,0.0014586705,0.0014285875,0.00028841323,0.0000040577593],"about_ca_topic_score_codex":0.003417591,"about_ca_topic_score_gemma":0.0036987192,"teacher_disagreement_score":0.003417591,"about_ca_system_score_codex":0.0006992732,"about_ca_system_score_gemma":0.0006723176,"threshold_uncertainty_score":0.0067954063},"labels":[],"label_agreement":null},{"id":"W2782461164","doi":"","title":"Computing EFMs consistent with equilibrium constants","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science","score_opus":0.02109923452714824,"score_gpt":0.2413523265706921,"score_spread":0.22025309204354387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782461164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11290011,0.0009025447,0.8471621,0.0021264965,0.0004036195,0.00010217613,0.0005328303,0.0013522101,0.034517914],"genre_scores_gemma":[0.78372824,0.00047958142,0.19984262,0.00060110894,0.00034287837,0.00022016073,0.00081786606,0.0008060178,0.013161575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993223,0.00026379927,0.00003496441,0.00017292592,0.000119888005,0.00008609471],"domain_scores_gemma":[0.9903144,0.0077685523,0.0004197265,0.00072434667,0.0004685488,0.0003043635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020790524,0.0011196374,0.0013255974,0.001803315,0.00091639976,0.0023854964,0.0023461774,0.0026868326,0.01879706],"category_scores_gemma":[0.023596052,0.0007312974,0.0009047743,0.001362788,0.0019080144,0.0045080953,0.002784981,0.0029495922,0.0015385103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002671368,0.00012939791,0.0008015324,0.00040932765,0.000093957984,0.00015499652,0.00012948939,0.2859361,0.0016792614,0.66134953,0.005641879,0.043407384],"study_design_scores_gemma":[0.000035504545,0.000015022766,0.00006796292,0.000022870747,0.000011365242,0.00001978479,0.000030984338,0.41821745,0.0005200545,0.5802798,0.00077094976,0.0000082586175],"about_ca_topic_score_codex":0.0008755361,"about_ca_topic_score_gemma":0.0012543704,"teacher_disagreement_score":0.01879706,"about_ca_system_score_codex":0.0011062657,"about_ca_system_score_gemma":0.0009300665,"threshold_uncertainty_score":0.06288248},"labels":[],"label_agreement":null},{"id":"W2782537722","doi":"10.1016/j.inffus.2018.01.007","title":"Alternating diffusion maps for multimodal data fusion","year":2018,"lang":"en","type":"article","venue":"Information Fusion","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Seventh Framework Programme; Israel Science Foundation","keywords":"Computer science; Sensor fusion; Focus (optics); Modalities; Filter (signal processing); Process (computing); Artificial intelligence; Representation (politics); Noise (video); Machine learning; Measure (data warehouse); Data mining; Pattern recognition (psychology); Computer vision","score_opus":0.033104130330928,"score_gpt":0.29201764562759935,"score_spread":0.25891351529667134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782537722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033983376,0.0004567443,0.9952396,0.00016852144,0.00003442284,0.00001784851,0.000046156336,0.00016853093,0.00046989974],"genre_scores_gemma":[0.35809186,0.0015080718,0.63204336,0.00023492999,0.0002146916,0.00028244208,0.00052511605,0.0002607881,0.0068388283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917954,0.0003633885,0.00006482627,0.00015952592,0.00018427194,0.000048508573],"domain_scores_gemma":[0.9976884,0.0015587163,0.00014929188,0.00022694819,0.00031288725,0.00006374982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00247044,0.00095119706,0.0013963677,0.0011989686,0.0005022424,0.0012848844,0.0014115457,0.0020917403,0.00215797],"category_scores_gemma":[0.008148977,0.00062035135,0.0011184019,0.0015730925,0.001108406,0.0026281932,0.0025031823,0.0020407303,0.00060533214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028231324,0.00009267139,0.0004625318,0.000359429,0.00025511952,0.00014127817,0.00020680601,0.5613862,0.009730104,0.1145533,0.0040922025,0.30843803],"study_design_scores_gemma":[0.000004967227,0.0000152182365,0.00006670982,0.0000064879764,0.000012395345,0.000019772882,0.000006394027,0.96677643,0.0010292324,0.031378753,0.0006721428,0.000011462432],"about_ca_topic_score_codex":0.0041188975,"about_ca_topic_score_gemma":0.0029626696,"teacher_disagreement_score":0.0041188975,"about_ca_system_score_codex":0.0008684696,"about_ca_system_score_gemma":0.0008712544,"threshold_uncertainty_score":0.01306504},"labels":[],"label_agreement":null},{"id":"W2782828279","doi":"10.4271/2017-36-0434","title":"Data Synchronization for Offline and Online Identification of Dynamic Systems","year":2017,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Synchronization (alternating current); Identification (biology); Data synchronization; Online and offline; Computer network; Operating system; Wireless sensor network","score_opus":0.028039558861993774,"score_gpt":0.30400349202561733,"score_spread":0.27596393316362355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782828279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03804632,0.00036005664,0.954321,0.00017634945,0.00017033875,0.000083047686,0.00007149156,0.0022053663,0.004565972],"genre_scores_gemma":[0.6961932,0.00028208623,0.29741555,0.00013534502,0.00007685414,0.000139223,0.0002925878,0.00047605982,0.004989197],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858886,0.00049414753,0.00009241126,0.00023605698,0.00050670886,0.00008178724],"domain_scores_gemma":[0.99695456,0.0015796194,0.0002273486,0.0007336146,0.00044745894,0.000057393718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012062432,0.0005537244,0.00046140348,0.0005508555,0.0004561198,0.00080217456,0.00074932864,0.00053490594,0.0053232173],"category_scores_gemma":[0.004490051,0.00025310085,0.00025605282,0.00042526348,0.0005467914,0.001514557,0.0010647248,0.00080621045,0.00178273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015895042,0.00027476286,0.002683555,0.0004230385,0.000049459082,0.00036118983,0.00041040775,0.10833781,0.17698704,0.014210275,0.006169382,0.6885036],"study_design_scores_gemma":[0.00010535351,0.00054578425,0.0019808747,0.0000550602,0.000026347096,0.00025282757,0.000154063,0.7703523,0.20974046,0.004816988,0.0119192675,0.000050752922],"about_ca_topic_score_codex":0.0009195576,"about_ca_topic_score_gemma":0.0010454357,"teacher_disagreement_score":0.0053232173,"about_ca_system_score_codex":0.0005163035,"about_ca_system_score_gemma":0.0006863104,"threshold_uncertainty_score":0.01780796},"labels":[],"label_agreement":null},{"id":"W2783007109","doi":"10.1017/9781009504942.007","title":"Neural-network quantum states","year":2025,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo; McMaster University","funders":"","keywords":"Quantum; Artificial neural network; Physics; Computer science; Quantum mechanics; Artificial intelligence","score_opus":0.01742442975390774,"score_gpt":0.20080722937166368,"score_spread":0.18338279961775594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783007109","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009482753,0.0683853,0.28204498,0.011183893,0.00304574,0.000068996625,0.0006382961,0.0006107229,0.6245394],"genre_scores_gemma":[0.30975574,0.09493435,0.123708904,0.0036294258,0.0025502967,0.00027552227,0.0009998226,0.000427153,0.46371868],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99986684,0.000029828945,0.0000052987857,0.000025804193,0.00006136095,0.000010858745],"domain_scores_gemma":[0.99985015,0.000086960805,0.0000067155784,0.000018880488,0.00002963267,0.000007658052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027843498,0.00041891864,0.0002847934,0.00045329286,0.00035517424,0.0011802994,0.00055055023,0.00082687906,0.01318145],"category_scores_gemma":[0.0011559933,0.00019813623,0.00023584533,0.00055170566,0.0009478664,0.0024544063,0.00071095076,0.001298512,0.0031173562],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000885227,0.000010381538,0.000035982404,0.00015706055,0.000007088363,0.000021962003,0.00005020708,0.007342549,0.00056118343,0.92199516,0.018492829,0.051316716],"study_design_scores_gemma":[0.0000042241363,0.0000088833285,0.00008692857,0.00012730248,0.000004956119,0.00006828603,0.000023627914,0.029964237,0.0006676869,0.8046975,0.1643355,0.000010750243],"about_ca_topic_score_codex":0.0009956203,"about_ca_topic_score_gemma":0.0012781243,"teacher_disagreement_score":0.01318145,"about_ca_system_score_codex":0.0010243868,"about_ca_system_score_gemma":0.000501084,"threshold_uncertainty_score":0.04409635},"labels":[],"label_agreement":null},{"id":"W2785235357","doi":"10.11575/prism/5399","title":"Synchronization and System Identification Using Symbolic Dynamics","year":2018,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Synchronization (alternating current); Identification (biology); Symbolic dynamics; Dynamics (music); Computer science; Psychology; Mathematics; Telecommunications; Biology","score_opus":0.024783129944639493,"score_gpt":0.3098597524499471,"score_spread":0.28507662250530763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785235357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0101985615,0.0005363074,0.9822567,0.00027208484,0.00005391363,0.000032828975,0.00002541934,0.0001552441,0.0064689047],"genre_scores_gemma":[0.7099088,0.0021283675,0.27479059,0.00012212347,0.00015934101,0.00032788518,0.00014365037,0.00008495554,0.012334323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99945194,0.00021531388,0.000022909258,0.00007296931,0.00021072004,0.00002616222],"domain_scores_gemma":[0.9993019,0.0004540115,0.0000858037,0.000062146595,0.00007996922,0.000016162001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061570393,0.0005209908,0.0005697097,0.00065512786,0.00043341494,0.0009960657,0.0003968749,0.0006861289,0.0022035702],"category_scores_gemma":[0.0022125167,0.00023106982,0.00052165566,0.0005740347,0.0011891695,0.0009570258,0.0012719587,0.00093691994,0.00037259207],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008414385,0.000030575433,0.0007462472,0.00024260796,0.000063475265,0.00017768673,0.00029855993,0.5108697,0.010650472,0.4016916,0.0009141236,0.07423078],"study_design_scores_gemma":[0.000008323153,0.000033017877,0.00015110927,0.00001944862,0.0000058540318,0.0000449812,0.000023566205,0.9356222,0.0019116027,0.059935655,0.0022310014,0.000013258873],"about_ca_topic_score_codex":0.0012879776,"about_ca_topic_score_gemma":0.0007628797,"teacher_disagreement_score":0.0022035702,"about_ca_system_score_codex":0.0006710252,"about_ca_system_score_gemma":0.00073834974,"threshold_uncertainty_score":0.0073717237},"labels":[],"label_agreement":null},{"id":"W2786067931","doi":"","title":"Improving with Practice: A Neural Model of Mathematical Development.","year":2016,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Task (project management); Basal ganglia; Artificial neural network; Recall; Network model; Process (computing); Artificial intelligence; Dyscalculia; Neuroscience; Working memory; Cognition; Spiking neural network; Thalamus; Machine learning; Psychology; Cognitive psychology","score_opus":0.016486400406202636,"score_gpt":0.2172310501067056,"score_spread":0.20074464970050296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786067931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10368907,0.0046205376,0.755003,0.009548236,0.00038989508,0.00012761535,0.0011247846,0.0005714599,0.12492545],"genre_scores_gemma":[0.8515665,0.0026952294,0.093917914,0.0005099244,0.00019824317,0.00038570038,0.00039474724,0.00016439341,0.05016732],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978775,0.000065073924,0.000012073727,0.00004658729,0.000049537364,0.00003898555],"domain_scores_gemma":[0.9993081,0.00030555294,0.00013323108,0.000060045535,0.000099368,0.000093698836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006364468,0.000730589,0.0005600434,0.0008648035,0.0007399209,0.0015928783,0.0017667557,0.0016633597,0.0127006015],"category_scores_gemma":[0.0032269352,0.00048683072,0.001146426,0.0007858995,0.0013805518,0.003213573,0.0011293966,0.001571282,0.0016233464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006371574,0.000049067057,0.0016514129,0.00009342717,0.00005258131,0.00023845497,0.0003884655,0.30334553,0.0011184888,0.66875124,0.003587191,0.020660486],"study_design_scores_gemma":[0.000029993113,0.000028268645,0.00041049346,0.000022257913,0.000019229012,0.0001321299,0.000034000688,0.7024733,0.0001347998,0.29230025,0.0044034817,0.000011856111],"about_ca_topic_score_codex":0.009909266,"about_ca_topic_score_gemma":0.009117673,"teacher_disagreement_score":0.0127006015,"about_ca_system_score_codex":0.001598076,"about_ca_system_score_gemma":0.0011724215,"threshold_uncertainty_score":0.04248768},"labels":[],"label_agreement":null},{"id":"W2786300701","doi":"10.1109/lsc.2017.8268138","title":"Artificial neural networks model design of Lorenz chaotic system for EEG pattern recognition and prediction","year":2017,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Artificial neural network; Chaotic; Computer science; Electroencephalography; Artificial intelligence; Pattern recognition (psychology); Lorenz system; Neuroscience; Psychology","score_opus":0.09623837690251046,"score_gpt":0.26030975588414834,"score_spread":0.16407137898163787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786300701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030403016,0.00093953573,0.95518506,0.00032473588,0.00012796691,0.00013379467,0.00009957029,0.00048863573,0.012297752],"genre_scores_gemma":[0.8480028,0.0012114746,0.13682044,0.00008416859,0.00008207906,0.00055832235,0.00028079166,0.00006296113,0.012896955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996855,0.00008646425,0.000020811092,0.00008498277,0.00009558533,0.000026621423],"domain_scores_gemma":[0.99980706,0.000050177405,0.00002193086,0.000012381994,0.00010086474,0.0000076274523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041269427,0.0006677598,0.0005056657,0.0003988192,0.00041593303,0.0007253409,0.0009212818,0.0007906842,0.0018908808],"category_scores_gemma":[0.0007689862,0.0003093949,0.0006061612,0.00036329246,0.0003014076,0.00067403214,0.00035193298,0.0006498741,0.0004969394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053492775,0.000023676253,0.0006237505,0.00009276623,0.000043642503,0.00008525049,0.00005607074,0.9595408,0.0046471786,0.0041338243,0.0005859066,0.030113615],"study_design_scores_gemma":[0.000002802389,0.00001541661,0.00010591352,0.0000048308575,0.0000057281973,0.000010194267,0.0000033424803,0.99824274,0.0005899204,0.0005418716,0.00047362666,0.0000036086344],"about_ca_topic_score_codex":0.0072171227,"about_ca_topic_score_gemma":0.004794441,"teacher_disagreement_score":0.0072171227,"about_ca_system_score_codex":0.00063712744,"about_ca_system_score_gemma":0.00066212704,"threshold_uncertainty_score":0.014350176},"labels":[],"label_agreement":null},{"id":"W2787719532","doi":"10.1167/16.12.27","title":"Statistical learning creates novel object associations via transitive relations","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Transitive relation; Categorical variable; Statistical learning; Object (grammar); Hierarchy; Sequence (biology); Base (topology); Superordinate goals; Artificial intelligence; Combinatorics; Task (project management); Psychology; Computer science; Mathematics; Communication; Social psychology; Machine learning","score_opus":0.01285443688363099,"score_gpt":0.2843856205891352,"score_spread":0.2715311837055042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787719532","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7896622,0.00011598992,0.20137289,0.00022320381,0.000035970672,0.00005712445,0.00011068468,0.00063312013,0.007788847],"genre_scores_gemma":[0.9525299,0.00009461659,0.04605418,0.000063040374,0.000014419225,0.000029637615,0.00016735266,0.00006481419,0.0009820444],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936277,0.000119484,0.000038261413,0.00027887998,0.00014663897,0.00005400007],"domain_scores_gemma":[0.9965634,0.0013456581,0.00071634504,0.0010174236,0.00017516135,0.00018204503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007130835,0.00031694202,0.00026781607,0.00038829164,0.00034009392,0.0009422963,0.0006500361,0.0003287541,0.0019858927],"category_scores_gemma":[0.0044776844,0.0005163983,0.0005762307,0.0002569033,0.0011848571,0.0024649152,0.0012976793,0.0009280837,0.00027216188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005480024,0.00048277964,0.037338924,0.00044158468,0.00018954322,0.0007111461,0.0021372293,0.024485637,0.5351256,0.067625664,0.0013574482,0.32955652],"study_design_scores_gemma":[0.00013865136,0.0016732485,0.09925032,0.000096720054,0.00023552417,0.0018140653,0.0008856758,0.35669667,0.25397092,0.26955983,0.015492466,0.00018585921],"about_ca_topic_score_codex":0.0007981395,"about_ca_topic_score_gemma":0.0012853905,"teacher_disagreement_score":0.0019858927,"about_ca_system_score_codex":0.00046923943,"about_ca_system_score_gemma":0.00039929932,"threshold_uncertainty_score":0.0066434145},"labels":[],"label_agreement":null},{"id":"W2790999378","doi":"10.1071/aseg2018abw10_3d","title":"Multidimensional Topology Transforms","year":2018,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Topology (electrical circuits); Computer science; Mathematics; Combinatorics","score_opus":0.014326458189972349,"score_gpt":0.2700944728066879,"score_spread":0.25576801461671556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790999378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008449302,0.0005494355,0.9735617,0.00032006536,0.0001690565,0.00013165064,0.0037824628,0.0024177239,0.01061861],"genre_scores_gemma":[0.20165996,0.0017732008,0.7689426,0.00026513275,0.00022451558,0.0005555039,0.016671196,0.0017587786,0.008149077],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979265,0.00034885603,0.00017918325,0.00057044305,0.0008603241,0.00011474258],"domain_scores_gemma":[0.99740416,0.0007617326,0.00025372917,0.0007990041,0.0006634067,0.00011787356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014100436,0.0012091318,0.0010206806,0.004426173,0.0006472826,0.0073662368,0.0017228911,0.0012875446,0.014504874],"category_scores_gemma":[0.0079223355,0.00059580186,0.0022564179,0.0032200573,0.0010729445,0.0051370077,0.0032050721,0.0021293883,0.0052423617],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016524798,0.0001067984,0.0033949544,0.0005584585,0.00012636806,0.00045838574,0.00095867494,0.13778713,0.008179474,0.37621564,0.029034775,0.4430141],"study_design_scores_gemma":[0.000028334902,0.00008053132,0.0016283614,0.0001863687,0.000036115853,0.001034863,0.00055271277,0.58311594,0.00614041,0.2703706,0.1367433,0.00008245506],"about_ca_topic_score_codex":0.001357773,"about_ca_topic_score_gemma":0.0015055822,"teacher_disagreement_score":0.014504874,"about_ca_system_score_codex":0.0010351633,"about_ca_system_score_gemma":0.000814674,"threshold_uncertainty_score":0.048523664},"labels":[],"label_agreement":null},{"id":"W2791755845","doi":"10.1007/s00221-018-5185-7","title":"Decoding auditory spatial and emotional information encoding using multivariate versus univariate techniques","year":2018,"lang":"en","type":"article","venue":"Experimental Brain Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Univariate; Multivariate statistics; Decoding methods; Encoding (memory); Psychology; Multivariate analysis; Speech recognition; Neuroscience; Audiology; Computer science; Medicine; Telecommunications; Machine learning","score_opus":0.10395443896540844,"score_gpt":0.4185273084413588,"score_spread":0.31457286947595037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791755845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34089646,0.0003338115,0.6534981,0.00025589432,0.000059787955,0.000067293884,0.00060566893,0.0010091985,0.0032736654],"genre_scores_gemma":[0.7850454,0.0004185277,0.21168986,0.000052930347,0.00008174366,0.00010113033,0.00048522846,0.0002689979,0.0018562449],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998215,0.000042089647,0.000011972908,0.000051642157,0.000044614077,0.000028322756],"domain_scores_gemma":[0.99909043,0.00051012816,0.000112494,0.00012429504,0.00012098288,0.00004154937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051440886,0.0004947093,0.00034839564,0.0011348488,0.00017139413,0.0008961201,0.00029382601,0.0003246936,0.0033975632],"category_scores_gemma":[0.0040855138,0.00024274054,0.00056785124,0.0010937955,0.00032968156,0.0014087826,0.0007325596,0.00050261355,0.00054165936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009980227,0.00011860011,0.0074649933,0.0002429866,0.00011927352,0.00016808732,0.00042822186,0.012429233,0.5117456,0.00809303,0.00096149184,0.45723042],"study_design_scores_gemma":[0.000094683375,0.0006227554,0.09812633,0.00006455484,0.00028522918,0.0011746773,0.00058082904,0.644206,0.20109953,0.04839347,0.005195895,0.00015610142],"about_ca_topic_score_codex":0.0010104232,"about_ca_topic_score_gemma":0.0019429798,"teacher_disagreement_score":0.0033975632,"about_ca_system_score_codex":0.00018098339,"about_ca_system_score_gemma":0.0003164689,"threshold_uncertainty_score":0.01136595},"labels":[],"label_agreement":null},{"id":"W2791777778","doi":"10.1109/tc.2018.2817237","title":"A Stochastic Computational Multi-Layer Perceptron with Backward Propagation","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Computers","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":111,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Stochastic computing; Computer science; Perceptron; Artificial neural network; Algorithm; Multilayer perceptron; Computation; Artificial intelligence","score_opus":0.026733666894722487,"score_gpt":0.2602225842534681,"score_spread":0.23348891735874563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791777778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011284358,0.00027959218,0.98575735,0.00010822987,0.000074516225,0.000033990655,0.000049687624,0.0009287825,0.0014835264],"genre_scores_gemma":[0.49885014,0.0005047806,0.49505734,0.00024839147,0.000060400474,0.00013565348,0.00021475078,0.000070326794,0.0048581655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996086,0.0000818347,0.000031435357,0.000074975025,0.00016119842,0.000041931224],"domain_scores_gemma":[0.99966943,0.0001121822,0.000047620808,0.000050795552,0.0001029371,0.000016928501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008019357,0.0005833007,0.00056303263,0.00046215445,0.0002823502,0.00065889553,0.0013243929,0.0007261369,0.0016497752],"category_scores_gemma":[0.0010565383,0.0004262408,0.0006920182,0.0005955838,0.00044033883,0.0008379502,0.00046883902,0.00089494686,0.00042446845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021304133,0.00011901102,0.0013948786,0.0002853958,0.0001983219,0.00019279776,0.0000628683,0.58391404,0.046852026,0.028006246,0.002970055,0.33579135],"study_design_scores_gemma":[0.0000075344624,0.00004524348,0.00014593829,0.0000051892803,0.000013092376,0.000030042833,0.0000014863552,0.9926214,0.004829647,0.0010178342,0.0012756004,0.0000069659854],"about_ca_topic_score_codex":0.002129906,"about_ca_topic_score_gemma":0.0036617527,"teacher_disagreement_score":0.002129906,"about_ca_system_score_codex":0.0005862687,"about_ca_system_score_gemma":0.00089776394,"threshold_uncertainty_score":0.0055190325},"labels":[],"label_agreement":null},{"id":"W2792919287","doi":"10.1038/nmeth.4642","title":"Statistics versus machine learning","year":2018,"lang":"en","type":"article","venue":"Nature Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1490,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"National Institute of Mental Health","keywords":"Computer science; Statistics; Computational biology; Artificial intelligence; Machine learning; Biology; Mathematics","score_opus":0.029821399663415408,"score_gpt":0.4125295686340756,"score_spread":0.3827081689706602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792919287","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077581652,0.039533854,0.8675465,0.023441523,0.0058388766,0.00004054062,0.00054884295,0.0011537555,0.05413797],"genre_scores_gemma":[0.4650272,0.04615608,0.35134733,0.01374853,0.034082517,0.0006171622,0.0014875454,0.0025607268,0.084973],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960925,0.0021622628,0.0001879351,0.0005074529,0.0009463341,0.00010342362],"domain_scores_gemma":[0.9808692,0.015017654,0.0009163187,0.0016556287,0.0011443556,0.0003967817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004210261,0.0008910843,0.0017028756,0.0025227582,0.00062305713,0.003235282,0.0011169193,0.0021305163,0.009107503],"category_scores_gemma":[0.02289576,0.0004156681,0.0005260171,0.0028492159,0.0045694835,0.0062640375,0.0014097603,0.004475145,0.004436928],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018566388,0.00001855067,0.00033770915,0.00012889852,0.000022866707,0.000019793148,0.00006249643,0.0021079557,0.00019903645,0.9329383,0.0105324285,0.053613394],"study_design_scores_gemma":[0.000006424763,0.000020276395,0.00023164356,0.00006119444,0.000010623013,0.00005159565,0.00002835625,0.0119013805,0.00026308323,0.9617439,0.02567149,0.000010062779],"about_ca_topic_score_codex":0.0007066113,"about_ca_topic_score_gemma":0.0005569596,"teacher_disagreement_score":0.009107503,"about_ca_system_score_codex":0.0010056659,"about_ca_system_score_gemma":0.0016157512,"threshold_uncertainty_score":0.03046763},"labels":[],"label_agreement":null},{"id":"W2798447324","doi":"","title":"On the Help of Bounded Shot Verifiers, Comparators and Standardisers for Learnability in Inductive Inference","year":2018,"lang":"en","type":"article","venue":"Algorithmic Learning Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Learnability; Inference; Inductive reasoning; Bounded function; Shot (pellet); Computer science; Comparator; Artificial intelligence; Mathematics; Engineering; Electrical engineering; Mathematical analysis","score_opus":0.02605099024266741,"score_gpt":0.2997311155748661,"score_spread":0.27368012533219865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798447324","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011877771,0.00060868723,0.972937,0.0019071709,0.00019707806,0.00006094315,0.0001395897,0.00070059526,0.011571147],"genre_scores_gemma":[0.6486618,0.0008741747,0.33444518,0.0017532808,0.00063559064,0.00037460812,0.0005176997,0.00080342044,0.011934162],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9904218,0.004308736,0.00058723113,0.0019690292,0.0019947777,0.00071845495],"domain_scores_gemma":[0.9519128,0.036217492,0.001298997,0.0078738015,0.001821826,0.00087506673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014328751,0.0012509765,0.0023197534,0.0027912904,0.0023370874,0.0063328245,0.005003528,0.0046691056,0.011508258],"category_scores_gemma":[0.06327005,0.001603654,0.0027221953,0.0022323914,0.01351695,0.026510064,0.010760858,0.011307446,0.0015801234],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011349718,0.000027228882,0.00018036996,0.00006480073,0.000021523432,0.00003486249,0.00013862072,0.0055549885,0.0004892081,0.97382337,0.000917187,0.018634247],"study_design_scores_gemma":[0.000012141479,0.000014328114,0.000030492745,0.000016267993,0.000009097408,0.000014842494,0.000013467272,0.01903206,0.00032897497,0.97998613,0.0005315796,0.000010588148],"about_ca_topic_score_codex":0.0015409536,"about_ca_topic_score_gemma":0.002231548,"teacher_disagreement_score":0.014328751,"about_ca_system_score_codex":0.0030896096,"about_ca_system_score_gemma":0.0026525739,"threshold_uncertainty_score":0.075778544},"labels":[],"label_agreement":null},{"id":"W2798901099","doi":"10.1017/9781107588493.008","title":"Non-linear Optimization","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Maxima and minima; Conjugate gradient method; Randomness; Gradient descent; Mathematical optimization; Saddle point; Stochastic gradient descent; Optimization problem; Stochastic optimization; Differential evolution; Nonlinear conjugate gradient method; Descent (aeronautics); Computer science; Meta-optimization; Mathematics; Artificial intelligence; Artificial neural network","score_opus":0.02807890753392667,"score_gpt":0.20851980342886597,"score_spread":0.1804408958949393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798901099","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017272324,0.01898844,0.72716194,0.0027638644,0.0014552545,0.00007364157,0.0005698959,0.0011809086,0.2460788],"genre_scores_gemma":[0.1141215,0.04882241,0.35689044,0.002164763,0.002010339,0.00057786825,0.0019342473,0.0020602988,0.47141805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999493,0.00008811766,0.000021605696,0.0000928799,0.0002791505,0.000025243555],"domain_scores_gemma":[0.999564,0.00023141604,0.000024978133,0.000066050874,0.00009292459,0.000020537891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005389374,0.0014558876,0.0011659114,0.0005733854,0.00047872396,0.001958561,0.0013257597,0.0013395591,0.03152269],"category_scores_gemma":[0.0015448682,0.0005046771,0.00089153106,0.0008406134,0.0010203852,0.0013789514,0.002174696,0.0019252672,0.016577227],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033193086,0.000052442887,0.00023947148,0.0011616808,0.000113147726,0.00017855578,0.00011560812,0.15111838,0.0030625628,0.49879414,0.11039849,0.23473234],"study_design_scores_gemma":[0.000020010486,0.0000415866,0.00025627617,0.00026843094,0.0000278124,0.00025970492,0.00003971553,0.19360858,0.0018139103,0.3590827,0.4445427,0.00003864873],"about_ca_topic_score_codex":0.0010256818,"about_ca_topic_score_gemma":0.0012505256,"teacher_disagreement_score":0.03152269,"about_ca_system_score_codex":0.0006640944,"about_ca_system_score_gemma":0.0008244197,"threshold_uncertainty_score":0.10545391},"labels":[],"label_agreement":null},{"id":"W2801473125","doi":"10.1007/978-981-10-8001-2_5","title":"Differential Entropy and Gaussian Channels","year":2018,"lang":"en","type":"book-chapter","venue":"Springer undergraduate texts in mathematics and technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Alphabet; Gaussian; Differential entropy; Discrete time and continuous time; Entropy (arrow of time); Waveform; Focus (optics); Algorithm; Computer science; Channel (broadcasting); Mathematics; Statistical physics; Theoretical computer science; Transfer entropy; Principle of maximum entropy; Artificial intelligence; Statistics; Telecommunications; Physics; Philosophy; Optics","score_opus":0.013584541187377674,"score_gpt":0.2309025376112108,"score_spread":0.21731799642383312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801473125","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.111343995,0.04591856,0.2869475,0.011279815,0.0038117555,0.00004586407,0.00072846585,0.00036540872,0.53955865],"genre_scores_gemma":[0.8524348,0.015666133,0.01337053,0.0011431192,0.004924307,0.00007153146,0.00034496814,0.00015749436,0.111887045],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999734,0.00006866662,0.000010848959,0.00004454343,0.00010866174,0.000033253425],"domain_scores_gemma":[0.9996032,0.0002130025,0.000042198615,0.000049699218,0.000056031753,0.000035881156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046424556,0.00058069493,0.0006779966,0.0012233567,0.00056995,0.0015251879,0.0004112506,0.0009795537,0.0057795257],"category_scores_gemma":[0.0014657449,0.00022473562,0.00038232942,0.0011962884,0.0020633198,0.0025129092,0.0011766774,0.0015153072,0.0009146481],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005165382,0.0000046905197,0.000039900722,0.000021197702,0.0000041704984,0.000015412748,0.000026682954,0.0017580059,0.00018907024,0.99004304,0.0023503157,0.005542275],"study_design_scores_gemma":[0.0000014559104,0.0000036061974,0.000088730216,0.0000069258504,0.0000021747478,0.00003303858,0.0000084006915,0.004021769,0.00009391059,0.99187297,0.0038621724,0.0000049869996],"about_ca_topic_score_codex":0.00041805,"about_ca_topic_score_gemma":0.0002483847,"teacher_disagreement_score":0.0057795257,"about_ca_system_score_codex":0.0009286432,"about_ca_system_score_gemma":0.00042866182,"threshold_uncertainty_score":0.019334495},"labels":[],"label_agreement":null},{"id":"W2802221970","doi":"","title":"Effects of Conduction Velocity and Spectral Comperssion on Fractal Parameters of Myoelectric Signals","year":2017,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Ottawa","funders":"","keywords":"Fractal; Context (archaeology); Spectral density; Fractal analysis; Compression (physics); Mathematics; Acoustics; Fractal dimension; Materials science; Mathematical analysis; Physics; Statistics; Geology","score_opus":0.015868486561241468,"score_gpt":0.25350916211276536,"score_spread":0.2376406755515239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802221970","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9773063,0.00010047745,0.021805901,0.000039317754,0.000008927726,0.000013268811,0.000035500343,0.00007035668,0.00061990245],"genre_scores_gemma":[0.99834967,0.000038996503,0.0015246643,0.0000030634153,0.0000015276675,0.000004214258,0.000023355622,0.000008050146,0.000046544243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980396,0.00006419072,0.000011495259,0.000023878136,0.00006809044,0.000028457198],"domain_scores_gemma":[0.9971654,0.002133985,0.00023375094,0.00021994939,0.00014338679,0.00010361226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006414757,0.00033542895,0.00021931845,0.0005059176,0.00012480536,0.00034160735,0.0001594305,0.0003393568,0.00042912606],"category_scores_gemma":[0.006162391,0.00013414727,0.00025900535,0.00022658662,0.000404764,0.00043038555,0.00027135582,0.00030680595,0.000046948895],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010356841,0.0002463903,0.013194546,0.00018081929,0.00010418384,0.00052542664,0.00035664544,0.6053455,0.33638713,0.00533987,0.00024149679,0.037042245],"study_design_scores_gemma":[0.000022107179,0.00047483062,0.01580552,0.000020243822,0.000028396495,0.00021686133,0.00003849509,0.942466,0.03910554,0.0015773062,0.00021454258,0.000030099181],"about_ca_topic_score_codex":0.0005282296,"about_ca_topic_score_gemma":0.00044157734,"teacher_disagreement_score":0.0006414757,"about_ca_system_score_codex":0.00019134025,"about_ca_system_score_gemma":0.0001490282,"threshold_uncertainty_score":0.0033925176},"labels":[],"label_agreement":null},{"id":"W2802284975","doi":"10.1139/tcsme-2016-0033","title":"ACTIVE HEAT DISSIPATION SYSTEM USING ADAPTIVE RECURRENT WAVELET NEURAL NETWORK CONTROL","year":2016,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Control theory (sociology); PID controller; Gradient descent; Controller (irrigation); Artificial neural network; Control system; Adaptive control; Computer science; Nonlinear system; Control engineering; Temperature control; Wavelet; Engineering; Control (management); Artificial intelligence","score_opus":0.01572165294746369,"score_gpt":0.2095762014808563,"score_spread":0.1938545485333926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802284975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038521674,0.0003025935,0.9562205,0.00009459204,0.00008077048,0.000036366648,0.0000131890265,0.0005017698,0.004228579],"genre_scores_gemma":[0.9450979,0.00026094436,0.05040941,0.00005012564,0.00005423897,0.00007759821,0.000035179615,0.000027550166,0.003987103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977237,0.00003301322,0.000017177661,0.00005687801,0.00009960064,0.000020996333],"domain_scores_gemma":[0.99990404,0.000020841522,0.000017025159,0.000012024786,0.000039607334,0.000006524788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031380545,0.00038412103,0.00042040844,0.00016019717,0.00025630585,0.00044348428,0.0008390265,0.00030086524,0.0008469054],"category_scores_gemma":[0.00036976003,0.00018159652,0.00034863767,0.00016740467,0.0003079397,0.0007204054,0.0004151254,0.00041787347,0.00016946052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039814177,0.00023515425,0.0018729555,0.00043216685,0.00013835821,0.0002649814,0.0003573599,0.40812707,0.19007057,0.028904488,0.0027607093,0.36643806],"study_design_scores_gemma":[0.00002523242,0.00010130674,0.0002723888,0.000006063484,0.000022161752,0.00002983453,0.000006590662,0.98923945,0.007595521,0.0009432939,0.0017491461,0.00000906638],"about_ca_topic_score_codex":0.0011082682,"about_ca_topic_score_gemma":0.0010119404,"teacher_disagreement_score":0.0011082682,"about_ca_system_score_codex":0.00024186267,"about_ca_system_score_gemma":0.00026988797,"threshold_uncertainty_score":0.002833128},"labels":[],"label_agreement":null},{"id":"W2803299525","doi":"","title":"Inductive Two-layer Modeling with Parametric Bregman Transfer.","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Parametric statistics; Layer (electronics); Computer science; Transfer (computing); Mathematics; Materials science; Statistics; Parallel computing","score_opus":0.06569738614582407,"score_gpt":0.31988227408625663,"score_spread":0.2541848879404326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803299525","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006447374,0.00031836744,0.98793775,0.00014799925,0.0000626544,0.00003755765,0.00011703831,0.0011838309,0.0037473736],"genre_scores_gemma":[0.62576103,0.00045519366,0.35282162,0.00021315727,0.00006270339,0.00031245436,0.00071158074,0.0005452092,0.019117018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99970263,0.000093904666,0.0000144494725,0.00006515618,0.000082874765,0.000041047428],"domain_scores_gemma":[0.99947995,0.00023474664,0.000039680803,0.0001063832,0.0001120304,0.000027310813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008555411,0.0007617562,0.00071504706,0.00048658135,0.0004359685,0.0010307255,0.0024623498,0.0013180966,0.0055924514],"category_scores_gemma":[0.002870402,0.0006257497,0.0008073193,0.00075931085,0.00052125484,0.0021078396,0.001751081,0.0021143116,0.0022442716],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016657893,0.000107754924,0.0005062712,0.00015049311,0.0001061407,0.00012377926,0.00014592693,0.82898533,0.0071537048,0.040701993,0.00383218,0.11801973],"study_design_scores_gemma":[0.000002590757,0.0000076134083,0.000031733038,0.000005129175,0.000004966595,0.000010313681,0.000004700072,0.9925343,0.0010191394,0.0057242494,0.0006516073,0.00000371829],"about_ca_topic_score_codex":0.0054102913,"about_ca_topic_score_gemma":0.007991128,"teacher_disagreement_score":0.0055924514,"about_ca_system_score_codex":0.00065866386,"about_ca_system_score_gemma":0.0010307359,"threshold_uncertainty_score":0.018708646},"labels":[],"label_agreement":null},{"id":"W2803675738","doi":"10.1002/lob.201120239","title":"INTEGRATING TERRESTRIAL AND AQUATIC P SCIENCE: EMERGING ISSUES WORKSHOP REPORT","year":2011,"lang":"en","type":"article","venue":"Limnology and Oceanography Bulletin","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Swift; Library science; Citation; Computer science","score_opus":0.02226627597070754,"score_gpt":0.2636877236554667,"score_spread":0.24142144768475918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803675738","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07645346,0.0915486,0.108441755,0.37569737,0.09374462,0.0010708021,0.006359712,0.0013414866,0.24534217],"genre_scores_gemma":[0.3419553,0.080390394,0.10971041,0.021308444,0.031919952,0.0008511915,0.010190529,0.0009626086,0.40271115],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985898,0.00023054803,0.00007396427,0.0002543246,0.0004356609,0.0004156594],"domain_scores_gemma":[0.9931465,0.0008304967,0.00022573519,0.00027586293,0.0027350807,0.0027862815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010392516,0.00081983244,0.00056014856,0.00097579055,0.0011646609,0.006338504,0.0017912826,0.002862564,0.025428692],"category_scores_gemma":[0.0044195424,0.000339788,0.0006766407,0.0013703568,0.0008899694,0.006682265,0.004574116,0.0036402147,0.008314964],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007515082,0.0007748045,0.0046283384,0.0013055812,0.00011778559,0.00052696647,0.0006721809,0.0020990227,0.01310684,0.020523673,0.51442105,0.44107234],"study_design_scores_gemma":[0.00009414747,0.00048422083,0.0045208787,0.00057743257,0.00010281668,0.00032380808,0.002764036,0.0036198306,0.010216503,0.022299796,0.954934,0.00006245998],"about_ca_topic_score_codex":0.003820124,"about_ca_topic_score_gemma":0.0061850827,"teacher_disagreement_score":0.025428692,"about_ca_system_score_codex":0.0012207377,"about_ca_system_score_gemma":0.009902365,"threshold_uncertainty_score":0.08506739},"labels":[],"label_agreement":null},{"id":"W2803832867","doi":"","title":"Mutual Information Neural Estimation.","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":598,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Mutual information; Estimation; Artificial intelligence; Artificial neural network; Engineering","score_opus":0.02579097961051308,"score_gpt":0.29717562734481257,"score_spread":0.2713846477342995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803832867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050638355,0.0040806048,0.9767104,0.0008660764,0.00037756347,0.00006725858,0.00040585603,0.00104918,0.011379264],"genre_scores_gemma":[0.3953602,0.0036820145,0.55516684,0.0006695456,0.0011698187,0.00044454422,0.0027555325,0.0009943934,0.039757174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813324,0.0008509242,0.00011282632,0.0003172795,0.00047637205,0.00010936586],"domain_scores_gemma":[0.9959358,0.0021177127,0.00036946125,0.0007873261,0.00069205184,0.00009759443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028530932,0.0011733561,0.0016389824,0.0014888345,0.00070579216,0.0017962185,0.0019436531,0.001806656,0.0069036554],"category_scores_gemma":[0.013961683,0.0008708992,0.000891554,0.0015788015,0.0012852106,0.0029895382,0.0025340612,0.0019944943,0.0032238201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037593796,0.00014203122,0.0018449565,0.0006091339,0.00041807332,0.00014792422,0.0001701281,0.20461954,0.004568189,0.17241122,0.04352654,0.5711664],"study_design_scores_gemma":[0.000020851938,0.000051925694,0.0010075365,0.00006903261,0.000070220696,0.000117484655,0.000026406924,0.8873065,0.004953613,0.093131684,0.013203035,0.00004169054],"about_ca_topic_score_codex":0.0025693497,"about_ca_topic_score_gemma":0.0038657628,"teacher_disagreement_score":0.0069036554,"about_ca_system_score_codex":0.0007526306,"about_ca_system_score_gemma":0.0011427526,"threshold_uncertainty_score":0.023095012},"labels":[],"label_agreement":null},{"id":"W2803926779","doi":"","title":"Implementing the Latent Heat Nudging (LHN) Algorithm in the Canadian Regional Deterministic Prediction System (RDPS)","year":2017,"lang":"en","type":"article","venue":"38th Conference on Radar Meteorology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Algorithm; Econometrics; Mathematics; Statistics","score_opus":0.05203808989630014,"score_gpt":0.28443039259736247,"score_spread":0.23239230270106231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803926779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26604253,0.00052366435,0.70115125,0.0011787972,0.0004898646,0.00030976563,0.0023001912,0.013057999,0.014945916],"genre_scores_gemma":[0.7448155,0.00010055693,0.24767162,0.00014204136,0.000035576642,0.00008632482,0.0010603516,0.00032103644,0.005766935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996276,0.00005371418,0.000019779309,0.00008855405,0.000101232064,0.00010905774],"domain_scores_gemma":[0.9993187,0.00013580277,0.000021685008,0.000049558104,0.00042061685,0.00005357063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011277072,0.000667491,0.0006427829,0.0005193145,0.0012162455,0.0010275166,0.0018598195,0.00076181674,0.0041974476],"category_scores_gemma":[0.003175151,0.0004079626,0.0004319151,0.0006363498,0.00040951534,0.00079579325,0.00084771035,0.000942677,0.0006269941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019669128,0.00006753704,0.0055182218,0.000044456323,0.000057988604,0.000065729764,0.00008665925,0.8526027,0.0019179124,0.003598228,0.004848028,0.13099581],"study_design_scores_gemma":[0.0000113441465,0.0000054727802,0.00035702976,0.0000020114617,0.00000477445,0.0000028451877,0.000009964264,0.99833137,0.0005118348,0.00030375496,0.0004536653,0.000005968279],"about_ca_topic_score_codex":0.85585743,"about_ca_topic_score_gemma":0.8568016,"teacher_disagreement_score":0.14414257,"about_ca_system_score_codex":0.0036809337,"about_ca_system_score_gemma":0.013482291,"threshold_uncertainty_score":0.28998286},"labels":[],"label_agreement":null},{"id":"W2807666102","doi":"10.11159/iccste18.106","title":"Traffic Delay Estimation Using Artificial Neural Network (ANN) at Unsignalized Intersections","year":2018,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Civil, Structural and Transportation Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Teknologi Malaysia","keywords":"Artificial neural network; Computer science; Estimation; Artificial intelligence; Engineering","score_opus":0.02360503276132502,"score_gpt":0.24569739320577796,"score_spread":0.22209236044445294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807666102","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.625531,0.0007065591,0.3672999,0.0001812967,0.000093992814,0.000042603548,0.0002272899,0.0007129039,0.005204502],"genre_scores_gemma":[0.9892392,0.00016402152,0.009488262,0.000012562521,0.0000066502744,0.000017981463,0.00008965388,0.000008375989,0.0009733475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981517,0.000035885303,0.000016800124,0.00005442975,0.000042908923,0.000034805485],"domain_scores_gemma":[0.99959725,0.00017753133,0.000068486544,0.000014525552,0.0001277154,0.0000144710475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033570538,0.00062050094,0.00034331557,0.00055164413,0.00023990405,0.0006153071,0.00050727255,0.0005187391,0.0005216947],"category_scores_gemma":[0.0010740304,0.00024656925,0.00036027166,0.0004034519,0.00014605894,0.0006369514,0.00025152232,0.00053729943,0.00011697655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006858589,0.000036614634,0.003935091,0.000034388853,0.000023686876,0.000056410277,0.000024612915,0.9764538,0.0014698178,0.0003191048,0.00013625268,0.017441615],"study_design_scores_gemma":[9.2546543e-7,0.000014537853,0.00064678345,0.0000028371846,0.0000042986476,0.00000519224,0.0000063933653,0.99866307,0.00045317336,0.00013728275,0.00006224621,0.000003281983],"about_ca_topic_score_codex":0.016575338,"about_ca_topic_score_gemma":0.009889469,"teacher_disagreement_score":0.016575338,"about_ca_system_score_codex":0.00081705337,"about_ca_system_score_gemma":0.00060073764,"threshold_uncertainty_score":0.032957733},"labels":[],"label_agreement":null},{"id":"W2807947879","doi":"10.48550/arxiv.1806.04418","title":"Quaternion Recurrent Neural Networks","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Quaternion; Computer science; Recurrent neural network; Artificial neural network; Artificial intelligence; Mathematics; Geometry","score_opus":0.07588062035796499,"score_gpt":0.19978785128774726,"score_spread":0.12390723092978227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807947879","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020383323,0.001798618,0.9633473,0.00042965685,0.00048587515,0.000059542108,0.00069593755,0.003382039,0.009417652],"genre_scores_gemma":[0.6654751,0.0030139238,0.3006784,0.0007160557,0.00041912202,0.00018048832,0.0029310207,0.0005027143,0.026083287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996158,0.00006652845,0.00003082833,0.00013029366,0.00011453896,0.00004192933],"domain_scores_gemma":[0.9995952,0.00010826861,0.00006571879,0.00008134194,0.00013107152,0.00001833409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004151167,0.00086518837,0.00056328916,0.00036318856,0.000278935,0.0010906289,0.001182107,0.0007857977,0.0064636157],"category_scores_gemma":[0.001936654,0.00031947516,0.0005057332,0.00061510387,0.0005386747,0.0017469765,0.00080212177,0.0009127281,0.0024728596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035987556,0.00008928679,0.0014712883,0.00045009336,0.0001775763,0.00037045532,0.00018324672,0.4106862,0.05045287,0.10235977,0.016748225,0.41665104],"study_design_scores_gemma":[0.000013531003,0.000052771753,0.0003376698,0.000023586317,0.000023440092,0.00008570391,0.000018412045,0.951189,0.010369997,0.02755839,0.010301688,0.000025687474],"about_ca_topic_score_codex":0.0030741098,"about_ca_topic_score_gemma":0.0034963905,"teacher_disagreement_score":0.0064636157,"about_ca_system_score_codex":0.0005571436,"about_ca_system_score_gemma":0.00044840574,"threshold_uncertainty_score":0.021622956},"labels":[],"label_agreement":null},{"id":"W2808883319","doi":"10.14288/1.0371125","title":"Statistical Learning Creates Novel Object Associations via Transitive Relations","year":2018,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Transitive relation; Computer science; Object (grammar); Statistical learning; Basis (linear algebra); Artificial intelligence; Cognitive science; Statistical inference; Cognition; Mechanism (biology); Machine learning; Cognitive psychology; Psychology; Mathematics; Epistemology; Neuroscience; Statistics","score_opus":0.013890486175136935,"score_gpt":0.2279113220543188,"score_spread":0.21402083587918186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808883319","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6394309,0.0001563345,0.34433883,0.0005732386,0.00009321479,0.00007979062,0.000187089,0.0012545016,0.013886131],"genre_scores_gemma":[0.9375299,0.00011557487,0.06061142,0.00009830326,0.000032599393,0.000031192318,0.00024256567,0.00010090517,0.0012374874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987326,0.00032970667,0.000073323616,0.00046903914,0.00031520292,0.00008018017],"domain_scores_gemma":[0.9939944,0.0029088606,0.0010251299,0.0015679852,0.00029957487,0.0002040818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014723017,0.00037502812,0.00030872753,0.00046136454,0.00037614972,0.0014651233,0.001142461,0.00046939045,0.0027635666],"category_scores_gemma":[0.01331695,0.0005334505,0.0006628569,0.00036241123,0.0018311283,0.004573315,0.0016163121,0.0014990967,0.00041489513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007276883,0.00050645985,0.02724742,0.0005887421,0.0002192163,0.0012370576,0.0045759548,0.031117797,0.31549034,0.24505825,0.0026272675,0.3706038],"study_design_scores_gemma":[0.00008513138,0.0005647051,0.023735996,0.00007681238,0.00016235923,0.0012150228,0.000782658,0.29787645,0.101602465,0.5635563,0.010194253,0.00014776096],"about_ca_topic_score_codex":0.00062545034,"about_ca_topic_score_gemma":0.00080519,"teacher_disagreement_score":0.0027635666,"about_ca_system_score_codex":0.00038795106,"about_ca_system_score_gemma":0.0004764911,"threshold_uncertainty_score":0.009245038},"labels":[],"label_agreement":null},{"id":"W282295065","doi":"10.1016/j.mee.2006.02.014","title":"A feed-forward time-multiplexed neural network with mixed-signal neuron–synapse arrays","year":2006,"lang":"en","type":"article","venue":"Microelectronic Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Multiplexing; Mixed-signal integrated circuit; Computer science; Artificial neural network; CMOS; Routing (electronic design automation); Electronic engineering; Time-division multiplexing; Node (physics); SIGNAL (programming language); Noise (video); Multiplication (music); Engineering; Embedded system; Integrated circuit; Artificial intelligence; Mathematics","score_opus":0.002607132546207445,"score_gpt":0.16131968498709456,"score_spread":0.1587125524408871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W282295065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13522893,0.0012189954,0.8445021,0.0006166273,0.0007551492,0.00018480675,0.00027791265,0.002586121,0.014629278],"genre_scores_gemma":[0.72105986,0.00029179075,0.26750147,0.0003117898,0.000111313384,0.00013148773,0.00013629455,0.0000360309,0.010419913],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977,0.000031802672,0.000013718056,0.000060264025,0.00010170336,0.000022617789],"domain_scores_gemma":[0.9998056,0.000043409585,0.000021973621,0.000019583538,0.00008786364,0.000021610145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003150291,0.00037063067,0.00038819114,0.00019784964,0.00039787727,0.00060218165,0.0017174599,0.000790259,0.0022137498],"category_scores_gemma":[0.00036259938,0.0003334749,0.00029807436,0.00032716888,0.00021686473,0.0006711873,0.00041929283,0.00051255437,0.00057247427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005975493,0.0004959564,0.0018319847,0.00045651794,0.00035568656,0.00043205722,0.00012947262,0.12071638,0.5263264,0.012106821,0.005403438,0.3311478],"study_design_scores_gemma":[0.000052959476,0.00037459034,0.0005819693,0.00002201599,0.00011396334,0.00019599205,0.0000093621775,0.9126003,0.080160424,0.0013375257,0.0045180065,0.000032930075],"about_ca_topic_score_codex":0.0015935305,"about_ca_topic_score_gemma":0.0060754777,"teacher_disagreement_score":0.0022137498,"about_ca_system_score_codex":0.0004487994,"about_ca_system_score_gemma":0.0004593096,"threshold_uncertainty_score":0.0074056983},"labels":[],"label_agreement":null},{"id":"W2864087850","doi":"10.5539/ijef.v10n8p141","title":"Forecasting a Mix of Temporal and Non-Temporal Economic Variables with a Mixture-of-Experts Neural Network","year":2018,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Flexibility (engineering); Artificial neural network; Computer science; Context (archaeology); Process (computing); Artificial intelligence; Genetic algorithm; Nonlinear system; Machine learning; Mathematical optimization; Mathematics; Statistics","score_opus":0.01467789278048568,"score_gpt":0.2255537460801191,"score_spread":0.21087585329963343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2864087850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15154806,0.0004539282,0.8460669,0.00022923674,0.000065193366,0.000045392277,0.00006235882,0.00017305711,0.0013559317],"genre_scores_gemma":[0.8306173,0.0002668874,0.16675757,0.000068476686,0.000050050192,0.00007696428,0.00010133004,0.000018352937,0.0020429762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991009,0.00035588193,0.00004731558,0.00021750871,0.00020115443,0.000077287616],"domain_scores_gemma":[0.9984864,0.0010249217,0.00017780837,0.00007138554,0.00018350212,0.00005584966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025652582,0.0008884857,0.0010953299,0.0009847371,0.0003389985,0.00096835184,0.0011638852,0.0013892025,0.0006593145],"category_scores_gemma":[0.005328838,0.0007612036,0.00097335683,0.0011587368,0.00046550474,0.002071284,0.0011464183,0.0013410802,0.00014693537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001483879,0.000054253793,0.002922833,0.000034165103,0.00013257308,0.00008126382,0.000063000116,0.9593976,0.0011846084,0.0047827354,0.00023873057,0.030959815],"study_design_scores_gemma":[0.0000023487937,0.000011191328,0.00018680234,0.0000016244455,0.0000070407555,0.0000064050955,0.0000044624885,0.9985222,0.00014935965,0.001036767,0.00006801151,0.0000037606997],"about_ca_topic_score_codex":0.005720033,"about_ca_topic_score_gemma":0.006330991,"teacher_disagreement_score":0.005720033,"about_ca_system_score_codex":0.00069142255,"about_ca_system_score_gemma":0.00064926496,"threshold_uncertainty_score":0.013566554},"labels":[],"label_agreement":null},{"id":"W2871883847","doi":"10.3389/fnhum.2018.00292","title":"Dynamic Neural Network Reconfiguration During the Generation and Reinstatement of Mnemonic Representations","year":2018,"lang":"en","type":"article","venue":"Frontiers in Human Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mnemonic; Control reconfiguration; Computer science; Artificial neural network; Neuroscience; Artificial intelligence; Psychology; Cognitive psychology; Embedded system","score_opus":0.019679519366623408,"score_gpt":0.27587379080859387,"score_spread":0.25619427144197043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2871883847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98413783,0.00009637497,0.013936952,0.00008389916,0.000013338913,0.000014133297,0.000057874167,0.00010087603,0.0015586622],"genre_scores_gemma":[0.9969157,0.00004241811,0.0026749324,0.000011286093,0.0000021346737,0.000011408832,0.00007994506,0.00001480483,0.00024747703],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998455,0.00003152532,0.000010485241,0.000040135546,0.000039988146,0.00003235508],"domain_scores_gemma":[0.99941325,0.00017678339,0.0001252854,0.00014143641,0.00006473529,0.00007856102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030114446,0.00018367246,0.00021259689,0.0002012642,0.00019041768,0.0006514833,0.0005020062,0.000249579,0.0013862494],"category_scores_gemma":[0.0031190135,0.00020418035,0.0002038555,0.00015830332,0.00045585787,0.0012436652,0.0008385361,0.00060434523,0.00014906659],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008237615,0.00035512605,0.061065696,0.00035325746,0.00031601082,0.000819762,0.0043891356,0.07564591,0.7053927,0.023425747,0.001376349,0.12603657],"study_design_scores_gemma":[0.00009183158,0.0012299708,0.33215195,0.000118441305,0.00020863788,0.001573642,0.0025945306,0.36976296,0.21356754,0.06852972,0.010018285,0.00015249288],"about_ca_topic_score_codex":0.0008661015,"about_ca_topic_score_gemma":0.0013470255,"teacher_disagreement_score":0.0013862494,"about_ca_system_score_codex":0.00035241072,"about_ca_system_score_gemma":0.00022694512,"threshold_uncertainty_score":0.00463748},"labels":[],"label_agreement":null},{"id":"W2883698364","doi":"10.71781/10841","title":"Deep Learning for Video Modelling","year":2017,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Deep learning; Computer science; Artificial intelligence; Data science","score_opus":0.06550884735099713,"score_gpt":0.3458475878308379,"score_spread":0.28033874047984075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883698364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035925081,0.005313339,0.97971857,0.0010945622,0.00034830658,0.000090437425,0.0012126751,0.0023777673,0.006251867],"genre_scores_gemma":[0.35109264,0.020378888,0.56210077,0.0008833863,0.000846279,0.0007849055,0.008600633,0.0012628115,0.05404967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993068,0.00018124291,0.00004640263,0.00019738269,0.00019042105,0.00007775107],"domain_scores_gemma":[0.9990729,0.00045223918,0.00009264618,0.00015392256,0.00017495787,0.00005328174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011004568,0.0014148639,0.0012591839,0.0018146071,0.0004649249,0.0029935576,0.0021792734,0.0022952603,0.012937054],"category_scores_gemma":[0.004682489,0.00088652136,0.0016761785,0.0023549562,0.0008296066,0.0029616458,0.0021648502,0.0034552896,0.005294233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022326602,0.000085807274,0.0010759017,0.0006181171,0.00015199951,0.00017978431,0.00014456006,0.48169348,0.0042397776,0.109798044,0.02381929,0.37796986],"study_design_scores_gemma":[0.000008186425,0.000015912608,0.000196738,0.00005979479,0.000010022204,0.000039948252,0.0000147857745,0.950322,0.0008479416,0.036728438,0.011741602,0.000014723271],"about_ca_topic_score_codex":0.019285662,"about_ca_topic_score_gemma":0.0150256455,"teacher_disagreement_score":0.019285662,"about_ca_system_score_codex":0.0022497345,"about_ca_system_score_gemma":0.0015223237,"threshold_uncertainty_score":0.043278694},"labels":[],"label_agreement":null},{"id":"W2884589569","doi":"10.1109/tcsii.2018.2860541","title":"Tunable Neuron With PWL Approximation Based on the Minimum Operator","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sigmoid function; Transfer function; Artificial neuron; Computer science; Artificial neural network; Operator (biology); Activation function; Piecewise linear function; Piecewise; Function (biology); Limit (mathematics); SIGNAL (programming language); Algorithm; Control theory (sociology); Mathematics; Artificial intelligence; Mathematical analysis; Engineering","score_opus":0.019689248023395813,"score_gpt":0.21881824646289194,"score_spread":0.19912899843949614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884589569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04048066,0.00049423304,0.9480574,0.0001684087,0.00011298125,0.000048306807,0.000060593546,0.0009363019,0.009641182],"genre_scores_gemma":[0.7949429,0.00034386292,0.19773374,0.00012985557,0.000031895273,0.000089939414,0.00006532171,0.00006209842,0.0066003427],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999881,0.000016620335,0.000006688149,0.000029896439,0.000047877085,0.000017839266],"domain_scores_gemma":[0.99992967,0.00001727383,0.000008312359,0.0000133176645,0.000023104558,0.000008232788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015128481,0.0002527254,0.00027322056,0.0001685158,0.0002887961,0.0004695191,0.0010484183,0.00042835885,0.0022145198],"category_scores_gemma":[0.0002647331,0.00015505539,0.00025589514,0.00024655892,0.00029086546,0.0007422133,0.0004084393,0.00051379635,0.00052250316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038931405,0.000090523106,0.0013116039,0.00045766332,0.00007510579,0.00055764854,0.00030642064,0.10302642,0.493937,0.07501313,0.003499724,0.32133538],"study_design_scores_gemma":[0.000029957866,0.00020463135,0.00044929874,0.000025757505,0.000027565886,0.00037532873,0.000023416942,0.9179312,0.065895215,0.0062210863,0.008788361,0.000028183447],"about_ca_topic_score_codex":0.0010808026,"about_ca_topic_score_gemma":0.0012971862,"teacher_disagreement_score":0.0022145198,"about_ca_system_score_codex":0.00042526127,"about_ca_system_score_gemma":0.00031149303,"threshold_uncertainty_score":0.007408321},"labels":[],"label_agreement":null},{"id":"W2885462593","doi":"10.5430/air.v7n2p1","title":"Using Monte Carlo method to estimate the behavior of neural training between balanced and unbalanced data in classification of patterns","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação Casimiro Montenegro Filho; Instituto Tecnológico de Aeronáutica","keywords":"Monte Carlo method; Computer science; Artificial neural network; Feedforward neural network; Artificial intelligence; Machine learning; Feed forward; Training (meteorology); Analogy; Algorithm; Data mining; Statistics; Mathematics; Engineering","score_opus":0.5927551417592783,"score_gpt":0.5681466352842042,"score_spread":0.024608506475074177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885462593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045581955,0.0002532367,0.9528756,0.00016316047,0.00003126801,0.000046189398,0.000050206065,0.00021792698,0.00078044814],"genre_scores_gemma":[0.7136266,0.00025191574,0.2836152,0.00020054195,0.000051025734,0.00033022955,0.00035095788,0.00016426461,0.0014092851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985696,0.0007443359,0.00006427714,0.00023393528,0.00029592897,0.000091860755],"domain_scores_gemma":[0.9760892,0.020597387,0.0009562613,0.001066339,0.0010961186,0.00019474854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049581006,0.0005640723,0.0008909536,0.0011305597,0.0006906916,0.0011647335,0.0012140909,0.0014468177,0.0015603482],"category_scores_gemma":[0.033765804,0.00055582763,0.00066375587,0.0007276422,0.0011764953,0.0020454475,0.00095211505,0.0014003329,0.00028652936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014119876,0.000042092764,0.003401812,0.00005235857,0.000069872265,0.000046947258,0.000066063636,0.96416026,0.0011494732,0.013979513,0.00031048464,0.01657992],"study_design_scores_gemma":[0.000004002398,0.000013413087,0.00024380292,0.000007680658,0.000005147156,0.000022566832,0.0000056350777,0.9950766,0.0005628829,0.003934367,0.00011730714,0.0000066561142],"about_ca_topic_score_codex":0.0050055846,"about_ca_topic_score_gemma":0.004024563,"teacher_disagreement_score":0.0050055846,"about_ca_system_score_codex":0.001123848,"about_ca_system_score_gemma":0.0009522978,"threshold_uncertainty_score":0.026221275},"labels":[],"label_agreement":null},{"id":"W2886415518","doi":"10.23919/wac.2018.8430387","title":"On Definition of Deep Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deep learning; Marketing buzz; Computer science; Artificial intelligence; World Wide Web","score_opus":0.023643107156487257,"score_gpt":0.24899330659956437,"score_spread":0.2253501994430771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886415518","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030996292,0.10666571,0.70713484,0.022701023,0.00550046,0.00014408444,0.0008562547,0.00042129756,0.1534767],"genre_scores_gemma":[0.22229885,0.12512305,0.53480476,0.03497513,0.019741764,0.0018367124,0.0020700144,0.0010889542,0.058060873],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99553776,0.0015386393,0.00037687717,0.0010875579,0.0011924079,0.0002668405],"domain_scores_gemma":[0.99698025,0.0019191937,0.00016200109,0.0002362127,0.00054782315,0.0001546033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003963906,0.0015399951,0.0012416372,0.003740447,0.0017967931,0.004742613,0.002313042,0.0028462275,0.007342927],"category_scores_gemma":[0.008845724,0.00065958884,0.0011435214,0.0040650303,0.0074465154,0.0095322095,0.003687823,0.008705745,0.0031520845],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007043811,0.000008014589,0.00006288938,0.000116489384,0.0000059576237,0.000025237852,0.000111928064,0.000593641,0.0001153373,0.9766788,0.0066724685,0.015602198],"study_design_scores_gemma":[0.00000478925,0.000017445924,0.0000935963,0.00027706477,0.000007298876,0.00011973285,0.000043985,0.0027431638,0.00023786163,0.8704615,0.1259762,0.000017325268],"about_ca_topic_score_codex":0.001556874,"about_ca_topic_score_gemma":0.00094482966,"teacher_disagreement_score":0.007342927,"about_ca_system_score_codex":0.0028439278,"about_ca_system_score_gemma":0.0017605267,"threshold_uncertainty_score":0.024564564},"labels":[],"label_agreement":null},{"id":"W2890431446","doi":"10.2139/ssrn.3244821","title":"Applying Deep Learning to Derivatives Valuation","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Valuation (finance); Deep learning; Artificial intelligence; Economics; Business; Computer science; Finance","score_opus":0.016067296459296238,"score_gpt":0.2744772459413095,"score_spread":0.2584099494820133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890431446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13142674,0.0012616185,0.84996295,0.0022407754,0.00031260855,0.000025746562,0.0001466864,0.0003721187,0.014250742],"genre_scores_gemma":[0.95665836,0.0004938844,0.034155704,0.00011952105,0.00010363551,0.0000130389135,0.00006908869,0.000053425243,0.008333342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997991,0.00007770463,0.0000121939665,0.000030513087,0.000051505103,0.000029036484],"domain_scores_gemma":[0.99887127,0.00065272243,0.000092477785,0.0001322956,0.00015912032,0.00009211338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009770064,0.0004148708,0.00054330315,0.00052592385,0.00029306812,0.0014334257,0.000549923,0.0008553674,0.004537066],"category_scores_gemma":[0.0065443013,0.00030370682,0.0002965347,0.0006454238,0.000896475,0.0022703845,0.0012089809,0.0016932084,0.0002602398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014230431,0.00013484492,0.0029557736,0.000116789946,0.00006151359,0.0001967697,0.00008893074,0.558755,0.0022696056,0.27705982,0.0037207026,0.15449792],"study_design_scores_gemma":[0.000005033529,0.000011266698,0.00018630385,0.000008779669,0.000003931221,0.000012413831,0.000006716286,0.8648638,0.00034608174,0.13392147,0.00063011143,0.000004048047],"about_ca_topic_score_codex":0.003279443,"about_ca_topic_score_gemma":0.0032975306,"teacher_disagreement_score":0.004537066,"about_ca_system_score_codex":0.00094001915,"about_ca_system_score_gemma":0.0008662757,"threshold_uncertainty_score":0.015178025},"labels":[],"label_agreement":null},{"id":"W2891927334","doi":"10.48550/arxiv.1810.10999","title":"Reversible Recurrent Neural Networks","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Recurrent neural network; Computer science; Sequence (biology); Forgetting; Encoder; Path (computing); Decoding methods; Artificial intelligence; Artificial neural network; Backpropagation; Flexibility (engineering); Algorithm","score_opus":0.05761062443858757,"score_gpt":0.18868274797848164,"score_spread":0.13107212353989406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891927334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014664104,0.0011472239,0.9695285,0.0003221388,0.0002079242,0.0000650877,0.0004381217,0.003771011,0.009855825],"genre_scores_gemma":[0.59037817,0.0021589233,0.37574238,0.0005645751,0.0002837587,0.00021634894,0.0021189456,0.00069398637,0.027842946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950385,0.000095495896,0.00003146911,0.00014100679,0.00016451612,0.00006363577],"domain_scores_gemma":[0.9992791,0.00025489728,0.00009157366,0.0001894122,0.00015454258,0.000030470763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050017587,0.0008952551,0.00081317447,0.00051867415,0.00035897628,0.0009279422,0.0019228451,0.0009773481,0.005985823],"category_scores_gemma":[0.0027623682,0.00045726533,0.0008969372,0.00064789853,0.0006062733,0.001763599,0.0009262405,0.0012950604,0.0022174171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017899269,0.00007372304,0.0010292414,0.00042864468,0.00019353742,0.00038150308,0.00011501157,0.53221536,0.028390825,0.08249307,0.01093874,0.34356135],"study_design_scores_gemma":[0.00001002488,0.000035191693,0.00019427112,0.000019560715,0.00002776442,0.00011003999,0.000011672363,0.9605305,0.009485489,0.023176322,0.0063777226,0.000021433556],"about_ca_topic_score_codex":0.004445167,"about_ca_topic_score_gemma":0.006439652,"teacher_disagreement_score":0.005985823,"about_ca_system_score_codex":0.0008128448,"about_ca_system_score_gemma":0.0008614936,"threshold_uncertainty_score":0.020024598},"labels":[],"label_agreement":null},{"id":"W2893382987","doi":"","title":"Attentional Alignment Networks.","year":2018,"lang":"en","type":"article","venue":"British Machine Vision Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence","score_opus":0.014032455483029779,"score_gpt":0.26634288016657254,"score_spread":0.25231042468354276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893382987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009885074,0.020040736,0.846368,0.0038600832,0.002109096,0.00017304592,0.005436008,0.01729845,0.09482955],"genre_scores_gemma":[0.27031285,0.0093928315,0.5197911,0.0019249795,0.0016744756,0.00055667746,0.017395742,0.0038856014,0.17506568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995449,0.000068152105,0.000024826119,0.0001789019,0.000118109434,0.00006517971],"domain_scores_gemma":[0.9993268,0.00018292139,0.000059081176,0.00019848715,0.00017092445,0.00006171745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006558901,0.0014554649,0.0010865896,0.0012793337,0.0008969564,0.0019550908,0.0025941024,0.0018284025,0.040052768],"category_scores_gemma":[0.0026464,0.00089336344,0.0006701921,0.0017538789,0.00065382774,0.003072339,0.0020582383,0.0020772556,0.020125031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020235883,0.00012042834,0.0008123795,0.00035843434,0.00014788916,0.00014055271,0.00008582152,0.029647177,0.0059173973,0.06775201,0.21108751,0.683728],"study_design_scores_gemma":[0.000082258186,0.000068582354,0.001760208,0.00014923813,0.00015603252,0.0003321803,0.000079214646,0.5243165,0.015964942,0.27979884,0.1772271,0.000064909065],"about_ca_topic_score_codex":0.006960898,"about_ca_topic_score_gemma":0.014416104,"teacher_disagreement_score":0.040052768,"about_ca_system_score_codex":0.00091298553,"about_ca_system_score_gemma":0.0008935601,"threshold_uncertainty_score":0.13398987},"labels":[],"label_agreement":null},{"id":"W2893706220","doi":"10.5555/3291125.3291126","title":"Numerical analysis near singularities in RBF networks","year":2018,"lang":"en","type":"article","venue":"DR-NTU (Nanyang Technological University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Gravitational singularity; Artificial neural network; Computer science; Radial basis function; Artificial intelligence; Mathematics; Applied mathematics; Mathematical analysis","score_opus":0.01170087366015612,"score_gpt":0.20577671627579644,"score_spread":0.19407584261564031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893706220","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36767438,0.0015311263,0.6073314,0.0016184067,0.00015052423,0.000076628006,0.00012791918,0.00048753436,0.021002091],"genre_scores_gemma":[0.97650844,0.00025159595,0.021148257,0.00007655248,0.000016416492,0.000044460598,0.000045475816,0.000050004513,0.0018587587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997907,0.000071383765,0.000012743439,0.00002684251,0.00006845434,0.000029944013],"domain_scores_gemma":[0.99875057,0.0007416302,0.00014045242,0.000060020622,0.00023164929,0.00007559871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009619683,0.00038756628,0.00053009,0.0006648166,0.0004915836,0.0006793264,0.00048563004,0.0011092066,0.0018083715],"category_scores_gemma":[0.006025586,0.0002287879,0.0005071725,0.0003458002,0.0011437066,0.0011888106,0.0007940877,0.0008126035,0.00016532725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071645736,0.000028840359,0.0017629423,0.0001077273,0.000022293796,0.00029868682,0.00017908556,0.89715767,0.004300344,0.08766042,0.00071840466,0.0076919845],"study_design_scores_gemma":[0.00000291786,0.0000046152354,0.00009411466,0.0000055660857,0.0000015345045,0.000012269815,0.00000926563,0.9938558,0.00020745775,0.0056959493,0.000107457614,0.0000030718859],"about_ca_topic_score_codex":0.003370171,"about_ca_topic_score_gemma":0.0014886502,"teacher_disagreement_score":0.003370171,"about_ca_system_score_codex":0.0006483998,"about_ca_system_score_gemma":0.0004956243,"threshold_uncertainty_score":0.006701112},"labels":[],"label_agreement":null},{"id":"W2893983273","doi":"10.3233/jifs-18343","title":"Topographic representation adds robustness to supervised learning","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Representation (politics); Deep learning; Machine learning; Internal model; Artificial neural network; Variety (cybernetics); Supervised learning","score_opus":0.03481996312498515,"score_gpt":0.29461613928130387,"score_spread":0.2597961761563187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893983273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05927365,0.00064319314,0.93064874,0.0010368584,0.00009519663,0.000044680866,0.00013243238,0.0009710883,0.007154149],"genre_scores_gemma":[0.9274334,0.0005629221,0.06859137,0.00021919822,0.00013858412,0.00006669649,0.00015000558,0.0001547658,0.0026830938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939334,0.00017480097,0.00003313208,0.00017465783,0.00017455027,0.00004948629],"domain_scores_gemma":[0.99638027,0.001698645,0.0003996074,0.0010439669,0.00037070917,0.00010687416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011768186,0.0005477087,0.0006919865,0.00056674174,0.00044079806,0.001046424,0.0011208236,0.0010512322,0.0018541922],"category_scores_gemma":[0.008627571,0.0004605646,0.0007994251,0.0003854467,0.0015866403,0.0021979085,0.0018256432,0.0013673622,0.0004594563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094275456,0.00004577195,0.0023970737,0.00020588866,0.00012050152,0.00023019436,0.00021573962,0.7940109,0.010345828,0.11403949,0.0019743429,0.07631999],"study_design_scores_gemma":[0.000009446346,0.000060336428,0.0005991741,0.000012825642,0.000017660386,0.00010017607,0.000022303946,0.90336686,0.0017488254,0.09215516,0.0018919673,0.000015144676],"about_ca_topic_score_codex":0.001879223,"about_ca_topic_score_gemma":0.0013819839,"teacher_disagreement_score":0.001879223,"about_ca_system_score_codex":0.0005354523,"about_ca_system_score_gemma":0.0006006414,"threshold_uncertainty_score":0.0062236786},"labels":[],"label_agreement":null},{"id":"W28944015","doi":"10.1002/ece3.3223","title":"Re-Configurable Hardware Based Fractal Neural Processor.","year":2006,"lang":"en","type":"article","venue":"Parallel and Distributed Computing Systems (ISCA)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Ministry of Natural Resources and Forestry","keywords":"Computer science; Fractal; Artificial neural network; Computer hardware; Computer architecture; Artificial intelligence; Mathematics","score_opus":0.012190510798905079,"score_gpt":0.22723803115566696,"score_spread":0.2150475203567619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W28944015","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22668926,0.004119792,0.6932262,0.0010187475,0.0010541488,0.0005000118,0.0012510772,0.02372965,0.048411153],"genre_scores_gemma":[0.7998885,0.0005006344,0.17750676,0.00052545953,0.00007692612,0.00024550926,0.0011034864,0.00027685415,0.019875903],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979764,0.000022332806,0.000016987407,0.000045356643,0.000079051126,0.000038579936],"domain_scores_gemma":[0.99967647,0.000068071175,0.000032562315,0.000088470595,0.000104334045,0.000030116109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002227334,0.0005268064,0.00028427743,0.0004770897,0.00022604458,0.0005525735,0.0015310374,0.00042911948,0.012859418],"category_scores_gemma":[0.0008753739,0.00015952859,0.00025245498,0.00031092812,0.00020041817,0.0008426202,0.000622744,0.0005656282,0.0021911394],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012142058,0.0002703085,0.0036015452,0.0004949197,0.0001396938,0.00069009664,0.00019396155,0.07976923,0.10308417,0.011821338,0.037281875,0.7614386],"study_design_scores_gemma":[0.00017106361,0.0008838459,0.0033106818,0.000104319275,0.000118522825,0.0011460439,0.0001362586,0.8883421,0.0427584,0.008621285,0.05432864,0.00007883734],"about_ca_topic_score_codex":0.0025622428,"about_ca_topic_score_gemma":0.005636868,"teacher_disagreement_score":0.012859418,"about_ca_system_score_codex":0.0005535933,"about_ca_system_score_gemma":0.00047675328,"threshold_uncertainty_score":0.043018997},"labels":[],"label_agreement":null},{"id":"W2894920870","doi":"10.1145/3241793.3241812","title":"FPGA Hardware Implementation and Optimization for Neural Network based Chaotic System Design","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Field-programmable gate array; Representation (politics); Artificial neural network; Software; Chaotic; Throughput; Computer hardware; Computer science; Floating point; Fixed point; Transfer function; Fixed-point arithmetic; Point (geometry); Computer engineering; Embedded system; Artificial intelligence; Algorithm; Operating system","score_opus":0.03555980588613085,"score_gpt":0.2816408104757526,"score_spread":0.2460810045896218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894920870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21742679,0.00068295235,0.74491256,0.00033546082,0.00016533682,0.00017663489,0.00017907085,0.003135768,0.032985393],"genre_scores_gemma":[0.840526,0.00018044953,0.15508851,0.000041555508,0.000014192195,0.000084383806,0.00010724848,0.00007460777,0.0038831045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998685,0.000036574624,0.000009905977,0.000013257445,0.000048023645,0.00002376581],"domain_scores_gemma":[0.9998048,0.000072257986,0.000023518185,0.000024663988,0.000067856796,0.0000070088663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021212488,0.0003128302,0.0001683568,0.0002802027,0.00017283125,0.00038162572,0.00041958952,0.00017226019,0.003981977],"category_scores_gemma":[0.0006057018,0.00016297912,0.0001461562,0.00021082113,0.0001115439,0.00025432778,0.0001247303,0.00026774278,0.0005156782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034681804,0.00010287454,0.002914017,0.0003803646,0.00007146117,0.00027996727,0.000095623516,0.596525,0.06528592,0.011349961,0.0033111225,0.31933683],"study_design_scores_gemma":[0.00006164056,0.00031769648,0.0018972241,0.000038969498,0.000033015156,0.00019272794,0.000033827946,0.94065624,0.047353446,0.0012846857,0.008114188,0.000016362716],"about_ca_topic_score_codex":0.0025601643,"about_ca_topic_score_gemma":0.004301697,"teacher_disagreement_score":0.003981977,"about_ca_system_score_codex":0.00041130077,"about_ca_system_score_gemma":0.0004716824,"threshold_uncertainty_score":0.013321042},"labels":[],"label_agreement":null},{"id":"W2895841736","doi":"10.1121/1.5067967","title":"It’s time to collaborate: What human linguists can learn from machine linguists","year":2018,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Autoencoder; Mandarin Chinese; Artificial intelligence; Unsupervised learning; SIGNAL (programming language); Cluster analysis; Tone (literature); Natural language processing; Speech recognition; Representation (politics); Supervised learning; Machine learning; Linguistics; Artificial neural network","score_opus":0.011887315904854336,"score_gpt":0.2714377665451885,"score_spread":0.25955045064033416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895841736","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025538282,0.023818739,0.052327283,0.74251366,0.00460321,0.000056775087,0.0004810533,0.00047135365,0.15018967],"genre_scores_gemma":[0.77486163,0.015430115,0.033801943,0.1449024,0.005425944,0.0003197308,0.0008751869,0.0008302841,0.023552807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9789725,0.014442894,0.0005379708,0.0024015678,0.0023005588,0.0013444248],"domain_scores_gemma":[0.94078827,0.037381213,0.002403059,0.009561535,0.004416468,0.0054494203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023513108,0.0008192722,0.0011527137,0.0033971402,0.011817486,0.018948311,0.0027664627,0.008889691,0.011006306],"category_scores_gemma":[0.07436311,0.0008411955,0.00082623947,0.0028297822,0.053497225,0.05959604,0.014567942,0.010878653,0.0049552834],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017423386,0.00016536658,0.010054522,0.00063763163,0.00015772304,0.00074667146,0.13256438,0.0011073283,0.0006699882,0.55972713,0.14081459,0.15318042],"study_design_scores_gemma":[0.000023699384,0.000028750383,0.000842812,0.00050883135,0.000015158914,0.00018611718,0.025475778,0.00062052964,0.00030104595,0.78181255,0.19012475,0.000059988466],"about_ca_topic_score_codex":0.0051293788,"about_ca_topic_score_gemma":0.005530083,"teacher_disagreement_score":0.023513108,"about_ca_system_score_codex":0.0039246967,"about_ca_system_score_gemma":0.0060079307,"threshold_uncertainty_score":0.12435067},"labels":[],"label_agreement":null},{"id":"W2896295793","doi":"10.1109/icci-cc.2018.8482067","title":"Convolutional Neural Networks: Estimating Relations in the Ising Model on Overfitting","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Overfitting; Convolutional neural network; Computer science; Artificial intelligence; Ising model; Pattern recognition (psychology); Contextual image classification; Deep learning; Pooling; Artificial neural network; Machine learning; Regularization (linguistics); Image (mathematics)","score_opus":0.029804191560334523,"score_gpt":0.2785406840356945,"score_spread":0.24873649247535995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896295793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11961914,0.0008146144,0.87634504,0.0005874567,0.000063871805,0.00006824303,0.00021607224,0.00079835346,0.0014872835],"genre_scores_gemma":[0.87322575,0.0008487822,0.12229693,0.00027689929,0.00010177564,0.00016112256,0.0006048547,0.000265355,0.0022185443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852353,0.0005798367,0.00009290021,0.00034826904,0.00031574973,0.000139748],"domain_scores_gemma":[0.9819916,0.013314813,0.0016572829,0.0014619452,0.0013212973,0.00025309026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006921551,0.0012583314,0.0015250404,0.0018403061,0.000599735,0.0013578649,0.0021103183,0.0016373269,0.0012353172],"category_scores_gemma":[0.040445548,0.00091884076,0.000926813,0.00167369,0.0015240373,0.0037547234,0.0014300703,0.0025307732,0.00026224784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013394994,0.000063752464,0.012415739,0.00012923703,0.00017933272,0.00016346332,0.00010604755,0.9167059,0.0017053556,0.024247203,0.0014694748,0.04268053],"study_design_scores_gemma":[0.0000021942146,0.000008817818,0.00053659437,0.00001235668,0.000008006945,0.000019136392,0.0000053940958,0.99103177,0.00038119723,0.007880994,0.00010720993,0.0000064369156],"about_ca_topic_score_codex":0.013867234,"about_ca_topic_score_gemma":0.010249424,"teacher_disagreement_score":0.013867234,"about_ca_system_score_codex":0.0023986138,"about_ca_system_score_gemma":0.0016251944,"threshold_uncertainty_score":0.03660506},"labels":[],"label_agreement":null},{"id":"W2896588250","doi":"10.1109/icci-cc.2018.8482017","title":"Artificial Neural Network Based Chaotic System Design for the Simulation of EEG Time Series","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Chaotic; Computer science; Artificial neural network; Lorenz system; Nonlinear system; Control theory (sociology); Series (stratigraphy); Time series; Generator (circuit theory); Artificial intelligence; Machine learning; Power (physics)","score_opus":0.040334040316385045,"score_gpt":0.2607991131696228,"score_spread":0.22046507285323774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896588250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03530014,0.00025475866,0.9581455,0.00013148479,0.00007305282,0.00014988963,0.00006618908,0.0003733659,0.005505641],"genre_scores_gemma":[0.7898847,0.0004379971,0.2028251,0.000047598463,0.000025650448,0.00079828553,0.00014771911,0.000073086245,0.0057598567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982893,0.00007818693,0.000009126401,0.00002378945,0.000049597977,0.000010264338],"domain_scores_gemma":[0.99971133,0.00015378607,0.000025407211,0.000016323324,0.000085347776,0.000007850186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047580284,0.00049598556,0.0004105001,0.00022914636,0.00026750704,0.0003593421,0.0005514843,0.0006644939,0.0018736173],"category_scores_gemma":[0.0012940674,0.00021750793,0.00043942218,0.0002023865,0.00028244362,0.00031972805,0.0003017103,0.00072659727,0.00025025618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038021593,0.000018782679,0.00026135187,0.00009226204,0.00002347237,0.00007051882,0.00005306157,0.97995573,0.005643917,0.0037532644,0.00020549244,0.009884197],"study_design_scores_gemma":[0.0000024627793,0.0000111946965,0.00003387564,0.0000025892346,0.0000021888125,0.0000036023323,0.0000018129875,0.9989543,0.0005266946,0.00021685072,0.0002431338,0.0000013716585],"about_ca_topic_score_codex":0.0032751814,"about_ca_topic_score_gemma":0.0031209579,"teacher_disagreement_score":0.0032751814,"about_ca_system_score_codex":0.00041172482,"about_ca_system_score_gemma":0.0005365517,"threshold_uncertainty_score":0.0065122247},"labels":[],"label_agreement":null},{"id":"W2896763107","doi":"10.20944/preprints201810.0494.v1","title":"Unsupervised Feature Learning in Time Series Prediction Using Continuous Deep Belief Network","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Convergence (economics); Feature (linguistics); Stability (learning theory); Process (computing); Series (stratigraphy); Machine learning; Time series; Artificial neural network; Deep belief network; Deep learning; Data mining; Pattern recognition (psychology)","score_opus":0.049110929021513657,"score_gpt":0.291537111956616,"score_spread":0.24242618293510237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896763107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03196549,0.00050180044,0.9661723,0.00013841795,0.000046877503,0.000016319414,0.000043829234,0.000351641,0.000763313],"genre_scores_gemma":[0.88821894,0.00033264857,0.10978679,0.0000897547,0.00005225484,0.00006534403,0.00014084796,0.000037354755,0.0012759923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996848,0.0000789095,0.000019677898,0.00009436163,0.00008357896,0.000038704744],"domain_scores_gemma":[0.9991436,0.0004405271,0.00009333277,0.00008577137,0.00019727933,0.00003943198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007759554,0.00062225095,0.00073010183,0.00041639485,0.00026507684,0.00077875354,0.0010456169,0.00081718457,0.00060645735],"category_scores_gemma":[0.002542823,0.0003770665,0.00044501497,0.0006205634,0.00051175745,0.0011110192,0.0009006814,0.0014667461,0.00014324443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012344246,0.00009066245,0.001734441,0.000088354216,0.000070818256,0.00008090083,0.000071560644,0.84210813,0.0053883702,0.008500724,0.0007474319,0.14099516],"study_design_scores_gemma":[0.0000020565935,0.0000070391366,0.000072579955,0.0000015196728,0.0000024673218,0.0000041648627,0.0000012308328,0.9986659,0.000336827,0.00083662284,0.00006780556,0.0000016749259],"about_ca_topic_score_codex":0.005015874,"about_ca_topic_score_gemma":0.0034719114,"teacher_disagreement_score":0.005015874,"about_ca_system_score_codex":0.00054607994,"about_ca_system_score_gemma":0.0006947367,"threshold_uncertainty_score":0.009973347},"labels":[],"label_agreement":null},{"id":"W2896803294","doi":"10.1109/ijcnn.2018.8489291","title":"Distance Correlation Autoencoder","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Distance correlation; Correlation; Artificial intelligence; Dimensionality reduction; Pattern recognition (psychology); Computer science; Curse of dimensionality; Regression; Reduction (mathematics); Mathematics; Machine learning; Artificial neural network; Statistics","score_opus":0.011103726954309412,"score_gpt":0.24356237188348104,"score_spread":0.23245864492917162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896803294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010531235,0.00027922203,0.98687184,0.00012756229,0.00004433128,0.000029857567,0.000039000475,0.00028258454,0.0017942821],"genre_scores_gemma":[0.46629497,0.00055915705,0.52278894,0.00042109384,0.00013717049,0.00018303366,0.00042727683,0.00021011787,0.008978239],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987005,0.00041561155,0.000073803,0.00028099565,0.00044126078,0.00008784281],"domain_scores_gemma":[0.9983981,0.0006896508,0.00011891372,0.0002677256,0.00047609466,0.000049548024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020058288,0.00075040513,0.0011775156,0.00055112725,0.00031757934,0.0009546316,0.0010657735,0.0011088573,0.0016769415],"category_scores_gemma":[0.0036997648,0.00040675935,0.000683237,0.00077933446,0.0007988152,0.0014709618,0.0012592209,0.001838297,0.0007035294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012564663,0.00014719086,0.0012975873,0.00015475877,0.00017827684,0.00009290704,0.00007843691,0.6691264,0.016029343,0.04730609,0.0031680395,0.26229534],"study_design_scores_gemma":[0.0000042614197,0.000028580434,0.00024287251,0.0000067732512,0.000008973383,0.00002605852,0.000004750884,0.99119604,0.003400771,0.0042016334,0.000872076,0.0000073102333],"about_ca_topic_score_codex":0.0020355491,"about_ca_topic_score_gemma":0.0025184986,"teacher_disagreement_score":0.0020355491,"about_ca_system_score_codex":0.0008953895,"about_ca_system_score_gemma":0.0010542184,"threshold_uncertainty_score":0.010607958},"labels":[],"label_agreement":null},{"id":"W2896811383","doi":"10.1109/ijcnn.2018.8489083","title":"Redundancy in Convolutional Neural Networks: Insights on Model Compression and Structure","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Redundancy (engineering); Computer science; Convolutional neural network; Pruning; Artificial intelligence; Machine learning","score_opus":0.013983403232222927,"score_gpt":0.24528660466178498,"score_spread":0.23130320142956207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896811383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4293313,0.002073722,0.55486,0.0025377187,0.00009531236,0.000074573676,0.00032946406,0.00071039604,0.009987564],"genre_scores_gemma":[0.95636487,0.00085517345,0.0408204,0.00014509658,0.00006919909,0.00004549636,0.00024389812,0.00011639817,0.0013394799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999446,0.00016516226,0.000032890584,0.00008319561,0.00021099488,0.000061830746],"domain_scores_gemma":[0.99648076,0.001919202,0.00044248614,0.00078027433,0.0002892175,0.00008799615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016410227,0.0008087476,0.0007119984,0.0012657039,0.00044050842,0.0011048107,0.0013079684,0.0010524973,0.0018332002],"category_scores_gemma":[0.012754071,0.00054501713,0.0005971014,0.00083238276,0.0011949104,0.0033535743,0.0013491131,0.0016204418,0.00027260336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026116936,0.00014547007,0.0077848225,0.00028503366,0.00014582064,0.0007016252,0.0006046335,0.67952096,0.028543478,0.17531176,0.0018167258,0.10487853],"study_design_scores_gemma":[0.000010273441,0.000083707295,0.0015680398,0.000038080154,0.00003409198,0.00018494303,0.000038417045,0.9088466,0.0051252614,0.083272666,0.0007808173,0.000017018061],"about_ca_topic_score_codex":0.001605605,"about_ca_topic_score_gemma":0.0020807844,"teacher_disagreement_score":0.0018332002,"about_ca_system_score_codex":0.0008079846,"about_ca_system_score_gemma":0.0005284988,"threshold_uncertainty_score":0.008678675},"labels":[],"label_agreement":null},{"id":"W2896818337","doi":"10.1109/ijcnn.2018.8489733","title":"Distinguishing Highly Correlated Patterns using a Context Based Aproach in Bidirectional Associative Memory","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Associative property; Noise (video); Context (archaeology); Correlation; Artificial neural network; Computer science; Cognition; Associative learning; Encoding (memory); Bidirectional associative memory; Recall; Content-addressable memory; Artificial intelligence; Pattern recognition (psychology); Cognitive psychology; Psychology; Mathematics; Neuroscience","score_opus":0.034129554258333396,"score_gpt":0.26954222640148195,"score_spread":0.23541267214314857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896818337","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90837216,0.00038478227,0.0887929,0.00005814357,0.000038374958,0.00008596652,0.000037440554,0.0001814698,0.002048779],"genre_scores_gemma":[0.9686968,0.00013937968,0.030723656,0.00002439637,0.000009216559,0.000046603913,0.000029404775,0.000007389071,0.000323088],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961406,0.00008101886,0.00004092345,0.000108118256,0.00010887971,0.000046998008],"domain_scores_gemma":[0.9990087,0.0003576081,0.00019358845,0.00024137615,0.00011185932,0.0000869105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005032267,0.0004794286,0.00039002864,0.0003763457,0.0003315036,0.00063163735,0.00054115726,0.00035934144,0.0011654173],"category_scores_gemma":[0.0023189334,0.0002151996,0.00024611832,0.00030041207,0.00054819207,0.0012447704,0.0011082871,0.00044893034,0.0001854311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022526872,0.0011363621,0.017280586,0.00046734797,0.00013409756,0.00049896183,0.0004948497,0.025035743,0.4788753,0.0077695623,0.00030478145,0.46574983],"study_design_scores_gemma":[0.00017966925,0.0060114325,0.042196453,0.00018353364,0.00046637558,0.0019039895,0.00066794263,0.4181662,0.49038768,0.03413275,0.0055122664,0.00019175495],"about_ca_topic_score_codex":0.00061208656,"about_ca_topic_score_gemma":0.0013674665,"teacher_disagreement_score":0.0011654173,"about_ca_system_score_codex":0.00017798766,"about_ca_system_score_gemma":0.00042750512,"threshold_uncertainty_score":0.0038987398},"labels":[],"label_agreement":null},{"id":"W2896855239","doi":"","title":"Système robuste de diagnostic décentralisé à l'aide d'automates cellulaires simples","year":2018,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Future Earth","funders":"","keywords":"Computer science; Computational biology; Biology","score_opus":0.02420777689303029,"score_gpt":0.23165695505381384,"score_spread":0.20744917816078356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896855239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031084983,0.0007000357,0.9621359,0.0004826478,0.00023215117,0.00010809848,0.00010802583,0.0028347555,0.002313425],"genre_scores_gemma":[0.7997908,0.00039552618,0.18402894,0.00030955684,0.00025851285,0.00029584763,0.00026295095,0.00024710206,0.014410793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979808,0.00045354792,0.000097883836,0.00068605214,0.0005434178,0.0002382847],"domain_scores_gemma":[0.9966709,0.0017956455,0.00024074332,0.00042299,0.00073278154,0.00013692818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020639256,0.0016847807,0.0029826532,0.00083783607,0.0012827807,0.0037841327,0.0015258626,0.003391683,0.0031314432],"category_scores_gemma":[0.007140513,0.0012715504,0.0012641395,0.0005136007,0.0020175339,0.0019417985,0.0020447322,0.0038063398,0.0013433511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026400595,0.0002481837,0.002134122,0.000507688,0.00042279097,0.0008169804,0.00051097583,0.68127465,0.07540267,0.03183039,0.00507618,0.19913526],"study_design_scores_gemma":[0.000107957014,0.00007662245,0.0008423066,0.000015483718,0.000020238707,0.00012140041,0.000017675087,0.9821813,0.011323211,0.0035996912,0.0016528784,0.000041336443],"about_ca_topic_score_codex":0.014440338,"about_ca_topic_score_gemma":0.008526551,"teacher_disagreement_score":0.014440338,"about_ca_system_score_codex":0.002255465,"about_ca_system_score_gemma":0.001833716,"threshold_uncertainty_score":0.02871257},"labels":[],"label_agreement":null},{"id":"W2896890299","doi":"10.1007/978-3-319-97846-8_4","title":"Classes of EDMs","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Biology; Computer science","score_opus":0.026261753675661758,"score_gpt":0.24385660202423387,"score_spread":0.21759484834857212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896890299","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008070038,0.010058378,0.37679031,0.002634644,0.0019676697,0.00012258865,0.0018400833,0.0019373465,0.59657896],"genre_scores_gemma":[0.2096446,0.013388553,0.1912485,0.0031220398,0.0029508392,0.0004340219,0.0034410465,0.001295855,0.5744746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997048,0.000035009696,0.00002144844,0.000099710895,0.000099587734,0.000039396597],"domain_scores_gemma":[0.99962604,0.00012296945,0.000022199622,0.00010908358,0.00009136814,0.000028354727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030772018,0.00069735857,0.00043751264,0.0009936653,0.00061296776,0.0017827826,0.0009792653,0.0009126954,0.04894024],"category_scores_gemma":[0.0014150498,0.00020966268,0.00058675086,0.0009719529,0.0008173987,0.0026295097,0.0013340526,0.0017694884,0.011235668],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025257335,0.000017346088,0.0000994764,0.00016419822,0.000011984283,0.000041571955,0.000056713685,0.0024503293,0.00097415375,0.75171715,0.03795919,0.20648256],"study_design_scores_gemma":[0.000009006753,0.000018490582,0.0001987216,0.00007539852,0.000008694285,0.00025368205,0.000035080862,0.00845852,0.0013567251,0.6729848,0.3165853,0.000015637612],"about_ca_topic_score_codex":0.0005208843,"about_ca_topic_score_gemma":0.00070130604,"teacher_disagreement_score":0.04894024,"about_ca_system_score_codex":0.00070518977,"about_ca_system_score_gemma":0.00045147713,"threshold_uncertainty_score":0.16372144},"labels":[],"label_agreement":null},{"id":"W2897318468","doi":"10.1109/icci-cc.2018.8482071","title":"Improving the Efficacy of Artificial Neural Network Training by Optimizing Training Data for the Simulation and Prediction of Electroencephalogram Chaotic Patterns","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Artificial neural network; Chaotic; Training (meteorology); MATLAB; Artificial intelligence; Lorenz system; Nonlinear system; Trajectory; Backpropagation; Machine learning; Pattern recognition (psychology)","score_opus":0.08449233891908499,"score_gpt":0.3018012881422351,"score_spread":0.21730894922315014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897318468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47471735,0.00073187804,0.5190684,0.0003661935,0.00009846316,0.000104298975,0.00013918757,0.0011930895,0.003581201],"genre_scores_gemma":[0.9311022,0.00018135326,0.06802418,0.00003141997,0.000009168803,0.00006812731,0.0001281762,0.0000586952,0.0003965678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996569,0.000119164295,0.000039485018,0.000060147435,0.00009608087,0.000028323982],"domain_scores_gemma":[0.99726105,0.0017967164,0.00016930392,0.00029703963,0.0004459782,0.000029932267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012991985,0.00066415325,0.00045761594,0.00038767417,0.00022825084,0.00044510554,0.00055820984,0.000629902,0.00066775794],"category_scores_gemma":[0.008454541,0.00025027743,0.00031616943,0.00030610443,0.00028544417,0.0008519353,0.00036899047,0.0006551083,0.00017448593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001466331,0.000119536664,0.0036392778,0.00014214539,0.00003896593,0.00009698838,0.00008265144,0.92073876,0.012877584,0.0007781946,0.00033689398,0.061002336],"study_design_scores_gemma":[0.00000531309,0.000042875956,0.00081785495,0.000012228728,0.000009294223,0.000014370537,0.0000097765,0.99145293,0.007194297,0.0002373201,0.00019877931,0.000004983086],"about_ca_topic_score_codex":0.0029653765,"about_ca_topic_score_gemma":0.002478375,"teacher_disagreement_score":0.0029653765,"about_ca_system_score_codex":0.00029208764,"about_ca_system_score_gemma":0.00039419945,"threshold_uncertainty_score":0.0068709254},"labels":[],"label_agreement":null},{"id":"W2897423697","doi":"10.1109/fuzz-ieee.2018.8491461","title":"Classification via Deep Fuzzy c-Means Clustering","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; MNIST database; Deep learning; Fuzzy logic; Cluster analysis; Classifier (UML); Convolutional neural network; Machine learning; Neuro-fuzzy; Extractor; Pattern recognition (psychology); Data mining; Fuzzy control system; Engineering","score_opus":0.02690076774169113,"score_gpt":0.263490884176425,"score_spread":0.23659011643473388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897423697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040628526,0.00038809015,0.9506969,0.0003514763,0.0001111793,0.00019605241,0.00059054344,0.0034238435,0.0036134622],"genre_scores_gemma":[0.30900872,0.00022170742,0.6834593,0.00018199663,0.000070663955,0.00021217015,0.0021247559,0.00021901478,0.004501746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984908,0.00014768787,0.000107713284,0.00047201244,0.00057357555,0.00020816503],"domain_scores_gemma":[0.9980574,0.0002953699,0.00016172096,0.00036046992,0.0010528138,0.00007220921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015253992,0.0011087148,0.0012165009,0.004480132,0.0013785384,0.0018455891,0.002544931,0.002441047,0.0032778427],"category_scores_gemma":[0.004754453,0.0004844158,0.0015256143,0.0026777035,0.00086516654,0.0013020675,0.001148878,0.0017445276,0.0023660238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034564908,0.00017841153,0.0027780535,0.0001077086,0.0001186278,0.000083591265,0.0001912106,0.30588067,0.0099751735,0.012555254,0.014559592,0.653226],"study_design_scores_gemma":[0.0000060464636,0.000012780859,0.0005313978,0.000010582969,0.0000057212546,0.00001880986,0.000024048406,0.9897259,0.0032342004,0.005106166,0.0013053949,0.000018956594],"about_ca_topic_score_codex":0.035380498,"about_ca_topic_score_gemma":0.030403536,"teacher_disagreement_score":0.035380498,"about_ca_system_score_codex":0.0026176274,"about_ca_system_score_gemma":0.0026264726,"threshold_uncertainty_score":0.0703491},"labels":[],"label_agreement":null},{"id":"W2897590818","doi":"","title":"Deep Neural Maps","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"MNIST database; Embedding; Computer science; Convolutional neural network; Visualization; Artificial intelligence; Representation (politics); Deep learning; Class (philosophy); Artificial neural network; Space (punctuation); Deep neural networks; Pattern recognition (psychology)","score_opus":0.06438476600813847,"score_gpt":0.1852727244961593,"score_spread":0.12088795848802084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897590818","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007300553,0.0012695118,0.9756586,0.00068953773,0.00026561238,0.000051890012,0.001301363,0.0033883299,0.010074685],"genre_scores_gemma":[0.30536518,0.0028341818,0.6660091,0.00056720135,0.0003173956,0.00029060102,0.0041222675,0.0009403969,0.019553754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993819,0.000118611286,0.00003319375,0.00016617973,0.00023972023,0.000060295348],"domain_scores_gemma":[0.99938893,0.0001888729,0.00004971914,0.00015369058,0.00016677848,0.000052012718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061808043,0.00097644527,0.0006648619,0.0013306235,0.0004885617,0.002507737,0.0015099151,0.0008611792,0.007930443],"category_scores_gemma":[0.0032076247,0.0004116944,0.0008877686,0.0013363151,0.0007528745,0.0033467251,0.0020936162,0.0016341973,0.0021991082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001482979,0.00008042336,0.0019199459,0.0003734436,0.00013730665,0.0002010489,0.0002639919,0.1475129,0.009019509,0.33416143,0.036454484,0.4697272],"study_design_scores_gemma":[0.000019074661,0.00005318976,0.0006626657,0.00007669566,0.000023621727,0.00020373099,0.00006289367,0.64748955,0.0074419146,0.27171946,0.07220714,0.00003998619],"about_ca_topic_score_codex":0.003226647,"about_ca_topic_score_gemma":0.0033102839,"teacher_disagreement_score":0.007930443,"about_ca_system_score_codex":0.00092507724,"about_ca_system_score_gemma":0.0008976635,"threshold_uncertainty_score":0.026529908},"labels":[],"label_agreement":null},{"id":"W2897790048","doi":"10.1109/ijcnn.2018.8489203","title":"Is There a Purpose to Network Redundancy?","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Redundancy (engineering); Computer science; Artificial neural network; Triple modular redundancy; Artificial intelligence; Content-addressable memory; Segmentation","score_opus":0.021834068743997927,"score_gpt":0.2750457551960809,"score_spread":0.253211686452083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897790048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16847278,0.024383977,0.60731196,0.12768932,0.0030261404,0.00027748273,0.0005583676,0.0026450218,0.065634914],"genre_scores_gemma":[0.9017599,0.006651976,0.07460336,0.007392956,0.0014320354,0.00025655326,0.00021393817,0.0003459478,0.00734329],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965463,0.0015455629,0.00019225056,0.00042384767,0.0009440464,0.00034792896],"domain_scores_gemma":[0.98198503,0.008855691,0.0017124544,0.0042818403,0.0026552852,0.0005096203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065429835,0.00091218424,0.0014181195,0.0010513759,0.00088478933,0.0025003762,0.0023583993,0.0023898259,0.004493416],"category_scores_gemma":[0.029418495,0.0004554101,0.0008179581,0.0008029049,0.005147163,0.010249608,0.001994627,0.0018940675,0.001210694],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008568642,0.00025682314,0.011561182,0.0024492,0.0007580096,0.0007233196,0.0015752789,0.037834607,0.017722268,0.5123749,0.023550581,0.39033696],"study_design_scores_gemma":[0.00021820355,0.00091457326,0.007824126,0.001213611,0.0005986857,0.0021706524,0.0012871771,0.0905187,0.018955568,0.7787876,0.097353175,0.00015786705],"about_ca_topic_score_codex":0.0007622387,"about_ca_topic_score_gemma":0.00086391275,"teacher_disagreement_score":0.0065429835,"about_ca_system_score_codex":0.0013926949,"about_ca_system_score_gemma":0.0011489765,"threshold_uncertainty_score":0.034603},"labels":[],"label_agreement":null},{"id":"W2898508560","doi":"10.1090/conm/717/14455","title":"Random phase infinite coherent states: Construction and dynamics","year":2018,"lang":"en","type":"other","venue":"Contemporary mathematics - American Mathematical Society","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Dynamics (music); Statistical physics; Phase (matter); Coherent states; Pure mathematics; Algebra over a field; Quantum mechanics; Physics","score_opus":0.019887986840458276,"score_gpt":0.28076447119618964,"score_spread":0.26087648435573135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898508560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39655092,0.00054293394,0.572661,0.0008749545,0.00011173535,0.0000708923,0.0001657849,0.0002990856,0.028722677],"genre_scores_gemma":[0.9520336,0.00020988715,0.040778182,0.00014741163,0.000043639586,0.000109860055,0.000092502974,0.00009008343,0.006494803],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99969566,0.00009287073,0.000011342102,0.000052529944,0.000091492315,0.00005616132],"domain_scores_gemma":[0.9995024,0.00016632804,0.000090115806,0.00007829039,0.00007233637,0.000090506306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056751986,0.0003071832,0.00047423595,0.00073600863,0.00052496,0.0012711642,0.0010490768,0.00076662586,0.0027891994],"category_scores_gemma":[0.0016356767,0.00030228237,0.00049928227,0.00044652348,0.0020078334,0.0021499794,0.0017887818,0.0011418859,0.00028940025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020866899,0.000030879422,0.00026515423,0.000024744351,0.000011658634,0.000086502274,0.00009136165,0.022571616,0.0039739986,0.9705423,0.00026851043,0.0021124354],"study_design_scores_gemma":[0.000027338883,0.000059791277,0.00039061927,0.000021201457,0.00001310871,0.00016457701,0.00007041541,0.57637703,0.002777934,0.41841343,0.0016475896,0.00003691367],"about_ca_topic_score_codex":0.0008901762,"about_ca_topic_score_gemma":0.0005753576,"teacher_disagreement_score":0.0027891994,"about_ca_system_score_codex":0.00081210426,"about_ca_system_score_gemma":0.0006254978,"threshold_uncertainty_score":0.009330809},"labels":[],"label_agreement":null},{"id":"W2898902424","doi":"","title":"EcoRNN: Efficient Computing of LSTM RNN on GPUs","year":2018,"lang":"en","type":"article","venue":"International Symposium on Microarchitecture","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Recurrent neural network; Parallel computing; Artificial intelligence; Artificial neural network","score_opus":0.007741681398862896,"score_gpt":0.25308549970870126,"score_spread":0.24534381830983837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898902424","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16699705,0.0020305987,0.7536202,0.0006906659,0.0010091059,0.00020991066,0.002146091,0.046110038,0.027186327],"genre_scores_gemma":[0.51561165,0.0004284034,0.4622155,0.0003610859,0.00013136989,0.00023089338,0.002966946,0.002050038,0.016004167],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997702,0.000034234057,0.000015799722,0.00006324972,0.00006822672,0.00004827343],"domain_scores_gemma":[0.9996295,0.00009034965,0.000017301316,0.000078351884,0.00014419515,0.000040332707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003301461,0.00085694436,0.00047526453,0.00049137254,0.0004677835,0.0009060653,0.0016604863,0.0005760597,0.012128096],"category_scores_gemma":[0.0015002362,0.00042924305,0.00042955618,0.0007439258,0.00026250898,0.0011519459,0.0007809291,0.0008260262,0.0023835122],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014534806,0.00023707208,0.004264928,0.00063772965,0.0003031484,0.00055385754,0.00028510194,0.22566997,0.054196306,0.016371513,0.08234289,0.61368394],"study_design_scores_gemma":[0.00006591736,0.000070293376,0.00053606334,0.000015608755,0.000024055722,0.000043198008,0.00003716738,0.9733218,0.014442095,0.0034724218,0.007955164,0.000016254671],"about_ca_topic_score_codex":0.013246223,"about_ca_topic_score_gemma":0.0333099,"teacher_disagreement_score":0.013246223,"about_ca_system_score_codex":0.0008394155,"about_ca_system_score_gemma":0.001322334,"threshold_uncertainty_score":0.040572464},"labels":[],"label_agreement":null},{"id":"W2899051854","doi":"10.1115/detc2018-85895","title":"Knowledge-Based Artificial Neural Network (KB-ANN) in Engineering: Associating Functional Architecture Modeling, Dimensional Analysis and Causal Graphs to Produce Optimized Topologies for KB-ANNs","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Network topology; Artificial neural network; Computer science; Artificial intelligence; Topology (electrical circuits); Curse of dimensionality; Convergence (economics); Machine learning; Engineering","score_opus":0.029635218637862143,"score_gpt":0.27053859895002974,"score_spread":0.2409033803121676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899051854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02589837,0.00032040404,0.9702388,0.000098554585,0.00002262738,0.000033435266,0.00003748377,0.00026370573,0.0030866808],"genre_scores_gemma":[0.5922894,0.0007327473,0.4044506,0.00009159804,0.000021761984,0.00020037442,0.00018242159,0.00008082875,0.0019502266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997576,0.00008392868,0.000021537056,0.00005401038,0.00006515399,0.000017664277],"domain_scores_gemma":[0.9992493,0.00047393655,0.0000700394,0.000075872485,0.00011864611,0.000012215734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005979637,0.0005793441,0.00039810166,0.0005079224,0.0002734465,0.0007460123,0.00074509607,0.0007140032,0.0010722746],"category_scores_gemma":[0.002310792,0.00036930505,0.0004441612,0.00051491044,0.0004423673,0.0011230203,0.0005343041,0.0006886182,0.0002888812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019366844,0.000026765907,0.00050651276,0.00009646377,0.000026975937,0.00005692481,0.000054478754,0.9102914,0.0036980992,0.009449649,0.00022002477,0.07555337],"study_design_scores_gemma":[0.000002177033,0.000025133088,0.00017703391,0.000018507957,0.000010964922,0.00003155536,0.0000144103815,0.9891366,0.0018390593,0.007937118,0.00080154097,0.0000060248462],"about_ca_topic_score_codex":0.0015383243,"about_ca_topic_score_gemma":0.0023300124,"teacher_disagreement_score":0.0015383243,"about_ca_system_score_codex":0.00045270383,"about_ca_system_score_gemma":0.00039607572,"threshold_uncertainty_score":0.0035871863},"labels":[],"label_agreement":null},{"id":"W2899060716","doi":"10.1016/j.ijar.2018.10.018","title":"FANCFIS: Fast adaptive neuro-complex fuzzy inference system","year":2018,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Univariate; Algorithm; Time series; Fuzzy logic; Data mining; Adaptive neuro fuzzy inference system; Gradient descent; Leverage (statistics); Artificial intelligence; Fuzzy control system; Machine learning; Multivariate statistics; Artificial neural network","score_opus":0.026792490004023197,"score_gpt":0.2894996418684409,"score_spread":0.26270715186441773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899060716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006045513,0.00049898436,0.9839934,0.00012837682,0.00021919026,0.00009407703,0.00034670965,0.0056577343,0.0030159089],"genre_scores_gemma":[0.34828132,0.00044175418,0.6408314,0.00024531333,0.000097458054,0.0003221972,0.0009355433,0.00030633085,0.008538728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971086,0.000043130574,0.000019208452,0.00007030141,0.00012590611,0.00003063851],"domain_scores_gemma":[0.99947256,0.00016585643,0.00002265989,0.000059333055,0.0002585617,0.000021057764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008761395,0.00079499284,0.00091968855,0.00070171314,0.000605183,0.0008394911,0.0014995463,0.0010083551,0.008748671],"category_scores_gemma":[0.0020156617,0.00030490532,0.0006003564,0.00057874835,0.00026221108,0.0007370049,0.0007436469,0.0012923465,0.0014192266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006481058,0.00011107363,0.0011092147,0.00032984593,0.00013466892,0.00021314599,0.00009558075,0.21965897,0.010351321,0.012412816,0.01578765,0.73914766],"study_design_scores_gemma":[0.000028350527,0.000045771598,0.00029057168,0.000017105262,0.000018914847,0.000073888914,0.000007083172,0.9881323,0.0036923517,0.0026265287,0.005054515,0.000012752883],"about_ca_topic_score_codex":0.020417783,"about_ca_topic_score_gemma":0.018992338,"teacher_disagreement_score":0.020417783,"about_ca_system_score_codex":0.00072709564,"about_ca_system_score_gemma":0.0011351169,"threshold_uncertainty_score":0.040597856},"labels":[],"label_agreement":null},{"id":"W2901504805","doi":"10.1007/978-3-642-22673-1","title":"Intelligent Computer Mathematics","year":2011,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Alberta","funders":"","keywords":"Computer science; Mathematics education; Calculus (dental); Mathematics; Medicine","score_opus":0.028485496172642105,"score_gpt":0.2559860838831774,"score_spread":0.2275005877105353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901504805","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018248974,0.01792709,0.06617748,0.0034840489,0.001924547,0.00006187311,0.0003646799,0.0007339402,0.9075014],"genre_scores_gemma":[0.03843504,0.01710052,0.03065282,0.00088141306,0.0018722892,0.000115143754,0.0007887531,0.0004091055,0.9097449],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996903,0.000040545543,0.0000134146285,0.00005994672,0.0001742995,0.00002147785],"domain_scores_gemma":[0.9997658,0.0000690298,0.00001281192,0.00008005611,0.00005418593,0.00001814162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027758096,0.00077464624,0.0006892315,0.0012237652,0.00073820027,0.0022369663,0.00064001954,0.0006240838,0.07560998],"category_scores_gemma":[0.00092605123,0.00032948234,0.00043046597,0.0011685197,0.0010699154,0.0028246993,0.0012367165,0.0017210516,0.033775948],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016023581,0.00003215635,0.00013447393,0.00023922167,0.000012884792,0.00003782972,0.00013837458,0.0013490103,0.0011016838,0.5043192,0.20620479,0.28641438],"study_design_scores_gemma":[0.0000053103604,0.000014729312,0.00025678493,0.000075950666,0.0000075384014,0.000109510474,0.000029390952,0.0024414223,0.00069449126,0.25187963,0.7444793,0.000006001265],"about_ca_topic_score_codex":0.0004602313,"about_ca_topic_score_gemma":0.0008895133,"teacher_disagreement_score":0.07560998,"about_ca_system_score_codex":0.001038263,"about_ca_system_score_gemma":0.0007114937,"threshold_uncertainty_score":0.2529406},"labels":[],"label_agreement":null},{"id":"W2903389744","doi":"10.1109/icpr.2018.8546120","title":"Prediction-based classification using learning on Riemannian manifolds","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Riemannian manifold; Artificial intelligence; Estimator; Pairwise comparison; Mathematics; Random forest; Pattern recognition (psychology); Computer science; Feature vector; Manifold (fluid mechanics); Positive-definite matrix; Algorithm; Machine learning; Statistics; Pure mathematics","score_opus":0.06601438344415028,"score_gpt":0.28224532225090404,"score_spread":0.21623093880675376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903389744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020100228,0.00048205984,0.97637993,0.00026092472,0.00005926133,0.00006124145,0.00012472567,0.0017836759,0.000747932],"genre_scores_gemma":[0.5795379,0.00056195137,0.41497937,0.0002785247,0.00021564674,0.00026185444,0.001315049,0.00027224643,0.002577457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825007,0.0005786409,0.0001556173,0.0004815753,0.0003965759,0.00013743108],"domain_scores_gemma":[0.9972133,0.001124599,0.00030838427,0.0005910944,0.0006389667,0.0001237099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022155198,0.0013491863,0.0019663076,0.0015480159,0.00067360926,0.0012273308,0.0020372528,0.0013553675,0.001540512],"category_scores_gemma":[0.006773648,0.0005974285,0.0016345389,0.0013006662,0.0009157381,0.002303198,0.0023529092,0.0020298536,0.0013327231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001641935,0.000108156826,0.0039148405,0.00015315787,0.00022675758,0.00015513565,0.00019116976,0.49148396,0.004415585,0.017403392,0.006847329,0.47493628],"study_design_scores_gemma":[0.000004634741,0.000028069213,0.00022835859,0.000007824292,0.000007316086,0.000021771963,0.000011015295,0.98647076,0.00068511796,0.012053374,0.00047338373,0.000008313187],"about_ca_topic_score_codex":0.0041578566,"about_ca_topic_score_gemma":0.0026060804,"teacher_disagreement_score":0.0041578566,"about_ca_system_score_codex":0.0011825608,"about_ca_system_score_gemma":0.00089046004,"threshold_uncertainty_score":0.011716902},"labels":[],"label_agreement":null},{"id":"W2903485739","doi":"","title":"Deep Learning Vector Quantization.","year":2016,"lang":"en","type":"article","venue":"The European Symposium on Artificial Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Vector quantization; Learning vector quantization; Computer science; Artificial intelligence; Quantization (signal processing); Linde–Buzo–Gray algorithm; Deep learning; Algorithm","score_opus":0.018081511643142,"score_gpt":0.2302442929815998,"score_spread":0.21216278133845778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903485739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011548094,0.031670716,0.9051666,0.0044251657,0.0033963236,0.00012566679,0.003419992,0.0055355313,0.034711875],"genre_scores_gemma":[0.3841361,0.014836287,0.4780137,0.0020510005,0.0014439401,0.00029846007,0.015536767,0.0011984471,0.10248528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945873,0.00010197603,0.000050890245,0.0001077252,0.00022261932,0.00005808934],"domain_scores_gemma":[0.99925214,0.00015235595,0.000044644836,0.00018746396,0.0003132172,0.00005016128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008707643,0.000746635,0.0010545132,0.00075444666,0.00045463824,0.0014593023,0.0015905284,0.0011114221,0.010969107],"category_scores_gemma":[0.002910819,0.00050596,0.0005055549,0.0013721596,0.0006979164,0.0021032167,0.0013620632,0.0026270545,0.004983303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017788901,0.00009978107,0.000697166,0.00032288858,0.000098059085,0.000051441522,0.000060617553,0.049244367,0.0037732746,0.08609855,0.15103754,0.70833844],"study_design_scores_gemma":[0.000045955327,0.000076572665,0.00074341777,0.00014799205,0.000045930523,0.0001053709,0.000042951164,0.72524947,0.01010133,0.18214871,0.08125332,0.00003904249],"about_ca_topic_score_codex":0.0067365547,"about_ca_topic_score_gemma":0.014479881,"teacher_disagreement_score":0.010969107,"about_ca_system_score_codex":0.00094402756,"about_ca_system_score_gemma":0.0010975777,"threshold_uncertainty_score":0.0366953},"labels":[],"label_agreement":null},{"id":"W2903996579","doi":"10.48550/arxiv.1812.05159","title":"An Empirical Study of Example Forgetting during Deep Neural Network Learning","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Forgetting; Computer science; Artificial neural network; Benchmark (surveying); Generalization; Artificial intelligence; Phenomenon; Set (abstract data type); Machine learning; Training set; Deep learning; Event (particle physics); Mathematics; Cognitive psychology; Psychology","score_opus":0.0691104631876883,"score_gpt":0.22241555740709432,"score_spread":0.15330509421940602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903996579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9787982,0.0011741327,0.018174144,0.00046990346,0.00003741523,0.000042988348,0.00040472954,0.0002282616,0.0006700503],"genre_scores_gemma":[0.99550724,0.00019125504,0.0031366767,0.000056709297,0.000022296343,0.00002190011,0.0007339275,0.000034530745,0.0002954004],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980958,0.00074040995,0.00019254618,0.00043243702,0.00038172642,0.00015713244],"domain_scores_gemma":[0.9061932,0.0711871,0.007857071,0.010033217,0.0034823788,0.0012469813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067425757,0.0005157595,0.00060705503,0.0008512658,0.0005424755,0.00075020426,0.001398266,0.0010019056,0.0013575061],"category_scores_gemma":[0.10421905,0.00039079477,0.0004157848,0.000981401,0.0016697397,0.002825344,0.0011464461,0.0022699956,0.00022084481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024779628,0.001277121,0.51210207,0.0009697301,0.0007527121,0.0014819215,0.0028914951,0.21367717,0.017271306,0.011776599,0.009716306,0.22560565],"study_design_scores_gemma":[0.00010376293,0.001125659,0.19655183,0.00022112778,0.00017182848,0.0024160095,0.0009404067,0.7549722,0.015166814,0.024386654,0.003808938,0.00013474989],"about_ca_topic_score_codex":0.0025342216,"about_ca_topic_score_gemma":0.00328735,"teacher_disagreement_score":0.0067425757,"about_ca_system_score_codex":0.00068905577,"about_ca_system_score_gemma":0.00039467213,"threshold_uncertainty_score":0.035658598},"labels":[],"label_agreement":null},{"id":"W2904474124","doi":"10.1016/j.procs.2018.11.102","title":"Generating Cognitive Context with Feature-Extracting Bidirectional Associative Memory","year":2018,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Content-addressable memory; Associative property; Feature (linguistics); Context (archaeology); Limiting; Recall; Cognition; Pattern recognition (psychology); Artificial intelligence; Bidirectional associative memory; Machine learning; Artificial neural network; Cognitive psychology","score_opus":0.01576960875963367,"score_gpt":0.26103662526969895,"score_spread":0.24526701651006527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904474124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5028936,0.00037555394,0.4908766,0.00014069673,0.00013640971,0.00013714984,0.00018539958,0.001253434,0.004001227],"genre_scores_gemma":[0.931694,0.00008457997,0.06730014,0.00005263051,0.000015229803,0.00005722934,0.00009786067,0.000021410266,0.0006770945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998654,0.000021334556,0.000011393283,0.00004720827,0.0000319756,0.000022664626],"domain_scores_gemma":[0.99958175,0.00013302325,0.000057609097,0.00010023069,0.000095166484,0.000032173954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033010173,0.0005403988,0.00035317554,0.00036647686,0.00025094431,0.0004272342,0.00057690695,0.0003410683,0.001210065],"category_scores_gemma":[0.0016258113,0.00014777885,0.00028115508,0.00032861842,0.00029502687,0.0009782931,0.00091841543,0.000462726,0.00024268316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067095936,0.00037359263,0.0072367853,0.00029666445,0.00012616359,0.0004163726,0.00022822866,0.040003233,0.26619548,0.013637566,0.0013130171,0.66950184],"study_design_scores_gemma":[0.00006747398,0.00085168914,0.006267738,0.000034646946,0.00019808172,0.0006480746,0.00015952133,0.731407,0.22576784,0.029786766,0.0047391932,0.00007194661],"about_ca_topic_score_codex":0.00064164476,"about_ca_topic_score_gemma":0.0010967447,"teacher_disagreement_score":0.001210065,"about_ca_system_score_codex":0.00015610718,"about_ca_system_score_gemma":0.0003576606,"threshold_uncertainty_score":0.0040480494},"labels":[],"label_agreement":null},{"id":"W2904658698","doi":"10.1109/lsc.2018.8572090","title":"Fitting Rank Order Data in the Age of Context","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Context (archaeology); Computer science; Rank (graph theory); Order (exchange); Data mining; Mathematics; Geology; Combinatorics","score_opus":0.06113187460889822,"score_gpt":0.309408479625866,"score_spread":0.24827660501696777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904658698","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11666361,0.002166608,0.87475514,0.001691087,0.00014498629,0.00008506762,0.0010064302,0.0014281439,0.0020589214],"genre_scores_gemma":[0.7453064,0.0016123374,0.24741514,0.000628277,0.00037512163,0.0002285015,0.0021634079,0.00033915183,0.0019317332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99509746,0.0024454433,0.00022665279,0.0010045005,0.0009716052,0.00025421122],"domain_scores_gemma":[0.97076595,0.018713623,0.0026603423,0.0054817838,0.0018974388,0.0004808071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073640794,0.0011347353,0.0016476882,0.0019432517,0.00082673493,0.0030949486,0.0016989504,0.0016746416,0.0015697724],"category_scores_gemma":[0.059752088,0.00048447258,0.0009139678,0.0023727145,0.001817642,0.0049890843,0.0024194885,0.0040156716,0.0009125094],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067749195,0.00028814466,0.06247795,0.0007668088,0.00032807785,0.00084818085,0.001074052,0.4788835,0.006254806,0.11733591,0.011025254,0.32003987],"study_design_scores_gemma":[0.000023546972,0.0001529032,0.0067620897,0.00008460834,0.000037345246,0.00035760383,0.0002899027,0.7886607,0.0027948902,0.1925449,0.008211409,0.000079974736],"about_ca_topic_score_codex":0.0028140577,"about_ca_topic_score_gemma":0.0034634813,"teacher_disagreement_score":0.0073640794,"about_ca_system_score_codex":0.0009028539,"about_ca_system_score_gemma":0.0012158575,"threshold_uncertainty_score":0.038945436},"labels":[],"label_agreement":null},{"id":"W2905297550","doi":"10.1109/csci.2017.58","title":"Multilayer Artificial Neural Network Design and Architecture Optimization for the Pattern Recognition and Prediction of EEG Signals Based on Hénon Map Chaotic System","year":2017,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Chaotic; Computer science; Artificial neural network; Artificial intelligence; Autoregressive model; Mean squared error; Pattern recognition (psychology); Nonlinear system; Machine learning; Mathematics","score_opus":0.05725985216562393,"score_gpt":0.24822461431294565,"score_spread":0.19096476214732172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905297550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113064736,0.0008539635,0.8801547,0.00016749678,0.00005195805,0.00007581837,0.000035407924,0.00046308598,0.005132746],"genre_scores_gemma":[0.87178916,0.0004077738,0.1251079,0.000043455446,0.000019444526,0.00015449137,0.000056258163,0.000033086475,0.0023884603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978656,0.000060684193,0.000014578685,0.00004390914,0.00006911383,0.000025235466],"domain_scores_gemma":[0.9997372,0.00009419826,0.00004348447,0.000017647364,0.00009751793,0.000009988626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053081504,0.00049911457,0.00042457852,0.00036436386,0.0002890776,0.00042794526,0.00045610344,0.00053918944,0.00074090087],"category_scores_gemma":[0.00090789306,0.00029180152,0.00045210423,0.00025857677,0.00022788528,0.00046938818,0.00028376997,0.00037154145,0.00016049515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005814813,0.000032750428,0.0008474845,0.00006927899,0.000048328886,0.000060360322,0.00004545178,0.9357176,0.011446007,0.0016820352,0.0002849079,0.04970765],"study_design_scores_gemma":[0.0000022671231,0.000027020615,0.00021918355,0.000003366683,0.000007944721,0.000009062709,0.0000022958784,0.9982002,0.0010778058,0.00026939117,0.00017841365,0.0000030508206],"about_ca_topic_score_codex":0.0033469098,"about_ca_topic_score_gemma":0.0030196744,"teacher_disagreement_score":0.0033469098,"about_ca_system_score_codex":0.00050268276,"about_ca_system_score_gemma":0.00047124084,"threshold_uncertainty_score":0.0066548586},"labels":[],"label_agreement":null},{"id":"W2906056877","doi":"","title":"Filtering Strategies for Robust Myoelectric Pattern Classification","year":2008,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology)","score_opus":0.0687814661268746,"score_gpt":0.2545382510412826,"score_spread":0.185756784914408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906056877","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007838914,0.00076716015,0.98978287,0.00011234633,0.00006410343,0.000023204284,0.00004983475,0.0002677316,0.0010938612],"genre_scores_gemma":[0.3443092,0.0017416588,0.6390287,0.0003997858,0.00030152904,0.00023785446,0.00064215006,0.00030221997,0.013036823],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999629,0.00006643233,0.000037662165,0.00010131044,0.00011573624,0.000049908398],"domain_scores_gemma":[0.9992919,0.00037675435,0.00005854672,0.00007928649,0.0001690879,0.000024519635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008691218,0.0008177131,0.0008573658,0.0008427596,0.00039165214,0.0011543487,0.0008665613,0.0010841215,0.0034049565],"category_scores_gemma":[0.002379217,0.0004376966,0.00079541566,0.00077089126,0.00037344408,0.0009292123,0.00075878756,0.00079658156,0.0015217084],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025963326,0.00009977351,0.00046561475,0.00014015946,0.000102390026,0.00006869659,0.00006358699,0.042899504,0.046956006,0.010270488,0.0028554779,0.89581877],"study_design_scores_gemma":[0.000030616226,0.00013308434,0.0013698189,0.000030401385,0.00007810898,0.0001564727,0.00002986234,0.96338475,0.018901596,0.012089578,0.003769818,0.000025977655],"about_ca_topic_score_codex":0.0024271854,"about_ca_topic_score_gemma":0.0030794851,"teacher_disagreement_score":0.0034049565,"about_ca_system_score_codex":0.0002972701,"about_ca_system_score_gemma":0.00051531213,"threshold_uncertainty_score":0.011390686},"labels":[],"label_agreement":null},{"id":"W2906568950","doi":"10.15353/jcvis.v4i1.326","title":"ConvART: Improving Adaptive Resonance Theory for Unsupervised Image Clustering","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adaptive resonance theory; Cluster analysis; Computer science; Artificial intelligence; Benchmark (surveying); Image (mathematics); Unsupervised learning; Pattern recognition (psychology); Convolutional neural network; Spectral clustering; Distortion (music); Stability (learning theory); Machine learning; Artificial neural network","score_opus":0.012384101255200234,"score_gpt":0.2747517400077667,"score_spread":0.26236763875256647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906568950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030100904,0.00018888697,0.99382234,0.000085204236,0.000039593615,0.000033393164,0.000059512382,0.0014246386,0.0013363935],"genre_scores_gemma":[0.14301553,0.00048037225,0.846618,0.00041782137,0.00017597272,0.0002516518,0.000824109,0.0010983845,0.0071181464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902713,0.00023646976,0.00003554135,0.0002554474,0.00037714644,0.000068309615],"domain_scores_gemma":[0.99869555,0.00045971232,0.00013359357,0.0002737523,0.0003774767,0.000059956332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015377552,0.0013700585,0.0010504014,0.0020227202,0.00072418747,0.0011610183,0.0028466904,0.0016601842,0.0032451488],"category_scores_gemma":[0.004024928,0.0006272144,0.0012855608,0.0013654402,0.0010832379,0.0021862607,0.0018399863,0.0018042781,0.0025050915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001728011,0.00015151763,0.00079543097,0.0002201944,0.00018680957,0.00014330141,0.00018772575,0.4923747,0.023492603,0.037551966,0.0154323885,0.42929065],"study_design_scores_gemma":[0.000006209015,0.0000191394,0.00010120369,0.000005753303,0.0000070939413,0.000028411101,0.000009152897,0.98727137,0.0026650464,0.007854191,0.0020221297,0.00001022388],"about_ca_topic_score_codex":0.0060249693,"about_ca_topic_score_gemma":0.007360487,"teacher_disagreement_score":0.0060249693,"about_ca_system_score_codex":0.000999544,"about_ca_system_score_gemma":0.00084933825,"threshold_uncertainty_score":0.011979759},"labels":[],"label_agreement":null},{"id":"W2907134525","doi":"","title":"Gradient-based Optimization of Neural Network Architecture.","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Computer science; Artificial neural network; Architecture; Artificial intelligence","score_opus":0.03574961797958973,"score_gpt":0.31452632339352865,"score_spread":0.2787767054139389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907134525","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017386151,0.0013247592,0.9720078,0.0007498192,0.00021369499,0.00008136482,0.00013000648,0.00077929423,0.007327026],"genre_scores_gemma":[0.612082,0.00083925424,0.37046456,0.0004207963,0.00017234319,0.00039188634,0.0005383296,0.00064136577,0.014449439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999706,0.00013518294,0.000013501112,0.00004364335,0.00006448691,0.00003719027],"domain_scores_gemma":[0.9991516,0.0004738728,0.00005897878,0.000058319125,0.00021468909,0.00004263277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012906631,0.0010167307,0.0008961043,0.00054532394,0.00032563973,0.0007387256,0.001150145,0.0015312517,0.004199507],"category_scores_gemma":[0.0062371404,0.0006798024,0.0003764261,0.00065362523,0.0005978571,0.0014458961,0.0008597854,0.0016882296,0.0011406438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102434096,0.000049102786,0.00039998768,0.00010204282,0.000050785457,0.000031057647,0.000037376027,0.90696746,0.0011317447,0.014057302,0.0076643983,0.069406375],"study_design_scores_gemma":[0.000007719022,0.000010657361,0.000037985516,0.000006628567,0.0000037014695,0.00000443634,0.000003092962,0.99510926,0.00017179371,0.0042914124,0.00035161548,0.0000016394187],"about_ca_topic_score_codex":0.0054463395,"about_ca_topic_score_gemma":0.007393405,"teacher_disagreement_score":0.0054463395,"about_ca_system_score_codex":0.00090118445,"about_ca_system_score_gemma":0.0012588899,"threshold_uncertainty_score":0.014048755},"labels":[],"label_agreement":null},{"id":"W2907153564","doi":"10.1109/access.2018.2890297","title":"Recurrent Neural Networks With Finite Memory Length","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China; Beijing Advanced Innovation Center for Big Data and Brain Computing; State Key Laboratory of Software Development Environment; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Recurrent neural network; Computer science; Heuristics; Intuition; Artificial intelligence; Artificial neural network; Stacking; Theoretical computer science; Convolutional neural network; Deep learning; Cognitive science","score_opus":0.020777434425578823,"score_gpt":0.2680468649198022,"score_spread":0.24726943049422334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907153564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0436078,0.0011908525,0.94785416,0.00033772222,0.00006150493,0.000023199556,0.00013745067,0.001410759,0.0053765406],"genre_scores_gemma":[0.8682209,0.0010399913,0.12565753,0.00018593382,0.000087647015,0.00008688162,0.00027716532,0.0001476444,0.0042963326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995485,0.00011672693,0.000034598987,0.0001271048,0.000103287384,0.00006973714],"domain_scores_gemma":[0.99882776,0.00062059675,0.00016160056,0.00022779728,0.00012441877,0.000037782833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008421949,0.00061288604,0.00061104936,0.00030559357,0.0002852513,0.0009874532,0.0010806266,0.00093044154,0.0016968247],"category_scores_gemma":[0.0043335757,0.00045070442,0.0005149019,0.00036851622,0.0008836911,0.0030849604,0.0009113628,0.0011544406,0.0005999972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022193429,0.000042444688,0.0012553826,0.0003084907,0.00013458401,0.00040094773,0.00024281167,0.55853724,0.02836684,0.26659772,0.0029329292,0.14095868],"study_design_scores_gemma":[0.0000066936427,0.000045374396,0.00015202338,0.000017575845,0.000019630115,0.000086614535,0.000011051412,0.9242088,0.0041016736,0.069638185,0.0016984458,0.000013927048],"about_ca_topic_score_codex":0.0010093556,"about_ca_topic_score_gemma":0.0014781,"teacher_disagreement_score":0.0016968247,"about_ca_system_score_codex":0.0005710462,"about_ca_system_score_gemma":0.0003754706,"threshold_uncertainty_score":0.0056764483},"labels":[],"label_agreement":null},{"id":"W2907184624","doi":"10.1109/newcas.2018.8585580","title":"Fully parallel FPGA Implementation of an Artificial Neural Network Tuned by Genetic Algorithm","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Field-programmable gate array; Computer science; Artificial neural network; Genetic algorithm; Process (computing); Layer (electronics); Algorithm; Gate array; Parallel computing; Computer hardware; Artificial intelligence; Machine learning","score_opus":0.016591030648646633,"score_gpt":0.2896844276301726,"score_spread":0.27309339698152596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907184624","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1954559,0.0004921129,0.7595875,0.00021415004,0.00027087037,0.00013470076,0.00017820021,0.0064805234,0.03718609],"genre_scores_gemma":[0.8018434,0.00011687975,0.19233423,0.00007438277,0.000024574021,0.00009047096,0.00012748501,0.00006704164,0.0053215344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999145,0.000015112091,0.000004395614,0.000019641655,0.00003093625,0.000015350424],"domain_scores_gemma":[0.9999201,0.000019483708,0.000009341212,0.000017332144,0.000027789742,0.0000060030507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011318677,0.00032842674,0.0002491458,0.00024384842,0.00017324077,0.0003594048,0.0006693033,0.00027134782,0.0035044092],"category_scores_gemma":[0.000193601,0.000111995374,0.00016281572,0.00021869596,0.000118667056,0.00021059954,0.00012696758,0.00025832787,0.0006485735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006025334,0.00025630588,0.0022931069,0.0004179181,0.00012640421,0.00056799466,0.00011156227,0.42643186,0.14795703,0.017445577,0.0070224362,0.39676723],"study_design_scores_gemma":[0.00008327209,0.0003483161,0.001082047,0.00002081975,0.000031787895,0.0002922753,0.000018772878,0.9398408,0.047278292,0.001817466,0.009167669,0.000018455412],"about_ca_topic_score_codex":0.0015969359,"about_ca_topic_score_gemma":0.0017681262,"teacher_disagreement_score":0.0035044092,"about_ca_system_score_codex":0.00030553006,"about_ca_system_score_gemma":0.00037748512,"threshold_uncertainty_score":0.011723399},"labels":[],"label_agreement":null},{"id":"W2907302002","doi":"10.1155/2019/5972620","title":"Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity","year":2019,"lang":"en","type":"article","venue":"Advances in Civil Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Formwork; Productivity; Artificial neural network; Computer science; Artificial intelligence; Engineering; Economics; Civil engineering; Economic growth","score_opus":0.0047846786377446155,"score_gpt":0.22119560271790523,"score_spread":0.2164109240801606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907302002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5907193,0.0024520054,0.3944744,0.00052341254,0.00026886654,0.00019872328,0.0013284724,0.0011215286,0.008913399],"genre_scores_gemma":[0.9524885,0.00090434746,0.043688755,0.000040285606,0.000030655567,0.00009418862,0.0006293201,0.00001999342,0.0021040512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954337,0.00010688736,0.000052277177,0.000104220606,0.00014295937,0.00005024054],"domain_scores_gemma":[0.99899334,0.00058933126,0.00010703637,0.00002932106,0.0002570391,0.000023912353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095291826,0.00078850857,0.00052879105,0.0014571755,0.00026457466,0.00078986184,0.00052459684,0.0009867281,0.0011249032],"category_scores_gemma":[0.00325185,0.00023663887,0.0005501308,0.0013842803,0.0001570138,0.0007586552,0.00032076583,0.0005455626,0.0003163829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016830339,0.00017725442,0.030319741,0.00019066942,0.0001125101,0.00019866269,0.00006500702,0.82343686,0.00223529,0.0006239141,0.0010172087,0.14145459],"study_design_scores_gemma":[0.0000029262305,0.000026278698,0.0040058903,0.000015737076,0.000011211612,0.000016159263,0.00002087257,0.99466527,0.00067436945,0.0002985081,0.00025625475,0.000006593117],"about_ca_topic_score_codex":0.01400671,"about_ca_topic_score_gemma":0.009193525,"teacher_disagreement_score":0.01400671,"about_ca_system_score_codex":0.00069106417,"about_ca_system_score_gemma":0.000632866,"threshold_uncertainty_score":0.02785033},"labels":[],"label_agreement":null},{"id":"W2907387033","doi":"10.1016/b978-012279670-8.50011-2","title":"ARTIFICIAL NEURAL NETWORKS","year":2002,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Neuroscience; Cognitive science; Biology; Psychology","score_opus":0.0255212454965309,"score_gpt":0.23112704262881334,"score_spread":0.20560579713228244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907387033","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049705408,0.060285594,0.5508309,0.002768024,0.0044445638,0.0001603996,0.00149322,0.0050904187,0.36995634],"genre_scores_gemma":[0.041332923,0.03065062,0.17472152,0.00096005603,0.0010768721,0.00033463308,0.0026673893,0.0010785597,0.7471775],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997454,0.000035353147,0.000017204924,0.0000567629,0.00013022718,0.000015062461],"domain_scores_gemma":[0.99966884,0.00011230298,0.000017548437,0.00008399449,0.00010202548,0.000015353562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003625443,0.0010641762,0.0012002788,0.0008437897,0.00036475173,0.0015172065,0.0010306195,0.0014622825,0.037835993],"category_scores_gemma":[0.0013969603,0.0005011547,0.00039176643,0.0016222664,0.00045269757,0.0017898594,0.0010973116,0.0014302989,0.025507942],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025682824,0.00005002652,0.00021083838,0.00047278035,0.000043390446,0.00007707474,0.00005372158,0.01650055,0.0018021748,0.031754352,0.13051525,0.8184942],"study_design_scores_gemma":[0.000021700225,0.000059829454,0.0009931023,0.00059580116,0.00007003182,0.0003988155,0.00005763397,0.119077966,0.0050280225,0.10840964,0.7652378,0.0000496778],"about_ca_topic_score_codex":0.0014853659,"about_ca_topic_score_gemma":0.0022463356,"teacher_disagreement_score":0.037835993,"about_ca_system_score_codex":0.0003910079,"about_ca_system_score_gemma":0.00047111846,"threshold_uncertainty_score":0.12657398},"labels":[],"label_agreement":null},{"id":"W2907862022","doi":"10.1016/b978-012646490-0/50017-2","title":"Neural Networks for Identification of Nonlinear Systems: An Overview","year":2000,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Identification (biology); Artificial neural network; Nonlinear system; Computer science; Nonlinear system identification; System identification; Artificial intelligence; Data mining; Physics; Biology","score_opus":0.037326071624014384,"score_gpt":0.28103526281681424,"score_spread":0.24370919119279985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907862022","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014471237,0.68426955,0.27552757,0.0012900631,0.0011606549,0.000071754876,0.000151552,0.0004809923,0.035600655],"genre_scores_gemma":[0.02072848,0.7574044,0.17344213,0.0011188688,0.002662883,0.00028398802,0.00046157918,0.00018865972,0.04370913],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996233,0.00006824506,0.00003294735,0.0000716185,0.0001831083,0.00002071513],"domain_scores_gemma":[0.999613,0.00022876803,0.000016572501,0.000024290266,0.00010326735,0.0000141603905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007584268,0.0014829766,0.001311576,0.001400443,0.00022559694,0.0016322682,0.0011664401,0.0019412412,0.0043487037],"category_scores_gemma":[0.0009532655,0.00092756614,0.0006951466,0.0025603727,0.0005417753,0.0023120025,0.00077588734,0.0017334536,0.0035300911],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074737276,0.00015232421,0.00040925902,0.0038268839,0.00015093773,0.00014309262,0.000111225636,0.024916008,0.0032001047,0.069496945,0.031808265,0.8657102],"study_design_scores_gemma":[0.000043271324,0.0001680484,0.0015750736,0.0021370775,0.000164352,0.0007369877,0.00008418264,0.101949096,0.0040276917,0.2067131,0.68228745,0.00011374693],"about_ca_topic_score_codex":0.0018789424,"about_ca_topic_score_gemma":0.002422803,"teacher_disagreement_score":0.0043487037,"about_ca_system_score_codex":0.00069986854,"about_ca_system_score_gemma":0.00067241467,"threshold_uncertainty_score":0.014547825},"labels":[],"label_agreement":null},{"id":"W2908176537","doi":"","title":"STOCHASTIC GRADIENT LANGEVIN DYNAMICS THAT EXPLOIT NEURAL NETWORK STRUCTURE","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Langevin dynamics; Exploit; Computer science; Artificial neural network; Dynamics (music); Statistical physics; Langevin equation; Artificial intelligence; Physics","score_opus":0.04822018521992996,"score_gpt":0.31713474569678946,"score_spread":0.26891456047685947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908176537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04843853,0.0003553477,0.93990016,0.00081023166,0.00039108083,0.000046653367,0.000074714146,0.00025580262,0.009727491],"genre_scores_gemma":[0.8449886,0.00065804255,0.13267174,0.00030892596,0.00019837875,0.00015016658,0.00018339512,0.00029526287,0.020545468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998536,0.000047453577,0.0000062467952,0.000022402703,0.00005031867,0.000020053712],"domain_scores_gemma":[0.99941707,0.00029615726,0.00005985711,0.00006327201,0.00009607019,0.00006772771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006904783,0.0005660939,0.0008443032,0.00040012712,0.0004002794,0.0009884142,0.0011593987,0.0014182181,0.002772211],"category_scores_gemma":[0.0026523883,0.00047777232,0.0005413236,0.00042717453,0.0010097332,0.0016994923,0.0013907341,0.0015952368,0.00039745038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047151043,0.00006544252,0.0004042109,0.00005209811,0.00004574405,0.00007129319,0.00005431013,0.68732214,0.0031016215,0.29302004,0.0017350684,0.014080802],"study_design_scores_gemma":[0.0000034453078,0.0000049969844,0.000021392903,0.0000018171432,0.000001810556,0.0000057781194,0.0000022613892,0.9834714,0.00011439297,0.01612896,0.00024036442,0.0000033860044],"about_ca_topic_score_codex":0.0017432576,"about_ca_topic_score_gemma":0.0024865745,"teacher_disagreement_score":0.002772211,"about_ca_system_score_codex":0.0006603465,"about_ca_system_score_gemma":0.0008072309,"threshold_uncertainty_score":0.009273946},"labels":[],"label_agreement":null},{"id":"W2909992598","doi":"10.1109/icmla.2018.00221","title":"Lorenz Chaotic System Artificial Neural Network Training with Single Time Series Input and Multiple Time Series Outputs for EEG Prediction","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Series (stratigraphy); Artificial neural network; Computer science; Chaotic; Electroencephalography; Time series; Training (meteorology); Lorenz system; Artificial intelligence; Pattern recognition (psychology); Machine learning; Neuroscience; Psychology","score_opus":0.023225023845566784,"score_gpt":0.21039037155667706,"score_spread":0.18716534771111026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909992598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34081003,0.001634101,0.6497871,0.00034645275,0.00015074624,0.00010518236,0.00016671169,0.000938401,0.0060612573],"genre_scores_gemma":[0.9058962,0.0004597377,0.091029175,0.000028210019,0.000027544895,0.000074726195,0.00012947015,0.00003268165,0.002322202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997465,0.00006489399,0.000021356627,0.00005768743,0.000082154496,0.000027453487],"domain_scores_gemma":[0.99958485,0.0001945439,0.00004342422,0.000039107617,0.0001259181,0.000012117009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063655234,0.0006209583,0.00046120843,0.00043691148,0.00035141985,0.00042185694,0.0004389974,0.0004905487,0.0012549028],"category_scores_gemma":[0.0021347061,0.00026278445,0.00042035317,0.0004601179,0.00019984115,0.0007152474,0.00039013964,0.00069012836,0.00031199623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002337896,0.000114190036,0.0050029345,0.0002237827,0.00010939571,0.0002057529,0.000103998376,0.7666826,0.018682607,0.0020065275,0.00089157606,0.20574278],"study_design_scores_gemma":[0.000003171497,0.000025567984,0.00079698354,0.00000578819,0.0000068811223,0.000016765975,0.0000072515745,0.9961808,0.0024798184,0.00028294258,0.000188673,0.000005375434],"about_ca_topic_score_codex":0.004748746,"about_ca_topic_score_gemma":0.0044201696,"teacher_disagreement_score":0.004748746,"about_ca_system_score_codex":0.00037768635,"about_ca_system_score_gemma":0.00044634155,"threshold_uncertainty_score":0.00944221},"labels":[],"label_agreement":null},{"id":"W2910279493","doi":"","title":"Designing Efficient Neural Attention Systems Towards Achieving Human-level Sharp Vision","year":2018,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Machine vision; Computer vision","score_opus":0.10085423005369301,"score_gpt":0.3803689614535364,"score_spread":0.2795147313998434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910279493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018350812,0.00027561994,0.97763914,0.0001933406,0.000074123614,0.0000807741,0.000038302165,0.0013411433,0.0020066793],"genre_scores_gemma":[0.57761896,0.00037319903,0.41191792,0.00046328155,0.000079688674,0.00017950483,0.00016281313,0.0003500952,0.0088545205],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995752,0.00007497917,0.000019994739,0.00014891366,0.000087006236,0.00009388552],"domain_scores_gemma":[0.99960035,0.000112959184,0.00003440559,0.00006719163,0.00014418903,0.00004094627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009293871,0.0009702736,0.0006819749,0.00037594468,0.00047026126,0.001354134,0.0013250376,0.0015664457,0.0048374697],"category_scores_gemma":[0.002374958,0.00060820574,0.000518201,0.00037621194,0.00048502377,0.0017379962,0.0015839512,0.0017147315,0.0015398638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035681773,0.00030748468,0.0011683799,0.00026971364,0.00015448213,0.00020648487,0.0002981719,0.2714758,0.20698997,0.02099172,0.008236345,0.48954466],"study_design_scores_gemma":[0.000017870289,0.000113101814,0.0003821437,0.000011518115,0.00003102183,0.00005242441,0.000047273832,0.95673627,0.03139345,0.008616323,0.002587712,0.0000108927825],"about_ca_topic_score_codex":0.004159843,"about_ca_topic_score_gemma":0.007266017,"teacher_disagreement_score":0.0048374697,"about_ca_system_score_codex":0.00096604857,"about_ca_system_score_gemma":0.0012312627,"threshold_uncertainty_score":0.016183019},"labels":[],"label_agreement":null},{"id":"W2910816794","doi":"10.1109/ucc-companion.2018.00082","title":"SinP[N]: A Fast Convergence Activation Function for Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundo para o Desenvolvimento das Ciências e da Tecnologia; Science and Technology Development Fund; Canadian Institute for Advanced Research","keywords":"Activation function; Function (biology); Computer science; Convergence (economics); Convolutional neural network; Artificial intelligence; Normalization (sociology); Derivative (finance); Algorithm; Artificial neural network","score_opus":0.01970040534562447,"score_gpt":0.24800858040033313,"score_spread":0.22830817505470866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910816794","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008719712,0.0005360048,0.98138785,0.00026859972,0.00012906952,0.00007669303,0.0002879627,0.004224295,0.004369894],"genre_scores_gemma":[0.23906206,0.0014079225,0.7311636,0.00057936704,0.00011918086,0.00074953504,0.0024463213,0.002517388,0.021954592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954236,0.00012990965,0.00002938982,0.00006782126,0.00017554763,0.00005494093],"domain_scores_gemma":[0.99928707,0.00025348214,0.00005033046,0.000081695485,0.0002894236,0.000037878115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011672337,0.001318128,0.00059596007,0.0007700561,0.00050094025,0.0007381454,0.0015939638,0.0011419322,0.006180357],"category_scores_gemma":[0.0028377115,0.0005104026,0.00068818784,0.0009719409,0.0006305468,0.0012533254,0.00088797713,0.0017330841,0.0031241726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003556758,0.00014628985,0.0010433842,0.00045332446,0.0001069905,0.0002731634,0.00013505455,0.39491197,0.022863155,0.03642131,0.036139596,0.5071502],"study_design_scores_gemma":[0.000017179258,0.00003879818,0.00017842023,0.00002436464,0.000009217765,0.000065794,0.000009582447,0.97471684,0.009496174,0.006125308,0.009303722,0.000014577498],"about_ca_topic_score_codex":0.007191704,"about_ca_topic_score_gemma":0.010524134,"teacher_disagreement_score":0.007191704,"about_ca_system_score_codex":0.0010080803,"about_ca_system_score_gemma":0.0020393606,"threshold_uncertainty_score":0.020675302},"labels":[],"label_agreement":null},{"id":"W2911364117","doi":"","title":"Proceedings of the 5th WSEAS international conference on Signal processing","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Signal processing; Computer science; Digital signal processing","score_opus":0.02683159864552165,"score_gpt":0.2641433230400935,"score_spread":0.23731172439457188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911364117","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046893664,0.023989888,0.7178792,0.008601185,0.055703446,0.0004082376,0.0015745455,0.0065118303,0.13843797],"genre_scores_gemma":[0.12658422,0.024113715,0.252167,0.0019423178,0.011171976,0.0002803388,0.007788047,0.002735103,0.5732173],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952114,0.000091382,0.00004039793,0.00007495296,0.00021516641,0.000056980483],"domain_scores_gemma":[0.9988279,0.00015172748,0.000034310742,0.00016018495,0.0006636925,0.00016222813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014614669,0.0012760011,0.0014738104,0.0015938606,0.0007257219,0.0033554938,0.00095585216,0.0012964588,0.052190248],"category_scores_gemma":[0.001631089,0.00025169933,0.0007218908,0.0011915193,0.0006620658,0.0012635232,0.0011258653,0.0015430562,0.028245214],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008649558,0.0004870009,0.0012610381,0.00044724336,0.00023374673,0.0004210481,0.00019266356,0.004877183,0.0389747,0.008575979,0.21535094,0.72831345],"study_design_scores_gemma":[0.00010140603,0.00066044624,0.005252314,0.00027347376,0.00033040662,0.0009724081,0.00031573625,0.093287684,0.052743156,0.012304641,0.8336679,0.000090517075],"about_ca_topic_score_codex":0.0035480405,"about_ca_topic_score_gemma":0.006488451,"teacher_disagreement_score":0.052190248,"about_ca_system_score_codex":0.00041644368,"about_ca_system_score_gemma":0.0010047216,"threshold_uncertainty_score":0.17459375},"labels":[],"label_agreement":null},{"id":"W2911864375","doi":"10.32470/ccn.2018.1253-0","title":"Incorporating an adaptive learning rate in a neural model of action selection","year":2018,"lang":"en","type":"article","venue":"2018 Conference on Cognitive Computational Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Action selection; Selection (genetic algorithm); Artificial intelligence; Action (physics); Artificial neural network; Machine learning; Neuroscience; Psychology","score_opus":0.14834550028290783,"score_gpt":0.34184773226564197,"score_spread":0.19350223198273414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911864375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07640366,0.0008372312,0.9144183,0.0016533791,0.00045443524,0.00008770053,0.00013935659,0.00051648583,0.0054894607],"genre_scores_gemma":[0.91174287,0.0006135672,0.077322796,0.00046648135,0.00023999262,0.00021623603,0.00009447049,0.00014408905,0.00915949],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994134,0.00019625382,0.00004646152,0.00015512183,0.00011426355,0.00007450617],"domain_scores_gemma":[0.9975116,0.0014519271,0.00019420114,0.00022679158,0.00045846635,0.00015707476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024343578,0.0007395116,0.001303163,0.0006121525,0.00043506807,0.0017955737,0.0028633182,0.00302052,0.0035901363],"category_scores_gemma":[0.009615071,0.0008979342,0.00084887876,0.00075480156,0.0009868433,0.0029112701,0.0012722726,0.0033541864,0.0008567255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014021162,0.00012394524,0.00094322336,0.00006881114,0.00009474434,0.00009052164,0.00008601268,0.9350884,0.002621292,0.020617891,0.0007893593,0.039335452],"study_design_scores_gemma":[0.0000066516545,0.0000141083965,0.00006855328,0.0000038118744,0.000010493312,0.000016572185,0.0000017757505,0.99722195,0.00013980121,0.00242027,0.00009031273,0.000005666973],"about_ca_topic_score_codex":0.005139265,"about_ca_topic_score_gemma":0.004978992,"teacher_disagreement_score":0.005139265,"about_ca_system_score_codex":0.0010155187,"about_ca_system_score_gemma":0.0011422526,"threshold_uncertainty_score":0.012874246},"labels":[],"label_agreement":null},{"id":"W2911969372","doi":"10.3390/e21020166","title":"Learning Entropy as a Learning-Based Information Concept","year":2019,"lang":"en","type":"article","venue":"Entropy","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Vysoká Škola Chemicko-technologická v Praze","keywords":"Novelty; Novelty detection; Probabilistic logic; Artificial intelligence; Computer science; Entropy (arrow of time); Machine learning; Information theory; Mathematics; Psychology","score_opus":0.004438258468596714,"score_gpt":0.21665262495542217,"score_spread":0.21221436648682546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911969372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024690386,0.0076132077,0.95169556,0.0023361747,0.00036923203,0.000045606484,0.00023938627,0.00013238359,0.012878029],"genre_scores_gemma":[0.8017033,0.009167083,0.18031868,0.000854607,0.002294292,0.00034250703,0.00034965028,0.000112842616,0.004857063],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99643034,0.0014170686,0.00025784844,0.0005624198,0.0011787573,0.00015350107],"domain_scores_gemma":[0.9878108,0.009576156,0.00075763854,0.000896439,0.00072044577,0.00023853334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004030217,0.0008243557,0.0010240439,0.0026614075,0.0005815826,0.0028874194,0.0012313888,0.0013023175,0.0013069946],"category_scores_gemma":[0.0112699615,0.00034176765,0.0009498527,0.0016552293,0.006177031,0.0066521983,0.0022260258,0.0021211992,0.0002679619],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028548602,0.000023080456,0.00080675003,0.00022230588,0.00006124225,0.00010133751,0.00019064656,0.023620892,0.0018206835,0.95089906,0.00081573793,0.021409618],"study_design_scores_gemma":[0.000005786,0.000050246417,0.00062923256,0.00005751486,0.000019368228,0.00012001495,0.00003400984,0.07528591,0.0008706083,0.9194463,0.0034463091,0.000034786986],"about_ca_topic_score_codex":0.00031463828,"about_ca_topic_score_gemma":0.0001796199,"teacher_disagreement_score":0.004030217,"about_ca_system_score_codex":0.0013925974,"about_ca_system_score_gemma":0.0005872661,"threshold_uncertainty_score":0.021314085},"labels":[],"label_agreement":null},{"id":"W2913172947","doi":"","title":"Proceedings of the 2014 Symposium on Symbolic-Numeric Computation","year":2014,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Symbolic computation; Symbolic-numeric computation; Computer science; Computation; The Symbolic; Symbolic data analysis; Symbolic trajectory evaluation; Theoretical computer science; Algebraic number; Reliability (semiconductor); Subject (documents); Algorithm; Algebra over a field; Mathematics; Model checking","score_opus":0.00655491936638028,"score_gpt":0.2151835258205986,"score_spread":0.20862860645421832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913172947","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008497009,0.111269005,0.31042105,0.037490714,0.082372315,0.00030435473,0.0022012426,0.0029934912,0.44445089],"genre_scores_gemma":[0.15295735,0.091252126,0.19308382,0.0059442925,0.04266288,0.0007145662,0.008310385,0.0032034365,0.5018711],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99753845,0.0007060083,0.00019897419,0.00040729242,0.00096360286,0.00018575888],"domain_scores_gemma":[0.9962167,0.0010492773,0.00010180988,0.00083269045,0.0013423674,0.0004572167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039149667,0.0013536807,0.0015900197,0.0018719274,0.001482162,0.006968734,0.0019634913,0.002205645,0.06256104],"category_scores_gemma":[0.00808303,0.00063562667,0.0015292495,0.0020286804,0.0022412369,0.004391252,0.0035323263,0.005026849,0.022351228],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033799923,0.00012458256,0.00084277446,0.00065644045,0.00020222159,0.00024404586,0.0003438252,0.006368142,0.0032331594,0.26029372,0.47543415,0.2519189],"study_design_scores_gemma":[0.00003625831,0.00006286336,0.00053172087,0.00035727237,0.0000589142,0.00022969322,0.00008375473,0.013790908,0.001723414,0.09822618,0.8848601,0.000039035058],"about_ca_topic_score_codex":0.002157923,"about_ca_topic_score_gemma":0.0022033826,"teacher_disagreement_score":0.06256104,"about_ca_system_score_codex":0.002540908,"about_ca_system_score_gemma":0.003066125,"threshold_uncertainty_score":0.20928752},"labels":[],"label_agreement":null},{"id":"W2913334542","doi":"","title":"L'élagage des réseaux neuronaux convolutionnels pour faciliter l'analyse du trafic routier","year":2019,"lang":"fr","type":"article","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.019133822839196266,"score_gpt":0.20595522780384323,"score_spread":0.18682140496464697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913334542","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08018896,0.003019895,0.89740354,0.0011939179,0.00042196468,0.00011827054,0.00090540445,0.006217445,0.010530624],"genre_scores_gemma":[0.4517528,0.0043700417,0.5095978,0.0006713573,0.00024323708,0.00018652818,0.0018605738,0.00094781816,0.030369963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918157,0.00012309694,0.00003815952,0.0002429733,0.0002571165,0.00015707579],"domain_scores_gemma":[0.99884903,0.00034487885,0.0000899436,0.00020232989,0.00046316287,0.000050623443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010479974,0.0013577259,0.00096280215,0.0012897453,0.00048301287,0.0023019088,0.0012510016,0.0013155431,0.005269743],"category_scores_gemma":[0.0027968357,0.0006172842,0.0010717624,0.0010954948,0.0007098801,0.0021189556,0.0008195309,0.0011626551,0.00208164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052882434,0.00011970913,0.004449473,0.00056344806,0.00026659732,0.0004668236,0.00028526792,0.09847515,0.18139647,0.012038857,0.012588868,0.6888205],"study_design_scores_gemma":[0.000023801178,0.00015894964,0.007112992,0.000120430064,0.000116769355,0.0002801822,0.00023434154,0.82365966,0.12694414,0.007161212,0.03411411,0.000073423274],"about_ca_topic_score_codex":0.026901612,"about_ca_topic_score_gemma":0.025835585,"teacher_disagreement_score":0.026901612,"about_ca_system_score_codex":0.0015453876,"about_ca_system_score_gemma":0.001253704,"threshold_uncertainty_score":0.053490043},"labels":[],"label_agreement":null},{"id":"W2914173415","doi":"10.5539/mas.v13n2p228","title":"High-Order Neural Networks are Equivalent to Ordinary Neural Networks","year":2019,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Equivalence (formal languages); Feedforward neural network; Computer science; Activation function; Multiplicative function; Transfer function; Function (biology); Artificial intelligence; Mathematics; Pure mathematics; Mathematical analysis","score_opus":0.014662150001771072,"score_gpt":0.24003273928870633,"score_spread":0.22537058928693526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914173415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06396526,0.0009381845,0.88500756,0.0010268209,0.00038838407,0.00007578472,0.00018009955,0.000267178,0.048150748],"genre_scores_gemma":[0.8464886,0.0014609607,0.13655026,0.00086380896,0.0005618326,0.0002123111,0.00037455437,0.00026345954,0.013224144],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99762195,0.00056430954,0.00018843272,0.00049534556,0.0008832061,0.00024673887],"domain_scores_gemma":[0.9973507,0.0012702493,0.0003044282,0.00043080034,0.0005108585,0.00013292649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015583578,0.00064052385,0.00067541306,0.00095986854,0.000656309,0.002236298,0.0010809612,0.0011418093,0.0050848373],"category_scores_gemma":[0.0076869205,0.00030089734,0.001392211,0.00044800673,0.0035399324,0.0048038512,0.0018447548,0.0029836344,0.00076371245],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022440881,0.000022123624,0.00018727657,0.00005823076,0.000020156625,0.00005446838,0.000100810335,0.017805057,0.0011862812,0.9706133,0.00049315876,0.009436609],"study_design_scores_gemma":[0.000011370179,0.000037224327,0.00021484727,0.000014541266,0.0000091359425,0.000054944252,0.000026061172,0.090048134,0.0007316426,0.9056739,0.0031682632,0.000009921277],"about_ca_topic_score_codex":0.0013947975,"about_ca_topic_score_gemma":0.000943319,"teacher_disagreement_score":0.0050848373,"about_ca_system_score_codex":0.0012419511,"about_ca_system_score_gemma":0.0007352592,"threshold_uncertainty_score":0.01701051},"labels":[],"label_agreement":null},{"id":"W2914223574","doi":"","title":"Proceedings of the 6th IAPR TC 3 International Workshop on Artificial Neural Networks in Pattern Recognition - Volume 8774","year":2014,"lang":"en","type":"article","venue":"Artificial Neural Networks in Pattern Recognition","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Volume (thermodynamics); Artificial neural network; Artificial intelligence; Computer science; Physics","score_opus":0.0334888035998317,"score_gpt":0.2514381802901355,"score_spread":0.2179493766903038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914223574","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033039887,0.06595686,0.5103275,0.01621697,0.08670984,0.0007334232,0.0027931812,0.0056031295,0.2786192],"genre_scores_gemma":[0.08501267,0.028561259,0.15089524,0.002195836,0.0112223225,0.00034248826,0.008632905,0.0030677079,0.7100696],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99867284,0.00039062343,0.00006637017,0.00020075477,0.0005434043,0.00012616522],"domain_scores_gemma":[0.99779046,0.00034747692,0.000058389836,0.00029212632,0.0012367808,0.00027470093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024122556,0.0012949605,0.0015881797,0.0015792323,0.00080552074,0.0036628332,0.0017333828,0.0015259172,0.04234049],"category_scores_gemma":[0.0029839538,0.0004307223,0.0009372378,0.0012216235,0.00080624264,0.0018623485,0.0018154286,0.0023179727,0.027041307],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052391016,0.00042531127,0.0010185465,0.0004602377,0.00017620299,0.00019772568,0.00016497518,0.004851344,0.008818864,0.0077289212,0.4595532,0.5160808],"study_design_scores_gemma":[0.000093983006,0.00049284636,0.006005716,0.00066048256,0.00030235018,0.00096576347,0.00037921252,0.06920254,0.019932108,0.024355486,0.8775216,0.00008797981],"about_ca_topic_score_codex":0.0046904744,"about_ca_topic_score_gemma":0.008090997,"teacher_disagreement_score":0.04234049,"about_ca_system_score_codex":0.00090363703,"about_ca_system_score_gemma":0.0016227182,"threshold_uncertainty_score":0.14164305},"labels":[],"label_agreement":null},{"id":"W2914580252","doi":"10.1016/j.ijar.2019.01.008","title":"Design methodology for Radial Basis Function Neural Networks classifier based on locally linear reconstruction and Conditional Fuzzy C-Means clustering","year":2019,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea; Ministry of Education","keywords":"Cluster analysis; Pattern recognition (psychology); Artificial intelligence; Radial basis function network; Fuzzy clustering; Radial basis function; Fuzzy classification; Fuzzy logic; Mathematics; Classifier (UML); Computer science; Artificial neural network; Fuzzy set; Data mining; Machine learning","score_opus":0.042644097106237046,"score_gpt":0.2870818511687789,"score_spread":0.24443775406254184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914580252","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012127031,0.0000797302,0.99814105,0.000027279195,0.000014069458,0.0000353016,0.000008250021,0.00015593632,0.00032563144],"genre_scores_gemma":[0.18312241,0.0003637139,0.8131251,0.00011389953,0.00008301934,0.0004382564,0.00017129011,0.000094300216,0.0024880848],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986652,0.00025738316,0.00009004479,0.0003998853,0.0005078023,0.00007973563],"domain_scores_gemma":[0.99905044,0.00014977495,0.00007785792,0.000054162596,0.000640411,0.000027328093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015635072,0.000758124,0.0011248082,0.0009079415,0.0007584329,0.0010713541,0.0020696367,0.0014256394,0.0018602879],"category_scores_gemma":[0.0019533937,0.0005460393,0.0009343334,0.00074564264,0.00053697685,0.0010027656,0.0006401815,0.0009217897,0.0010500782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002733488,0.00012361872,0.0011660184,0.00049898843,0.00014701943,0.00017403613,0.0001879204,0.36021438,0.055579536,0.026842818,0.0048934,0.54989886],"study_design_scores_gemma":[0.000009745149,0.00005894028,0.00020716354,0.0000097250395,0.000023406154,0.00007403993,0.000013009855,0.98956656,0.0066561345,0.0018091118,0.0015563812,0.000015831625],"about_ca_topic_score_codex":0.0055422643,"about_ca_topic_score_gemma":0.003253597,"teacher_disagreement_score":0.0055422643,"about_ca_system_score_codex":0.00087417534,"about_ca_system_score_gemma":0.0016982696,"threshold_uncertainty_score":0.011020064},"labels":[],"label_agreement":null},{"id":"W2915445509","doi":"10.4018/ijssci.2018100102","title":"Preventing Model Overfitting and Underfitting in Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Convolutional neural network; Regularization (linguistics); Machine learning; Adaptability; Deep learning; Pattern recognition (psychology); Set (abstract data type); Artificial neural network","score_opus":0.02465964199239385,"score_gpt":0.3058860702326626,"score_spread":0.2812264282402688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915445509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0677589,0.0009260911,0.9277732,0.0008236433,0.00007805006,0.000061271305,0.00004916231,0.0011784753,0.0013511433],"genre_scores_gemma":[0.80718416,0.0009980561,0.18850379,0.00065114617,0.000069999995,0.0002299703,0.00027086868,0.0005368256,0.0015552678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963223,0.00152656,0.00032799816,0.0005060279,0.0010621316,0.00025493515],"domain_scores_gemma":[0.98007625,0.013667843,0.00171884,0.002612917,0.0016810434,0.00024309476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008505819,0.0014791095,0.0018052765,0.0012692206,0.00084265036,0.0016263237,0.0018485722,0.0017743306,0.0008948339],"category_scores_gemma":[0.052121595,0.0010817467,0.0012796634,0.0008504834,0.0022325232,0.0030544067,0.0028776447,0.0032288914,0.00023735515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020347856,0.00009874041,0.0061408854,0.00035000042,0.00025847091,0.00035723386,0.00030321107,0.8616459,0.006786031,0.029286861,0.0018856865,0.09268352],"study_design_scores_gemma":[0.000007963242,0.000050661904,0.0003863312,0.00004280742,0.000026547405,0.00006385305,0.000023868784,0.9763179,0.0037089635,0.018872077,0.00048587908,0.000013323665],"about_ca_topic_score_codex":0.003912392,"about_ca_topic_score_gemma":0.005189976,"teacher_disagreement_score":0.008505819,"about_ca_system_score_codex":0.00139074,"about_ca_system_score_gemma":0.0021101546,"threshold_uncertainty_score":0.044983625},"labels":[],"label_agreement":null},{"id":"W2915508016","doi":"10.4018/ijcini.2019010103","title":"Chaotic System Design Based on Recurrent Artificial Neural Network for the Simulation of EEG Time Series","year":2019,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Chaotic; Computer science; Artificial neural network; Lorenz system; Nonlinear system; Series (stratigraphy); Control theory (sociology); Time series; Generator (circuit theory); Artificial intelligence; Machine learning; Power (physics)","score_opus":0.02786026597270924,"score_gpt":0.29338591597158314,"score_spread":0.2655256499988739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915508016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08866705,0.0003787653,0.90298647,0.00016581459,0.00008151688,0.00012065823,0.00007066213,0.0005978878,0.0069312914],"genre_scores_gemma":[0.9054748,0.0002897433,0.09084941,0.000024831592,0.000015141729,0.00030696613,0.00009550302,0.000054569493,0.0028890949],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998486,0.00006517608,0.000008815325,0.000024202885,0.00004071227,0.000012450668],"domain_scores_gemma":[0.9997576,0.000121096695,0.000028846773,0.000015250463,0.00006819158,0.000009053661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044351027,0.00049571664,0.0004888815,0.00027670135,0.00025127325,0.00035042438,0.0005862877,0.0005900484,0.0014016272],"category_scores_gemma":[0.0010884886,0.0002191813,0.0005765395,0.00018621156,0.0002504481,0.0003473314,0.0002983075,0.0005515519,0.00017936542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026537307,0.000015278909,0.00028626694,0.000048232232,0.00002193195,0.000059181944,0.00004080753,0.9876522,0.003412149,0.00247995,0.00012485031,0.005832604],"study_design_scores_gemma":[0.0000016957862,0.0000085616,0.000027934628,0.0000012687619,0.000001923167,0.0000027504598,0.0000013543098,0.99948514,0.00024925775,0.00013561154,0.000083355015,0.0000011257418],"about_ca_topic_score_codex":0.0043277503,"about_ca_topic_score_gemma":0.00356933,"teacher_disagreement_score":0.0043277503,"about_ca_system_score_codex":0.00036931614,"about_ca_system_score_gemma":0.0004828663,"threshold_uncertainty_score":0.008605123},"labels":[],"label_agreement":null},{"id":"W2916313152","doi":"10.1109/icassp.2019.8682914","title":"Ising-dropout: A Regularization Method for Training and Compression of Deep Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"MNIST database; Overfitting; Computer science; Artificial intelligence; Dropout (neural networks); Initialization; Regularization (linguistics); Artificial neural network; Machine learning; Inference; Deep learning; Deep neural networks; Pattern recognition (psychology)","score_opus":0.02080000613618969,"score_gpt":0.28621397382706876,"score_spread":0.26541396769087905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916313152","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013403581,0.00040090532,0.9829525,0.0002461416,0.00009606584,0.000078023106,0.00012912459,0.0016359034,0.0010577145],"genre_scores_gemma":[0.384876,0.00097163767,0.60459065,0.00038759923,0.00018499464,0.00050960714,0.0009994261,0.0006702686,0.006809838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937963,0.00015507474,0.00006855958,0.00008505761,0.00025148873,0.000060166432],"domain_scores_gemma":[0.9990841,0.00034422832,0.00011216613,0.00021685305,0.00020011837,0.00004252878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018222834,0.0009660784,0.00077257503,0.00082676683,0.00054038875,0.00061815995,0.0016820768,0.0012547086,0.0017622773],"category_scores_gemma":[0.004690632,0.00040203944,0.00088940817,0.0011094587,0.00082804676,0.0014438701,0.0011193911,0.0025360906,0.0004978926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042121118,0.00026779354,0.0018387644,0.0002692714,0.0001845865,0.00031048746,0.00024227439,0.54045177,0.028466104,0.037893254,0.008000402,0.38165408],"study_design_scores_gemma":[0.000011913719,0.000035770878,0.00018279362,0.00001240564,0.000011206086,0.000033155582,0.0000060070306,0.9861373,0.007836185,0.0044481587,0.001274859,0.000010339965],"about_ca_topic_score_codex":0.0037117961,"about_ca_topic_score_gemma":0.0048353057,"teacher_disagreement_score":0.0037117961,"about_ca_system_score_codex":0.0010608677,"about_ca_system_score_gemma":0.0011836066,"threshold_uncertainty_score":0.009637237},"labels":[],"label_agreement":null},{"id":"W2917005233","doi":"10.1007/s12594-019-1153-5","title":"Predicting Porosity by Multivariate Regression and Probabilistic Neural Network using Model-based and Coloured Inversion as External Attributes: A Quantitative Comparison","year":2019,"lang":"en","type":"article","venue":"Journal of the Geological Society of India","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geology; Multivariate statistics; Probabilistic logic; Porosity; Inversion (geology); Regression; Artificial neural network; Hydrogeology; Regression analysis; Artificial intelligence; Geomorphology; Geotechnical engineering; Statistics; Computer science; Mathematics","score_opus":0.03589381740949168,"score_gpt":0.29023899048705776,"score_spread":0.2543451730775661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917005233","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6547259,0.00027192256,0.34069937,0.00014696586,0.00002571409,0.000052306328,0.00049284875,0.0012693322,0.0023157573],"genre_scores_gemma":[0.9713488,0.000086436135,0.027716985,0.000013229761,0.000007719944,0.000029908671,0.0003007791,0.00003875582,0.00045730124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993204,0.00017013687,0.000035413013,0.00015491537,0.00025981746,0.000059409344],"domain_scores_gemma":[0.9982014,0.0009565666,0.00021955054,0.0001288569,0.00044640378,0.000047240082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020317836,0.0008275187,0.0005774229,0.0015001412,0.00017700503,0.00096913177,0.0007183586,0.0006801439,0.00095660466],"category_scores_gemma":[0.005270983,0.00030418352,0.0007361749,0.0010199244,0.00048476638,0.0015223165,0.00073638506,0.00054898346,0.0001904727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000191408,0.000088867615,0.02308533,0.00006775091,0.00009779438,0.00005038379,0.000037724494,0.92301494,0.0045729075,0.0005799604,0.00016442203,0.0480486],"study_design_scores_gemma":[0.0000024469784,0.000013349512,0.0036440396,0.0000032348394,0.000008626856,0.000010554292,0.0000070988294,0.99501026,0.001047564,0.00020515607,0.000039502593,0.000008108247],"about_ca_topic_score_codex":0.011080136,"about_ca_topic_score_gemma":0.0077025583,"teacher_disagreement_score":0.011080136,"about_ca_system_score_codex":0.0007553005,"about_ca_system_score_gemma":0.0007407142,"threshold_uncertainty_score":0.022031307},"labels":[],"label_agreement":null},{"id":"W2927683843","doi":"10.5430/air.v8n1p41","title":"A three-stage learning algorithm for deep multilayer perceptron with effective weight initialisation based on sparse auto-encoder","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Autoencoder; Pattern recognition (psychology); Deep learning; Artificial neural network; Feature (linguistics); Stage (stratigraphy); Algorithm; Multilayer perceptron; Backpropagation; Perceptron; Feature extraction; Encoder; Convolutional neural network; Machine learning","score_opus":0.1028277012464106,"score_gpt":0.3815450569620924,"score_spread":0.2787173557156818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927683843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022977088,0.00006918844,0.99642974,0.00003430104,0.00001950212,0.000067599496,0.00001541111,0.0007295511,0.0003370225],"genre_scores_gemma":[0.11645845,0.00014629535,0.8796557,0.0001624643,0.000040955016,0.000558096,0.00023570455,0.00016707693,0.0025751628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992175,0.00018011469,0.00007470628,0.00014864517,0.0002860438,0.00009308756],"domain_scores_gemma":[0.99912256,0.00030308965,0.00008204914,0.00010970409,0.00032639212,0.000056267563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016724612,0.0013905395,0.0013155382,0.00092404085,0.0005314383,0.0008342802,0.0024992942,0.0017381718,0.003114397],"category_scores_gemma":[0.0032406545,0.0009280757,0.00090386736,0.0008838737,0.0006914756,0.0017177189,0.0018856127,0.0025393711,0.001389102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000239899,0.0001747371,0.0011023352,0.000222494,0.00012353808,0.0001616168,0.0001509909,0.40063483,0.017958025,0.012404465,0.0040511116,0.562776],"study_design_scores_gemma":[0.000021675869,0.000051874176,0.00008877118,0.0000067496567,0.0000092810615,0.000036628626,0.0000056760346,0.99469674,0.0027053824,0.001655356,0.0007114245,0.000010377863],"about_ca_topic_score_codex":0.0045414274,"about_ca_topic_score_gemma":0.006490855,"teacher_disagreement_score":0.0045414274,"about_ca_system_score_codex":0.00077400904,"about_ca_system_score_gemma":0.0016877997,"threshold_uncertainty_score":0.010418713},"labels":[],"label_agreement":null},{"id":"W2934497975","doi":"","title":"Classifier with Hierarchical Topographical Maps as Internal Representation.","year":2015,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Machine learning; Data mining","score_opus":0.07523832574978477,"score_gpt":0.35001370036302765,"score_spread":0.27477537461324286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2934497975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05843187,0.0014018641,0.91862935,0.0008145627,0.00040201942,0.00020945871,0.0019070614,0.004029466,0.014174302],"genre_scores_gemma":[0.74056286,0.00062968757,0.239806,0.00043103413,0.00025700586,0.00024405484,0.004366242,0.0002239569,0.013479289],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999703,0.000056966455,0.000016854321,0.000074283525,0.00008862486,0.000060272403],"domain_scores_gemma":[0.9994165,0.00017682221,0.00004096944,0.00013918288,0.00019062473,0.000035965506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048028099,0.0004590144,0.00067955203,0.00087163056,0.0003864182,0.0012976236,0.0012540473,0.0011900977,0.0047264015],"category_scores_gemma":[0.0024962907,0.00021559803,0.0006764157,0.0010659731,0.00027765587,0.0016961118,0.00095351133,0.0009039704,0.0026050787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003734777,0.0001897902,0.0024589784,0.00021038213,0.00016403808,0.00019333277,0.0001001022,0.08804378,0.018331015,0.020656848,0.040386457,0.8288918],"study_design_scores_gemma":[0.000027830105,0.00008780116,0.000994657,0.000028638735,0.000053262174,0.00012442257,0.00006116341,0.966575,0.0061141867,0.02134438,0.0045740283,0.000014588031],"about_ca_topic_score_codex":0.003633959,"about_ca_topic_score_gemma":0.004548136,"teacher_disagreement_score":0.0047264015,"about_ca_system_score_codex":0.00044565002,"about_ca_system_score_gemma":0.00077323127,"threshold_uncertainty_score":0.015811443},"labels":[],"label_agreement":null},{"id":"W2938038604","doi":"10.3819/ccbr.2019.140011","title":"Probability Learning by Perceptrons and People","year":2022,"lang":"en","type":"article","venue":"Comparative Cognition & Behavior Reviews","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perceptron; Margin (machine learning); White (mutation); Animal learning; Psychology; Artificial intelligence; Mathematics education; Cognitive psychology; Computer science; Machine learning; Biology; Artificial neural network","score_opus":0.12260791524826518,"score_gpt":0.33690398657982346,"score_spread":0.21429607133155826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938038604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0393239,0.009029954,0.8662913,0.021440482,0.0012657276,0.000083399034,0.000668142,0.0014253695,0.060471706],"genre_scores_gemma":[0.8423874,0.006918525,0.112112075,0.002096057,0.0014952932,0.00019864703,0.00077848404,0.00041146018,0.03360221],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99813026,0.0009648378,0.00007669324,0.00046170698,0.00023541655,0.00013102117],"domain_scores_gemma":[0.99160707,0.00635619,0.00042549195,0.00068199774,0.00059906096,0.0003301944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042831027,0.00079771294,0.00091903826,0.0013243317,0.00059278874,0.003645677,0.0012455703,0.0019233935,0.011774641],"category_scores_gemma":[0.020858768,0.0007261674,0.0011305199,0.0010709867,0.0030289537,0.0067060827,0.0016736287,0.0033029735,0.0023720113],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023655778,0.00012008808,0.004536654,0.00036099253,0.0003037002,0.00010147625,0.0003504381,0.10137068,0.00044675276,0.73476046,0.023443162,0.13396901],"study_design_scores_gemma":[0.000028969936,0.000033224936,0.00074923516,0.00007927886,0.000027233822,0.000047249952,0.000040026694,0.20849587,0.00031655043,0.7849856,0.005171039,0.000025690357],"about_ca_topic_score_codex":0.0037541077,"about_ca_topic_score_gemma":0.0024646607,"teacher_disagreement_score":0.011774641,"about_ca_system_score_codex":0.0016188872,"about_ca_system_score_gemma":0.0008265848,"threshold_uncertainty_score":0.039390087},"labels":[],"label_agreement":null},{"id":"W2940891384","doi":"10.4018/ijssci.2019010102","title":"Evaluating the Effects of Size and Precision of Training Data on ANN Training Performance for the Prediction of Chaotic Time Series Patterns","year":2019,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Chaotic; Artificial neural network; Training (meteorology); Time series; Mean squared error; Attractor; Artificial intelligence; Nonlinear system; Series (stratigraphy); Machine learning; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.07629571614617801,"score_gpt":0.35016898690959725,"score_spread":0.2738732707634192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940891384","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9417475,0.0017686624,0.053305306,0.00024408601,0.00014493304,0.00012313781,0.00016224364,0.0005095256,0.0019945824],"genre_scores_gemma":[0.97551835,0.0003497387,0.023402024,0.00003271499,0.000016089396,0.00005903357,0.00019047693,0.000043277912,0.00038832883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986749,0.0003985186,0.0002415725,0.00024883045,0.0003519486,0.00008421128],"domain_scores_gemma":[0.9770558,0.017343359,0.0011157617,0.0017084135,0.002608654,0.00016806506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035460468,0.0008738151,0.0005534484,0.00048413902,0.00034323192,0.00061759964,0.00049243716,0.0010048877,0.0005096785],"category_scores_gemma":[0.02797261,0.0002817164,0.00036236443,0.00046986938,0.00042899698,0.0011512932,0.00038715138,0.0006591599,0.00015945468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0037857129,0.0011021346,0.03828405,0.0008717941,0.00038643263,0.0006302353,0.00040412295,0.6064572,0.07775999,0.00078352075,0.0008907675,0.268644],"study_design_scores_gemma":[0.000099676225,0.0027507518,0.03608801,0.00015001751,0.00022996533,0.0002602675,0.00017758975,0.8671212,0.09168314,0.0005018588,0.0008636783,0.00007373337],"about_ca_topic_score_codex":0.0020200028,"about_ca_topic_score_gemma":0.0016620781,"teacher_disagreement_score":0.0035460468,"about_ca_system_score_codex":0.00027043687,"about_ca_system_score_gemma":0.00040370165,"threshold_uncertainty_score":0.018753469},"labels":[],"label_agreement":null},{"id":"W2942879675","doi":"10.1016/j.plrev.2019.04.008","title":"The Borsuk-Ulam theorem solves the curse of dimensionality: Comment on “The unreasonable effectiveness of small neural ensembles in high-dimensional brain” by Alexander N. Gorban et al.","year":2019,"lang":"en","type":"review","venue":"Physics of Life Reviews","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Curse of dimensionality; Artificial intelligence; Curse; Artificial neural network; Mathematical economics; Mathematics; Pattern recognition (psychology); Computer science; Machine learning; Philosophy","score_opus":0.08158565909551829,"score_gpt":0.335243104734152,"score_spread":0.2536574456386337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942879675","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015330398,0.54571074,0.0013838301,0.3976534,0.05164682,0.00000712029,0.0001308323,0.00006645666,0.0032475241],"genre_scores_gemma":[0.0060463976,0.44768,0.0013348421,0.4307481,0.10665249,0.00008107951,0.00013647288,0.0001060858,0.00721456],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979765,0.00048933236,0.00021715889,0.00029600805,0.0008762795,0.00014479566],"domain_scores_gemma":[0.9912717,0.0058480585,0.00041982165,0.00029337007,0.001936913,0.00023008486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058933254,0.0010694711,0.0017369019,0.001538024,0.0010132828,0.0022455554,0.0036570935,0.007834232,0.0030898578],"category_scores_gemma":[0.012222285,0.0004870001,0.0009463996,0.0020053505,0.0071197567,0.0070439135,0.002074021,0.020073121,0.005369878],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004190136,0.0000096815575,0.000053698248,0.00081049895,0.00003417138,0.000095124116,0.000052702384,0.00011863988,0.0001315441,0.04884188,0.9145766,0.035233557],"study_design_scores_gemma":[0.000030425917,0.000034854093,0.00018521861,0.00061990315,0.000022863844,0.0003457997,0.000032735083,0.00013174085,0.00026229944,0.06277838,0.9355235,0.000032200936],"about_ca_topic_score_codex":0.0023430565,"about_ca_topic_score_gemma":0.0028467,"teacher_disagreement_score":0.007834232,"about_ca_system_score_codex":0.0016179958,"about_ca_system_score_gemma":0.0022769428,"threshold_uncertainty_score":0.03116721},"labels":[],"label_agreement":null},{"id":"W2944876085","doi":"10.1155/2019/9852134","title":"Quality Prediction Model Based on Novel Elman Neural Network Ensemble","year":2019,"lang":"en","type":"article","venue":"Complexity","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Social Science Foundation of Jiangsu Province; Government of Jiangsu Province; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial neural network; Generalization; Ensemble forecasting; Artificial intelligence; Quality (philosophy); Ensemble learning; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.09145736791529512,"score_gpt":0.3034035623250433,"score_spread":0.21194619440974816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944876085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03116877,0.0005040111,0.966009,0.00012178825,0.00004927744,0.000019748202,0.00004600852,0.00029116604,0.0017901178],"genre_scores_gemma":[0.89896315,0.0005786635,0.09606939,0.00017912623,0.000078032375,0.000111879875,0.00024797453,0.000046648795,0.0037251776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994443,0.000087693705,0.00003302216,0.00018509601,0.00019366104,0.000056266686],"domain_scores_gemma":[0.99944204,0.00015059017,0.000092804315,0.000045978857,0.0002475809,0.000020932743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010131617,0.0007647225,0.0010391076,0.00056338555,0.00037934116,0.0006797914,0.0013947781,0.00075052603,0.00076225464],"category_scores_gemma":[0.0015119791,0.00035467127,0.0006845127,0.0006582776,0.00033592273,0.0015554427,0.00062903966,0.00093389244,0.00020089725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035859557,0.000027703169,0.0013386946,0.000023389312,0.000067215326,0.000038676924,0.000028169852,0.951208,0.0015428624,0.0016884537,0.00042659658,0.04357428],"study_design_scores_gemma":[9.805505e-7,0.0000060566663,0.000104261555,0.0000010784285,0.000004762331,0.000004256271,0.0000010655808,0.99928683,0.00021012321,0.00031344953,0.00006527359,0.0000019000798],"about_ca_topic_score_codex":0.0072025727,"about_ca_topic_score_gemma":0.005402822,"teacher_disagreement_score":0.0072025727,"about_ca_system_score_codex":0.00070144306,"about_ca_system_score_gemma":0.0005417539,"threshold_uncertainty_score":0.014321268},"labels":[],"label_agreement":null},{"id":"W2944935347","doi":"10.3390/e21050525","title":"Evaluating Approximations and Heuristic Measures of Integrated Information","year":2019,"lang":"en","type":"article","venue":"Entropy","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Horizon 2020 Framework Programme; Norges Forskningsråd; European Commission","keywords":"Heuristic; Computer science; Approximations of π; Mathematical optimization; Mathematics; Applied mathematics; Artificial intelligence","score_opus":0.03142982204694847,"score_gpt":0.2807008610099895,"score_spread":0.249271038963041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944935347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42623132,0.0009034557,0.56805944,0.00048782027,0.00006256309,0.00016441451,0.00023797996,0.00046825697,0.003384731],"genre_scores_gemma":[0.89734876,0.0001460924,0.10183272,0.00006035814,0.000018863735,0.00013341871,0.00022424123,0.000046280085,0.0001893725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99471325,0.002881651,0.00034630767,0.0006008512,0.0011869294,0.00027104293],"domain_scores_gemma":[0.8905171,0.09234826,0.005406072,0.0075849374,0.0032621971,0.0008813461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013703836,0.0010099891,0.0012177995,0.0027788258,0.0005618698,0.0022550668,0.0019126119,0.0015556249,0.00081190077],"category_scores_gemma":[0.14404276,0.00068512664,0.0006404573,0.0013125595,0.0028666856,0.0052517247,0.0025691898,0.0017622573,0.00012261295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027687397,0.00008111986,0.013709602,0.0002077332,0.00018540211,0.000089906156,0.0003886788,0.91029835,0.0008184453,0.04794083,0.00039954655,0.025603408],"study_design_scores_gemma":[0.000012782496,0.000093687864,0.0013122811,0.000036109937,0.000014569572,0.000030674673,0.00008888589,0.97560096,0.00074681337,0.021858403,0.00018039995,0.000024348883],"about_ca_topic_score_codex":0.0052222954,"about_ca_topic_score_gemma":0.0030318017,"teacher_disagreement_score":0.013703836,"about_ca_system_score_codex":0.003447819,"about_ca_system_score_gemma":0.0020153096,"threshold_uncertainty_score":0.072473705},"labels":[],"label_agreement":null},{"id":"W2945214814","doi":"","title":"Computing with uncertainty truth degrees: a convolution-based degrees","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Permission; License; Redistribution (election); Computer science; World Wide Web; Computer security; Internet privacy; Law and economics; Law; Political science; Sociology","score_opus":0.019013283127391766,"score_gpt":0.2723140589766176,"score_spread":0.25330077584922583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945214814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015363684,0.00060265657,0.979996,0.000991838,0.000081753744,0.000047377573,0.00019166427,0.00033290684,0.0023920992],"genre_scores_gemma":[0.54341567,0.0010368014,0.4484739,0.00061441783,0.0005849861,0.00015568665,0.00084500335,0.0002668665,0.0046067275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968419,0.000976763,0.00021780375,0.00086945994,0.00079692935,0.0002972202],"domain_scores_gemma":[0.9930166,0.0042059915,0.00043134912,0.0010668898,0.00088217383,0.00039701792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036737812,0.0009100773,0.0019043993,0.0019635155,0.0010788203,0.004058443,0.0028654332,0.0022726376,0.0057798517],"category_scores_gemma":[0.022591196,0.0008983253,0.001994516,0.0024013652,0.0024940476,0.007823499,0.0053651077,0.0041190484,0.0009858293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005841365,0.00016961659,0.0034384264,0.00030385895,0.0002854571,0.00018601566,0.000492186,0.41527373,0.0034241443,0.18483293,0.007618079,0.3833915],"study_design_scores_gemma":[0.00001621683,0.000058215548,0.0003320393,0.000052855135,0.000041456886,0.000077332756,0.000036461533,0.8794958,0.0013729773,0.11662697,0.0018673462,0.000022341586],"about_ca_topic_score_codex":0.0052921493,"about_ca_topic_score_gemma":0.0045273723,"teacher_disagreement_score":0.0057798517,"about_ca_system_score_codex":0.0022630922,"about_ca_system_score_gemma":0.00229128,"threshold_uncertainty_score":0.019429028},"labels":[],"label_agreement":null},{"id":"W2945544107","doi":"10.1007/978-3-030-20912-4_13","title":"On Learning and Convergence of RBF Networks in Regression Estimation and Classification","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convergence (economics); Artificial intelligence; Regression; Estimation; Machine learning; Artificial neural network; Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.01644847171202781,"score_gpt":0.26043386129300233,"score_spread":0.24398538958097452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945544107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026640424,0.0037553888,0.9910806,0.0003106028,0.00013464906,0.000027629576,0.00002661404,0.00012757853,0.0018729604],"genre_scores_gemma":[0.16888142,0.009655167,0.8060636,0.00054225186,0.0010880529,0.00033493826,0.00036878503,0.0005826772,0.012483114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961331,0.0022772122,0.0002491975,0.0004753553,0.000677287,0.00018777401],"domain_scores_gemma":[0.9785429,0.017797591,0.00047067084,0.00086325174,0.0021119406,0.0002135942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012863845,0.0019304528,0.0025597543,0.0016300428,0.000681351,0.0019247743,0.0029879226,0.003428742,0.0027632588],"category_scores_gemma":[0.047692657,0.0014074356,0.0019852454,0.002629507,0.003247235,0.004490825,0.0036398063,0.0062149186,0.0009991289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022684963,0.00007999505,0.0011555691,0.0005135769,0.00019466477,0.00008850981,0.00034859407,0.54159075,0.002099201,0.20171241,0.0070798025,0.24491003],"study_design_scores_gemma":[0.0000083888535,0.000028405771,0.00017443477,0.000054227716,0.000019982068,0.000029809704,0.000015410493,0.9475575,0.0004928483,0.05004686,0.001556833,0.00001534447],"about_ca_topic_score_codex":0.008037966,"about_ca_topic_score_gemma":0.004443291,"teacher_disagreement_score":0.012863845,"about_ca_system_score_codex":0.0015930095,"about_ca_system_score_gemma":0.0010622011,"threshold_uncertainty_score":0.06803131},"labels":[],"label_agreement":null},{"id":"W2946118415","doi":"10.17485/ijst/2019/v12i15/139608","title":"Forecasting the stock price time series via all components of multi resolution","year":2019,"lang":"en","type":"article","venue":"Indian Journal of Science and Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wavelet; Computer science; Artificial neural network; Backpropagation; Wavelet transform; Time series; Series (stratigraphy); Stock market; Component (thermodynamics); Spline (mechanical); Discrete wavelet transform; Econometrics; Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.02225248090212489,"score_gpt":0.2401748235100382,"score_spread":0.2179223426079133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946118415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19873792,0.0008441645,0.79741603,0.00023244455,0.00010781363,0.00004255761,0.00011669156,0.00033123253,0.0021710838],"genre_scores_gemma":[0.91359854,0.0007895357,0.083462246,0.00003004327,0.00003976228,0.000036869507,0.00013395475,0.000028200706,0.0018808137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998318,0.00002490924,0.000010436394,0.000039034214,0.0000779417,0.000015815605],"domain_scores_gemma":[0.9998448,0.000053583142,0.00003088861,0.000016075279,0.00004794535,0.0000067824026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044586632,0.0005260825,0.0003711492,0.0005020019,0.0001601237,0.0006561483,0.0004493435,0.0004637723,0.00071729184],"category_scores_gemma":[0.0011942191,0.00019572413,0.0006061459,0.0006993692,0.00018783307,0.0010311577,0.00029347185,0.0005879077,0.00027757726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016234693,0.00014801435,0.009087929,0.00019105514,0.00016852599,0.00020198428,0.000092890215,0.6903609,0.024940036,0.0053646727,0.00070047635,0.2685812],"study_design_scores_gemma":[0.0000022108322,0.000019644742,0.0012785175,0.0000049903147,0.000015340805,0.000015338961,0.0000042069896,0.99654996,0.0013266837,0.00056684355,0.00021229355,0.0000039170136],"about_ca_topic_score_codex":0.0034858417,"about_ca_topic_score_gemma":0.0022088166,"teacher_disagreement_score":0.0034858417,"about_ca_system_score_codex":0.00023473866,"about_ca_system_score_gemma":0.00034608613,"threshold_uncertainty_score":0.0069310665},"labels":[],"label_agreement":null},{"id":"W2946296475","doi":"10.1007/s40815-019-00642-w","title":"A New Approach to Multiple Time Series Prediction Using MIMO Fuzzy Aggregation Models with Modular Neural Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Adaptive neuro fuzzy inference system; Computer science; Mean squared error; Artificial neural network; Modular neural network; Data mining; Modular design; Fuzzy logic; Backpropagation; MIMO; Inference; Artificial intelligence; Neuro-fuzzy; Machine learning; Computational intelligence; Stock exchange; Cluster analysis; Fuzzy control system; Mathematics; Statistics","score_opus":0.01860133035764733,"score_gpt":0.22891474434988895,"score_spread":0.21031341399224163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946296475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008143029,0.00015102162,0.9906944,0.000057439214,0.00006447357,0.000014019015,0.000019034484,0.0001081412,0.00074836274],"genre_scores_gemma":[0.64222676,0.0004367868,0.35306296,0.00009949287,0.00022917395,0.00012009466,0.00011207908,0.000060069866,0.003652582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996747,0.00007608844,0.000027111495,0.00009116466,0.000102916245,0.000027988517],"domain_scores_gemma":[0.99955875,0.00016963997,0.00005365406,0.000054212695,0.00013945822,0.000024291821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006601278,0.00075551897,0.0009604481,0.00055670564,0.0004754179,0.00078192394,0.0011174057,0.0008637992,0.0013230527],"category_scores_gemma":[0.0012288311,0.0004273511,0.0010289071,0.0006921009,0.00032141202,0.001390219,0.00092492515,0.00094662776,0.00031589766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010863944,0.00012348132,0.0010474974,0.00013494195,0.00023851685,0.00023119216,0.000109822184,0.81470776,0.013604281,0.028664406,0.0011387579,0.13989067],"study_design_scores_gemma":[0.0000015670536,0.000011131617,0.00006607185,0.0000018215467,0.000008230019,0.000007870121,0.0000023459916,0.99785525,0.0002852893,0.0016270998,0.00012977746,0.0000034866778],"about_ca_topic_score_codex":0.0029172115,"about_ca_topic_score_gemma":0.0042374083,"teacher_disagreement_score":0.0029172115,"about_ca_system_score_codex":0.00043596624,"about_ca_system_score_gemma":0.00042917143,"threshold_uncertainty_score":0.0058004856},"labels":[],"label_agreement":null},{"id":"W2946836103","doi":"10.3389/fnins.2019.00390","title":"Robust Adaptive Recurrent Cerebellar Model Neural Network for Non-linear System Based on GPSO","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"Xiamen University of Technology; Xiamen University; Natural Science Foundation of Fujian Province","keywords":"Artificial neural network; Computer science; Artificial intelligence; Neuroscience; Psychology","score_opus":0.031919445234148934,"score_gpt":0.23816955692080802,"score_spread":0.20625011168665908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946836103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034404792,0.0013164904,0.9552369,0.00029125647,0.00022343632,0.00007286669,0.000052953743,0.0009964765,0.007404853],"genre_scores_gemma":[0.9595543,0.00051137095,0.035192378,0.00010779887,0.000052607415,0.00015338983,0.00009321806,0.000035102225,0.0042998022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997327,0.000043658,0.000019842682,0.00008103434,0.00008496773,0.00003785621],"domain_scores_gemma":[0.9997596,0.000055095665,0.000052025953,0.00002092602,0.0000978583,0.00001448754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044204653,0.0008753989,0.0007573593,0.00026161075,0.0005190134,0.0008633421,0.0010246183,0.00086229964,0.0015461398],"category_scores_gemma":[0.0009243217,0.00030084743,0.0005513098,0.0003347825,0.0005658506,0.00065358385,0.00060345826,0.00079286884,0.0003489602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001479913,0.00006606977,0.001186654,0.0002529406,0.00012689891,0.00033392318,0.00016156057,0.8806511,0.011599295,0.012031345,0.0020717634,0.09137042],"study_design_scores_gemma":[0.00001681718,0.0000582486,0.00017527964,0.000009291332,0.000021867943,0.000037054808,0.0000061729625,0.99722785,0.0008140831,0.000989849,0.0006348405,0.00000866725],"about_ca_topic_score_codex":0.009311767,"about_ca_topic_score_gemma":0.009271092,"teacher_disagreement_score":0.009311767,"about_ca_system_score_codex":0.0005713396,"about_ca_system_score_gemma":0.00076010684,"threshold_uncertainty_score":0.01851511},"labels":[],"label_agreement":null},{"id":"W2949190276","doi":"","title":"On the difficulty of training Recurrent Neural Networks","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Constraint (computer-aided design); Perspective (graphical); Computer science; Artificial neural network; Simple (philosophy); Norm (philosophy); Artificial intelligence; Gradient descent; Mathematical optimization; Algorithm; Applied mathematics; Mathematics; Geometry; Epistemology","score_opus":0.1297134932191586,"score_gpt":0.20029158105233916,"score_spread":0.07057808783318056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949190276","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047331005,0.0048449244,0.9262916,0.01007275,0.0004431949,0.00011710806,0.00030114135,0.0014890273,0.009109224],"genre_scores_gemma":[0.7266534,0.0047830925,0.25144994,0.0024463371,0.0016322163,0.00055146724,0.00089744554,0.0008649056,0.010721198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977303,0.0012295011,0.00019288693,0.00037191203,0.000367095,0.000108389686],"domain_scores_gemma":[0.96361214,0.032629162,0.0007660522,0.0012574211,0.0014341909,0.00030106056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069873333,0.0015495835,0.0017363301,0.0006906053,0.0008311091,0.0020589053,0.002243487,0.0035679687,0.004669994],"category_scores_gemma":[0.060435787,0.0010511245,0.0006641036,0.0008836869,0.002397424,0.007492326,0.0030098855,0.005343717,0.001233724],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051928003,0.0001320966,0.0027143653,0.00081415544,0.00017609193,0.0006433103,0.00045759007,0.7165919,0.0031923342,0.120619886,0.015697217,0.1384418],"study_design_scores_gemma":[0.000036133508,0.000060172715,0.0002595321,0.00006312785,0.00001575661,0.00007970176,0.00004883151,0.91840434,0.0008707491,0.07880941,0.0013348144,0.000017400671],"about_ca_topic_score_codex":0.0034110781,"about_ca_topic_score_gemma":0.003149687,"teacher_disagreement_score":0.0069873333,"about_ca_system_score_codex":0.0010281673,"about_ca_system_score_gemma":0.00079861877,"threshold_uncertainty_score":0.036952972},"labels":[],"label_agreement":null},{"id":"W2949207763","doi":"10.48550/arxiv.1611.02120","title":"Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial neural network; Computer science; Stochastic neural network; Deep neural networks; Artificial intelligence; Recurrent neural network","score_opus":0.061535756809425565,"score_gpt":0.20057069766927027,"score_spread":0.1390349408598447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949207763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012074776,0.00068420003,0.98237485,0.00030863556,0.000051828956,0.00009506703,0.000043521413,0.00042611206,0.003940955],"genre_scores_gemma":[0.3939556,0.0008370781,0.59736484,0.00044450862,0.00008374793,0.00085251086,0.00025730184,0.00038248935,0.0058219633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992894,0.00036141067,0.000030432742,0.00011317945,0.00015839009,0.000047128502],"domain_scores_gemma":[0.9988405,0.00073312933,0.00010903643,0.00008175296,0.0002034634,0.00003207393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018542433,0.0017397262,0.0010370259,0.0006676281,0.0003555057,0.0010674772,0.0011162296,0.0016502072,0.002527204],"category_scores_gemma":[0.0044713486,0.0007724602,0.0007112368,0.00057317695,0.00079031626,0.0009661046,0.0011106911,0.0017253967,0.00072655786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002411035,0.000023515768,0.00023791548,0.00006550105,0.000028686138,0.000031519896,0.000025122927,0.9673093,0.0012061305,0.0030492062,0.0008273459,0.027171766],"study_design_scores_gemma":[0.0000061527708,0.000015942704,0.000033842563,0.000013264064,0.00000488893,0.000007441914,0.000005520391,0.99650383,0.00045490425,0.002388747,0.00056218274,0.0000033676868],"about_ca_topic_score_codex":0.0019130108,"about_ca_topic_score_gemma":0.0021632286,"teacher_disagreement_score":0.002527204,"about_ca_system_score_codex":0.0008976105,"about_ca_system_score_gemma":0.00094396184,"threshold_uncertainty_score":0.009806275},"labels":[],"label_agreement":null},{"id":"W2949688604","doi":"10.48550/arxiv.1301.7033","title":"Quantum Hall Effect in Fractal Graphene: Growth and Properties of Graphlocons","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Graphene; Dendrite (mathematics); Limiting; Snowflake; Condensed matter physics; Materials science; Chemical vapor deposition; Fractal; Diffusion; Chemical physics; Copper; Surface diffusion; Symmetry (geometry); Nanotechnology; Chemistry; Physics; Thermodynamics; Geometry; Physical chemistry","score_opus":0.040571105846193015,"score_gpt":0.1709367853100125,"score_spread":0.1303656794638195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949688604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99738103,0.00017084675,0.00083206553,0.00005299783,0.000009945964,0.0000027146625,0.00007996475,0.000025068806,0.0014453286],"genre_scores_gemma":[0.99908125,0.000060687376,0.0005273196,0.0000051762067,0.0000028643742,0.0000014706927,0.000046977882,0.000003965217,0.0002702326],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999651,0.0000053630715,0.0000013954776,0.0000071487884,0.000013179241,0.000007755216],"domain_scores_gemma":[0.9998946,0.000031701376,0.000028682993,0.000012854672,0.000017156039,0.000014958284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004687633,0.000089317226,0.00008686376,0.00023909524,0.00014687821,0.00021365905,0.00015679328,0.00022720969,0.00042546482],"category_scores_gemma":[0.0002483442,0.000064603475,0.0000894995,0.00014543912,0.00027881723,0.0001319813,0.00013507479,0.000106248466,0.000053850625],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000971073,0.000025538426,0.0013703763,0.0001158536,0.000017914579,0.00022909392,0.00010843614,0.0086379815,0.98003906,0.006179587,0.00037223773,0.0028068854],"study_design_scores_gemma":[0.00003843611,0.0002873305,0.022237284,0.000028268318,0.000030928935,0.00065231463,0.00015235913,0.14037189,0.8251266,0.006997003,0.004017684,0.000059965252],"about_ca_topic_score_codex":0.00080147467,"about_ca_topic_score_gemma":0.001094129,"teacher_disagreement_score":0.00080147467,"about_ca_system_score_codex":0.00025715522,"about_ca_system_score_gemma":0.00007681958,"threshold_uncertainty_score":0.0018657446},"labels":[],"label_agreement":null},{"id":"W2949982775","doi":"10.13140/rg.2.1.3661.0407","title":"Difference Target Propagation","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Infinitesimal; Backpropagation; Computer science; Propagation of uncertainty; Nonlinear system; Relation (database); Wave propagation; Encoder; Algorithm; Artificial intelligence; Artificial neural network; Mathematics; Physics; Mathematical analysis","score_opus":0.05482958240973031,"score_gpt":0.17745346291754363,"score_spread":0.12262388050781332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949982775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071054185,0.0004485872,0.9817732,0.0005596186,0.0002702206,0.000078990066,0.00030860835,0.0024804203,0.0069748615],"genre_scores_gemma":[0.46152985,0.0009504469,0.49170274,0.0012168266,0.0003836113,0.00036148782,0.0018199108,0.0013918135,0.040643368],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877065,0.00022069461,0.000069005415,0.00031487874,0.00049187464,0.00013281089],"domain_scores_gemma":[0.995808,0.0020410905,0.0002182412,0.0007557439,0.0010428043,0.0001340861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019164409,0.0015637031,0.0014114019,0.0012025349,0.00066755107,0.0020794512,0.0032403464,0.0025912223,0.012305491],"category_scores_gemma":[0.010133759,0.00064117037,0.0010420261,0.001473809,0.0015307495,0.003787789,0.0026619986,0.0035377704,0.00425814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005600401,0.00022918031,0.0017781989,0.00036570523,0.00016520263,0.00027323473,0.00021112274,0.3757018,0.008801871,0.15464474,0.024957014,0.432312],"study_design_scores_gemma":[0.000025280773,0.000045512854,0.00012577664,0.000025581996,0.00002654668,0.00007549101,0.000011733622,0.93979484,0.005506384,0.04959267,0.0047526523,0.00001758394],"about_ca_topic_score_codex":0.0043229964,"about_ca_topic_score_gemma":0.0055235336,"teacher_disagreement_score":0.012305491,"about_ca_system_score_codex":0.00190723,"about_ca_system_score_gemma":0.0015818629,"threshold_uncertainty_score":0.041166008},"labels":[],"label_agreement":null},{"id":"W2950100795","doi":"10.1016/j.neunet.2019.06.005","title":"Combining Hopfield neural networks, with applications to grid-based mathematics puzzles","year":2019,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hopfield network; Artificial neural network; Computer science; Constraint (computer-aided design); Simple (philosophy); Set (abstract data type); Grid; Solver; Selection (genetic algorithm); Mathematical optimization; Artificial intelligence; Theoretical computer science; Algorithm; Mathematics","score_opus":0.01403382173678859,"score_gpt":0.23842341870740036,"score_spread":0.22438959697061178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950100795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07315776,0.0033833073,0.8667795,0.0030665325,0.00061511854,0.000084195206,0.00013716698,0.00054366345,0.052232847],"genre_scores_gemma":[0.78603053,0.002714721,0.19999185,0.00029187725,0.00040868865,0.000098386474,0.00010515802,0.00010600502,0.010252795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976605,0.00009859992,0.000016973996,0.00003582811,0.000060335497,0.000022347482],"domain_scores_gemma":[0.99902,0.0005778507,0.00008589164,0.00010322549,0.00014486014,0.00006827735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094523304,0.0006661031,0.0006686507,0.0012731871,0.0007365321,0.0018965057,0.0012275325,0.0012437606,0.003600798],"category_scores_gemma":[0.0056849476,0.00032087776,0.00051280065,0.0013922992,0.0010263245,0.002663712,0.0019593681,0.0010106816,0.00046138855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001350138,0.000073899166,0.002086351,0.00020960682,0.00010095829,0.00022986713,0.0002206653,0.6453437,0.0020042898,0.21664639,0.00370312,0.12924613],"study_design_scores_gemma":[0.000007872203,0.000009306452,0.00013642242,0.000012897092,0.0000092870705,0.000020619384,0.000030683117,0.85982233,0.00038594907,0.13782848,0.0017263362,0.000009773692],"about_ca_topic_score_codex":0.0043688505,"about_ca_topic_score_gemma":0.005651153,"teacher_disagreement_score":0.0043688505,"about_ca_system_score_codex":0.0007760829,"about_ca_system_score_gemma":0.0005273125,"threshold_uncertainty_score":0.01204592},"labels":[],"label_agreement":null},{"id":"W2951025565","doi":"","title":"State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations","year":2019,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Reification (Marxism); Generalization; Computer science; State (computer science); Theoretical computer science; Artificial intelligence; Algorithm; Mathematics","score_opus":0.022436667099194292,"score_gpt":0.2829833811041134,"score_spread":0.26054671400491913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951025565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06212186,0.00063748873,0.9337736,0.00045104494,0.0001032206,0.000045665947,0.00012399968,0.0011352214,0.0016080486],"genre_scores_gemma":[0.84229094,0.00053306576,0.15113427,0.000308462,0.000096456264,0.00012931449,0.00053539313,0.00022022975,0.0047518276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934036,0.00023417614,0.00004315754,0.00022059339,0.0000942908,0.00006755815],"domain_scores_gemma":[0.99698275,0.0016594254,0.00023361032,0.00071864866,0.00033960128,0.00006600697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002285273,0.0012467649,0.0013563675,0.00059926935,0.00044711403,0.00095446064,0.0020099012,0.0016693622,0.0023378083],"category_scores_gemma":[0.007732585,0.0007077964,0.0008826114,0.00070963625,0.00070105615,0.0031453574,0.0014773147,0.0028694964,0.0006657851],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024270819,0.00012661617,0.0013383738,0.000050240807,0.000099447214,0.00006336991,0.00011436789,0.76403445,0.002995559,0.012227447,0.0019249732,0.21678248],"study_design_scores_gemma":[0.000004466486,0.000012561742,0.00005823716,0.0000036141469,0.000006122559,0.0000046508194,0.0000022084305,0.9961719,0.0003920404,0.0032336426,0.00010789967,0.0000027032386],"about_ca_topic_score_codex":0.009557621,"about_ca_topic_score_gemma":0.008606377,"teacher_disagreement_score":0.009557621,"about_ca_system_score_codex":0.0008362971,"about_ca_system_score_gemma":0.00083703373,"threshold_uncertainty_score":0.019003987},"labels":[],"label_agreement":null},{"id":"W2951195401","doi":"10.48550/arxiv.1805.08239","title":"The Roles of Supervised Machine Learning in Systems Neuroscience","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Toolbox; Systems neuroscience; Computational neuroscience; Computer science; Neuroscience; Artificial intelligence; Machine learning; Cognitive science; Psychology; Central nervous system","score_opus":0.06087281412528758,"score_gpt":0.1886398697415559,"score_spread":0.1277670556162683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951195401","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009324883,0.25402418,0.58468467,0.08748302,0.0031261784,0.0000824594,0.00030545137,0.0006153726,0.06035375],"genre_scores_gemma":[0.48284805,0.21088915,0.26953283,0.009728139,0.018227622,0.00040769452,0.0003971329,0.00031502417,0.007654372],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950989,0.0030524146,0.00021465227,0.0005569972,0.00095030846,0.00012672639],"domain_scores_gemma":[0.9787022,0.017660087,0.00040477977,0.0013888958,0.0014370213,0.00040710648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010102754,0.00093545316,0.0012499606,0.0019616755,0.0012075278,0.0042957645,0.0013391414,0.0024932732,0.0024020313],"category_scores_gemma":[0.017530857,0.0004913407,0.00054956716,0.0014821184,0.011043466,0.00856273,0.0027704728,0.0060423277,0.00088105025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025871459,0.000026684156,0.000594999,0.00045151188,0.00003830773,0.00004076414,0.00022676948,0.007103174,0.00024177915,0.91394347,0.006426992,0.07087964],"study_design_scores_gemma":[0.0000047231943,0.0000128498505,0.00016862604,0.0001362413,0.0000047300546,0.000027778662,0.000032466607,0.010366817,0.00014875308,0.9751932,0.013888776,0.000014952187],"about_ca_topic_score_codex":0.0011835165,"about_ca_topic_score_gemma":0.0008344428,"teacher_disagreement_score":0.010102754,"about_ca_system_score_codex":0.0022491715,"about_ca_system_score_gemma":0.0021748657,"threshold_uncertainty_score":0.053429067},"labels":[],"label_agreement":null},{"id":"W2951308445","doi":"","title":"Structure Discovery in Nonparametric Regression through Compositional Kernel Search","year":2013,"lang":"en","type":"preprint","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":192,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Kernel (algebra); Extrapolation; Nonparametric statistics; Computer science; Kernel method; Kernel embedding of distributions; Nonparametric regression; Kernel regression; Range (aeronautics); Artificial intelligence; Regression; Machine learning; Data mining; Mathematics; Pattern recognition (psychology); Regression analysis; Support vector machine; Statistics; Engineering; Discrete mathematics","score_opus":0.020504118666902854,"score_gpt":0.2806218579701827,"score_spread":0.26011773930327986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951308445","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0131199015,0.00043541013,0.98517966,0.00019609719,0.000014594728,0.0000378295,0.000052424926,0.00043323968,0.000530869],"genre_scores_gemma":[0.4254844,0.0005589119,0.5701873,0.00029128618,0.00010631186,0.0002882944,0.00060642645,0.00025835348,0.0022187713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977094,0.0013856187,0.000090954454,0.00028803066,0.00043036757,0.000095687414],"domain_scores_gemma":[0.99366814,0.004254638,0.0004879788,0.00089404534,0.00049119466,0.00020389754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046137637,0.0011440079,0.0022226602,0.0016731094,0.0007791544,0.0015296267,0.0021130242,0.0019547243,0.002148017],"category_scores_gemma":[0.017638175,0.0008517165,0.0012431447,0.0019557637,0.0019486396,0.003500312,0.0032898667,0.0025906786,0.0010222158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038552663,0.00025160887,0.0033625043,0.00034173732,0.00024675077,0.00015644205,0.000157958,0.6771827,0.0043720985,0.116278924,0.0032154561,0.1940483],"study_design_scores_gemma":[0.000021630645,0.000030917614,0.0000922996,0.000009047453,0.000010243097,0.000015150882,0.000007701971,0.9632679,0.00036007303,0.035900395,0.00027697394,0.0000076959805],"about_ca_topic_score_codex":0.0017351261,"about_ca_topic_score_gemma":0.0033016675,"teacher_disagreement_score":0.0046137637,"about_ca_system_score_codex":0.00084524986,"about_ca_system_score_gemma":0.0017008268,"threshold_uncertainty_score":0.024400175},"labels":[],"label_agreement":null},{"id":"W2951603627","doi":"","title":"On the Number of Linear Regions of Deep Neural Networks","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":488,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Université de Montréal","funders":"","keywords":"Rectifier (neural networks); Piecewise linear function; Computation; Artificial neural network; Computer science; Feedforward neural network; Feed forward; Deep learning; Activation function; Homogeneous space; Piecewise; Algorithm; Artificial intelligence; Mathematics; Stochastic neural network; Recurrent neural network; Geometry","score_opus":0.05273342306533113,"score_gpt":0.19882131165962197,"score_spread":0.14608788859429084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951603627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33396277,0.003666758,0.6323458,0.0051825996,0.00013821054,0.00013980438,0.0013056722,0.0009966564,0.022261802],"genre_scores_gemma":[0.91454774,0.0014944194,0.075688355,0.0005096213,0.00025353418,0.00029112445,0.00087511464,0.00048352906,0.005856522],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979005,0.00068845926,0.00011412987,0.0004688893,0.0005373074,0.00029068164],"domain_scores_gemma":[0.9635846,0.030235961,0.002131129,0.0018944595,0.0010405738,0.0011132886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002759005,0.0011766002,0.0014705664,0.0013295067,0.00097079336,0.003654542,0.002385795,0.0018537571,0.005993531],"category_scores_gemma":[0.030049497,0.0010918379,0.0015973032,0.0010127699,0.0035635263,0.012220006,0.0037925148,0.0046440717,0.0006920999],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008894222,0.00015338048,0.0051492774,0.00059670804,0.00013656536,0.0003354726,0.00064020295,0.3290299,0.0067967423,0.6225513,0.004081494,0.029639557],"study_design_scores_gemma":[0.000039524948,0.00005820339,0.0005778075,0.000038038837,0.00002575585,0.00008455357,0.000048180067,0.47209117,0.0014197049,0.52452475,0.0010673539,0.000025000187],"about_ca_topic_score_codex":0.0016576989,"about_ca_topic_score_gemma":0.0013649084,"teacher_disagreement_score":0.005993531,"about_ca_system_score_codex":0.002248689,"about_ca_system_score_gemma":0.001171293,"threshold_uncertainty_score":0.020050406},"labels":[],"label_agreement":null},{"id":"W2951993850","doi":"","title":"Using Fast Weights to Attend to the Recent Past","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Sequence (biology); Artificial neural network; Computer science; Variable (mathematics); Neural system; Artificial intelligence; Type (biology); Psychology; Mathematics; Neuroscience; Biology; Ecology","score_opus":0.11943616466131095,"score_gpt":0.2158102249668421,"score_spread":0.09637406030553115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951993850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14755526,0.0019335509,0.83128935,0.0017799818,0.0009410425,0.00011001539,0.00047755294,0.0025109404,0.0134022795],"genre_scores_gemma":[0.8540286,0.0015280793,0.13095492,0.00037967006,0.00030442973,0.00015992933,0.00042919398,0.0003874853,0.011827692],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995622,0.00006657706,0.00003850749,0.00015047476,0.00012309417,0.000059146623],"domain_scores_gemma":[0.9975764,0.0008195085,0.00029295462,0.00070805603,0.00044162583,0.00016141597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011700641,0.0009854838,0.00068379194,0.0010830971,0.00059958413,0.0017389785,0.0019891171,0.00123848,0.00616986],"category_scores_gemma":[0.00964052,0.00076073944,0.00047417468,0.0011707296,0.0010164721,0.0054391255,0.00205275,0.0022279634,0.0013774585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058350404,0.0002979592,0.0054458263,0.00045856115,0.0002753334,0.00030108757,0.0006532097,0.19022423,0.047942232,0.1573826,0.011433104,0.5850024],"study_design_scores_gemma":[0.000079305355,0.00016089849,0.0025364975,0.00007736607,0.000113260554,0.00031542245,0.00008253266,0.76066315,0.0136543745,0.20924515,0.012992441,0.00007955518],"about_ca_topic_score_codex":0.0029173254,"about_ca_topic_score_gemma":0.0035197972,"teacher_disagreement_score":0.00616986,"about_ca_system_score_codex":0.0008306844,"about_ca_system_score_gemma":0.00087525585,"threshold_uncertainty_score":0.020640194},"labels":[],"label_agreement":null},{"id":"W2952577166","doi":"10.48550/arxiv.0811.1081","title":"Parallel GPU Implementation of Iterative PCA Algorithms","year":2008,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Principal component analysis; Orthogonalization; Graphics processing unit; Computer science; Orthogonality; CUDA; Algorithm; Graphics; Computation; Parallel computing; Artificial intelligence; Mathematics; Computer graphics (images)","score_opus":0.05920165061853042,"score_gpt":0.32530347009718524,"score_spread":0.2661018194786548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952577166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042782508,0.00040730933,0.9208147,0.0002971116,0.00020858334,0.00009743285,0.0005405121,0.011098372,0.023753477],"genre_scores_gemma":[0.26498884,0.00032098367,0.7214951,0.00013138991,0.000051365325,0.000307971,0.0011763555,0.0010350528,0.010492831],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951303,0.00009144638,0.000028458091,0.00007338294,0.00021624156,0.00007738028],"domain_scores_gemma":[0.9993383,0.000119207914,0.000028310313,0.0001289079,0.00035091062,0.00003422596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036117266,0.0008640033,0.0008416525,0.00066366163,0.0006083,0.0013043992,0.0013654524,0.00071403786,0.010008911],"category_scores_gemma":[0.0020959966,0.00033128177,0.00060969853,0.0013047229,0.00032800683,0.00087682257,0.00087925966,0.00087259494,0.003016951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005985871,0.00022790919,0.0029469826,0.00028034512,0.00018205402,0.00044701886,0.00037653896,0.43611076,0.026996799,0.045614876,0.040772773,0.44544545],"study_design_scores_gemma":[0.00004617478,0.000031610412,0.00035138577,0.000009471988,0.000011534067,0.00007564641,0.000034175027,0.9745882,0.0076494324,0.007811578,0.009377086,0.000013741187],"about_ca_topic_score_codex":0.010685635,"about_ca_topic_score_gemma":0.010868094,"teacher_disagreement_score":0.010685635,"about_ca_system_score_codex":0.000768539,"about_ca_system_score_gemma":0.0014396174,"threshold_uncertainty_score":0.033483088},"labels":[],"label_agreement":null},{"id":"W2952800213","doi":"10.48550/arxiv.1811.10396","title":"Learning to Skip Ineffectual Recurrent Computations in LSTMs","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Computation; Pruning; Inference; Set (abstract data type); Recurrent neural network; Process (computing); Speedup; Computer engineering; Parallel computing; Artificial intelligence; Algorithm; Artificial neural network; Programming language","score_opus":0.0630774526983908,"score_gpt":0.2118791832265717,"score_spread":0.1488017305281809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952800213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08977878,0.00042396493,0.90398216,0.00026526846,0.00014602607,0.000055184104,0.00016190734,0.0029260162,0.0022608151],"genre_scores_gemma":[0.7680049,0.00031598864,0.22719377,0.00026080216,0.00007439875,0.00012166379,0.0004392843,0.00028634132,0.003302757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996655,0.000056548837,0.00002895916,0.00009318216,0.00009420946,0.00006156158],"domain_scores_gemma":[0.99921834,0.0003919422,0.00009752162,0.00012588922,0.00012787741,0.00003831401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061597273,0.001006626,0.00071964,0.00034809666,0.00034013577,0.0006894417,0.0012639994,0.00065961527,0.0024201286],"category_scores_gemma":[0.0036217715,0.00051530375,0.00046981793,0.00039570322,0.0005496062,0.0018807013,0.0009289246,0.001390879,0.00066372164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004496422,0.00022655263,0.0021984323,0.00043123437,0.00017993124,0.0005408731,0.00027493955,0.48800275,0.07826074,0.02655751,0.0070149545,0.39586252],"study_design_scores_gemma":[0.0000099031895,0.000036604906,0.00014159585,0.0000093266735,0.000014431417,0.000040553732,0.0000113374,0.98174185,0.010609684,0.006742401,0.0006359421,0.0000063337025],"about_ca_topic_score_codex":0.0021635178,"about_ca_topic_score_gemma":0.0061929193,"teacher_disagreement_score":0.0024201286,"about_ca_system_score_codex":0.00056471024,"about_ca_system_score_gemma":0.000886926,"threshold_uncertainty_score":0.0080961585},"labels":[],"label_agreement":null},{"id":"W2957236078","doi":"10.48550/arxiv.1907.05550","title":"Faster Neural Network Training with Data Echoing","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Training (meteorology); Artificial neural network; Computer science; Artificial intelligence; Training set; Machine learning; Psychology; Geography","score_opus":0.16860512160132593,"score_gpt":0.20413053186558897,"score_spread":0.035525410264263035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957236078","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5358704,0.0016005617,0.39781308,0.0014518978,0.000900182,0.00021321038,0.0010122177,0.050318748,0.010819702],"genre_scores_gemma":[0.77239186,0.0002482539,0.21905376,0.00048099348,0.00010486329,0.00016735084,0.0016345147,0.0011148134,0.0048036436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989334,0.0001380813,0.00009496102,0.00035526295,0.00033469533,0.00014362007],"domain_scores_gemma":[0.9956176,0.002023122,0.0001893829,0.001288939,0.00072187313,0.0001590954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015375876,0.0010360897,0.0007970595,0.0006021762,0.00042504809,0.0010747137,0.0019588778,0.0009313408,0.004474718],"category_scores_gemma":[0.010168035,0.0007084929,0.00045803402,0.0010959014,0.0007372101,0.004188922,0.0012622367,0.0022080063,0.001300815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002644049,0.0009595464,0.0109498985,0.0005526632,0.00029920472,0.00030167477,0.00052062277,0.27828273,0.10071761,0.008041961,0.024904614,0.5718254],"study_design_scores_gemma":[0.00010356653,0.0002415727,0.0015849943,0.000017539183,0.000031681462,0.000065426575,0.000063201085,0.9422471,0.04896379,0.0028735292,0.0037738883,0.00003373561],"about_ca_topic_score_codex":0.0073388275,"about_ca_topic_score_gemma":0.009123895,"teacher_disagreement_score":0.0073388275,"about_ca_system_score_codex":0.0008986091,"about_ca_system_score_gemma":0.001400427,"threshold_uncertainty_score":0.014969468},"labels":[],"label_agreement":null},{"id":"W2957648571","doi":"","title":"Support Vector Machine with Graphical Network Structures in Features","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Data Mining in Pattern Recognition","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Western University","funders":"","keywords":"Computer science; Support vector machine; Graphical model; Theoretical computer science; Artificial intelligence","score_opus":0.01844727399585286,"score_gpt":0.2645528993714113,"score_spread":0.24610562537555844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957648571","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01546001,0.0002823974,0.97909147,0.0003969285,0.000113589354,0.00007951572,0.000610052,0.0024766736,0.0014893282],"genre_scores_gemma":[0.5160333,0.00043405706,0.4758482,0.0002617563,0.00021517384,0.0004185571,0.0022132443,0.00017473985,0.004400987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991185,0.00025578475,0.00006289869,0.00019171942,0.0003074119,0.000063682244],"domain_scores_gemma":[0.9984781,0.0008018617,0.00011717256,0.0002675003,0.00029119686,0.000044261073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006818151,0.0008105314,0.0009265874,0.00087483774,0.0002599794,0.0013053444,0.0012530133,0.001100775,0.0037658503],"category_scores_gemma":[0.0072816005,0.00038925768,0.0006632064,0.001367622,0.0003878444,0.0018128952,0.00101374,0.001400039,0.0019873676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005884231,0.00026381682,0.001254618,0.00024809878,0.00011550331,0.00020983693,0.000042549564,0.21846645,0.006792593,0.02695437,0.011136776,0.73392695],"study_design_scores_gemma":[0.00003486319,0.00011363559,0.00018263767,0.000013905839,0.00001968255,0.000042551586,0.000006416699,0.97938454,0.0020836147,0.01663942,0.0014688272,0.000009960353],"about_ca_topic_score_codex":0.0016463287,"about_ca_topic_score_gemma":0.0013305506,"teacher_disagreement_score":0.0037658503,"about_ca_system_score_codex":0.0003658883,"about_ca_system_score_gemma":0.0007288525,"threshold_uncertainty_score":0.012598038},"labels":[],"label_agreement":null},{"id":"W2958054060","doi":"10.71781/15275","title":"Méthode d'inférence par bootstrap pour l'estimateur sisVIVE en randomisation mendélienne","year":2018,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Statistics","score_opus":0.10970809852073524,"score_gpt":0.3606403212851097,"score_spread":0.25093222276437444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958054060","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002699332,0.0003365928,0.9949733,0.00016296927,0.0001305552,0.00013362715,0.00022948776,0.00047018254,0.00086382916],"genre_scores_gemma":[0.09390875,0.00091830094,0.89564,0.00033297532,0.0002928434,0.0019140175,0.0010845396,0.00066965976,0.005238944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97392786,0.01940547,0.00087413663,0.0023778407,0.0027631049,0.0006516318],"domain_scores_gemma":[0.94221574,0.049212053,0.0012079155,0.0041260095,0.0029860921,0.00025218938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025715454,0.0016654934,0.0034892408,0.0031545355,0.0013026219,0.002819317,0.003255046,0.0021247528,0.016745873],"category_scores_gemma":[0.104362205,0.001028037,0.0039783986,0.0027832256,0.0021713213,0.0019082796,0.0025074708,0.005610103,0.0044692624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012311232,0.00029651372,0.012030362,0.0017036503,0.0020606425,0.0010463784,0.0011335907,0.12403213,0.0055581825,0.4176605,0.012831361,0.42041564],"study_design_scores_gemma":[0.0004052507,0.00058739434,0.008048869,0.0006882843,0.0004550912,0.0007129997,0.00031710314,0.66321856,0.0075332895,0.26774916,0.050037917,0.00024604253],"about_ca_topic_score_codex":0.009395083,"about_ca_topic_score_gemma":0.0059927073,"teacher_disagreement_score":0.025715454,"about_ca_system_score_codex":0.0013250988,"about_ca_system_score_gemma":0.0040257326,"threshold_uncertainty_score":0.13599795},"labels":[],"label_agreement":null},{"id":"W2959400106","doi":"10.1088/1742-6596/1237/2/022030","title":"Performance Analysis of Various Activation Functions in Artificial Neural Networks","year":2019,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":192,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Activation function; Artificial neural network; Computer science; Artificial intelligence; Principal (computer security); Function (biology); Affect (linguistics); Machine learning; Psychology; Biology; Communication","score_opus":0.01991495602824132,"score_gpt":0.2342995755777208,"score_spread":0.2143846195494795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959400106","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6058218,0.008058721,0.37001333,0.0010810805,0.00029386827,0.00011819722,0.00032305572,0.0009867515,0.013303261],"genre_scores_gemma":[0.9792214,0.00097170885,0.01809773,0.000045593333,0.00002759677,0.000058483234,0.00021345976,0.00006883154,0.0012952108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855405,0.000653167,0.00010416742,0.00013051735,0.00040171284,0.0001563261],"domain_scores_gemma":[0.9953768,0.0030695186,0.00025427045,0.00016193517,0.0010678165,0.00006959872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004533008,0.0010462302,0.0007169846,0.0014005617,0.00044639685,0.00119753,0.0006926216,0.0012063144,0.0008161806],"category_scores_gemma":[0.011536977,0.00018888748,0.0005760761,0.0012471753,0.0005271454,0.00081971183,0.0005310853,0.0007357527,0.00025110296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004335904,0.00008946619,0.0041847015,0.00029353728,0.00009856846,0.00010316595,0.00008766743,0.9230979,0.0042732256,0.0048455484,0.0007247799,0.061767913],"study_design_scores_gemma":[0.0000046709292,0.00008542807,0.0013881171,0.000024573457,0.000017089884,0.00002375204,0.000024526398,0.99412584,0.0030205944,0.000980247,0.00029334624,0.000011791115],"about_ca_topic_score_codex":0.004147732,"about_ca_topic_score_gemma":0.0013929941,"teacher_disagreement_score":0.004533008,"about_ca_system_score_codex":0.0009187441,"about_ca_system_score_gemma":0.0005418099,"threshold_uncertainty_score":0.023973107},"labels":[],"label_agreement":null},{"id":"W2963216439","doi":"","title":"Neural Networks Trained to Solve Differential Equations Learn General Representations","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Generality; MNIST database; Artificial neural network; Computer science; Curse of dimensionality; Artificial intelligence; Domain (mathematical analysis); Canonical correlation; Set (abstract data type); Partial differential equation; Algorithm; Applied mathematics; Mathematics; Pattern recognition (psychology); Mathematical analysis","score_opus":0.06741741632035102,"score_gpt":0.21339420517390192,"score_spread":0.14597678885355092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963216439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1370108,0.00022369776,0.8590049,0.00036136733,0.000028998587,0.00006394097,0.0001406367,0.0006340095,0.0025316922],"genre_scores_gemma":[0.8432149,0.00032640653,0.15322061,0.00015804067,0.000049399627,0.00012954007,0.00040130696,0.00016356327,0.002336261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929976,0.00020816707,0.000049860613,0.0001838889,0.00017070773,0.00008753548],"domain_scores_gemma":[0.9967326,0.0013922574,0.00050942035,0.0008419307,0.00038302236,0.00014081216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002077565,0.0010330884,0.00082252274,0.000897068,0.0004033875,0.0010239494,0.0011984874,0.00095136155,0.0013877578],"category_scores_gemma":[0.013679888,0.00076961296,0.0008816557,0.0009107457,0.0014347791,0.0035484198,0.0020884501,0.0027918757,0.0003565273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054990654,0.000050822702,0.0041041304,0.00007639241,0.000118106036,0.00007917514,0.0001634993,0.8882593,0.007178197,0.05156614,0.0008012659,0.047547877],"study_design_scores_gemma":[0.0000037059256,0.000015348085,0.0004814769,0.000004425158,0.0000039833126,0.000010379876,0.0000090455915,0.9759547,0.0011002118,0.02222265,0.00018836142,0.0000057426364],"about_ca_topic_score_codex":0.0045500016,"about_ca_topic_score_gemma":0.00549905,"teacher_disagreement_score":0.0045500016,"about_ca_system_score_codex":0.0013002997,"about_ca_system_score_gemma":0.0011623828,"threshold_uncertainty_score":0.010987341},"labels":[],"label_agreement":null},{"id":"W2963235055","doi":"10.48550/arxiv.1806.04418","title":"Quaternion Recurrent Neural Networks","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Quaternion; Computer science; Recurrent neural network; Artificial neural network; Artificial intelligence; Mathematics; Geometry","score_opus":0.01821758132357187,"score_gpt":0.24182575704995743,"score_spread":0.22360817572638556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963235055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016126378,0.0014838913,0.97142166,0.00034496567,0.00038503727,0.000051170202,0.0005280482,0.0025860032,0.007072801],"genre_scores_gemma":[0.6458348,0.0025743041,0.32578054,0.0006286143,0.00038073823,0.00018902346,0.0023654706,0.0005390449,0.021707507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957246,0.0000868759,0.00003521977,0.00013956125,0.00012181446,0.000044097957],"domain_scores_gemma":[0.99951506,0.00014690487,0.00007990013,0.000088917346,0.00014821485,0.000020931477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045782447,0.00093770406,0.0006519318,0.00036566844,0.00027065614,0.0011006645,0.0011619206,0.0008975981,0.00662814],"category_scores_gemma":[0.0022364247,0.00033515837,0.00053078745,0.00062412844,0.00057788705,0.0016539838,0.0007540804,0.0009384003,0.0025838555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031702302,0.0000783708,0.0011396584,0.00048759856,0.00016242942,0.00039013836,0.00016957145,0.5309023,0.04644033,0.09884072,0.013374011,0.30769783],"study_design_scores_gemma":[0.0000125112265,0.000048393373,0.0002441625,0.000021897953,0.000018831042,0.000085665626,0.000014053811,0.9581324,0.008570535,0.024911573,0.007918697,0.000021225802],"about_ca_topic_score_codex":0.002383238,"about_ca_topic_score_gemma":0.0025673476,"teacher_disagreement_score":0.00662814,"about_ca_system_score_codex":0.000510015,"about_ca_system_score_gemma":0.00044404776,"threshold_uncertainty_score":0.022173285},"labels":[],"label_agreement":null},{"id":"W2963619462","doi":"","title":"Techniques for Learning Binary Stochastic Feedforward Neural Networks","year":2015,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Benchmark (surveying); Perceptron; Estimator; Generalization; Artificial neural network; Binary number; Feed forward; Stochastic neural network; Artificial intelligence; Machine learning; Feedforward neural network; Binary classification; Structured prediction; Algorithm; Recurrent neural network; Mathematics; Statistics; Support vector machine; Engineering","score_opus":0.07169717858938203,"score_gpt":0.20062601579234018,"score_spread":0.12892883720295817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963619462","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077437502,0.00023434576,0.9909151,0.00012996829,0.00002970996,0.000028231163,0.000025700941,0.00028804244,0.0006049658],"genre_scores_gemma":[0.38797015,0.0004888053,0.60764825,0.0003065585,0.00016690424,0.00032118935,0.00031067003,0.0001871324,0.002600308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984592,0.00062526466,0.00013056658,0.00019078009,0.0005033331,0.00009076718],"domain_scores_gemma":[0.99493283,0.0033037474,0.00054906815,0.00034288253,0.0007457136,0.00012590423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004104706,0.0007701365,0.00093551556,0.0015293864,0.00048800898,0.0008513388,0.0014486132,0.0013074701,0.0027055487],"category_scores_gemma":[0.016764734,0.0006635418,0.00063407107,0.0012378726,0.00089070614,0.0018443658,0.002151207,0.0017575116,0.0006445094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022750658,0.00011337709,0.0015308228,0.00030467243,0.00009973303,0.000095512325,0.00016274366,0.72079873,0.005341668,0.06378137,0.0017793702,0.20576455],"study_design_scores_gemma":[0.000010661206,0.000018926396,0.000096850345,0.000016663314,0.000005575324,0.000016520244,0.0000037156815,0.98523974,0.0007085016,0.0135799255,0.00029851473,0.000004401079],"about_ca_topic_score_codex":0.0017121988,"about_ca_topic_score_gemma":0.0016713708,"teacher_disagreement_score":0.004104706,"about_ca_system_score_codex":0.00089256576,"about_ca_system_score_gemma":0.0007436696,"threshold_uncertainty_score":0.021708012},"labels":[],"label_agreement":null},{"id":"W2963859684","doi":"10.4230/lipics.itcs.2018.22","title":"Convergence Results for Neural Networks via Electrodynamics","year":2018,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gradient descent; Electron; Convergence (economics); Node (physics); Artificial neural network; Physics; Function (biology); Equivalence (formal languages); Descent (aeronautics); Mathematics; Topology (electrical circuits); Computer science; Mathematical analysis; Quantum mechanics; Combinatorics; Discrete mathematics; Artificial intelligence","score_opus":0.011468294227940972,"score_gpt":0.25449760773054636,"score_spread":0.2430293135026054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963859684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027426658,0.0030465845,0.9432549,0.0032452412,0.00020124897,0.00007777745,0.0001334329,0.0003803124,0.022233803],"genre_scores_gemma":[0.7511125,0.00433744,0.21195966,0.0019016868,0.0006519299,0.00081226503,0.00051802216,0.00059584767,0.028110718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987483,0.000567292,0.00006137496,0.0002298362,0.0002703067,0.00012290952],"domain_scores_gemma":[0.99079996,0.0072724307,0.00039677048,0.00034889524,0.0009107307,0.0002712109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004397285,0.0018561461,0.001128846,0.0017078926,0.0009388394,0.0014254717,0.0019292663,0.0025772685,0.0051573133],"category_scores_gemma":[0.02261153,0.00075118936,0.001328512,0.00083870156,0.0033566963,0.0044125994,0.0036565564,0.004253359,0.00091100344],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106060535,0.00006860981,0.0013762641,0.00028927543,0.00012932313,0.00012762961,0.00023452572,0.34531897,0.0019983375,0.62211096,0.0031246115,0.02511549],"study_design_scores_gemma":[0.000015788512,0.000044153978,0.00015837532,0.000042212378,0.000012021248,0.000028123824,0.000018281036,0.76614976,0.00041803252,0.23210016,0.001002228,0.000010957952],"about_ca_topic_score_codex":0.0030110748,"about_ca_topic_score_gemma":0.0022057425,"teacher_disagreement_score":0.0051573133,"about_ca_system_score_codex":0.0021557966,"about_ca_system_score_gemma":0.0010766182,"threshold_uncertainty_score":0.023255348},"labels":[],"label_agreement":null},{"id":"W2964176828","doi":"","title":"Deep Neural Maps","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"MNIST database; Embedding; Computer science; Artificial intelligence; Visualization; Convolutional neural network; Deep learning; Representation (politics); Artificial neural network; Data visualization; Class (philosophy); Pattern recognition (psychology); Deep neural networks","score_opus":0.04971398233387822,"score_gpt":0.17389272758089544,"score_spread":0.12417874524701722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964176828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006883884,0.0012369686,0.9764473,0.0006468713,0.00025968207,0.000051984385,0.0012347623,0.0035578366,0.009680684],"genre_scores_gemma":[0.29451016,0.0027999764,0.6770631,0.00056480465,0.00030334658,0.00028886876,0.0041374257,0.00094470277,0.019387675],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993926,0.00011254788,0.00003278996,0.00016078241,0.0002411803,0.000060167673],"domain_scores_gemma":[0.9993994,0.00018464835,0.000048250928,0.00014924785,0.00016717055,0.000051237646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006227909,0.0009881948,0.0006763664,0.0012923281,0.0004957304,0.0024586094,0.0015616727,0.00085445016,0.00802855],"category_scores_gemma":[0.0030984094,0.00041598262,0.00092168525,0.001297264,0.00073313015,0.003327004,0.0020504782,0.0016261983,0.002266345],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014849944,0.000083833,0.0018799399,0.00037275598,0.0001409213,0.00020089875,0.0002519616,0.15484804,0.009065744,0.30412528,0.036785103,0.49209717],"study_design_scores_gemma":[0.000019838382,0.0000564839,0.000635048,0.0000756477,0.000024604884,0.00020136048,0.00006008868,0.6753473,0.007609469,0.24488416,0.07104502,0.000040944262],"about_ca_topic_score_codex":0.0034088336,"about_ca_topic_score_gemma":0.0035727895,"teacher_disagreement_score":0.00802855,"about_ca_system_score_codex":0.0009363318,"about_ca_system_score_gemma":0.0009252936,"threshold_uncertainty_score":0.026858151},"labels":[],"label_agreement":null},{"id":"W2964232029","doi":"10.1016/j.neunet.2019.06.009","title":"Depth with nonlinearity creates no bad local minima in ResNets","year":2019,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Cegep Edouard Montpetit","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Maxima and minima; Nonlinear system; Computer science; Artificial neural network; Residual; Artificial intelligence; Mathematical optimization; Algorithm; Mathematics; Physics; Mathematical analysis","score_opus":0.008581145360445082,"score_gpt":0.22896164907717761,"score_spread":0.22038050371673254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964232029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28490037,0.00063512125,0.69445556,0.0014573343,0.00018086044,0.00009407099,0.00020900319,0.0011016977,0.01696594],"genre_scores_gemma":[0.9549732,0.00019737463,0.038338866,0.00053379807,0.00004689444,0.00008457348,0.00012458404,0.00029324685,0.005407379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990528,0.00023951197,0.00005460154,0.00020265159,0.00023971374,0.00021073516],"domain_scores_gemma":[0.9976374,0.0011557981,0.00034549716,0.0003493958,0.00024524875,0.00026667977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017410641,0.0012012625,0.0014670038,0.0006369502,0.0009003274,0.0013964982,0.0012937796,0.0018444124,0.0039322516],"category_scores_gemma":[0.0084760375,0.00076298503,0.0012975569,0.0002799936,0.0028791695,0.0044518104,0.003669375,0.0028864983,0.0006300227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078810955,0.00012090155,0.004203319,0.00050792785,0.00023577224,0.00075688824,0.00038169482,0.5280882,0.030209785,0.39935586,0.0040819906,0.031269506],"study_design_scores_gemma":[0.000047765192,0.00027688028,0.0006202892,0.000044177905,0.00004948568,0.00024992184,0.000085745545,0.7407395,0.0068118325,0.24959768,0.0014319614,0.0000447457],"about_ca_topic_score_codex":0.000930809,"about_ca_topic_score_gemma":0.0014692813,"teacher_disagreement_score":0.0039322516,"about_ca_system_score_codex":0.0011131528,"about_ca_system_score_gemma":0.00069108594,"threshold_uncertainty_score":0.013154626},"labels":[],"label_agreement":null},{"id":"W2964309400","doi":"","title":"Revisiting Natural Gradient for Deep Networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Hessian matrix; Stochastic gradient descent; Gradient descent; Robustness (evolution); Diagonal; Generalization; Computer science; Metric (unit); Deep learning; Subspace topology; Algorithm; Artificial intelligence; Mathematics; Mathematical optimization; Applied mathematics; Artificial neural network","score_opus":0.009799498980287287,"score_gpt":0.2366033906371499,"score_spread":0.22680389165686263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964309400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037258852,0.001542451,0.9501421,0.0008478009,0.00029358373,0.00019575533,0.00033894205,0.0025215975,0.0068589705],"genre_scores_gemma":[0.41790193,0.0006015387,0.575613,0.00054447417,0.00016055425,0.00024112659,0.0006761843,0.00078613055,0.0034750223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976845,0.0010330598,0.00011436412,0.0003604569,0.0006834241,0.00012420479],"domain_scores_gemma":[0.99324894,0.003721466,0.0003506915,0.0009866835,0.0014354805,0.00025669596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063181506,0.0016754168,0.0011711573,0.0014649775,0.00082276226,0.0019374663,0.002234035,0.0019321329,0.0037196402],"category_scores_gemma":[0.025330264,0.0005898904,0.00077915477,0.0009097101,0.0019021056,0.005234388,0.0028959285,0.0026750714,0.000792396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000349217,0.00026136058,0.0032993967,0.00035878984,0.00015180018,0.00010239479,0.00015252836,0.71145225,0.0034732816,0.09990432,0.008876719,0.17161804],"study_design_scores_gemma":[0.000022827975,0.00008104837,0.00015429597,0.000024051438,0.0000079782885,0.00002543305,0.000011232407,0.9716665,0.0012963349,0.02508413,0.0016165477,0.000009541352],"about_ca_topic_score_codex":0.007159683,"about_ca_topic_score_gemma":0.011069185,"teacher_disagreement_score":0.007159683,"about_ca_system_score_codex":0.0020633216,"about_ca_system_score_gemma":0.0027149688,"threshold_uncertainty_score":0.033414006},"labels":[],"label_agreement":null},{"id":"W2965031958","doi":"10.1016/j.knosys.2019.104874","title":"Logic-driven autoencoders","year":2019,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Deanship of Scientific Research, King Saud University; King Abdulaziz University","keywords":"Autoencoder; Decoding methods; Encoding (memory); Computer science; Generalization; Artificial intelligence; Fuzzy logic; Set (abstract data type); Representation (politics); Deep learning; Algorithm; Programming language; Mathematics","score_opus":0.019810383405822776,"score_gpt":0.2540926165889562,"score_spread":0.23428223318313343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965031958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01901874,0.000668729,0.97046566,0.0003508723,0.0001647414,0.00005156655,0.00014973848,0.0009456709,0.0081843985],"genre_scores_gemma":[0.81848854,0.0006619209,0.16855618,0.00046390208,0.0001534793,0.00010788664,0.0004121663,0.000096473785,0.011059419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996947,0.00006276282,0.000021358659,0.00007835311,0.000103412305,0.000039405706],"domain_scores_gemma":[0.9989598,0.00055791636,0.00004910891,0.00011476482,0.0002923089,0.000026101403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006661653,0.00048625932,0.00050426385,0.00036083694,0.0002801733,0.0010009597,0.00089750823,0.0008155801,0.004331165],"category_scores_gemma":[0.0026095258,0.00032097078,0.00044508625,0.000336518,0.00047785114,0.0010286164,0.0006136108,0.0011360715,0.0013021872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001879808,0.00015196104,0.0008396989,0.00019412846,0.00013932923,0.00013272061,0.00007388846,0.50788087,0.013382909,0.05697337,0.0051221624,0.41492102],"study_design_scores_gemma":[0.000005919854,0.000014896186,0.0001228967,0.000011176035,0.000011581683,0.000022810982,0.0000045321312,0.986129,0.0022405914,0.010720233,0.00071097893,0.000005291115],"about_ca_topic_score_codex":0.0028961983,"about_ca_topic_score_gemma":0.0046574767,"teacher_disagreement_score":0.004331165,"about_ca_system_score_codex":0.0006066782,"about_ca_system_score_gemma":0.00072286045,"threshold_uncertainty_score":0.0144892335},"labels":[],"label_agreement":null},{"id":"W2965142132","doi":"10.1109/mis.2019.2932667","title":"Nonlinear Gain Approximation Structure Using Manifold Learning on a Vertical Manipulator","year":2019,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Mitacs","keywords":"Computer science; Dimensionality reduction; Nonlinear system; Dimension (graph theory); Representation (politics); Manifold (fluid mechanics); Nonlinear dimensionality reduction; Reduction (mathematics); Process (computing); Algorithm; Artificial intelligence; Machine learning; Mathematical optimization; Mathematics; Geometry","score_opus":0.03340582474930846,"score_gpt":0.269584898193182,"score_spread":0.2361790734438735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965142132","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039623473,0.000106874715,0.95610225,0.00013919683,0.000018980689,0.000035176083,0.000031962096,0.00034669635,0.0035954874],"genre_scores_gemma":[0.61030394,0.00024134738,0.37992188,0.0000613881,0.000040773928,0.00012235639,0.00019582445,0.00008576723,0.009026827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979895,0.000049505903,0.000006756565,0.00004832913,0.00008132446,0.000015125743],"domain_scores_gemma":[0.99978966,0.000091337875,0.000026889087,0.000037667716,0.00004343515,0.000011032724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003828718,0.00052743894,0.00043202133,0.00044880755,0.00041107967,0.00050855096,0.00047652857,0.0007102851,0.002959599],"category_scores_gemma":[0.0010006921,0.0002476233,0.00059723377,0.0003439206,0.0008203936,0.0008516739,0.00087864045,0.0010520879,0.0006555836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008876546,0.000080546015,0.0008289422,0.00008835938,0.00003321139,0.00015875214,0.000266591,0.81288004,0.026094154,0.047581416,0.0011606775,0.11073859],"study_design_scores_gemma":[0.0000022703794,0.000052624167,0.00014353693,0.00000398467,0.0000016620528,0.000017291097,0.000011981124,0.99281234,0.0011450986,0.0048442455,0.00095943734,0.0000056221],"about_ca_topic_score_codex":0.002376649,"about_ca_topic_score_gemma":0.0019405317,"teacher_disagreement_score":0.002959599,"about_ca_system_score_codex":0.00046209208,"about_ca_system_score_gemma":0.00040331003,"threshold_uncertainty_score":0.009900868},"labels":[],"label_agreement":null},{"id":"W2967148694","doi":"10.18280/ts.360202","title":"New Analog Processing Technique in Multichannel Neural Signal Recording with Reduce Data Rate and Reduce Power Consumption","year":2019,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Power consumption; Computer science; Signal processing; SIGNAL (programming language); Power (physics); Analog signal; Computer hardware; Digital signal processing","score_opus":0.03933106548648735,"score_gpt":0.2809784017005358,"score_spread":0.24164733621404844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967148694","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07203917,0.008900398,0.89987385,0.001061363,0.0006587614,0.0001755112,0.00017266051,0.00070951297,0.016408592],"genre_scores_gemma":[0.4748464,0.005655175,0.504844,0.00060278276,0.0004644865,0.00020970717,0.00017236755,0.00007356569,0.01313145],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996462,0.00005251773,0.000021416077,0.00007899905,0.00018146621,0.000019312567],"domain_scores_gemma":[0.9997243,0.0000846566,0.00003712214,0.000043193148,0.00009608679,0.000014709777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029816362,0.00031290803,0.00022814289,0.0004818963,0.0002611826,0.00047640415,0.0006083556,0.00045351827,0.0031095182],"category_scores_gemma":[0.00051906536,0.00016669839,0.000267895,0.0006180021,0.00037553132,0.0010089821,0.000338804,0.0004943,0.0005705816],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035604692,0.0001671941,0.0010436287,0.0008501131,0.00006094533,0.0002872453,0.00025417173,0.0066394294,0.61139315,0.016853994,0.0026205862,0.35947356],"study_design_scores_gemma":[0.000112894246,0.0022934964,0.006100207,0.00021408348,0.00021558958,0.0030317025,0.00026062195,0.18707141,0.6869118,0.011900074,0.10175706,0.0001309535],"about_ca_topic_score_codex":0.00038318455,"about_ca_topic_score_gemma":0.0006142638,"teacher_disagreement_score":0.0031095182,"about_ca_system_score_codex":0.00027384303,"about_ca_system_score_gemma":0.00027026725,"threshold_uncertainty_score":0.010402381},"labels":[],"label_agreement":null},{"id":"W2970661753","doi":"","title":"Deep Scale-spaces: Equivariance Over Scale","year":2019,"lang":"en","type":"article","venue":"UvA-DARE (University of Amsterdam)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Equivariant map; Generalization; Scale (ratio); Computer science; Artificial intelligence; Convolutional neural network; Scale invariance; Deep learning; Construct (python library); Invariant (physics); Pattern recognition (psychology); Scale space; Theoretical computer science; Image (mathematics); Mathematics; Pure mathematics; Image processing; Mathematical analysis; Geography","score_opus":0.007938050193676001,"score_gpt":0.1959451032571959,"score_spread":0.18800705306351992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970661753","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031399205,0.00047932114,0.9630161,0.00031310934,0.00008478186,0.00002453049,0.00016008051,0.00043166007,0.0040912875],"genre_scores_gemma":[0.7319933,0.0010183194,0.25912964,0.0003783124,0.00026159242,0.000108531574,0.00043066352,0.0003197654,0.0063598217],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991916,0.00018237985,0.00004247215,0.00022404839,0.0002774068,0.0000820815],"domain_scores_gemma":[0.9985176,0.00044110906,0.00026988267,0.00043143582,0.00019154034,0.00014849145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011960287,0.0006761421,0.0005682297,0.000809661,0.000367802,0.0020133513,0.0009997031,0.000602834,0.0030549804],"category_scores_gemma":[0.0047683427,0.00036389817,0.0008121634,0.0010029676,0.0019719442,0.0031551714,0.002782982,0.0020990497,0.0007283231],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015665496,0.000055204924,0.0025258933,0.00014004744,0.00008109098,0.00028044358,0.00032785174,0.0480082,0.015690275,0.80173165,0.0037036932,0.12729903],"study_design_scores_gemma":[0.00001811404,0.00012486309,0.0015640511,0.000030998588,0.00003033852,0.0003140528,0.000090656016,0.3776542,0.0087105,0.5969201,0.0144991195,0.000043067248],"about_ca_topic_score_codex":0.0007703141,"about_ca_topic_score_gemma":0.0008055193,"teacher_disagreement_score":0.0030549804,"about_ca_system_score_codex":0.00056361035,"about_ca_system_score_gemma":0.0004335984,"threshold_uncertainty_score":0.010219932},"labels":[],"label_agreement":null},{"id":"W2972588912","doi":"10.1007/978-1-4471-7452-3_28","title":"Neural Network Circuits and Parallel Implementations","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Implementation; Computer science; Artificial neural network; Parallel computing; Computer architecture; Electronic circuit; Parallel processing; Computer engineering; Artificial intelligence; Programming language; Engineering; Electrical engineering","score_opus":0.031044525600579506,"score_gpt":0.26013577715787406,"score_spread":0.22909125155729454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972588912","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053793546,0.027133852,0.5011412,0.002854327,0.0026673975,0.000060423623,0.00025629124,0.0015345487,0.4589726],"genre_scores_gemma":[0.16657893,0.035662632,0.24942054,0.0012805704,0.0020507374,0.00029556904,0.0006109238,0.00094940496,0.5431507],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99985313,0.000019443314,0.0000063771477,0.000029185643,0.00007658597,0.000015146614],"domain_scores_gemma":[0.9998381,0.0000680714,0.000008500169,0.00003408883,0.00004431747,0.0000068544173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019524046,0.00063115347,0.00045731026,0.0005223834,0.00031571038,0.001507888,0.0010145521,0.0008541315,0.027184375],"category_scores_gemma":[0.000916822,0.00042739997,0.00030083023,0.0010816328,0.0007561597,0.0022593979,0.0006909513,0.0013689734,0.0068542864],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035731013,0.000027072332,0.00006604555,0.0002632248,0.000026582908,0.00004165599,0.000041056745,0.02270897,0.0023270203,0.6408795,0.04149443,0.29208872],"study_design_scores_gemma":[0.000016042135,0.00002039981,0.00014937614,0.00009997385,0.00002265754,0.00013299607,0.000020374338,0.083569035,0.0027460959,0.6761907,0.23701485,0.000017468963],"about_ca_topic_score_codex":0.0008333463,"about_ca_topic_score_gemma":0.001236422,"teacher_disagreement_score":0.027184375,"about_ca_system_score_codex":0.0008155622,"about_ca_system_score_gemma":0.0004965497,"threshold_uncertainty_score":0.09094077},"labels":[],"label_agreement":null},{"id":"W2976797453","doi":"10.29169/1927-5129.2019.15.02","title":"Basic Ideas of Information Thermodynamics","year":2019,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Carnot cycle; Thermodynamic system; Fundamental thermodynamic relation; Thermodynamics; Laws of thermodynamics; Entropy (arrow of time); Statistical physics; Second law of thermodynamics; Mathematics; Thermodynamic process; Information theory; Thermodynamic equilibrium; Maximum entropy thermodynamics; Gibbs free energy; Thermodynamic free energy; Non-equilibrium thermodynamics; Physics; Binary entropy function; Principle of maximum entropy; Material properties","score_opus":0.009390957943228391,"score_gpt":0.22911878064890162,"score_spread":0.21972782270567323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976797453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006073845,0.033042774,0.6761151,0.016101047,0.003055945,0.00032388247,0.001539748,0.0006335067,0.26311412],"genre_scores_gemma":[0.5048226,0.04987414,0.35389194,0.015212856,0.013519383,0.002944935,0.0015135905,0.0006131365,0.05760737],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966356,0.0010524445,0.0002774005,0.0005528775,0.001216523,0.00026524856],"domain_scores_gemma":[0.9968612,0.0017621403,0.00022672217,0.0006084285,0.00043246985,0.0001090901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003304743,0.0014221714,0.0013782983,0.004165121,0.0017852071,0.005515603,0.0024882653,0.0034395794,0.0072128098],"category_scores_gemma":[0.0059467377,0.0006084139,0.0020375154,0.0028240024,0.010782395,0.008903647,0.0034765387,0.004514522,0.0030440642],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000189008,0.0000031998397,0.000022109258,0.0000517445,0.0000051793604,0.00002024982,0.00007413005,0.00046510025,0.000081349724,0.99555135,0.0013291755,0.0023944243],"study_design_scores_gemma":[0.0000035978242,0.000006260879,0.000028610493,0.000050869505,0.0000029649077,0.00005122962,0.000016210548,0.0013452526,0.00010582104,0.9736299,0.024751924,0.000007466953],"about_ca_topic_score_codex":0.00097162795,"about_ca_topic_score_gemma":0.00046315667,"teacher_disagreement_score":0.0072128098,"about_ca_system_score_codex":0.0025407157,"about_ca_system_score_gemma":0.0019342675,"threshold_uncertainty_score":0.024129272},"labels":[],"label_agreement":null},{"id":"W2977860868","doi":"10.1109/ijcnn.2019.8851792","title":"A Novel LSTM Approach for Asynchronous Multivariate Time Series Prediction","year":2019,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Univariate; Pipeline (software); Asynchronous communication; Multivariate statistics; Interpolation (computer graphics); Artificial intelligence; Convergence (economics); Recurrent neural network; Time series; Series (stratigraphy); Representation (politics); Set (abstract data type); Encoding (memory); Artificial neural network; Pattern recognition (psychology); Machine learning","score_opus":0.012740237266728592,"score_gpt":0.22127547952858287,"score_spread":0.20853524226185427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977860868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068805115,0.0001795106,0.99056,0.000113469076,0.00007887406,0.0000218861,0.00012872918,0.00084945804,0.0011876281],"genre_scores_gemma":[0.4460267,0.00045204614,0.5452534,0.00019762065,0.00017699749,0.00015210282,0.00058338256,0.00018319057,0.006974621],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999824,0.000031840962,0.000011785977,0.00005946559,0.000051964347,0.000020979542],"domain_scores_gemma":[0.9998485,0.000045540037,0.000019587722,0.000026401734,0.000048145073,0.000011903611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003344402,0.0005554646,0.00033876378,0.0003044751,0.00022825299,0.0003715988,0.0009666461,0.0005677533,0.0029220227],"category_scores_gemma":[0.0009979018,0.00024162525,0.000421553,0.0005953097,0.00023158109,0.000970951,0.0006450726,0.0010771474,0.0007097678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013991965,0.000096893724,0.00073365076,0.00016546907,0.00012275,0.00017444613,0.00011150475,0.35586587,0.05663852,0.023351645,0.0056884997,0.5569109],"study_design_scores_gemma":[0.000003087189,0.000023489625,0.0001224032,0.000004620783,0.000008604386,0.000022377071,0.000004122268,0.9916637,0.003616417,0.0031612152,0.0013646327,0.0000053681515],"about_ca_topic_score_codex":0.0034930147,"about_ca_topic_score_gemma":0.0059111924,"teacher_disagreement_score":0.0034930147,"about_ca_system_score_codex":0.00043859213,"about_ca_system_score_gemma":0.00075521704,"threshold_uncertainty_score":0.009775162},"labels":[],"label_agreement":null},{"id":"W2978782468","doi":"10.31234/osf.io/u4ry6","title":"Statistical Learning Ability as a Measure of Cognitive Function","year":2019,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cognition; Psychology; Task (project management); Reliability (semiconductor); Test (biology); Function (biology); Measure (data warehouse); Cognitive psychology; Cognitive science; Computer science; Power (physics); Neuroscience; Biology; Data mining","score_opus":0.01323795730313118,"score_gpt":0.2548084287418949,"score_spread":0.24157047143876373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978782468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9487391,0.002913338,0.025228329,0.00048420142,0.0001642438,0.00021242606,0.0020097918,0.00025914618,0.01998952],"genre_scores_gemma":[0.99053496,0.000554414,0.005955217,0.00014633097,0.000064955406,0.00012807608,0.0007829463,0.000024271552,0.0018087075],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980227,0.0005123509,0.00016815093,0.00041110226,0.0007833396,0.000102459664],"domain_scores_gemma":[0.99007505,0.004979454,0.0019089899,0.0013452682,0.0011129286,0.0005782366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025443824,0.0005042881,0.0004349959,0.0025355865,0.00023746767,0.0012346237,0.0008650215,0.0007391463,0.0028541975],"category_scores_gemma":[0.0150531735,0.00010416545,0.0004345439,0.00185447,0.0012070547,0.0013056913,0.0012804271,0.0010702178,0.00052618736],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056213996,0.0010354148,0.7358219,0.0003524241,0.0008269898,0.00028504757,0.0015613198,0.0048710164,0.014809017,0.0084039895,0.0051983795,0.22627236],"study_design_scores_gemma":[0.00002065547,0.0013338687,0.9696305,0.00008827102,0.00013627998,0.0005418956,0.00036411246,0.0053786165,0.0049880478,0.012608604,0.004832932,0.0000762366],"about_ca_topic_score_codex":0.0019165119,"about_ca_topic_score_gemma":0.002067301,"teacher_disagreement_score":0.0028541975,"about_ca_system_score_codex":0.00046913835,"about_ca_system_score_gemma":0.00047675965,"threshold_uncertainty_score":0.013456166},"labels":[],"label_agreement":null},{"id":"W2978953460","doi":"10.1109/ijcnn.2019.8852461","title":"Modulation of Activation Function in Triangular Recurrent Neural Networks for Time Series Modeling","year":2019,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Activation function; Control theory (sociology); Computer science; Artificial neural network; Function (biology); Forgetting; Diagonal; Series (stratigraphy); Eigenvalues and eigenvectors; Recurrent neural network; Chaotic; State variable; Algorithm; Mathematics; Artificial intelligence; Physics","score_opus":0.019258946067200973,"score_gpt":0.23969238739516732,"score_spread":0.22043344132796636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978953460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019685514,0.00022733133,0.97846395,0.000053445798,0.00003011948,0.000021022475,0.00001750794,0.00025682893,0.0012443241],"genre_scores_gemma":[0.76522195,0.0003616752,0.23229028,0.000054748936,0.00003982899,0.00009544524,0.00006288087,0.00007927735,0.0017938737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998342,0.00005793884,0.000011707243,0.00003658404,0.000044609922,0.000014981992],"domain_scores_gemma":[0.99977523,0.00010950367,0.000034003308,0.000035956597,0.00003641786,0.000008891466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004441627,0.00041408773,0.00024574972,0.00019268994,0.00014777138,0.00031435164,0.00063112285,0.0003672034,0.0008579122],"category_scores_gemma":[0.0016096898,0.00017159755,0.0003420936,0.00023914746,0.00028004913,0.00054444437,0.00028722873,0.00055815827,0.00023240679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014104666,0.000050357583,0.0009251095,0.000108146705,0.000051044266,0.00014303492,0.00017760998,0.7289323,0.06496725,0.025359586,0.00078648917,0.17835791],"study_design_scores_gemma":[0.0000017273826,0.000016241262,0.00006379856,0.000002083901,0.0000036287604,0.0000123543205,0.0000022751904,0.9961171,0.002224412,0.001190738,0.00036263006,0.0000029720863],"about_ca_topic_score_codex":0.0015439844,"about_ca_topic_score_gemma":0.0020879663,"teacher_disagreement_score":0.0015439844,"about_ca_system_score_codex":0.00033068226,"about_ca_system_score_gemma":0.00019190299,"threshold_uncertainty_score":0.0030699968},"labels":[],"label_agreement":null},{"id":"W2979310190","doi":"10.1109/tfuzz.2019.2946520","title":"Interpretable Deep Convolutional Fuzzy Classifier","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Classifier (UML); Fuzzy logic; Convolutional neural network; Machine learning; Cluster analysis; Deep learning; Feature extraction; Data mining; Pattern recognition (psychology)","score_opus":0.013531351735030728,"score_gpt":0.22477570167537708,"score_spread":0.21124434994034635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979310190","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048353598,0.00024768765,0.9445379,0.00023902717,0.0000735277,0.000075522665,0.00035553199,0.0013959742,0.0047212387],"genre_scores_gemma":[0.66512954,0.00021413018,0.3261916,0.00022303162,0.000060898605,0.00014136628,0.00075365196,0.00004945736,0.0072362903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996947,0.000031344967,0.000023722034,0.00007602251,0.000120458935,0.000053738284],"domain_scores_gemma":[0.99949706,0.0001261385,0.00003650657,0.00008375579,0.00023795517,0.000018589964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055770914,0.0005889354,0.0005972204,0.0006761589,0.00039326242,0.0009087667,0.0010680238,0.00111627,0.0030077884],"category_scores_gemma":[0.0017595558,0.00024364181,0.00058018655,0.00036545587,0.00037440474,0.00079853286,0.0005443717,0.0008671036,0.0007874505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029051176,0.00014351714,0.0021367266,0.000103755614,0.000065358014,0.00024754275,0.00011872394,0.36548975,0.027575558,0.027516898,0.0059469836,0.57036465],"study_design_scores_gemma":[0.000005898432,0.00001529902,0.00023039895,0.000007039129,0.000007546613,0.000028729355,0.000008077439,0.99168724,0.0034121168,0.0037760318,0.0008157994,0.0000057265597],"about_ca_topic_score_codex":0.008788657,"about_ca_topic_score_gemma":0.011951797,"teacher_disagreement_score":0.008788657,"about_ca_system_score_codex":0.0011396257,"about_ca_system_score_gemma":0.0010240976,"threshold_uncertainty_score":0.017475009},"labels":[],"label_agreement":null},{"id":"W2979766873","doi":"10.1109/fuzz-ieee.2019.8858809","title":"Improved Deep Fuzzy Clustering for Accurate and Interpretable Classifiers","year":2019,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Interpretability; Computer science; Cluster analysis; Deep learning; MNIST database; Machine learning; Fuzzy logic; Fuzzy clustering; Classifier (UML); Neuro-fuzzy; Artificial neural network; Data mining; Pattern recognition (psychology); Fuzzy control system","score_opus":0.013984550706079138,"score_gpt":0.24835311190880205,"score_spread":0.23436856120272292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979766873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051649015,0.00069923536,0.94182336,0.00058630994,0.00008287688,0.000079754536,0.00046682998,0.0017175627,0.0028950567],"genre_scores_gemma":[0.38902247,0.00029145938,0.6040893,0.00027123225,0.00006131331,0.00008394942,0.0014392245,0.00017927973,0.0045617204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988201,0.00020437111,0.00009515712,0.00031571797,0.00044800987,0.000116544805],"domain_scores_gemma":[0.99782,0.0006523471,0.00015019144,0.00051941443,0.0007897994,0.00006817414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019154114,0.0009843942,0.0010346367,0.0017492914,0.00087785465,0.0016912855,0.0018954003,0.0021123637,0.0028736768],"category_scores_gemma":[0.0057325945,0.00045449717,0.0011465374,0.0011011384,0.00072536856,0.0023632206,0.0012137069,0.0024720493,0.0014479368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043143082,0.0001789653,0.0027879255,0.00018394027,0.00013060457,0.00016563965,0.0003408023,0.46359137,0.022461269,0.041623365,0.009167914,0.45893684],"study_design_scores_gemma":[0.000010916757,0.000024599976,0.00037641535,0.00001711918,0.000010028055,0.000037860285,0.00003760684,0.9759266,0.005242178,0.01691147,0.0013906698,0.000014493433],"about_ca_topic_score_codex":0.009964749,"about_ca_topic_score_gemma":0.01461852,"teacher_disagreement_score":0.009964749,"about_ca_system_score_codex":0.00221204,"about_ca_system_score_gemma":0.001390264,"threshold_uncertainty_score":0.019813538},"labels":[],"label_agreement":null},{"id":"W2980121538","doi":"10.1109/ccece.2019.8861880","title":"Universal Functions Originator—Part I: Design","year":2019,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial neural network; Support vector machine; Cluster analysis; Artificial intelligence; Nonlinear system; Machine learning; Exponential function; Algorithm; Mathematical optimization; Mathematics","score_opus":0.017079277017417262,"score_gpt":0.2146202205010299,"score_spread":0.19754094348361265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980121538","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006361937,0.0021564695,0.9579398,0.00037714659,0.00037745188,0.000242882,0.00013772877,0.0023344595,0.03007214],"genre_scores_gemma":[0.2981645,0.0030663263,0.655547,0.0005738751,0.00040213717,0.0011180602,0.0004423678,0.0012450325,0.039440665],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99917704,0.000172778,0.00005893901,0.00024284456,0.0002772655,0.00007102347],"domain_scores_gemma":[0.99944896,0.00010539383,0.00007716905,0.00013758695,0.00019917171,0.0000316259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095997506,0.0011159829,0.0008196106,0.0008949059,0.0007189268,0.0021149071,0.0014899231,0.001149772,0.010055596],"category_scores_gemma":[0.0019803718,0.00052031263,0.0007601021,0.00082648086,0.0013334696,0.0021460503,0.0011889633,0.0012340918,0.0046670362],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029389505,0.000090342306,0.001108499,0.001032884,0.00006591368,0.00027225915,0.00064675265,0.03202813,0.059950586,0.3831142,0.013259835,0.5081368],"study_design_scores_gemma":[0.00008273271,0.0009956324,0.0011324966,0.00042979498,0.00010732415,0.0014599238,0.00015227996,0.26202542,0.104085475,0.08016838,0.5491692,0.00019127435],"about_ca_topic_score_codex":0.00066224724,"about_ca_topic_score_gemma":0.00031342782,"teacher_disagreement_score":0.010055596,"about_ca_system_score_codex":0.00096911925,"about_ca_system_score_gemma":0.0007801585,"threshold_uncertainty_score":0.03363931},"labels":[],"label_agreement":null},{"id":"W2983041270","doi":"10.18280/ts.360402","title":"Generating Random Numbers from Biological Signals in LabVIEW Environment and Statistical Analysis","year":2019,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Statistical analysis; Statistics; Mathematics","score_opus":0.01750220091416922,"score_gpt":0.23600663606527883,"score_spread":0.21850443515110962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983041270","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005441189,0.000038766317,0.99130464,0.000032941112,0.000017308757,0.0000544658,0.00006585223,0.0026015842,0.00044324787],"genre_scores_gemma":[0.22215033,0.00016827133,0.7745788,0.000067673056,0.000026351372,0.0006612849,0.00049414014,0.00045693628,0.0013963061],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986904,0.000535014,0.000092752576,0.00021936776,0.00040084802,0.000061552346],"domain_scores_gemma":[0.99718213,0.0018186653,0.0002109191,0.00034600968,0.00040234483,0.000039971605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023342462,0.0006748649,0.00043743575,0.00076349964,0.00022961549,0.0008082888,0.0006929936,0.00036319636,0.0025193503],"category_scores_gemma":[0.0075365794,0.00024470783,0.00039455833,0.0005533303,0.00043590035,0.0007404347,0.0004099579,0.0007172238,0.0007910226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006543803,0.00023243966,0.006304031,0.0005684232,0.00018738746,0.0006967158,0.0005936228,0.2832272,0.06434283,0.03727455,0.008472341,0.597446],"study_design_scores_gemma":[0.00005400612,0.00019761307,0.0012381268,0.00002828225,0.000021069784,0.00034620543,0.00004392459,0.93307287,0.04853389,0.0099123,0.0065070116,0.00004467459],"about_ca_topic_score_codex":0.00046871504,"about_ca_topic_score_gemma":0.00038446192,"teacher_disagreement_score":0.0025193503,"about_ca_system_score_codex":0.00025187916,"about_ca_system_score_gemma":0.00041875438,"threshold_uncertainty_score":0.012344837},"labels":[],"label_agreement":null},{"id":"W2984869307","doi":"10.7554/elife.47314.027","title":"Decision letter: Brian 2, an intuitive and efficient neural simulator","year":2019,"lang":"en","type":"peer-review","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network","funders":"","keywords":"Computer science; Code (set theory); Artificial intelligence; Point (geometry); Protocol (science); Artificial neural network; Programming language; Machine learning; Theoretical computer science; Set (abstract data type)","score_opus":0.02230031853648442,"score_gpt":0.3058889372327247,"score_spread":0.2835886186962403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984869307","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005007711,0.0012120146,0.22984906,0.29564136,0.07601842,0.0015321381,0.03275768,0.0524477,0.30553386],"genre_scores_gemma":[0.048904642,0.0024142868,0.14058737,0.109881535,0.0115113575,0.0034117822,0.02545433,0.024986759,0.63284796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980825,0.0003792631,0.00013378597,0.00023177224,0.0009813709,0.00019139428],"domain_scores_gemma":[0.9918154,0.002712037,0.00025506498,0.0010644613,0.0030356983,0.0011174555],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026543809,0.0008113373,0.00073787285,0.00063848577,0.0014123727,0.0022504837,0.0020897416,0.00463134,0.32877326],"category_scores_gemma":[0.030454388,0.00075208466,0.0008652552,0.00057761534,0.00087524956,0.0027622103,0.002143297,0.0036251973,0.15114391],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066675086,0.000009458667,0.00007349244,0.000040184634,0.0000024484214,0.00006503064,0.000021604037,0.00033692195,0.0003703589,0.004098874,0.9826146,0.012300462],"study_design_scores_gemma":[0.00008356721,0.000027558803,0.00010829324,0.00007333597,0.0000046327127,0.00011092575,0.00003947243,0.0033727214,0.0013049261,0.007955552,0.9868897,0.00002921519],"about_ca_topic_score_codex":0.0027612455,"about_ca_topic_score_gemma":0.0065136557,"teacher_disagreement_score":0.32877326,"about_ca_system_score_codex":0.0015214041,"about_ca_system_score_gemma":0.0030667696,"threshold_uncertainty_score":0.95742345},"labels":[],"label_agreement":null},{"id":"W2985015595","doi":"10.18280/isi.240408","title":"Estimation of Neural Sources from EEG Measurements Using Sequential Monte Carlo Method","year":2019,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Monte Carlo method; Electroencephalography; Computer science; Estimation; Artificial intelligence; Pattern recognition (psychology); Econometrics; Statistics; Mathematics; Psychology; Engineering; Neuroscience","score_opus":0.03754845204386229,"score_gpt":0.27823199424392664,"score_spread":0.24068354220006435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985015595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016789438,0.000091939706,0.9824835,0.00004358625,0.000014779678,0.000027414419,0.000023834942,0.00015415253,0.00037140163],"genre_scores_gemma":[0.6661548,0.0003699081,0.33136395,0.000053722284,0.000058503934,0.00023168488,0.00019577115,0.00007099056,0.0015006703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996884,0.00010670934,0.00001936229,0.00006877188,0.000091225826,0.000025633184],"domain_scores_gemma":[0.99862623,0.00097145216,0.00012697322,0.00008000696,0.00015523176,0.000040094517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008255654,0.000716682,0.00073843345,0.0007004638,0.00038455852,0.00054860004,0.0008125737,0.0007771146,0.0011642638],"category_scores_gemma":[0.003238896,0.0005496801,0.0006572689,0.0006681468,0.000531961,0.0007715523,0.00051162404,0.00072921417,0.00021067464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009068807,0.00002937919,0.0011049148,0.000048996546,0.00004477891,0.00009350998,0.000041223597,0.969343,0.0024612509,0.0051536527,0.00019333727,0.021395264],"study_design_scores_gemma":[0.0000031590946,0.0000051171937,0.00007630587,0.0000013280024,0.000002176467,0.000009071528,0.0000014567016,0.9988096,0.000274301,0.0007497732,0.000064904256,0.0000028479762],"about_ca_topic_score_codex":0.00933927,"about_ca_topic_score_gemma":0.0069745206,"teacher_disagreement_score":0.00933927,"about_ca_system_score_codex":0.00045133883,"about_ca_system_score_gemma":0.0010152729,"threshold_uncertainty_score":0.018569827},"labels":[],"label_agreement":null},{"id":"W2990138404","doi":"10.7551/mitpress/7503.001.0001","title":"Advances in Neural Information Processing Systems 19","year":2007,"lang":"en","type":"book","venue":"The MIT Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15653,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Presentation (obstetrics); Computer science; Artificial intelligence; Artificial neural network; Neural system; Information processing; Computation; Cognitive science; Computational neuroscience; Data science; Psychology; Neuroscience; Medicine; Algorithm","score_opus":0.023245989065769888,"score_gpt":0.2595556331874428,"score_spread":0.2363096441216729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990138404","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033562297,0.50110465,0.16749345,0.022912038,0.014822886,0.00012329716,0.0011953466,0.0009926294,0.28799954],"genre_scores_gemma":[0.06156762,0.40401644,0.09121122,0.00511268,0.012927145,0.0002771601,0.0028955333,0.00056591287,0.42142627],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958533,0.000059667524,0.000036619185,0.000083591774,0.00021204771,0.000022703396],"domain_scores_gemma":[0.99947196,0.0002080378,0.000022975817,0.000069633046,0.0001932544,0.00003411214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006749652,0.0009210619,0.0010017725,0.001299311,0.0004530166,0.0026026976,0.0008673601,0.0011614176,0.02786906],"category_scores_gemma":[0.0018699854,0.00042640616,0.00054126896,0.002411208,0.0009385894,0.0026147198,0.0009680253,0.0024073527,0.011421731],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003926205,0.00004097027,0.0003473961,0.0009174998,0.00011539693,0.00016357191,0.0001705248,0.0071083587,0.0015075298,0.18498592,0.30149657,0.503107],"study_design_scores_gemma":[0.00001144922,0.00003198211,0.0006697824,0.00031737206,0.000030719082,0.00035039443,0.000048142014,0.013570904,0.00080314395,0.17824689,0.8058966,0.000022645538],"about_ca_topic_score_codex":0.0017971847,"about_ca_topic_score_gemma":0.0023729245,"teacher_disagreement_score":0.02786906,"about_ca_system_score_codex":0.0011227321,"about_ca_system_score_gemma":0.00095819734,"threshold_uncertainty_score":0.09323132},"labels":[],"label_agreement":null},{"id":"W2991479744","doi":"10.1007/978-3-030-31985-4_6","title":"Three-Phase Circuits","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Waveform; Electronic circuit; Amplitude; Phase (matter); Power (physics); Electronic engineering; Electrical engineering; Physics; Computer science; Engineering; Optics; Voltage","score_opus":0.03662403152957885,"score_gpt":0.2643072707150422,"score_spread":0.22768323918546332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991479744","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009773954,0.0034125955,0.22027853,0.0010949436,0.0015357792,0.00012327176,0.00042069235,0.0010529639,0.7623072],"genre_scores_gemma":[0.32865465,0.0052681514,0.0846843,0.0011360031,0.00051977136,0.0002582511,0.00070068496,0.00060796237,0.57817024],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998864,0.000012468391,0.0000036153485,0.000026474645,0.000054579334,0.00001648753],"domain_scores_gemma":[0.99992347,0.000016326288,0.0000063132934,0.000021167718,0.000023651328,0.000009062377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012958782,0.00055451825,0.00034522772,0.00048127017,0.00056331576,0.0014354768,0.00091633294,0.00081297377,0.052498892],"category_scores_gemma":[0.00033123474,0.00026588124,0.0003747699,0.0006248748,0.0006783857,0.0014865162,0.0007784223,0.00094871846,0.011827836],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051023828,0.000037486232,0.000054662953,0.00019683385,0.00001646775,0.000043683136,0.00007918372,0.0026610799,0.01592497,0.81515807,0.025999444,0.13977702],"study_design_scores_gemma":[0.000032668548,0.000122482,0.00022129843,0.00013842889,0.00003351609,0.00038910896,0.000058501675,0.01978515,0.025532607,0.4306418,0.5230133,0.0000311298],"about_ca_topic_score_codex":0.00024215016,"about_ca_topic_score_gemma":0.00056725513,"teacher_disagreement_score":0.052498892,"about_ca_system_score_codex":0.0006589722,"about_ca_system_score_gemma":0.00031862225,"threshold_uncertainty_score":0.17562628},"labels":[],"label_agreement":null},{"id":"W2993014117","doi":"10.1080/03610926.2021.1891438","title":"Model averages sharpened into Occam’s razors: Deep learning enhanced by Rényi entropy","year":2021,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"occam; Occam's razor; Entropy (arrow of time); Artificial intelligence; Computer science; Mathematics; Psychology; Statistics; Physics","score_opus":0.020972446219122393,"score_gpt":0.3676529641585918,"score_spread":0.3466805179394694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993014117","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021643553,0.0007406271,0.97584414,0.00027025238,0.00006756277,0.000018319204,0.00005542445,0.00052440393,0.00083573343],"genre_scores_gemma":[0.63993394,0.0011287631,0.35469776,0.0003822282,0.00024434648,0.00010938392,0.00030649168,0.00031172376,0.0028853323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990878,0.00036983256,0.00004914088,0.00013481222,0.00028606478,0.000072274175],"domain_scores_gemma":[0.99690324,0.0016578282,0.00031126983,0.00056991546,0.00043301593,0.00012474165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00359102,0.0009339889,0.0012540383,0.00082492974,0.00035793555,0.001259972,0.0017932121,0.0008937892,0.0011247414],"category_scores_gemma":[0.009795626,0.0005799413,0.0009011217,0.0008496941,0.00085312297,0.0032047508,0.0019914585,0.0030702017,0.00035372627],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030136126,0.000067670786,0.003563564,0.00012009555,0.0002544482,0.00009844247,0.00014701117,0.7103407,0.006712292,0.050499197,0.002993012,0.22490217],"study_design_scores_gemma":[0.000004718779,0.000036832007,0.00021550519,0.000008327945,0.000011692631,0.000016805561,0.000003854651,0.9841702,0.0013074031,0.0138131175,0.00039865632,0.000012876966],"about_ca_topic_score_codex":0.0028759802,"about_ca_topic_score_gemma":0.0035562178,"teacher_disagreement_score":0.00359102,"about_ca_system_score_codex":0.00071680185,"about_ca_system_score_gemma":0.00079249794,"threshold_uncertainty_score":0.018991351},"labels":[],"label_agreement":null},{"id":"W2994682872","doi":"10.1108/lhtn-09-2019-0067","title":"Deep learning for librarians","year":2019,"lang":"en","type":"article","venue":"Library Hi Tech News","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Computer science; Deep learning; World Wide Web; Information retrieval; Artificial intelligence","score_opus":0.010111928597726454,"score_gpt":0.21426420174177588,"score_spread":0.20415227314404943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994682872","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05462554,0.018228242,0.7665523,0.025713162,0.0014572575,0.00018976691,0.0041453727,0.013840442,0.11524785],"genre_scores_gemma":[0.52681595,0.008176434,0.21544194,0.003679541,0.0007712144,0.00021859756,0.004776532,0.0009756891,0.23914418],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995554,0.00014204119,0.000017917346,0.0000909721,0.00013485896,0.000058756334],"domain_scores_gemma":[0.998833,0.00030948195,0.00008245201,0.00023365536,0.00044566614,0.00009580997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010447596,0.00047805705,0.0006312522,0.0013618376,0.0007577127,0.0019568466,0.00093119894,0.0015399533,0.016770734],"category_scores_gemma":[0.0044889324,0.00047401246,0.00045849162,0.0018298684,0.0005683608,0.0030498512,0.0017963389,0.0019995673,0.0064526345],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012272476,0.0001992308,0.0020800182,0.00020954374,0.000081826794,0.000039816758,0.00013188008,0.026659364,0.0014215537,0.029462371,0.12328065,0.816311],"study_design_scores_gemma":[0.000030361016,0.00008462112,0.0021965036,0.00025417987,0.00008627604,0.00006315137,0.00023331374,0.66430026,0.005551317,0.21733986,0.10981312,0.00004695321],"about_ca_topic_score_codex":0.01076559,"about_ca_topic_score_gemma":0.015532655,"teacher_disagreement_score":0.016770734,"about_ca_system_score_codex":0.0013211677,"about_ca_system_score_gemma":0.00163709,"threshold_uncertainty_score":0.056103647},"labels":[],"label_agreement":null},{"id":"W2994791542","doi":"10.1007/978-3-319-21840-3","title":"Algorithms and Data Structures","year":2015,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Carleton University","funders":"","keywords":"Computer science; Algorithm","score_opus":0.03946967750347083,"score_gpt":0.29903758412407555,"score_spread":0.2595679066206047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994791542","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015634184,0.012838792,0.90801364,0.001803729,0.001283723,0.00012828951,0.0013372925,0.0036640053,0.069367215],"genre_scores_gemma":[0.035779886,0.017984465,0.79384035,0.0014092588,0.0021587224,0.0007041578,0.0075266915,0.0029229843,0.13767354],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99846065,0.00024021018,0.00014656178,0.0003913176,0.0006942938,0.00006700489],"domain_scores_gemma":[0.9982951,0.00056683173,0.000049769555,0.00081176637,0.00023550932,0.000040981897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010884606,0.0015235738,0.0016411688,0.0021129185,0.0008895429,0.0046909126,0.002067548,0.0013329592,0.03685845],"category_scores_gemma":[0.0049425866,0.00097230775,0.0012593037,0.00497536,0.0015603334,0.0056539327,0.0026666997,0.0033703218,0.036182366],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039134888,0.0000446643,0.00015933592,0.0004734094,0.000043349988,0.00003792776,0.00009051809,0.004639866,0.0014045819,0.28555652,0.08879801,0.6187126],"study_design_scores_gemma":[0.000019647203,0.000028920113,0.00018951694,0.00016700584,0.000028052773,0.00022467737,0.000037011338,0.022388725,0.0024356898,0.687986,0.2864728,0.000021879487],"about_ca_topic_score_codex":0.00056668825,"about_ca_topic_score_gemma":0.0005354961,"teacher_disagreement_score":0.03685845,"about_ca_system_score_codex":0.000969122,"about_ca_system_score_gemma":0.0013075611,"threshold_uncertainty_score":0.12330377},"labels":[],"label_agreement":null},{"id":"W2994861143","doi":"10.1109/ipta.2019.8936079","title":"Subtractive Perceptrons for Learning Images: A Preliminary Report","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; MNIST database; Perceptron; Artificial intelligence; Artificial neural network; Network topology; Machine learning; Deep learning; Benchmark (surveying); Task (project management); Pattern recognition (psychology)","score_opus":0.02487556330652957,"score_gpt":0.2982258250669944,"score_spread":0.27335026176046484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994861143","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03445922,0.034266476,0.8790171,0.0062006703,0.0023268342,0.00031759572,0.0012658574,0.0041021076,0.038044125],"genre_scores_gemma":[0.3502834,0.035804067,0.49727738,0.0023104856,0.0030904284,0.00062243803,0.007230803,0.0010639935,0.10231695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899036,0.00028880948,0.000054568867,0.00017004987,0.000428112,0.0000681646],"domain_scores_gemma":[0.9979856,0.0008450171,0.00005490692,0.00023734191,0.00072173506,0.00015526774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026036967,0.0010512312,0.0007620059,0.0009224539,0.0002852551,0.001308744,0.0013732564,0.0011818514,0.010341004],"category_scores_gemma":[0.0061675096,0.0006483053,0.0009250151,0.0015374449,0.00066486065,0.0025443002,0.0011487931,0.0031651573,0.0038911486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008358583,0.0008922814,0.0019965465,0.00089136575,0.00030277704,0.00037827247,0.00030364076,0.12717211,0.01687408,0.059149656,0.081267945,0.7099354],"study_design_scores_gemma":[0.00006873488,0.00055858213,0.0017597313,0.00012879251,0.00008807958,0.00025248574,0.00009278036,0.8530685,0.021493956,0.040150173,0.082270555,0.00006768086],"about_ca_topic_score_codex":0.0051813545,"about_ca_topic_score_gemma":0.0036002249,"teacher_disagreement_score":0.010341004,"about_ca_system_score_codex":0.0009627155,"about_ca_system_score_gemma":0.0007138783,"threshold_uncertainty_score":0.03459418},"labels":[],"label_agreement":null},{"id":"W2995476327","doi":"10.15131/shef.data.11298953.v1","title":"NeurIPS 2019 poster: Nonlinear scaling of resource allocation in sensory bottlenecks","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sensory system; Scaling; Resource allocation; Computer science; Nonlinear system; Resource (disambiguation); Mathematics; Psychology; Physics; Neuroscience; Computer network; Geometry","score_opus":0.02234434314622812,"score_gpt":0.24494020168254177,"score_spread":0.22259585853631364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995476327","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06103165,0.025246821,0.28802067,0.062085528,0.06055575,0.0006494149,0.012619078,0.007980631,0.48181057],"genre_scores_gemma":[0.25073516,0.007822132,0.039758656,0.002542033,0.0044222577,0.00030237524,0.0044271457,0.0014627168,0.6885276],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997689,0.000036350662,0.000008021085,0.000057828685,0.0000749543,0.000053972373],"domain_scores_gemma":[0.9990994,0.0001695261,0.000020971234,0.00007136237,0.00040804647,0.00023079463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010075882,0.0009293385,0.0006346575,0.00046678915,0.0009255913,0.0023655533,0.0012857248,0.0014053314,0.12646839],"category_scores_gemma":[0.0016148777,0.000326635,0.00039798155,0.00057469634,0.000601733,0.0015363715,0.0015179913,0.0018704988,0.023241838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011471461,0.000118413,0.00038919997,0.000402183,0.0000783444,0.0002461738,0.00008126532,0.016421331,0.02708693,0.021436771,0.80830413,0.124288104],"study_design_scores_gemma":[0.00040415334,0.00057828723,0.0050438386,0.00035159825,0.00015482461,0.00057750026,0.0003423919,0.26496375,0.045819804,0.08376143,0.5977721,0.00023020957],"about_ca_topic_score_codex":0.007497051,"about_ca_topic_score_gemma":0.016817464,"teacher_disagreement_score":0.12646839,"about_ca_system_score_codex":0.0020769048,"about_ca_system_score_gemma":0.0014976263,"threshold_uncertainty_score":0.4230789},"labels":[],"label_agreement":null},{"id":"W2996151541","doi":"","title":"A Constructive Prediction of the Generalization Error Across Scales","year":2020,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Generalization; Dependency (UML); Computer science; Constructive; Construct (python library); Artificial neural network; Artificial intelligence; Functional dependency; Scale (ratio); Range (aeronautics); Scaling; Generalization error; Machine learning; Algorithm; Theoretical computer science; Data mining; Mathematics; Process (computing)","score_opus":0.0703062179486579,"score_gpt":0.34140394478371966,"score_spread":0.2710977268350617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996151541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028192107,0.0005747124,0.9646341,0.0020390912,0.000102250764,0.00005607204,0.00014351883,0.0005713632,0.0036868423],"genre_scores_gemma":[0.73647887,0.0013784865,0.25349998,0.0015366612,0.00047501008,0.0004991515,0.00047018557,0.00091945997,0.004742248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953755,0.0020219914,0.0002208025,0.0011984868,0.0009179999,0.00026510606],"domain_scores_gemma":[0.9258045,0.05629306,0.0041190796,0.0079106875,0.0047848574,0.0010878259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01608292,0.0018845855,0.0015223671,0.0021390906,0.00090444635,0.0020319363,0.0024137844,0.0027103298,0.0032556914],"category_scores_gemma":[0.11276769,0.0011236233,0.0012991225,0.0012205394,0.0049907705,0.007708398,0.0046281517,0.0050853468,0.00065326685],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016112185,0.00012433254,0.0072597167,0.00050680264,0.00018276423,0.00041695306,0.00074348634,0.5305266,0.008375833,0.37210447,0.006638741,0.07295918],"study_design_scores_gemma":[0.000012020423,0.000054709097,0.0014231782,0.000078226185,0.000018524013,0.000115545445,0.000051973042,0.859309,0.0011869109,0.13688007,0.00084245246,0.00002736393],"about_ca_topic_score_codex":0.0024749457,"about_ca_topic_score_gemma":0.0019067463,"teacher_disagreement_score":0.01608292,"about_ca_system_score_codex":0.0017559965,"about_ca_system_score_gemma":0.0015205808,"threshold_uncertainty_score":0.08505565},"labels":[],"label_agreement":null},{"id":"W2996823581","doi":"10.1609/aaai.v34i04.5883","title":"Residual Neural Processes","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation","keywords":"Universality (dynamical systems); Residual; Computer science; Convergence (economics); Simple (philosophy); Stochastic process; Artificial intelligence; Set (abstract data type); Process (computing); Mathematical optimization; Bayesian probability; Inference; Algorithm; Machine learning; Mathematics; Statistics","score_opus":0.1184773588599628,"score_gpt":0.2946823630960368,"score_spread":0.176205004236074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996823581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012384306,0.0002797424,0.980508,0.00036392122,0.00005895951,0.000035359706,0.00014189369,0.00052208616,0.0057057273],"genre_scores_gemma":[0.67832595,0.00090677844,0.30516937,0.0003357713,0.00017781706,0.00022510641,0.00046111355,0.00018434552,0.014213707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937904,0.00014599583,0.000031755026,0.00022288451,0.00015965612,0.00006055062],"domain_scores_gemma":[0.99871314,0.0006208369,0.0001547793,0.00022393279,0.00023171748,0.000055625667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014986792,0.0007028272,0.0006303501,0.00064081274,0.00034758018,0.0012893087,0.0019823762,0.0012899799,0.0044713207],"category_scores_gemma":[0.0057462184,0.0003470297,0.0007971495,0.00061774947,0.001299482,0.0025381432,0.001231231,0.0020356847,0.0010733788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083157545,0.000057780464,0.0014058239,0.0001346359,0.00008398169,0.00015817743,0.00015067968,0.49112394,0.007337634,0.42163542,0.002680686,0.075148135],"study_design_scores_gemma":[0.000007945777,0.000026758422,0.0001881571,0.000013019068,0.000010099674,0.000039527004,0.000009517744,0.92956054,0.0012383494,0.06701075,0.0018839617,0.000011386056],"about_ca_topic_score_codex":0.002845286,"about_ca_topic_score_gemma":0.0021537398,"teacher_disagreement_score":0.0044713207,"about_ca_system_score_codex":0.0008257539,"about_ca_system_score_gemma":0.00071676326,"threshold_uncertainty_score":0.014958084},"labels":[],"label_agreement":null},{"id":"W2997059872","doi":"10.48550/arxiv.2001.01686","title":"A Deep Neuro-Fuzzy Network for Image Classification","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Neuro-fuzzy; Adaptive neuro fuzzy inference system; MNIST database; Computer science; Artificial intelligence; Fuzzy logic; Artificial neural network; Pooling; Generalization; Connectionism; Fuzzy classification; Fuzzy set operations; Machine learning; Data mining; Fuzzy control system; Mathematics","score_opus":0.07587787656689056,"score_gpt":0.20280818595778294,"score_spread":0.1269303093908924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997059872","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02669694,0.0012089916,0.9642882,0.000557633,0.0002122912,0.00005978938,0.00045913318,0.001294949,0.005222097],"genre_scores_gemma":[0.49608165,0.00085711735,0.48776954,0.00040445753,0.00012936576,0.00013746809,0.00094442954,0.00008372394,0.0135921175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998259,0.00002396037,0.00001007392,0.00005546065,0.00005669746,0.000027868224],"domain_scores_gemma":[0.99979347,0.00005372791,0.000015775247,0.00003512551,0.00008483395,0.000017144866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004378118,0.00056594063,0.00040102928,0.0004731457,0.00041618233,0.000621972,0.0010092778,0.0010229694,0.0027473138],"category_scores_gemma":[0.0011171774,0.00027461615,0.0004470554,0.000536166,0.00037422063,0.0011229984,0.00080695015,0.0012606442,0.00082261994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028028814,0.00017725257,0.0015616663,0.00014204392,0.0001352379,0.00018965227,0.00008357964,0.3255192,0.031444672,0.035369866,0.012456233,0.5926403],"study_design_scores_gemma":[0.000006782896,0.000031646014,0.00027073803,0.000011455786,0.000013713553,0.00004152544,0.0000066813363,0.9770107,0.0048373397,0.014913511,0.0028468198,0.000009073359],"about_ca_topic_score_codex":0.0053534023,"about_ca_topic_score_gemma":0.008419818,"teacher_disagreement_score":0.0053534023,"about_ca_system_score_codex":0.0007469994,"about_ca_system_score_gemma":0.0007199177,"threshold_uncertainty_score":0.010644436},"labels":[],"label_agreement":null},{"id":"W2997197218","doi":"","title":"DeepLABNet: End-to-end Learning of Deep Radial Basis Networks","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"End-to-end principle; Radial basis function; Deep learning; Artificial intelligence; Computer science; Artificial neural network; Activation function; Basis (linear algebra); Radial basis function network; Function (biology); Mathematics","score_opus":0.0058472219097668665,"score_gpt":0.2467689429445632,"score_spread":0.24092172103479634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997197218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055712555,0.0001871454,0.979704,0.00015161162,0.00006845739,0.000077888166,0.00033389917,0.012325169,0.001580537],"genre_scores_gemma":[0.21294263,0.00030536915,0.7734252,0.0003623828,0.000075600525,0.00043838995,0.002398499,0.0016283876,0.008423496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993975,0.00012654124,0.00002956631,0.00015237651,0.00021536376,0.00007858361],"domain_scores_gemma":[0.9991291,0.00030066154,0.00006587622,0.00021163867,0.00022576115,0.00006703019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014659327,0.0016257068,0.00093925005,0.0005581508,0.0004841492,0.001362003,0.0027057081,0.0018807405,0.00940966],"category_scores_gemma":[0.004740358,0.0007536731,0.0006206175,0.0005693112,0.00068327505,0.002529246,0.0023645272,0.003373706,0.004712745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041728804,0.00030929304,0.0011098074,0.00024836115,0.00013055389,0.00024995412,0.00012600611,0.4819291,0.012014567,0.024849214,0.032208633,0.44640726],"study_design_scores_gemma":[0.000012565592,0.00002564036,0.000057924615,0.000007773116,0.0000033625233,0.000018196673,0.000005691305,0.9866052,0.0045803227,0.006719342,0.001956782,0.00000712734],"about_ca_topic_score_codex":0.004005394,"about_ca_topic_score_gemma":0.0074409633,"teacher_disagreement_score":0.00940966,"about_ca_system_score_codex":0.00086563535,"about_ca_system_score_gemma":0.0014671297,"threshold_uncertainty_score":0.031478465},"labels":[],"label_agreement":null},{"id":"W2997330246","doi":"10.48550/arxiv.1912.11511","title":"An Analysis of the Expressiveness of Deep Neural Network Architectures Based on Their Lipschitz Constants","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Lipschitz continuity; Computer science; Range (aeronautics); Constant (computer programming); Artificial neural network; Function (biology); Activation function; Nonlinear system; Deep neural networks; Power (physics); Artificial intelligence; Mathematics; Pure mathematics","score_opus":0.035380485072528375,"score_gpt":0.19125375846263187,"score_spread":0.15587327339010348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997330246","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16503878,0.0020428796,0.81225246,0.0022678366,0.00006944052,0.000059775204,0.0003825825,0.00046927587,0.017416999],"genre_scores_gemma":[0.9251398,0.001897904,0.06697344,0.0004704712,0.00012582137,0.00017604452,0.00028089795,0.00026118275,0.004674323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847347,0.0004193891,0.000109744,0.00033670285,0.0005106901,0.00014992805],"domain_scores_gemma":[0.9839585,0.012758334,0.0011039728,0.0011640105,0.0005591427,0.00045605548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003296058,0.0010475891,0.0007607282,0.0008812917,0.00058535807,0.0022954305,0.0019385326,0.0015554879,0.0026609115],"category_scores_gemma":[0.02428205,0.00091107935,0.0011405369,0.0005365627,0.0027854433,0.006963347,0.0028411192,0.0040340694,0.00030081047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022437547,0.000053660915,0.0019536046,0.00033136882,0.000075722026,0.0003537651,0.00041703595,0.4361395,0.013684784,0.51990366,0.000946182,0.025916345],"study_design_scores_gemma":[0.000013145446,0.00005970623,0.00038139845,0.000068373614,0.000026524263,0.00014817527,0.00003455766,0.7392197,0.0026475843,0.25636134,0.0010178621,0.000021529264],"about_ca_topic_score_codex":0.0010260777,"about_ca_topic_score_gemma":0.0008247151,"teacher_disagreement_score":0.003296058,"about_ca_system_score_codex":0.0021710899,"about_ca_system_score_gemma":0.0007955138,"threshold_uncertainty_score":0.017431438},"labels":[],"label_agreement":null},{"id":"W2998437255","doi":"","title":"Understanding BatchNorm in Ternary Training","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ternary operation; Artificial neural network; Binary number; Computer science; Function (biology); Training (meteorology); Artificial intelligence; Mathematics; Arithmetic; Physics","score_opus":0.04719880229903626,"score_gpt":0.2811962168183445,"score_spread":0.23399741451930825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998437255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0211733,0.00072852976,0.97122496,0.0010890716,0.00013465206,0.00004536948,0.0000980307,0.00057932263,0.0049267597],"genre_scores_gemma":[0.564279,0.0014576856,0.4208747,0.0013408482,0.00040657964,0.00031357456,0.00044847152,0.0005626027,0.010316543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986331,0.00048377967,0.00008572312,0.00027891988,0.0003955716,0.00012292768],"domain_scores_gemma":[0.9956175,0.002725703,0.00032996503,0.0006415325,0.0005530888,0.00013226163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032187416,0.0007715005,0.0006875251,0.00047501884,0.00059102016,0.0019242832,0.0016801418,0.0014124856,0.0036281436],"category_scores_gemma":[0.014892583,0.0005060119,0.00047678984,0.0006006695,0.0024077408,0.004497274,0.0020496363,0.002928161,0.0007320058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005375075,0.0001299842,0.002887641,0.00030930227,0.000063844935,0.0003443396,0.00063127093,0.2915656,0.014610214,0.38545847,0.007675481,0.29578626],"study_design_scores_gemma":[0.000018904122,0.000060716346,0.00041554603,0.0000425803,0.000013329854,0.00009338664,0.00003483984,0.84548074,0.008255226,0.14217012,0.003395769,0.000018756758],"about_ca_topic_score_codex":0.00373676,"about_ca_topic_score_gemma":0.003699021,"teacher_disagreement_score":0.00373676,"about_ca_system_score_codex":0.0012256912,"about_ca_system_score_gemma":0.0011968133,"threshold_uncertainty_score":0.01702255},"labels":[],"label_agreement":null},{"id":"W2998736262","doi":"10.11121/ijocta.01.2020.00756","title":"New complex-valued activation functions","year":2020,"lang":"en","type":"article","venue":"An International Journal of Optimization and Control Theories & Applications (IJOCTA)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Ellipse; Activation function; Function (biology); Complex plane; Plane (geometry); Property (philosophy); Computer science; Mathematics; Topology (electrical circuits); Mathematical analysis; Geometry; Artificial intelligence; Combinatorics; Artificial neural network","score_opus":0.01569589080655479,"score_gpt":0.262996213048756,"score_spread":0.2473003222422012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998736262","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016545087,0.0007425874,0.9735391,0.00032098903,0.00019825147,0.000030494666,0.000060928436,0.00021568913,0.008346826],"genre_scores_gemma":[0.60038346,0.0017288262,0.37932456,0.00047478676,0.00022974014,0.00021412299,0.0002180324,0.0001663108,0.017260129],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996989,0.00007015737,0.000020850466,0.00006445252,0.0001029686,0.000042684693],"domain_scores_gemma":[0.9996499,0.00008805554,0.00003392565,0.00004138605,0.00015393156,0.000032745364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005870747,0.00066064106,0.0003400085,0.00048375523,0.00028974316,0.00093184324,0.00085422146,0.0011274581,0.0029148061],"category_scores_gemma":[0.0014240355,0.0001871323,0.00033997642,0.000518283,0.0006403548,0.0015248792,0.00072732643,0.0008838619,0.0007927533],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022274446,0.00009248389,0.0012108241,0.00034555673,0.00007195472,0.00054999103,0.00025306488,0.26862925,0.10747652,0.31807724,0.0065678153,0.29650268],"study_design_scores_gemma":[0.000016152624,0.0000649361,0.000363768,0.000040328396,0.000017225218,0.00033005807,0.000030169305,0.9188857,0.014331198,0.04755442,0.018333284,0.000032726868],"about_ca_topic_score_codex":0.00040886685,"about_ca_topic_score_gemma":0.0005235935,"teacher_disagreement_score":0.0029148061,"about_ca_system_score_codex":0.00046210227,"about_ca_system_score_gemma":0.00035097022,"threshold_uncertainty_score":0.009751022},"labels":[],"label_agreement":null},{"id":"W2999648422","doi":"10.1109/tfuzz.2020.2966172","title":"Prediction Intervals for Granular Data Streams Based on Evolving Type-2 Fuzzy Granular Neural Network Dynamic Ensemble","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Data stream mining; Artificial neural network; Data mining; Interval (graph theory); Merge (version control); Classifier (UML); Granular computing; Fuzzy logic; Artificial intelligence; Ensemble learning; Machine learning; Rough set; Mathematics","score_opus":0.04767566778005918,"score_gpt":0.26535296047389173,"score_spread":0.21767729269383254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999648422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07199861,0.00040305482,0.92559254,0.00011347388,0.000052822423,0.0000456382,0.00013626245,0.00042411863,0.0012335118],"genre_scores_gemma":[0.9018116,0.0002491428,0.09652335,0.00005812413,0.000040473715,0.0000820952,0.0003716988,0.000042609365,0.0008210214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930406,0.00009797382,0.00006354785,0.00019754052,0.00025936984,0.00007762869],"domain_scores_gemma":[0.9987895,0.00052251725,0.00016073551,0.00013186457,0.00033216548,0.000063262254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013028559,0.00075931917,0.0008755791,0.0010587404,0.00046616257,0.0010868117,0.0011695699,0.00057427795,0.00077638874],"category_scores_gemma":[0.0045901975,0.00027659055,0.0006361821,0.0009583379,0.0003720247,0.0018489381,0.00086827093,0.0010598258,0.00012923563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019087123,0.00005314855,0.0041900906,0.000055984703,0.00007272572,0.00012934036,0.00013331899,0.8308313,0.002543501,0.004652394,0.00081487285,0.15633245],"study_design_scores_gemma":[0.0000018187776,0.000009277045,0.00025550148,0.0000027059602,0.0000058128444,0.000009397506,0.0000084003805,0.9980368,0.0004282474,0.001136978,0.00010129416,0.0000038053893],"about_ca_topic_score_codex":0.007412575,"about_ca_topic_score_gemma":0.0054035243,"teacher_disagreement_score":0.007412575,"about_ca_system_score_codex":0.0008533795,"about_ca_system_score_gemma":0.00056454854,"threshold_uncertainty_score":0.014738858},"labels":[],"label_agreement":null},{"id":"W3003624513","doi":"10.4018/978-1-6684-2408-7.ch013","title":"Development of the Enhanced Piece-Wise Linear Neural Network Algorithm","year":2021,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Sigmoid function; Artificial neural network; Algorithm; Computer science; Dual (grammatical number); Nonlinear system; Artificial intelligence","score_opus":0.018804111626734666,"score_gpt":0.24117692100018842,"score_spread":0.22237280937345374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003624513","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016231779,0.00013744894,0.9954904,0.000044705597,0.00003904289,0.000028004422,0.000030312362,0.0005712129,0.002035799],"genre_scores_gemma":[0.037274987,0.00023401335,0.9557356,0.0000778179,0.000047499412,0.00011098477,0.0002223998,0.00017371714,0.0061229574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994925,0.000108233646,0.000032022435,0.00012895299,0.00020732924,0.00003084544],"domain_scores_gemma":[0.9992067,0.0002599775,0.000047186662,0.00010808502,0.00036267724,0.000015420346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089159596,0.00064645155,0.00080240925,0.0006063522,0.00024464625,0.0008504571,0.0013053692,0.0007400259,0.0048568547],"category_scores_gemma":[0.0025855764,0.00053241674,0.0006415457,0.00067531556,0.0002930586,0.0014031363,0.00088245916,0.0014479527,0.0029743693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007103717,0.000049741895,0.00040138126,0.00016264818,0.000042814612,0.00010539954,0.00005296962,0.28004047,0.012108822,0.017426439,0.004105983,0.6854322],"study_design_scores_gemma":[0.0000044694475,0.000015387826,0.00007814195,0.0000089235855,0.000005205711,0.00003694288,0.0000027789515,0.9909746,0.0024421471,0.0025081157,0.003918574,0.0000048103147],"about_ca_topic_score_codex":0.0016629036,"about_ca_topic_score_gemma":0.0020471662,"teacher_disagreement_score":0.0048568547,"about_ca_system_score_codex":0.0004777336,"about_ca_system_score_gemma":0.00072172127,"threshold_uncertainty_score":0.016247809},"labels":[],"label_agreement":null},{"id":"W3004346138","doi":"10.1109/tcyb.2020.2964011","title":"Hierarchical Granular Computing-Based Model and Its Reinforcement Structural Learning for Construction of Long-Term Prediction Intervals","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Probabilistic logic; Scheduling (production processes); Term (time); Software deployment; Granular computing; Reliability (semiconductor); Process (computing); Monte Carlo method; Reinforcement; Artificial intelligence; Machine learning; Mathematical optimization; Engineering; Mathematics","score_opus":0.02657488675183108,"score_gpt":0.2606205866532678,"score_spread":0.23404569990143673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004346138","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024955302,0.00039065632,0.97173834,0.00023274356,0.00004306017,0.000037549617,0.00007375623,0.00023644684,0.0022921169],"genre_scores_gemma":[0.92926127,0.00036976254,0.06813323,0.000088646695,0.000034052326,0.00013182458,0.00013127385,0.000037511396,0.0018124041],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993926,0.00013053717,0.000042818083,0.00015803376,0.00018057025,0.00009534978],"domain_scores_gemma":[0.9990798,0.0004550705,0.00014090141,0.000081189595,0.00018789641,0.00005506056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096659776,0.0005335151,0.0010327342,0.00054062833,0.00048945966,0.0011601018,0.0013276993,0.0008677271,0.0016704266],"category_scores_gemma":[0.002833066,0.00031590337,0.0007468125,0.00077172846,0.0007099217,0.0011746332,0.00093302527,0.0014199318,0.00018617339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043820557,0.000019481467,0.00057461375,0.00002926236,0.00001589094,0.000047097856,0.00003717995,0.96974784,0.0006500208,0.013174552,0.0003529243,0.015307281],"study_design_scores_gemma":[0.000001510533,0.0000047398516,0.000038904964,0.0000015448022,0.0000023381097,0.0000030821338,0.0000018271979,0.9981456,0.000074783595,0.0016619487,0.00006209328,0.0000016900784],"about_ca_topic_score_codex":0.015055917,"about_ca_topic_score_gemma":0.0067067025,"teacher_disagreement_score":0.015055917,"about_ca_system_score_codex":0.0011720251,"about_ca_system_score_gemma":0.001363765,"threshold_uncertainty_score":0.029936552},"labels":[],"label_agreement":null},{"id":"W3005385294","doi":"","title":"ERP evidence of fast learning of a second language vocabulary: New labels and existing concepts","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"N400; Computer science; Vocabulary; Session (web analytics); Natural language processing; Noun; Artificial intelligence; Cognition; Psychology; Speech recognition; Event-related potential; Linguistics","score_opus":0.034255405613259876,"score_gpt":0.2891500973763245,"score_spread":0.2548946917630647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005385294","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9950021,0.0001199405,0.0023080825,0.000044883163,0.0000093336685,0.000012411722,0.000067616544,0.000032398868,0.0024031904],"genre_scores_gemma":[0.99776864,0.00006161124,0.0009993836,0.000029714449,0.0000062174004,0.000019870518,0.00006582719,0.000010516371,0.0010382668],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.999884,0.000013589661,0.000005815899,0.00003802612,0.000044689685,0.000013840312],"domain_scores_gemma":[0.9994553,0.00025361456,0.00009522408,0.0000794698,0.000057816444,0.000058710906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017953773,0.00019934366,0.00018364792,0.00014957973,0.000057476536,0.00023039927,0.00014977055,0.00034118394,0.0027991775],"category_scores_gemma":[0.0016442796,0.00010382431,0.00009697437,0.00009965683,0.0003363972,0.00035780776,0.00034675223,0.0004455929,0.00029216395],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048653915,0.00014097897,0.008066487,0.0001433346,0.000018592202,0.0005854522,0.00046272133,0.0000805776,0.9636137,0.00046892712,0.00018292798,0.025749717],"study_design_scores_gemma":[0.000098091594,0.0019012613,0.8201256,0.000028151806,0.000049852348,0.004947091,0.0007704876,0.0012819319,0.16489123,0.0032205046,0.0026562652,0.000029525725],"about_ca_topic_score_codex":0.0002295855,"about_ca_topic_score_gemma":0.00027137136,"teacher_disagreement_score":0.0027991775,"about_ca_system_score_codex":0.00007702467,"about_ca_system_score_gemma":0.000075464726,"threshold_uncertainty_score":0.009364188},"labels":[],"label_agreement":null},{"id":"W3005870079","doi":"10.1007/978-3-030-39442-4_16","title":"Clustering and Classification to Evaluate Data Reduction via Johnson-Lindenstrauss Transform","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Cluster analysis; Matrix (chemical analysis); Transformation (genetics); Distortion (music); Transformation matrix; Mathematics; Reduction (mathematics); Distance matrix; Pattern recognition (psychology); Meaning (existential); Artificial intelligence; Computer science; Data mining; Combinatorics; Physics; Psychology; Geometry","score_opus":0.07885324836982317,"score_gpt":0.323428647988419,"score_spread":0.24457539961859587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005870079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019936265,0.00063840835,0.9728001,0.00027662347,0.00013602873,0.00009861987,0.00030429015,0.0018728842,0.003936815],"genre_scores_gemma":[0.30580795,0.00034656932,0.6819896,0.00019060515,0.00013520311,0.0003057679,0.001653519,0.0006450167,0.008925716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975193,0.00079658924,0.00016857096,0.00031909256,0.0010345662,0.00016179062],"domain_scores_gemma":[0.9965922,0.001415115,0.00015298641,0.00046863957,0.0012716332,0.00009934756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031585402,0.0009266099,0.0015462826,0.0020174705,0.00073577795,0.0018168809,0.001729481,0.0013887647,0.0047832737],"category_scores_gemma":[0.010139371,0.0002875918,0.0008535149,0.0016764698,0.0010500281,0.0015276347,0.0013586909,0.0015571172,0.001833737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052961946,0.00039006854,0.0024263717,0.00031192973,0.00016603949,0.0000844879,0.00012766068,0.2871733,0.017351737,0.07847463,0.016202156,0.596762],"study_design_scores_gemma":[0.000009313648,0.000056518777,0.00046402597,0.000012105793,0.000016175907,0.000028380018,0.000025760826,0.9832078,0.0051291697,0.009559623,0.0014809134,0.000010189454],"about_ca_topic_score_codex":0.0060258345,"about_ca_topic_score_gemma":0.006429613,"teacher_disagreement_score":0.0060258345,"about_ca_system_score_codex":0.0012920437,"about_ca_system_score_gemma":0.0016974718,"threshold_uncertainty_score":0.016704142},"labels":[],"label_agreement":null},{"id":"W3006266603","doi":"10.48550/arxiv.1911.08068","title":"Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Simple (philosophy); Computer science; Fuzzy logic; Artificial intelligence; Theoretical computer science; Machine learning; Epistemology","score_opus":0.12408611336713649,"score_gpt":0.23457599468580373,"score_spread":0.11048988131866724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006266603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005678742,0.00005048952,0.99291635,0.000112292284,0.000022447171,0.000020646592,0.000017822422,0.00021315835,0.000968008],"genre_scores_gemma":[0.46385306,0.00019593688,0.5308053,0.00026975028,0.00008968317,0.00019377505,0.00009255685,0.00015880093,0.004341177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967957,0.000096983946,0.000017539222,0.00008477027,0.000083903804,0.000037232054],"domain_scores_gemma":[0.999321,0.00035087875,0.000060751045,0.00013063726,0.00008246744,0.000054311302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008348738,0.00062477356,0.00058493303,0.0003951214,0.00031127245,0.00061213877,0.001230139,0.0010227081,0.0037007087],"category_scores_gemma":[0.0031078365,0.0003545766,0.00044310713,0.00043599054,0.001096294,0.0015890818,0.0012140648,0.0015540954,0.0005231764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024324836,0.00010276613,0.00060997554,0.000115425006,0.00005630314,0.00010361996,0.00017146165,0.6543824,0.02320866,0.11687192,0.0022304477,0.2019038],"study_design_scores_gemma":[0.000009827783,0.00003739508,0.000040497933,0.0000051418747,0.000004187217,0.000021539923,0.0000054980646,0.9718046,0.0024827945,0.024730012,0.0008530554,0.0000053908125],"about_ca_topic_score_codex":0.0010235597,"about_ca_topic_score_gemma":0.001263989,"teacher_disagreement_score":0.0037007087,"about_ca_system_score_codex":0.0004770649,"about_ca_system_score_gemma":0.00058091915,"threshold_uncertainty_score":0.0123800635},"labels":[],"label_agreement":null},{"id":"W3007285736","doi":"","title":"ICE-BeeM: Identifiable Conditional Energy-Based Deep Models.","year":2020,"lang":"en","type":"article","venue":"Työväentutkimus Vuosikirja","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Nationale de la Recherche; Canadian Institute for Advanced Research","keywords":"Energy (signal processing); Econometrics; Geology; Economics; Statistics; Mathematics","score_opus":0.028734488663608226,"score_gpt":0.21740707735462336,"score_spread":0.18867258869101514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007285736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0231515,0.0024873607,0.9505214,0.0009573019,0.00035368523,0.0000882599,0.0039857826,0.012717784,0.005736831],"genre_scores_gemma":[0.49286228,0.0014986874,0.45191678,0.0014129953,0.00021202218,0.0006212968,0.021929987,0.0032570113,0.026288988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999785,0.00005194394,0.000010431647,0.00006396072,0.00005352641,0.000035129633],"domain_scores_gemma":[0.9994684,0.00029019534,0.000029682436,0.00009660838,0.000078104866,0.000037039445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000715076,0.0013116223,0.0010829738,0.0004932743,0.00033000892,0.001077907,0.0030943307,0.0019301699,0.008083144],"category_scores_gemma":[0.0034422872,0.0007974963,0.0010199803,0.0006287109,0.00042802255,0.0022624624,0.002259227,0.0041428073,0.0030031505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037716157,0.00016594371,0.00094757974,0.00024676687,0.00027144176,0.00016146552,0.00006180466,0.7218088,0.003116984,0.022645082,0.04558838,0.20460862],"study_design_scores_gemma":[0.000012674774,0.000012729759,0.00006399176,0.000014023864,0.000007752206,0.000012385131,0.0000038622197,0.9897752,0.00065389,0.008136351,0.0013022152,0.000004826007],"about_ca_topic_score_codex":0.006965392,"about_ca_topic_score_gemma":0.016412767,"teacher_disagreement_score":0.008083144,"about_ca_system_score_codex":0.00071955455,"about_ca_system_score_gemma":0.001260894,"threshold_uncertainty_score":0.02704078},"labels":[],"label_agreement":null},{"id":"W3009388678","doi":"10.5539/ijsp.v9n2p1","title":"Efficient Estimation of Interval-Valued Symbolic Data Regression Model","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Minimax; Regression analysis; Mathematics; Range (aeronautics); Statistics; Interval (graph theory); Regression; Interval estimation; Symbolic data analysis; Regression diagnostic; Estimation; Interval data; Econometrics; Confidence interval; Computer science; Polynomial regression; Mathematical optimization","score_opus":0.060999271763761576,"score_gpt":0.3312858458769387,"score_spread":0.27028657411317714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009388678","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006410365,0.00020923678,0.9927764,0.000049513117,0.000010210226,0.000010835754,0.000040167135,0.00020871263,0.00028463773],"genre_scores_gemma":[0.49517983,0.0010839021,0.4993604,0.00010360237,0.00010441164,0.00021927446,0.0009653506,0.00024394739,0.002739244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974753,0.0012138126,0.0001183438,0.00050990714,0.0005672296,0.000115418625],"domain_scores_gemma":[0.99518764,0.00336952,0.0004270737,0.00036129943,0.00059177453,0.00006265492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032813093,0.00088467676,0.0015532648,0.0010941072,0.00034091066,0.0012904187,0.0014940789,0.0008509556,0.001905284],"category_scores_gemma":[0.013846527,0.00038427155,0.00090806436,0.0013419254,0.00057930517,0.0017104017,0.0011834454,0.0019675274,0.00080903864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002683084,0.00010407999,0.004122842,0.00051417114,0.0001745279,0.0002652227,0.00026671772,0.6102471,0.010906044,0.056585953,0.00247798,0.31406716],"study_design_scores_gemma":[0.0000030589933,0.00001655003,0.000336301,0.000008740986,0.000008023017,0.000029004881,0.000009416057,0.99305224,0.00081288855,0.0053473087,0.00036655238,0.00000996157],"about_ca_topic_score_codex":0.0025063416,"about_ca_topic_score_gemma":0.0017054555,"teacher_disagreement_score":0.0032813093,"about_ca_system_score_codex":0.0005403146,"about_ca_system_score_gemma":0.0010883269,"threshold_uncertainty_score":0.017353415},"labels":[],"label_agreement":null},{"id":"W3009966687","doi":"10.3233/jifs-179689","title":"Online identification of nonlinear systems using neo-fuzzy supported brain emotional learning network","year":2020,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Identifier; Computer science; Fuzzy logic; Identification (biology); Artificial intelligence; Artificial neural network; Neuro-fuzzy; Mean squared error; Machine learning; Pattern recognition (psychology); Fuzzy control system; Mathematics; Statistics","score_opus":0.04789653651898411,"score_gpt":0.2895856835326977,"score_spread":0.24168914701371358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009966687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02155432,0.00013627563,0.97671604,0.000055839788,0.00002094693,0.000024536363,0.000011554268,0.0002451025,0.0012354188],"genre_scores_gemma":[0.79493296,0.00016592046,0.20234571,0.00008576743,0.000021608132,0.00012780614,0.00005864911,0.000024919547,0.0022365665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998103,0.000045940127,0.000014133069,0.000045886183,0.00006129379,0.000022432987],"domain_scores_gemma":[0.9996445,0.00018269618,0.000054923392,0.000026276193,0.00008108362,0.000010493836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058110245,0.00041051244,0.0003684182,0.00022787138,0.00024420765,0.00041740786,0.0004971175,0.00053216337,0.00087855756],"category_scores_gemma":[0.0014621615,0.00015384321,0.00032032118,0.00015794071,0.00028346496,0.0005109115,0.00057449995,0.0006090585,0.00018619292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002496725,0.00010732301,0.0034463662,0.00018442063,0.00008870685,0.0002188069,0.00026355096,0.5933592,0.025103914,0.009742541,0.00079125597,0.3664443],"study_design_scores_gemma":[0.0000044413473,0.000033487588,0.0003359377,0.000005750382,0.000005812409,0.00003037363,0.000006500061,0.99664295,0.0019458961,0.0006714427,0.00031274845,0.0000046650293],"about_ca_topic_score_codex":0.0014651393,"about_ca_topic_score_gemma":0.0015990684,"teacher_disagreement_score":0.0014651393,"about_ca_system_score_codex":0.00028976167,"about_ca_system_score_gemma":0.00035231214,"threshold_uncertainty_score":0.0030732155},"labels":[],"label_agreement":null},{"id":"W3011439611","doi":"10.5220/0009141408360845","title":"Self-organized Cognitive Algebraic Neural Network","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada; Research Canada","funders":"","keywords":"Computer science; Artificial neural network; Cognition; Algebraic number; Artificial intelligence; Cognitive science; Mathematics; Psychology; Neuroscience","score_opus":0.016185458585880448,"score_gpt":0.22546561212812496,"score_spread":0.20928015354224452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011439611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32578573,0.000602061,0.6376519,0.00092056143,0.00022696886,0.00005954075,0.00018831604,0.00041327658,0.03415168],"genre_scores_gemma":[0.9681828,0.00022567321,0.024862781,0.00008366737,0.000050957104,0.000030843683,0.0000678547,0.00002220041,0.0064730835],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998553,0.00003785479,0.000006557275,0.00003424667,0.000043169395,0.000022923641],"domain_scores_gemma":[0.99940324,0.00017809388,0.000095575655,0.00006770579,0.00018539968,0.00006999297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026265194,0.00019007176,0.0003951932,0.0004490553,0.00058644864,0.0009818139,0.0007262626,0.00049912476,0.002635251],"category_scores_gemma":[0.0017634832,0.00014853253,0.00035149118,0.00034153054,0.0007907294,0.0012436446,0.0011153292,0.000502404,0.000285055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011750689,0.000055185836,0.0014323328,0.000105228675,0.00005347621,0.00016261186,0.00024022163,0.21083495,0.0084626395,0.74448586,0.0022593795,0.031790674],"study_design_scores_gemma":[0.000015244407,0.000034806286,0.00041446422,0.0000058501323,0.0000125351635,0.00007032559,0.0000418696,0.76854897,0.0008737254,0.22852486,0.0014409996,0.000016449654],"about_ca_topic_score_codex":0.0013619758,"about_ca_topic_score_gemma":0.0015618429,"teacher_disagreement_score":0.002635251,"about_ca_system_score_codex":0.0005353183,"about_ca_system_score_gemma":0.0006184369,"threshold_uncertainty_score":0.008815765},"labels":[],"label_agreement":null},{"id":"W3011624340","doi":"10.1016/j.cub.2020.01.030","title":"Neural Networks: How a Multi-Layer Network Learns to Disentangle Exogenous from Self-Generated Signals","year":2020,"lang":"en","type":"letter","venue":"Current Biology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial neural network; Biology; Nervous system network models; Artificial intelligence; Layer (electronics); Physical neural network; Types of artificial neural networks; Computer science; Time delay neural network; Self-organization; Nanotechnology","score_opus":0.07146060198656526,"score_gpt":0.2914345708567242,"score_spread":0.21997396887015894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011624340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0152081875,0.011026072,0.5145799,0.37642503,0.026405517,0.000102920516,0.0003823159,0.001367011,0.054503143],"genre_scores_gemma":[0.57762885,0.011755551,0.18585287,0.103142515,0.025640309,0.00037271477,0.00032384318,0.0006417914,0.09464158],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972004,0.00007788625,0.000012536826,0.000050115796,0.00011198873,0.000027403898],"domain_scores_gemma":[0.99910444,0.00057731837,0.00004797633,0.00007999292,0.000119824836,0.00007040029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085925293,0.00036800187,0.00032757776,0.00021499123,0.00043644442,0.0016008988,0.0009437586,0.004458749,0.0030073496],"category_scores_gemma":[0.0047245785,0.00025825648,0.00017934953,0.00023910664,0.001581378,0.002050682,0.0007193458,0.004106408,0.0018637433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004596041,0.000078472054,0.0010373372,0.00028858977,0.00012721853,0.00096579123,0.00013124869,0.02334365,0.013438959,0.3188071,0.27200192,0.36932012],"study_design_scores_gemma":[0.00014995657,0.00005446716,0.00043472063,0.00009506954,0.00003463154,0.00040887904,0.000037915324,0.25458282,0.010149019,0.5786831,0.1553176,0.000051830328],"about_ca_topic_score_codex":0.0008471177,"about_ca_topic_score_gemma":0.0012337585,"teacher_disagreement_score":0.004458749,"about_ca_system_score_codex":0.00075577066,"about_ca_system_score_gemma":0.0003397441,"threshold_uncertainty_score":0.010060608},"labels":[],"label_agreement":null},{"id":"W3012024403","doi":"10.1162/neco_a_01276","title":"The Stochastic Delta Rule: Faster and More Accurate Deep Learning Through Adaptive Weight Noise","year":2020,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Overfitting; Dropout (neural networks); Computer science; Artificial neural network; Bayes' theorem; Algorithm; Benchmark (surveying); Random variable; Standard deviation; Noise (video); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Mathematics; Machine learning; Bayesian probability; Statistics; Image (mathematics)","score_opus":0.028784244134236067,"score_gpt":0.26673971326088186,"score_spread":0.23795546912664578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012024403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057741474,0.0006448878,0.9354084,0.00058536493,0.00020089094,0.00007546158,0.00011115379,0.0033178523,0.0019145014],"genre_scores_gemma":[0.63747746,0.0003267146,0.355829,0.0007030408,0.00012098313,0.00015832989,0.00043769847,0.00041753374,0.0045293025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858046,0.000385061,0.00011947133,0.0002969338,0.0005158507,0.00010225827],"domain_scores_gemma":[0.9972796,0.0009452719,0.0002709005,0.00075550785,0.00060995115,0.00013873413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003336208,0.0010041498,0.0013301438,0.00073570816,0.00031061465,0.0012403565,0.0028663327,0.0013490912,0.0023139424],"category_scores_gemma":[0.007842985,0.00056148757,0.0007221775,0.0006742583,0.0009959224,0.0024501255,0.0019483418,0.0027682092,0.0009701276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002381711,0.00022540255,0.0015541826,0.000087854285,0.00011033351,0.00012645299,0.00006236168,0.7809908,0.0068602595,0.022516048,0.0053953403,0.18183272],"study_design_scores_gemma":[0.000010290464,0.000017309563,0.000036874277,0.000003239626,0.000002873573,0.000006862888,0.0000015297817,0.9963258,0.00076287444,0.0026161252,0.00021354991,0.000002660976],"about_ca_topic_score_codex":0.005704152,"about_ca_topic_score_gemma":0.005444581,"teacher_disagreement_score":0.005704152,"about_ca_system_score_codex":0.0010664094,"about_ca_system_score_gemma":0.001614053,"threshold_uncertainty_score":0.01764375},"labels":[],"label_agreement":null},{"id":"W3015079983","doi":"10.5220/0009175102490257","title":"Activation adaptation in neural networks","year":2020,"lang":"en","type":"article","venue":"Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Adaptation (eye); Computer science; Artificial neural network; Artificial intelligence; Neuroscience; Psychology","score_opus":0.024356432665153624,"score_gpt":0.22369347873982146,"score_spread":0.19933704607466785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015079983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006891211,0.0018927089,0.98580444,0.00037968662,0.00013490366,0.00004260933,0.000048719376,0.0006337359,0.004171995],"genre_scores_gemma":[0.54625505,0.0040995954,0.43382078,0.0006938067,0.00035861327,0.0004844986,0.00026848447,0.00054573966,0.0134734735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912614,0.00035656797,0.00005496731,0.00019626442,0.00019696925,0.00006903551],"domain_scores_gemma":[0.9990416,0.0005417535,0.00007040916,0.00012611783,0.00019305202,0.000026989492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016841255,0.000955269,0.0007891934,0.00060040795,0.00045708517,0.0012289855,0.0015008779,0.0018928158,0.0024623908],"category_scores_gemma":[0.0060257465,0.0005653234,0.0007010157,0.0009836756,0.0012647122,0.0019082234,0.0012288053,0.0023068695,0.0012228715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009870065,0.00004851473,0.0008670235,0.000262414,0.00009625385,0.00010940806,0.00017030539,0.6678485,0.006569478,0.11333253,0.003901463,0.20669548],"study_design_scores_gemma":[0.00001083525,0.00002856639,0.00019527822,0.000035323126,0.000015165652,0.00006438898,0.000017431435,0.9245438,0.0025199056,0.06734448,0.005206513,0.000018205725],"about_ca_topic_score_codex":0.0019315454,"about_ca_topic_score_gemma":0.0014602955,"teacher_disagreement_score":0.0024623908,"about_ca_system_score_codex":0.0009090534,"about_ca_system_score_gemma":0.0006606124,"threshold_uncertainty_score":0.008906662},"labels":[],"label_agreement":null},{"id":"W3015357480","doi":"10.1101/2020.04.08.031484","title":"Developing a novel recurrent neural network architecture with fewer parameters and good learning performance","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Ministry of Education, Culture, Sports, Science and Technology; Institute of Genetics; Japan Agency for Medical Research and Development","keywords":"Recurrent neural network; Computer science; Memorization; Artificial intelligence; Task (project management); Deep learning; Machine learning; Architecture; Sequence learning; Core (optical fiber); Artificial neural network; Engineering; Cognitive psychology; Psychology","score_opus":0.021640886472785805,"score_gpt":0.21368761939538788,"score_spread":0.19204673292260208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015357480","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07015078,0.00035537904,0.92344576,0.0002271391,0.00011484227,0.000096981,0.00019853981,0.0025293678,0.002881237],"genre_scores_gemma":[0.35505658,0.00028704005,0.63813126,0.0002777541,0.000055957844,0.00027492805,0.00086087757,0.0002417469,0.0048138336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997254,0.000042849213,0.000021659082,0.00010352037,0.000076102966,0.000030334033],"domain_scores_gemma":[0.9996587,0.0000720299,0.000033513214,0.000050855302,0.00016617677,0.000018829767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071881333,0.0006940862,0.0005157868,0.00032438236,0.00022557436,0.00043765115,0.0010028717,0.00078830984,0.0014866104],"category_scores_gemma":[0.0011835808,0.00040958324,0.00056035735,0.00031544623,0.0002634256,0.001238766,0.00052897254,0.00081203156,0.00079446007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019090621,0.00017418867,0.0015836012,0.0002520718,0.00018425057,0.0002741495,0.000115774295,0.48810887,0.16799031,0.011885956,0.0049278177,0.32431218],"study_design_scores_gemma":[0.000022597102,0.00010972749,0.00019897961,0.0000076043816,0.000028274057,0.000068567555,0.0000052618834,0.9804434,0.016074093,0.0012151125,0.0018153768,0.000010926181],"about_ca_topic_score_codex":0.0024142195,"about_ca_topic_score_gemma":0.0048479,"teacher_disagreement_score":0.0024142195,"about_ca_system_score_codex":0.0004645765,"about_ca_system_score_gemma":0.00073119364,"threshold_uncertainty_score":0.004973173},"labels":[],"label_agreement":null},{"id":"W3015826724","doi":"10.36227/techrxiv.12100713.v1","title":"Capsule Networks: An Alternative Approach to Image Classification Using Convolutional Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Artificial neural network","score_opus":0.09721064983910437,"score_gpt":0.3110722037266361,"score_spread":0.2138615538875317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015826724","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008282055,0.0005678195,0.9875031,0.00035971988,0.00012408111,0.00003682228,0.000084518135,0.00042340977,0.0026183971],"genre_scores_gemma":[0.44547868,0.002274098,0.5267873,0.0008596367,0.00054359034,0.00022411215,0.00083718443,0.0004340051,0.02256148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936634,0.00014736525,0.000035189616,0.00015649354,0.00021093241,0.00008373365],"domain_scores_gemma":[0.99906105,0.00038811454,0.000101349586,0.00017183185,0.00020687937,0.00007075098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011806553,0.0012762302,0.0009154149,0.0013061528,0.00042617923,0.0018538242,0.001858847,0.0014842255,0.0023903113],"category_scores_gemma":[0.0032939562,0.00059618556,0.0012204034,0.0014835529,0.0011434931,0.003272962,0.002284047,0.002522187,0.0007607692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003676448,0.00013416818,0.002151817,0.00036515377,0.00034957012,0.00021290906,0.00016186133,0.28058317,0.023005446,0.26348013,0.010399219,0.4187889],"study_design_scores_gemma":[0.000010103962,0.00007585413,0.00024543455,0.000019293104,0.000026605372,0.000047583602,0.000010495463,0.95520616,0.007364761,0.031372096,0.0056028008,0.000018871251],"about_ca_topic_score_codex":0.0036062736,"about_ca_topic_score_gemma":0.0027981515,"teacher_disagreement_score":0.0036062736,"about_ca_system_score_codex":0.00095440703,"about_ca_system_score_gemma":0.00083979,"threshold_uncertainty_score":0.00799644},"labels":[],"label_agreement":null},{"id":"W3021934271","doi":"10.4006/0836-1398-28.4.507","title":"Clarification of the causes of ferromagnetic resonance and the value of the g-factor","year":2015,"lang":"en","type":"article","venue":"Physics Essays","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Physics; Value (mathematics); Ferromagnetic resonance; Factor (programming language); Resonance (particle physics); Ferromagnetism; Nuclear magnetic resonance; Condensed matter physics; Quantum mechanics; Statistics; Computer science; Mathematics; Magnetization","score_opus":0.029575291755232543,"score_gpt":0.24053076185826447,"score_spread":0.21095547010303192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021934271","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119417064,0.030661602,0.33840427,0.118258774,0.010155727,0.00008476631,0.00047266702,0.0008266865,0.38171852],"genre_scores_gemma":[0.9410451,0.006312896,0.025373999,0.0041233557,0.0033567336,0.000065920016,0.00008816068,0.00022753044,0.019406332],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993043,0.00023051769,0.000041173913,0.00014698277,0.00019653038,0.00008042532],"domain_scores_gemma":[0.99634874,0.0023594876,0.00021812764,0.0006348564,0.00032214494,0.00011676233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023477606,0.0006836597,0.0009336718,0.0012694256,0.0012663956,0.0022295658,0.0020842557,0.0039062854,0.0063337754],"category_scores_gemma":[0.010360698,0.00040157253,0.00066045695,0.0005897375,0.009560483,0.0058714137,0.0016474546,0.004062898,0.00077138434],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016046923,0.0000072482853,0.00008930637,0.00004575657,0.0000066353405,0.00012263487,0.0001058369,0.0013402038,0.0006164193,0.9926069,0.0021172701,0.002925792],"study_design_scores_gemma":[0.0000073927613,0.0000056952435,0.0001176738,0.00002052059,0.0000037794339,0.00009799269,0.000049971124,0.0030218351,0.00044058944,0.9905095,0.005714504,0.000010512765],"about_ca_topic_score_codex":0.0009513792,"about_ca_topic_score_gemma":0.0009919306,"teacher_disagreement_score":0.0063337754,"about_ca_system_score_codex":0.0012938547,"about_ca_system_score_gemma":0.0008789774,"threshold_uncertainty_score":0.021188557},"labels":[],"label_agreement":null},{"id":"W3022564173","doi":"","title":"Let’s deplatform the deplatformers","year":2019,"lang":"en","type":"article","venue":"The Sydney eScholarship Repository (The University of Sydney)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.009704522166559804,"score_gpt":0.18371956852412322,"score_spread":0.1740150463575634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022564173","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00794322,0.004914427,0.003950671,0.6360998,0.051736552,0.00011713339,0.00029417776,0.0012317393,0.29371232],"genre_scores_gemma":[0.043650858,0.0028384144,0.00222167,0.27161345,0.0058444454,0.000097746946,0.00017720324,0.0011993013,0.6723569],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953335,0.0008812141,0.00015493357,0.0006041581,0.0015726518,0.0014535221],"domain_scores_gemma":[0.9904304,0.00065095356,0.0004820708,0.00084096694,0.0029825417,0.004613096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026755217,0.0013275545,0.0007163757,0.0010879947,0.015345774,0.010102215,0.0017407102,0.008525052,0.1419978],"category_scores_gemma":[0.017421136,0.00089307665,0.0008346905,0.0005757383,0.0071476903,0.012374759,0.007994984,0.014373369,0.09748872],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041708583,0.00005421232,0.0010019419,0.000055794208,0.000007589701,0.00033758912,0.0044134078,0.00002869967,0.000464064,0.015723228,0.94325083,0.03462087],"study_design_scores_gemma":[0.0000055775286,0.000023392844,0.000329148,0.00006868539,0.0000027683197,0.0001929192,0.0036769975,0.000019918012,0.00016101166,0.0024318204,0.9930698,0.000017956556],"about_ca_topic_score_codex":0.023732617,"about_ca_topic_score_gemma":0.05176727,"teacher_disagreement_score":0.1419978,"about_ca_system_score_codex":0.0035734044,"about_ca_system_score_gemma":0.0053883004,"threshold_uncertainty_score":0.47502995},"labels":[],"label_agreement":null},{"id":"W3023430131","doi":"10.1016/b978-0-12-804291-5.00010-6","title":"Deep learning","year":2016,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":254,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Artificial neural network; Key (lock); Backpropagation; Field (mathematics); Convolutional neural network; Machine learning; Boltzmann machine; Mathematics","score_opus":0.013688224297948103,"score_gpt":0.22799208283197922,"score_spread":0.2143038585340311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023430131","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043864027,0.023964187,0.49059096,0.0034074013,0.0024869028,0.00008308005,0.0023064956,0.008000787,0.4647737],"genre_scores_gemma":[0.033042386,0.013819353,0.1013295,0.0008696879,0.0006449761,0.00013227627,0.004240552,0.0014429803,0.84447825],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998271,0.000011798733,0.000008253954,0.0000451243,0.000090225236,0.000017618951],"domain_scores_gemma":[0.9998324,0.00004066171,0.000008263347,0.000053770804,0.000048549482,0.000016373853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025453567,0.001028377,0.00062284997,0.00071369397,0.00031569114,0.0014811292,0.0008098524,0.0010140598,0.06936386],"category_scores_gemma":[0.00077666773,0.00045664815,0.00039293565,0.0011419127,0.00043081515,0.0018379295,0.0015381646,0.0017815764,0.041569334],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017985743,0.000031116142,0.00011325875,0.000196791,0.000017090792,0.00002831906,0.000029535027,0.007618049,0.0019549744,0.04161565,0.15139286,0.79698443],"study_design_scores_gemma":[0.00001196409,0.000033339486,0.000548599,0.00029788504,0.000027019256,0.00021430926,0.000028079721,0.06315244,0.0054676807,0.16120382,0.76898366,0.000031277134],"about_ca_topic_score_codex":0.0021345986,"about_ca_topic_score_gemma":0.0042749518,"teacher_disagreement_score":0.06936386,"about_ca_system_score_codex":0.00057094416,"about_ca_system_score_gemma":0.0007562206,"threshold_uncertainty_score":0.23204523},"labels":[],"label_agreement":null},{"id":"W3023630834","doi":"10.48550/arxiv.2004.13838","title":"How Chaotic Are Recurrent Neural Networks?","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Chaotic; Artificial neural network; Computer science; Artificial intelligence","score_opus":0.09824412102651865,"score_gpt":0.18710524594517544,"score_spread":0.08886112491865679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023630834","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5923321,0.024822285,0.29715014,0.037568152,0.0009393475,0.00007089002,0.0014563713,0.00095134706,0.04470934],"genre_scores_gemma":[0.9856704,0.0035755322,0.0077284547,0.00056949863,0.0002445917,0.000027124885,0.00020282876,0.000112551614,0.001868923],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989784,0.0003889027,0.00006597976,0.00026058077,0.00022594504,0.00008027819],"domain_scores_gemma":[0.9922076,0.0048837857,0.001276035,0.0006443751,0.00080228935,0.00018597592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016119735,0.00034095356,0.0005804042,0.00080033485,0.00050267583,0.0026344026,0.00067214685,0.001433584,0.0018050452],"category_scores_gemma":[0.031447604,0.00046838942,0.00032825614,0.0008953541,0.00213565,0.006687312,0.0007397351,0.0010630662,0.00041506722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055780646,0.00006841509,0.072263084,0.001018603,0.0006060874,0.0009462524,0.0028689494,0.1458001,0.0071696895,0.5269173,0.015380758,0.22640309],"study_design_scores_gemma":[0.000037821606,0.00006356189,0.011692483,0.00018432646,0.00009516025,0.00061381306,0.00077383994,0.16225109,0.0031910066,0.80755,0.01345985,0.00008709241],"about_ca_topic_score_codex":0.0021846872,"about_ca_topic_score_gemma":0.0013396728,"teacher_disagreement_score":0.0026344026,"about_ca_system_score_codex":0.0008428234,"about_ca_system_score_gemma":0.0004186512,"threshold_uncertainty_score":0.0085250735},"labels":[],"label_agreement":null},{"id":"W3023897519","doi":"10.1007/978-3-030-47358-7_27","title":"Low-Dimensional Dynamics of Encoding and Learning in Recurrent Neural Networks","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Encoding (memory); Recurrent neural network; Artificial neural network; Artificial intelligence; Dynamics (music)","score_opus":0.013666152127828568,"score_gpt":0.23380113292594623,"score_spread":0.22013498079811766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023897519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17971724,0.004360338,0.7935759,0.0019807445,0.00035797432,0.000029506813,0.0003820128,0.0006685316,0.018927844],"genre_scores_gemma":[0.94407845,0.0016605877,0.04041886,0.0001397493,0.00015721355,0.000052942334,0.0002788792,0.00017522306,0.0130380625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983585,0.00005963679,0.000011624446,0.000037421818,0.000035111756,0.000020185653],"domain_scores_gemma":[0.9990853,0.0005776407,0.00009129503,0.0000794575,0.00011571631,0.000050430124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044392142,0.00031587717,0.00043759734,0.00024998837,0.0002538682,0.001332977,0.00073518424,0.0008698119,0.0037426658],"category_scores_gemma":[0.004406499,0.0003770758,0.00030255862,0.0003889637,0.0006861871,0.0024501674,0.0007336148,0.0012099769,0.00062128267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015840976,0.00007014838,0.0011799692,0.00023495095,0.00005435421,0.00016436403,0.00035311276,0.46515,0.017968025,0.43022674,0.0053594187,0.07908054],"study_design_scores_gemma":[0.0000047294916,0.000014977953,0.00021794591,0.000011298902,0.000004737338,0.000030398041,0.000018081855,0.91774553,0.00073483266,0.08061036,0.0005977544,0.00000933327],"about_ca_topic_score_codex":0.0015417292,"about_ca_topic_score_gemma":0.0018551481,"teacher_disagreement_score":0.0037426658,"about_ca_system_score_codex":0.0006320827,"about_ca_system_score_gemma":0.00026178468,"threshold_uncertainty_score":0.0125204325},"labels":[],"label_agreement":null},{"id":"W3024298383","doi":"10.1061/9780784482957.017","title":"Extension of Naturalized Flow Using Linear Regression","year":2020,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2020","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Extension (predicate logic); Computer science; Regression; Flow (mathematics); Mathematics; Statistics; Programming language; Geometry","score_opus":0.01654954614972516,"score_gpt":0.21893684681486367,"score_spread":0.2023873006651385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024298383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09650575,0.00031206943,0.8747732,0.0002806405,0.0001713036,0.00021047526,0.00797467,0.015055494,0.00471639],"genre_scores_gemma":[0.45392025,0.0002993012,0.52073395,0.00016312205,0.00012953152,0.0004355926,0.018648518,0.0016223604,0.0040473794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818295,0.00047040978,0.00013262621,0.0007479484,0.00035706555,0.00010889204],"domain_scores_gemma":[0.99461126,0.002001391,0.00070205634,0.0011052352,0.0014940738,0.00008599281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036950272,0.0010010388,0.0005843103,0.0022902905,0.0003914928,0.0011023366,0.0018181691,0.00046435776,0.003270409],"category_scores_gemma":[0.015226412,0.00051528605,0.0012101209,0.002311444,0.00034616893,0.0019005276,0.0011793403,0.00158073,0.0017198782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021347568,0.0003404685,0.08277864,0.0001989165,0.00037144957,0.00020791116,0.00038317085,0.5259113,0.00299856,0.00971995,0.015134648,0.36174154],"study_design_scores_gemma":[0.000016057089,0.000034133256,0.00684143,0.000024015035,0.000022520913,0.000035407502,0.000035437974,0.9780393,0.00216257,0.004043118,0.0087098675,0.000036159745],"about_ca_topic_score_codex":0.02299512,"about_ca_topic_score_gemma":0.013444785,"teacher_disagreement_score":0.02299512,"about_ca_system_score_codex":0.0007705927,"about_ca_system_score_gemma":0.0013492415,"threshold_uncertainty_score":0.045722544},"labels":[],"label_agreement":null},{"id":"W3026831842","doi":"10.1007/978-3-642-02614-0","title":"Intelligent Computer Mathematics","year":2009,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Computation; Symbolic computation; Algebra over a field; Theoretical computer science; Algorithm; Mathematics; Pure mathematics","score_opus":0.019427436264534245,"score_gpt":0.260039717935297,"score_spread":0.24061228167076276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3026831842","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018125959,0.015975628,0.065447934,0.003635088,0.0019358371,0.000059572838,0.000369207,0.00073151663,0.91003275],"genre_scores_gemma":[0.039784066,0.015413259,0.030508958,0.0008970477,0.001827383,0.00011086941,0.0007576267,0.00039845842,0.9103022],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970585,0.00003801902,0.000012606858,0.00005864291,0.00016442419,0.000020345185],"domain_scores_gemma":[0.99977154,0.00006524655,0.000012278629,0.000079613834,0.000053506654,0.000017835697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000276927,0.000748342,0.00067566935,0.001186232,0.000732355,0.002140743,0.0006214275,0.0006192225,0.07285507],"category_scores_gemma":[0.00092026143,0.00031662732,0.00042141395,0.0011142683,0.0010618014,0.0027605323,0.0011685942,0.0017083008,0.03294085],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015294003,0.000030400155,0.00013408434,0.0002107838,0.000012251655,0.0000362336,0.00013064219,0.0012664794,0.001011876,0.53114223,0.20492236,0.26108733],"study_design_scores_gemma":[0.000005307044,0.000014160758,0.0002616823,0.000069845686,0.0000076151227,0.000107675696,0.000028119573,0.0024623307,0.0006753646,0.26393992,0.7324222,0.0000058025807],"about_ca_topic_score_codex":0.0004746151,"about_ca_topic_score_gemma":0.0009118775,"teacher_disagreement_score":0.07285507,"about_ca_system_score_codex":0.0010238888,"about_ca_system_score_gemma":0.00068414985,"threshold_uncertainty_score":0.24372447},"labels":[],"label_agreement":null},{"id":"W3030459265","doi":"10.31651/2076-5886-2019-1-42-53","title":"CLASSIFICATION MODEL BUILDING USING MACHINE LEARNING METHODS","year":2020,"lang":"en","type":"article","venue":"CHERKASY UNIVERSITY BULLETIN APPLIED MATHEMATICS INFORMATICS","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Society for Neuroeconomics","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence","score_opus":0.06736105748279236,"score_gpt":0.27735254825318884,"score_spread":0.20999149077039647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030459265","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052644806,0.00021479066,0.991482,0.00018297113,0.00008332386,0.00006360218,0.00011900618,0.0009252522,0.0016645055],"genre_scores_gemma":[0.19580129,0.0006539235,0.7943562,0.0001046516,0.000119391814,0.00042837646,0.0012485842,0.0003148515,0.0069728293],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99890196,0.0003027535,0.00010225315,0.00027432246,0.00033411427,0.00008457446],"domain_scores_gemma":[0.9985896,0.0007081849,0.00006924854,0.00021618418,0.00038395342,0.000032891377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016837575,0.0007734398,0.0010501232,0.0014074924,0.00069280114,0.002347898,0.0011859608,0.0009778417,0.0046384037],"category_scores_gemma":[0.003960872,0.00064066675,0.0017669247,0.000984112,0.0004602618,0.0016149684,0.0009453591,0.0019835832,0.0033197147],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015485453,0.00015924321,0.0021025848,0.0002759043,0.00015281793,0.00018117297,0.00017313879,0.32040957,0.010968585,0.029143661,0.0058322055,0.63044626],"study_design_scores_gemma":[0.000006982638,0.000020315762,0.00027911822,0.000023755249,0.000029413202,0.000043957356,0.000023510374,0.9796861,0.0041001,0.011526041,0.0042451764,0.000015427086],"about_ca_topic_score_codex":0.0050986717,"about_ca_topic_score_gemma":0.003450435,"teacher_disagreement_score":0.0050986717,"about_ca_system_score_codex":0.00075926166,"about_ca_system_score_gemma":0.0017384819,"threshold_uncertainty_score":0.015516996},"labels":[],"label_agreement":null},{"id":"W3034370310","doi":"","title":"Detecting Out-of-Distribution Examples with Gram Matrices","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Dalhousie University","funders":"","keywords":"Gram; Computer science; n-gram; Distribution (mathematics); Mathematics; Artificial intelligence; Geology","score_opus":0.05855191741722271,"score_gpt":0.29597505400348045,"score_spread":0.23742313658625774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034370310","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61965936,0.0006041713,0.36965564,0.0006594876,0.00016863726,0.00010102109,0.00089791307,0.004763612,0.003490134],"genre_scores_gemma":[0.9121309,0.00018418695,0.083730094,0.00014185139,0.00008752355,0.000041039773,0.001529484,0.0003034179,0.0018515798],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982158,0.00045858705,0.000111101435,0.00034666582,0.0006524117,0.00021538624],"domain_scores_gemma":[0.99245995,0.0041374485,0.00075355003,0.0009347312,0.001332389,0.00038198582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012847253,0.00086037297,0.00075322716,0.0023050199,0.0007516422,0.0011996055,0.0010576346,0.0019537301,0.0020991527],"category_scores_gemma":[0.010628351,0.000426879,0.00048041242,0.0012575921,0.0007589112,0.0018944977,0.0019180841,0.0016308444,0.0011468241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027338313,0.00089213776,0.16180566,0.00049365463,0.00035662428,0.006069094,0.0010976685,0.04935011,0.14112909,0.022912858,0.022674663,0.59048456],"study_design_scores_gemma":[0.000047645743,0.00016279955,0.021495024,0.000052525353,0.00005609593,0.0031127606,0.00043792152,0.91707,0.038570877,0.013682452,0.005262438,0.000049460505],"about_ca_topic_score_codex":0.0017886908,"about_ca_topic_score_gemma":0.003761075,"teacher_disagreement_score":0.0023050199,"about_ca_system_score_codex":0.00045054723,"about_ca_system_score_gemma":0.00062734174,"threshold_uncertainty_score":0.007022381},"labels":[],"label_agreement":null},{"id":"W3034957745","doi":"10.1109/tfuzz.2020.3001740","title":"Design of Reinforced Fuzzy Radial Basis Function Neural Network Classifier Driven With the Aid of Iterative Learning Techniques and Support Vector-Based Clustering","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Changjiang Scholar Program of Chinese Ministry of Education; Korea Electric Power Corporation","keywords":"Softmax function; Computer science; Artificial intelligence; Outlier; Pattern recognition (psychology); Classifier (UML); Cluster analysis; Artificial neural network; Robustness (evolution); Machine learning","score_opus":0.02554668025093662,"score_gpt":0.22339706468954515,"score_spread":0.19785038443860853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034957745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009166367,0.00021298889,0.9883703,0.0000902491,0.000041556592,0.00006501825,0.0000256434,0.00052962673,0.0014982012],"genre_scores_gemma":[0.41895825,0.00042763792,0.575085,0.00019253977,0.000072619936,0.00042060929,0.0002357442,0.000082651655,0.0045250086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991714,0.00013903281,0.000061938736,0.00022414558,0.00032082797,0.00008267864],"domain_scores_gemma":[0.99915576,0.00012639137,0.00008322137,0.00004208249,0.0005659938,0.000026476424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010234461,0.0007385031,0.00083249784,0.00078307075,0.0005210149,0.00081665907,0.0016941387,0.0013188915,0.0010341889],"category_scores_gemma":[0.0018517015,0.0003589814,0.0005631802,0.00056477025,0.00036232523,0.0010633153,0.0005101186,0.00082627934,0.0007072755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022963954,0.00011199157,0.0021499298,0.00023183714,0.00010555739,0.00020168464,0.00013124483,0.41873547,0.031534143,0.010466101,0.0043556555,0.53174675],"study_design_scores_gemma":[0.000005235356,0.000028503078,0.00016784716,0.000007096094,0.000008792323,0.000033488883,0.00000661652,0.995067,0.0033437905,0.0006274629,0.0006945122,0.000009590381],"about_ca_topic_score_codex":0.0060010636,"about_ca_topic_score_gemma":0.0046930416,"teacher_disagreement_score":0.0060010636,"about_ca_system_score_codex":0.0008917345,"about_ca_system_score_gemma":0.0013965865,"threshold_uncertainty_score":0.011932254},"labels":[],"label_agreement":null},{"id":"W3035148950","doi":"10.18280/ria.340204","title":"Test-Cost Sensitive Ensemble of Classifiers Using Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Artificial intelligence; Computer science; Machine learning; Ensemble learning; Reinforcement; Test (biology); Pattern recognition (psychology); Psychology; Social psychology; Biology","score_opus":0.07861826366056356,"score_gpt":0.2861415320650758,"score_spread":0.20752326840451224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035148950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06326852,0.0005344672,0.9329631,0.00028597738,0.00008263764,0.00013171083,0.00004172229,0.00080325897,0.0018886088],"genre_scores_gemma":[0.8675245,0.00013280903,0.1300935,0.00016752584,0.000060883314,0.00018164184,0.00011224768,0.00006741328,0.0016594703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998178,0.0006151557,0.00010193154,0.00040827968,0.00048390706,0.00021260713],"domain_scores_gemma":[0.9945755,0.0030511438,0.0005238585,0.00053965085,0.0010161903,0.0002936906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035471204,0.0010893154,0.002203262,0.0008157025,0.00050642854,0.0010006936,0.0018781734,0.001511142,0.0013236186],"category_scores_gemma":[0.008682505,0.0004955811,0.0007350907,0.00054831227,0.0008370872,0.0015209059,0.0015099252,0.0017756933,0.0003150474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023786596,0.00024339743,0.002866315,0.0000690903,0.00014849882,0.000102454345,0.00006661608,0.83467215,0.0035777516,0.003725668,0.0013867833,0.15290338],"study_design_scores_gemma":[0.000008334104,0.0000485558,0.00013896216,0.00000358644,0.000010371159,0.00001488554,0.000003822467,0.99782866,0.0006132458,0.0011894755,0.00013537388,0.0000047763206],"about_ca_topic_score_codex":0.0039071813,"about_ca_topic_score_gemma":0.0029539946,"teacher_disagreement_score":0.0039071813,"about_ca_system_score_codex":0.001236624,"about_ca_system_score_gemma":0.0013120982,"threshold_uncertainty_score":0.018759191},"labels":[],"label_agreement":null},{"id":"W3035547404","doi":"10.48550/arxiv.2007.00611","title":"Gradient Temporal-Difference Learning with Regularized Corrections","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Temporal difference learning; Divergence (linguistics); Computer science; Artificial neural network; Soundness; Range (aeronautics); Stability (learning theory); Artificial intelligence; Algorithm; Reinforcement learning; Machine learning","score_opus":0.05664012624384622,"score_gpt":0.15808612261247043,"score_spread":0.10144599636862421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035547404","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006851744,0.00023325281,0.99063474,0.00023718941,0.000111898,0.000043140233,0.00002912574,0.00072834356,0.0011304143],"genre_scores_gemma":[0.4289167,0.00023492011,0.56290066,0.0005316422,0.0001755884,0.00025493783,0.00024677347,0.0005404054,0.006198367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99889046,0.000461765,0.00007002006,0.00023624222,0.00025908402,0.00008245189],"domain_scores_gemma":[0.99459153,0.0035015426,0.00032667827,0.00057936454,0.00081995514,0.00018095442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031621063,0.0010194014,0.0013163039,0.0005456723,0.00039794328,0.0010593584,0.0021007583,0.0016741938,0.003808013],"category_scores_gemma":[0.017821625,0.000502021,0.0007118447,0.00059434737,0.0012777202,0.0019853492,0.0016250645,0.002574092,0.0010557596],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004452176,0.00018974348,0.0015816868,0.00025882982,0.000104219034,0.00013172861,0.0001738977,0.7022942,0.004293078,0.056931235,0.0068331636,0.22676304],"study_design_scores_gemma":[0.000019635474,0.000035340854,0.000041716903,0.00000709846,0.0000041729536,0.000015223905,0.000003843055,0.9912402,0.0005502998,0.007476687,0.000600667,0.0000051632105],"about_ca_topic_score_codex":0.0039003915,"about_ca_topic_score_gemma":0.002974604,"teacher_disagreement_score":0.0039003915,"about_ca_system_score_codex":0.00089431775,"about_ca_system_score_gemma":0.0019534363,"threshold_uncertainty_score":0.016722977},"labels":[],"label_agreement":null},{"id":"W3036913634","doi":"10.48550/arxiv.2006.12253","title":"Advantages of biologically-inspired adaptive neural activation in RNNs during learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Recurrent neural network; Artificial intelligence; Computer science; Neural activity; Neuroscience; Cognitive science; Artificial neural network; Psychology","score_opus":0.08413204027054547,"score_gpt":0.1982148206611387,"score_spread":0.11408278039059325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036913634","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35792005,0.0007896886,0.6308584,0.00059316267,0.000056480138,0.000039015897,0.000037407677,0.0005375754,0.009168173],"genre_scores_gemma":[0.97936785,0.00012209531,0.019793812,0.000031501677,0.00000889855,0.000021511676,0.0000122138445,0.000037964808,0.0006041707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998455,0.000069127265,0.0000074095674,0.000031228752,0.0000295812,0.000017213357],"domain_scores_gemma":[0.9994673,0.0002944261,0.00006125343,0.00009740964,0.00005110624,0.000028540906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064813753,0.00038096073,0.00024714402,0.0001234127,0.00020755689,0.000360418,0.00060585275,0.0006281949,0.0010041139],"category_scores_gemma":[0.0032478322,0.00016638143,0.00022628825,0.00015208755,0.0006407089,0.0008985941,0.0005769875,0.00055517,0.00021355519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020053396,0.00008445401,0.0015815553,0.00011787593,0.00003432676,0.00015819972,0.00021444366,0.8152626,0.072353855,0.042456955,0.0004830482,0.0670521],"study_design_scores_gemma":[0.000007457009,0.000070872695,0.00038270545,0.000007926044,0.0000066308894,0.000047293004,0.000017387474,0.9804482,0.005888169,0.012617316,0.00049878587,0.0000072448265],"about_ca_topic_score_codex":0.0005440499,"about_ca_topic_score_gemma":0.0005453897,"teacher_disagreement_score":0.0010041139,"about_ca_system_score_codex":0.00035475596,"about_ca_system_score_gemma":0.0002469257,"threshold_uncertainty_score":0.003427744},"labels":[],"label_agreement":null},{"id":"W3037251213","doi":"","title":"Improving the EDCM Mixture Model with Expectation Propagation.","year":2020,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.060539999301135815,"score_gpt":0.31347901441989157,"score_spread":0.2529390151187558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037251213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046930187,0.0006434886,0.991569,0.00026933796,0.00014263006,0.00002806143,0.00021399734,0.0013470681,0.0010934565],"genre_scores_gemma":[0.19304565,0.0006674507,0.79149467,0.0006733904,0.00023749808,0.00016014403,0.0026313183,0.0011692696,0.009920514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998511,0.0005789934,0.0000777242,0.00032055477,0.0003740317,0.00013771316],"domain_scores_gemma":[0.99706644,0.001742447,0.00012665128,0.00033303662,0.0006140132,0.00011731976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028833984,0.0014874954,0.0013350504,0.0011656525,0.0006277257,0.001488673,0.0033642983,0.0024324316,0.0055657243],"category_scores_gemma":[0.009915378,0.0009683324,0.0015609175,0.0016066144,0.00078620145,0.0034200212,0.0030366005,0.004240693,0.0035978036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008039071,0.0003047899,0.0020071103,0.00023801143,0.0004294748,0.00017897232,0.00013825425,0.515173,0.0067006988,0.024524178,0.018064689,0.43143696],"study_design_scores_gemma":[0.000017154696,0.000018234627,0.0001252713,0.00001123525,0.000026655292,0.000029848441,0.000008237894,0.99027574,0.0017324878,0.0063419593,0.0014015363,0.000011706756],"about_ca_topic_score_codex":0.016004767,"about_ca_topic_score_gemma":0.020690503,"teacher_disagreement_score":0.016004767,"about_ca_system_score_codex":0.0008928989,"about_ca_system_score_gemma":0.0017692416,"threshold_uncertainty_score":0.031823218},"labels":[],"label_agreement":null},{"id":"W3037464880","doi":"10.1007/978-3-030-50516-5_2","title":"Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backpropagation; Computer science; Artificial neural network; Feedforward neural network; Feature (linguistics); Artificial intelligence; Projection (relational algebra); Kernel (algebra); Feed forward; Perspective (graphical); Pattern recognition (psychology); Training (meteorology); Time delay neural network; Layer (electronics); Algorithm; Generalization; Rprop; Training set; Types of artificial neural networks; Mathematics","score_opus":0.030173577112763826,"score_gpt":0.2565689401822738,"score_spread":0.22639536306950994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037464880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001155622,0.0013289633,0.9944891,0.00006280314,0.00012740455,0.000022313212,0.00009296976,0.0008133973,0.001907423],"genre_scores_gemma":[0.036259856,0.0024976195,0.94215065,0.00008949071,0.00015005624,0.00029680663,0.00069609884,0.00058042794,0.017279008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946207,0.0001581358,0.000049130416,0.00009200425,0.00020828626,0.000030449759],"domain_scores_gemma":[0.99935395,0.00038126705,0.00002477318,0.00009605871,0.00012969392,0.000014201249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088102336,0.0017737055,0.0011304542,0.00044987368,0.0003066536,0.00093556714,0.0013228485,0.0013528533,0.010708579],"category_scores_gemma":[0.0030027553,0.0008050316,0.00083257194,0.0014003409,0.0007105864,0.001659583,0.0010003853,0.0025393327,0.00546388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013257244,0.000079455625,0.00021450526,0.00074749615,0.00011196302,0.00018387457,0.00010323807,0.15941654,0.009627386,0.03732244,0.017576585,0.774484],"study_design_scores_gemma":[0.000028336222,0.000086471504,0.00024137595,0.00009351056,0.000035524234,0.00016960989,0.00001798369,0.93709844,0.008733184,0.038029265,0.015441449,0.000024928624],"about_ca_topic_score_codex":0.0021530953,"about_ca_topic_score_gemma":0.0034395289,"teacher_disagreement_score":0.010708579,"about_ca_system_score_codex":0.0003676297,"about_ca_system_score_gemma":0.0005216457,"threshold_uncertainty_score":0.035823762},"labels":[],"label_agreement":null},{"id":"W3037679484","doi":"","title":"On Learnability wih Computable Learners.","year":2020,"lang":"en","type":"article","venue":"Algorithmic Learning Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Waterloo","funders":"","keywords":"Learnability; Computer science; Artificial intelligence","score_opus":0.013372520359430193,"score_gpt":0.22632398920355495,"score_spread":0.21295146884412475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037679484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062790155,0.0062456205,0.8308516,0.008317406,0.00046227247,0.0000935668,0.00053574506,0.0003496374,0.09035393],"genre_scores_gemma":[0.8732202,0.0049626203,0.09555941,0.0015486804,0.0014656474,0.0003800554,0.0011414179,0.00027355572,0.021448418],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964457,0.0015344352,0.00019438734,0.0006308303,0.0008511511,0.00034358737],"domain_scores_gemma":[0.966079,0.028931389,0.0007656344,0.0023059098,0.0012662085,0.0006517752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040245284,0.0008743331,0.001180151,0.002025799,0.0011705885,0.0031294639,0.0015643835,0.0019776847,0.008459911],"category_scores_gemma":[0.042123366,0.00052776485,0.0010236874,0.001484046,0.0060086083,0.01251983,0.004249364,0.005661682,0.0010353975],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025565178,0.000016785187,0.00035081294,0.00008173737,0.000017062854,0.000028369626,0.00011987422,0.004496818,0.00014942451,0.98077285,0.0016805162,0.012260109],"study_design_scores_gemma":[0.000007028171,0.000009845496,0.00011935379,0.000022534372,0.0000041803823,0.000021157235,0.000019511659,0.010759193,0.000097749864,0.98771393,0.0012221134,0.0000033770575],"about_ca_topic_score_codex":0.0013702856,"about_ca_topic_score_gemma":0.001136569,"teacher_disagreement_score":0.008459911,"about_ca_system_score_codex":0.0017066379,"about_ca_system_score_gemma":0.0009989071,"threshold_uncertainty_score":0.02830118},"labels":[],"label_agreement":null},{"id":"W3037834087","doi":"","title":"Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models.","year":2020,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Graphical model; Computer science; Inference; Amortized analysis; Theoretical computer science; Programming language; Data structure; Artificial intelligence","score_opus":0.11644873757317396,"score_gpt":0.34251640125676824,"score_spread":0.22606766368359427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037834087","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003122391,0.00031059157,0.9937571,0.0003186239,0.00007249198,0.00009666625,0.00030169295,0.0013538856,0.0006665088],"genre_scores_gemma":[0.10884741,0.0003503182,0.88293654,0.00046490112,0.00030006282,0.00081686577,0.0020698253,0.000752868,0.0034612296],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940044,0.0038419855,0.00031907542,0.00068090745,0.0007493009,0.0004042121],"domain_scores_gemma":[0.96321106,0.02994498,0.00087191677,0.0037823766,0.001473766,0.00071586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011819367,0.0022394592,0.0028112987,0.0024713546,0.0014195917,0.003063153,0.0056936145,0.0032191272,0.012017253],"category_scores_gemma":[0.05194046,0.002083995,0.0023944995,0.0029556241,0.0033349746,0.0067851297,0.005227091,0.00661775,0.002655598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010136883,0.00045989858,0.0019536838,0.00043157375,0.00032783073,0.00022724071,0.00028864818,0.35430035,0.0017256951,0.41200173,0.0185292,0.20874044],"study_design_scores_gemma":[0.00006279882,0.000027735143,0.000099522,0.000028657554,0.000025464504,0.000026224894,0.000015993528,0.79948753,0.00055876514,0.19830425,0.0013493564,0.000013646086],"about_ca_topic_score_codex":0.009548254,"about_ca_topic_score_gemma":0.01595769,"teacher_disagreement_score":0.012017253,"about_ca_system_score_codex":0.002788531,"about_ca_system_score_gemma":0.0061107716,"threshold_uncertainty_score":0.06250751},"labels":[],"label_agreement":null},{"id":"W3037838597","doi":"10.6084/m9.figshare.14040183.v1","title":"Fast computation of latent correlations","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computation; Statistical physics; Computer science; Algorithm; Physics","score_opus":0.04462821908483772,"score_gpt":0.27641034708021583,"score_spread":0.23178212799537812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037838597","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037889466,0.0017505193,0.87130594,0.0009680844,0.00026499742,0.00025783927,0.04746848,0.03455194,0.0055427453],"genre_scores_gemma":[0.21013619,0.00080588984,0.6685572,0.0005191177,0.00011841098,0.00091078674,0.11000272,0.0030184265,0.005931349],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983523,0.00048855756,0.00008995402,0.0005312688,0.00034996925,0.00018790843],"domain_scores_gemma":[0.997532,0.00090053724,0.0001445619,0.000831723,0.000508005,0.00008315057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002689549,0.0018967981,0.0016479094,0.00199803,0.0008552232,0.0022721663,0.002653153,0.0013043726,0.008598239],"category_scores_gemma":[0.015298265,0.000878964,0.0021258257,0.003407075,0.00055148196,0.002769813,0.0032491556,0.0026932675,0.0073314696],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006624619,0.0002786426,0.014602751,0.0008803535,0.00060598116,0.00029988846,0.00039366735,0.28181016,0.0059883674,0.041580684,0.22726686,0.4256302],"study_design_scores_gemma":[0.000104136256,0.000034589073,0.0021494394,0.000050785507,0.000049755945,0.000110711204,0.00008309445,0.92415935,0.0029660978,0.049327612,0.020920018,0.00004451523],"about_ca_topic_score_codex":0.0134206265,"about_ca_topic_score_gemma":0.025405757,"teacher_disagreement_score":0.0134206265,"about_ca_system_score_codex":0.0015107032,"about_ca_system_score_gemma":0.0034100653,"threshold_uncertainty_score":0.02876395},"labels":[],"label_agreement":null},{"id":"W3038842541","doi":"10.3389/fnsys.2020.00034","title":"Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting","year":2020,"lang":"en","type":"article","venue":"Frontiers in Systems Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Lethbridge","keywords":"Cluster analysis; Spike sorting; Computer science; Artificial intelligence; Sorting; Algorithm; Sorting algorithm; Artificial neural network; Pattern recognition (psychology)","score_opus":0.149823267577796,"score_gpt":0.3180830068570847,"score_spread":0.1682597392792887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038842541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1633842,0.003044107,0.8206258,0.0008038754,0.0003603285,0.00067140046,0.00074439193,0.002273143,0.008092787],"genre_scores_gemma":[0.5270705,0.0012096012,0.46598944,0.00025648662,0.00013563223,0.0005170424,0.0025760627,0.00042919477,0.0018160178],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98908955,0.003126167,0.0012248459,0.0014197387,0.004774989,0.00036480624],"domain_scores_gemma":[0.97774804,0.009911365,0.001716172,0.0021296262,0.008100166,0.00039454715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013749677,0.0016321477,0.0011839658,0.005006506,0.0016042618,0.0042415047,0.001984672,0.002417466,0.0018223989],"category_scores_gemma":[0.046824966,0.00035211467,0.0015233308,0.003300847,0.0011147037,0.0026459503,0.002017773,0.0014063442,0.0011681024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001668233,0.000589434,0.027901908,0.0011820534,0.0009952055,0.00020406827,0.0005509849,0.20141883,0.019467738,0.018872026,0.0054400307,0.7217095],"study_design_scores_gemma":[0.00006105741,0.0010515678,0.018817415,0.00022702636,0.00022626662,0.00042425073,0.00050706195,0.9201873,0.036284126,0.01685881,0.005218085,0.0001369982],"about_ca_topic_score_codex":0.003798925,"about_ca_topic_score_gemma":0.0038463203,"teacher_disagreement_score":0.013749677,"about_ca_system_score_codex":0.0022138823,"about_ca_system_score_gemma":0.0029299872,"threshold_uncertainty_score":0.07271612},"labels":[],"label_agreement":null},{"id":"W3043460766","doi":"10.1016/j.autcon.2020.103348","title":"Neuro-fuzzy systems in construction engineering and management research","year":2020,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Sciences and Engineering Research Council; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Computer science; Fuzzy logic; Identification (biology); Maxima and minima; Convergence (economics); Machine learning; Fuzzy set; Artificial intelligence; Management science; Industrial engineering; Data mining; Engineering; Mathematics","score_opus":0.03236979421865804,"score_gpt":0.271942178678385,"score_spread":0.23957238445972695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043460766","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051788785,0.3896098,0.39807373,0.019812563,0.0020556585,0.00028024212,0.00050491566,0.00024194647,0.13763231],"genre_scores_gemma":[0.68751425,0.16137023,0.13962385,0.0018354511,0.0008296797,0.00026511934,0.00024770957,0.000024339497,0.008289433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982626,0.0007515569,0.0001350332,0.00016539913,0.000612755,0.0000726622],"domain_scores_gemma":[0.9979254,0.0013589831,0.00021608017,0.00008748347,0.0003670561,0.00004504234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033465105,0.0004934124,0.0006939483,0.002802843,0.0008703546,0.0025313173,0.0008079223,0.0017079487,0.0022780686],"category_scores_gemma":[0.004426997,0.00019680746,0.0005657648,0.004124221,0.0018625249,0.0022996815,0.0009335486,0.0012050058,0.00031881977],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005899086,0.000095305666,0.006299035,0.0029184916,0.00023861084,0.00038472927,0.0012758217,0.07037049,0.0010717165,0.4803463,0.0071136146,0.42982686],"study_design_scores_gemma":[0.000023898952,0.00019425718,0.011980563,0.0047441707,0.0001594146,0.0005122661,0.0035903773,0.12372789,0.0018156675,0.6496144,0.20349881,0.00013834839],"about_ca_topic_score_codex":0.008693631,"about_ca_topic_score_gemma":0.010212635,"teacher_disagreement_score":0.008693631,"about_ca_system_score_codex":0.0027169746,"about_ca_system_score_gemma":0.0030431598,"threshold_uncertainty_score":0.019713104},"labels":[],"label_agreement":null},{"id":"W3043476194","doi":"10.5220/0009883401740180","title":"Efficient Construction of Neural Networks Lyapunov Functions with Domain of Attraction Maximization","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Lyapunov function; Attraction; Domain (mathematical analysis); Lyapunov optimization; Artificial neural network; Computer science; Nonlinear system; Maximization; Lyapunov redesign; Mathematical optimization; Control theory (sociology); Mathematics; Artificial intelligence; Mathematical analysis; Physics; Control (management)","score_opus":0.01196095467852004,"score_gpt":0.1996063739329411,"score_spread":0.18764541925442105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043476194","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009075422,0.00011035875,0.9881738,0.00008754591,0.000025940048,0.000036318703,0.000024971172,0.000114953196,0.0023506945],"genre_scores_gemma":[0.54140854,0.00035788256,0.4490835,0.00009713674,0.00007207075,0.00045687376,0.00023649259,0.00023029781,0.008057152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974734,0.00010847424,0.000014917277,0.00003464837,0.000069788475,0.000024858537],"domain_scores_gemma":[0.99921286,0.00047428886,0.000051112624,0.00004330131,0.00018408224,0.000034429075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000982701,0.0008162778,0.0007892227,0.0006883502,0.0003715393,0.0006785027,0.0006667868,0.00075415015,0.0025818446],"category_scores_gemma":[0.0034921037,0.00048673348,0.0005163461,0.0003825969,0.0004900355,0.00068086997,0.0015026361,0.0011571391,0.0006216508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088283006,0.000057319165,0.00040947096,0.00019632648,0.000033000717,0.00010266118,0.000114385795,0.7731381,0.0076706246,0.09053012,0.0027855565,0.12487416],"study_design_scores_gemma":[0.000004501827,0.000017636748,0.000042238895,0.000007479436,0.0000020641098,0.000012346356,0.0000074268173,0.98585343,0.00086747843,0.012646673,0.00053539296,0.0000034529808],"about_ca_topic_score_codex":0.0010597879,"about_ca_topic_score_gemma":0.0013596268,"teacher_disagreement_score":0.0025818446,"about_ca_system_score_codex":0.00070031226,"about_ca_system_score_gemma":0.0009679202,"threshold_uncertainty_score":0.008637071},"labels":[],"label_agreement":null},{"id":"W3044276988","doi":"","title":"Low-Resource Neural Network Modelling for Universal Dependency Parsing","year":2015,"lang":"en","type":"article","venue":"Empirical Methods in Natural Language Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Parsing; Dependency grammar; Dependency (UML); Artificial neural network; Artificial intelligence; Resource (disambiguation); Natural language processing; Computer network","score_opus":0.07949367515106834,"score_gpt":0.41001134372687936,"score_spread":0.330517668575811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044276988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02560513,0.0004439971,0.9689622,0.0005397766,0.00006158111,0.000026043146,0.00039449867,0.00069348584,0.0032733579],"genre_scores_gemma":[0.7781634,0.00072570326,0.20845053,0.00021566023,0.00013074193,0.00023979129,0.0013085537,0.00071395945,0.01005172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946195,0.000262005,0.000031504755,0.00012712603,0.000057975238,0.000059450747],"domain_scores_gemma":[0.99648297,0.0028030837,0.00010456384,0.00035185096,0.00018934299,0.000068137415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017863314,0.00067375053,0.0011506025,0.00091084634,0.0006451018,0.0017932977,0.0024596774,0.0016883647,0.007228578],"category_scores_gemma":[0.012065165,0.00090197776,0.00092571246,0.0014688553,0.0009003588,0.0050817532,0.0015854344,0.0031573018,0.0011055361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010789983,0.00005428618,0.0007181022,0.00011664795,0.00007244438,0.00012675885,0.00011383272,0.80918133,0.0011439074,0.13974713,0.002964171,0.045653492],"study_design_scores_gemma":[0.000002531635,0.0000017096392,0.00005109473,0.0000040368577,0.0000048251823,0.000006936749,0.0000033349002,0.9490854,0.00011096057,0.050542176,0.00018391093,0.0000031014274],"about_ca_topic_score_codex":0.009068697,"about_ca_topic_score_gemma":0.015144796,"teacher_disagreement_score":0.009068697,"about_ca_system_score_codex":0.0013717571,"about_ca_system_score_gemma":0.0013836971,"threshold_uncertainty_score":0.024182022},"labels":[],"label_agreement":null},{"id":"W3045465472","doi":"10.3390/math9243216","title":"The Representation Theory of Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Quiver; Artificial neural network; Representation (politics); Computer science; Theoretical computer science; Moduli space; Algebraic number; Artificial intelligence; Mathematics; Algebra over a field; Pure mathematics","score_opus":0.03786282891710747,"score_gpt":0.28661472823504436,"score_spread":0.2487518993179369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045465472","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014802608,0.005379195,0.91481996,0.0037002817,0.0004276609,0.000050949202,0.00027065125,0.00025115747,0.060297552],"genre_scores_gemma":[0.722685,0.010569061,0.23402497,0.0020659526,0.0018811862,0.00047876802,0.0007053096,0.00032278002,0.02726688],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99857914,0.0006206651,0.000086216394,0.00024314543,0.00035396588,0.00011685308],"domain_scores_gemma":[0.9983841,0.0008508755,0.00014065405,0.00028055056,0.0002734212,0.00007044473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020675776,0.0008126365,0.0008048897,0.0018135002,0.0011080977,0.004309842,0.0014434776,0.0017539038,0.0061425935],"category_scores_gemma":[0.0047130026,0.00045350118,0.0012466331,0.0015738499,0.005040698,0.008376523,0.0021186299,0.0032332896,0.0013077559],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002140604,0.000002114572,0.00002811766,0.000024404782,0.000005363341,0.000010726595,0.0000294986,0.0021832038,0.00008446693,0.9949203,0.00039668047,0.0023129378],"study_design_scores_gemma":[0.0000021889198,0.000005104557,0.00002439325,0.00001691059,0.0000025821262,0.00001681088,0.0000103602015,0.014076665,0.00007154895,0.9828569,0.0029122762,0.0000043113096],"about_ca_topic_score_codex":0.001401078,"about_ca_topic_score_gemma":0.000728972,"teacher_disagreement_score":0.0061425935,"about_ca_system_score_codex":0.0021095665,"about_ca_system_score_gemma":0.00085903285,"threshold_uncertainty_score":0.020549},"labels":[],"label_agreement":null},{"id":"W3046467949","doi":"10.11159/icsta20.05","title":"Some Statistical Applications of Generative Neural Networks","year":2020,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Actua","funders":"","keywords":"Computer science; Artificial neural network; Generative grammar; Artificial intelligence","score_opus":0.028892641850667394,"score_gpt":0.27683013077839996,"score_spread":0.24793748892773257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046467949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011962655,0.0210607,0.92699444,0.008659891,0.0005264698,0.000036527832,0.0003348629,0.0003775579,0.030046936],"genre_scores_gemma":[0.5885653,0.04516798,0.3238913,0.004054082,0.0044599287,0.0002947724,0.0006853685,0.0004670977,0.032414127],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927455,0.00034497774,0.000040831794,0.00012351142,0.00017281529,0.00004324253],"domain_scores_gemma":[0.9973821,0.0020153616,0.000141118,0.00018903388,0.00019596182,0.00007639874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018871669,0.0010419904,0.0006253926,0.0015742576,0.0006357299,0.0012684072,0.0010770304,0.0016385637,0.004060558],"category_scores_gemma":[0.0074435025,0.0006311845,0.0013333694,0.0019599707,0.0022145282,0.0015176248,0.0014609604,0.0030411268,0.00068165397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020554307,0.000030779844,0.001690271,0.0001644336,0.00007884683,0.0002822004,0.00021132447,0.12001268,0.00071839115,0.81713766,0.0049915016,0.054661334],"study_design_scores_gemma":[0.0000056634162,0.00001071649,0.0004924555,0.000058865033,0.000010380835,0.000116327894,0.00001826967,0.17500198,0.00020573029,0.8155854,0.00847536,0.000018984374],"about_ca_topic_score_codex":0.0049320753,"about_ca_topic_score_gemma":0.0048742564,"teacher_disagreement_score":0.0049320753,"about_ca_system_score_codex":0.0014383025,"about_ca_system_score_gemma":0.00058504037,"threshold_uncertainty_score":0.013583958},"labels":[],"label_agreement":null},{"id":"W3046517452","doi":"10.1109/tcsi.2020.3010723","title":"Hardware-Algorithm Co-Design of a Compressed Fuzzy Active Learning Method","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Reduction (mathematics); Algorithm; Fuzzy logic; Cluster analysis; Compressed sensing; Computer engineering; Artificial intelligence; Mathematics","score_opus":0.03658260580358209,"score_gpt":0.2610879669025987,"score_spread":0.22450536109901664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046517452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008911927,0.0001011293,0.98745024,0.00009733201,0.00004793306,0.00007170511,0.000019642068,0.0007710659,0.0025291224],"genre_scores_gemma":[0.45150372,0.00012255566,0.54361355,0.00017733518,0.00005521604,0.00038665728,0.00011149799,0.00007391352,0.003955517],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966156,0.000051938765,0.000019831168,0.00006675859,0.00016607982,0.000033861204],"domain_scores_gemma":[0.99960095,0.00012266183,0.00003079,0.00006210333,0.0001620885,0.000021495582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040031713,0.00035074208,0.00043705726,0.00040936976,0.00032416408,0.0006884755,0.001466244,0.00063565996,0.004672027],"category_scores_gemma":[0.0010903275,0.00021253264,0.00031403083,0.00028425953,0.00034560295,0.0007244611,0.00059911597,0.000631631,0.00093537424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042166354,0.00022970345,0.000869189,0.0002030053,0.00007441494,0.00015253905,0.00015528053,0.33257112,0.0606134,0.019672632,0.0034658,0.5815712],"study_design_scores_gemma":[0.000021800193,0.00006353334,0.00007126431,0.0000051397383,0.0000068829263,0.000040372695,0.000008202069,0.98884785,0.008003482,0.0008483963,0.002077513,0.00000551719],"about_ca_topic_score_codex":0.0020624578,"about_ca_topic_score_gemma":0.0023217294,"teacher_disagreement_score":0.004672027,"about_ca_system_score_codex":0.0004928396,"about_ca_system_score_gemma":0.0008578026,"threshold_uncertainty_score":0.01562947},"labels":[],"label_agreement":null},{"id":"W3046712532","doi":"10.11159/icsta20.134","title":"The Effect of Spectral Line Components on Coefficients in Time Series Regression Models","year":2020,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Trent University; Queen's University","funders":"","keywords":"Series (stratigraphy); Time series; Computer science; Regression analysis; Line (geometry); Linear regression; Regression; Statistics; Mathematics; Geology","score_opus":0.024009482037408096,"score_gpt":0.2723022455742938,"score_spread":0.24829276353688567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046712532","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14296968,0.0006208913,0.8538344,0.0004757992,0.00008531586,0.00006563433,0.00011258644,0.00070404727,0.0011316391],"genre_scores_gemma":[0.7900715,0.0008392331,0.20666505,0.0002503102,0.00013392737,0.00014128482,0.00042810413,0.00045623744,0.0010143991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9740641,0.019573098,0.0012452664,0.0020284003,0.0025107404,0.0005784276],"domain_scores_gemma":[0.6040774,0.36335632,0.011276925,0.014765623,0.00606332,0.00046037696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0401742,0.0013116647,0.001225756,0.0015515619,0.00079448626,0.0024180643,0.0015682933,0.0024843544,0.0013240107],"category_scores_gemma":[0.29562217,0.0012791083,0.0015034219,0.0018732321,0.0023495753,0.0048469296,0.002309686,0.0029252004,0.00049849524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082549365,0.00015620988,0.07188652,0.00040666608,0.0010742574,0.00084143516,0.0011030814,0.60382956,0.010044906,0.04000802,0.0014450862,0.26837876],"study_design_scores_gemma":[0.00004207833,0.00014768905,0.015368536,0.000106535925,0.00018048586,0.00017221455,0.00010951386,0.9598725,0.007470063,0.0154820215,0.00096092146,0.00008744564],"about_ca_topic_score_codex":0.0075819935,"about_ca_topic_score_gemma":0.006439993,"teacher_disagreement_score":0.0401742,"about_ca_system_score_codex":0.0010925954,"about_ca_system_score_gemma":0.0012722701,"threshold_uncertainty_score":0.21246403},"labels":[],"label_agreement":null},{"id":"W3046826865","doi":"10.22215/etd/2010-09365","title":"Improving the performance of neural networks through parallel processing in the cell broadband engine","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Library and Archives Canada","funders":"","keywords":"Computer science; Broadband; Telecommunications","score_opus":0.009124704150264706,"score_gpt":0.23553142112857545,"score_spread":0.22640671697831075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046826865","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4970742,0.0016420161,0.46487972,0.0013185028,0.00036341028,0.000070058515,0.00019259664,0.0043729297,0.030086556],"genre_scores_gemma":[0.8922763,0.0004972741,0.09739169,0.00010833239,0.000053294876,0.000028335757,0.00011444592,0.00014894553,0.009381406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998636,0.000023930263,0.0000075788516,0.00002965772,0.000044723995,0.000030448517],"domain_scores_gemma":[0.999622,0.00015331488,0.000022643511,0.000049505823,0.0001300282,0.000022515687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037527454,0.00042518525,0.00031817664,0.00030416783,0.00030834248,0.00082571025,0.000674062,0.00044219423,0.0032309133],"category_scores_gemma":[0.001566175,0.0002223939,0.0001780586,0.0004098469,0.0002330383,0.0009844463,0.0003621436,0.00071572064,0.0008360389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005373832,0.00026130214,0.0015425674,0.00008990009,0.00006273285,0.00014316784,0.00006310307,0.5671421,0.051844172,0.01056399,0.0048431945,0.36290646],"study_design_scores_gemma":[0.000013097793,0.000040250736,0.00023214624,0.000003371535,0.00001135792,0.000012737639,0.000008254217,0.9767625,0.019557677,0.0025246227,0.0008288196,0.0000052553532],"about_ca_topic_score_codex":0.006184561,"about_ca_topic_score_gemma":0.010112983,"teacher_disagreement_score":0.006184561,"about_ca_system_score_codex":0.00084049156,"about_ca_system_score_gemma":0.0007569462,"threshold_uncertainty_score":0.012297094},"labels":[],"label_agreement":null},{"id":"W3048124370","doi":"10.3934/ipi.2020045","title":"A new initialization method based on normed statistical spaces in deep networks","year":2020,"lang":"en","type":"article","venue":"Inverse Problems and Imaging","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Initialization; Computer science; Norm (philosophy); Artificial neural network; Artificial intelligence; Algorithm; Applied mathematics; Mathematical optimization; Mathematics","score_opus":0.023164715129789223,"score_gpt":0.2645463437617822,"score_spread":0.24138162863199297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048124370","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013285108,0.000117897805,0.9976997,0.000069079164,0.000066982124,0.000015187869,0.000023863722,0.00021927137,0.00045956761],"genre_scores_gemma":[0.049664605,0.00040604014,0.9452371,0.0001587741,0.00020085,0.00016045092,0.00029544358,0.00047541645,0.0034012664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986872,0.0004733383,0.000105876636,0.00027540835,0.0003855806,0.000072612456],"domain_scores_gemma":[0.99813086,0.0005949289,0.00019352809,0.00030196694,0.0006424653,0.00013643687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023809897,0.0012974297,0.001048719,0.0013258304,0.0006619917,0.0013915807,0.0015850533,0.0014984873,0.0018059433],"category_scores_gemma":[0.006557263,0.00086640153,0.0012248177,0.0013021971,0.001738084,0.0032138848,0.0022794649,0.0035862445,0.0012637627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002635995,0.0000990605,0.0015084962,0.00025465185,0.00012175589,0.00019196284,0.00029550714,0.38808864,0.042020056,0.22335577,0.010817261,0.33298326],"study_design_scores_gemma":[0.000011812994,0.00005784157,0.00019647658,0.000023564815,0.000014243131,0.00008141363,0.000011764594,0.9527165,0.01120113,0.029370455,0.006267142,0.000047646194],"about_ca_topic_score_codex":0.00179648,"about_ca_topic_score_gemma":0.0019089208,"teacher_disagreement_score":0.0023809897,"about_ca_system_score_codex":0.0010383846,"about_ca_system_score_gemma":0.0011782611,"threshold_uncertainty_score":0.012592077},"labels":[],"label_agreement":null},{"id":"W3049569209","doi":"10.1038/s41467-021-26568-2","title":"Correspondence between neuroevolution and gradient descent","year":2021,"lang":"en","type":"preprint","venue":"Nature Communications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Vector Institute; University of Ottawa","funders":"Lawrence Berkeley National Laboratory; Office of Science; Basic Energy Sciences; National Energy Research Scientific Computing Center; U.S. Department of Energy","keywords":"Neuroevolution; Gradient descent; Artificial neural network; Limit (mathematics); Computer science; Function (biology); Stochastic gradient descent; Mathematics; Applied mathematics; Artificial intelligence; Algorithm; Mathematical analysis","score_opus":0.03742233270969061,"score_gpt":0.30666050368995323,"score_spread":0.26923817098026265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049569209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032259297,0.00076557975,0.94007903,0.0014765466,0.00014081685,0.000046099,0.000046252953,0.00028463418,0.024901796],"genre_scores_gemma":[0.83987695,0.0009497833,0.14751059,0.000780157,0.00021141702,0.00028725603,0.00009165149,0.00039462018,0.009897531],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988501,0.0004725972,0.00004463607,0.00017518729,0.00036493057,0.0000925277],"domain_scores_gemma":[0.99558264,0.0031728079,0.0003320077,0.0004077771,0.00033976662,0.00016508604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016574053,0.00050099206,0.00078845664,0.0006646766,0.000619541,0.0013775141,0.0009789882,0.0017450018,0.0032706142],"category_scores_gemma":[0.0131106125,0.0003893508,0.00055721076,0.000597701,0.0026746043,0.0018285782,0.0018223381,0.002188574,0.0004322368],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002624372,0.000021037646,0.00043952244,0.000052793093,0.000024784269,0.000077295634,0.00013251313,0.11867885,0.0009825078,0.86547124,0.00089591334,0.013197212],"study_design_scores_gemma":[0.000015854528,0.00003176267,0.00041927028,0.000024484181,0.000007697049,0.00010806885,0.000020067151,0.4756772,0.00065983,0.52026767,0.0027525881,0.000015545707],"about_ca_topic_score_codex":0.0014997992,"about_ca_topic_score_gemma":0.0010109566,"teacher_disagreement_score":0.0032706142,"about_ca_system_score_codex":0.0014348631,"about_ca_system_score_gemma":0.00090660335,"threshold_uncertainty_score":0.010941327},"labels":[],"label_agreement":null},{"id":"W3080427797","doi":"10.1007/978-1-0716-0826-5_3","title":"Siamese Neural Networks: An Overview","year":2020,"lang":"en","type":"review","venue":"Methods in molecular biology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":670,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Krembil Foundation","funders":"","keywords":"Artificial neural network; Computer science; Similarity (geometry); Artificial intelligence; Perceptron; Curse of dimensionality; Representation (politics); Pattern recognition (psychology); Rank (graph theory); Feedforward neural network; Cosine similarity; Euclidean distance; Machine learning; Pearson product-moment correlation coefficient; Convolutional neural network; Mathematics; Statistics","score_opus":0.12986869723055464,"score_gpt":0.49266200524932324,"score_spread":0.36279330801876863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080427797","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015149916,0.9881621,0.007834039,0.00059416814,0.0005462787,0.000015242367,0.000063225954,0.000048392445,0.0025851133],"genre_scores_gemma":[0.0016881013,0.9889712,0.0058565265,0.0003710794,0.0010127197,0.000031838437,0.000118294585,0.000022388984,0.0019278732],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996284,0.00006733792,0.00004693678,0.00007440844,0.00015799538,0.000024825353],"domain_scores_gemma":[0.9991291,0.00048226677,0.000055486573,0.00003390403,0.00025097196,0.00004825007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011529773,0.0016822666,0.0018173652,0.0027419156,0.00026258233,0.0017206562,0.0017584263,0.0019132849,0.0044099754],"category_scores_gemma":[0.0017880806,0.00076299306,0.0007792049,0.004644195,0.00094449765,0.0031308776,0.0011798409,0.0026900095,0.0029738545],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005278106,0.00012591436,0.00019536137,0.008507699,0.0001528085,0.00006256129,0.000039718772,0.0033424958,0.0007533041,0.024018457,0.052430023,0.9103189],"study_design_scores_gemma":[0.00003846056,0.00012992429,0.0008964703,0.0034965845,0.000177831,0.00064764754,0.000040052502,0.0068923444,0.0010290842,0.034241345,0.95233494,0.000075169955],"about_ca_topic_score_codex":0.0029054377,"about_ca_topic_score_gemma":0.003585491,"teacher_disagreement_score":0.0044099754,"about_ca_system_score_codex":0.0010912098,"about_ca_system_score_gemma":0.0018099138,"threshold_uncertainty_score":0.014752805},"labels":[],"label_agreement":null},{"id":"W308069672","doi":"","title":"Signal and noise in perceptual learning","year":2001,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto; Government of Canada","keywords":"Perception; Noise (video); Speech recognition; Perceptual learning; SIGNAL (programming language); Computer science; Psychology; Acoustics; Artificial intelligence; Physics; Neuroscience","score_opus":0.011199532766519985,"score_gpt":0.23382590501329206,"score_spread":0.22262637224677206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W308069672","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81344557,0.009087483,0.06424334,0.0043586674,0.0002825925,0.00013294825,0.000107517015,0.00023283885,0.10810905],"genre_scores_gemma":[0.9825196,0.0016418358,0.0098614255,0.00033976426,0.00011930032,0.000058436286,0.00004032882,0.000052110896,0.0053671934],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99710816,0.0010824321,0.00012373379,0.0006062222,0.0009083574,0.00017111207],"domain_scores_gemma":[0.9856489,0.010816344,0.0013670922,0.0008018002,0.0008884045,0.0004775458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003800145,0.0003610162,0.0004994241,0.0007568337,0.00045615586,0.0027324765,0.00063079956,0.00090774207,0.004193816],"category_scores_gemma":[0.025876114,0.0003319827,0.00038625355,0.0005334531,0.00316586,0.002715129,0.0018315685,0.0014295249,0.00054352067],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018352949,0.0010329443,0.03985725,0.0011007364,0.00022194057,0.00031952403,0.006257018,0.019306934,0.047255185,0.2882348,0.0024655315,0.5921129],"study_design_scores_gemma":[0.00018518191,0.0022036233,0.18472472,0.00034575825,0.00018324175,0.0006349338,0.001197646,0.062199254,0.025685757,0.69943315,0.022990204,0.00021657973],"about_ca_topic_score_codex":0.0012915597,"about_ca_topic_score_gemma":0.0009202619,"teacher_disagreement_score":0.004193816,"about_ca_system_score_codex":0.0009945362,"about_ca_system_score_gemma":0.0008299019,"threshold_uncertainty_score":0.020097315},"labels":[],"label_agreement":null},{"id":"W3082212141","doi":"10.1007/978-3-319-49583-5","title":"Algorithms and Architectures for Parallel Processing","year":2016,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":173,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Parallel computing; Parallel processing","score_opus":0.016590815646636992,"score_gpt":0.2676664535930699,"score_spread":0.2510756379464329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082212141","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017813339,0.0070579383,0.95083934,0.0007948272,0.0011098773,0.000096498225,0.0002667615,0.0035743732,0.03447916],"genre_scores_gemma":[0.04216345,0.009716127,0.89287794,0.00041524673,0.0014057406,0.0004747626,0.001074271,0.001340011,0.050532486],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99931896,0.00011898901,0.000058208974,0.00011112096,0.0003292512,0.00006341452],"domain_scores_gemma":[0.9991854,0.00024429793,0.000036454632,0.00029182917,0.00021427867,0.000027744096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006218979,0.0018059915,0.0012071743,0.0011766305,0.0008066729,0.0025319518,0.0020325196,0.0010607833,0.022628479],"category_scores_gemma":[0.002390124,0.00088350923,0.0008291273,0.0030932645,0.0009817801,0.0031782917,0.0015116113,0.0025438005,0.011837269],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014616815,0.00006992955,0.00015873894,0.0006897123,0.0000977648,0.00007744616,0.00007824877,0.04517818,0.004612363,0.32867426,0.075233676,0.54498345],"study_design_scores_gemma":[0.00006927441,0.00005149181,0.00015467643,0.00010003723,0.00005577998,0.00018233214,0.00002897942,0.17984055,0.005632278,0.6583417,0.1555128,0.00003013676],"about_ca_topic_score_codex":0.0008739679,"about_ca_topic_score_gemma":0.0009622914,"teacher_disagreement_score":0.022628479,"about_ca_system_score_codex":0.0008005846,"about_ca_system_score_gemma":0.0010086693,"threshold_uncertainty_score":0.075699806},"labels":[],"label_agreement":null},{"id":"W3088907756","doi":"10.1371/journal.pone.0238454","title":"Biological batch normalisation: How intrinsic plasticity improves learning in deep neural networks","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Infomax; Learning rule; Computer science; Artificial intelligence; Robustness (evolution); Artificial neural network; Machine learning; Biology; Channel (broadcasting)","score_opus":0.05685182642939146,"score_gpt":0.21252667384352203,"score_spread":0.15567484741413057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088907756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06922648,0.00061216723,0.9214319,0.00093933294,0.00017610916,0.00005066664,0.00008137517,0.0019715438,0.005510404],"genre_scores_gemma":[0.8275741,0.0003688143,0.16690882,0.0004222515,0.000116003765,0.00009248415,0.00009437518,0.00053699105,0.0038862107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990584,0.00023400829,0.00006467621,0.00026154402,0.00030022627,0.000081079226],"domain_scores_gemma":[0.99642605,0.0016554695,0.00039619228,0.00090314395,0.00045920207,0.0001599617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026970906,0.00066761696,0.0006450978,0.00045331707,0.0003643319,0.0012301326,0.0021125155,0.0010754695,0.0020171027],"category_scores_gemma":[0.014816446,0.00035729117,0.000599663,0.00037370692,0.0018793439,0.0031708947,0.0014440214,0.0021822075,0.0006210873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043476597,0.00020112339,0.004252797,0.00033484126,0.00014135691,0.0002572426,0.00027129878,0.46074045,0.10224646,0.13046405,0.00374413,0.29691157],"study_design_scores_gemma":[0.00003351629,0.0002655906,0.0009597118,0.000029208182,0.000027108095,0.00016138393,0.000021988802,0.8731213,0.044182956,0.07825665,0.0028996365,0.00004093501],"about_ca_topic_score_codex":0.0010854758,"about_ca_topic_score_gemma":0.0012477731,"teacher_disagreement_score":0.0026970906,"about_ca_system_score_codex":0.0008367797,"about_ca_system_score_gemma":0.0008236773,"threshold_uncertainty_score":0.014263749},"labels":[],"label_agreement":null},{"id":"W3090647408","doi":"10.1109/ijcnn48605.2020.9207460","title":"Should I Stay or Should I Grow? A Dynamic Self-Governed Growth for Determining Hidden Layer Size in a Multilayer Perceptron","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Perceptron; Computer science; Population; Task (project management); Upper and lower bounds; Algorithm; Constraint (computer-aided design); Multilayer perceptron; Network topology; Layer (electronics); Set (abstract data type); Artificial intelligence; Mathematics; Artificial neural network; Engineering","score_opus":0.07954576087304424,"score_gpt":0.3190943325781591,"score_spread":0.23954857170511487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090647408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15292582,0.00034656478,0.84051895,0.00086691533,0.000081289545,0.00007695151,0.000063978434,0.00045418562,0.004665265],"genre_scores_gemma":[0.83463264,0.00020354222,0.1623147,0.00018399244,0.000033286982,0.0001671091,0.00006810837,0.000093793904,0.0023028674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994179,0.00017709553,0.000037595437,0.00015313936,0.00014149644,0.00007266087],"domain_scores_gemma":[0.9974082,0.0016850663,0.0003191182,0.0001761649,0.00028828142,0.00012308055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017132742,0.00042116322,0.00045602437,0.00039632953,0.00045900984,0.0009434934,0.0014556521,0.001242192,0.0013324178],"category_scores_gemma":[0.009431664,0.00040738785,0.00043592777,0.0003307018,0.0011678555,0.0027576606,0.001016569,0.0013408253,0.00033214988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029142314,0.00014385022,0.0063544437,0.000303323,0.00007948392,0.00039026674,0.00071716553,0.7234142,0.049547937,0.066020705,0.0023458696,0.15039136],"study_design_scores_gemma":[0.000008651748,0.000036576927,0.00035480454,0.00001723077,0.000008258832,0.000037475005,0.000023939967,0.9846731,0.0028465064,0.011613807,0.00036639994,0.000013299563],"about_ca_topic_score_codex":0.0013341499,"about_ca_topic_score_gemma":0.001358401,"teacher_disagreement_score":0.0017132742,"about_ca_system_score_codex":0.0008147388,"about_ca_system_score_gemma":0.00067507074,"threshold_uncertainty_score":0.0090608},"labels":[],"label_agreement":null},{"id":"W3090717756","doi":"10.1109/iscas45731.2020.9180463","title":"CORDIC-SNN: On-FPGA STDP Learning with Izhikevich Neurons","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Field-programmable gate array; CORDIC; Computer science; Computer hardware","score_opus":0.023283297305124274,"score_gpt":0.22462376887270646,"score_spread":0.2013404715675822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090717756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20208034,0.0013827622,0.74166244,0.00043086676,0.0004923653,0.00021294657,0.00042982225,0.011944685,0.041363686],"genre_scores_gemma":[0.8425956,0.00040546554,0.14437377,0.00013784967,0.00003863205,0.000066577784,0.0002719952,0.00022935418,0.011880755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999138,0.000011284605,0.0000059485646,0.000021643422,0.00003487062,0.000012419886],"domain_scores_gemma":[0.9998833,0.000018041106,0.00001395029,0.000034703018,0.00003917645,0.000010727522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015609647,0.00029231858,0.00027469368,0.0002041753,0.00018708967,0.00042099482,0.00087451184,0.00023023457,0.006454108],"category_scores_gemma":[0.00029361402,0.000080764905,0.00015252162,0.00022669771,0.00018804595,0.00036686435,0.0002980599,0.00028682235,0.0008818254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070821797,0.00024070124,0.0030500002,0.00058197416,0.00012403892,0.00067292934,0.00013371767,0.2003293,0.1812235,0.027052013,0.01523396,0.5706496],"study_design_scores_gemma":[0.000066835135,0.00042030687,0.0014615402,0.000044028457,0.00005152015,0.00037588438,0.000023378454,0.82518744,0.14587016,0.0038615365,0.022609135,0.000028317],"about_ca_topic_score_codex":0.0026544174,"about_ca_topic_score_gemma":0.0046025063,"teacher_disagreement_score":0.006454108,"about_ca_system_score_codex":0.00051150407,"about_ca_system_score_gemma":0.00057344226,"threshold_uncertainty_score":0.021591127},"labels":[],"label_agreement":null},{"id":"W3090953785","doi":"10.1109/acit49673.2020.9208945","title":"Automated Object Recognition System based on Convolutional Autoencoder","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Solana Networks (Canada)","funders":"","keywords":"Autoencoder; Artificial intelligence; Computer science; Preprocessor; Pattern recognition (psychology); Feature extraction; Convolutional neural network; Cognitive neuroscience of visual object recognition; Computer vision; Object (grammar); Contextual image classification; Perceptron; Artificial neural network; Image (mathematics)","score_opus":0.03272756309610853,"score_gpt":0.23768320232150703,"score_spread":0.2049556392253985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090953785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1267083,0.00045215903,0.8555571,0.0001257796,0.00020797578,0.00025956717,0.0003333953,0.009175015,0.007180751],"genre_scores_gemma":[0.66661125,0.00028803424,0.32309315,0.00011032692,0.000042426283,0.00025147758,0.0005847932,0.00008287995,0.008935628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996326,0.000031255197,0.000016890399,0.00011206311,0.00016793604,0.000039214694],"domain_scores_gemma":[0.9997352,0.00004378537,0.000023122722,0.00005208813,0.00013069429,0.000015106398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000386207,0.0003794873,0.0005355426,0.0004193232,0.00031307995,0.0004984057,0.00083252956,0.0005329052,0.0019309648],"category_scores_gemma":[0.0004234962,0.00022927707,0.000331624,0.0002951501,0.00023531704,0.00068623683,0.0003280947,0.00035204756,0.0008038986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046301217,0.00031681647,0.0028964244,0.00028275876,0.00015636867,0.0002846839,0.00015952258,0.108066104,0.36902446,0.008052213,0.0043769293,0.5059207],"study_design_scores_gemma":[0.000032013413,0.00027212233,0.0038480244,0.000020880056,0.000051238145,0.0001723765,0.000016209999,0.88874537,0.0973092,0.001096995,0.008397572,0.000037898208],"about_ca_topic_score_codex":0.0049484014,"about_ca_topic_score_gemma":0.0038653626,"teacher_disagreement_score":0.0049484014,"about_ca_system_score_codex":0.00054606434,"about_ca_system_score_gemma":0.00072001625,"threshold_uncertainty_score":0.009839237},"labels":[],"label_agreement":null},{"id":"W3091586704","doi":"10.1109/iscas45731.2020.9180548","title":"Towards Efficient On-Chip Learning using Equilibrium Propagation","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Backpropagation; Pruning; Computation; Quantization (signal processing); Artificial neural network; Deep learning; Computer engineering; Field (mathematics); Chip; Artificial intelligence; Algorithm; Telecommunications","score_opus":0.047932426698258944,"score_gpt":0.27026120137660753,"score_spread":0.22232877467834858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091586704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021963157,0.00031156678,0.9700004,0.00030993033,0.00006280855,0.00003833819,0.000027943093,0.0013917774,0.0058940947],"genre_scores_gemma":[0.5038078,0.0004482589,0.48810902,0.0003018891,0.000050949024,0.00010419593,0.00009497576,0.00016434585,0.006918549],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995784,0.000097207376,0.00001687815,0.000050639734,0.00019572527,0.00006108737],"domain_scores_gemma":[0.99929,0.00036234444,0.000043265227,0.000119425626,0.00016022148,0.000024707233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005382174,0.0005139031,0.00049550994,0.00038589473,0.000357565,0.0009881011,0.0015715625,0.0006813779,0.003554417],"category_scores_gemma":[0.0021446245,0.0002899601,0.00023935163,0.0005010291,0.00065373676,0.0014243909,0.0008702513,0.0011755147,0.0009737427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003414328,0.0002494524,0.0010052285,0.0002657388,0.000072016526,0.00016069756,0.00016603505,0.3823023,0.05046528,0.14884824,0.006701223,0.4094224],"study_design_scores_gemma":[0.000018592049,0.000042031737,0.000063305175,0.000009772006,0.000007687124,0.000026853764,0.000012697988,0.9741818,0.010284121,0.012542868,0.0028041196,0.0000061092946],"about_ca_topic_score_codex":0.0027278077,"about_ca_topic_score_gemma":0.0050896425,"teacher_disagreement_score":0.003554417,"about_ca_system_score_codex":0.0007707132,"about_ca_system_score_gemma":0.0011556844,"threshold_uncertainty_score":0.01189065},"labels":[],"label_agreement":null},{"id":"W3091966033","doi":"10.1364/networks.2020.new1b.4","title":"Forecasting SNR margins has low-complexity","year":2020,"lang":"en","type":"article","venue":"OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Ciena (Canada)","funders":"","keywords":"Margin (machine learning); Signal-to-noise ratio (imaging); Computer science; Artificial neural network; Noise (video); Artificial intelligence; Deep neural networks; Algorithm; Pattern recognition (psychology); Machine learning; Telecommunications","score_opus":0.04859311026454356,"score_gpt":0.2518846167001651,"score_spread":0.20329150643562155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091966033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5747289,0.001873309,0.4006725,0.001222552,0.00029102736,0.00009383626,0.005688006,0.0056877513,0.009742141],"genre_scores_gemma":[0.9544305,0.00023414684,0.03793382,0.00011402334,0.000097907316,0.000035558154,0.0050888,0.0001456471,0.0019195401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997181,0.000041337906,0.00001507162,0.00010855065,0.00007982397,0.00003714344],"domain_scores_gemma":[0.9988508,0.0005229961,0.00014016757,0.00017545694,0.00026684804,0.000043763244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074012927,0.0012342082,0.0004219679,0.00053518417,0.0002164233,0.0006398427,0.0005118528,0.0007131066,0.0011674667],"category_scores_gemma":[0.0037150031,0.00027979293,0.00043446998,0.00036979263,0.00027229995,0.00091294816,0.00035279497,0.0010653747,0.00074712466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003755483,0.000096662414,0.01505677,0.000093836585,0.000091850736,0.000107654785,0.000026805652,0.83789295,0.0078828465,0.001066367,0.0077795973,0.12952901],"study_design_scores_gemma":[0.000011180119,0.000056363424,0.005244291,0.000007054172,0.000010302094,0.000030842577,0.000007094794,0.98756903,0.004664322,0.001389728,0.0009960165,0.000013782949],"about_ca_topic_score_codex":0.0071440064,"about_ca_topic_score_gemma":0.009949773,"teacher_disagreement_score":0.0071440064,"about_ca_system_score_codex":0.00048358052,"about_ca_system_score_gemma":0.00036429055,"threshold_uncertainty_score":0.01420486},"labels":[],"label_agreement":null},{"id":"W3092111998","doi":"10.1007/978-3-030-59830-3_25","title":"Manifold-Based Classifier Ensembles","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Classifier (UML); Computer science; Nonlinear system; Manifold (fluid mechanics); Riemannian geometry; Artificial intelligence; Nonlinear dimensionality reduction; Riemannian manifold; Pattern recognition (psychology); Mathematics; Pure mathematics; Physics; Dimensionality reduction","score_opus":0.025799406950174516,"score_gpt":0.24103383246622082,"score_spread":0.2152344255160463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092111998","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014810756,0.0018633201,0.97353244,0.00023432935,0.00032285863,0.000052179446,0.00019775462,0.0011498532,0.007836488],"genre_scores_gemma":[0.39787942,0.0024513504,0.5642778,0.00029822535,0.0008000108,0.00022606178,0.0026536451,0.00063790753,0.030775491],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856466,0.00041066663,0.00007242126,0.00029086595,0.00056466827,0.00009667856],"domain_scores_gemma":[0.9983165,0.0004679992,0.00006206868,0.00044251885,0.00064122584,0.000069622496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013588514,0.000926338,0.001904915,0.0011424115,0.00071533705,0.0012398278,0.0014401267,0.0012483115,0.004801217],"category_scores_gemma":[0.0034827988,0.0006811039,0.0012592534,0.0013104535,0.00044984513,0.002169313,0.0022272174,0.002131226,0.0035308225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012843586,0.00011321246,0.0007645096,0.00010129251,0.0002380321,0.00005919106,0.00009238334,0.17355174,0.0085393265,0.034123614,0.017188787,0.76509947],"study_design_scores_gemma":[0.0000037106786,0.00006531305,0.00031337322,0.000015818154,0.000029648292,0.00008012769,0.000016334026,0.9671701,0.0024195663,0.02429332,0.005577993,0.00001461713],"about_ca_topic_score_codex":0.0009980396,"about_ca_topic_score_gemma":0.0022705302,"teacher_disagreement_score":0.004801217,"about_ca_system_score_codex":0.00047415524,"about_ca_system_score_gemma":0.0005387227,"threshold_uncertainty_score":0.016061664},"labels":[],"label_agreement":null},{"id":"W3092384192","doi":"10.48550/arxiv.2006.08305","title":"Inner Ensemble Networks: Average Ensemble as an Effective Regularizer","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Ensemble learning; Computer science; Ensemble forecasting; Artificial intelligence","score_opus":0.04090951258387337,"score_gpt":0.19580795857965685,"score_spread":0.15489844599578348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092384192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013694563,0.00037530545,0.981739,0.00018297268,0.000091769245,0.000025670559,0.00010804051,0.0010239528,0.0027587388],"genre_scores_gemma":[0.4381826,0.00071346,0.54693884,0.0005146922,0.00029077366,0.0002262799,0.00093272526,0.000851284,0.011349285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920255,0.00023615902,0.000037646027,0.00017469212,0.00025410476,0.00009486566],"domain_scores_gemma":[0.99879223,0.0004117315,0.00009954998,0.00035409848,0.0002766971,0.00006577744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017370566,0.00096272235,0.0010508457,0.00067657593,0.00048671468,0.0009312468,0.0018041587,0.0011122121,0.002536238],"category_scores_gemma":[0.003952361,0.0004799215,0.000859072,0.000781413,0.00074224325,0.0020288022,0.0021016102,0.0025297874,0.0009586598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001763008,0.00011540388,0.0014842554,0.00009268612,0.00017040304,0.00009528073,0.000119404795,0.71040875,0.006497901,0.041801244,0.008449532,0.23058876],"study_design_scores_gemma":[0.0000030452497,0.000020465217,0.00007911091,0.0000069920584,0.00000946873,0.00001747502,0.000004356386,0.9897887,0.0014041822,0.0073931757,0.0012682131,0.0000048343004],"about_ca_topic_score_codex":0.0028634788,"about_ca_topic_score_gemma":0.005936548,"teacher_disagreement_score":0.0028634788,"about_ca_system_score_codex":0.000602535,"about_ca_system_score_gemma":0.00094719033,"threshold_uncertainty_score":0.009186566},"labels":[],"label_agreement":null},{"id":"W3093046553","doi":"10.1145/3466752.3480106","title":"FPRaker: A Processing Element For Accelerating Neural Network Training","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Toronto Public Health; University of Toronto","funders":"","keywords":"Accumulator (cryptography); Computer science; Operand; Floating point; Quantization (signal processing); Artificial neural network; Training (meteorology); Pruning; Algorithm; Artificial intelligence; Computer hardware","score_opus":0.10457217051179975,"score_gpt":0.31795773425713897,"score_spread":0.21338556374533924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093046553","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023796577,0.000723825,0.9376829,0.00029118493,0.0004088149,0.00013056332,0.00039731181,0.02479549,0.011773437],"genre_scores_gemma":[0.20329836,0.00042611238,0.7708541,0.0005148796,0.00014475238,0.0003214803,0.001171566,0.0020238222,0.021244902],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956673,0.00004633306,0.00002688563,0.00009566042,0.00021029293,0.00005407417],"domain_scores_gemma":[0.9994374,0.00019826164,0.000050334384,0.000101820304,0.00016798414,0.000044241653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004903729,0.0009460281,0.00045538147,0.0005922128,0.0005125734,0.0009690878,0.0021697558,0.0007533808,0.016558433],"category_scores_gemma":[0.0019785678,0.0005468743,0.00044034573,0.00059375766,0.00040234366,0.0014924095,0.0009430081,0.0016731763,0.004879639],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011772795,0.00029709225,0.002639829,0.0006764133,0.00022124719,0.0005533744,0.00036990974,0.09547958,0.1634642,0.027477361,0.056322962,0.65132076],"study_design_scores_gemma":[0.00010784094,0.00044830533,0.00094059523,0.00007885885,0.00007231763,0.00042339618,0.00005750014,0.73565143,0.18363921,0.0116263535,0.06687642,0.000077878154],"about_ca_topic_score_codex":0.0015267034,"about_ca_topic_score_gemma":0.0032654994,"teacher_disagreement_score":0.016558433,"about_ca_system_score_codex":0.0006802181,"about_ca_system_score_gemma":0.00096724165,"threshold_uncertainty_score":0.055393517},"labels":[],"label_agreement":null},{"id":"W309569989","doi":"10.1007/978-1-4471-5571-3_10","title":"Radial Basis Function Networks","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Radial basis function; Interpolation (computer graphics); Regularization (linguistics); Hierarchical RBF; Radial basis function network; Function approximation; Basis function; Applied mathematics; Computer science; Algorithm; Mathematical optimization; Function space; Mathematics; Artificial intelligence; Set (abstract data type); Basis (linear algebra); Artificial neural network; Mathematical analysis; Geometry","score_opus":0.015631718830099468,"score_gpt":0.19645381298462578,"score_spread":0.1808220941545263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W309569989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022149275,0.038080987,0.68089247,0.0013074293,0.0016331525,0.00006002387,0.00056474586,0.0028223805,0.27242392],"genre_scores_gemma":[0.03627864,0.038761165,0.23968522,0.0006720474,0.0010607962,0.00014863342,0.001859516,0.0013673824,0.68016654],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99974674,0.00002914438,0.000009991922,0.000053898137,0.00014430824,0.000015915766],"domain_scores_gemma":[0.9998011,0.00005395589,0.00001208701,0.000036880723,0.0000856543,0.000010372828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030123693,0.0012308788,0.00093436457,0.00080239435,0.00032984914,0.0013344618,0.0010856682,0.0012100593,0.025518605],"category_scores_gemma":[0.000984739,0.0003907679,0.00042819823,0.0014891479,0.00042646978,0.0016858235,0.0010607288,0.0013732682,0.028382214],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001929846,0.00002978447,0.00008080276,0.00028521073,0.000021810516,0.000041633146,0.000029540939,0.017357932,0.0019142146,0.051297415,0.10504793,0.8238744],"study_design_scores_gemma":[0.000007902382,0.000042109335,0.00052417687,0.00035134528,0.000038113427,0.00040711043,0.000036145135,0.12391465,0.00655483,0.13709824,0.7309643,0.00006110382],"about_ca_topic_score_codex":0.0012281272,"about_ca_topic_score_gemma":0.0022385803,"teacher_disagreement_score":0.025518605,"about_ca_system_score_codex":0.00048927614,"about_ca_system_score_gemma":0.00052334275,"threshold_uncertainty_score":0.085368216},"labels":[],"label_agreement":null},{"id":"W3099022131","doi":"","title":"Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mutual information; Generalization; Conditional mutual information; Computer science; Algorithm; Sample complexity; Sample (material); Mathematics; Artificial intelligence; Theoretical computer science","score_opus":0.017739944479120598,"score_gpt":0.2671884721861898,"score_spread":0.2494485277070692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099022131","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009847768,0.0011613951,0.98472345,0.0008175629,0.00007257405,0.00003236325,0.000073856674,0.00018286433,0.0030881541],"genre_scores_gemma":[0.5960857,0.0025146538,0.39282033,0.0012072162,0.00073997513,0.00044522446,0.00035637085,0.00072074035,0.0051098033],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9909457,0.0037448662,0.00051561935,0.0016036455,0.002613156,0.000576967],"domain_scores_gemma":[0.9201509,0.063005336,0.004187095,0.006895335,0.0044954824,0.0012658417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018178191,0.002475671,0.002383949,0.0038172093,0.0013379266,0.0032207249,0.003689181,0.0032412955,0.0026803596],"category_scores_gemma":[0.09934791,0.0012950654,0.002536046,0.0024416859,0.007742745,0.009121503,0.009771379,0.009629199,0.00045276925],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015866543,0.00006765775,0.0014098062,0.00021572161,0.00017488156,0.00013556705,0.0003815508,0.3518868,0.0030931858,0.61653346,0.0013830187,0.024559591],"study_design_scores_gemma":[0.000009846341,0.00005509217,0.00036070606,0.0000584121,0.00003098639,0.00004373892,0.000018580162,0.7376121,0.0014674006,0.25935236,0.00095374964,0.000037016205],"about_ca_topic_score_codex":0.0025682296,"about_ca_topic_score_gemma":0.0022743845,"teacher_disagreement_score":0.018178191,"about_ca_system_score_codex":0.004085642,"about_ca_system_score_gemma":0.0020699135,"threshold_uncertainty_score":0.09613663},"labels":[],"label_agreement":null},{"id":"W3101581426","doi":"","title":"Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Initialization; Artificial neural network; Kernel (algebra); Chaotic; Artificial intelligence; Tangent; Computer science; Linearization; Deep learning; Nonlinear system; Machine learning; Geometry; Algorithm; Mathematics; Physics; Discrete mathematics","score_opus":0.021154375713718625,"score_gpt":0.263866539708851,"score_spread":0.24271216399513237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101581426","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9128288,0.0017734087,0.080344975,0.0008689053,0.00004157743,0.000058810034,0.00044001653,0.00030400473,0.0033394536],"genre_scores_gemma":[0.99457586,0.00022251757,0.004406659,0.000035906178,0.000019178946,0.000020495063,0.00028981987,0.00006612683,0.0003634251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992937,0.00027096394,0.00004165675,0.00017271035,0.00014998611,0.00007095275],"domain_scores_gemma":[0.9865067,0.00929375,0.0018065174,0.0012471242,0.0007333629,0.00041254182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003138174,0.00050048827,0.00043822746,0.0011836602,0.00037195766,0.0011576059,0.00061135076,0.0009310854,0.0011558947],"category_scores_gemma":[0.02928485,0.00029659606,0.00041860808,0.00086014427,0.0022001967,0.003832416,0.0012257829,0.0017285848,0.00019097606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011054417,0.0003792952,0.16002955,0.0006058439,0.0003509612,0.0006687836,0.0021994272,0.6032231,0.015396965,0.08535464,0.006591543,0.124094434],"study_design_scores_gemma":[0.00002273207,0.00029060803,0.06836916,0.000084727406,0.00003134209,0.00041887563,0.00039436016,0.89916104,0.0041459333,0.025114916,0.0018965844,0.0000697061],"about_ca_topic_score_codex":0.0019370817,"about_ca_topic_score_gemma":0.0012419601,"teacher_disagreement_score":0.003138174,"about_ca_system_score_codex":0.0010927998,"about_ca_system_score_gemma":0.00036153264,"threshold_uncertainty_score":0.016596437},"labels":[],"label_agreement":null},{"id":"W3104722529","doi":"","title":"Untangling tradeoffs between recurrence and self-attention in artificial neural networks","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.030068948169692702,"score_gpt":0.2506744529650285,"score_spread":0.2206055047953358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104722529","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6708954,0.0035006623,0.3083331,0.0051554693,0.0002056088,0.00004730536,0.00012120016,0.0005443287,0.0111969225],"genre_scores_gemma":[0.9908296,0.00022232726,0.007186425,0.00011735863,0.000072801144,0.00002480532,0.000024882369,0.000054054177,0.0014677176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990338,0.00049832586,0.000061850355,0.00017466361,0.00012690735,0.00010445568],"domain_scores_gemma":[0.96732616,0.028061489,0.0015684331,0.0016716843,0.0008423126,0.0005300072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037373512,0.00035949872,0.0008378269,0.00059948355,0.0006885982,0.0015542435,0.0015375724,0.0018738785,0.0032082559],"category_scores_gemma":[0.04480108,0.0005136036,0.0003590957,0.00045284673,0.0012769683,0.005285366,0.0015968486,0.0018180315,0.0003174858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010221672,0.00035781032,0.014358293,0.00053865014,0.00020606394,0.00039032064,0.0016431827,0.34737116,0.012614114,0.40375796,0.0048908177,0.21284951],"study_design_scores_gemma":[0.000036297366,0.0001168814,0.0018774029,0.000027040389,0.000035653306,0.00007603449,0.00008951263,0.8066214,0.0014652098,0.18921882,0.00041225553,0.000023580818],"about_ca_topic_score_codex":0.001345581,"about_ca_topic_score_gemma":0.0023489213,"teacher_disagreement_score":0.0037373512,"about_ca_system_score_codex":0.00086288847,"about_ca_system_score_gemma":0.0005806114,"threshold_uncertainty_score":0.019765198},"labels":[],"label_agreement":null},{"id":"W3104945874","doi":"10.1038/s41467-020-19656-2","title":"Publisher Correction: Mnemonic prediction errors bias hippocampal states","year":2020,"lang":"en","type":"erratum","venue":"Nature Communications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mnemonic; Computer science; Hippocampal formation; Neuroscience; Artificial intelligence; Cognitive psychology; Psychology","score_opus":0.034115580368234005,"score_gpt":0.28157755927021727,"score_spread":0.24746197890198326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104945874","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006381752,0.0012934675,0.003826981,0.05005687,0.91537696,0.000032641474,0.005980616,0.0014625998,0.021331726],"genre_scores_gemma":[0.04545984,0.0055309464,0.010643273,0.030929297,0.11279111,0.00019007082,0.008745617,0.004973019,0.78073686],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971323,0.00036735987,0.0004768088,0.0005313422,0.0013363421,0.00015587531],"domain_scores_gemma":[0.9625498,0.010292381,0.0015710914,0.0055610696,0.019230809,0.0007948589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035418188,0.0018487753,0.0015412522,0.0037397824,0.0033058408,0.0045038434,0.0029864744,0.0034714327,0.16327241],"category_scores_gemma":[0.070164874,0.00090970023,0.001139077,0.003913972,0.001956842,0.0028805824,0.0022759247,0.005769055,0.07819305],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034770444,0.0000037847467,0.00014996335,0.00007449738,0.000010434068,0.00015291906,0.000043395874,0.000088026165,0.000057117813,0.0032552122,0.9848673,0.011262488],"study_design_scores_gemma":[0.000060649658,0.000023886563,0.0015959808,0.00036935194,0.00005895552,0.0005998347,0.0001339094,0.00085704593,0.001288621,0.0108815795,0.98407346,0.000056828678],"about_ca_topic_score_codex":0.02080596,"about_ca_topic_score_gemma":0.030879928,"teacher_disagreement_score":0.16327241,"about_ca_system_score_codex":0.0028027282,"about_ca_system_score_gemma":0.0044264337,"threshold_uncertainty_score":0.54620063},"labels":[],"label_agreement":null},{"id":"W3105027165","doi":"","title":"Regularized linear autoencoders recover the principal components, eventually","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Regularization (linguistics); Feature learning; Computer science; Simple (philosophy); Representation (politics); Principal component analysis; Gradient descent; Mathematical optimization; Mathematics; Artificial intelligence; Artificial neural network; Algorithm; Applied mathematics","score_opus":0.036751842957258835,"score_gpt":0.24326561935089744,"score_spread":0.2065137763936386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105027165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007778968,0.00028663507,0.98908174,0.00025986138,0.000059164653,0.000020145952,0.00012193614,0.00055593596,0.0018355836],"genre_scores_gemma":[0.35193607,0.0009360944,0.6284795,0.00039598704,0.0001837193,0.0001336511,0.00072388805,0.0004742233,0.01673691],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930847,0.00019907934,0.0000419724,0.00022242563,0.00016570922,0.00006238382],"domain_scores_gemma":[0.99820733,0.00068945496,0.00016667633,0.0005401009,0.00034940476,0.000047055655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001777712,0.0013040227,0.0006621327,0.00065035705,0.00038592707,0.0012591771,0.001025669,0.0010390205,0.0034391545],"category_scores_gemma":[0.008406925,0.00083121326,0.00070828904,0.0007587766,0.0013193953,0.0031056495,0.00136194,0.002782347,0.0022065497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017448458,0.000074421405,0.0022561105,0.00022346286,0.00019578933,0.00012859686,0.00021472535,0.43964118,0.013541619,0.23718552,0.009592048,0.29677206],"study_design_scores_gemma":[0.000008494797,0.000030036068,0.00060575583,0.000027714577,0.00001736323,0.000045254048,0.000021033165,0.899099,0.004549004,0.09036694,0.005214238,0.000015221597],"about_ca_topic_score_codex":0.0023421193,"about_ca_topic_score_gemma":0.003614773,"teacher_disagreement_score":0.0034391545,"about_ca_system_score_codex":0.0006457314,"about_ca_system_score_gemma":0.0011311468,"threshold_uncertainty_score":0.011505127},"labels":[],"label_agreement":null},{"id":"W3107829318","doi":"10.18280/ria.340509","title":"Synchronization Control of High-Order Inertial Hopfield Neural Network with Time Delay","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Synchronization (alternating current); Control theory (sociology); Artificial neural network; Inertial frame of reference; Computer science; Inertia; Differential (mechanical device); Hopfield network; Differential equation; Term (time); Delay differential equation; Control (management); Mathematics; Engineering; Artificial intelligence; Physics; Mathematical analysis","score_opus":0.014868858359314601,"score_gpt":0.21947817158754793,"score_spread":0.20460931322823334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107829318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22461846,0.00064456253,0.7648479,0.0002555527,0.00012358969,0.00003114507,0.000035056073,0.00017614034,0.009267717],"genre_scores_gemma":[0.9940963,0.00013060418,0.0045328513,0.000014860822,0.0000110033725,0.000013574388,0.000008620476,0.000004780479,0.0011874331],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983525,0.000027962062,0.000010957569,0.0000554305,0.000038299222,0.00003205322],"domain_scores_gemma":[0.99983263,0.000050364997,0.00004496449,0.000011152654,0.00004305979,0.000017899614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004405559,0.00044571035,0.00027278689,0.00019207176,0.00041423342,0.00049010443,0.00051229453,0.00032400806,0.00075254956],"category_scores_gemma":[0.00083793554,0.00013535291,0.00028422542,0.00020189887,0.0005360285,0.00061976304,0.00054588844,0.00044202106,0.00007008464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000304401,0.000066788045,0.0019866864,0.00020647285,0.00008201386,0.00031284094,0.00042577926,0.8034663,0.05106703,0.080222204,0.00090369425,0.0609558],"study_design_scores_gemma":[0.00001882585,0.00006826544,0.00035490756,0.000004284997,0.0000137631105,0.000024251192,0.000016886683,0.99087137,0.0022986783,0.0059354464,0.00038502392,0.000008371393],"about_ca_topic_score_codex":0.004420856,"about_ca_topic_score_gemma":0.0035152277,"teacher_disagreement_score":0.004420856,"about_ca_system_score_codex":0.0005533601,"about_ca_system_score_gemma":0.0004983558,"threshold_uncertainty_score":0.008790255},"labels":[],"label_agreement":null},{"id":"W3109018030","doi":"10.5430/jct.v9n4p40","title":"Investigation of Mathematics-Specific Trend Variables in PISA Studies with Neural Networks and Linear Regression","year":2020,"lang":"en","type":"article","venue":"Journal of Curriculum and Teaching","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Turkish; Artificial neural network; Regression analysis; Linear regression; Multilayer perceptron; Mathematics education; Mathematics; Statistics; Artificial intelligence; Computer science","score_opus":0.03640088409897355,"score_gpt":0.26986446184671986,"score_spread":0.23346357774774631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109018030","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9853521,0.00063042017,0.012217984,0.00014803428,0.00003894064,0.000082481456,0.00017075193,0.000030759842,0.0013284567],"genre_scores_gemma":[0.99602,0.00015066625,0.0031910848,0.000012288856,0.000011494886,0.00006431714,0.00021235073,0.00000690104,0.00033094286],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948027,0.0030476118,0.00042248573,0.00070176873,0.0007605831,0.0002647267],"domain_scores_gemma":[0.98416036,0.010264676,0.0024198727,0.0009208895,0.0019028193,0.0003313514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008893528,0.0005005231,0.00053564954,0.0028448773,0.0005460646,0.0012691838,0.0007812309,0.00041374896,0.001115985],"category_scores_gemma":[0.028009074,0.00025746605,0.0009041197,0.0036936165,0.00046482697,0.0014200744,0.0011772632,0.0010423616,0.00018896133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016432711,0.00011143992,0.96578807,0.00008268971,0.0002711069,0.000092227776,0.00075729867,0.002009436,0.00030504077,0.00053346227,0.0001511018,0.029733803],"study_design_scores_gemma":[0.000009653523,0.0002992522,0.9552552,0.00008023919,0.00024241346,0.000094711,0.0021523894,0.03952883,0.0006759166,0.0007420161,0.0009029681,0.000016440312],"about_ca_topic_score_codex":0.009587802,"about_ca_topic_score_gemma":0.012120033,"teacher_disagreement_score":0.009587802,"about_ca_system_score_codex":0.00073251873,"about_ca_system_score_gemma":0.0012389318,"threshold_uncertainty_score":0.047034085},"labels":[],"label_agreement":null},{"id":"W3109607710","doi":"10.3233/jifs-200500","title":"An integrated neural network with nonlinear output structure for interval-valued data","year":2020,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Nonlinear system; Interval (graph theory); Particle swarm optimization; Artificial neural network; Computer science; Feature (linguistics); Algorithm; Divergence (linguistics); Variable (mathematics); Artificial intelligence; Mathematics","score_opus":0.06328901124714652,"score_gpt":0.29789570037744073,"score_spread":0.2346066891302942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109607710","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008012464,0.00031078808,0.9899775,0.00007842958,0.000043151675,0.000013591868,0.000046312034,0.00022423579,0.00129361],"genre_scores_gemma":[0.7564621,0.0006333905,0.23752701,0.0001512023,0.00005213058,0.00014875467,0.0002635254,0.000060289593,0.0047015436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996146,0.00006978233,0.00002307097,0.0001266436,0.00013221941,0.000033567136],"domain_scores_gemma":[0.9996861,0.00009485231,0.00003627251,0.00003858612,0.00012862642,0.000015649937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007451792,0.000615415,0.0005832126,0.00040006306,0.00025201606,0.000879566,0.0014105935,0.00080542674,0.0011307175],"category_scores_gemma":[0.0013860957,0.0003672277,0.0005459561,0.00060856354,0.00044767885,0.001587318,0.00081509404,0.0012678329,0.00030788247],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008971008,0.000050062205,0.0013045621,0.00012533176,0.00007826832,0.0000899599,0.000087722,0.86909556,0.005271509,0.01555517,0.0009832742,0.1072688],"study_design_scores_gemma":[0.0000017404493,0.000011025457,0.00006600554,0.0000036603512,0.0000057013717,0.0000093429535,0.0000020475143,0.9979219,0.0003344132,0.0013383974,0.00030279325,0.000002998704],"about_ca_topic_score_codex":0.006325672,"about_ca_topic_score_gemma":0.004959945,"teacher_disagreement_score":0.006325672,"about_ca_system_score_codex":0.00067141897,"about_ca_system_score_gemma":0.00065789913,"threshold_uncertainty_score":0.012577713},"labels":[],"label_agreement":null},{"id":"W3109891917","doi":"10.1109/ccece47787.2020.9255825","title":"A Cloud-based Architecture for Automated Grading of Computer-Aided Design Student Work Using Deep Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Cloud computing; Computer science; Convolutional neural network; Deep learning; Grading (engineering); Implementation; Artificial intelligence; Software engineering; Software; Architecture; Computer architecture; Task (project management); Machine learning; Computer engineering; Multimedia; Operating system; Systems engineering","score_opus":0.05352751151852896,"score_gpt":0.2931374961487213,"score_spread":0.23960998463019234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109891917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05122445,0.0006465516,0.9041654,0.00054023496,0.0002681583,0.00031375082,0.0010352182,0.032508347,0.00929797],"genre_scores_gemma":[0.67745304,0.0004409769,0.30248138,0.00043941903,0.000061735875,0.000309531,0.0023410472,0.00031304944,0.016159799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971634,0.000028792949,0.00001633837,0.000087525885,0.00010658296,0.00004444433],"domain_scores_gemma":[0.99969923,0.00003877025,0.000032012755,0.00006702132,0.000114551694,0.00004843735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002681812,0.0006708922,0.00044408964,0.0004933009,0.00033274604,0.00078361906,0.0021677455,0.0006178922,0.007063919],"category_scores_gemma":[0.00063105155,0.00028224633,0.00045676588,0.0006955913,0.00021738872,0.00090966246,0.0009122277,0.00071889453,0.002998155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011286783,0.00083584356,0.0055942037,0.00021415466,0.0001461372,0.0004793181,0.00010871217,0.23262899,0.028407976,0.0070018168,0.038654055,0.68480015],"study_design_scores_gemma":[0.000032408494,0.00008218235,0.00062321615,0.000011592305,0.00001693624,0.00005390123,0.000013183353,0.984835,0.008702335,0.001428184,0.004186171,0.0000148925665],"about_ca_topic_score_codex":0.016476255,"about_ca_topic_score_gemma":0.023019912,"teacher_disagreement_score":0.016476255,"about_ca_system_score_codex":0.0014829307,"about_ca_system_score_gemma":0.001680117,"threshold_uncertainty_score":0.03276068},"labels":[],"label_agreement":null},{"id":"W3110136086","doi":"10.1142/9789811226830_0014","title":"On the Use of Interval Arithmetic for the Branch and Bound Delta-Lognormal Parameter Extraction of Rapid Human Movements","year":2020,"lang":"en","type":"book-chapter","venue":"Series in machine perception and artificial intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Log-normal distribution; Interval (graph theory); Mathematics; Arithmetic; Extraction (chemistry); Interval arithmetic; Statistics; Combinatorics; Mathematical analysis; Chromatography; Chemistry","score_opus":0.11622400116974799,"score_gpt":0.31776276508857926,"score_spread":0.20153876391883127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110136086","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010116579,0.0018377911,0.99128366,0.00007344524,0.00015097158,0.000014903615,0.000061320876,0.00039416752,0.005172156],"genre_scores_gemma":[0.03414494,0.0037019104,0.9570194,0.00014367657,0.0003514449,0.00007489833,0.00021141172,0.0004300656,0.0039223214],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99906224,0.0003042024,0.00009672295,0.00016671586,0.0003330534,0.00003709671],"domain_scores_gemma":[0.9963031,0.0028893098,0.000095824296,0.0003376404,0.0003330585,0.000041115996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002771025,0.0014148927,0.0009992674,0.0016744025,0.00047955223,0.0023971607,0.002330517,0.0010156832,0.0082275495],"category_scores_gemma":[0.011115391,0.00055057567,0.0008804695,0.003241405,0.0015710233,0.0029142005,0.0016324796,0.0035009407,0.0035997597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016470283,0.000042858526,0.00049998413,0.00030563737,0.00007170679,0.0001578444,0.00023267856,0.07316556,0.0064446814,0.25589666,0.010195771,0.65282196],"study_design_scores_gemma":[0.000015644999,0.000081085615,0.000893289,0.00021906431,0.000060882598,0.00033059105,0.00007505719,0.6314992,0.0056295986,0.32513052,0.03598446,0.0000805349],"about_ca_topic_score_codex":0.0021260919,"about_ca_topic_score_gemma":0.0026678296,"teacher_disagreement_score":0.0082275495,"about_ca_system_score_codex":0.00055128144,"about_ca_system_score_gemma":0.0005564656,"threshold_uncertainty_score":0.027523935},"labels":[],"label_agreement":null},{"id":"W3110476581","doi":"10.31234/osf.io/nh9bq","title":"Differences in Learning Across the Lifespan Emerge via Resource-Rational Computations","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Satisficing; Sampling (signal processing); Inference; Computer science; Cognitive psychology; Cognitive resource theory; Anchoring; Bounded rationality; Value (mathematics); Computation; Cognition; Artificial intelligence; Resource (disambiguation); Machine learning; Perspective (graphical); Adaptive sampling; Psychology; Social psychology; Statistics; Mathematics; Algorithm","score_opus":0.04683048685337083,"score_gpt":0.2986522657276741,"score_spread":0.2518217788743033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110476581","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98724514,0.00013182983,0.010790527,0.00010798556,0.000006587249,0.000014346633,0.00014296516,0.00003889783,0.0015217712],"genre_scores_gemma":[0.9947509,0.00005694923,0.0047399327,0.000037278005,0.00000334371,0.000015226509,0.00009588437,0.000009808478,0.00029070504],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995078,0.00009682858,0.000034202396,0.00020670341,0.00009816197,0.000056347104],"domain_scores_gemma":[0.99643266,0.0011796562,0.0010375724,0.00079389475,0.00032180085,0.00023438115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018266514,0.00022857008,0.00029970406,0.00055282266,0.00017023236,0.0008546823,0.00034126022,0.00043114377,0.0011422705],"category_scores_gemma":[0.011432143,0.00028096643,0.0003563299,0.00022481686,0.0007979277,0.0011697921,0.0007443456,0.0005704024,0.0001272477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077727006,0.0003337734,0.7373845,0.00018194463,0.0004593746,0.0006660518,0.0042457767,0.01435515,0.082791716,0.036082357,0.00084460096,0.12187742],"study_design_scores_gemma":[0.000033179018,0.00030390397,0.932725,0.00003077038,0.00009175167,0.00037994736,0.0003717444,0.017250944,0.0067429994,0.040826842,0.0011916639,0.000051306935],"about_ca_topic_score_codex":0.0019389949,"about_ca_topic_score_gemma":0.0021446152,"teacher_disagreement_score":0.0019389949,"about_ca_system_score_codex":0.00043666334,"about_ca_system_score_gemma":0.00034269033,"threshold_uncertainty_score":0.009660423},"labels":[],"label_agreement":null},{"id":"W3112928663","doi":"10.32628/cseit1949137","title":"A Novel Delay Dependent Stability Analysis of Neural Networks Using LMI Approach","year":2019,"lang":"en","type":"article","venue":"International Journal of Scientific Research in Computer Science Engineering and Information Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Stability (learning theory); Artificial neural network; Control theory (sociology); Computer science; Mathematics; Artificial intelligence; Control (management); Machine learning","score_opus":0.03548140449252666,"score_gpt":0.3060650848677248,"score_spread":0.27058368037519814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112928663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005347091,0.00046179432,0.98782104,0.000118312506,0.000089703884,0.00002753453,0.000029287137,0.00016428807,0.005940906],"genre_scores_gemma":[0.8577061,0.0014775983,0.11856809,0.00019165779,0.00021504523,0.0003653347,0.0001671677,0.00018914258,0.021119813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997845,0.000056002755,0.000010485601,0.000058768346,0.00007079465,0.000019395386],"domain_scores_gemma":[0.9997507,0.00012115696,0.000026975546,0.000013991205,0.000080137695,0.000007101875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047135874,0.0009763067,0.00085459877,0.0003413398,0.0004005572,0.000841214,0.0007007035,0.0008273405,0.0037038603],"category_scores_gemma":[0.0008516861,0.00029473897,0.00067023106,0.0002857611,0.00046986583,0.00071948295,0.000666237,0.0011037162,0.0006024348],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010188619,0.0000624423,0.0003350304,0.00048582023,0.00013016141,0.00031794695,0.00019282436,0.8079888,0.032986175,0.058441527,0.0028643967,0.096092924],"study_design_scores_gemma":[0.0000031971974,0.000031770473,0.000079718455,0.000009547726,0.000010568934,0.000024264367,0.000006690683,0.99453187,0.0011171913,0.003401639,0.0007769308,0.0000065344348],"about_ca_topic_score_codex":0.0015967822,"about_ca_topic_score_gemma":0.0017038252,"teacher_disagreement_score":0.0037038603,"about_ca_system_score_codex":0.0006240922,"about_ca_system_score_gemma":0.00048335607,"threshold_uncertainty_score":0.012390673},"labels":[],"label_agreement":null},{"id":"W3113561531","doi":"10.13033/isahp.y2020.015","title":"ANP SUPERMATRICES: LESSONS FROM HIERARCHIES","year":2020,"lang":"en","type":"article","venue":"ISAHP proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science","score_opus":0.0387023024691459,"score_gpt":0.25942723256960204,"score_spread":0.22072493010045613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113561531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009571127,0.004421406,0.8924786,0.026230656,0.00081094285,0.00021089906,0.00039521145,0.00052318,0.06535798],"genre_scores_gemma":[0.2242119,0.0031465085,0.76218206,0.0020263568,0.0006241757,0.0005463949,0.000389007,0.00036588483,0.00650774],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99074906,0.0057524643,0.00044584784,0.0007666497,0.0020256224,0.0002602992],"domain_scores_gemma":[0.97174793,0.019571127,0.0011188957,0.0026025206,0.0036216457,0.0013378714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0145045,0.0010353255,0.0010001647,0.002421197,0.0035454673,0.0063086525,0.0032873391,0.0016197907,0.01227564],"category_scores_gemma":[0.0366867,0.0009180965,0.0012864609,0.0032890926,0.0076648695,0.010754153,0.0048461407,0.0058279107,0.0017779997],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026046777,0.000038676495,0.0005418769,0.00024612274,0.00003191969,0.00005549025,0.0017469752,0.010597286,0.00009713381,0.91028976,0.009647651,0.06668112],"study_design_scores_gemma":[0.000013127541,0.000010266241,0.00011925532,0.000099946585,0.0000049765363,0.000017887149,0.00032486685,0.016106067,0.000071838556,0.9672193,0.016002918,0.000009498035],"about_ca_topic_score_codex":0.013221024,"about_ca_topic_score_gemma":0.017786935,"teacher_disagreement_score":0.0145045,"about_ca_system_score_codex":0.005622057,"about_ca_system_score_gemma":0.0055143996,"threshold_uncertainty_score":0.07670808},"labels":[],"label_agreement":null},{"id":"W3115597439","doi":"10.1109/tsmc.2020.3041775","title":"Looking Back on the Actor–Critic Architecture","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cybernetics; Architecture; Computer science; Cognitive science; Control (management); Artificial intelligence; Data science; Management science; Engineering ethics; Epistemology; Psychology; Engineering; History; Philosophy","score_opus":0.025095680351848493,"score_gpt":0.21573506530828165,"score_spread":0.19063938495643315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115597439","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01281375,0.025926977,0.72250813,0.07171133,0.0051429057,0.00004367255,0.0003335722,0.0008253174,0.16069435],"genre_scores_gemma":[0.60426253,0.035530467,0.16235851,0.021981934,0.005438655,0.00015816893,0.0006776195,0.0012001263,0.16839197],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999148,0.0003342635,0.000032918277,0.00023901634,0.00019129406,0.0000545931],"domain_scores_gemma":[0.9990079,0.000543442,0.000043046308,0.0001314974,0.00021295158,0.000061266044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014895472,0.0007495875,0.0005522612,0.00045322243,0.00066873356,0.003033513,0.0010301552,0.002043037,0.010444268],"category_scores_gemma":[0.005370523,0.0004646985,0.0005304795,0.00046904202,0.0020128894,0.005230816,0.0008950649,0.0060432674,0.0047755963],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105223575,0.000025311017,0.0004859377,0.00015425235,0.000103546205,0.00008468582,0.00028506384,0.02378579,0.00087997067,0.89811903,0.01605148,0.059919793],"study_design_scores_gemma":[0.000030363128,0.00006952695,0.0003614078,0.000339665,0.000056982954,0.00009672442,0.00011567624,0.071246944,0.0015910604,0.79571176,0.13032469,0.00005517055],"about_ca_topic_score_codex":0.0037840928,"about_ca_topic_score_gemma":0.0032168878,"teacher_disagreement_score":0.010444268,"about_ca_system_score_codex":0.0013006793,"about_ca_system_score_gemma":0.00089851575,"threshold_uncertainty_score":0.034939528},"labels":[],"label_agreement":null},{"id":"W3117735472","doi":"","title":"Risk Bounds for Reservoir Computing","year":2020,"lang":"en","type":"article","venue":"Alexandria (UniSG) (University of St.Gallen)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universität Konstanz; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Agence Nationale de la Recherche; Ottawa Hospital Research Institute; National Science Foundation","keywords":"Generalization; Consistency (knowledge bases); Reservoir computing; Multivariate statistics; Computer science; Applied mathematics; Statistical learning theory; Minification; Mathematical optimization; Mathematics; Artificial intelligence; Artificial neural network; Machine learning","score_opus":0.028083880141783406,"score_gpt":0.21886579641185266,"score_spread":0.19078191627006927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117735472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0093145445,0.0015899922,0.98218656,0.0008068362,0.00006982688,0.000026038208,0.00006333198,0.00017241047,0.0057703597],"genre_scores_gemma":[0.7581211,0.004778637,0.22515373,0.00074934185,0.0007248958,0.00042982437,0.00040925396,0.00058196514,0.009051265],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963844,0.0011557383,0.00020883883,0.00059648196,0.0012912265,0.00036334264],"domain_scores_gemma":[0.9689538,0.023847848,0.0017012535,0.0023246615,0.0025597506,0.0006127401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006577796,0.001702956,0.0015985012,0.0018055538,0.00092677807,0.0031708118,0.0023032245,0.0017213263,0.003731254],"category_scores_gemma":[0.042895176,0.00068931363,0.001465715,0.0011141702,0.0038706374,0.00635854,0.005017875,0.005379871,0.0006781142],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058958816,0.000032228603,0.00064541167,0.00019844343,0.000076193595,0.000097741904,0.00012564992,0.37564874,0.0026987395,0.6033041,0.0011004861,0.016013281],"study_design_scores_gemma":[0.0000030082365,0.000030471487,0.00014800587,0.000037629343,0.000013132283,0.000042145195,0.000013294993,0.7989834,0.000963692,0.19898322,0.00076619524,0.000015803318],"about_ca_topic_score_codex":0.0013912899,"about_ca_topic_score_gemma":0.0009881539,"teacher_disagreement_score":0.006577796,"about_ca_system_score_codex":0.0025783866,"about_ca_system_score_gemma":0.0014000509,"threshold_uncertainty_score":0.03478712},"labels":[],"label_agreement":null},{"id":"W3119612495","doi":"10.33137/juls.v14i1.35833","title":"Visualization of Layers Within a Convolutional Neural Network Using Gradient Activation Maps","year":2020,"lang":"en","type":"article","venue":"Journal of Undergraduate Life Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Convolutional neural network; Visualization; Computer science; Artificial intelligence","score_opus":0.06446286893220793,"score_gpt":0.2983334559458125,"score_spread":0.23387058701360458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119612495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22618105,0.00075598893,0.7342414,0.0014856183,0.0003430148,0.00025073803,0.0036212641,0.021603229,0.011517616],"genre_scores_gemma":[0.6471578,0.00054833305,0.34207425,0.00018984648,0.000037314712,0.00033041966,0.0018195435,0.0014728945,0.0063696345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983776,0.000033950026,0.000010180764,0.000034265606,0.00005304428,0.000030773015],"domain_scores_gemma":[0.9994923,0.00025467123,0.00005030634,0.000050363364,0.00011378995,0.0000386044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043225678,0.0012647177,0.00028509225,0.00090831314,0.00029280112,0.0009733662,0.0007517866,0.00056820456,0.012292742],"category_scores_gemma":[0.0020540154,0.00039984554,0.00069089467,0.00038578967,0.00026495504,0.0009308729,0.00076077756,0.0008985227,0.00090941123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001028567,0.00026042803,0.010476568,0.0010959756,0.00021620428,0.0011785057,0.0012366003,0.52029765,0.10612298,0.01458047,0.0339405,0.30956554],"study_design_scores_gemma":[0.000024060644,0.00009691078,0.003159646,0.000096205404,0.000028836283,0.00014039074,0.0001090228,0.9551922,0.028538587,0.0057944027,0.0067762397,0.000043556232],"about_ca_topic_score_codex":0.007447375,"about_ca_topic_score_gemma":0.0085405065,"teacher_disagreement_score":0.012292742,"about_ca_system_score_codex":0.00069863506,"about_ca_system_score_gemma":0.00088996603,"threshold_uncertainty_score":0.04112333},"labels":[],"label_agreement":null},{"id":"W3120280695","doi":"10.3390/mca26010017","title":"Data Augmentation and Feature Selection for Automatic Model Recommendation in Computational Physics","year":2021,"lang":"en","type":"article","venue":"Mathematical and Computational Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Computer science; Dimensionality reduction; Curse of dimensionality; Discretization; Machine learning; Feature selection; Artificial intelligence; Variety (cybernetics); Perceptron; Nonlinear system; Feature (linguistics); Data mining; Pattern recognition (psychology); Artificial neural network; Mathematics; Physics","score_opus":0.04969621143905954,"score_gpt":0.3185783979166909,"score_spread":0.26888218647763135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120280695","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021942418,0.0004515091,0.97416717,0.00036998736,0.000073703675,0.00008918128,0.00024687065,0.0021292146,0.00052988104],"genre_scores_gemma":[0.3926223,0.00047906008,0.60254073,0.0002787576,0.0001796202,0.00043653807,0.0018261715,0.0002804214,0.0013564116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984201,0.00071691285,0.00012610984,0.00030079993,0.0003276054,0.00010834191],"domain_scores_gemma":[0.9955521,0.0024955573,0.00027861894,0.0008838824,0.00068873144,0.00010106608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030515522,0.0012413457,0.0018303851,0.0016982525,0.000861746,0.0011434277,0.0017000748,0.0014934494,0.0017899086],"category_scores_gemma":[0.009387867,0.0007167933,0.0015958559,0.0018590045,0.0008239983,0.0016148639,0.0012913259,0.0022010943,0.0011884368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045557268,0.0005778079,0.004384195,0.00024169845,0.00021193433,0.00017617775,0.00021409376,0.27458373,0.012044769,0.0075527388,0.01060162,0.68895566],"study_design_scores_gemma":[0.000013380457,0.000044064374,0.0004067137,0.000011391185,0.000012709361,0.000027101383,0.000017655602,0.99025613,0.0030547823,0.0052336776,0.0009092318,0.000013151598],"about_ca_topic_score_codex":0.0032630605,"about_ca_topic_score_gemma":0.0045855786,"teacher_disagreement_score":0.0032630605,"about_ca_system_score_codex":0.00060181785,"about_ca_system_score_gemma":0.0010827978,"threshold_uncertainty_score":0.016138315},"labels":[],"label_agreement":null},{"id":"W3122174941","doi":"10.1038/s42256-021-00401-3","title":"Variational neural annealing","year":2021,"lang":"en","type":"preprint","venue":"Nature Machine Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Perimeter Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada; Compute Canada","keywords":"Parameterized complexity; Simulated annealing; Hamiltonian (control theory); Statistical physics; Computer science; Mathematical optimization; Quantum annealing; Applied mathematics; Annealing (glass); Autoregressive model; Quantum; Mathematics; Algorithm; Physics; Quantum mechanics; Quantum computer","score_opus":0.01440587569263979,"score_gpt":0.29534659283285103,"score_spread":0.28094071714021124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122174941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017170157,0.0026746043,0.8870595,0.0027149399,0.0013070445,0.00011637313,0.00034540857,0.0004979401,0.08811405],"genre_scores_gemma":[0.5398114,0.0015648839,0.30966896,0.0013642594,0.00080263265,0.00047128976,0.0007858171,0.0018262242,0.1437046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958545,0.00019474987,0.00001199035,0.00008272766,0.00009036143,0.000034677167],"domain_scores_gemma":[0.99938726,0.00032937576,0.00003277369,0.0001137309,0.00009027686,0.00004648508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087234424,0.00062244135,0.0013722386,0.00056780013,0.00083850784,0.001455948,0.0012668883,0.0020709834,0.013354532],"category_scores_gemma":[0.0039991685,0.00078038534,0.00077726005,0.00071814546,0.0014999699,0.0013767265,0.0019221673,0.0024663764,0.002029444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063203675,0.00003999808,0.00022797144,0.00010337903,0.00007288719,0.000030817704,0.00006546432,0.21182072,0.0009906499,0.741101,0.012442314,0.033041507],"study_design_scores_gemma":[0.000017378085,0.0000136342305,0.00012772916,0.000022068134,0.000013248896,0.000017893612,0.000011775432,0.6761538,0.0004126102,0.31539768,0.007801471,0.000010675902],"about_ca_topic_score_codex":0.0030876996,"about_ca_topic_score_gemma":0.0041302554,"teacher_disagreement_score":0.013354532,"about_ca_system_score_codex":0.0012825576,"about_ca_system_score_gemma":0.0008836026,"threshold_uncertainty_score":0.04467541},"labels":[],"label_agreement":null},{"id":"W3122551441","doi":"10.5281/zenodo.4532597","title":"NNGeometry: Easy and Fast Fisher Information Matrices and Neural Tangent Kernels in PyTorch","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial neural network; Linear algebra; Computer science; Fisher information; Tangent; TRACE (psycholinguistics); Matrix norm; Matrix (chemical analysis); Cardinality (data modeling); Theoretical computer science; Algebra over a field; Algorithm; Artificial intelligence; Mathematics; Machine learning; Data mining; Pure mathematics; Geometry","score_opus":0.019899264996780186,"score_gpt":0.2261883662038573,"score_spread":0.2062891012070771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122551441","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030822156,0.00049085653,0.70078796,0.00032109185,0.00049457216,0.0001272732,0.0067665526,0.2794664,0.008463024],"genre_scores_gemma":[0.092507735,0.0008040842,0.68983006,0.0006744167,0.00026223197,0.00073920377,0.031700842,0.13892183,0.04455961],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854183,0.0001925705,0.00008638625,0.00028219644,0.00073388824,0.00016315776],"domain_scores_gemma":[0.9983431,0.00044445033,0.000057232774,0.0006839784,0.00037468525,0.00009658579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015428131,0.0019213622,0.0015217621,0.0014164035,0.0006999316,0.002419431,0.00468348,0.0017965151,0.11135794],"category_scores_gemma":[0.009886682,0.0019099003,0.0015958408,0.0012601027,0.00091227726,0.004783238,0.004799948,0.0038143853,0.07432261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075557537,0.00018170508,0.0013614917,0.00058747624,0.00021977982,0.00027141735,0.00024106148,0.01980641,0.006384705,0.03782484,0.49627495,0.4360906],"study_design_scores_gemma":[0.0005218893,0.00013710832,0.0023549895,0.0002854347,0.00007288021,0.00050442346,0.00011164318,0.48918924,0.036399756,0.16677622,0.30333075,0.00031568555],"about_ca_topic_score_codex":0.006249676,"about_ca_topic_score_gemma":0.012534911,"teacher_disagreement_score":0.11135794,"about_ca_system_score_codex":0.0011541328,"about_ca_system_score_gemma":0.0015032981,"threshold_uncertainty_score":0.37252945},"labels":[],"label_agreement":null},{"id":"W3123042577","doi":"10.1162/neco_a_01510","title":"Invariance, Encodings, and Generalization: Learning Identity Effects With Neural Networks","year":2022,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Concordia University","funders":"","keywords":"Identity (music); Computer science; Generalization; Inference; Artificial neural network; Adversarial system; Artificial intelligence; Class (philosophy); Theoretical computer science; Algorithm; Mathematics","score_opus":0.00921551634621746,"score_gpt":0.23059884219356644,"score_spread":0.221383325847349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123042577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11416366,0.00035120666,0.8771364,0.0012651371,0.00007580708,0.00007173493,0.000110572066,0.0004694397,0.006356078],"genre_scores_gemma":[0.91256183,0.00025540075,0.08495099,0.00030974956,0.00006976624,0.00009806785,0.00011376893,0.00012572434,0.0015147119],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785477,0.0009803434,0.000113829534,0.0004681603,0.00040873347,0.00017418517],"domain_scores_gemma":[0.9865807,0.008121151,0.0011878391,0.0033373185,0.00051584095,0.0002570871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045998353,0.00087100704,0.0007413586,0.0004911904,0.00057190243,0.0014326137,0.0013563837,0.0012286158,0.0022690066],"category_scores_gemma":[0.032131158,0.0005286292,0.0008896938,0.0004636757,0.0046128226,0.0060739284,0.0035258066,0.0034255923,0.00024267068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004017592,0.00020192658,0.0045341398,0.0002264099,0.00014544226,0.0002136483,0.0006427871,0.39010677,0.012107127,0.4203813,0.0018312384,0.16920757],"study_design_scores_gemma":[0.000027877313,0.000115638104,0.00075005455,0.000028399885,0.000023378649,0.00005367485,0.00004118841,0.5710805,0.005092997,0.42204842,0.00071999186,0.000017915478],"about_ca_topic_score_codex":0.0013252092,"about_ca_topic_score_gemma":0.0010993142,"teacher_disagreement_score":0.0045998353,"about_ca_system_score_codex":0.0012405525,"about_ca_system_score_gemma":0.00063313206,"threshold_uncertainty_score":0.024326563},"labels":[],"label_agreement":null},{"id":"W3123611107","doi":"10.20944/preprints201809.0327.v1","title":"Localization Parameters for Interacting Particles in Disordered Two- Dimensional Lattices","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Delocalized electron; Condensed matter physics; Physics; Range (aeronautics); Electron delocalization; Statistical physics; Materials science; Quantum mechanics","score_opus":0.14257471070366795,"score_gpt":0.37545715099771243,"score_spread":0.23288244029404448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123611107","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.951756,0.00078959746,0.039068796,0.00065286894,0.000043614673,0.00003839384,0.00020682582,0.00023717647,0.0072068684],"genre_scores_gemma":[0.99665844,0.00010010429,0.0023236168,0.000026379339,0.000007525916,0.00002907076,0.00007941734,0.000032311735,0.0007431132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973696,0.00007322689,0.000021126241,0.000051101015,0.0000573006,0.00006022165],"domain_scores_gemma":[0.9973955,0.0012805387,0.00044658183,0.00016113555,0.00025967858,0.0004564915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089853496,0.0003823841,0.00042011304,0.0023313109,0.0006198838,0.0016881452,0.00097465597,0.0012290247,0.0025899876],"category_scores_gemma":[0.005368205,0.0003983133,0.00028864687,0.0007106182,0.0019913684,0.0023115934,0.0010346526,0.0008129876,0.00033677983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072003424,0.00026287095,0.009984474,0.00041542234,0.000112249276,0.00089348335,0.0012208283,0.47645614,0.04282893,0.4535144,0.0030106145,0.010580531],"study_design_scores_gemma":[0.000109372886,0.00007348741,0.0034125564,0.000049041624,0.000017162625,0.00024547533,0.00033015374,0.8568198,0.0052505094,0.13285695,0.0006925861,0.00014287444],"about_ca_topic_score_codex":0.002056936,"about_ca_topic_score_gemma":0.0011024346,"teacher_disagreement_score":0.0025899876,"about_ca_system_score_codex":0.001108085,"about_ca_system_score_gemma":0.0004819627,"threshold_uncertainty_score":0.008664429},"labels":[],"label_agreement":null},{"id":"W3123918979","doi":"10.1007/s12652-020-02846-7","title":"Special issue on advances in data intelligence and modelling","year":2021,"lang":"en","type":"editorial","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computational intelligence; Computer science; Business intelligence; Data science; Artificial intelligence; Data mining","score_opus":0.05797450472645445,"score_gpt":0.3285673794574398,"score_spread":0.27059287473098537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123918979","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022224893,0.0049794484,0.00025709593,0.01378745,0.9787249,0.000016505208,0.00006333927,0.000053782063,0.0020952716],"genre_scores_gemma":[0.0002821614,0.0030634275,0.00014643745,0.0042283763,0.9801539,0.00002148829,0.000052681797,0.00005760147,0.011993895],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99159896,0.0012425774,0.000882175,0.0009979174,0.0048025274,0.0004758528],"domain_scores_gemma":[0.96763355,0.012499223,0.002130282,0.0014400209,0.011762191,0.0045346743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010557684,0.0050482443,0.0067492174,0.008953457,0.0033098892,0.013020325,0.0036220434,0.011216704,0.037206147],"category_scores_gemma":[0.024419399,0.0015318358,0.0035127301,0.0035374542,0.0029058782,0.0059753885,0.003015057,0.01503356,0.019993592],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029351424,0.000013007932,0.00002186219,0.00017229284,0.000021353024,0.000045506786,0.000006395573,0.000047412035,0.000058622758,0.0005604591,0.99300474,0.006018869],"study_design_scores_gemma":[0.000051546256,0.000029413479,0.00023800496,0.00029952519,0.00005131284,0.00012405557,0.000020495689,0.00043905922,0.000095550575,0.0030244382,0.99560827,0.000018287734],"about_ca_topic_score_codex":0.0015409963,"about_ca_topic_score_gemma":0.0057189395,"teacher_disagreement_score":0.037206147,"about_ca_system_score_codex":0.0045118225,"about_ca_system_score_gemma":0.0040247543,"threshold_uncertainty_score":0.124466956},"labels":[],"label_agreement":null},{"id":"W3124431014","doi":"10.1007/978-1-4419-9514-8","title":"Random Matrices, Random Processes and Integrable Systems","year":2011,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Integrable system; Mathematics; Pure mathematics; Computer science","score_opus":0.0170965322558114,"score_gpt":0.21585271421148775,"score_spread":0.19875618195567635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124431014","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002431478,0.47847858,0.0849493,0.004520575,0.006381317,0.000062960826,0.00064162124,0.00088923005,0.42164502],"genre_scores_gemma":[0.03117958,0.26855534,0.044509817,0.0022746308,0.011642062,0.00025927302,0.0013538375,0.000668372,0.63955706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997347,0.000034479624,0.000012458002,0.000043584085,0.0001631165,0.000011515649],"domain_scores_gemma":[0.9996947,0.00017550272,0.000026964852,0.00003407763,0.00005041884,0.000018363371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028526055,0.0014585637,0.0012717611,0.0017526544,0.00044206274,0.0019563409,0.00076924206,0.0012517405,0.022455817],"category_scores_gemma":[0.0011134242,0.0005517183,0.00045938976,0.003177426,0.0011202546,0.0024374307,0.00063238846,0.0018788152,0.012402944],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002078793,0.00007693842,0.00013342386,0.0010544017,0.00003546795,0.000090961665,0.0001256513,0.00601273,0.0014988801,0.37699065,0.34062383,0.27333635],"study_design_scores_gemma":[0.000014497316,0.000025805422,0.00041020493,0.00028943663,0.000016704955,0.00032295182,0.00003590554,0.0055066375,0.0003165945,0.35545102,0.63758165,0.000028677983],"about_ca_topic_score_codex":0.0008819355,"about_ca_topic_score_gemma":0.0021625245,"teacher_disagreement_score":0.022455817,"about_ca_system_score_codex":0.0006269935,"about_ca_system_score_gemma":0.0007463283,"threshold_uncertainty_score":0.07512218},"labels":[],"label_agreement":null},{"id":"W3125121244","doi":"10.21314/jcf.2016.209","title":"The efficient application of automatic differentiation for computing gradients in financial applications","year":2016,"lang":"en","type":"article","venue":"The Journal of Computational Finance","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Computational finance; Automatic differentiation; Speedup; Computation; Mode (computer interface); Function (biology); Monte Carlo method; Algorithm; Finance; Parallel computing; Mathematics","score_opus":0.00798150592505802,"score_gpt":0.24873530124578358,"score_spread":0.24075379532072555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125121244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019521518,0.0005722992,0.9735241,0.00031670005,0.00009107931,0.00004526672,0.000047020276,0.0010328157,0.004849223],"genre_scores_gemma":[0.34742847,0.0005967335,0.6474372,0.00018541433,0.00007360938,0.000093628776,0.00014233682,0.00042745072,0.0036151821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962044,0.00013466223,0.000029632643,0.00004320151,0.00013069798,0.000041311087],"domain_scores_gemma":[0.9983676,0.0010318927,0.00009172882,0.00020033773,0.0002533528,0.000055029184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009268466,0.0008404007,0.00060111104,0.00086972205,0.0004801315,0.0009984242,0.00083863956,0.00066213775,0.0037545504],"category_scores_gemma":[0.007015608,0.00035195076,0.00043737423,0.00088969036,0.0008067741,0.0013956391,0.0012781,0.0013112857,0.001567576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002854246,0.00013824708,0.0024995666,0.00039456683,0.000058766997,0.00031581393,0.00025092394,0.35000452,0.020727556,0.17020021,0.0068770936,0.44824728],"study_design_scores_gemma":[0.000015927246,0.000024573505,0.0001875621,0.000014496764,0.000004874483,0.00005921251,0.000013970855,0.9562768,0.0037196686,0.03765904,0.00201508,0.000008856548],"about_ca_topic_score_codex":0.0036569368,"about_ca_topic_score_gemma":0.0047723725,"teacher_disagreement_score":0.0037545504,"about_ca_system_score_codex":0.00062119536,"about_ca_system_score_gemma":0.0012184955,"threshold_uncertainty_score":0.012560248},"labels":[],"label_agreement":null},{"id":"W3125391873","doi":"","title":"Diagnostics for the bootstrap and fast double bootstrap","year":2017,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Bootstrapping (finance); Series (stratigraphy); Context (archaeology); Computer science; Bootstrap model; Autocorrelation; Bootstrap aggregating; Gauge (firearms); Econometrics; Artificial intelligence; Statistics; Mathematics; Particle physics; Physics","score_opus":0.09886542386916725,"score_gpt":0.3691208963967289,"score_spread":0.27025547252756166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125391873","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012386771,0.0013094483,0.98168474,0.0007835032,0.00020416363,0.000115194474,0.0003305217,0.000508132,0.00267761],"genre_scores_gemma":[0.51926106,0.0013309991,0.47267964,0.00083168794,0.0006997219,0.0010076707,0.0011880863,0.00040170384,0.0025994624],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9813809,0.012593346,0.00079837634,0.0013708727,0.0032940076,0.00056258787],"domain_scores_gemma":[0.86817694,0.10212085,0.008523133,0.011648053,0.008251436,0.0012795879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022476615,0.0013778723,0.0019564815,0.0045568966,0.00094774505,0.0025438743,0.0021264218,0.0038099762,0.005630706],"category_scores_gemma":[0.20565636,0.00075626996,0.0012721138,0.0027024548,0.003513201,0.0040940945,0.0034292473,0.0037853064,0.001812369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005991843,0.000103884384,0.010409061,0.00062186,0.00022336436,0.00053658296,0.00045811775,0.06798226,0.002792545,0.7458589,0.01065047,0.15976375],"study_design_scores_gemma":[0.00012363408,0.00026831502,0.0034388863,0.00032341428,0.000048240152,0.00061119517,0.00016448446,0.38888642,0.0032792357,0.59111565,0.011622028,0.00011844763],"about_ca_topic_score_codex":0.0010384243,"about_ca_topic_score_gemma":0.0005426772,"teacher_disagreement_score":0.022476615,"about_ca_system_score_codex":0.0010899942,"about_ca_system_score_gemma":0.0018892994,"threshold_uncertainty_score":0.118869126},"labels":[],"label_agreement":null},{"id":"W3126057260","doi":"","title":"A Fixed-bandwidth View of the Pre-asymptotic Inference for Kernel Smoothing with Time Series Data","year":2015,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematics; Smoothing; Estimator; Autocorrelation; Applied mathematics; Statistics; Kernel smoother; Covariance; Asymptotically optimal algorithm; Heteroscedasticity; Mathematical optimization; Kernel method; Computer science","score_opus":0.057459538394700145,"score_gpt":0.3325889624313482,"score_spread":0.2751294240366481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126057260","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020381445,0.00006968718,0.9971916,0.000092052105,0.000014244445,0.000015768246,0.000011718402,0.00008815092,0.00047852294],"genre_scores_gemma":[0.36048338,0.00052765186,0.63587546,0.00025962907,0.0002604519,0.00034743667,0.00017773376,0.00034579478,0.001722483],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9869667,0.00827867,0.00058489974,0.0014792798,0.0023295076,0.0003610335],"domain_scores_gemma":[0.92289215,0.0628348,0.0032802173,0.0071711745,0.0034471785,0.00037449595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02322365,0.0010152565,0.0013254812,0.0020658858,0.0007219786,0.0020302576,0.003951573,0.0021871096,0.0028205037],"category_scores_gemma":[0.13113071,0.00077899743,0.0018443335,0.0015870173,0.0039264844,0.00424575,0.0022766404,0.0041927746,0.00057934166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013298371,0.000093214105,0.0032662214,0.00024049234,0.0002598951,0.00025935663,0.00033722568,0.22452699,0.0035193886,0.6700273,0.0009269631,0.09640994],"study_design_scores_gemma":[0.000021383143,0.000086517124,0.0011190106,0.00007550189,0.000055303302,0.00012780222,0.000045868164,0.68573844,0.0031114593,0.30750254,0.0020635538,0.000052713036],"about_ca_topic_score_codex":0.0023938674,"about_ca_topic_score_gemma":0.0013197523,"teacher_disagreement_score":0.02322365,"about_ca_system_score_codex":0.0016041908,"about_ca_system_score_gemma":0.0019059777,"threshold_uncertainty_score":0.1228199},"labels":[],"label_agreement":null},{"id":"W3128105035","doi":"10.1007/978-3-030-62368-5_1","title":"Discrete-Time Signals","year":2021,"lang":"en","type":"book-chapter","venue":"Digital Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Aperiodic graph; Discrete-time signal; Algorithm; Computer science; Aliasing; Bounded function; Signal processing; Power of two; Sampling (signal processing); SIGNAL (programming language); Impulse (physics); Scaling; Mathematics; Analog signal; Digital signal processing; Filter (signal processing); Mathematical analysis; Signal transfer function; Computer vision; Computer hardware","score_opus":0.01715785887862713,"score_gpt":0.23325030411143857,"score_spread":0.21609244523281143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128105035","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019915304,0.015555595,0.42586172,0.0011984824,0.005103756,0.00006945071,0.0006646325,0.0015912146,0.54796356],"genre_scores_gemma":[0.042723008,0.01889615,0.076532744,0.001024954,0.002883336,0.000099182005,0.0012191494,0.0005574406,0.856064],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997781,0.000021569154,0.000009217252,0.000046171608,0.00013456715,0.000010437217],"domain_scores_gemma":[0.99980813,0.00007329205,0.000008738815,0.000049321716,0.00004661642,0.0000139283175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016188395,0.0007812038,0.00043585175,0.0007227368,0.000280832,0.0015512753,0.00056771946,0.0006474899,0.05425828],"category_scores_gemma":[0.0005940557,0.00019783399,0.00022060172,0.0009908783,0.0005892363,0.0011076208,0.0006781329,0.0011151838,0.031571563],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003576733,0.00003700223,0.00007998139,0.00042779915,0.000018860144,0.00011380881,0.00013546502,0.0030634252,0.01373634,0.3008028,0.104918376,0.5766303],"study_design_scores_gemma":[0.0000069821517,0.00005434302,0.00024173272,0.00010648627,0.000010747647,0.00037418024,0.000034915287,0.008038558,0.005195967,0.06851947,0.91740215,0.000014533691],"about_ca_topic_score_codex":0.0002290996,"about_ca_topic_score_gemma":0.00033162432,"teacher_disagreement_score":0.05425828,"about_ca_system_score_codex":0.00030132508,"about_ca_system_score_gemma":0.00022973944,"threshold_uncertainty_score":0.18151206},"labels":[],"label_agreement":null},{"id":"W3128467522","doi":"","title":"Gradient Descent Resists Compositionality","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Principle of compositionality; Computer science; Gradient descent; Generalization; Artificial intelligence; Property (philosophy); Artificial neural network; Independence (probability theory); Process (computing); Machine learning; Natural language processing; Theoretical computer science; Programming language; Mathematics; Epistemology","score_opus":0.018964801629831298,"score_gpt":0.24865871040440052,"score_spread":0.22969390877456922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128467522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05828572,0.0001973248,0.93293697,0.00065317645,0.000053433938,0.00010539467,0.000028217868,0.0008489323,0.006890834],"genre_scores_gemma":[0.7564841,0.00018119333,0.23602901,0.00077943254,0.000066292654,0.00022386598,0.00011020578,0.000488758,0.005637103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969598,0.0011623608,0.00017222173,0.0005335486,0.00089643913,0.00027562137],"domain_scores_gemma":[0.9910685,0.0049814875,0.0007873124,0.0020203625,0.0008702831,0.00027205856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006834955,0.0013176058,0.0013724294,0.00068248954,0.0010795236,0.0011548185,0.001231593,0.0014548919,0.0027864312],"category_scores_gemma":[0.028735563,0.00090552284,0.000848459,0.00039467998,0.0034262908,0.0030677042,0.0038265751,0.0030043714,0.0007380163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036295663,0.00014134657,0.0030728895,0.00033853354,0.00017539297,0.00028789305,0.0004967162,0.4785761,0.021487802,0.39874512,0.00238043,0.09393481],"study_design_scores_gemma":[0.000031269323,0.0001304169,0.00024961733,0.000029324066,0.00001825091,0.00007501923,0.0000309167,0.83212024,0.00507575,0.16042426,0.0018016784,0.000013258295],"about_ca_topic_score_codex":0.0014687259,"about_ca_topic_score_gemma":0.0020515,"teacher_disagreement_score":0.006834955,"about_ca_system_score_codex":0.0009654422,"about_ca_system_score_gemma":0.0015718985,"threshold_uncertainty_score":0.036147118},"labels":[],"label_agreement":null},{"id":"W3129180326","doi":"","title":"Feedforward Legendre Memory Unit","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Feed forward; Recurrent neural network; MNIST database; Artificial intelligence; Inference; Transformer; Language model; Artificial neural network","score_opus":0.0230980867924338,"score_gpt":0.24936672373582613,"score_spread":0.22626863694339233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129180326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054276112,0.0022842195,0.90946037,0.0007792583,0.0008981488,0.00012150346,0.000865888,0.011416948,0.019897556],"genre_scores_gemma":[0.7438387,0.0008561771,0.21545465,0.0010082043,0.000224689,0.00021680539,0.0017559773,0.00056335446,0.03608139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996425,0.00005568067,0.000026538859,0.000119895776,0.00008077166,0.000074610725],"domain_scores_gemma":[0.99956065,0.00011318244,0.00004111149,0.0001105632,0.00014620296,0.000028267827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006370009,0.0009444485,0.000812868,0.00043443707,0.00044414343,0.0010911911,0.002454118,0.0012534437,0.010025068],"category_scores_gemma":[0.0018620241,0.00043635396,0.00070726307,0.00046031532,0.0006998242,0.0020233036,0.00088244234,0.0015038857,0.003348343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007937203,0.0002191372,0.0022105305,0.0004414177,0.00021013971,0.0005471509,0.00021913114,0.25287533,0.047383837,0.053229704,0.03937232,0.6024976],"study_design_scores_gemma":[0.000020697862,0.00013643365,0.0003123313,0.000026805543,0.000043776065,0.00014398369,0.000020240448,0.96332586,0.018630387,0.0084892195,0.008820546,0.000029639696],"about_ca_topic_score_codex":0.005567187,"about_ca_topic_score_gemma":0.008922871,"teacher_disagreement_score":0.010025068,"about_ca_system_score_codex":0.00080535887,"about_ca_system_score_gemma":0.000910465,"threshold_uncertainty_score":0.03353715},"labels":[],"label_agreement":null},{"id":"W3130587930","doi":"10.1016/b978-0-323-90933-4.00007-3","title":"Introduction to artificial neural networks (ANN)","year":2021,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial neural network; Representation (politics); Computer science; Artificial intelligence; Artificial neuron; Meaning (existential); Epistemology; Philosophy","score_opus":0.016444503234615618,"score_gpt":0.23539192976094883,"score_spread":0.2189474265263332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130587930","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014073331,0.24954355,0.19218117,0.0053765406,0.011402994,0.000112720176,0.0022663816,0.0032478399,0.53446156],"genre_scores_gemma":[0.012734092,0.19394514,0.14824215,0.0027353289,0.004422074,0.00024741254,0.0026277793,0.0009439724,0.63410205],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997365,0.000024928706,0.000019369647,0.000056833785,0.00014485029,0.000017464707],"domain_scores_gemma":[0.9995708,0.00020958084,0.000019148334,0.000039619972,0.00013483004,0.000025899493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033026387,0.0010659696,0.00074307906,0.0012189292,0.0002834128,0.0018322452,0.00086997735,0.0012421955,0.07181693],"category_scores_gemma":[0.0011975516,0.0004591794,0.00046428497,0.0025546923,0.0004087511,0.0019039422,0.0008130818,0.001971024,0.03695088],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025269555,0.000059685826,0.00017032253,0.0010604037,0.000026207785,0.000099474724,0.000056809185,0.004898457,0.001895912,0.055362854,0.2567451,0.67959946],"study_design_scores_gemma":[0.000004031076,0.000017048063,0.00024072356,0.00040690886,0.000009863689,0.00017934134,0.000020085898,0.003792088,0.00047265476,0.03847488,0.9563669,0.000015488073],"about_ca_topic_score_codex":0.0011688587,"about_ca_topic_score_gemma":0.001910235,"teacher_disagreement_score":0.07181693,"about_ca_system_score_codex":0.00050048233,"about_ca_system_score_gemma":0.00074637844,"threshold_uncertainty_score":0.24025154},"labels":[],"label_agreement":null},{"id":"W3130818702","doi":"10.1016/b978-0-323-90933-4.00005-x","title":"Working with any number of hidden layers","year":2021,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Milestone; Computer science; Function (biology); Architecture; Artificial neural network; Theoretical computer science; Artificial intelligence; Geography; Cartography","score_opus":0.02347897410162359,"score_gpt":0.2402529195166323,"score_spread":0.21677394541500872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130818702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046881153,0.005797353,0.86083615,0.0016039104,0.0021809735,0.000039456732,0.00041493363,0.0062565007,0.11818261],"genre_scores_gemma":[0.051009413,0.006941209,0.4523896,0.001280328,0.00071255345,0.00012235042,0.0013269689,0.0017494678,0.48446804],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99979514,0.000020977635,0.00001685388,0.000067130415,0.00007068159,0.000029345376],"domain_scores_gemma":[0.9996475,0.000106287865,0.000016106478,0.00014878999,0.00006115338,0.000020254252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043267148,0.001690178,0.00061351486,0.00035679238,0.00042709595,0.0020219225,0.0020012045,0.0014327031,0.06171763],"category_scores_gemma":[0.0012843717,0.0007773475,0.0009995288,0.0007328853,0.00051178766,0.0050631175,0.001629363,0.0027389731,0.027069855],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067552995,0.00004962232,0.00020872797,0.00052627164,0.00010577969,0.00019905853,0.00010422082,0.019491002,0.0143554015,0.0446192,0.05788875,0.8623845],"study_design_scores_gemma":[0.00003559379,0.00009800804,0.0006261634,0.00057668897,0.00021346164,0.00077122974,0.00013392024,0.26094264,0.032536052,0.35147408,0.3525159,0.00007631637],"about_ca_topic_score_codex":0.0017053727,"about_ca_topic_score_gemma":0.003377911,"teacher_disagreement_score":0.06171763,"about_ca_system_score_codex":0.00040403096,"about_ca_system_score_gemma":0.00055747526,"threshold_uncertainty_score":0.20646602},"labels":[],"label_agreement":null},{"id":"W3132401450","doi":"10.1162/neco_a_01557","title":"How to Represent Part-Whole Hierarchies in a Neural Network","year":2022,"lang":"en","type":"article","venue":"Neural Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Interpretability; Parsing; Transformer; Computer science; Artificial intelligence; Artificial neural network; Hierarchy; Representation (politics); Parse tree; Natural language processing; Theoretical computer science; Machine learning; Engineering; Voltage","score_opus":0.02746012427718318,"score_gpt":0.2652115562248038,"score_spread":0.2377514319476206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132401450","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007412868,0.00037479255,0.98192203,0.0015417222,0.00011488656,0.000056710276,0.0002422426,0.0017080322,0.0066268416],"genre_scores_gemma":[0.1831658,0.00081448606,0.8068721,0.00056820986,0.000071649585,0.00017980489,0.0004944156,0.0004995433,0.007333979],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956554,0.000119969576,0.000035016437,0.00013196892,0.000105388935,0.000042084375],"domain_scores_gemma":[0.9994823,0.00018698051,0.000030502895,0.00016847845,0.00009483703,0.000036950838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093208614,0.0005874092,0.0005135684,0.00066020136,0.00078216166,0.0023439694,0.0013628835,0.001352626,0.00825177],"category_scores_gemma":[0.003955513,0.0005797106,0.0008849499,0.0007069733,0.0013787071,0.0076719206,0.00152207,0.002124759,0.0024736207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012643237,0.00004626166,0.0007864309,0.0003005529,0.000085638036,0.00014050846,0.00063389324,0.111918055,0.011162816,0.51978284,0.013169232,0.3418473],"study_design_scores_gemma":[0.000021489197,0.000027929593,0.0001651596,0.000077342185,0.000034168625,0.00009547144,0.000117989795,0.4085647,0.005563216,0.56579155,0.019508738,0.00003223109],"about_ca_topic_score_codex":0.0050366395,"about_ca_topic_score_gemma":0.0063214917,"teacher_disagreement_score":0.00825177,"about_ca_system_score_codex":0.0008899422,"about_ca_system_score_gemma":0.0009777597,"threshold_uncertainty_score":0.027604878},"labels":[],"label_agreement":null},{"id":"W3133450490","doi":"10.21608/bfemu.2021.146280","title":"A Biometric System for Personal Identification Using Modular Neural Nets.(Dept.E)","year":2021,"lang":"en","type":"article","venue":"MEJ Mansoura Engineering Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Biometrics; Modular design; Identification (biology); Computer science; DEPT; Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Medicine; Biology; Botany","score_opus":0.021773473489749277,"score_gpt":0.23408629702217196,"score_spread":0.21231282353242267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133450490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039536417,0.0014670524,0.9232123,0.00062160863,0.000669031,0.0003838223,0.0009187612,0.011916943,0.021274043],"genre_scores_gemma":[0.4035195,0.0010314158,0.5213001,0.00088144554,0.00030734725,0.00054700347,0.0017309524,0.00021868582,0.07046356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996598,0.000053632066,0.000017791812,0.00007934897,0.00015863235,0.00003091896],"domain_scores_gemma":[0.9997304,0.00003514551,0.00003056682,0.000054324537,0.000114656264,0.000034951678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058954133,0.00045246418,0.00042363533,0.0006190505,0.00035501143,0.00049610616,0.0006318459,0.00071644294,0.018596804],"category_scores_gemma":[0.000540719,0.00017569214,0.0002615287,0.0005291697,0.00019545056,0.00075204915,0.00080066855,0.0006849049,0.010404936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062515284,0.00017928069,0.002896072,0.0002238548,0.000045527726,0.00029422675,0.000097261975,0.0047813863,0.1180956,0.0075484747,0.029299814,0.8359135],"study_design_scores_gemma":[0.00018185771,0.0015222534,0.022297643,0.00018834132,0.00022125992,0.0032632262,0.00015677563,0.50463194,0.21469057,0.011396399,0.24125169,0.00019807713],"about_ca_topic_score_codex":0.0007660737,"about_ca_topic_score_gemma":0.0010728387,"teacher_disagreement_score":0.018596804,"about_ca_system_score_codex":0.00044230788,"about_ca_system_score_gemma":0.00040498402,"threshold_uncertainty_score":0.062212467},"labels":[],"label_agreement":null},{"id":"W3133928912","doi":"10.1145/3434074.3447193","title":"Estimating Levels of Engagement for Social Human-Robot Interaction using Legendre Memory Units","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Legendre polynomials; Task (project management); Computer science; Artificial neural network; Perception; Class (philosophy); Robot; State (computer science); Training (meteorology); Artificial intelligence; Machine learning; Cognitive psychology; Psychology; Mathematics; Algorithm; Engineering; Neuroscience","score_opus":0.2862960006228849,"score_gpt":0.4004391219715979,"score_spread":0.11414312134871302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133928912","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76023924,0.00022183111,0.23565708,0.00012163228,0.000035610083,0.00007776221,0.00013686901,0.00044912985,0.0030608422],"genre_scores_gemma":[0.98334044,0.000030881445,0.015764302,0.000020467683,0.0000055604123,0.00002587861,0.00006775474,0.000011275271,0.0007334399],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997267,0.000060303963,0.000011422435,0.00010614129,0.000045822613,0.0000496344],"domain_scores_gemma":[0.9992335,0.00040366035,0.00012352859,0.000071998016,0.00008819792,0.00007924323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005701608,0.0004533661,0.00028641737,0.00043653848,0.00018343881,0.00056996464,0.0004782724,0.00046683985,0.0019569239],"category_scores_gemma":[0.003279993,0.00017063004,0.00022963125,0.00022583778,0.00037750526,0.0009876338,0.0007378644,0.00063867564,0.0004016822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027733753,0.00065992307,0.105082236,0.0002769012,0.00020666729,0.00028918064,0.0022840528,0.12150394,0.18944408,0.004606074,0.0013590746,0.5715145],"study_design_scores_gemma":[0.000018174993,0.0008113304,0.06768743,0.0000324426,0.00004071527,0.00013509182,0.00041220855,0.8995324,0.026222082,0.0041145645,0.00094100717,0.000052637646],"about_ca_topic_score_codex":0.0019249619,"about_ca_topic_score_gemma":0.0040481985,"teacher_disagreement_score":0.0019569239,"about_ca_system_score_codex":0.00041526175,"about_ca_system_score_gemma":0.0002296457,"threshold_uncertainty_score":0.0065464973},"labels":[],"label_agreement":null},{"id":"W3134134779","doi":"10.47839/ijc.8.1.651","title":"AN IMPROVED ARCHITECTURE FOR COMPETITIVE AND COOPERATIVE NEURONS (CCNS) IN NEURAL NETWORKS","year":2014,"lang":"en","type":"article","venue":"International Journal of Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Content-addressable memory; Artificial neural network; Bidirectional associative memory; Associative property; Recall; Artificial intelligence; Algorithm; Mathematics","score_opus":0.008417527868436367,"score_gpt":0.2750034447492628,"score_spread":0.26658591688082645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134134779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051565554,0.0016060335,0.9305326,0.00045246395,0.00031872085,0.00010664232,0.00008947222,0.0011656246,0.014162907],"genre_scores_gemma":[0.8394428,0.0005255741,0.15066922,0.00029027506,0.00016026995,0.00016157364,0.00010040625,0.00006187631,0.008587977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959487,0.00007166367,0.000027215561,0.0001164908,0.00012456278,0.000065267566],"domain_scores_gemma":[0.9995952,0.000067147026,0.000029726658,0.00006723018,0.00020164606,0.000039103845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058400445,0.0006996575,0.0005812556,0.00032548764,0.0005785225,0.00078783184,0.0032524401,0.0016713269,0.0022604838],"category_scores_gemma":[0.0011680346,0.00025040153,0.00044996373,0.00057982,0.0006217186,0.001231953,0.0009197315,0.0010461675,0.00072265865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003720753,0.00023111494,0.0014342141,0.00035315676,0.00015926079,0.000484961,0.00037810946,0.55967367,0.051471215,0.12894031,0.00968095,0.24682091],"study_design_scores_gemma":[0.000016879298,0.00014306657,0.00014241778,0.000009927608,0.00003100069,0.00010612224,0.000011058359,0.981434,0.0038469145,0.010357484,0.0038850682,0.000016020762],"about_ca_topic_score_codex":0.00650227,"about_ca_topic_score_gemma":0.006687559,"teacher_disagreement_score":0.00650227,"about_ca_system_score_codex":0.00084308087,"about_ca_system_score_gemma":0.0008416349,"threshold_uncertainty_score":0.0129288435},"labels":[],"label_agreement":null},{"id":"W3134848548","doi":"10.1109/mdat.2021.3063356","title":"Training Binarized Neural Networks Using Ternary Multipliers","year":2021,"lang":"en","type":"article","venue":"IEEE Design and Test","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Ternary operation; Artificial intelligence; Computer science; Pattern recognition (psychology)","score_opus":0.07940189450894619,"score_gpt":0.27393791180575633,"score_spread":0.19453601729681014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134848548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017351545,0.00090958783,0.9734813,0.001174598,0.0010841903,0.00005183414,0.00011003206,0.0010328578,0.0048040533],"genre_scores_gemma":[0.3448616,0.0010824145,0.62676615,0.0011766235,0.00087174954,0.00023246587,0.00047295378,0.0003970989,0.024138942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976164,0.00006866425,0.000021078846,0.000058352507,0.00006991524,0.000020275658],"domain_scores_gemma":[0.99897707,0.00056521536,0.00006782494,0.00006353348,0.00028773837,0.0000386004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010379124,0.0006599614,0.00053066073,0.00047439846,0.0002505871,0.0009903904,0.00083567534,0.00097291317,0.00855323],"category_scores_gemma":[0.0060662874,0.0003981601,0.00034426767,0.0007321973,0.0003625339,0.0014368821,0.000743592,0.0017941325,0.001405717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040947826,0.0001007256,0.00064777385,0.00022571921,0.00012182662,0.00010188542,0.000069703274,0.49884674,0.010373452,0.050146483,0.019409448,0.4195467],"study_design_scores_gemma":[0.00001300428,0.000018974675,0.0000529728,0.000010079517,0.000006069791,0.000009817026,0.0000029251833,0.99213636,0.0016086025,0.004438652,0.0016985367,0.0000039500956],"about_ca_topic_score_codex":0.002503742,"about_ca_topic_score_gemma":0.0037344263,"teacher_disagreement_score":0.00855323,"about_ca_system_score_codex":0.0004995834,"about_ca_system_score_gemma":0.0006947512,"threshold_uncertainty_score":0.028613448},"labels":[],"label_agreement":null},{"id":"W3135230929","doi":"10.47302/jsr.2020540201","title":"A numerical study of entropy and residual entropy estimators based on smooth density estimators for non-negative random variables","year":2021,"lang":"en","type":"article","venue":"Journal of Statistical Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Mathematics; Maximum entropy probability distribution; Entropy (arrow of time); Residual; Joint entropy; Entropy estimation; Histogram; Random variable; Poisson distribution; Probability density function; Statistics; Residual entropy; Extremum estimator; Principle of maximum entropy; Applied mathematics; Rényi entropy; Binary entropy function; Statistical physics; M-estimator; Algorithm; Computer science; Physics; Artificial intelligence; Configuration entropy; Thermodynamics","score_opus":0.04317620171551941,"score_gpt":0.3791857417861424,"score_spread":0.336009540070623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135230929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14744256,0.0029588598,0.84549284,0.00055796746,0.00010863694,0.00006326014,0.00004672247,0.00022828455,0.003100817],"genre_scores_gemma":[0.8534808,0.0007062966,0.14431258,0.00010013165,0.00010223753,0.000068793954,0.000089781744,0.000084703584,0.0010546083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982936,0.0010289267,0.000053882966,0.00014403406,0.00041248882,0.00006710104],"domain_scores_gemma":[0.94645953,0.04822061,0.0018089766,0.0015471905,0.0015752547,0.00038843133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007687309,0.0005238788,0.0006982374,0.0013059997,0.0003632341,0.0007253043,0.0010033705,0.0011095358,0.00082005747],"category_scores_gemma":[0.05880278,0.000328243,0.00041496218,0.0008129168,0.0023438358,0.0028426002,0.00095798605,0.0014141472,0.000090073234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018678355,0.000053263728,0.0055706054,0.00019408122,0.000089108216,0.0001411563,0.00016843522,0.84593177,0.0035987012,0.11545469,0.00054678426,0.028064663],"study_design_scores_gemma":[0.0000061975456,0.000036446334,0.0006614935,0.00001609862,0.000006086816,0.000039737308,0.00001004452,0.98979354,0.0007142055,0.008527069,0.00017612778,0.000013003064],"about_ca_topic_score_codex":0.0010294801,"about_ca_topic_score_gemma":0.0008062031,"teacher_disagreement_score":0.007687309,"about_ca_system_score_codex":0.00087925344,"about_ca_system_score_gemma":0.00051492127,"threshold_uncertainty_score":0.040654898},"labels":[],"label_agreement":null},{"id":"W3136877468","doi":"10.1139/cjp-2021-0006","title":"A neural network approach for solution of the Schrödinger equation for a particle in the Pöschl–Teller potential","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Physics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Delhi","keywords":"Physics; Artificial neural network; Schrödinger equation; Wave function; Ground state; Energy (signal processing); Work (physics); Particle (ecology); Quantum; Applied mathematics; Quantum mechanics; Statistical physics; Artificial intelligence; Computer science; Mathematics","score_opus":0.04660970425702989,"score_gpt":0.23996040081355552,"score_spread":0.19335069655652562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136877468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04224944,0.0008250593,0.94787097,0.0007439501,0.000118621,0.00004984887,0.000054772976,0.00010955759,0.00797773],"genre_scores_gemma":[0.5748347,0.0013384521,0.4089683,0.00033608926,0.00014112887,0.00034473796,0.000127809,0.00006178177,0.0138469925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998727,0.000046971894,0.0000067254655,0.000016473676,0.000045182904,0.000011969514],"domain_scores_gemma":[0.99981135,0.00011467746,0.000014278449,0.000008807543,0.000041253305,0.000009616863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004056869,0.00027781422,0.0003323523,0.00032473664,0.00034274702,0.00050253596,0.00067819015,0.0010377928,0.0014891444],"category_scores_gemma":[0.00093011797,0.00022096992,0.00031418356,0.0004081248,0.0005446406,0.0007367462,0.0005010241,0.00087635865,0.00022409672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037549227,0.000037200924,0.00033718944,0.00009592428,0.000027679569,0.00009640557,0.000052963154,0.91229033,0.005003884,0.05723311,0.00064677215,0.024140967],"study_design_scores_gemma":[0.0000022630607,0.0000058416977,0.000030019553,0.0000023351854,0.000001487168,0.0000044592516,0.000002725431,0.99540955,0.0001777495,0.004202715,0.00015828513,0.0000026698783],"about_ca_topic_score_codex":0.0050531393,"about_ca_topic_score_gemma":0.004937034,"teacher_disagreement_score":0.0050531393,"about_ca_system_score_codex":0.00057196297,"about_ca_system_score_gemma":0.0009230465,"threshold_uncertainty_score":0.010047436},"labels":[],"label_agreement":null},{"id":"W3136903971","doi":"10.1007/s11571-020-09598-2","title":"Neural computing in four spatial dimensions","year":2020,"lang":"en","type":"article","venue":"Cognitive Neurodynamics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Computation; Dimension (graph theory); Set (abstract data type); Artificial neural network; Surface (topology); Topology (electrical circuits); Feature (linguistics); Artificial intelligence; Architecture; Theoretical computer science; Volume (thermodynamics); Quantum; Pattern recognition (psychology); Algorithm; Geometry; Mathematics; Pure mathematics; Physics","score_opus":0.027688232317526075,"score_gpt":0.25288985251756196,"score_spread":0.2252016202000359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136903971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4045253,0.006836285,0.422699,0.0077222395,0.0011091782,0.00007914094,0.00035332757,0.0002935881,0.15638193],"genre_scores_gemma":[0.9405541,0.0012305011,0.048364505,0.00021743943,0.00013601083,0.00006215093,0.000046653335,0.00002621421,0.009362529],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978215,0.000067110595,0.0000121522335,0.000030861,0.00006763212,0.00004011092],"domain_scores_gemma":[0.9995828,0.00017785157,0.000039493276,0.000057484245,0.00009165157,0.000050804618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003782243,0.000284439,0.00044121547,0.0006846286,0.0008043745,0.0023441392,0.0006047431,0.0007379994,0.005001672],"category_scores_gemma":[0.001807862,0.00020412002,0.00034395815,0.00095354306,0.0026700622,0.003059499,0.0013425908,0.0010056486,0.00030899904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030590258,0.000012499096,0.00038445278,0.00004243693,0.000015011366,0.00003084877,0.000120857425,0.027776288,0.00073866476,0.9609571,0.0006909955,0.009200313],"study_design_scores_gemma":[0.000009878132,0.000012174994,0.0003883669,0.000011505745,0.000006854949,0.000021041678,0.0001053773,0.09307296,0.0003296414,0.902756,0.0032726002,0.000013669275],"about_ca_topic_score_codex":0.0024067126,"about_ca_topic_score_gemma":0.0018906065,"teacher_disagreement_score":0.005001672,"about_ca_system_score_codex":0.000982197,"about_ca_system_score_gemma":0.0006886425,"threshold_uncertainty_score":0.016732216},"labels":[],"label_agreement":null},{"id":"W3141207700","doi":"10.1109/wi.2007.62","title":"Growing Hierarchical Self-Organizing Maps for Web Mining","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Hierarchy; Web mining; Curse of dimensionality; Web intelligence; Data mining; Data Web; Web modeling; Web mapping; Self-organizing map; Machine learning; Information retrieval; The Internet; Artificial intelligence; World Wide Web; Web page; Artificial neural network","score_opus":0.016236894674188946,"score_gpt":0.2568059178200497,"score_spread":0.24056902314586073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141207700","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015776379,0.0030618734,0.97467756,0.00065030786,0.000119083896,0.00008118439,0.00020602459,0.0011245713,0.0043031573],"genre_scores_gemma":[0.4193597,0.0023376008,0.5736627,0.00020127496,0.00022676031,0.00033454187,0.00045705392,0.00013694288,0.003283473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995763,0.00016951324,0.000018919452,0.00005522658,0.00015607565,0.000023977336],"domain_scores_gemma":[0.99899954,0.00059895276,0.000081150705,0.00011187259,0.00018216854,0.000026321068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007389065,0.00039807815,0.0005391059,0.0012715468,0.00044186454,0.00065468944,0.00094994676,0.0007309863,0.0011199259],"category_scores_gemma":[0.0038918047,0.00028008118,0.00050870195,0.0015170779,0.0004938052,0.0010610112,0.0006504274,0.0007147384,0.00039564853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058608148,0.00006762755,0.0012984712,0.00027051743,0.00009690452,0.00028479975,0.00023204766,0.67203623,0.0031129017,0.071806364,0.011151063,0.23958452],"study_design_scores_gemma":[0.000004404559,0.0000056301124,0.00025662404,0.000008634111,0.0000059659587,0.000027033198,0.000015180977,0.95425874,0.00037155463,0.042922635,0.0021157807,0.000007906265],"about_ca_topic_score_codex":0.004872138,"about_ca_topic_score_gemma":0.0047348994,"teacher_disagreement_score":0.004872138,"about_ca_system_score_codex":0.00061027665,"about_ca_system_score_gemma":0.00049355463,"threshold_uncertainty_score":0.009687543},"labels":[],"label_agreement":null},{"id":"W3145041349","doi":"10.1016/j.bdr.2021.100254","title":"Scaling the Growing Neural Gas for Visual Cluster Analysis","year":2021,"lang":"en","type":"article","venue":"Big Data Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Clutter; Visualization; Neural gas; Big data; Cluster (spacecraft); Artificial intelligence; Unsupervised learning; Graph; Scalability; Artificial neural network; Topology (electrical circuits); Machine learning; Pattern recognition (psychology); Theoretical computer science; Data mining; Recurrent neural network; Database","score_opus":0.40066876961771286,"score_gpt":0.45786006405529445,"score_spread":0.057191294437581586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145041349","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04925209,0.000722344,0.93811256,0.00058971747,0.00015904197,0.00011134582,0.00036052242,0.004463575,0.0062288893],"genre_scores_gemma":[0.5120638,0.0005591228,0.48163822,0.00035006588,0.00007346602,0.00022787358,0.0008245702,0.001179941,0.0030829576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994228,0.0001774257,0.000023554667,0.00014851331,0.00017810262,0.000049592443],"domain_scores_gemma":[0.99823666,0.0008786274,0.00012342927,0.00027793017,0.00040988313,0.00007358667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010626216,0.0011748798,0.00071380084,0.0015232568,0.0006399537,0.001578096,0.0014925331,0.0011032269,0.0034320073],"category_scores_gemma":[0.007950963,0.00046968,0.0009206284,0.0013218222,0.00084970176,0.0026367928,0.0015636181,0.0013311635,0.0009216772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001683516,0.000055442757,0.0017331318,0.00015498968,0.00005791349,0.00014380633,0.00028739567,0.8251445,0.006881826,0.019004252,0.0062391674,0.1401293],"study_design_scores_gemma":[0.000004907086,0.000008829605,0.00012359465,0.000007505154,0.0000039460315,0.000020486772,0.00002716037,0.98961395,0.001132252,0.007952059,0.0010981079,0.0000072784915],"about_ca_topic_score_codex":0.010077187,"about_ca_topic_score_gemma":0.008350087,"teacher_disagreement_score":0.010077187,"about_ca_system_score_codex":0.0013562908,"about_ca_system_score_gemma":0.0008654885,"threshold_uncertainty_score":0.020037055},"labels":[],"label_agreement":null},{"id":"W3146058829","doi":"10.1016/j.neunet.2021.03.035","title":"Quantifying the separability of data classes in neural networks","year":2021,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Deutsche Forschungsgemeinschaft","keywords":"Artificial neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Machine learning","score_opus":0.09719686209684632,"score_gpt":0.3354928062548781,"score_spread":0.2382959441580318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146058829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32428408,0.0015612476,0.66845614,0.000680051,0.000117415504,0.00009894914,0.0006412317,0.00075561035,0.0034052068],"genre_scores_gemma":[0.9131178,0.0002805694,0.08459429,0.00016835664,0.000065692046,0.00012986685,0.0006102503,0.00014700889,0.00088621693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970086,0.00074547215,0.00028292747,0.00069448876,0.0009284995,0.00034009528],"domain_scores_gemma":[0.98259765,0.01325368,0.0013873639,0.0016381295,0.00072711945,0.00039606343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004655647,0.0008507262,0.0010665176,0.002699589,0.00062781933,0.0027665643,0.0013458387,0.0021436869,0.0013884406],"category_scores_gemma":[0.033949107,0.00038716782,0.00074042554,0.0017457545,0.0036673604,0.0038896147,0.0031192107,0.0021199055,0.00027829217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010520542,0.00029060768,0.041410558,0.0008341839,0.00043736983,0.0005995213,0.0013095711,0.44295087,0.044820663,0.11291582,0.0020702055,0.35130855],"study_design_scores_gemma":[0.00003101752,0.0002370263,0.015698364,0.00012182998,0.000042988795,0.0003824493,0.0002312174,0.7757055,0.013087652,0.19212012,0.0022499776,0.000091842034],"about_ca_topic_score_codex":0.0013296732,"about_ca_topic_score_gemma":0.00075191515,"teacher_disagreement_score":0.004655647,"about_ca_system_score_codex":0.001217481,"about_ca_system_score_gemma":0.00068205845,"threshold_uncertainty_score":0.024621665},"labels":[],"label_agreement":null},{"id":"W3154784987","doi":"10.2316/j.2021.206-0613","title":"FEATURE EXTRACTION OF MOTOR IMAGERY EEG SIGNALS BASED ON MULTI-SCALE RECURRENCE PLOT AND SDA","year":2021,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Administration of Surveying, Mapping and Geoinformation of China; Wuhan University of Technology; Wuhan University","keywords":"Motor imagery; Electroencephalography; Artificial intelligence; Pattern recognition (psychology); Computer science; Plot (graphics); Feature extraction; Scale (ratio); Recurrence plot; Class (philosophy); Psychology; Mathematics; Geography; Cartography; Statistics; Brain–computer interface; Neuroscience; Physics","score_opus":0.018315555287318198,"score_gpt":0.2911296863332035,"score_spread":0.2728141310458853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154784987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3255617,0.0009880979,0.66774356,0.00019694693,0.00008840124,0.00012506764,0.00086243975,0.0019988965,0.0024347748],"genre_scores_gemma":[0.8718144,0.0005655925,0.124883235,0.000035349196,0.00007630265,0.000108654356,0.0009394857,0.000116344316,0.001460638],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998939,0.00001354203,0.000012428453,0.000032705353,0.000032114705,0.000015355641],"domain_scores_gemma":[0.9997327,0.000086496446,0.00005308557,0.0000326491,0.00007850553,0.00001658826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029483932,0.0005203778,0.00036562505,0.001222082,0.00012446202,0.0003678595,0.00023436229,0.00025605207,0.0014033073],"category_scores_gemma":[0.0012471753,0.00015077803,0.0005881173,0.0010678087,0.0001450635,0.0005467526,0.00022704098,0.00028368505,0.00045315825],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047854526,0.0001092795,0.01238537,0.00031868895,0.00017810446,0.0006145592,0.00022619712,0.023711272,0.24212699,0.002015281,0.0031887065,0.71464694],"study_design_scores_gemma":[0.000028467864,0.00027617367,0.12117634,0.00004979493,0.00021335795,0.001483818,0.0001938499,0.80570436,0.06435165,0.0025753586,0.0038708688,0.00007589338],"about_ca_topic_score_codex":0.0010527661,"about_ca_topic_score_gemma":0.0014713198,"teacher_disagreement_score":0.0014033073,"about_ca_system_score_codex":0.00009122176,"about_ca_system_score_gemma":0.00017226281,"threshold_uncertainty_score":0.0046945214},"labels":[],"label_agreement":null},{"id":"W3155637371","doi":"10.1007/978-3-030-73103-8_50","title":"Probabilistic Feature Selection for Interpretable Random Forest Model","year":2021,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Random forest; Feature selection; Probabilistic logic; Computer science; Selection (genetic algorithm); Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Forestry; Machine learning; Geography","score_opus":0.01756435180286139,"score_gpt":0.2651151918306172,"score_spread":0.24755084002775582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155637371","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037060515,0.0005289414,0.9944537,0.00007562966,0.000026626398,0.000017170321,0.00012824246,0.00040893056,0.00065467897],"genre_scores_gemma":[0.32740933,0.0015133934,0.6632361,0.0001451492,0.00020259243,0.00025698723,0.0019164108,0.000370766,0.004949295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995285,0.00017607046,0.000025880523,0.000115003975,0.00011833637,0.0000362001],"domain_scores_gemma":[0.9993062,0.00047775262,0.000037689373,0.00007584996,0.000088712615,0.000013792901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090661884,0.0005762535,0.0009781081,0.0006481147,0.0002723719,0.0007082033,0.0013456732,0.0008647553,0.0028244872],"category_scores_gemma":[0.0024369806,0.00033699506,0.0009502917,0.0010425935,0.00034904672,0.0014873743,0.0005151461,0.0012641726,0.0008690341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014920723,0.000083747094,0.0006482364,0.00027165774,0.000098606884,0.0001819538,0.000072679395,0.5219786,0.006561019,0.048534792,0.00984384,0.41157568],"study_design_scores_gemma":[0.000004904919,0.000010554559,0.0000966975,0.000007899174,0.000008486814,0.00003994399,0.0000024857397,0.9743707,0.00040891758,0.024240045,0.0008034794,0.00000584197],"about_ca_topic_score_codex":0.0020311659,"about_ca_topic_score_gemma":0.0023687673,"teacher_disagreement_score":0.0028244872,"about_ca_system_score_codex":0.00041800138,"about_ca_system_score_gemma":0.00037786804,"threshold_uncertainty_score":0.009448826},"labels":[],"label_agreement":null},{"id":"W3157712042","doi":"10.1109/icpr48806.2021.9413168","title":"Comparison of Stacking-based Classifier Ensembles using Euclidean and Riemannian Geometries","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stacking; Classifier (UML); Artificial intelligence; Computer science; Pattern recognition (psychology); Convolutional neural network; Machine learning; Random forest; Euclidean geometry; Artificial neural network; Algorithm; Mathematics; Geometry; Physics","score_opus":0.0878959423652703,"score_gpt":0.36023166554029307,"score_spread":0.27233572317502275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157712042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31388947,0.015390038,0.6414023,0.0011271827,0.0015413373,0.0007853234,0.0039493074,0.010689216,0.011225912],"genre_scores_gemma":[0.6821396,0.0030129338,0.29504764,0.00037562958,0.00056141475,0.00044368676,0.01216541,0.0006037761,0.0056499057],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99398094,0.0015026277,0.000608436,0.0013995141,0.0019822689,0.00052614964],"domain_scores_gemma":[0.9897775,0.0043961355,0.0006762501,0.0017671691,0.002861568,0.0005213987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063235504,0.0039318763,0.0030847182,0.0060467795,0.0013215415,0.002507874,0.002580471,0.0023637197,0.002809023],"category_scores_gemma":[0.01648529,0.000736749,0.0026636855,0.0027250785,0.00076150685,0.005194856,0.002366957,0.0024988854,0.0023802153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010151801,0.00040558612,0.015447291,0.00035365025,0.0010635213,0.00018058122,0.00018564978,0.21663949,0.0033148802,0.004261028,0.016005095,0.741128],"study_design_scores_gemma":[0.000046484467,0.0008084503,0.0041680993,0.00007972302,0.000267061,0.00018993655,0.0001870714,0.9765654,0.00781734,0.006207604,0.0035940395,0.000068684196],"about_ca_topic_score_codex":0.006400293,"about_ca_topic_score_gemma":0.0073873154,"teacher_disagreement_score":0.006400293,"about_ca_system_score_codex":0.0017562639,"about_ca_system_score_gemma":0.002046013,"threshold_uncertainty_score":0.033442497},"labels":[],"label_agreement":null},{"id":"W3159308542","doi":"10.1109/access.2021.3076668","title":"Expanded Neo-Fuzzy Adaptive Decayed Brain Emotional Learning Network for Online Time Series Predication","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Fuzzy logic; Artificial intelligence; Mean squared error; Machine learning; Mathematics; Statistics","score_opus":0.03092880170229588,"score_gpt":0.30178055625898076,"score_spread":0.27085175455668486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159308542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09860881,0.0006124143,0.89637923,0.00022294164,0.00007019548,0.000058171117,0.000051836698,0.00038539164,0.0036110142],"genre_scores_gemma":[0.944115,0.00019893616,0.052455064,0.00009148702,0.000017505889,0.000072630995,0.00008131655,0.000011268769,0.002956681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997843,0.000042580916,0.000016841808,0.000059246882,0.00006227552,0.000034740675],"domain_scores_gemma":[0.99970263,0.00010232731,0.00002840524,0.000024315577,0.00012585704,0.000016430444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006357303,0.00045272286,0.00043603065,0.00026764942,0.00026476136,0.00050672865,0.0008303544,0.00059983844,0.0012569651],"category_scores_gemma":[0.0012769288,0.0001700677,0.00049581024,0.00022881605,0.00035481603,0.00064467837,0.0006012176,0.0007425967,0.00016518794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023306395,0.00011660062,0.0027636662,0.0000844675,0.000094403,0.00018963621,0.00012494945,0.7954234,0.011270038,0.0077689947,0.0010515746,0.1808792],"study_design_scores_gemma":[0.0000037443358,0.000027485778,0.00016577373,0.0000024994754,0.0000073215792,0.000014020245,0.0000038842954,0.99812275,0.0006370043,0.00083038653,0.0001813762,0.0000036295569],"about_ca_topic_score_codex":0.003202279,"about_ca_topic_score_gemma":0.003260701,"teacher_disagreement_score":0.003202279,"about_ca_system_score_codex":0.000499183,"about_ca_system_score_gemma":0.00043135654,"threshold_uncertainty_score":0.006367326},"labels":[],"label_agreement":null},{"id":"W3160249802","doi":"10.1109/icsmc.2004.1400872","title":"Nonlinear state-space modeling using recurrent multilayer perceptrons with unscented Kalman filter","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Extended Kalman filter; Kalman filter; Computer science; Unscented transform; Control theory (sociology); Perceptron; Convergence (economics); Nonlinear system; State space; State-space representation; Generalization; Artificial neural network; Invariant extended Kalman filter; Recurrent neural network; Filter (signal processing); Algorithm; Artificial intelligence; Mathematics; Computer vision","score_opus":0.037404120814556095,"score_gpt":0.2812360978752415,"score_spread":0.2438319770606854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160249802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062563745,0.00014586875,0.992421,0.00004830247,0.000023779672,0.000011133293,0.0000275934,0.00044592883,0.00062003057],"genre_scores_gemma":[0.68981206,0.0006304655,0.3056922,0.000068073976,0.00006465911,0.00012461931,0.00019282263,0.00010331333,0.0033117621],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996296,0.000120571196,0.000034285535,0.000071905364,0.00011138726,0.00003217994],"domain_scores_gemma":[0.999519,0.00018812103,0.00011222651,0.000076697914,0.00009000908,0.000013941989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008919171,0.0006348124,0.0007054923,0.0002788385,0.0002665709,0.00063714175,0.0008852843,0.0007045339,0.00083743467],"category_scores_gemma":[0.0020417734,0.00041438406,0.0009558439,0.00037295063,0.00039704732,0.0011961819,0.0005988814,0.00095393514,0.0004104189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006580553,0.000024813238,0.0005769848,0.000069537775,0.000066706554,0.00007696469,0.00006822778,0.93823344,0.005065923,0.007961581,0.00036809975,0.047422003],"study_design_scores_gemma":[0.0000017003825,0.000009245609,0.00005447717,0.0000021700241,0.0000042505003,0.0000072444805,0.0000013659869,0.9981737,0.0005962925,0.0009771662,0.00016870392,0.000003606761],"about_ca_topic_score_codex":0.006572417,"about_ca_topic_score_gemma":0.006220567,"teacher_disagreement_score":0.006572417,"about_ca_system_score_codex":0.00046203792,"about_ca_system_score_gemma":0.0005007545,"threshold_uncertainty_score":0.013068318},"labels":[],"label_agreement":null},{"id":"W3160470259","doi":"10.18280/ts.380220","title":"Fast Job Recognition and Sorting Based on Image Processing","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sorting; Artificial intelligence; Computer science; Template matching; Matching (statistics); Wavelet; Pattern recognition (psychology); Image processing; Noise (video); Wavelet transform; Similarity (geometry); Computer vision; Image (mathematics); Algorithm; Mathematics","score_opus":0.030173374399731834,"score_gpt":0.2487763122712787,"score_spread":0.21860293787154686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160470259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042056993,0.0003565564,0.9513102,0.00009673716,0.00015796209,0.000091700684,0.0000745936,0.0018766468,0.003978486],"genre_scores_gemma":[0.3714655,0.00042838033,0.61928713,0.00016978153,0.00010979737,0.00011032091,0.00028043543,0.00013199277,0.008016673],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995302,0.00003520915,0.000019268602,0.00009297773,0.0002623544,0.000059900434],"domain_scores_gemma":[0.9996275,0.00009539984,0.000044922435,0.00006987047,0.00013296034,0.000029289546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031325337,0.00048723974,0.0005591404,0.0011176867,0.00038784457,0.0007735729,0.00080714433,0.00067955884,0.0016941725],"category_scores_gemma":[0.0007383318,0.00026350096,0.00037262915,0.0009852007,0.00045617542,0.0010459597,0.00062049914,0.00041412402,0.0012121674],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033861178,0.00012645072,0.0012305266,0.00015281086,0.000019644265,0.00016466179,0.00015481058,0.0137389805,0.25440577,0.004811898,0.0029105823,0.7219452],"study_design_scores_gemma":[0.00004167485,0.0003849979,0.006673276,0.000029780154,0.00004153772,0.0006084988,0.00015019631,0.68686384,0.28515893,0.0059583434,0.013990812,0.000098158314],"about_ca_topic_score_codex":0.0011295,"about_ca_topic_score_gemma":0.0012245814,"teacher_disagreement_score":0.0016941725,"about_ca_system_score_codex":0.00028578728,"about_ca_system_score_gemma":0.0005981888,"threshold_uncertainty_score":0.0056675673},"labels":[],"label_agreement":null},{"id":"W3160882100","doi":"10.1109/icassp39728.2021.9413943","title":"Collaborative Intelligence: Challenges and Opportunities","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Key (lock); Signal processing; Emphasis (telecommunications); Resilience (materials science); Data science; State (computer science); Computer security; Telecommunications","score_opus":0.08193814224678338,"score_gpt":0.28667394985194644,"score_spread":0.20473580760516308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160882100","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015531848,0.18940729,0.4070416,0.28297853,0.0035839886,0.00014603836,0.00021576569,0.0005092046,0.10058574],"genre_scores_gemma":[0.64990175,0.15375456,0.15060672,0.017674323,0.013250207,0.00047712316,0.00035570512,0.00018681902,0.013792778],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.987229,0.006341572,0.00052563025,0.0017208005,0.0032952928,0.0008877153],"domain_scores_gemma":[0.96598524,0.025158167,0.0010817628,0.003008699,0.0025873706,0.002178689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017486235,0.00076678983,0.0015348763,0.0016939903,0.0035992279,0.013206046,0.0037761293,0.007563016,0.0038714313],"category_scores_gemma":[0.023567077,0.0006029084,0.0006153651,0.0024966921,0.010455017,0.026876196,0.009017437,0.007861905,0.0014855016],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003876954,0.00005112808,0.00060905836,0.00048762647,0.000030160594,0.00012937214,0.00090863655,0.0028468089,0.00018166371,0.83698076,0.01604541,0.14169063],"study_design_scores_gemma":[0.0000125725655,0.000043808846,0.00023034653,0.0003921751,0.000010654884,0.0003315684,0.0015514821,0.013893535,0.00021316092,0.8862107,0.097072445,0.000037426016],"about_ca_topic_score_codex":0.0012720332,"about_ca_topic_score_gemma":0.0008245289,"teacher_disagreement_score":0.017486235,"about_ca_system_score_codex":0.0017779642,"about_ca_system_score_gemma":0.00305273,"threshold_uncertainty_score":0.09247714},"labels":[],"label_agreement":null},{"id":"W3161660208","doi":"10.1007/s002249910005","title":"The Characterization of Data-Accumulating Algorithms","year":2000,"lang":"en","type":"article","venue":"Theory of Computing Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Speedup; Algorithm; Computer science; Parallelism (grammar); Computation; Characterization (materials science); Class (philosophy); Geodetic datum; Unitary state; Parallel algorithm; Parallel computing; Artificial intelligence","score_opus":0.04788059028256705,"score_gpt":0.2927200907847261,"score_spread":0.24483950050215905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161660208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04467928,0.0016041845,0.9427801,0.0014676971,0.00016354313,0.00014691682,0.0002693741,0.00059187715,0.008297112],"genre_scores_gemma":[0.51148224,0.0023318236,0.46897027,0.0009407044,0.00080956786,0.0004444014,0.001339387,0.0008691768,0.01281243],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99375993,0.0019278107,0.00076680677,0.0013657976,0.0016402309,0.00053929834],"domain_scores_gemma":[0.9269058,0.04848177,0.004616203,0.009827108,0.008214624,0.0019545504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010004352,0.0014008958,0.0017548108,0.0038426935,0.0021344968,0.008473361,0.0056021786,0.0035171157,0.0060737557],"category_scores_gemma":[0.08555424,0.0011135553,0.0017898038,0.0041758204,0.0048197075,0.014773443,0.004532212,0.004561556,0.0014765476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030623234,0.0001188788,0.0025287576,0.00028965776,0.000053028663,0.000089162386,0.00029389415,0.034668438,0.002355405,0.891954,0.0032530979,0.06408935],"study_design_scores_gemma":[0.000042466887,0.00013697932,0.0005511903,0.0001027708,0.00004095549,0.0002590367,0.000072167546,0.3691851,0.0050481395,0.6194402,0.005084153,0.00003688292],"about_ca_topic_score_codex":0.0010440686,"about_ca_topic_score_gemma":0.00068652624,"teacher_disagreement_score":0.010004352,"about_ca_system_score_codex":0.0025548392,"about_ca_system_score_gemma":0.0036024835,"threshold_uncertainty_score":0.05290872},"labels":[],"label_agreement":null},{"id":"W3163997880","doi":"10.5281/zenodo.4778712","title":"Maximally Predictive Ensemble Dynamics from Data","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Computer science","score_opus":0.058261569343294746,"score_gpt":0.276793479839955,"score_spread":0.21853191049666026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163997880","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032671553,0.00044705594,0.0037170465,0.0003214552,0.00028362917,0.00009908202,0.98151714,0.0073528634,0.002994474],"genre_scores_gemma":[0.005346119,0.00013568392,0.0038573137,0.00007556248,0.00002272305,0.0002491773,0.98863286,0.0003446089,0.0013359364],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915826,0.00021794958,0.00006325393,0.000213774,0.00023518506,0.00011154176],"domain_scores_gemma":[0.9978259,0.0007039981,0.00009865449,0.0008720959,0.00038440546,0.000114869414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013658992,0.0031775862,0.0011599897,0.0023086413,0.00078421674,0.0017702153,0.0029619108,0.0022214127,0.039418835],"category_scores_gemma":[0.007476281,0.000659802,0.0024265724,0.0025474073,0.00055199175,0.001137384,0.0019472371,0.0026194856,0.04709849],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019908135,0.000103697654,0.00106257,0.00068873673,0.00013741347,0.000060494778,0.000031056676,0.012344244,0.00036802105,0.0016311939,0.9707422,0.012631358],"study_design_scores_gemma":[0.0013169434,0.00026712625,0.009371578,0.000618658,0.00020670834,0.00041008348,0.00012791896,0.088702366,0.004718164,0.023553422,0.8705234,0.00018360386],"about_ca_topic_score_codex":0.011371558,"about_ca_topic_score_gemma":0.020430954,"teacher_disagreement_score":0.039418835,"about_ca_system_score_codex":0.0011737976,"about_ca_system_score_gemma":0.0012912946,"threshold_uncertainty_score":0.1318692},"labels":[],"label_agreement":null},{"id":"W3164293641","doi":"10.1155/2021/9946573","title":"Comparative Study on Feature-Based Scoring Using Vector Space Modelling System","year":2021,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Taif University","keywords":"Novelty; Computer science; Cosine similarity; Similarity (geometry); Feature vector; Artificial intelligence; Data mining; Feature (linguistics); Notation; Vector space model; Machine learning; Function (biology); Novelty detection; Pattern recognition (psychology); Mathematics; Image (mathematics)","score_opus":0.05909417464206701,"score_gpt":0.27087848649377205,"score_spread":0.21178431185170504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164293641","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81742483,0.0011852306,0.16455783,0.00024288952,0.00013451745,0.00025326633,0.00042158592,0.0022344035,0.013545464],"genre_scores_gemma":[0.97574246,0.00022681926,0.021682793,0.000018264269,0.000018336172,0.000060813407,0.00046744017,0.00003488199,0.0017481904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968033,0.0012537815,0.0003103367,0.00032920754,0.001127345,0.00017605472],"domain_scores_gemma":[0.99622774,0.0016252613,0.0001636204,0.00032278264,0.0015743206,0.00008628285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003248893,0.00049428607,0.0007510247,0.0017002003,0.00044835598,0.0015170657,0.00065004046,0.00053836446,0.0030704646],"category_scores_gemma":[0.008830278,0.00012015423,0.00056477403,0.0013068763,0.00023237224,0.0016097395,0.00061904016,0.0002480723,0.00072483096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023645957,0.00066159334,0.029600168,0.0007836108,0.00035116976,0.00041865258,0.001283754,0.048063226,0.036029954,0.006588604,0.0060316073,0.867823],"study_design_scores_gemma":[0.00010883772,0.0027827122,0.06657939,0.00006981734,0.00040585894,0.00042987196,0.001225616,0.8777945,0.038056154,0.0035109215,0.008893441,0.00014289701],"about_ca_topic_score_codex":0.003752052,"about_ca_topic_score_gemma":0.0016357563,"teacher_disagreement_score":0.003752052,"about_ca_system_score_codex":0.00065718405,"about_ca_system_score_gemma":0.00044469777,"threshold_uncertainty_score":0.017182052},"labels":[],"label_agreement":null},{"id":"W3167805910","doi":"","title":"Parallelizing Legendre Memory Unit Training","year":2021,"lang":"en","type":"article","venue":"UWSpace (University of Waterloo)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Recurrent neural network; Leverage (statistics); Machine translation; Artificial intelligence; Benchmark (surveying); Inference; Transformer; Machine learning; Artificial neural network","score_opus":0.033739505906773146,"score_gpt":0.2117834451219137,"score_spread":0.17804393921514056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167805910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07414361,0.00094610156,0.8906832,0.00046867286,0.00047924015,0.00014163746,0.0004778213,0.021362748,0.011296981],"genre_scores_gemma":[0.57469857,0.0003373467,0.4049736,0.0004763995,0.00011419557,0.00023623767,0.0019971724,0.0011074053,0.016059062],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995665,0.00007298394,0.00002969597,0.00014864349,0.00010904525,0.00007321287],"domain_scores_gemma":[0.9995092,0.000119348326,0.000026272337,0.00016122621,0.00016128235,0.00002265975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074458256,0.000891774,0.0007108905,0.00051743025,0.00033543006,0.0007781969,0.0020186335,0.00075658166,0.0070682284],"category_scores_gemma":[0.0024803896,0.00039166858,0.00059052603,0.0005579846,0.00047167117,0.0017784779,0.0010257115,0.0013179893,0.0025439588],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033759838,0.00015013218,0.0014448767,0.00019494361,0.00012765588,0.00023787381,0.0001579224,0.20847833,0.03218227,0.013922333,0.02162026,0.72114575],"study_design_scores_gemma":[0.000021192054,0.000066624736,0.00025923466,0.000011889433,0.000022463099,0.00006294532,0.000026602778,0.9686636,0.021627005,0.0040943567,0.0051273946,0.000016708318],"about_ca_topic_score_codex":0.008042734,"about_ca_topic_score_gemma":0.016033782,"teacher_disagreement_score":0.008042734,"about_ca_system_score_codex":0.0007937332,"about_ca_system_score_gemma":0.0011557759,"threshold_uncertainty_score":0.02364552},"labels":[],"label_agreement":null},{"id":"W3168604437","doi":"10.48550/arxiv.2106.08443","title":"Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reproducing kernel Hilbert space; Representer theorem; Hilbert space; Eigenfunction; Kernel (algebra); Mathematics; Space (punctuation); Kernel method; Applied mathematics; Computer science; Algebra over a field; Calculus (dental); Artificial intelligence; Algorithm; Pure mathematics; Kernel principal component analysis; Eigenvalues and eigenvectors; Support vector machine; Physics","score_opus":0.08495214814039256,"score_gpt":0.2186239364882141,"score_spread":0.13367178834782154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168604437","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005294905,0.55945754,0.40322563,0.0032602379,0.0016479696,0.000056687273,0.00033252744,0.00054953544,0.026174977],"genre_scores_gemma":[0.10958039,0.69973636,0.16387905,0.0017090682,0.007182183,0.0002155251,0.000931696,0.00041349907,0.016352257],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993051,0.00020441516,0.000067398614,0.00013885333,0.0002456524,0.000038517745],"domain_scores_gemma":[0.9989594,0.00071461743,0.00007169779,0.00006323363,0.00015805596,0.000032944314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015863555,0.001303474,0.0013707066,0.0026421028,0.00035730313,0.0013566543,0.00069504295,0.0013506236,0.003360971],"category_scores_gemma":[0.0027668062,0.00052260625,0.0008087365,0.0041991402,0.0015819406,0.0041947975,0.00089851447,0.00244023,0.001579554],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066372195,0.00014702331,0.0013361907,0.0039451756,0.00017314652,0.0002665486,0.0004687778,0.015107616,0.0028658055,0.43815747,0.04901904,0.4884468],"study_design_scores_gemma":[0.000016774138,0.00018684221,0.002756409,0.00085568917,0.0000933483,0.0015989785,0.0002110388,0.04908577,0.0029434394,0.5413355,0.40078065,0.00013559488],"about_ca_topic_score_codex":0.0011050486,"about_ca_topic_score_gemma":0.0006158735,"teacher_disagreement_score":0.003360971,"about_ca_system_score_codex":0.0007611532,"about_ca_system_score_gemma":0.00089348387,"threshold_uncertainty_score":0.011243522},"labels":[],"label_agreement":null},{"id":"W3168905660","doi":"","title":"Efficient Statistical Tests: A Neural Tangent Kernel Approach","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kernel (algebra); Computer science; Artificial neural network; Artificial intelligence; Mathematics; Discrete mathematics","score_opus":0.021596919365886542,"score_gpt":0.2600193260423124,"score_spread":0.2384224066764259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168905660","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024869216,0.000078544705,0.99655056,0.00007501953,0.000016900727,0.00002400919,0.000040270494,0.00040643764,0.00032134575],"genre_scores_gemma":[0.25251952,0.0002588777,0.74060655,0.000112325564,0.00014398618,0.00028155054,0.0005821175,0.00056003075,0.0049351235],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99506444,0.002515139,0.00037091284,0.00055152306,0.0012688121,0.00022923207],"domain_scores_gemma":[0.97663474,0.016191987,0.00090289523,0.0030695025,0.0027515579,0.00044934443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062047425,0.0011124526,0.0022589755,0.0020440212,0.00074702885,0.0021333466,0.0040496965,0.0017151657,0.007147342],"category_scores_gemma":[0.03693935,0.0008804632,0.0012609372,0.0022478472,0.001467184,0.0040323325,0.003541344,0.0033053714,0.0021491998],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060188514,0.00031380868,0.0028586574,0.00027601916,0.000243765,0.00019491233,0.00014520227,0.244624,0.004680538,0.12121113,0.0052996743,0.61955047],"study_design_scores_gemma":[0.00001721928,0.000040472354,0.00033184857,0.000009182153,0.00001613234,0.00004273929,0.0000122764195,0.9664814,0.0009039965,0.03139761,0.00073367887,0.000013445886],"about_ca_topic_score_codex":0.0054795006,"about_ca_topic_score_gemma":0.0055856192,"teacher_disagreement_score":0.007147342,"about_ca_system_score_codex":0.0011900851,"about_ca_system_score_gemma":0.0032997066,"threshold_uncertainty_score":0.032814205},"labels":[],"label_agreement":null},{"id":"W3170614471","doi":"10.1109/iccicc50026.2020.9450265","title":"A Neural Network Based Algorithm Selector for Radar Task Scheduling","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Artificial neural network; Recurrent neural network; Scheduling (production processes); Schedule; Algorithm; Selection (genetic algorithm); Radar; Task (project management); Artificial intelligence; Machine learning; Mathematical optimization; Engineering; Mathematics","score_opus":0.022932948712594534,"score_gpt":0.2435568855287821,"score_spread":0.22062393681618758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170614471","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014004619,0.0002718887,0.9819775,0.000083390056,0.00006554319,0.000109125394,0.000053250264,0.002012774,0.001421936],"genre_scores_gemma":[0.30821124,0.00027600126,0.68595445,0.0002119364,0.00012559762,0.0003763151,0.0002994897,0.00027882648,0.0042662243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992342,0.00020382875,0.000060862618,0.0001619339,0.00026219423,0.00007695627],"domain_scores_gemma":[0.9989767,0.00036786462,0.00018353571,0.000077621014,0.0003499518,0.00004436268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015234113,0.00094471534,0.00083044777,0.0009330092,0.0004222232,0.00082355604,0.0011035164,0.0007498122,0.002876222],"category_scores_gemma":[0.0036588449,0.00031096162,0.000372005,0.0005918536,0.00031273178,0.00080403185,0.00040313607,0.000971248,0.0009862375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049674365,0.00018715319,0.0013550319,0.00013275488,0.00007950787,0.000081869155,0.00004698647,0.37948203,0.018772379,0.0046790014,0.004051656,0.59063494],"study_design_scores_gemma":[0.000023472596,0.00008241231,0.00018097826,0.0000068936747,0.000009988341,0.000029754145,0.000003938719,0.9921882,0.0058819065,0.0004950058,0.0010877798,0.000009614759],"about_ca_topic_score_codex":0.003300097,"about_ca_topic_score_gemma":0.0041592564,"teacher_disagreement_score":0.003300097,"about_ca_system_score_codex":0.0007203765,"about_ca_system_score_gemma":0.0014994786,"threshold_uncertainty_score":0.009621918},"labels":[],"label_agreement":null},{"id":"W3170889573","doi":"10.1088/2632-2153/ac9885","title":"Twin neural network regression is a semi-supervised regression algorithm","year":2022,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Vector Institute; University of Waterloo; Perimeter Institute","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Council Canada; Compute Canada","keywords":"Regression; Artificial neural network; Artificial intelligence; Computer science; Data point; Regression analysis; Training set; Point (geometry); Linear regression; Pattern recognition (psychology); Machine learning; Algorithm; Mathematics; Statistics","score_opus":0.009164884751049228,"score_gpt":0.2518464698018076,"score_spread":0.24268158505075835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170889573","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009532707,0.0001710453,0.9883762,0.00008598567,0.00004070078,0.000038066642,0.00004838055,0.0011237052,0.00058311934],"genre_scores_gemma":[0.31580418,0.0001789923,0.67808044,0.00018607071,0.00008931773,0.00019430727,0.00065974775,0.0004903182,0.004316488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981446,0.00078021095,0.0001016758,0.00047461645,0.0003982299,0.00010055874],"domain_scores_gemma":[0.9972506,0.0012896393,0.00023733117,0.00040714463,0.0007234555,0.0000918414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032619035,0.0010529221,0.0016006009,0.00089030986,0.00042457285,0.00094254233,0.0021061592,0.0014360489,0.0020993324],"category_scores_gemma":[0.006537292,0.0007321764,0.00095352984,0.0009116599,0.00080635137,0.0015496535,0.0014012961,0.0018404197,0.0011903923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003884952,0.00015754932,0.0017973466,0.00013700452,0.00021457626,0.00009950793,0.0000917436,0.62528694,0.007767144,0.008612594,0.0045745065,0.3508726],"study_design_scores_gemma":[0.0000050094495,0.000015860775,0.00006027548,0.000003365168,0.0000041233807,0.000013386154,0.0000016597294,0.99807835,0.00052916014,0.0010397149,0.0002461566,0.0000029825794],"about_ca_topic_score_codex":0.003748329,"about_ca_topic_score_gemma":0.0038236063,"teacher_disagreement_score":0.003748329,"about_ca_system_score_codex":0.0006176609,"about_ca_system_score_gemma":0.0011969603,"threshold_uncertainty_score":0.017250836},"labels":[],"label_agreement":null},{"id":"W3171498330","doi":"10.48550/arxiv.2106.07476","title":"Training Graph Neural Networks with 1000 Layers","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"King Abdullah University of Science and Technology","keywords":"Training (meteorology); Computer science; Graph; Artificial neural network; Engineering management; Library science; Engineering; Artificial intelligence; Meteorology; Theoretical computer science","score_opus":0.06147416077163236,"score_gpt":0.17494586882073343,"score_spread":0.11347170804910106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171498330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4581642,0.0056782346,0.46031797,0.003841309,0.0013941147,0.000452956,0.009957622,0.039621003,0.0205726],"genre_scores_gemma":[0.7326626,0.0011291332,0.23286836,0.0013995797,0.00013145269,0.00033942325,0.018606026,0.0012199694,0.011643454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994129,0.00011101075,0.00003331542,0.00024248341,0.00010375591,0.000096529795],"domain_scores_gemma":[0.9990638,0.00037547437,0.00007182745,0.00022804894,0.00020356225,0.00005730261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001009206,0.0020748205,0.00085579336,0.0010123767,0.0005737175,0.0009742132,0.0023147033,0.002016004,0.005013475],"category_scores_gemma":[0.004666677,0.0008737073,0.0016605462,0.001371223,0.0008394148,0.0026722471,0.0010194271,0.0029174334,0.0020028015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002653127,0.00023106264,0.0034678462,0.0003368209,0.00027059478,0.0001412075,0.00007181029,0.77590376,0.003905064,0.006760008,0.033304103,0.1753424],"study_design_scores_gemma":[0.000026378815,0.00003682506,0.00033464204,0.00001827256,0.00001825798,0.000018976443,0.000017879858,0.99021626,0.0016683983,0.0060287407,0.0016066564,0.000008706853],"about_ca_topic_score_codex":0.02066323,"about_ca_topic_score_gemma":0.03856276,"teacher_disagreement_score":0.02066323,"about_ca_system_score_codex":0.0025216162,"about_ca_system_score_gemma":0.0017247833,"threshold_uncertainty_score":0.0410859},"labels":[],"label_agreement":null},{"id":"W3171506507","doi":"10.1109/iccicc50026.2020.9450259","title":"Designing a Cost Function to Assess a Neural Network to Detect Distributed Denial of Service Attacks","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Denial-of-service attack; Function (biology); Computer science; Artificial neural network; Variance (accounting); Data mining; Component (thermodynamics); Measure (data warehouse); Service (business); The Internet; Artificial intelligence","score_opus":0.0634175671188904,"score_gpt":0.2835671416040132,"score_spread":0.2201495744851228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171506507","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054841008,0.00019489555,0.94295335,0.00024119765,0.000034483946,0.0000940287,0.000037771486,0.00025605957,0.0013472522],"genre_scores_gemma":[0.870667,0.0001828413,0.12686752,0.000095488445,0.00003631134,0.00024099379,0.00011478038,0.000059799604,0.0017353042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990024,0.0003536532,0.0000578177,0.00012634174,0.00035114257,0.00010861496],"domain_scores_gemma":[0.9980775,0.00093427615,0.00016266809,0.00009914584,0.0006688583,0.000057447185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024843095,0.0009947194,0.00072211656,0.0012433579,0.0004067714,0.0009345672,0.00090442004,0.0014414967,0.0009195519],"category_scores_gemma":[0.007306484,0.00028348522,0.0004896438,0.0005256209,0.00065318996,0.0014962592,0.0008398948,0.000783129,0.00021476217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010806391,0.00007920327,0.002200965,0.00005786727,0.00005732071,0.000053068175,0.000022822358,0.93818116,0.0063623902,0.00498172,0.00050583616,0.047389545],"study_design_scores_gemma":[0.0000014566311,0.000024021412,0.0002524629,0.0000029912196,0.0000044992257,0.000009889225,0.0000032943544,0.99806434,0.0008770485,0.0006843184,0.0000714734,0.000004186363],"about_ca_topic_score_codex":0.0041884664,"about_ca_topic_score_gemma":0.0022396413,"teacher_disagreement_score":0.0041884664,"about_ca_system_score_codex":0.0014933889,"about_ca_system_score_gemma":0.0008834642,"threshold_uncertainty_score":0.013138413},"labels":[],"label_agreement":null},{"id":"W3172058676","doi":"","title":"On Monotonic Linear Interpolation of Neural Network Parameters","year":2021,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Interpolation (computer graphics); Monotonic function; Artificial neural network; Linear interpolation; Computer science; Mathematics; Mathematical optimization; Applied mathematics; Artificial intelligence; Mathematical analysis; Pattern recognition (psychology)","score_opus":0.03636786631191663,"score_gpt":0.3014350248448471,"score_spread":0.26506715853293045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172058676","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009140187,0.00064215023,0.98627603,0.00014944948,0.00007169339,0.00003821248,0.00006328506,0.00029454392,0.0033244381],"genre_scores_gemma":[0.3017598,0.001362786,0.6834914,0.00030396183,0.00023229896,0.00024815943,0.00046928742,0.0006490817,0.011483199],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997895,0.0011187659,0.000112156,0.000334972,0.00040967917,0.0001294068],"domain_scores_gemma":[0.9920204,0.0058129574,0.0003207834,0.00093328604,0.00078179827,0.00013075076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053794472,0.0011236282,0.0012973542,0.0010669389,0.000519486,0.0012636689,0.0023474758,0.0013168663,0.0055011497],"category_scores_gemma":[0.019235544,0.00082372356,0.000921348,0.0013079765,0.0014618718,0.003474362,0.0029542095,0.003180988,0.0011602145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069611875,0.00012682893,0.0010231882,0.00044469142,0.00008941172,0.00016502426,0.00027400564,0.49349135,0.00834625,0.16888852,0.0043784305,0.32207623],"study_design_scores_gemma":[0.000009533668,0.000048818547,0.00012675255,0.000035804744,0.000011442028,0.00002918538,0.000017616896,0.96954614,0.0014538032,0.027406864,0.0013026452,0.000011408642],"about_ca_topic_score_codex":0.004450228,"about_ca_topic_score_gemma":0.0039232224,"teacher_disagreement_score":0.0055011497,"about_ca_system_score_codex":0.0010416463,"about_ca_system_score_gemma":0.001108179,"threshold_uncertainty_score":0.028449595},"labels":[],"label_agreement":null},{"id":"W3172526945","doi":"10.1109/tii.2021.3062036","title":"Spatiotemporal Prediction for Energy System of Steel Industry by Generalized Tensor Granularity Based Evolving Type-2 Fuzzy Neural Network","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Granularity; Robustness (evolution); Computer science; Fuzzy logic; Artificial neural network; Adaptability; Data mining; Cluster analysis; Artificial intelligence; Neuro-fuzzy; Fuzzy set; Fuzzy control system; Mathematical optimization; Mathematics","score_opus":0.03656403556839609,"score_gpt":0.24387182137482183,"score_spread":0.20730778580642573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172526945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17149669,0.0006004804,0.82482064,0.00030484123,0.00006782007,0.000030884756,0.00015412232,0.00022837662,0.0022961993],"genre_scores_gemma":[0.9761204,0.0001452257,0.022908287,0.00003259885,0.000015784564,0.00002531618,0.000090489775,0.00000871404,0.0006531751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984837,0.000027698637,0.000009829881,0.00005553912,0.00003461828,0.000024033432],"domain_scores_gemma":[0.99977976,0.00008901013,0.000040247778,0.00001599191,0.00006175438,0.0000132444775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042296428,0.00051248673,0.00039744982,0.00029645866,0.00025318775,0.00047381566,0.0006209888,0.00051061186,0.0006137187],"category_scores_gemma":[0.0011076038,0.00016872192,0.0004903243,0.0003359759,0.00028757317,0.00063515373,0.00040404257,0.0006169854,0.00006354606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057119698,0.00001861942,0.0020731862,0.000026437297,0.000026745154,0.0000563133,0.000034840366,0.96488434,0.0016111367,0.0028117823,0.0003685585,0.02803097],"study_design_scores_gemma":[5.966421e-7,0.0000026370467,0.000093623705,5.1304977e-7,0.0000012029847,0.000001692029,0.0000012580076,0.9994843,0.00006615807,0.00033026963,0.000017001918,8.4826644e-7],"about_ca_topic_score_codex":0.018501418,"about_ca_topic_score_gemma":0.012424692,"teacher_disagreement_score":0.018501418,"about_ca_system_score_codex":0.0006912678,"about_ca_system_score_gemma":0.0004765288,"threshold_uncertainty_score":0.03678751},"labels":[],"label_agreement":null},{"id":"W3172744965","doi":"10.2316/j.2021.206-0618","title":"BACK-PROPAGATION NEURAL NETWORK–BASED MODELLING FOR SOIL HEAVY METAL","year":2021,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Environmental science; Heavy metals; Backpropagation; Computer science; Artificial intelligence; Environmental chemistry; Chemistry","score_opus":0.02812564992378832,"score_gpt":0.269617798033951,"score_spread":0.24149214811016265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172744965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12731645,0.001776595,0.8577117,0.00042587562,0.00024851438,0.000083311614,0.0011799468,0.0016111389,0.009646439],"genre_scores_gemma":[0.9325192,0.001106058,0.051329724,0.00007250064,0.000046961868,0.00018723705,0.0007812224,0.00015422807,0.013802862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999876,0.000024148261,0.000010078943,0.000034839886,0.000037032227,0.000017732335],"domain_scores_gemma":[0.9996824,0.00016293029,0.000026707216,0.000015616706,0.00010236994,0.000010039735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028716685,0.00085763336,0.0009489328,0.00049419556,0.00041396593,0.00091888546,0.0013680761,0.0016151736,0.0028164457],"category_scores_gemma":[0.00090822484,0.00061098294,0.00067689543,0.0010380187,0.00039632237,0.0010241444,0.00049306115,0.0010204741,0.0008549172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025938247,0.000014815916,0.0003699972,0.000041923675,0.000021609909,0.00002231081,0.000011415937,0.9904365,0.0009997931,0.00033682527,0.00023093763,0.007487965],"study_design_scores_gemma":[0.0000015106108,0.0000033142146,0.0000952417,0.0000019686865,0.0000025654144,0.000002007349,0.0000016679656,0.99934953,0.00017600636,0.00023390041,0.0001302238,0.0000020680454],"about_ca_topic_score_codex":0.041830692,"about_ca_topic_score_gemma":0.031122204,"teacher_disagreement_score":0.041830692,"about_ca_system_score_codex":0.00082482473,"about_ca_system_score_gemma":0.0009321309,"threshold_uncertainty_score":0.08317441},"labels":[],"label_agreement":null},{"id":"W3173541058","doi":"10.1609/aaai.v35i7.16761","title":"Learning Intuitive Physics with Multimodal Generative Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Object (grammar); Human–computer interaction; Autoencoder; Motion (physics); Perception; Modality (human–computer interaction); Tactile sensor; Modalities; Generative model; Generative grammar; Deep learning; Robot","score_opus":0.020414495915912562,"score_gpt":0.23658533149075292,"score_spread":0.21617083557484035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173541058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035086956,0.00029131677,0.9608497,0.0004932657,0.00003638053,0.00003668825,0.00015741054,0.0005573604,0.0024909507],"genre_scores_gemma":[0.87378806,0.0004032907,0.11982634,0.00032740715,0.00009768505,0.00020467982,0.00038867103,0.0002130025,0.004750893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975663,0.000091856964,0.000008040699,0.00007267441,0.000043559194,0.000027372329],"domain_scores_gemma":[0.9987005,0.0010179047,0.000092641334,0.00008112441,0.000057356327,0.00005046991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080818834,0.0008454907,0.00078804244,0.00075336546,0.0003567723,0.0010918223,0.00095574645,0.0012312358,0.0031716065],"category_scores_gemma":[0.0036654442,0.0009820627,0.0012253433,0.0004032889,0.0011557989,0.0014238497,0.001562292,0.0016204363,0.00033052656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038253278,0.000026515268,0.0008825558,0.000048734077,0.000050590053,0.00006522044,0.00009041559,0.9461984,0.0010474699,0.030655464,0.0007754279,0.020120984],"study_design_scores_gemma":[0.0000036349368,0.0000058053947,0.000061329854,0.0000036437732,0.0000024188325,0.000006647107,0.0000044544545,0.98467743,0.000058361897,0.015016497,0.00015696818,0.0000028340276],"about_ca_topic_score_codex":0.0062028705,"about_ca_topic_score_gemma":0.009252968,"teacher_disagreement_score":0.0062028705,"about_ca_system_score_codex":0.00097343617,"about_ca_system_score_gemma":0.00058615836,"threshold_uncertainty_score":0.012333572},"labels":[],"label_agreement":null},{"id":"W3177786904","doi":"10.1016/j.simpa.2021.100105","title":"Generative locally linear embedding: A module for manifold unfolding and visualization","year":2021,"lang":"en","type":"article","venue":"Software Impacts","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonlinear dimensionality reduction; Dimensionality reduction; Manifold (fluid mechanics); Visualization; Embedding; Computer science; Nonlinear system; Manifold alignment; Artificial intelligence; Curse of dimensionality; Feature extraction; Isomap; Pattern recognition (psychology); Algorithm","score_opus":0.025209437874609577,"score_gpt":0.3135421521626475,"score_spread":0.288332714288038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177786904","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019747904,0.000029292782,0.94444543,0.00005248154,0.000021971147,0.000039749342,0.00033784506,0.052249413,0.00084900786],"genre_scores_gemma":[0.08964632,0.00013596096,0.87912196,0.00027598723,0.00005649542,0.00047061787,0.0030420893,0.020543525,0.0067070974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994479,0.000092933195,0.000038883565,0.00016248417,0.00020389704,0.000054051507],"domain_scores_gemma":[0.9988728,0.0004576067,0.0000783394,0.0003317933,0.00018122539,0.00007820743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010316898,0.0013046346,0.0005754204,0.0012230258,0.00040576173,0.001490615,0.0022125305,0.0011286701,0.022622347],"category_scores_gemma":[0.004862611,0.000686317,0.0013056073,0.0006733009,0.00070156506,0.002492072,0.0032235305,0.0016507619,0.008605185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068829156,0.00035042362,0.0038461133,0.00061269384,0.00039747453,0.00075751013,0.0010559296,0.054557376,0.06234045,0.053580955,0.07759523,0.74421763],"study_design_scores_gemma":[0.00009567485,0.0001194389,0.0015310955,0.00006187917,0.000045809473,0.0005472696,0.000095881915,0.76474226,0.10463156,0.059657548,0.06834482,0.00012670182],"about_ca_topic_score_codex":0.00065709697,"about_ca_topic_score_gemma":0.0012665571,"teacher_disagreement_score":0.022622347,"about_ca_system_score_codex":0.0004048005,"about_ca_system_score_gemma":0.00055567775,"threshold_uncertainty_score":0.0756793},"labels":[],"label_agreement":null},{"id":"W3184573320","doi":"10.1038/s41592-021-01232-1","title":"Graphical assessment of tests and classifiers","year":2021,"lang":"en","type":"article","venue":"Nature Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.021486921062327205,"score_gpt":0.43116926000491496,"score_spread":0.40968233894258776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184573320","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02864801,0.0006775467,0.95252144,0.0013748555,0.0002651902,0.00023470432,0.0019449649,0.003367131,0.010966099],"genre_scores_gemma":[0.77503103,0.00045526426,0.21191366,0.0006986532,0.00045037628,0.0005741939,0.004074979,0.00088426896,0.0059175105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9766717,0.013573521,0.00091547944,0.0026733926,0.005394701,0.00077120913],"domain_scores_gemma":[0.88120747,0.08775061,0.005122596,0.010727027,0.013776653,0.0014156369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01389783,0.0015569757,0.0015225542,0.006998193,0.0007170307,0.004898609,0.0028425,0.0030133545,0.016152699],"category_scores_gemma":[0.12004464,0.0005733201,0.001861364,0.002472509,0.002314526,0.004427897,0.0027597987,0.0022646012,0.0034960648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017803485,0.00039552813,0.03033627,0.0010399433,0.0006281963,0.00047825323,0.0004006258,0.1543302,0.007181788,0.19977902,0.032876074,0.5707737],"study_design_scores_gemma":[0.00012538415,0.00029613386,0.0071218386,0.00020062641,0.00017774582,0.00023202134,0.00013288118,0.6648722,0.006097836,0.31313112,0.0075434423,0.00006868788],"about_ca_topic_score_codex":0.0018829851,"about_ca_topic_score_gemma":0.0019397967,"teacher_disagreement_score":0.016152699,"about_ca_system_score_codex":0.0015796405,"about_ca_system_score_gemma":0.0014587707,"threshold_uncertainty_score":0.07349968},"labels":[],"label_agreement":null},{"id":"W3185543031","doi":"10.1017/9781108938051","title":"Machine Learning Fundamentals: A Concise Introduction","year":2021,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Variety (cybernetics); Transformer; Natural language processing; Engineering","score_opus":0.014705518128074965,"score_gpt":0.23333198973586414,"score_spread":0.21862647160778917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185543031","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012473542,0.19572002,0.28412464,0.011821584,0.019954113,0.00031701248,0.0036917794,0.004302498,0.478821],"genre_scores_gemma":[0.011725649,0.16574863,0.15109695,0.010534436,0.01893785,0.0007781055,0.0053254096,0.0031220932,0.6327309],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99931276,0.00009657372,0.000048007067,0.00013979242,0.00037151703,0.00003133992],"domain_scores_gemma":[0.9992403,0.00038745222,0.000034070625,0.00007359268,0.00021678809,0.000047798247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007343727,0.001911186,0.00095317705,0.0022318445,0.0006755779,0.0030696136,0.0012903627,0.0014776714,0.047527526],"category_scores_gemma":[0.0020268923,0.00085411646,0.00076508557,0.0029681933,0.0011074767,0.004536797,0.0013623955,0.0042186836,0.06136361],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019514868,0.000056878096,0.00012053087,0.00083837326,0.000017946883,0.00009279823,0.0001405423,0.0022535608,0.0011020845,0.11447535,0.6231023,0.2577802],"study_design_scores_gemma":[0.0000019682866,0.00001361714,0.00013711296,0.00019762012,0.0000025642128,0.00016320842,0.000015568103,0.0009612783,0.00014178929,0.047512144,0.95084333,0.000009767342],"about_ca_topic_score_codex":0.0007616647,"about_ca_topic_score_gemma":0.0010732873,"teacher_disagreement_score":0.047527526,"about_ca_system_score_codex":0.0010232681,"about_ca_system_score_gemma":0.00084795954,"threshold_uncertainty_score":0.15899539},"labels":[],"label_agreement":null},{"id":"W3185972108","doi":"","title":"Améliorer la généralisation de l'équité en apprentissage grâce à l'Optimisation Distributionnellement Robuste","year":2021,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Humanities; Mathematics; Art","score_opus":0.01666243276905302,"score_gpt":0.23295457846509693,"score_spread":0.2162921456960439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185972108","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019944191,0.0003599244,0.97576845,0.00056881574,0.00018817234,0.00003577184,0.000043947693,0.00055708707,0.0025336419],"genre_scores_gemma":[0.4628707,0.0008936384,0.49665174,0.0004907778,0.0005014698,0.00032624975,0.00032715738,0.0015760025,0.03636229],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872667,0.00046012492,0.000059063434,0.0002853623,0.00035340912,0.00011545546],"domain_scores_gemma":[0.99677,0.002140272,0.0001391096,0.00029035486,0.00055147585,0.00010872545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028428272,0.0020028367,0.001795843,0.00073982246,0.0006091659,0.0022631213,0.001205155,0.003913048,0.0053635533],"category_scores_gemma":[0.008961102,0.00074392505,0.001561764,0.00057395076,0.0017141292,0.0020710367,0.0019007763,0.0041408995,0.001619285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033559208,0.00010431383,0.00036349677,0.00020645338,0.0001459201,0.00025853873,0.00016301216,0.87561727,0.016240599,0.02279752,0.0027289337,0.081038386],"study_design_scores_gemma":[0.000021896423,0.000034464603,0.00016194611,0.000008828985,0.000010213379,0.000030380204,0.000010259966,0.9936079,0.0023858142,0.002533029,0.0011853719,0.000009853654],"about_ca_topic_score_codex":0.012944638,"about_ca_topic_score_gemma":0.008214264,"teacher_disagreement_score":0.012944638,"about_ca_system_score_codex":0.0015864725,"about_ca_system_score_gemma":0.0013859688,"threshold_uncertainty_score":0.025738597},"labels":[],"label_agreement":null},{"id":"W3186423796","doi":"","title":"Double Back-Propagation and Differential Machine Learning","year":2021,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Overfitting; Computer science; Convergence (economics); Taylor series; Backpropagation; Mean squared error; Artificial neural network; Interpolation (computer graphics); Applied mathematics; Stochastic gradient descent; Algorithm; Gradient descent; Mathematical optimization; Artificial intelligence; Mathematics; Statistics","score_opus":0.01566934731034529,"score_gpt":0.22297484205682053,"score_spread":0.20730549474647525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186423796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016645897,0.00036045793,0.9965557,0.00017473089,0.00007909579,0.000011363575,0.000024677678,0.00012585093,0.0010035702],"genre_scores_gemma":[0.255615,0.0015647907,0.7299853,0.00043330758,0.00030282387,0.00018753963,0.00021090877,0.00021453452,0.011485681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884975,0.00029498452,0.00008869036,0.00023260187,0.00043655405,0.00009744224],"domain_scores_gemma":[0.9980438,0.00091362087,0.00016851844,0.0002898204,0.0005091143,0.00007517982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027371817,0.001364512,0.0010190799,0.0010787786,0.00041551082,0.0013371144,0.0022341753,0.0018899569,0.0018333923],"category_scores_gemma":[0.007190727,0.0007246355,0.001127646,0.0010397037,0.0018353226,0.002422068,0.0022149833,0.002929856,0.0007789017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007569423,0.0000440407,0.00090246927,0.00025707448,0.000096297816,0.00014791929,0.00013745017,0.61331934,0.004459676,0.2383897,0.0019712737,0.14019907],"study_design_scores_gemma":[0.0000046116606,0.000024159848,0.000061161176,0.000014757328,0.0000073291963,0.00003531069,0.000003764258,0.97074294,0.0014583857,0.02528517,0.002351134,0.000011317306],"about_ca_topic_score_codex":0.005022284,"about_ca_topic_score_gemma":0.0034664974,"teacher_disagreement_score":0.005022284,"about_ca_system_score_codex":0.0016221444,"about_ca_system_score_gemma":0.0010836992,"threshold_uncertainty_score":0.014475763},"labels":[],"label_agreement":null},{"id":"W3192044085","doi":"10.26083/tuprints-00019052","title":"In Theory and Practice - On the Rate of Convergence of Implementable Neural Network Regression Estimates","year":2021,"lang":"en","type":"dissertation","venue":"TUbilio (Technical University of Darmstadt)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universität Stuttgart; Concordia University","keywords":"Artificial neural network; Gradient descent; Piecewise; Mathematics; Rate of convergence; Univariate; Nonparametric regression; Regression; Convergence (economics); Mathematical optimization; Multivariate statistics; Computer science; Artificial intelligence; Statistics","score_opus":0.015237562096618945,"score_gpt":0.2913119508511578,"score_spread":0.2760743887545388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192044085","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007906348,0.0015574291,0.9795989,0.002367615,0.00012837125,0.00008560997,0.00006789281,0.00033014917,0.007957696],"genre_scores_gemma":[0.39663374,0.00451645,0.5866498,0.0016030626,0.00060954,0.0013133201,0.00039544978,0.0010706261,0.007208012],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9753465,0.014789127,0.0012352206,0.0028083571,0.005002961,0.00081780634],"domain_scores_gemma":[0.84749514,0.12619494,0.004602217,0.013562539,0.007281609,0.00086356734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0328976,0.0025208185,0.0018609131,0.0019701894,0.00104796,0.0057624755,0.0033584007,0.005086121,0.0058717867],"category_scores_gemma":[0.20609172,0.0014115961,0.0018010776,0.0016477493,0.006500896,0.0101923,0.006722923,0.009729091,0.0018684559],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024687455,0.0001148864,0.002736834,0.00055460015,0.00014354353,0.0001384916,0.00040989686,0.16363326,0.0023087205,0.7577267,0.0027071272,0.06927908],"study_design_scores_gemma":[0.00008460126,0.00024941744,0.00073915516,0.00034608686,0.000047026435,0.00019442143,0.00010876919,0.5817357,0.0046225386,0.4042222,0.0076028677,0.00004723139],"about_ca_topic_score_codex":0.0017122494,"about_ca_topic_score_gemma":0.0007370063,"teacher_disagreement_score":0.0328976,"about_ca_system_score_codex":0.003572357,"about_ca_system_score_gemma":0.0025086508,"threshold_uncertainty_score":0.17398125},"labels":[],"label_agreement":null},{"id":"W3192289198","doi":"10.1109/cec45853.2021.9504995","title":"Complexity-Based Lambda Layer for Time Series Prediction","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Mitacs","keywords":"Computer science; Time series; Artificial intelligence; Dependency (UML); Series (stratigraphy); Algorithm; Lambda; Artificial neural network; Machine learning; Performic acid; Data mining","score_opus":0.03882802511716696,"score_gpt":0.2617908040216567,"score_spread":0.22296277890448976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192289198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08991233,0.0005842624,0.89848566,0.00029171904,0.00018172455,0.000121895246,0.0004410667,0.00591032,0.0040709805],"genre_scores_gemma":[0.72035563,0.00040356905,0.26699668,0.00018667804,0.000067436566,0.00025233076,0.00077701185,0.00026597432,0.010694843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996848,0.000045913952,0.000034701887,0.00007006706,0.00010623882,0.000058424976],"domain_scores_gemma":[0.99925655,0.00027322696,0.00006678196,0.000100077086,0.0002615722,0.000041661322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011436854,0.00082328886,0.0005153907,0.0005680329,0.00036757308,0.0010237688,0.0012262891,0.000694374,0.0052684136],"category_scores_gemma":[0.0026317865,0.00041042562,0.0007580742,0.00044033356,0.00028234185,0.0013789153,0.0008816264,0.001427472,0.0014383503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007484621,0.00027714527,0.0032737781,0.00021837949,0.00022736086,0.00023868153,0.00014948769,0.6282176,0.025887813,0.0109914085,0.0049658855,0.32480407],"study_design_scores_gemma":[0.0000064420806,0.000028520595,0.00036464937,0.000006176393,0.0000124649905,0.0000110853125,0.000004682228,0.9944041,0.003335369,0.0012235723,0.0005957926,0.0000070456354],"about_ca_topic_score_codex":0.007883357,"about_ca_topic_score_gemma":0.007652874,"teacher_disagreement_score":0.007883357,"about_ca_system_score_codex":0.0009835388,"about_ca_system_score_gemma":0.0011400247,"threshold_uncertainty_score":0.017624617},"labels":[],"label_agreement":null},{"id":"W3193033925","doi":"","title":"LibGuides: Martin - Quarter 4 Research Paper: Databases & Periodicals","year":2021,"lang":"en","type":"libguides","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Database; Computer science; World Wide Web; History; Archaeology","score_opus":0.09995944784397119,"score_gpt":0.36867052910323556,"score_spread":0.26871108125926435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193033925","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012068052,0.0037481417,0.002339771,0.0045729103,0.006721067,0.00014517906,0.06736913,0.01865032,0.89524674],"genre_scores_gemma":[0.005102054,0.003653603,0.0025112713,0.00058754504,0.0023665254,0.00013397232,0.047274154,0.008127747,0.93024296],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998329,0.0001781928,0.0001564852,0.00041072987,0.0007702912,0.00015531585],"domain_scores_gemma":[0.99316275,0.001076267,0.0004173974,0.0012703312,0.002899298,0.0011739671],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015581775,0.0018029972,0.0025416804,0.010461022,0.001970283,0.015437333,0.0021748566,0.0021756855,0.7215975],"category_scores_gemma":[0.012452204,0.00087787095,0.0009277522,0.017376568,0.00091437704,0.005155131,0.0027139669,0.0016615465,0.7052044],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036386387,0.000014160748,0.00007266019,0.00010865943,0.000005557319,0.0000103063,0.0000068892455,0.0000573861,0.000051774456,0.0019757785,0.9625335,0.03512699],"study_design_scores_gemma":[0.000034205023,0.000022281569,0.000731148,0.00026078566,0.000012846606,0.000056151177,0.000038723272,0.0006225231,0.00048395462,0.004209393,0.9935088,0.000019147265],"about_ca_topic_score_codex":0.007839241,"about_ca_topic_score_gemma":0.0072796126,"teacher_disagreement_score":0.2784025,"about_ca_system_score_codex":0.0025923771,"about_ca_system_score_gemma":0.0039738077,"threshold_uncertainty_score":0.39710736},"labels":[],"label_agreement":null},{"id":"W3196116926","doi":"10.1007/978-3-030-82922-3_7","title":"Spectral Analysis of Random Processes","year":2021,"lang":"en","type":"book-chapter","venue":"Studies in systems, decision and control","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fourier transform; Frequency domain; Fourier analysis; Mathematics; Spectral analysis; Statistical physics; Computer science; Mathematical analysis; Physics; Quantum mechanics; Spectroscopy","score_opus":0.038660005184533164,"score_gpt":0.309224551133561,"score_spread":0.27056454594902785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196116926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01822396,0.04405091,0.6984146,0.0036849268,0.00180274,0.00003440926,0.00024175088,0.00040048684,0.23314618],"genre_scores_gemma":[0.66564095,0.042194903,0.1045101,0.001435,0.0062276577,0.00019664459,0.00063898956,0.0005172465,0.17863856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996511,0.00013258816,0.000012673538,0.000055957855,0.00012657348,0.000021188933],"domain_scores_gemma":[0.99915385,0.0005426994,0.00004944047,0.00008444127,0.00012920098,0.000040307637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006641346,0.0006736655,0.00067742117,0.0011032784,0.00033940224,0.0016502833,0.0004325684,0.0007529663,0.005580936],"category_scores_gemma":[0.0026669241,0.00023541959,0.00042394554,0.000974779,0.0014155451,0.0013351077,0.00070078793,0.0014353346,0.0014270025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042654337,0.000007905418,0.00003744656,0.000049742466,0.000008271606,0.000012552036,0.00005208026,0.004578902,0.00058516,0.97887623,0.0046848576,0.011102491],"study_design_scores_gemma":[0.0000023880234,0.000006054686,0.00015595388,0.000023550274,0.0000039969946,0.00002792134,0.000020101226,0.029237626,0.00016287749,0.9564908,0.013862641,0.0000060028947],"about_ca_topic_score_codex":0.00057374896,"about_ca_topic_score_gemma":0.0003455657,"teacher_disagreement_score":0.005580936,"about_ca_system_score_codex":0.0006015512,"about_ca_system_score_gemma":0.00028905808,"threshold_uncertainty_score":0.018670082},"labels":[],"label_agreement":null},{"id":"W3197173456","doi":"10.71781/10933","title":"Advances in parameterisation, optimisation and pruning of neural networks","year":2020,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Insight SFI Research Centre for Data Analytics; Science Foundation Ireland; Compute Canada; Samsung; Canada Research Chairs; Nuance Foundation; Canadian Institute for Advanced Research","keywords":"Pruning; Artificial neural network; Computer science; Artificial intelligence; Deep neural networks; Machine learning; Mathematics; Biology; Horticulture","score_opus":0.005583749612858575,"score_gpt":0.183264452175427,"score_spread":0.17768070256256843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197173456","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013079738,0.012266565,0.96687585,0.00041580305,0.00019415059,0.000050565497,0.00010060476,0.00097652525,0.0060402653],"genre_scores_gemma":[0.4091392,0.02629685,0.546479,0.00038807018,0.00051209464,0.00034101293,0.0007651761,0.0009620331,0.015116514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988703,0.0003098479,0.00013962266,0.00021281099,0.00037766594,0.00008964212],"domain_scores_gemma":[0.99813616,0.00096329517,0.00016121045,0.00033037382,0.00037467512,0.00003422083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017498628,0.0015889684,0.0014588222,0.0012065219,0.00042593715,0.0018704437,0.002114354,0.0016996664,0.0019182112],"category_scores_gemma":[0.007249423,0.000803786,0.0012323563,0.0015809602,0.0007523449,0.0024022255,0.0011897223,0.002193949,0.0009828864],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008428666,0.000050702503,0.0008379444,0.00040941406,0.00013775418,0.00009513394,0.000099708646,0.6238913,0.007989943,0.01995286,0.0020931277,0.34435782],"study_design_scores_gemma":[0.000016024604,0.000056989822,0.00045947116,0.0000939925,0.000050835883,0.00010169315,0.000028268942,0.96405655,0.005691194,0.01647082,0.012950034,0.000024191286],"about_ca_topic_score_codex":0.007912343,"about_ca_topic_score_gemma":0.0077427374,"teacher_disagreement_score":0.007912343,"about_ca_system_score_codex":0.0013490147,"about_ca_system_score_gemma":0.0012023189,"threshold_uncertainty_score":0.015732586},"labels":[],"label_agreement":null},{"id":"W3197786435","doi":"10.1088/1755-1315/833/1/012065","title":"Rock mass performance monitoring with topology preserving quantised vector spaces","year":2021,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Laurentian University","funders":"","keywords":"Rock mass classification; Topology (electrical circuits); Metric (unit); Computer science; Mathematics; Geology; Engineering; Geotechnical engineering","score_opus":0.016038536020252975,"score_gpt":0.2132843567230036,"score_spread":0.19724582070275062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197786435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06167973,0.0001310731,0.9334645,0.000098437806,0.000035532365,0.000048347105,0.00027555614,0.0016635098,0.0026032766],"genre_scores_gemma":[0.7750801,0.00012886849,0.22237074,0.00003622901,0.00003198376,0.00008518548,0.0003551879,0.0001234348,0.0017882169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995072,0.00011920771,0.00003570993,0.00011801367,0.00018135898,0.00003848059],"domain_scores_gemma":[0.99918646,0.000262307,0.00014398275,0.00016048594,0.00021539324,0.000031428975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056439947,0.0004887813,0.0003887615,0.0013133954,0.00021710823,0.0015012975,0.00059955555,0.00032512544,0.0018200531],"category_scores_gemma":[0.0025191298,0.00018943634,0.00025491236,0.0011518575,0.0007665151,0.0016139138,0.0008999371,0.00046606635,0.0004329828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003349464,0.00006828838,0.0027222966,0.00016081652,0.00004583113,0.00011039894,0.00054032996,0.4762949,0.027086794,0.036959097,0.0017944768,0.4538818],"study_design_scores_gemma":[0.000009101246,0.00008623123,0.001565798,0.000022896598,0.00000766896,0.000040851206,0.00008275609,0.969475,0.009476132,0.016618574,0.0025869666,0.000028034161],"about_ca_topic_score_codex":0.0030447908,"about_ca_topic_score_gemma":0.0018913228,"teacher_disagreement_score":0.0030447908,"about_ca_system_score_codex":0.0005507168,"about_ca_system_score_gemma":0.00031766505,"threshold_uncertainty_score":0.006088674},"labels":[],"label_agreement":null},{"id":"W3198084506","doi":"10.1109/ijcnn54540.2023.10191403","title":"On the Space of Coefficients of a Feedforward Neural Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Feedforward neural network; Feed forward; Artificial neural network; Space (punctuation); Computer science; Control theory (sociology); Control engineering; Artificial intelligence; Control (management); Engineering","score_opus":0.021572903083660673,"score_gpt":0.24993611597741142,"score_spread":0.22836321289375075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198084506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08873311,0.00035671596,0.89703333,0.00037949465,0.00005525797,0.00005499783,0.0004454831,0.00018054536,0.012761047],"genre_scores_gemma":[0.7516774,0.00086725864,0.23786871,0.00019450225,0.00012452537,0.0003338929,0.0011717128,0.00022868319,0.0075333035],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985864,0.00042944172,0.000091162816,0.00023414077,0.00048224596,0.00017658777],"domain_scores_gemma":[0.99642426,0.0022465023,0.0003015021,0.00016928324,0.0006522876,0.00020618622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002288782,0.0010377651,0.000842662,0.0017012213,0.0006210497,0.0022276435,0.0013130334,0.0007749496,0.005684554],"category_scores_gemma":[0.011479068,0.00042798562,0.0007689302,0.0010613697,0.0020296122,0.0035837356,0.0017782366,0.002491731,0.0010137222],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010137054,0.0000315083,0.0005264577,0.0001356163,0.000020705647,0.00010033458,0.00021825924,0.07934476,0.0038728663,0.8944189,0.0010969318,0.020132259],"study_design_scores_gemma":[0.00001929053,0.000054108958,0.00018548277,0.000060949984,0.000012795599,0.000054797336,0.00006818124,0.36036864,0.0027468118,0.6344848,0.0019176415,0.000026587844],"about_ca_topic_score_codex":0.001918662,"about_ca_topic_score_gemma":0.0014675306,"teacher_disagreement_score":0.005684554,"about_ca_system_score_codex":0.0012980285,"about_ca_system_score_gemma":0.00101031,"threshold_uncertainty_score":0.019016743},"labels":[],"label_agreement":null},{"id":"W3198769081","doi":"10.1007/978-3-030-63591-6_12","title":"Comparing Regularization Techniques Applied to a Perceptron","year":2021,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Overfitting; Regularization (linguistics); Computer science; Artificial neural network; Artificial intelligence; Perceptron; Machine learning; Deep neural networks; Multilayer perceptron","score_opus":0.025079295666965016,"score_gpt":0.2531904974517191,"score_spread":0.2281112017847541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198769081","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19956964,0.0053984565,0.7824892,0.0010461952,0.0009718875,0.00019789847,0.00030509118,0.0021622963,0.007859244],"genre_scores_gemma":[0.5742041,0.0012091307,0.41515943,0.00024152099,0.0002839843,0.0002270266,0.00069060957,0.0008956242,0.00708858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959567,0.0023928639,0.0002268892,0.0003701615,0.0008623446,0.00019102414],"domain_scores_gemma":[0.98118985,0.014413899,0.000312269,0.0014240157,0.002360113,0.00029982283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00942133,0.0011503911,0.0015870158,0.0021213202,0.0007754346,0.001944844,0.0019570272,0.002799606,0.0027384288],"category_scores_gemma":[0.024014948,0.00050312956,0.0014520412,0.0021828795,0.0010098332,0.002531239,0.0016227916,0.0023490398,0.0005374738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023432602,0.0005111656,0.002290135,0.0004048144,0.0005898677,0.00006219425,0.0001518723,0.6497776,0.004761964,0.024432521,0.0063337004,0.308341],"study_design_scores_gemma":[0.000038446084,0.00022169559,0.00067552016,0.000016993687,0.000052364914,0.000014319321,0.00003429181,0.9881417,0.001114121,0.009077897,0.0005971186,0.00001545936],"about_ca_topic_score_codex":0.0063956706,"about_ca_topic_score_gemma":0.0051547424,"teacher_disagreement_score":0.00942133,"about_ca_system_score_codex":0.001352664,"about_ca_system_score_gemma":0.0013208236,"threshold_uncertainty_score":0.04982537},"labels":[],"label_agreement":null},{"id":"W3198959915","doi":"10.1109/mwscas47672.2021.9531921","title":"Frequency Response of Linear Time-Varying Circuits Using Iterated Laplace Transform","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Laplace transform; Iterated function; Laplace transform applied to differential equations; Inverse Laplace transform; Two-sided Laplace transform; Mathematics; Electronic circuit; Applied mathematics; Laplace–Stieltjes transform; Linear circuit; Algorithm; Computer science; Control theory (sociology); Fourier transform; Mathematical analysis; Equivalent circuit; Fractional Fourier transform; Fourier analysis; Engineering; Artificial intelligence; Electrical engineering; Voltage","score_opus":0.027935322815402806,"score_gpt":0.2722172767388131,"score_spread":0.24428195392341026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198959915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03372899,0.00028589298,0.95795786,0.00011559716,0.000031189018,0.000019564888,0.000028880555,0.00028530875,0.00754674],"genre_scores_gemma":[0.9066308,0.00061491044,0.086983226,0.00009121516,0.00003986925,0.000055676235,0.000075223346,0.00008188421,0.005427169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99987555,0.000031440035,0.0000046763544,0.00001814311,0.000059740254,0.0000104558185],"domain_scores_gemma":[0.99976355,0.00015505178,0.000022561073,0.000018296998,0.000034181467,0.0000064057103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002955604,0.00025600154,0.0002048119,0.00022213427,0.00009826109,0.00043611525,0.0003289347,0.00043544002,0.002934003],"category_scores_gemma":[0.001149715,0.00010941465,0.00025293682,0.00025178815,0.0003446994,0.00057320675,0.00030721922,0.00047396726,0.0003389694],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020300678,0.0000942259,0.0011448408,0.00031018446,0.0000716256,0.00045948123,0.0006353492,0.3745461,0.23757955,0.22037992,0.0016271757,0.16294846],"study_design_scores_gemma":[0.0000075226876,0.000048962756,0.00039011246,0.0000141865485,0.000007671688,0.00016116168,0.000026580356,0.9740363,0.007758094,0.015861409,0.001676686,0.000011389139],"about_ca_topic_score_codex":0.0006490957,"about_ca_topic_score_gemma":0.00049712486,"teacher_disagreement_score":0.002934003,"about_ca_system_score_codex":0.000247099,"about_ca_system_score_gemma":0.00015125655,"threshold_uncertainty_score":0.009815216},"labels":[],"label_agreement":null},{"id":"W3200427552","doi":"10.1109/ijcnn52387.2021.9533892","title":"Volume-preserving Recurrent Neural Networks (VPRNN)","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Recurrent neural network; Computer science; MNIST database; Sublinear function; Artificial neural network; Generalization; Artificial intelligence; Algorithm; Sequence (biology); Pattern recognition (psychology); Mathematics; Discrete mathematics","score_opus":0.018063681230117618,"score_gpt":0.2474682127166056,"score_spread":0.22940453148648798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200427552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019381234,0.00035216514,0.97770464,0.00010645349,0.00004385051,0.000028544115,0.00007498937,0.0006582782,0.0016498795],"genre_scores_gemma":[0.6560481,0.0008173047,0.33587793,0.00028999493,0.00011104706,0.00015117283,0.00059522846,0.00035262018,0.005756608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995166,0.00011801219,0.00003269245,0.0001497814,0.00012995461,0.000053004394],"domain_scores_gemma":[0.99887556,0.00048700915,0.00018538274,0.000235245,0.00017898646,0.000037782876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092196185,0.00083453703,0.0006809919,0.0003665015,0.00026227708,0.00076155184,0.0015198056,0.0007135206,0.00163259],"category_scores_gemma":[0.0040020016,0.0003728413,0.0006583033,0.0004699223,0.0009718743,0.0020717601,0.0009817235,0.0011872392,0.00059119734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015231638,0.000058190024,0.00090377673,0.00016776884,0.00010827199,0.000165122,0.00012404368,0.6290001,0.025336785,0.067438364,0.0026026303,0.27394268],"study_design_scores_gemma":[0.00000359261,0.000054866905,0.00011413181,0.000007072896,0.0000118560265,0.000054084518,0.0000057950638,0.97873926,0.004735966,0.015205254,0.0010592052,0.000008896215],"about_ca_topic_score_codex":0.002091395,"about_ca_topic_score_gemma":0.00291339,"teacher_disagreement_score":0.002091395,"about_ca_system_score_codex":0.00057994464,"about_ca_system_score_gemma":0.0006841042,"threshold_uncertainty_score":0.0054615736},"labels":[],"label_agreement":null},{"id":"W3200732009","doi":"10.21203/rs.3.rs-790408/v1","title":"Modeling of Chaotic Processes by Means of Antisymmetric Neural ODEs","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Native Mental Health Association of Canada","funders":"","keywords":"Chaotic; Antisymmetric relation; Ode; Computer science; Mathematics; Applied mathematics; Econometrics; Artificial intelligence; Mathematical physics","score_opus":0.09659594823893142,"score_gpt":0.38270555022868535,"score_spread":0.28610960198975394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200732009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11098643,0.00011963213,0.8842735,0.00010772587,0.00003745767,0.00003495293,0.000046760015,0.00015117455,0.0042421794],"genre_scores_gemma":[0.91027576,0.00021792356,0.085478134,0.000023969247,0.000023100625,0.00007591296,0.00004077804,0.000026853553,0.0038375254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998845,0.000044549244,0.000008531132,0.000018597704,0.000033586923,0.000010136353],"domain_scores_gemma":[0.999838,0.000055029668,0.000035883557,0.00002132197,0.00003834073,0.000011408986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003347308,0.00031143188,0.00029735544,0.0002822805,0.00018974635,0.00033976484,0.00047542265,0.00037785523,0.0008979479],"category_scores_gemma":[0.00070523954,0.00013027768,0.000323824,0.00018607435,0.00046626388,0.0006222572,0.0003976998,0.00038671336,0.00014096586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033817247,0.000023663515,0.00053587987,0.000041634503,0.000017520248,0.00009813391,0.000048669634,0.92396104,0.019099278,0.04735857,0.00016281643,0.008619094],"study_design_scores_gemma":[0.0000012302313,0.0000070751394,0.00003474097,0.000001072586,0.0000012040722,0.0000089348705,0.0000015384039,0.9969041,0.00069672975,0.0021665646,0.0001753993,0.0000014102977],"about_ca_topic_score_codex":0.0011475589,"about_ca_topic_score_gemma":0.0009708784,"teacher_disagreement_score":0.0011475589,"about_ca_system_score_codex":0.00028108485,"about_ca_system_score_gemma":0.00027062953,"threshold_uncertainty_score":0.0030038953},"labels":[],"label_agreement":null},{"id":"W3201587969","doi":"","title":"Conformal Embedding Flows: Tractable Density Estimation on Learned Manifolds","year":2021,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Conformal map; Embedding; Estimation; Computer science; Mathematics; Mathematical optimization; Mathematical analysis; Artificial intelligence; Economics","score_opus":0.03983173517339811,"score_gpt":0.3204656192178434,"score_spread":0.2806338840444453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201587969","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014470274,0.00038001582,0.9823412,0.0005001215,0.000054630687,0.00004660816,0.00014926305,0.00038042883,0.0016775649],"genre_scores_gemma":[0.61684877,0.0014059333,0.36875907,0.00051597494,0.00038703214,0.00033657489,0.0011877042,0.00068807584,0.009870816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904066,0.0004339365,0.00004365496,0.00021265983,0.00019757228,0.000071465845],"domain_scores_gemma":[0.99240315,0.0058672377,0.00034327107,0.0007032833,0.0004280258,0.00025500084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025834586,0.0014329412,0.0018167807,0.0012985131,0.0007098138,0.0020805546,0.0023195622,0.0027627253,0.004451802],"category_scores_gemma":[0.022592897,0.0015548866,0.0009744314,0.001325268,0.0018934861,0.0053120893,0.0048906147,0.0040806876,0.00076176744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017230613,0.000106438376,0.00148412,0.0002867821,0.00007255027,0.00017986716,0.00021349959,0.49915177,0.001837175,0.39478782,0.008899678,0.092807904],"study_design_scores_gemma":[0.000007711456,0.00000775627,0.000058131372,0.000007031425,0.0000032803202,0.000013797435,0.000008053961,0.8994314,0.00013823519,0.099918135,0.00040172608,0.0000047466874],"about_ca_topic_score_codex":0.004749611,"about_ca_topic_score_gemma":0.0036346156,"teacher_disagreement_score":0.004749611,"about_ca_system_score_codex":0.0015136832,"about_ca_system_score_gemma":0.0011504473,"threshold_uncertainty_score":0.014892817},"labels":[],"label_agreement":null},{"id":"W3205221690","doi":"","title":"How Chaotic Are Recurrent Neural Networks","year":2020,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Recurrent neural network; Computer science; Chaotic; CHAOS (operating system); Work (physics); Process (computing); Artificial neural network; Artificial intelligence; Engineering","score_opus":0.07907329506073459,"score_gpt":0.3174880274877981,"score_spread":0.2384147324270635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205221690","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6794097,0.007409147,0.23268925,0.015026548,0.00066927314,0.00006595672,0.0011247471,0.0012294232,0.062376004],"genre_scores_gemma":[0.990209,0.0011192801,0.0055488315,0.00032981124,0.00011603617,0.000015835953,0.0001730392,0.000129633,0.0023586233],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992229,0.0002481972,0.000049529306,0.00020311507,0.00019684673,0.00007932526],"domain_scores_gemma":[0.9950836,0.0025208117,0.00091198453,0.00053637865,0.0007750334,0.00017218868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011934589,0.00028603507,0.00044330064,0.00062776037,0.0005332957,0.0025717744,0.00054232863,0.0011708775,0.0025240497],"category_scores_gemma":[0.023144638,0.00038568198,0.0003229552,0.00059491326,0.0013760928,0.005403719,0.00071010843,0.0007563845,0.0006004554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068835955,0.00008314841,0.06899139,0.0008807146,0.0005872981,0.0012042247,0.0040491936,0.20171921,0.015368741,0.5094661,0.020405298,0.17655641],"study_design_scores_gemma":[0.000058218808,0.000084436855,0.016979342,0.00019387585,0.00014327037,0.0008554869,0.0012745836,0.2950324,0.006229771,0.65903974,0.019971466,0.00013744226],"about_ca_topic_score_codex":0.0027004608,"about_ca_topic_score_gemma":0.0014539582,"teacher_disagreement_score":0.0027004608,"about_ca_system_score_codex":0.0007191741,"about_ca_system_score_gemma":0.0005207969,"threshold_uncertainty_score":0.008443773},"labels":[],"label_agreement":null},{"id":"W3206780676","doi":"10.14428/esann/2021.es2021-158","title":"Temperature as a Regularizer for Semantic Segmentation","year":2021,"lang":"en","type":"article","venue":"ESANN 2021 proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Natural language processing; Image segmentation; Pattern recognition (psychology)","score_opus":0.009165308501573871,"score_gpt":0.2524738199965949,"score_spread":0.24330851149502103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206780676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036998566,0.00039071063,0.9531257,0.00048061647,0.00014528495,0.000056229153,0.00022086481,0.0044901907,0.004091897],"genre_scores_gemma":[0.640632,0.00026461662,0.34824196,0.0006658135,0.00012579691,0.00017211822,0.0010991719,0.0019620326,0.006836685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986613,0.00031973433,0.00006309743,0.00050610286,0.00028549877,0.00016430973],"domain_scores_gemma":[0.9986884,0.00047475545,0.000119104596,0.0004174632,0.00022952081,0.00007083151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002027719,0.0013215219,0.00095504103,0.0007617217,0.0005555305,0.0015156022,0.0014872524,0.0016488511,0.0029317525],"category_scores_gemma":[0.0052678464,0.0006321667,0.0010018708,0.000657842,0.001514803,0.0028538012,0.0022100874,0.0033114185,0.0013832782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045841787,0.00014604136,0.0015161291,0.00012238875,0.0001344879,0.00011062262,0.00014323906,0.7483175,0.029640285,0.02987758,0.00803283,0.18150046],"study_design_scores_gemma":[0.000008270745,0.000036725352,0.00022002011,0.000010864879,0.000013036959,0.000049688686,0.000011308445,0.97456884,0.0121713085,0.011317093,0.0015806376,0.000012115494],"about_ca_topic_score_codex":0.0023642168,"about_ca_topic_score_gemma":0.003075491,"teacher_disagreement_score":0.0029317525,"about_ca_system_score_codex":0.0014044478,"about_ca_system_score_gemma":0.0010159177,"threshold_uncertainty_score":0.01072371},"labels":[],"label_agreement":null},{"id":"W3207172758","doi":"10.1007/978-3-030-70296-0_87","title":"A Hybrid Chaotic Activation Function for Artificial Neural Networks","year":2021,"lang":"en","type":"book-chapter","venue":"Transactions on computational science and computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Sigmoid function; Chaotic; Artificial neural network; Activation function; Logistic function; Artificial neuron; Neuron; Computer science; Function (biology); Logistic map; Artificial intelligence; Biological neuron model; Neuroscience; Psychology; Machine learning; Biology","score_opus":0.0434444603108069,"score_gpt":0.27441997343694974,"score_spread":0.23097551312614284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207172758","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011409639,0.0020439748,0.9776178,0.00016374887,0.00018182861,0.000026344434,0.00005308943,0.00035649037,0.008147012],"genre_scores_gemma":[0.5386744,0.0031289116,0.41936862,0.00018616601,0.00024421565,0.00019916549,0.00026452664,0.0002978669,0.037636098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999169,0.000021233694,0.000006124665,0.00001258422,0.000034881672,0.000008308655],"domain_scores_gemma":[0.9999176,0.000028451748,0.000005254639,0.000011762232,0.00003180185,0.0000050496274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019980627,0.00036047457,0.00037063364,0.0002562477,0.00022225462,0.00051255926,0.00059092214,0.00064248464,0.0025353718],"category_scores_gemma":[0.000380697,0.00015282967,0.00028795341,0.00048827118,0.0002841853,0.00056683057,0.0005085131,0.0005405742,0.00073345157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002246433,0.000045406705,0.0003316532,0.00035936126,0.000084766194,0.00017985079,0.00009235076,0.5034835,0.05498306,0.08138354,0.006820298,0.35201147],"study_design_scores_gemma":[0.000005393734,0.000029161813,0.00008518344,0.000009376494,0.000008155744,0.000055397424,0.000005255099,0.9858986,0.0028234816,0.005866707,0.0052065216,0.000006705816],"about_ca_topic_score_codex":0.00077416614,"about_ca_topic_score_gemma":0.000870326,"teacher_disagreement_score":0.0025353718,"about_ca_system_score_codex":0.00028446887,"about_ca_system_score_gemma":0.00024582585,"threshold_uncertainty_score":0.008481681},"labels":[],"label_agreement":null},{"id":"W3209208313","doi":"10.5281/zenodo.204453","title":"Computer Generated-Human Morph Stimuli","year":2016,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Communication; Artificial intelligence; Psychology","score_opus":0.04789732021344297,"score_gpt":0.25875994292615434,"score_spread":0.21086262271271136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209208313","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41329858,0.0025150843,0.08974011,0.0010094844,0.002349147,0.024876041,0.2515516,0.012137496,0.20252247],"genre_scores_gemma":[0.48581025,0.0019070413,0.12131173,0.002731717,0.00039593666,0.031163257,0.27237517,0.00420641,0.08009842],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996136,0.000081829494,0.00004564629,0.000105554034,0.0001193897,0.000034067263],"domain_scores_gemma":[0.99940753,0.00025704247,0.000021294241,0.00017230686,0.00010598352,0.000035764882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051456725,0.0009281341,0.0005922449,0.00048353735,0.00028220055,0.0005553179,0.0009324708,0.0007174351,0.115541086],"category_scores_gemma":[0.0022634754,0.0003420796,0.00033080837,0.0005333956,0.00027807258,0.00045931464,0.00086232385,0.00059863145,0.0162859],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00894335,0.0023305516,0.0027099238,0.004457101,0.00016717995,0.0014834655,0.0006951706,0.0068887533,0.23960583,0.01246137,0.4017121,0.3185452],"study_design_scores_gemma":[0.002930016,0.0034319186,0.0852762,0.0006093771,0.0002853791,0.010927296,0.0005609467,0.03330546,0.084000446,0.018410759,0.75977755,0.00048471827],"about_ca_topic_score_codex":0.000990926,"about_ca_topic_score_gemma":0.0013243014,"teacher_disagreement_score":0.115541086,"about_ca_system_score_codex":0.00042212926,"about_ca_system_score_gemma":0.00020189965,"threshold_uncertainty_score":0.38652343},"labels":[],"label_agreement":null},{"id":"W3209488382","doi":"10.1049/pbte098e_ch5","title":"Convolutional neural networks","year":2021,"lang":"en","type":"book-chapter","venue":"IET eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Convolutional neural network; Pooling; Convolution (computer science); Computer science; Padding; STRIDE; Layer (electronics); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Materials science","score_opus":0.02182879966860502,"score_gpt":0.22223883868926486,"score_spread":0.20041003902065985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209488382","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004502637,0.06295412,0.75454265,0.0026972892,0.0027388262,0.000108630804,0.0025208918,0.003627947,0.16630708],"genre_scores_gemma":[0.09488834,0.11816662,0.48882446,0.0022444776,0.0015749331,0.00031973567,0.008124964,0.0016907663,0.2841657],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99975175,0.000026399079,0.000014492497,0.00006399593,0.000117012954,0.000026315327],"domain_scores_gemma":[0.9998259,0.000068555666,0.000010536216,0.000026174655,0.000055305423,0.000013588196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002872342,0.0010165311,0.00054396095,0.00072690786,0.0003511747,0.0015165046,0.0010258113,0.0011163816,0.025876567],"category_scores_gemma":[0.0012285783,0.0003684458,0.0004802022,0.0014522867,0.00047219207,0.002099445,0.00084650563,0.0015587475,0.016697217],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045925983,0.00004869852,0.00040276413,0.0009064876,0.000079087426,0.00018727519,0.00010104252,0.039471906,0.008651151,0.24280335,0.13001518,0.5772872],"study_design_scores_gemma":[0.0000088242205,0.000037758688,0.0005020654,0.00034833202,0.000038892125,0.00038448864,0.00003139738,0.09197789,0.006436398,0.17536214,0.7248276,0.000044280816],"about_ca_topic_score_codex":0.0021944905,"about_ca_topic_score_gemma":0.0037873155,"teacher_disagreement_score":0.025876567,"about_ca_system_score_codex":0.00079667004,"about_ca_system_score_gemma":0.0007266824,"threshold_uncertainty_score":0.08656567},"labels":[],"label_agreement":null},{"id":"W3209680301","doi":"10.1155/2021/3394666","title":"Chaotic Behavior Analysis of a New Incommensurate Fractional‐Order Hopfield Neural Network System","year":2021,"lang":"en","type":"article","venue":"Complexity","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Taif University","keywords":"Attractor; Phase portrait; Chaotic; Multistability; Lyapunov exponent; Artificial neural network; Phase space; Bifurcation; Hopfield network; Statistical physics; Mathematics; Computer science; Applied mathematics; Mathematical analysis; Physics; Nonlinear system; Artificial intelligence","score_opus":0.07348130697687542,"score_gpt":0.3013420973259188,"score_spread":0.22786079034904339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209680301","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76072395,0.0008359958,0.22192743,0.00037509436,0.00006619742,0.000037337286,0.00009461295,0.00016332661,0.015776027],"genre_scores_gemma":[0.99469334,0.000099857476,0.004094957,0.0000105333165,0.000005314993,0.000010928104,0.00001660686,0.0000023402893,0.0010660682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992883,0.00001302268,0.0000037847808,0.000017028406,0.000023658604,0.000013725941],"domain_scores_gemma":[0.99993265,0.000015340853,0.000019429142,0.000005989464,0.00001880567,0.000007707865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018067355,0.00028927144,0.0003062854,0.0003007207,0.0003827968,0.00047913688,0.00043610422,0.0005445158,0.0007075587],"category_scores_gemma":[0.0003327435,0.0000997892,0.00027882998,0.00021472081,0.0004403659,0.00046579988,0.0002848583,0.00025103524,0.00004088487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011452191,0.0000388862,0.002592171,0.000110522575,0.00006988471,0.0006828044,0.00020371549,0.93621737,0.024202459,0.021539824,0.0003585343,0.013869257],"study_design_scores_gemma":[0.000003446004,0.00001326356,0.00032502107,0.0000021971355,0.000004939196,0.00002704801,0.000008363818,0.9978923,0.00052038394,0.00106005,0.00013919333,0.000003781578],"about_ca_topic_score_codex":0.0065227803,"about_ca_topic_score_gemma":0.0034677726,"teacher_disagreement_score":0.0065227803,"about_ca_system_score_codex":0.0006554427,"about_ca_system_score_gemma":0.0003455155,"threshold_uncertainty_score":0.012969613},"labels":[],"label_agreement":null},{"id":"W3209845743","doi":"10.5281/zenodo.2621560","title":"fcomitani/SimpSOM: v1.3.4","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Computer science","score_opus":0.023049068504998386,"score_gpt":0.23555566279559353,"score_spread":0.21250659429059515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209845743","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026223864,0.00031759593,0.27317548,0.00024974206,0.00029464057,0.00017788964,0.044823803,0.6680597,0.010278696],"genre_scores_gemma":[0.059766445,0.00097164634,0.46644145,0.0012474281,0.00018538527,0.0020550783,0.14113902,0.29467967,0.033513945],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995383,0.000049846694,0.000033333177,0.000113806585,0.00017095718,0.000093751616],"domain_scores_gemma":[0.9993801,0.00020306297,0.00003370513,0.000155426,0.00016033897,0.0000672556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009977672,0.0020306748,0.0012755024,0.0013198169,0.00087577227,0.0019646278,0.0056862794,0.0012753225,0.09535722],"category_scores_gemma":[0.0038054266,0.0013439219,0.0022536071,0.0014577445,0.00074855244,0.0026781121,0.0029550958,0.0030012967,0.054283697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004402438,0.00013116295,0.0013015682,0.002025366,0.00033754038,0.00024260403,0.000377596,0.017310848,0.008741891,0.013791605,0.8450124,0.1102872],"study_design_scores_gemma":[0.00036813872,0.0000929656,0.0021145067,0.0003213265,0.00014260973,0.0004359045,0.00012739094,0.31384805,0.038711835,0.05795151,0.5855836,0.00030205396],"about_ca_topic_score_codex":0.008277323,"about_ca_topic_score_gemma":0.013024193,"teacher_disagreement_score":0.09535722,"about_ca_system_score_codex":0.0008864238,"about_ca_system_score_gemma":0.0019539245,"threshold_uncertainty_score":0.31900167},"labels":[],"label_agreement":null},{"id":"W3209956318","doi":"10.11648/j.ajcst.20210403.13","title":"Activation Maximization with a Prior in Speech Data","year":2021,"lang":"en","type":"article","venue":"American Journal of Computer Science and Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Spectrogram; Computer science; Visualization; Speech recognition; Artificial intelligence; Feature (linguistics); Artificial neural network; Generative model; Pattern recognition (psychology); Generator (circuit theory); Data set; Natural language processing; Generative grammar","score_opus":0.0117489391602111,"score_gpt":0.2504850406289335,"score_spread":0.2387361014687224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209956318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022739446,0.0005803945,0.97336584,0.00065998564,0.000040314862,0.00003507145,0.00013151036,0.0003552226,0.0020923296],"genre_scores_gemma":[0.77956706,0.00094510894,0.20594344,0.00047740736,0.00015142508,0.00030526635,0.0006308479,0.00033030874,0.011649154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926454,0.00036215418,0.000033892396,0.0001805738,0.0000949954,0.00006380464],"domain_scores_gemma":[0.99703836,0.0023705652,0.00012677496,0.00020014466,0.00020125404,0.00006285165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002661672,0.0009233702,0.00077392237,0.000436604,0.0002877023,0.0008077548,0.0009162338,0.0013169133,0.0031553393],"category_scores_gemma":[0.0067856945,0.0005758256,0.0006678963,0.0004986906,0.0013988494,0.0014866139,0.0014367328,0.002409981,0.00073942845],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015481214,0.00004447001,0.00057009776,0.00016393563,0.00004184277,0.00013369933,0.00013955998,0.90157866,0.0048844316,0.03707924,0.0019015723,0.05330769],"study_design_scores_gemma":[0.0000041691496,0.000014095539,0.00011271878,0.000008719985,0.0000036521787,0.000015689659,0.0000062508266,0.988952,0.0011108369,0.009248559,0.0005188966,0.0000044979324],"about_ca_topic_score_codex":0.0024497968,"about_ca_topic_score_gemma":0.0024433485,"teacher_disagreement_score":0.0031553393,"about_ca_system_score_codex":0.00088696706,"about_ca_system_score_gemma":0.00079960725,"threshold_uncertainty_score":0.014076471},"labels":[],"label_agreement":null},{"id":"W3210120391","doi":"10.1080/08839514.2021.1972251","title":"Neural Network Architecture Selection Using Particle Swarm Optimization Technique","year":2021,"lang":"en","type":"article","venue":"Applied Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Hyperparameter; Artificial neural network; Particle swarm optimization; Selection (genetic algorithm); Artificial intelligence; Field (mathematics); Hyperparameter optimization; Machine learning; Pattern recognition (psychology); Algorithm; Mathematics; Support vector machine","score_opus":0.041315197071781676,"score_gpt":0.2807478089014073,"score_spread":0.23943261182962566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210120391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033472735,0.00050034584,0.96154314,0.00013710326,0.00006696085,0.00014187298,0.00003883977,0.00059627515,0.0035027075],"genre_scores_gemma":[0.63065624,0.0005851277,0.3635618,0.00012505535,0.00006217657,0.0004706451,0.00018263541,0.00007665527,0.0042796507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999686,0.00007866509,0.000022303113,0.000066092405,0.000106179185,0.000040802584],"domain_scores_gemma":[0.99970067,0.000113602975,0.000037810478,0.000019828578,0.00011741239,0.000010714071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074959715,0.001013235,0.00094324123,0.0009897623,0.00041636924,0.00072442525,0.00068052683,0.0008872244,0.0014758813],"category_scores_gemma":[0.0013775358,0.00044924565,0.00063606363,0.00070674496,0.0002938535,0.00051811896,0.0003615354,0.0006262512,0.00032801586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006258088,0.000065318796,0.0011240554,0.000111952555,0.00008166694,0.00014140217,0.00006685682,0.8346423,0.009045451,0.002114575,0.0012043774,0.15133955],"study_design_scores_gemma":[0.000010664383,0.0000360822,0.00029597775,0.000007362373,0.0000139908225,0.000025307496,0.000007832483,0.9972114,0.0014570357,0.0005151501,0.00041461524,0.0000045651973],"about_ca_topic_score_codex":0.0032534979,"about_ca_topic_score_gemma":0.0031958504,"teacher_disagreement_score":0.0032534979,"about_ca_system_score_codex":0.00044595113,"about_ca_system_score_gemma":0.0007018419,"threshold_uncertainty_score":0.0064691305},"labels":[],"label_agreement":null},{"id":"W3213216575","doi":"10.1016/j.fss.2021.10.007","title":"Double iterative learning-based polynomial based-RBFNNs driven by the aid of support vector-based kernel fuzzy clustering and least absolute shrinkage deviations","year":2021,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Key Technology Research and Development Program of Shandong; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning","keywords":"Robustness (evolution); Support vector machine; Fuzzy logic; Mathematics; Gaussian function; Cluster analysis; Polynomial; Polynomial regression; Algorithm; Mean squared error; Kernel (algebra); Radial basis function; Gaussian; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Linear regression; Computer science; Statistics","score_opus":0.018914217153650888,"score_gpt":0.2475154380309017,"score_spread":0.22860122087725082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213216575","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0096201105,0.00020193725,0.9885661,0.00005063543,0.000047021873,0.00001871311,0.000019938285,0.00030380822,0.0011718309],"genre_scores_gemma":[0.5662564,0.0003522426,0.42818528,0.00008327199,0.00009581248,0.00011634703,0.00021138144,0.00016033427,0.0045389496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992355,0.00014897708,0.000051315008,0.00018953331,0.00029864162,0.000075970995],"domain_scores_gemma":[0.99892396,0.000228475,0.00009658182,0.0001015549,0.00060494925,0.0000445053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012031015,0.0005840331,0.0013933881,0.00046390184,0.0005324333,0.0010011446,0.0018509597,0.00113588,0.0019153438],"category_scores_gemma":[0.0031962788,0.00037321917,0.0008188643,0.00081218896,0.00052460533,0.0012748702,0.001061845,0.0012418897,0.0009240981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031348827,0.00011995873,0.0010815784,0.00023172561,0.00011928952,0.000087283064,0.00017205924,0.6132482,0.018324899,0.021612708,0.0028240154,0.34186473],"study_design_scores_gemma":[0.0000027882656,0.000015018454,0.00006996306,0.0000030348745,0.000005603691,0.00001394726,0.000003282045,0.99801254,0.00090832496,0.0006927321,0.00026750818,0.000005338392],"about_ca_topic_score_codex":0.005098222,"about_ca_topic_score_gemma":0.0051293448,"teacher_disagreement_score":0.005098222,"about_ca_system_score_codex":0.00064165756,"about_ca_system_score_gemma":0.0012510713,"threshold_uncertainty_score":0.010137081},"labels":[],"label_agreement":null},{"id":"W3214485347","doi":"","title":"Dynamic Inference with Neural Interpreters","year":2021,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Inference; Artificial neural network; Interpreter; Artificial intelligence; Leverage (statistics); Unary operation; Transformer; Generalization; Reuse; Machine learning; Programming language; Mathematics","score_opus":0.010235428346110546,"score_gpt":0.24974414536721334,"score_spread":0.2395087170211028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214485347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04567585,0.0002771407,0.94347626,0.00045015768,0.00008145848,0.000057900805,0.00012316847,0.005032717,0.0048253736],"genre_scores_gemma":[0.64661163,0.00028427108,0.34566733,0.00038762743,0.000083260624,0.00013463515,0.00036842408,0.000445399,0.006017325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994836,0.00012361586,0.000044798588,0.00018250031,0.00010510018,0.000060420065],"domain_scores_gemma":[0.99841154,0.0007003667,0.00009467023,0.00053851603,0.00018834966,0.000066558656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001426461,0.0005832165,0.00062263064,0.00048758188,0.0003599315,0.0012649815,0.001982967,0.0009866026,0.0038829783],"category_scores_gemma":[0.0060346546,0.0006917498,0.0010424291,0.00038192308,0.0013856865,0.0043311073,0.0018577246,0.0021881028,0.0009038359],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004160103,0.00018684781,0.0022392364,0.00026201995,0.00015603205,0.0003402328,0.00052841194,0.44155997,0.025197493,0.13745715,0.004730524,0.3869261],"study_design_scores_gemma":[0.000012812755,0.000024579886,0.00011879852,0.000012076988,0.000020988791,0.000042138163,0.00001663423,0.94260305,0.005550355,0.04954651,0.0020419832,0.000010190426],"about_ca_topic_score_codex":0.0026310934,"about_ca_topic_score_gemma":0.0040983213,"teacher_disagreement_score":0.0038829783,"about_ca_system_score_codex":0.00093696325,"about_ca_system_score_gemma":0.00095051853,"threshold_uncertainty_score":0.012989819},"labels":[],"label_agreement":null},{"id":"W3214966871","doi":"10.3389/fneur.2021.674275","title":"Multivariate FMRI Signatures of Learning in a Hebb Repetition Paradigm With Tone Sequences","year":2021,"lang":"en","type":"article","venue":"Frontiers in Neurology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Multivariate statistics; Repetition (rhetorical device); Tone (literature); Multivariate analysis; Psychology; Artificial intelligence; Neuroscience; Computer science; Speech recognition; Machine learning; Linguistics","score_opus":0.008371316178733889,"score_gpt":0.24013698971887765,"score_spread":0.23176567354014377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214966871","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941963,0.000053826854,0.004201161,0.00008063094,0.000008009839,0.000029706458,0.00006619351,0.00004450312,0.0013196148],"genre_scores_gemma":[0.99646926,0.000074859905,0.002456795,0.00005618095,0.000014035556,0.00003655485,0.0001230766,0.0000160448,0.0007531872],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999441,0.000010940498,0.000002563352,0.000014729606,0.000012014572,0.000015539621],"domain_scores_gemma":[0.999778,0.00008248533,0.00004554373,0.000018256442,0.000020474186,0.000055274508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028602767,0.00015273993,0.00016222901,0.0002573176,0.00009788579,0.00017818734,0.00017889691,0.0002988102,0.0016406071],"category_scores_gemma":[0.00080648274,0.000121751735,0.00011178306,0.00012401551,0.00026234533,0.00020807683,0.00014710205,0.00046674526,0.00014326301],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007086611,0.00019410547,0.0037301728,0.00003169407,0.00002879499,0.00023995334,0.0000977749,0.00039804276,0.9816799,0.00024526607,0.00015824835,0.012487313],"study_design_scores_gemma":[0.0001704122,0.0035138116,0.76717544,0.000024829787,0.00011211998,0.0032934055,0.00021419657,0.024274992,0.19715649,0.0028041066,0.0012019952,0.000058157482],"about_ca_topic_score_codex":0.0006849164,"about_ca_topic_score_gemma":0.001589172,"teacher_disagreement_score":0.0016406071,"about_ca_system_score_codex":0.00009437186,"about_ca_system_score_gemma":0.00016423696,"threshold_uncertainty_score":0.0054883957},"labels":[],"label_agreement":null},{"id":"W3216527274","doi":"10.1016/j.neucom.2021.11.028","title":"Development of two-phase logic-oriented fuzzy AND/OR network","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"King Khalid University","keywords":"Computer science; Fuzzy logic; Curse of dimensionality; Artificial neural network; Convergence (economics); Overhead (engineering); Artificial intelligence; Adaptive neuro fuzzy inference system; Neuro-fuzzy; Fuzzy electronics; Network architecture; Machine learning; Fuzzy control system","score_opus":0.03561016172973484,"score_gpt":0.3073776669698933,"score_spread":0.27176750524015847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216527274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009186727,0.0002821258,0.9785454,0.000117296266,0.0001455583,0.00012375327,0.000054929224,0.00055492605,0.010989235],"genre_scores_gemma":[0.30104217,0.00058906706,0.68715477,0.00022974284,0.000066302695,0.0002111672,0.00025550282,0.00007360656,0.010377651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997633,0.000029244762,0.000014796741,0.000058544574,0.00011080942,0.000023325276],"domain_scores_gemma":[0.9998135,0.000024722394,0.000012330449,0.00001837815,0.000117678675,0.000013559912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003447457,0.0004195449,0.00042762776,0.00037109735,0.00046117153,0.0007334317,0.0012842678,0.00058882823,0.0034864396],"category_scores_gemma":[0.00046479795,0.00024137912,0.00039065897,0.00030777595,0.00025111542,0.0008343378,0.00060384406,0.0005775622,0.00089130935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023530246,0.00028066727,0.0011300201,0.00061625004,0.000083579915,0.00046796422,0.00025282794,0.13721605,0.15709326,0.133317,0.0053307507,0.5639763],"study_design_scores_gemma":[0.00003666946,0.00022966151,0.0004032823,0.00004359866,0.000051832692,0.0002566728,0.000040604948,0.9185091,0.043685496,0.015602788,0.021108387,0.00003180233],"about_ca_topic_score_codex":0.002273982,"about_ca_topic_score_gemma":0.0029223212,"teacher_disagreement_score":0.0034864396,"about_ca_system_score_codex":0.00045383352,"about_ca_system_score_gemma":0.0010004215,"threshold_uncertainty_score":0.011663258},"labels":[],"label_agreement":null},{"id":"W3217110397","doi":"10.18280/ts.380527","title":"Simple, Flexible Method to Extract Digital Image Features","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Process (computing); Image (mathematics); Feature extraction; Identifier; Pattern recognition (psychology); Simple (philosophy); Digital image; Computer vision; Feature (linguistics); Extraction (chemistry); Image processing","score_opus":0.018032035435576816,"score_gpt":0.29724021346956614,"score_spread":0.27920817803398934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217110397","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051098713,0.0005394704,0.9896917,0.00008897905,0.00013361695,0.00018533618,0.00020713618,0.0013697142,0.002674181],"genre_scores_gemma":[0.06669823,0.0006818843,0.9226798,0.0001383886,0.000117259304,0.00033044064,0.00058400293,0.00020305776,0.00856684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995497,0.000028218981,0.000030344094,0.00009857098,0.00026500295,0.00002810867],"domain_scores_gemma":[0.99959964,0.00008674125,0.00003909727,0.00010174908,0.00015315684,0.000019651206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034120964,0.00067208376,0.00050762977,0.0018634311,0.00029041324,0.00067948626,0.00074003934,0.00070156873,0.0067471974],"category_scores_gemma":[0.0010780714,0.00035157468,0.0004950098,0.0011015853,0.00038225818,0.0012913302,0.0008199723,0.0008261607,0.0036557796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009739561,0.00005339925,0.00049270014,0.00034531098,0.000034946384,0.0002227971,0.00008051005,0.0019304454,0.2936905,0.0048883236,0.005950559,0.6922131],"study_design_scores_gemma":[0.00010828474,0.00043140713,0.007973695,0.00016945087,0.00013608711,0.0042900844,0.00018428026,0.15619883,0.63203883,0.01482399,0.18340072,0.00024445108],"about_ca_topic_score_codex":0.00036300495,"about_ca_topic_score_gemma":0.00070908124,"teacher_disagreement_score":0.0067471974,"about_ca_system_score_codex":0.00019318258,"about_ca_system_score_gemma":0.00030502654,"threshold_uncertainty_score":0.022571564},"labels":[],"label_agreement":null},{"id":"W323170945","doi":"10.1016/j.csda.2013.03.019","title":"Nonparametric meta-analysis of independent samples of records","year":2013,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Nonparametric statistics; Quantile; Sample (material); Statistics; Confidence interval; Mathematics; Population; Econometrics; Medicine","score_opus":0.13473331688474383,"score_gpt":0.3399723815747199,"score_spread":0.20523906468997605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W323170945","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14484379,0.08214526,0.7480625,0.002301751,0.0048542167,0.0031905428,0.009877809,0.0021959501,0.002528157],"genre_scores_gemma":[0.816213,0.005342079,0.16401228,0.0011271652,0.00080498715,0.00596297,0.0037754136,0.00053573307,0.0022263331],"study_design_codex":"meta_analysis","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.66910285,0.26705438,0.014999893,0.036225628,0.010463628,0.0021536003],"domain_scores_gemma":[0.56735754,0.34833327,0.015513242,0.06353097,0.0041395677,0.001125448],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1270147,0.0031199637,0.011758013,0.0062527033,0.001462681,0.005177431,0.0072147725,0.0050119795,0.0063013746],"category_scores_gemma":[0.34970063,0.0022786593,0.031608753,0.006387243,0.0025991313,0.004691576,0.0030199299,0.005851131,0.00086051476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013981203,0.000308205,0.044079166,0.013605037,0.8567402,0.0006198792,0.00052116095,0.010206182,0.0015140355,0.007122315,0.0040101386,0.047292434],"study_design_scores_gemma":[0.008651473,0.0043629906,0.044520557,0.0034698371,0.76966405,0.0017080926,0.0008358739,0.05829934,0.004018308,0.09103597,0.012938226,0.0004952047],"about_ca_topic_score_codex":0.002983809,"about_ca_topic_score_gemma":0.0036160578,"teacher_disagreement_score":0.8729853,"about_ca_system_score_codex":0.0017696501,"about_ca_system_score_gemma":0.0027997433,"threshold_uncertainty_score":0.671726},"labels":[],"label_agreement":null},{"id":"W337566769","doi":"10.22237/jmasm/1209615480","title":"Using Connectionist Models to Evaluate Examinees’ Response Patterns to Achievement Tests","year":2008,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Connectionism; Set (abstract data type); Artificial neural network; Artificial intelligence; Task (project management); Cognition; Hierarchy; Test (biology); Machine learning; Computer science; Pattern recognition (psychology); Mathematics; Psychology","score_opus":0.1993683463561494,"score_gpt":0.42767081956611475,"score_spread":0.22830247320996536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W337566769","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7618602,0.00017123127,0.23277274,0.00046819105,0.000040342173,0.00029305645,0.0002292884,0.0007084511,0.0034564591],"genre_scores_gemma":[0.96802866,0.00009927705,0.030376099,0.000032062995,0.000014266319,0.00019366358,0.00019072587,0.000026784526,0.0010384915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987337,0.0007855647,0.000052594638,0.00016072392,0.00019146563,0.00007599949],"domain_scores_gemma":[0.9920332,0.006413708,0.00052726787,0.00038472432,0.00049451523,0.00014642377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032193873,0.00095683296,0.00054385676,0.0015829455,0.00033032367,0.0012718048,0.00086839666,0.0010038505,0.0017038446],"category_scores_gemma":[0.021594826,0.0005181417,0.0005775862,0.0009049088,0.00052550394,0.0012707186,0.0006497632,0.0011172871,0.00031111375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009799853,0.0007320234,0.13586901,0.000109293316,0.0006569499,0.00032159724,0.0005896259,0.71267635,0.003467293,0.009295707,0.0010826832,0.13421953],"study_design_scores_gemma":[0.000025836916,0.000109370194,0.009894333,0.0000092340915,0.00002375428,0.00005683279,0.000040540865,0.9827791,0.00041610483,0.0064677023,0.00016029607,0.000017035129],"about_ca_topic_score_codex":0.009274951,"about_ca_topic_score_gemma":0.009144668,"teacher_disagreement_score":0.009274951,"about_ca_system_score_codex":0.0017936204,"about_ca_system_score_gemma":0.0006115038,"threshold_uncertainty_score":0.018441975},"labels":[],"label_agreement":null},{"id":"W34363959","doi":"10.1016/j.neuroimage.2021.118457","title":"Training Activation Function in Neuro-Wavelet Parametric Modeling","year":2000,"lang":"en","type":"article","venue":"Information Processing and Management of Uncertainty","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Tax Foundation; Ontario Brain Institute","keywords":"Computer science; Wavelet; Parametric statistics; Artificial intelligence; Function (biology); Activation function; Pattern recognition (psychology); Artificial neural network; Mathematics; Statistics","score_opus":0.031210915383747435,"score_gpt":0.24822842066968495,"score_spread":0.21701750528593752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W34363959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016474558,0.0002406163,0.9819338,0.00013070334,0.000014826365,0.00003506154,0.00008407418,0.00024660488,0.0008398587],"genre_scores_gemma":[0.7162913,0.000805803,0.27760014,0.00008697155,0.00004340032,0.0005434395,0.00033260955,0.0002664819,0.004029859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996604,0.00019641075,0.000014120024,0.0000485509,0.000051622905,0.000028974648],"domain_scores_gemma":[0.9987685,0.0010071462,0.00007071003,0.00007402196,0.000061824256,0.00001782698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015807494,0.0005925665,0.00047978727,0.0006237366,0.00020602609,0.000736538,0.00079958973,0.0010799076,0.001433653],"category_scores_gemma":[0.0061012623,0.00041274325,0.00072163704,0.00082607934,0.00049723976,0.00063233904,0.0006578452,0.0012238515,0.0004994493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030323494,0.000027804705,0.0007365989,0.000047812315,0.00004500762,0.000057274025,0.000048635608,0.95518786,0.0020297,0.007325501,0.00033072906,0.03413271],"study_design_scores_gemma":[9.596945e-7,0.0000050007416,0.00013476227,0.0000034743305,0.0000023269406,0.0000071182785,0.0000029564226,0.9975375,0.00022093815,0.0018798559,0.00020279955,0.000002351316],"about_ca_topic_score_codex":0.0033314847,"about_ca_topic_score_gemma":0.0030661207,"teacher_disagreement_score":0.0033314847,"about_ca_system_score_codex":0.0004659522,"about_ca_system_score_gemma":0.00045986575,"threshold_uncertainty_score":0.008359849},"labels":[],"label_agreement":null},{"id":"W36848714","doi":"10.1016/j.janxdis.2023.102681","title":"Robot Learning from Human Demonstrations: confronto tra DMM e GMM","year":2013,"lang":"en","type":"article","venue":"Journal of Anxiety Disorders","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Mental Health; Canadian Institutes of Health Research","keywords":"Artificial intelligence; Mixture model; Humanities; Computer science; Art","score_opus":0.01020415572529588,"score_gpt":0.22876623301214258,"score_spread":0.2185620772868467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W36848714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.795104,0.000337813,0.1665643,0.0007192902,0.00019835414,0.00078595494,0.00084177876,0.0036609045,0.03178755],"genre_scores_gemma":[0.9560845,0.00014680266,0.035812017,0.000109469765,0.00001770394,0.00031641466,0.00032640304,0.00006781095,0.007118797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976057,0.00011275222,0.00001050677,0.00004584392,0.000036530517,0.000033710345],"domain_scores_gemma":[0.9990907,0.0005893021,0.000077914985,0.00013487613,0.00004070881,0.00006651443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006260214,0.00041116826,0.00029022945,0.00024487378,0.00017398054,0.0003482548,0.0005477638,0.0005276378,0.01838878],"category_scores_gemma":[0.0037074958,0.00017080127,0.00024232385,0.00012285521,0.0003689964,0.00047898848,0.0010036587,0.00043777822,0.00163908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0058560185,0.0027861912,0.0117359245,0.00084044866,0.0001332361,0.0010205406,0.001447678,0.20218389,0.06313619,0.0051631504,0.0128362,0.69286066],"study_design_scores_gemma":[0.0006373402,0.010637371,0.03486772,0.00023279262,0.00011177067,0.00071221095,0.0011576685,0.84223443,0.043835845,0.01300089,0.052419163,0.00015277778],"about_ca_topic_score_codex":0.001286937,"about_ca_topic_score_gemma":0.0021271736,"teacher_disagreement_score":0.01838878,"about_ca_system_score_codex":0.00022281242,"about_ca_system_score_gemma":0.00027517258,"threshold_uncertainty_score":0.061516583},"labels":[],"label_agreement":null},{"id":"W37158840","doi":"10.1186/s12885-023-10891-6","title":"Discriminatory Components for Pattern Classification.","year":2009,"lang":"en","type":"article","venue":"European Society for Fuzzy Logic and Technology Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Computer science; Uncorrelated; Fuzzy logic; Fuzzy set; Benchmark (surveying); Data mining; Machine learning; Mathematics; Statistics","score_opus":0.05695077975895907,"score_gpt":0.2674216129674613,"score_spread":0.21047083320850224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W37158840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060391452,0.0028700628,0.91435426,0.001033166,0.0011499734,0.0018420318,0.005570287,0.004247086,0.008541681],"genre_scores_gemma":[0.48440278,0.0011999211,0.49290928,0.0004343336,0.00040145146,0.003334825,0.008767632,0.0003858772,0.008163938],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963959,0.0010284508,0.0004777197,0.0008419485,0.0009489227,0.0003070151],"domain_scores_gemma":[0.99545056,0.0024141867,0.00023734306,0.0006293504,0.0011008851,0.00016773229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050252634,0.0017020458,0.0017996108,0.004526857,0.0008823554,0.0032925976,0.0019172253,0.0016816318,0.012692927],"category_scores_gemma":[0.02077715,0.00035843748,0.0021642765,0.0041714525,0.0007688803,0.0015320049,0.0015998071,0.0021090845,0.0051161386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011525896,0.00042288224,0.01335798,0.000760287,0.0005728331,0.0002443511,0.00022732279,0.024974978,0.017112378,0.013871252,0.015135578,0.9121675],"study_design_scores_gemma":[0.00014062149,0.00046871332,0.02014266,0.00031202188,0.00047410224,0.0006541812,0.00032242315,0.9007686,0.014652937,0.039423328,0.022485852,0.00015454314],"about_ca_topic_score_codex":0.0049599195,"about_ca_topic_score_gemma":0.0029155186,"teacher_disagreement_score":0.012692927,"about_ca_system_score_codex":0.0011250403,"about_ca_system_score_gemma":0.0024442493,"threshold_uncertainty_score":0.04246205},"labels":[],"label_agreement":null},{"id":"W4200042261","doi":"10.3390/s21248218","title":"Evidential Data Fusion for Characterization of Pavement Surface Conditions during Winter Using a Multi-Sensor Approach","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Université de Sherbrooke","keywords":"Sensor fusion; Road surface; Computer science; Deep learning; Data mining; State (computer science); Microphone; Artificial intelligence; Real-time computing; Engineering; Telecommunications; Civil engineering","score_opus":0.09274216694963608,"score_gpt":0.320363633219294,"score_spread":0.22762146626965793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200042261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1383576,0.00029704013,0.85957044,0.00017200285,0.00005178197,0.00003168878,0.00020463612,0.00038248126,0.0009323978],"genre_scores_gemma":[0.9048876,0.00011255987,0.09422568,0.000039074996,0.000022262606,0.000025273965,0.00021273426,0.000018991852,0.00045586706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995933,0.000090374466,0.000027697302,0.00012338419,0.00012498692,0.00004032944],"domain_scores_gemma":[0.99952316,0.00015152976,0.000078897785,0.00008936097,0.00013270642,0.000024376495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007432802,0.000581208,0.00047086077,0.00096597953,0.00020301831,0.0005801959,0.0005691791,0.00059068337,0.00052197726],"category_scores_gemma":[0.0015302007,0.00025825793,0.0005988893,0.0006463493,0.000413103,0.001361623,0.0008399768,0.00085736543,0.00014929559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064563716,0.00033542776,0.012597613,0.00027622658,0.00024728486,0.00031839331,0.00053125585,0.44487408,0.16703771,0.009736572,0.0014990112,0.3619007],"study_design_scores_gemma":[0.000005605185,0.00007407652,0.004711492,0.000007270054,0.000022187105,0.000040960862,0.000046603865,0.97413754,0.01714558,0.0032069783,0.0005772467,0.000024441826],"about_ca_topic_score_codex":0.0009820926,"about_ca_topic_score_gemma":0.0016674958,"teacher_disagreement_score":0.0009820926,"about_ca_system_score_codex":0.0004227867,"about_ca_system_score_gemma":0.00033781966,"threshold_uncertainty_score":0.0039309263},"labels":[],"label_agreement":null},{"id":"W4200079396","doi":"10.18280/ria.350606","title":"A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Backpropagation; Stock (firearms); Artificial neural network; Econometrics; Computer science; Singular spectrum analysis; Profit (economics); Financial market; Stock market index; Stock market; Economics; Financial economics; Artificial intelligence; Finance; Engineering; Microeconomics","score_opus":0.04953438294427867,"score_gpt":0.26946880156168307,"score_spread":0.2199344186174044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200079396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09297868,0.0016448463,0.8985633,0.00034501468,0.00021579061,0.000084274645,0.00010600917,0.0011467289,0.0049152514],"genre_scores_gemma":[0.9306851,0.0008231438,0.062076326,0.00012182187,0.00010893808,0.00012951568,0.00017527014,0.000042348805,0.005837554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997495,0.00004430889,0.000022013537,0.000059855665,0.00009006418,0.000034152967],"domain_scores_gemma":[0.9998062,0.00007349671,0.000017843735,0.0000096828635,0.000082170925,0.000010666875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005848335,0.0007903667,0.0009011629,0.0006996778,0.00039043315,0.00081927644,0.0011223145,0.0010682833,0.0011445137],"category_scores_gemma":[0.0008099944,0.0003908143,0.00085695746,0.00070583425,0.00034669077,0.0011714011,0.00052143197,0.0006934009,0.00030057106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105242434,0.00010104324,0.001446574,0.000072192866,0.000115504765,0.000110856985,0.000046796817,0.9030102,0.0043046298,0.0014874054,0.00069851085,0.08850103],"study_design_scores_gemma":[0.000002587308,0.00001120703,0.00010984289,0.00000198826,0.0000070862266,0.0000054436905,0.0000012943914,0.9993655,0.00021057166,0.0001969676,0.00008489644,0.0000027035333],"about_ca_topic_score_codex":0.017117819,"about_ca_topic_score_gemma":0.010599309,"teacher_disagreement_score":0.017117819,"about_ca_system_score_codex":0.0005103997,"about_ca_system_score_gemma":0.0007768735,"threshold_uncertainty_score":0.034036398},"labels":[],"label_agreement":null},{"id":"W4200390302","doi":"10.1017/9781108938051.012","title":"Ensemble Learning","year":2021,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Content (measure theory); Artificial intelligence; Mathematics","score_opus":0.022733681145037505,"score_gpt":0.1959381394636759,"score_spread":0.17320445831863837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200390302","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004446506,0.017019635,0.8048067,0.001911432,0.002809465,0.0001833401,0.004222972,0.0059392937,0.1586607],"genre_scores_gemma":[0.11861606,0.029144298,0.48759195,0.0019141893,0.003344025,0.0006054577,0.0284541,0.003315966,0.32701403],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993793,0.00012933259,0.000029242841,0.00015972256,0.00025309756,0.000049352024],"domain_scores_gemma":[0.99926585,0.00022428611,0.000022327227,0.00020790129,0.00023713475,0.00004252703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001288118,0.001025463,0.0011388849,0.0011530718,0.0004244739,0.001487819,0.0014388312,0.00091024337,0.056863647],"category_scores_gemma":[0.0027467234,0.00029462605,0.00088102004,0.0019169252,0.00026969198,0.0020275754,0.0012520369,0.0014893855,0.031529907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005268729,0.00004886096,0.00042568645,0.00024720532,0.00010613043,0.000030245697,0.000019611962,0.025794769,0.0010620651,0.019318722,0.16879821,0.78409576],"study_design_scores_gemma":[0.000026474921,0.00016855604,0.0016735698,0.0003942796,0.00013370783,0.000457929,0.000066462926,0.34984717,0.006846327,0.10103111,0.5392722,0.000082135586],"about_ca_topic_score_codex":0.0013638189,"about_ca_topic_score_gemma":0.0028085755,"teacher_disagreement_score":0.056863647,"about_ca_system_score_codex":0.0004566292,"about_ca_system_score_gemma":0.0005494171,"threshold_uncertainty_score":0.19022787},"labels":[],"label_agreement":null},{"id":"W4200493517","doi":"10.1007/978-3-030-79375-3_4","title":"BJT and FET Models","year":2021,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Bipolar junction transistor; Biasing; Bipolar transistor biasing; Transistor; Amplifier; Electronic engineering; Electronic circuit; Field-effect transistor; Electrical engineering; Materials science; Engineering; Voltage; CMOS","score_opus":0.033603185193114345,"score_gpt":0.218585234209287,"score_spread":0.18498204901617266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200493517","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004658822,0.009680223,0.65585554,0.0030101568,0.0019899725,0.00006049813,0.0023801709,0.004541927,0.31782272],"genre_scores_gemma":[0.18207487,0.013843622,0.22802557,0.0015967856,0.0012312309,0.00023659444,0.007083231,0.0046189083,0.56128925],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997869,0.00003267255,0.00000871177,0.000054064687,0.00009158324,0.000026139436],"domain_scores_gemma":[0.99972624,0.00009675309,0.000013347794,0.00007817003,0.000063815976,0.000021752541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038553623,0.00094633334,0.00086505566,0.0006236322,0.00060244254,0.0023845683,0.0017232373,0.0015020437,0.052733336],"category_scores_gemma":[0.0018021187,0.00047854168,0.0009618478,0.0013239698,0.00068606716,0.0036252078,0.0011501401,0.0026122213,0.024633208],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008416303,0.0000440964,0.0002150718,0.00022690107,0.000055932327,0.00011745165,0.00006154059,0.08381393,0.0020867323,0.5540421,0.08957178,0.26968032],"study_design_scores_gemma":[0.000012258494,0.00002342152,0.00016478258,0.000104911735,0.000029442774,0.00024966119,0.000032832664,0.29001987,0.0022379835,0.53996116,0.16713375,0.00002976984],"about_ca_topic_score_codex":0.0042332876,"about_ca_topic_score_gemma":0.005541531,"teacher_disagreement_score":0.052733336,"about_ca_system_score_codex":0.0010626272,"about_ca_system_score_gemma":0.0007186664,"threshold_uncertainty_score":0.17641056},"labels":[],"label_agreement":null},{"id":"W4200513961","doi":"10.1007/s13202-021-01405-w","title":"Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm","year":2021,"lang":"en","type":"article","venue":"Journal of Petroleum Exploration and Production Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Adaptive neuro fuzzy inference system; Artificial neural network; Computer science; Neuro-fuzzy; Algorithm; Artificial intelligence; Production (economics); Fuzzy logic; Machine learning; Engineering; Fuzzy control system","score_opus":0.049038293896668965,"score_gpt":0.26776448080425264,"score_spread":0.21872618690758366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200513961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11629702,0.0006500296,0.8760016,0.00031065865,0.000100085825,0.000093218034,0.00013556212,0.00044723367,0.005964606],"genre_scores_gemma":[0.9420794,0.00038150026,0.052840896,0.000046727655,0.000029050852,0.00016658503,0.00015557151,0.000017864424,0.004282364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997806,0.00005141915,0.00001846718,0.00005389575,0.00006979965,0.00002579498],"domain_scores_gemma":[0.99976975,0.00010372053,0.000033374265,0.00000843815,0.00007661991,0.000008152099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005742022,0.0006297091,0.00078135007,0.0005088608,0.00044583212,0.0007036927,0.00082561147,0.0009861421,0.0012069207],"category_scores_gemma":[0.00089395227,0.0002893782,0.00080970995,0.0004991376,0.00028584394,0.0006474155,0.00031033464,0.00082782394,0.0001712285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000257314,0.00001811036,0.00047555065,0.000024819527,0.000018760327,0.000037225138,0.000024360143,0.9868221,0.00093814573,0.001045421,0.00014240624,0.010427341],"study_design_scores_gemma":[0.0000013956845,0.000006652186,0.00005903295,0.0000012429894,0.0000021133599,0.0000026255075,0.0000011928919,0.99967074,0.00008981933,0.00010685889,0.00005691851,0.0000013732929],"about_ca_topic_score_codex":0.022369131,"about_ca_topic_score_gemma":0.014106815,"teacher_disagreement_score":0.022369131,"about_ca_system_score_codex":0.0006923147,"about_ca_system_score_gemma":0.0009312868,"threshold_uncertainty_score":0.04447788},"labels":[],"label_agreement":null},{"id":"W4200629188","doi":"","title":"Using Audio Loops for Instrument Family Recognition in Machine Learning Tasks","year":2021,"lang":"en","type":"article","venue":"DergiPark (Istanbul University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Psychology; Artificial intelligence","score_opus":0.06069670695350057,"score_gpt":0.23851870190792956,"score_spread":0.177821994954429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200629188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26991415,0.002659431,0.70769495,0.00039235456,0.00027983225,0.0002883076,0.0025141204,0.008492273,0.007764609],"genre_scores_gemma":[0.6634939,0.0006105659,0.32458472,0.00015340076,0.00013717747,0.00036762818,0.0057078823,0.00017514674,0.0047696326],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992211,0.00022550326,0.00007101611,0.00021974066,0.00016643273,0.00009623702],"domain_scores_gemma":[0.9990324,0.00054567814,0.000081711914,0.00013568359,0.00016174452,0.000042897947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010018789,0.0010511881,0.00056304014,0.0012542432,0.0004025921,0.00090212165,0.00073694345,0.00071419403,0.002775031],"category_scores_gemma":[0.0029439349,0.00018738478,0.0004915815,0.0014214836,0.00025636159,0.001112734,0.0007491237,0.00090256316,0.0016301839],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004704173,0.00033114958,0.0058337855,0.00023998211,0.00007686326,0.00014518414,0.00013125942,0.04143984,0.0213691,0.0011577535,0.006557982,0.92224675],"study_design_scores_gemma":[0.00006512184,0.0005478198,0.012179836,0.00007409673,0.00007058252,0.00019202282,0.0002940529,0.9356314,0.031658,0.005643006,0.01358963,0.000054355638],"about_ca_topic_score_codex":0.002471325,"about_ca_topic_score_gemma":0.003313599,"teacher_disagreement_score":0.002775031,"about_ca_system_score_codex":0.00029191643,"about_ca_system_score_gemma":0.00041189758,"threshold_uncertainty_score":0.009283423},"labels":[],"label_agreement":null},{"id":"W4205304882","doi":"10.23952/jano.3.2021.3.06","title":"Improved sample efficiency by episodic memory hit ratio deep Q-networks","year":2021,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Episodic memory; Sample (material); Computer science; Psychology; Neuroscience; Chemistry; Cognition","score_opus":0.0051715406517908015,"score_gpt":0.2061447720945865,"score_spread":0.20097323144279572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205304882","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0673583,0.0005555066,0.9283635,0.00028487813,0.000054250817,0.00007549102,0.000036706606,0.0008824351,0.0023889923],"genre_scores_gemma":[0.86521846,0.00018741724,0.13089022,0.00035949174,0.000038479677,0.00012150742,0.00008812589,0.00010321922,0.0029930815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939597,0.00018650296,0.00004493808,0.00013689397,0.00014000635,0.000095689786],"domain_scores_gemma":[0.998171,0.0010580017,0.00018956355,0.0001943295,0.0002694799,0.00011765229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018564838,0.00072359,0.0010356278,0.00035507875,0.0003302091,0.000692063,0.0016727127,0.000968338,0.0021243081],"category_scores_gemma":[0.005549446,0.0003748333,0.0003283776,0.00030216397,0.0007691141,0.0015110084,0.0012666513,0.0010951557,0.00034261716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004391721,0.00030655658,0.003095892,0.00014069202,0.000084645086,0.00013519025,0.00012691242,0.6809681,0.0066811577,0.018780105,0.0025961578,0.28664547],"study_design_scores_gemma":[0.00002465956,0.00005608886,0.00010619583,0.000004543559,0.000008134132,0.00001646462,0.000004242501,0.99575377,0.0009981261,0.0027790372,0.00024455888,0.0000041980516],"about_ca_topic_score_codex":0.0025436287,"about_ca_topic_score_gemma":0.002922063,"teacher_disagreement_score":0.0025436287,"about_ca_system_score_codex":0.0007074953,"about_ca_system_score_gemma":0.0013333262,"threshold_uncertainty_score":0.009818196},"labels":[],"label_agreement":null},{"id":"W4206158547","doi":"10.1017/s0140525x02260069","title":"Modeling consciousness","year":2002,"lang":"en","type":"article","venue":"Behavioral and Brain Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Connectionism; Consciousness; Unconscious mind; Cascade; Cognitive science; Extension (predicate logic); Computer science; Cognition; Artificial intelligence; Feature (linguistics); Correlation; Psychology; Artificial neural network; Mathematics; Neuroscience; Philosophy; Psychoanalysis; Programming language; Linguistics","score_opus":0.096199473295399,"score_gpt":0.31234253527038797,"score_spread":0.21614306197498895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206158547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04780398,0.0026893644,0.795726,0.007226852,0.0004135053,0.00007694558,0.000469226,0.00058931357,0.14500481],"genre_scores_gemma":[0.8879455,0.0017346115,0.08684629,0.0005152467,0.0001655043,0.00015189381,0.0002859036,0.00010699363,0.022248095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998666,0.00004003203,0.0000044323488,0.00003807753,0.000028520222,0.000022399725],"domain_scores_gemma":[0.99981827,0.00007253022,0.000013350385,0.000041716634,0.000025776326,0.000028411969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034439028,0.00035711564,0.00030970183,0.00038730938,0.00047704013,0.0014607544,0.00088480447,0.00092636014,0.0064854603],"category_scores_gemma":[0.0013830635,0.00022726145,0.0004686706,0.00024770357,0.0011041111,0.0030184572,0.0012584517,0.0010777562,0.00076750194],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010893284,0.000010148642,0.00025414155,0.000034366614,0.000015147759,0.00004329763,0.00009661344,0.039927218,0.0005719347,0.94718945,0.0024320057,0.009414792],"study_design_scores_gemma":[0.000007430111,0.00001180886,0.00012998849,0.000017601915,0.000008823443,0.000046115496,0.000030180876,0.25000075,0.0004234283,0.73875326,0.010561472,0.000009065088],"about_ca_topic_score_codex":0.0022153966,"about_ca_topic_score_gemma":0.001970749,"teacher_disagreement_score":0.0064854603,"about_ca_system_score_codex":0.0008014502,"about_ca_system_score_gemma":0.0007187079,"threshold_uncertainty_score":0.021696031},"labels":[],"label_agreement":null},{"id":"W4206171822","doi":"10.1007/978-981-16-6054-2_15","title":"Dynamic Graph Neural Networks","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Collège Boréal","funders":"","keywords":"Computer science","score_opus":0.01228363295004534,"score_gpt":0.2209167375992955,"score_spread":0.20863310464925017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206171822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006432521,0.020531714,0.5601724,0.0031782626,0.002450514,0.00007115827,0.00112739,0.0029398508,0.40309626],"genre_scores_gemma":[0.15713531,0.027490938,0.18517709,0.0013974024,0.0013977588,0.00019391679,0.0036097304,0.0016353689,0.62196255],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9999167,0.000011052661,0.0000025321792,0.000026090001,0.00003538889,0.0000082374245],"domain_scores_gemma":[0.99985266,0.000056735378,0.000008588959,0.000035575304,0.000034682536,0.0000116935225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013646156,0.0006153235,0.00039745023,0.0006228274,0.00026531983,0.00085034006,0.0007054876,0.0007191655,0.026741916],"category_scores_gemma":[0.00074872083,0.00029332418,0.00025202977,0.00111434,0.00041507237,0.0014414191,0.00067454367,0.0012469176,0.010082474],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002831488,0.000041546617,0.00012602724,0.00019897941,0.000030015224,0.000052499865,0.000036803347,0.055962965,0.0026785478,0.18012512,0.1809066,0.5798125],"study_design_scores_gemma":[0.0000093094295,0.000030875362,0.0004144914,0.00013068572,0.000028916065,0.00018643575,0.00003517754,0.24627478,0.0034539963,0.4062154,0.3431914,0.000028591776],"about_ca_topic_score_codex":0.001678471,"about_ca_topic_score_gemma":0.0033645094,"teacher_disagreement_score":0.026741916,"about_ca_system_score_codex":0.0005241445,"about_ca_system_score_gemma":0.0003666369,"threshold_uncertainty_score":0.08946061},"labels":[],"label_agreement":null},{"id":"W4206520803","doi":"10.1049/pbte098e_ch4","title":"Artificial neural network","year":2021,"lang":"en","type":"book-chapter","venue":"IET eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Sigmoid function; Gradient descent; Hyperbolic function; Stochastic gradient descent; Activation function; Overfitting; Artificial neural network; Backpropagation; Norm (philosophy); Mathematics; Gradient method; Tangent; Applied mathematics; Algorithm; Computer science; Mathematical analysis; Artificial intelligence; Geometry","score_opus":0.033847364609325054,"score_gpt":0.23664103603398587,"score_spread":0.20279367142466082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206520803","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005272767,0.06255323,0.7084257,0.006307144,0.006779238,0.0003138732,0.003195255,0.0043950034,0.20275778],"genre_scores_gemma":[0.11971676,0.102744415,0.48228464,0.004345111,0.0025244767,0.0008980301,0.010149711,0.001171314,0.2761656],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99929535,0.000121030775,0.000056885146,0.00016287652,0.00032245307,0.000041283834],"domain_scores_gemma":[0.9995459,0.00016838766,0.000033228276,0.00006879493,0.00016332213,0.000020308686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007671743,0.0011345363,0.00097051123,0.0010062808,0.0005078282,0.0026728956,0.0019371021,0.0021327762,0.025270129],"category_scores_gemma":[0.0033884225,0.0003494161,0.0006785131,0.0015835854,0.0005698259,0.002333913,0.0014409058,0.0020908297,0.020547923],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080036494,0.0000648452,0.0007655632,0.001463401,0.00016470354,0.00023662996,0.00009824388,0.055747323,0.004338282,0.108168036,0.13211295,0.69675994],"study_design_scores_gemma":[0.000025063697,0.00007693234,0.0007894559,0.00085132505,0.00007436256,0.00048131018,0.00008910216,0.18184656,0.004422513,0.15467927,0.6565867,0.00007738605],"about_ca_topic_score_codex":0.0017469107,"about_ca_topic_score_gemma":0.0022579043,"teacher_disagreement_score":0.025270129,"about_ca_system_score_codex":0.00078913325,"about_ca_system_score_gemma":0.0008699529,"threshold_uncertainty_score":0.08453703},"labels":[],"label_agreement":null},{"id":"W4206560839","doi":"10.1002/9780470549148.ch2","title":"Signal Processing","year":2009,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Signal processing; Computer science; SIGNAL (programming language); Waveform; Speech recognition; Algorithm; Pattern recognition (psychology); Artificial intelligence; Telecommunications; Programming language","score_opus":0.01313900404974945,"score_gpt":0.24695295130314807,"score_spread":0.2338139472533986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206560839","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040767626,0.019881746,0.6945497,0.0015751306,0.0060701687,0.00039878936,0.002934467,0.006857047,0.2636562],"genre_scores_gemma":[0.041336365,0.024627423,0.2970562,0.0019469237,0.0034732053,0.00048039094,0.009849823,0.0016532885,0.6195764],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99967074,0.000032056076,0.000020363475,0.00007507728,0.00018216495,0.000019549774],"domain_scores_gemma":[0.9996792,0.00005858327,0.000010495902,0.00007458984,0.00016249606,0.00001454808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004056604,0.0008256371,0.00055262033,0.00097383786,0.0003494571,0.0016097265,0.0006905757,0.0008116161,0.08097961],"category_scores_gemma":[0.0010230386,0.00021741897,0.0004103035,0.0010577363,0.00029798632,0.0009999021,0.0007489542,0.00076964457,0.088446476],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005922451,0.00004609068,0.00014068927,0.00031609266,0.00003255319,0.00008677465,0.000053317286,0.0019775452,0.020054761,0.015457134,0.094929,0.8668468],"study_design_scores_gemma":[0.000021448308,0.00013322532,0.0012517276,0.0002830387,0.00003865879,0.0004829283,0.00008179369,0.017820224,0.018035678,0.025103668,0.9367058,0.000041812797],"about_ca_topic_score_codex":0.00046314692,"about_ca_topic_score_gemma":0.00059403275,"teacher_disagreement_score":0.08097961,"about_ca_system_score_codex":0.00018543957,"about_ca_system_score_gemma":0.0003819739,"threshold_uncertainty_score":0.27090383},"labels":[],"label_agreement":null},{"id":"W4206613742","doi":"10.3390/math9243216","title":"The Representation Theory of Neural Networks","year":2021,"lang":"en","type":"article","venue":"MDPI (MDPI AG)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Quiver; Artificial neural network; Representation (politics); Computer science; Theoretical computer science; Moduli space; Algebraic number; Artificial intelligence; Mathematics; Algebra over a field; Pure mathematics","score_opus":0.02064930643601671,"score_gpt":0.2633804152011367,"score_spread":0.24273110876512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206613742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011845899,0.005419141,0.91908437,0.003689008,0.0004242169,0.00005235186,0.00026369584,0.00023959643,0.058981646],"genre_scores_gemma":[0.68724823,0.011586915,0.2685039,0.0021733372,0.0020559893,0.0005583093,0.0007520314,0.0003196508,0.026801614],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99847454,0.0006634273,0.00009812803,0.0002622764,0.00038033136,0.00012136404],"domain_scores_gemma":[0.9982672,0.00091926917,0.00014357864,0.00030453046,0.0002945739,0.00007082554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022411176,0.00082018954,0.0008512772,0.0018900961,0.0011289681,0.004539076,0.0015080839,0.0018263002,0.0061193146],"category_scores_gemma":[0.0048486595,0.00046982084,0.0013364905,0.0016259575,0.0052916068,0.008353962,0.0021628446,0.0033547126,0.0013107948],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020663908,0.0000020461769,0.000026628031,0.000025036845,0.0000055037217,0.0000105964355,0.000029116683,0.0022714657,0.00007507556,0.9948724,0.00038586475,0.0022942938],"study_design_scores_gemma":[0.0000022993304,0.000005414887,0.000023632518,0.000018618406,0.0000027798906,0.000016721917,0.000010072193,0.014651846,0.00006753511,0.98217404,0.003022373,0.000004610581],"about_ca_topic_score_codex":0.0015111006,"about_ca_topic_score_gemma":0.0007710794,"teacher_disagreement_score":0.0061193146,"about_ca_system_score_codex":0.0021909382,"about_ca_system_score_gemma":0.0009362016,"threshold_uncertainty_score":0.020471156},"labels":[],"label_agreement":null},{"id":"W4206704501","doi":"10.36227/techrxiv.17791574.v1","title":"Fast Quaternion Product Units for Learning Disentangled Representations in SO(3)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Quaternion; Equivariant map; Rotation (mathematics); Invariant (physics); Computer science; Algorithm; Artificial neural network; Point cloud; Theoretical computer science; Mathematics; Artificial intelligence; Pure mathematics; Geometry","score_opus":0.048208531833498276,"score_gpt":0.3184664304738697,"score_spread":0.2702578986403714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206704501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016799038,0.00020519129,0.98027474,0.0001051292,0.000054932752,0.000047835172,0.00012332542,0.0011086651,0.0012811439],"genre_scores_gemma":[0.43707022,0.00046675347,0.55625373,0.00025388954,0.000079603626,0.00030993868,0.00090449094,0.00028375577,0.0043775793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967647,0.00005180676,0.000027243676,0.00008336008,0.00011885031,0.000042325526],"domain_scores_gemma":[0.99958843,0.00011808011,0.00006059988,0.00010009228,0.00009471152,0.000038054815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005381274,0.0008239517,0.0007726457,0.00066690886,0.0003404735,0.0009475125,0.0019056811,0.00087130256,0.004224535],"category_scores_gemma":[0.0021464813,0.0004224364,0.00076339446,0.0014627945,0.0007171753,0.0023907165,0.00177398,0.0013468317,0.0012594188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018690164,0.000097784,0.0011445363,0.00017203594,0.00007947897,0.00012385647,0.00018088956,0.39814368,0.013684111,0.08089444,0.0061074663,0.49918482],"study_design_scores_gemma":[0.000005163046,0.000024575365,0.00006340546,0.0000049646565,0.000004235809,0.000013164611,0.000010778171,0.9831986,0.0021566718,0.013563687,0.00094826525,0.000006556472],"about_ca_topic_score_codex":0.0062064365,"about_ca_topic_score_gemma":0.007013762,"teacher_disagreement_score":0.0062064365,"about_ca_system_score_codex":0.0007152604,"about_ca_system_score_gemma":0.0011820006,"threshold_uncertainty_score":0.0141325},"labels":[],"label_agreement":null},{"id":"W4206917572","doi":"10.1002/adts.202100459","title":"Interpretability of Neural Networks with Probability Density Functions","year":2022,"lang":"en","type":"article","venue":"Advanced Theory and Simulations","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Sigmoid function; Interpretability; Probability density function; Artificial neural network; Variable (mathematics); Probability distribution; Computer science; Cumulative distribution function; Function (biology); Artificial intelligence; Feature (linguistics); Perceptron; Binary function; Mathematics; Statistics","score_opus":0.007086387723102946,"score_gpt":0.22490462530889488,"score_spread":0.21781823758579194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206917572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08024445,0.0009041097,0.9114229,0.001810731,0.00012579709,0.00004723288,0.0002451921,0.0004317186,0.004767816],"genre_scores_gemma":[0.9361779,0.0005166237,0.061512228,0.00023863063,0.0001255233,0.000085968,0.00022339546,0.000058322497,0.001061437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970101,0.0017425861,0.00017933603,0.00044044835,0.00051098363,0.00011649448],"domain_scores_gemma":[0.976267,0.019504786,0.0015167778,0.0012268993,0.0013344896,0.00015000813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005274262,0.0008646791,0.00080197206,0.0017541217,0.00036204382,0.0028110754,0.0010548512,0.0012677461,0.0019070584],"category_scores_gemma":[0.03466766,0.00044576306,0.00095149205,0.00091985555,0.001870425,0.003559301,0.0010620706,0.0017236836,0.00022729722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016364928,0.00005464257,0.00666919,0.00019510453,0.00014171352,0.0005864968,0.00030455866,0.7187394,0.0016129947,0.23089038,0.0012070534,0.039434835],"study_design_scores_gemma":[0.0000064517203,0.000010224887,0.000508506,0.000024381256,0.000008757528,0.000046278128,0.00002510523,0.87819386,0.00029039528,0.12061474,0.00026457995,0.0000067211904],"about_ca_topic_score_codex":0.0026490605,"about_ca_topic_score_gemma":0.00096168736,"teacher_disagreement_score":0.005274262,"about_ca_system_score_codex":0.0017920978,"about_ca_system_score_gemma":0.0004714035,"threshold_uncertainty_score":0.027893305},"labels":[],"label_agreement":null},{"id":"W4210247913","doi":"10.36227/techrxiv.19101512","title":"Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Unsupervised learning; Machine learning; Bridge (graph theory); Artificial neural network; Kernel (algebra); Supervised learning; Nonlinear system; Representation (politics); Fault detection and isolation; Mathematics","score_opus":0.03208151151129831,"score_gpt":0.28833446555087494,"score_spread":0.25625295403957665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210247913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010376685,0.00013979658,0.9888364,0.00013924825,0.000008102198,0.000012724478,0.000015823174,0.00006829796,0.00040286343],"genre_scores_gemma":[0.806749,0.00046072603,0.19094771,0.00012935133,0.0000683871,0.000096414224,0.00012946477,0.00006419816,0.0013547562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992932,0.00026846852,0.000043063905,0.0001715505,0.000174399,0.000049270937],"domain_scores_gemma":[0.9969313,0.0018186364,0.00046661656,0.0004309257,0.00029179378,0.00006074702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014870415,0.00066244387,0.00072063063,0.00061138516,0.00034748108,0.001063361,0.000882307,0.00084860943,0.0007144034],"category_scores_gemma":[0.005670185,0.00028031197,0.0006973921,0.00033523346,0.0014081086,0.0019896566,0.0016303811,0.0013551681,0.00015130774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012317466,0.00007859974,0.0028821684,0.0002344432,0.00012767565,0.00012325821,0.00042128886,0.69119453,0.005780932,0.16211154,0.0007759159,0.13614643],"study_design_scores_gemma":[0.0000038781604,0.000029184843,0.00028721074,0.000012140937,0.000010396822,0.000027769049,0.000017023449,0.9530682,0.0014927428,0.04450878,0.0005327256,0.000009850275],"about_ca_topic_score_codex":0.0010169346,"about_ca_topic_score_gemma":0.0010331898,"teacher_disagreement_score":0.0014870415,"about_ca_system_score_codex":0.0007857019,"about_ca_system_score_gemma":0.0007348862,"threshold_uncertainty_score":0.007864356},"labels":[],"label_agreement":null},{"id":"W4210460553","doi":"10.36227/techrxiv.19101512.v1","title":"Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Unsupervised learning; Machine learning; Artificial neural network; Bridge (graph theory); Nonlinear system; Kernel (algebra); Supervised learning; Representation (politics); Fault detection and isolation; Mathematics","score_opus":0.03208151151129831,"score_gpt":0.28833446555087494,"score_spread":0.25625295403957665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210460553","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010376685,0.00013979658,0.9888364,0.00013924825,0.000008102198,0.000012724478,0.000015823174,0.00006829796,0.00040286343],"genre_scores_gemma":[0.806749,0.00046072603,0.19094771,0.00012935133,0.0000683871,0.000096414224,0.00012946477,0.00006419816,0.0013547562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992932,0.00026846852,0.000043063905,0.0001715505,0.000174399,0.000049270937],"domain_scores_gemma":[0.9969313,0.0018186364,0.00046661656,0.0004309257,0.00029179378,0.00006074702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014870415,0.00066244387,0.00072063063,0.00061138516,0.00034748108,0.001063361,0.000882307,0.00084860943,0.0007144034],"category_scores_gemma":[0.005670185,0.00028031197,0.0006973921,0.00033523346,0.0014081086,0.0019896566,0.0016303811,0.0013551681,0.00015130774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012317466,0.00007859974,0.0028821684,0.0002344432,0.00012767565,0.00012325821,0.00042128886,0.69119453,0.005780932,0.16211154,0.0007759159,0.13614643],"study_design_scores_gemma":[0.0000038781604,0.000029184843,0.00028721074,0.000012140937,0.000010396822,0.000027769049,0.000017023449,0.9530682,0.0014927428,0.04450878,0.0005327256,0.000009850275],"about_ca_topic_score_codex":0.0010169346,"about_ca_topic_score_gemma":0.0010331898,"teacher_disagreement_score":0.0014870415,"about_ca_system_score_codex":0.0007857019,"about_ca_system_score_gemma":0.0007348862,"threshold_uncertainty_score":0.007864356},"labels":[],"label_agreement":null},{"id":"W4210695660","doi":"10.1109/icbase53849.2021.00083","title":"The AlexNet, LeNet-5 and VGG NET applied to CIFAR-10","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Exploit; Net (polyhedron); Artificial intelligence; Convolutional neural network; Artificial neural network; Pattern recognition (psychology); Mathematics","score_opus":0.009545993534144143,"score_gpt":0.22203917948870952,"score_spread":0.21249318595456537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210695660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16686419,0.023891952,0.648937,0.005561938,0.005172809,0.0014203916,0.0075039165,0.04663392,0.09401389],"genre_scores_gemma":[0.4957221,0.010095672,0.4194596,0.0019798155,0.0004795037,0.0007745058,0.014930179,0.0017463973,0.05481227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921656,0.00010936152,0.000045061668,0.00023275542,0.00024814002,0.00014819237],"domain_scores_gemma":[0.99959666,0.000044612894,0.000029353292,0.00011185868,0.00016708382,0.000050462473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015743701,0.0024283908,0.00080693397,0.0023449766,0.00090767286,0.0015702948,0.002092432,0.0015602271,0.0033793543],"category_scores_gemma":[0.001964064,0.0005904076,0.0006705738,0.0018800952,0.0007450731,0.0025040659,0.0011988779,0.002387257,0.003001615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063033734,0.00038499455,0.0051206853,0.0007242555,0.00071417406,0.00042226684,0.00014022364,0.10427368,0.020831367,0.021063296,0.106268704,0.7394259],"study_design_scores_gemma":[0.00016512343,0.0006915336,0.007290485,0.0003602595,0.0003602882,0.00094481924,0.00020382147,0.76754475,0.096035585,0.03825749,0.087956615,0.00018917616],"about_ca_topic_score_codex":0.019133292,"about_ca_topic_score_gemma":0.023952289,"teacher_disagreement_score":0.019133292,"about_ca_system_score_codex":0.0015922914,"about_ca_system_score_gemma":0.001969834,"threshold_uncertainty_score":0.038043857},"labels":[],"label_agreement":null},{"id":"W4210725933","doi":"10.3386/w29708","title":"Addressing Endogeneity Using a Two-stage Copula Generated Regressor Approach","year":2022,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Endogeneity; Copula (linguistics); Econometrics; Stage (stratigraphy); Economics; Statistics; Mathematics; Geology","score_opus":0.7581970708045123,"score_gpt":0.5724356794634216,"score_spread":0.1857613913410907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210725933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039192503,0.0001009383,0.9947231,0.00015572368,0.00002637111,0.00007982326,0.000078314806,0.0001392044,0.00077732926],"genre_scores_gemma":[0.36529627,0.0007291269,0.6264517,0.0005642206,0.0001392821,0.0006516172,0.0006657222,0.00022302114,0.005279144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9955472,0.0027566268,0.00020676327,0.000630122,0.00064553204,0.00021383057],"domain_scores_gemma":[0.98050284,0.013045596,0.0020245381,0.0021317715,0.002055506,0.00023970677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008898779,0.0013831195,0.0014691778,0.0018058962,0.00061836414,0.0015021783,0.0022710855,0.0013297139,0.0049489085],"category_scores_gemma":[0.031547535,0.00075023284,0.001654903,0.001774442,0.001005276,0.0017128607,0.0018161684,0.0024771104,0.00052042276],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009796934,0.00023259452,0.019478332,0.00049387227,0.0008140694,0.0007879355,0.0003503151,0.5317006,0.0025301108,0.2822258,0.0056464416,0.15564202],"study_design_scores_gemma":[0.000026159942,0.000043968128,0.0020801,0.000042401,0.00008584946,0.00007510179,0.000034606488,0.95099056,0.0014565458,0.042022906,0.0031107445,0.000031065672],"about_ca_topic_score_codex":0.009919976,"about_ca_topic_score_gemma":0.0097356355,"teacher_disagreement_score":0.009919976,"about_ca_system_score_codex":0.0011511912,"about_ca_system_score_gemma":0.0040098955,"threshold_uncertainty_score":0.0470618},"labels":[],"label_agreement":null},{"id":"W4210870123","doi":"10.1101/2022.02.06.22270548","title":"TOWARDS A SINGLE PARAMETER FOR THE ASSESSMENT OF EEG OSCILLATIONS","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Amplitude; Electroencephalography; Flow (mathematics); Clockwise; Boundary (topology); Scalp; Physics; Mathematical analysis; Mathematics; Pattern recognition (psychology); Computer science; Artificial intelligence; Geometry; Psychology; Optics; Neuroscience; Biology","score_opus":0.06520624619116387,"score_gpt":0.3363678607855719,"score_spread":0.271161614594408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210870123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27606443,0.0019998278,0.714864,0.0004151312,0.00019532251,0.0003043342,0.0019717573,0.001897828,0.0022873583],"genre_scores_gemma":[0.83188695,0.00045030852,0.16551305,0.00007472554,0.00019629757,0.0003079022,0.0009913292,0.0001972761,0.00038230536],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99802065,0.0008404155,0.00019001644,0.00054195366,0.00034735675,0.00005955362],"domain_scores_gemma":[0.9904907,0.0060421806,0.0011401378,0.0011659756,0.000870333,0.00029071738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036650365,0.001437904,0.0012646152,0.0036974908,0.00019661317,0.0017434276,0.0006627186,0.0013354332,0.0013408299],"category_scores_gemma":[0.020410724,0.00034419695,0.0006211521,0.0020716537,0.00090932834,0.0019766733,0.0010846443,0.0013037638,0.00072305097],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020824643,0.0007762582,0.14567739,0.0012061325,0.0008139896,0.00050140696,0.0006843943,0.11631339,0.18078251,0.007541061,0.0026732844,0.54094774],"study_design_scores_gemma":[0.0001253265,0.0013208057,0.12233849,0.00033589944,0.000288338,0.0010523982,0.00043245833,0.8040719,0.044126295,0.022387916,0.0032147707,0.00030544438],"about_ca_topic_score_codex":0.0003923061,"about_ca_topic_score_gemma":0.00024271439,"teacher_disagreement_score":0.0036974908,"about_ca_system_score_codex":0.00032566272,"about_ca_system_score_gemma":0.00037615135,"threshold_uncertainty_score":0.019382775},"labels":[],"label_agreement":null},{"id":"W4211044774","doi":"10.1007/978-1-4471-7452-3_11","title":"Radial Basis Function Networks","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Radial basis function; Artificial neural network; Multilayer perceptron; Computer science; Artificial intelligence; Probabilistic neural network; Perceptron; Feedforward neural network; Radial basis function network; Pattern recognition (psychology); Time delay neural network","score_opus":0.014956208356058646,"score_gpt":0.20098105818775672,"score_spread":0.18602484983169806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211044774","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020375839,0.0352345,0.53352106,0.001538938,0.0023096502,0.00006706405,0.0007499949,0.0031169946,0.42142424],"genre_scores_gemma":[0.024542186,0.025030587,0.13864648,0.0005866404,0.000938617,0.00010581125,0.001743552,0.001089357,0.80731684],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99972504,0.000031127856,0.0000098609,0.00005831994,0.00015903899,0.000016598166],"domain_scores_gemma":[0.99979204,0.000055837423,0.0000121418825,0.000037847414,0.00009022553,0.000011913614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028713152,0.0011814957,0.0008492606,0.000849886,0.0003665382,0.0015677157,0.0010564011,0.001302037,0.04157738],"category_scores_gemma":[0.0010082951,0.00037103068,0.00037908414,0.001578588,0.00041256775,0.001652767,0.0011274774,0.0012703,0.04504469],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002098184,0.000030361738,0.00007369151,0.00029167274,0.000019046987,0.000040396873,0.00002707598,0.012377227,0.00179106,0.051808868,0.14998735,0.7835323],"study_design_scores_gemma":[0.0000065000654,0.000037732694,0.00040999803,0.00027876697,0.00002724292,0.00032014117,0.00002938911,0.07531349,0.004471654,0.0894517,0.8296086,0.00004485118],"about_ca_topic_score_codex":0.0012060049,"about_ca_topic_score_gemma":0.002242023,"teacher_disagreement_score":0.04157738,"about_ca_system_score_codex":0.00048981275,"about_ca_system_score_gemma":0.0005030816,"threshold_uncertainty_score":0.13909018},"labels":[],"label_agreement":null},{"id":"W4211049083","doi":"10.1007/978-1-4471-7452-3_20","title":"Kernel Methods","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Kernel (algebra); Computer science; Artificial intelligence; Pattern recognition (psychology); Mathematics; Pure mathematics","score_opus":0.038020444496068236,"score_gpt":0.31608939759521054,"score_spread":0.2780689530991423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211049083","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017752143,0.0080159465,0.864172,0.0006790305,0.00095747877,0.000046434565,0.0004404247,0.0034421622,0.120471425],"genre_scores_gemma":[0.050994422,0.01048154,0.30336124,0.00056199497,0.0010677491,0.00015183788,0.0022903855,0.0026359714,0.62845486],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99961215,0.000052108302,0.000017735974,0.00009072763,0.00020302026,0.000024233465],"domain_scores_gemma":[0.99959403,0.000098852586,0.000020064706,0.00012483203,0.0001418884,0.000020365718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004120623,0.0009985223,0.00087266526,0.0010937612,0.000404457,0.001632569,0.0009364104,0.0010434457,0.043901075],"category_scores_gemma":[0.0013548371,0.0004516661,0.0005806523,0.0011557725,0.00055720384,0.0019681763,0.0014503836,0.0017918983,0.045305595],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036801728,0.000056569705,0.00014094796,0.00029891118,0.00004241973,0.00003534196,0.00006115973,0.009660199,0.0038369154,0.13775732,0.13339806,0.7146753],"study_design_scores_gemma":[0.000015438289,0.00004792852,0.00072518905,0.0001470749,0.00004767188,0.0004180741,0.00005285943,0.14316688,0.010047266,0.19669266,0.6485794,0.000059544003],"about_ca_topic_score_codex":0.0007536727,"about_ca_topic_score_gemma":0.0011056525,"teacher_disagreement_score":0.043901075,"about_ca_system_score_codex":0.00041615558,"about_ca_system_score_gemma":0.0004999757,"threshold_uncertainty_score":0.14686376},"labels":[],"label_agreement":null},{"id":"W4211117539","doi":"10.1007/978-1-4471-7452-3_25","title":"Combining Multiple Learners: Data Fusion and Ensemble Learning","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Viewpoints; Brainstorming; Ensemble learning; Computer science; Artificial intelligence; Voting; Majority rule; Machine learning; Domain (mathematical analysis); Training set; Mathematics","score_opus":0.055424232122556046,"score_gpt":0.25664581083791754,"score_spread":0.2012215787153615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211117539","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015344289,0.022379031,0.9616004,0.0006494536,0.0007138626,0.000029304614,0.00010769843,0.00051472196,0.012471018],"genre_scores_gemma":[0.05600492,0.032878026,0.86870235,0.0005336001,0.0020028464,0.00013776196,0.00076323375,0.00046766107,0.03850963],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991003,0.00021246716,0.000056085235,0.00017889908,0.00041256708,0.000039697556],"domain_scores_gemma":[0.9991264,0.00047155182,0.000038984843,0.00012360066,0.0002149912,0.000024543693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00208519,0.0010137941,0.0014874104,0.0012294223,0.00032530617,0.0023896145,0.0019267474,0.0016945129,0.005523468],"category_scores_gemma":[0.0032999655,0.0006084577,0.0010552468,0.0032230297,0.0005756069,0.003779431,0.0018035107,0.0029575655,0.0039722887],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044140197,0.00006369344,0.00027853603,0.0003661465,0.00016988207,0.00005859819,0.00010033208,0.031863645,0.0026500733,0.046676047,0.025789756,0.8919391],"study_design_scores_gemma":[0.000013827931,0.000103369624,0.0009152605,0.00036844605,0.00015751556,0.0005954635,0.000107549844,0.5941218,0.008912094,0.25788417,0.1367303,0.00009019481],"about_ca_topic_score_codex":0.0005839518,"about_ca_topic_score_gemma":0.0010131762,"teacher_disagreement_score":0.005523468,"about_ca_system_score_codex":0.0003954694,"about_ca_system_score_gemma":0.00046516582,"threshold_uncertainty_score":0.018477857},"labels":[],"label_agreement":null},{"id":"W4211235501","doi":"10.1007/978-1-4471-7452-3_6","title":"Multilayer Perceptrons: Other Learing Techniques","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Perceptron; Computer science; Artificial intelligence; Artificial neural network","score_opus":0.02685268768894598,"score_gpt":0.25148190694668626,"score_spread":0.2246292192577403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211235501","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020876355,0.0052696276,0.93685544,0.001443138,0.00088347844,0.00003211062,0.0004437719,0.003641283,0.049343433],"genre_scores_gemma":[0.064538665,0.012993982,0.65034795,0.0010528965,0.001427,0.000098933735,0.0013850686,0.0018345074,0.2663209],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996338,0.000069972666,0.000026844824,0.00006176432,0.00017892923,0.000028614428],"domain_scores_gemma":[0.9993907,0.00028471262,0.000035344176,0.00013978628,0.000121865975,0.000027500571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008391428,0.00111214,0.0007672021,0.00075709814,0.00037636832,0.0017831017,0.0011683939,0.001247543,0.032404322],"category_scores_gemma":[0.0028516336,0.0005813659,0.0007428164,0.0013001498,0.0006359866,0.0040527536,0.0015117193,0.002652029,0.0136122415],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007318204,0.00005743445,0.00020885053,0.000499496,0.00005514464,0.00006521247,0.00009997886,0.023651034,0.004633074,0.09818555,0.09603021,0.77644074],"study_design_scores_gemma":[0.000023758437,0.00005260755,0.0006366239,0.00026368536,0.000072616,0.00024026727,0.0000644424,0.32305953,0.01093349,0.40372905,0.26085865,0.000065323526],"about_ca_topic_score_codex":0.0011380641,"about_ca_topic_score_gemma":0.0023976637,"teacher_disagreement_score":0.032404322,"about_ca_system_score_codex":0.00045929322,"about_ca_system_score_gemma":0.000461092,"threshold_uncertainty_score":0.108403265},"labels":[],"label_agreement":null},{"id":"W4211266147","doi":"10.1103/physrevresearch.4.043143","title":"Decomposing neural networks as mappings of correlation functions","year":2022,"lang":"en","type":"preprint","venue":"Physical Review Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie","keywords":"MNIST database; Artificial neural network; Computer science; Transformation (genetics); Artificial intelligence; Iterated function; Correlation; Layer (electronics); Task (project management); Machine learning; Pattern recognition (psychology); Data mining; Mathematics","score_opus":0.09878273249964624,"score_gpt":0.440113722855962,"score_spread":0.34133099035631576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211266147","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061372958,0.0032688212,0.9155497,0.0021505333,0.0001539578,0.000059619706,0.0002005766,0.0002503557,0.016993491],"genre_scores_gemma":[0.85688967,0.0069594304,0.12194177,0.00057422067,0.000409021,0.0002024152,0.00025775877,0.00023659816,0.012529149],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99959284,0.00016913755,0.00002131416,0.00007331989,0.00010393406,0.00003949861],"domain_scores_gemma":[0.9989127,0.0005680981,0.00017967346,0.00015228866,0.0001269779,0.000060314997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011009496,0.000755311,0.0004289003,0.0011598201,0.00027092747,0.0017603852,0.0007695566,0.0009522394,0.0023682006],"category_scores_gemma":[0.0036646887,0.00042334403,0.00061786897,0.00079590885,0.0016167861,0.003756684,0.0010359886,0.0014208711,0.00041757224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003239448,0.000019492087,0.0005911294,0.00012733744,0.00004795819,0.00013256667,0.00012505267,0.13226405,0.004118934,0.8345629,0.0013469661,0.026631236],"study_design_scores_gemma":[0.000005415637,0.000015894857,0.0004183057,0.000028503113,0.00000791395,0.000057967678,0.00002402243,0.37560463,0.00059338467,0.621305,0.0019277695,0.000011167953],"about_ca_topic_score_codex":0.0010292209,"about_ca_topic_score_gemma":0.000629911,"teacher_disagreement_score":0.0023682006,"about_ca_system_score_codex":0.0010059294,"about_ca_system_score_gemma":0.0005321395,"threshold_uncertainty_score":0.007922471},"labels":[],"label_agreement":null},{"id":"W4212895448","doi":"10.1109/conf-spml54095.2021.00056","title":"Big Data Classification and Machine Learning Using Zillow Estimates","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Big data; Machine learning; Data mining","score_opus":0.21303911436452905,"score_gpt":0.3159000880098156,"score_spread":0.10286097364528657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212895448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0524223,0.0033053218,0.93626994,0.00086454686,0.00023062798,0.00011579142,0.0015173003,0.0011348563,0.004139347],"genre_scores_gemma":[0.60954916,0.002231149,0.37906083,0.00013114166,0.00039907687,0.00018991262,0.0044543133,0.0001943197,0.0037900351],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993187,0.00013873607,0.0000561965,0.00020665937,0.00022183836,0.000057897538],"domain_scores_gemma":[0.99680734,0.0017190399,0.00044946524,0.000327735,0.0006065285,0.000089991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00152965,0.00084847317,0.0006056282,0.0037965975,0.00040953702,0.0025772378,0.0009074153,0.00062718836,0.001980873],"category_scores_gemma":[0.012918429,0.00038651086,0.0007054698,0.0027122472,0.0005174241,0.0026422134,0.00094636396,0.0012551347,0.0006990887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045893402,0.00012584533,0.05216638,0.0003733702,0.0005024106,0.00023018997,0.00028158986,0.34892702,0.0042137555,0.08876494,0.017355924,0.48659965],"study_design_scores_gemma":[0.000018595247,0.000028564815,0.005990833,0.000063896936,0.000038686583,0.000035669436,0.000050239832,0.9517205,0.0014063308,0.033936422,0.0066742,0.000036022633],"about_ca_topic_score_codex":0.010921982,"about_ca_topic_score_gemma":0.010426861,"teacher_disagreement_score":0.010921982,"about_ca_system_score_codex":0.0010743779,"about_ca_system_score_gemma":0.00076731545,"threshold_uncertainty_score":0.021716833},"labels":[],"label_agreement":null},{"id":"W4220783810","doi":"10.18280/i2m.210102","title":"Artificial Neural Networks Oriented Testbed for Multiantenna Wireless Application","year":2022,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Testbed; Computer science; Artificial neural network; Feed forward; Feedforward neural network; Backpropagation; Wireless; Algorithm; Wireless network; Mean squared error; Computational complexity theory; Artificial intelligence; Mathematics; Engineering; Computer network; Telecommunications; Statistics; Control engineering","score_opus":0.024812648316628508,"score_gpt":0.28407333401937035,"score_spread":0.25926068570274186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220783810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27854508,0.00059110636,0.69281787,0.00056488276,0.0005509354,0.00037999736,0.0009110648,0.0049915733,0.020647572],"genre_scores_gemma":[0.8869091,0.00024475245,0.105614714,0.00009113994,0.00001993545,0.00032938432,0.0005678115,0.00007506783,0.006148028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996823,0.000085023516,0.00002056957,0.000045359368,0.00012986985,0.000036761663],"domain_scores_gemma":[0.99962926,0.00010025543,0.000046507997,0.00007190891,0.00012109801,0.000030996285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046120584,0.00042641858,0.00025530174,0.00026281603,0.0002188837,0.00042876235,0.0007213362,0.0004213931,0.002269419],"category_scores_gemma":[0.0007476488,0.00010913268,0.00021502936,0.00021380089,0.00019882621,0.00044991568,0.00034858898,0.00048951054,0.00048750872],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084133947,0.0007237761,0.007882658,0.00086924894,0.00017241086,0.0008947181,0.00018069743,0.49780077,0.27614173,0.020648226,0.0097257905,0.18411863],"study_design_scores_gemma":[0.000042393345,0.001040229,0.0030918075,0.00004677088,0.000036292986,0.00023810363,0.000075826254,0.8197618,0.15575686,0.0047032684,0.0151727935,0.00003378113],"about_ca_topic_score_codex":0.00073480426,"about_ca_topic_score_gemma":0.0008202459,"teacher_disagreement_score":0.002269419,"about_ca_system_score_codex":0.0003304084,"about_ca_system_score_gemma":0.000314378,"threshold_uncertainty_score":0.007591963},"labels":[],"label_agreement":null},{"id":"W4220797294","doi":"10.1109/tit.2022.3155349","title":"IEEE Transactions on Information Theory publication information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.009156959172592861,"score_gpt":0.21540977404581182,"score_spread":0.20625281487321898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220797294","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073632444,0.041065272,0.4078097,0.025557896,0.04692853,0.0007379434,0.02859816,0.005462615,0.43647674],"genre_scores_gemma":[0.12103247,0.07721286,0.099727295,0.0049459753,0.012654195,0.0008991606,0.04606473,0.002194734,0.63526857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980058,0.0003531626,0.00016168886,0.00020072536,0.0011487462,0.00012988472],"domain_scores_gemma":[0.9946779,0.0017836735,0.0001828315,0.0013241188,0.0018538217,0.0001776851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020620713,0.0012995908,0.0020753695,0.0038026476,0.00096002803,0.004441838,0.0011496176,0.0020986642,0.18658403],"category_scores_gemma":[0.011094833,0.00058022304,0.0007657712,0.005049121,0.00082062976,0.0042192605,0.0017576806,0.0025192474,0.07861321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016382568,0.00012273522,0.0006897795,0.00085827295,0.00006272511,0.00014853044,0.00005241059,0.0032309913,0.0011106146,0.07595082,0.524427,0.3931823],"study_design_scores_gemma":[0.00006565506,0.00010452829,0.0013115945,0.0008803995,0.00015325854,0.00051396637,0.00011317045,0.033855367,0.0028811428,0.1821534,0.77789456,0.00007305823],"about_ca_topic_score_codex":0.0014865134,"about_ca_topic_score_gemma":0.0012459535,"teacher_disagreement_score":0.18658403,"about_ca_system_score_codex":0.001008677,"about_ca_system_score_gemma":0.0021853258,"threshold_uncertainty_score":0.62418574},"labels":[],"label_agreement":null},{"id":"W4220839546","doi":"10.31219/osf.io/cew42","title":"CogNGen: Constructing the Kernel of a Hyperdimensional Predictive Processing Cognitive Architecture","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Cognitive architecture; Computer science; Cognition; Artificial intelligence; Cognitive model; Machine learning; Generative model; Soar; Cognitive science; Generative grammar; Psychology","score_opus":0.018907674835636584,"score_gpt":0.26507165789323717,"score_spread":0.2461639830576006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220839546","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04206079,0.000087270106,0.95192957,0.00035336387,0.000025392712,0.00003330715,0.000060745428,0.00055083964,0.0048987647],"genre_scores_gemma":[0.64312303,0.00016260857,0.35270864,0.00017597075,0.000027778618,0.00012293762,0.00014506245,0.00011593458,0.0034180963],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997528,0.000070953,0.000010486368,0.00007124973,0.000059275084,0.00003522418],"domain_scores_gemma":[0.9993838,0.00021707847,0.000053140782,0.00018401472,0.000107182525,0.000054731314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005759968,0.00030831632,0.00041002824,0.00041245355,0.000383886,0.0012332204,0.0011046818,0.00060025114,0.002835435],"category_scores_gemma":[0.002222178,0.0003246447,0.00079510227,0.00029145426,0.0018405732,0.0021275217,0.0016127272,0.0011502721,0.00039897513],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013091454,0.00006893726,0.0025876535,0.00009767314,0.00009859723,0.00019662462,0.00039949745,0.3153956,0.007471785,0.5779277,0.0020894434,0.093535535],"study_design_scores_gemma":[0.000010633103,0.000025834905,0.0002346954,0.000010499847,0.000011712498,0.00003703154,0.000024150695,0.8122887,0.0014650751,0.18406948,0.0018123754,0.00000972573],"about_ca_topic_score_codex":0.0032077162,"about_ca_topic_score_gemma":0.0027503502,"teacher_disagreement_score":0.0032077162,"about_ca_system_score_codex":0.0008431339,"about_ca_system_score_gemma":0.00083385425,"threshold_uncertainty_score":0.009485424},"labels":[],"label_agreement":null},{"id":"W4225909377","doi":"10.4018/978-1-6684-2408-7.ch074","title":"Chaotic System Design Based on Recurrent Artificial Neural Network for the Simulation of EEG Time Series","year":2021,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Chaotic; Artificial neural network; Lorenz system; Computer science; Nonlinear system; Control theory (sociology); Series (stratigraphy); Time series; Generator (circuit theory); Artificial intelligence; Control engineering; Machine learning; Engineering; Control (management)","score_opus":0.03885519931200261,"score_gpt":0.25657192227025494,"score_spread":0.21771672295825234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225909377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022548793,0.0008806169,0.9624173,0.00012032829,0.00010657234,0.000079022655,0.00007713205,0.00071279483,0.013057549],"genre_scores_gemma":[0.64578533,0.0015019904,0.33328307,0.000065771565,0.000053936896,0.0004670693,0.00030933254,0.00021202852,0.018321514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999105,0.000033680983,0.000005020584,0.00001607235,0.000029882554,0.0000048891607],"domain_scores_gemma":[0.99989116,0.000055412034,0.000010849931,0.000009664725,0.000029019282,0.000003956286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002655352,0.0004695032,0.0004073047,0.00019971236,0.00017150333,0.00036076352,0.0005576621,0.00052716554,0.0027307514],"category_scores_gemma":[0.0005570045,0.00020398402,0.00049962074,0.00023600909,0.0001841554,0.0003375632,0.00024778084,0.0005386475,0.00052546535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000335745,0.0000211032,0.00027825809,0.00014253771,0.000036188685,0.000104969404,0.000060556576,0.9468458,0.009059174,0.009266959,0.0009328457,0.033218082],"study_design_scores_gemma":[0.0000019801787,0.000010430964,0.000038007216,0.0000039099887,0.000003171912,0.000009573349,0.0000019348317,0.9981029,0.0005313529,0.0005661835,0.00072874647,0.0000017222394],"about_ca_topic_score_codex":0.0018274862,"about_ca_topic_score_gemma":0.002163387,"teacher_disagreement_score":0.0027307514,"about_ca_system_score_codex":0.00028527115,"about_ca_system_score_gemma":0.00030657463,"threshold_uncertainty_score":0.009135306},"labels":[],"label_agreement":null},{"id":"W4226079145","doi":"10.4018/978-1-6684-2408-7.ch012","title":"Evaluating the Effects of Size and Precision of Training Data on ANN Training Performance for the Prediction of Chaotic Time Series Patterns","year":2021,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Chaotic; Artificial neural network; Training (meteorology); Computer science; Time series; Attractor; Mean squared error; Artificial intelligence; Nonlinear system; Series (stratigraphy); Machine learning; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.07798245354231945,"score_gpt":0.30406718325883847,"score_spread":0.22608472971651902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226079145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8815961,0.0069420873,0.09987676,0.0003909045,0.00027915498,0.00021473497,0.0004585611,0.0011727427,0.009069036],"genre_scores_gemma":[0.9334016,0.001451558,0.062316842,0.0000572502,0.000031275827,0.00010553427,0.00059258816,0.00012613104,0.0019171535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989322,0.0003532424,0.00013889087,0.00020678918,0.0003156618,0.00005327403],"domain_scores_gemma":[0.98554415,0.011387331,0.00059321773,0.0009383678,0.0014414091,0.00009546773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029274402,0.00093627255,0.0006093755,0.0005448122,0.00026169035,0.0008684294,0.0005651523,0.0009342208,0.0010344165],"category_scores_gemma":[0.01598714,0.00024690613,0.00031683932,0.0007481281,0.00030902287,0.0011651481,0.00037516435,0.0005695685,0.00039252776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022814628,0.000679461,0.01960996,0.0010801473,0.00028782318,0.00042018524,0.00026208872,0.4405053,0.054919396,0.00096967793,0.0016730535,0.47731143],"study_design_scores_gemma":[0.000054450553,0.0017689135,0.022116324,0.0001961818,0.00017780246,0.000228919,0.00014432771,0.9046971,0.068178594,0.0007112495,0.0016655951,0.000060545895],"about_ca_topic_score_codex":0.0015686465,"about_ca_topic_score_gemma":0.0015082353,"teacher_disagreement_score":0.0029274402,"about_ca_system_score_codex":0.0002880333,"about_ca_system_score_gemma":0.00029085993,"threshold_uncertainty_score":0.015481949},"labels":[],"label_agreement":null},{"id":"W4226106744","doi":"10.1109/tfuzz.2022.3157947","title":"On Fractional Tikhonov Regularization: Application to the Adaptive Network-Based Fuzzy Inference System for Regression Problems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"European Social Fund; National Natural Science Foundation of China","keywords":"Tikhonov regularization; Adaptive neuro fuzzy inference system; Regularization (linguistics); Regularization perspectives on support vector machines; Mathematics; Backpropagation; Algorithm; Early stopping; Computer science; Mathematical optimization; Artificial intelligence; Fuzzy logic; Artificial neural network; Fuzzy control system; Inverse problem","score_opus":0.02242840776839986,"score_gpt":0.24892544113112472,"score_spread":0.22649703336272486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226106744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041810144,0.00053501414,0.993666,0.00016131742,0.00004000864,0.000013900814,0.000008447126,0.00007387432,0.0013204716],"genre_scores_gemma":[0.50181514,0.0017625766,0.49262047,0.00019813479,0.0001747934,0.00011571696,0.000069503156,0.00008560733,0.0031579945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994481,0.0002153607,0.000029610133,0.00006715955,0.00021131492,0.0000282764],"domain_scores_gemma":[0.9993048,0.00047417896,0.00005010726,0.000051743365,0.000105867366,0.000013291988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013016181,0.0006250952,0.0007339819,0.000602142,0.0004096157,0.0006809524,0.0006208,0.0011599539,0.0007634952],"category_scores_gemma":[0.0030640066,0.00021739659,0.00070043677,0.00066054944,0.0009633798,0.000683677,0.00060685433,0.0013479976,0.00020186692],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005614134,0.000032164327,0.0003639373,0.00015843437,0.00006472919,0.00012773708,0.00011163814,0.8699656,0.008924066,0.043965764,0.00058971567,0.07564003],"study_design_scores_gemma":[0.0000017389351,0.000011211857,0.000054314787,0.0000062334507,0.0000036064544,0.000018781557,0.0000029379603,0.99221987,0.00054959697,0.0065244166,0.00060254964,0.0000046077394],"about_ca_topic_score_codex":0.005374839,"about_ca_topic_score_gemma":0.0038481879,"teacher_disagreement_score":0.005374839,"about_ca_system_score_codex":0.0006140715,"about_ca_system_score_gemma":0.00075207034,"threshold_uncertainty_score":0.010687113},"labels":[],"label_agreement":null},{"id":"W4226248578","doi":"10.1109/tit.2022.3163833","title":"IEEE Transactions on Information Theory publication information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Information theory; Computer science; Computer security; Mathematics; Statistics","score_opus":0.009156959172592861,"score_gpt":0.21540977404581182,"score_spread":0.20625281487321898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226248578","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073632444,0.041065272,0.4078097,0.025557896,0.04692853,0.0007379434,0.02859816,0.005462615,0.43647674],"genre_scores_gemma":[0.12103247,0.07721286,0.099727295,0.0049459753,0.012654195,0.0008991606,0.04606473,0.002194734,0.63526857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980058,0.0003531626,0.00016168886,0.00020072536,0.0011487462,0.00012988472],"domain_scores_gemma":[0.9946779,0.0017836735,0.0001828315,0.0013241188,0.0018538217,0.0001776851],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020620713,0.0012995908,0.0020753695,0.0038026476,0.00096002803,0.004441838,0.0011496176,0.0020986642,0.18658403],"category_scores_gemma":[0.011094833,0.00058022304,0.0007657712,0.005049121,0.00082062976,0.0042192605,0.0017576806,0.0025192474,0.07861321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016382568,0.00012273522,0.0006897795,0.00085827295,0.00006272511,0.00014853044,0.00005241059,0.0032309913,0.0011106146,0.07595082,0.524427,0.3931823],"study_design_scores_gemma":[0.00006565506,0.00010452829,0.0013115945,0.0008803995,0.00015325854,0.00051396637,0.00011317045,0.033855367,0.0028811428,0.1821534,0.77789456,0.00007305823],"about_ca_topic_score_codex":0.0014865134,"about_ca_topic_score_gemma":0.0012459535,"teacher_disagreement_score":0.813416,"about_ca_system_score_codex":0.001008677,"about_ca_system_score_gemma":0.0021853258,"threshold_uncertainty_score":0.62418574},"labels":[],"label_agreement":null},{"id":"W4226326665","doi":"10.1109/tit.2021.3137975","title":"IEEE Transactions on Information Theory publication information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.009156959172592861,"score_gpt":0.21540977404581182,"score_spread":0.20625281487321898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226326665","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073632444,0.041065272,0.4078097,0.025557896,0.04692853,0.0007379434,0.02859816,0.005462615,0.43647674],"genre_scores_gemma":[0.12103247,0.07721286,0.099727295,0.0049459753,0.012654195,0.0008991606,0.04606473,0.002194734,0.63526857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980058,0.0003531626,0.00016168886,0.00020072536,0.0011487462,0.00012988472],"domain_scores_gemma":[0.9946779,0.0017836735,0.0001828315,0.0013241188,0.0018538217,0.0001776851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020620713,0.0012995908,0.0020753695,0.0038026476,0.00096002803,0.004441838,0.0011496176,0.0020986642,0.18658403],"category_scores_gemma":[0.011094833,0.00058022304,0.0007657712,0.005049121,0.00082062976,0.0042192605,0.0017576806,0.0025192474,0.07861321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016382568,0.00012273522,0.0006897795,0.00085827295,0.00006272511,0.00014853044,0.00005241059,0.0032309913,0.0011106146,0.07595082,0.524427,0.3931823],"study_design_scores_gemma":[0.00006565506,0.00010452829,0.0013115945,0.0008803995,0.00015325854,0.00051396637,0.00011317045,0.033855367,0.0028811428,0.1821534,0.77789456,0.00007305823],"about_ca_topic_score_codex":0.0014865134,"about_ca_topic_score_gemma":0.0012459535,"teacher_disagreement_score":0.18658403,"about_ca_system_score_codex":0.001008677,"about_ca_system_score_gemma":0.0021853258,"threshold_uncertainty_score":0.62418574},"labels":[],"label_agreement":null},{"id":"W4226367741","doi":"10.1109/tit.2022.3147661","title":"IEEE Transactions on Information Theory publication information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Statistics; Mathematics","score_opus":0.009156959172592861,"score_gpt":0.21540977404581182,"score_spread":0.20625281487321898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226367741","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073632444,0.041065272,0.4078097,0.025557896,0.04692853,0.0007379434,0.02859816,0.005462615,0.43647674],"genre_scores_gemma":[0.12103247,0.07721286,0.099727295,0.0049459753,0.012654195,0.0008991606,0.04606473,0.002194734,0.63526857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980058,0.0003531626,0.00016168886,0.00020072536,0.0011487462,0.00012988472],"domain_scores_gemma":[0.9946779,0.0017836735,0.0001828315,0.0013241188,0.0018538217,0.0001776851],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020620713,0.0012995908,0.0020753695,0.0038026476,0.00096002803,0.004441838,0.0011496176,0.0020986642,0.18658403],"category_scores_gemma":[0.011094833,0.00058022304,0.0007657712,0.005049121,0.00082062976,0.0042192605,0.0017576806,0.0025192474,0.07861321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016382568,0.00012273522,0.0006897795,0.00085827295,0.00006272511,0.00014853044,0.00005241059,0.0032309913,0.0011106146,0.07595082,0.524427,0.3931823],"study_design_scores_gemma":[0.00006565506,0.00010452829,0.0013115945,0.0008803995,0.00015325854,0.00051396637,0.00011317045,0.033855367,0.0028811428,0.1821534,0.77789456,0.00007305823],"about_ca_topic_score_codex":0.0014865134,"about_ca_topic_score_gemma":0.0012459535,"teacher_disagreement_score":0.813416,"about_ca_system_score_codex":0.001008677,"about_ca_system_score_gemma":0.0021853258,"threshold_uncertainty_score":0.62418574},"labels":[],"label_agreement":null},{"id":"W4226368216","doi":"10.1109/access.2022.3170042","title":"Activation Function Modulation in Generative Triangular Recurrent Neural Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Computer science; Recurrent neural network; Activation function; Artificial neural network; Chaotic; Algorithm; Artificial intelligence","score_opus":0.04103157873423388,"score_gpt":0.29435286194465743,"score_spread":0.2533212832104236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226368216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04063826,0.00024236608,0.9567259,0.00012854155,0.00002319179,0.000022569762,0.000023038187,0.00034812163,0.0018480755],"genre_scores_gemma":[0.9373159,0.00017746851,0.060400724,0.00007095251,0.000020230125,0.00004750583,0.00005054038,0.00004489285,0.0018718633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997447,0.000103645056,0.000013451101,0.000053647673,0.000055007007,0.000029422275],"domain_scores_gemma":[0.99927455,0.00047947836,0.000084157284,0.00007681104,0.0000662656,0.000018841423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008706988,0.00046189528,0.0003359637,0.00023223578,0.00016124356,0.000405754,0.00082750135,0.00061785517,0.00080336595],"category_scores_gemma":[0.0032852648,0.00026728754,0.0003560372,0.00025055508,0.00066600845,0.00067871227,0.0005157482,0.00078757707,0.00021218842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043746713,0.0000138039395,0.00034495178,0.000022824795,0.000013900653,0.00004865502,0.000060479433,0.9437737,0.005098166,0.012831023,0.00024315563,0.037505593],"study_design_scores_gemma":[9.792876e-7,0.00000668042,0.00003371336,0.0000013675883,0.0000013363821,0.000005578821,0.0000013018332,0.9978249,0.0005835358,0.0014570494,0.00008196501,0.0000016434647],"about_ca_topic_score_codex":0.001451142,"about_ca_topic_score_gemma":0.0013452229,"teacher_disagreement_score":0.001451142,"about_ca_system_score_codex":0.0005209491,"about_ca_system_score_gemma":0.00019496029,"threshold_uncertainty_score":0.004604757},"labels":[],"label_agreement":null},{"id":"W4229335653","doi":"10.7287/peerj-cs.951v0.2/reviews/1","title":"Peer Review #1 of \"Deep learning methods for inverse problems (v0.2)\"","year":2022,"lang":"en","type":"peer-review","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Inverse; Computer science; Artificial intelligence; Psychology; Mathematics; Geometry","score_opus":0.09311610629671357,"score_gpt":0.40710407858531356,"score_spread":0.3139879722886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229335653","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019630525,0.0142574115,0.1723802,0.09919731,0.18928726,0.0062474688,0.016435372,0.025245441,0.45731905],"genre_scores_gemma":[0.056371238,0.00799207,0.053564437,0.0074974513,0.019196944,0.001784837,0.02035676,0.009297163,0.8239392],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9885605,0.0021112838,0.0006238831,0.0009911681,0.007083502,0.00062971184],"domain_scores_gemma":[0.9092707,0.009302449,0.0024511202,0.0097952,0.063286655,0.005893911],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009793078,0.0009177689,0.0015671015,0.0040789554,0.0028753497,0.004624627,0.0028757844,0.0021780636,0.20128086],"category_scores_gemma":[0.07335228,0.0005985775,0.00078530033,0.002855312,0.0010804449,0.0038637638,0.0040651094,0.002105039,0.16195282],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004840713,0.000028500484,0.0007195068,0.00040349184,0.000025811376,0.00009516093,0.00005648599,0.00041134388,0.00082979514,0.0036519594,0.83409995,0.15962964],"study_design_scores_gemma":[0.000027723248,0.000030346193,0.00094133185,0.00014223497,0.000014890674,0.00011774736,0.0000661719,0.0029876307,0.0015476418,0.003563392,0.9905353,0.00002564079],"about_ca_topic_score_codex":0.0031847334,"about_ca_topic_score_gemma":0.009113121,"teacher_disagreement_score":0.9902069,"about_ca_system_score_codex":0.0014377122,"about_ca_system_score_gemma":0.0069565615,"threshold_uncertainty_score":0.6733515},"labels":[],"label_agreement":null},{"id":"W4229693134","doi":"10.1109/ccece.1993.332192","title":"Generalized artificial neural networks (GANN)","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Computer science; Computation; Quadratic equation; Relation (database); Interpretation (philosophy); Bounded function; Artificial intelligence; Nonlinear system; Very-large-scale integration; Theoretical computer science; Algorithm; Mathematics; Programming language; Data mining; Embedded system","score_opus":0.03716053456968826,"score_gpt":0.24148579264007725,"score_spread":0.20432525807038898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229693134","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014315322,0.00306139,0.96088105,0.0005099213,0.00045367962,0.000060609334,0.00032985094,0.0016369938,0.018751092],"genre_scores_gemma":[0.4552657,0.005026965,0.50493443,0.0010823971,0.00031932362,0.00032521764,0.0010357434,0.00026283634,0.03174733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996488,0.000103103666,0.000021487122,0.000090449495,0.000106618965,0.000029484194],"domain_scores_gemma":[0.999711,0.00008722202,0.000037530648,0.000064190805,0.00008736862,0.000012655058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047030137,0.0005576818,0.00065956055,0.00045060695,0.00023306119,0.000801441,0.0013137782,0.00094954413,0.0032403925],"category_scores_gemma":[0.0013267486,0.00023502982,0.00065770413,0.0007329412,0.00056815386,0.000932433,0.00064989517,0.0010055667,0.000983033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007355653,0.000029505016,0.00092570897,0.0003056461,0.00016028747,0.0002079223,0.000096619486,0.58193797,0.0059830486,0.19844776,0.008898581,0.20293331],"study_design_scores_gemma":[0.000009446708,0.0000333403,0.0002732902,0.000022784472,0.00002073007,0.00011410493,0.0000099205545,0.9322243,0.001201744,0.04824252,0.017832503,0.000015446414],"about_ca_topic_score_codex":0.002789218,"about_ca_topic_score_gemma":0.0044320016,"teacher_disagreement_score":0.0032403925,"about_ca_system_score_codex":0.00057492126,"about_ca_system_score_gemma":0.00040261666,"threshold_uncertainty_score":0.010840237},"labels":[],"label_agreement":null},{"id":"W4230236456","doi":"10.1002/0470009403.ch15","title":"Mathematical Modeling in Electrochemistry","year":2005,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Reading (process); Management science; Engineering drawing; Engineering; Linguistics; Philosophy","score_opus":0.01572925272377324,"score_gpt":0.24617327450856633,"score_spread":0.2304440217847931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230236456","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006104896,0.019284684,0.6830294,0.008345689,0.0022011972,0.00006951033,0.00046390167,0.0004558603,0.28004485],"genre_scores_gemma":[0.3599968,0.042324822,0.23641181,0.0033117693,0.004599173,0.00046765272,0.0009026341,0.00068945304,0.35129586],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974006,0.00008188372,0.000014093449,0.000039315524,0.00010802788,0.0000167448],"domain_scores_gemma":[0.9998035,0.00009665174,0.000016037346,0.000032298045,0.000043489963,0.000008031817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044746342,0.0008081749,0.00088999735,0.0008162238,0.0005326307,0.0017323872,0.0012319321,0.0014375205,0.011697336],"category_scores_gemma":[0.0010192698,0.00035995277,0.00087955553,0.00088892266,0.0013071899,0.0030282035,0.00082411733,0.00187599,0.003603596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014761642,0.0000064969013,0.000015382306,0.000052629443,0.0000032720134,0.000015849448,0.000021101516,0.005344475,0.00021785959,0.985246,0.003985203,0.0050903144],"study_design_scores_gemma":[0.0000026591124,0.000005458299,0.000048252008,0.000038409442,0.000004415858,0.000045018813,0.000014459087,0.03811173,0.00039463484,0.91784114,0.04348655,0.00000734035],"about_ca_topic_score_codex":0.0009823968,"about_ca_topic_score_gemma":0.000703789,"teacher_disagreement_score":0.011697336,"about_ca_system_score_codex":0.001049596,"about_ca_system_score_gemma":0.0005489324,"threshold_uncertainty_score":0.039131463},"labels":[],"label_agreement":null},{"id":"W4230520697","doi":"10.1016/b978-008044046-0/50473-5","title":"Controlling approximation error","year":2003,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Compute Canada","funders":"","keywords":"Computer science","score_opus":0.022261591277079863,"score_gpt":0.24112091222845358,"score_spread":0.21885932095137373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230520697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006594611,0.0039959275,0.8706507,0.00046214528,0.0006130301,0.000029069563,0.00016042084,0.0034659014,0.11402815],"genre_scores_gemma":[0.25825137,0.005847797,0.29004174,0.00050414674,0.00047289632,0.00014231635,0.0006801312,0.0019301326,0.44212946],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997756,0.000022563001,0.000009620997,0.00005272271,0.00012047316,0.000018943176],"domain_scores_gemma":[0.99973804,0.00010077268,0.000017591863,0.0000813254,0.0000491912,0.000013040201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030198944,0.000752646,0.00069279317,0.00044216664,0.00025974624,0.00128349,0.00080551545,0.0007571364,0.027391626],"category_scores_gemma":[0.0013581287,0.00033096422,0.00028365312,0.0005500296,0.00056813844,0.0011462888,0.0010613191,0.00127043,0.009303593],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011433297,0.00006262224,0.00028751008,0.0001991226,0.000031638312,0.000085791406,0.00009012014,0.04756038,0.024973975,0.0865744,0.03385754,0.8061626],"study_design_scores_gemma":[0.000030632767,0.000105600484,0.0006423225,0.00015974345,0.000057819278,0.00029737578,0.00005172981,0.6937746,0.038774353,0.0891781,0.17689359,0.00003422381],"about_ca_topic_score_codex":0.0016975357,"about_ca_topic_score_gemma":0.0018793206,"teacher_disagreement_score":0.027391626,"about_ca_system_score_codex":0.00038096018,"about_ca_system_score_gemma":0.0002790933,"threshold_uncertainty_score":0.091634095},"labels":[],"label_agreement":null},{"id":"W4230766745","doi":"10.24124/2019/58993","title":"Neighbouring proximity: A key impact factor of deep machine learning","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"University of Northern British Columbia","keywords":"Deep learning; Artificial intelligence; Computer science; Key (lock); Machine learning; Autopilot; Engineering","score_opus":0.017184561604882843,"score_gpt":0.2812909143772621,"score_spread":0.26410635277237926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230766745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4049354,0.008995642,0.49196908,0.0043555954,0.00081134064,0.0002937691,0.00074699445,0.0020644197,0.08582777],"genre_scores_gemma":[0.97349554,0.0011870809,0.01829661,0.00020970324,0.00016540174,0.0000516438,0.0002813431,0.000267411,0.0060453974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981719,0.00034628544,0.00007466384,0.0003654008,0.00085982855,0.00018192444],"domain_scores_gemma":[0.9858941,0.009422569,0.0008247494,0.0014216172,0.0016691232,0.00076790026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025285075,0.0006671959,0.00088356895,0.0008146612,0.0010519805,0.001960483,0.0011612844,0.0010473137,0.010352287],"category_scores_gemma":[0.029348036,0.00037249332,0.00042258704,0.000977926,0.0016025298,0.0048255916,0.0027194626,0.0023645398,0.0019104613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012538799,0.0005778363,0.057406344,0.0011207595,0.00019334964,0.0009489152,0.0009697529,0.11201853,0.039355222,0.095649615,0.016662968,0.6738428],"study_design_scores_gemma":[0.00012330899,0.0014772284,0.058290917,0.0004886313,0.000404551,0.0018906079,0.0010696206,0.6433265,0.05166001,0.2027421,0.038338292,0.00018824838],"about_ca_topic_score_codex":0.0021320446,"about_ca_topic_score_gemma":0.002487727,"teacher_disagreement_score":0.010352287,"about_ca_system_score_codex":0.0007787395,"about_ca_system_score_gemma":0.0009011071,"threshold_uncertainty_score":0.03463185},"labels":[],"label_agreement":null},{"id":"W4230903954","doi":"10.4018/9781591400370.ch004","title":"A Biologically Inspired Neural Network Approach to Real-Time Map Building and Path Planning","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Motion planning; Artificial neural network; Path (computing); Robot; Artificial intelligence; Spiking neural network; Real-time computing; Computer network","score_opus":0.033342279512994705,"score_gpt":0.24456732647773874,"score_spread":0.21122504696474403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230903954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020779665,0.0053082877,0.9744261,0.00028833808,0.00021343908,0.000021683469,0.000051681876,0.0003442215,0.0172683],"genre_scores_gemma":[0.1534118,0.013110726,0.7821501,0.00027075334,0.00027768523,0.00024023704,0.00027867928,0.00018436079,0.050075702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999229,0.00001343428,0.000004332019,0.000021943944,0.000030266563,0.000007197891],"domain_scores_gemma":[0.99994695,0.000022117347,0.0000057995985,0.000007188577,0.00001382,0.000004046761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001622041,0.00070448656,0.00041341365,0.0004149796,0.00022067328,0.0007679734,0.0013092179,0.0010607515,0.0039672884],"category_scores_gemma":[0.00028403822,0.0003025866,0.00053090614,0.0009050558,0.0005020789,0.0009141136,0.0005691528,0.0010967808,0.0010544087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003977994,0.000053917203,0.000242424,0.00060544594,0.00008395962,0.0003239163,0.00013865708,0.45206603,0.023593262,0.1663074,0.008918657,0.34762648],"study_design_scores_gemma":[0.000008737161,0.000050480106,0.00024417744,0.000076871074,0.000030775613,0.0003371047,0.000027033124,0.8555699,0.005313557,0.08447543,0.053837683,0.00002819659],"about_ca_topic_score_codex":0.0012681886,"about_ca_topic_score_gemma":0.0019103704,"teacher_disagreement_score":0.0039672884,"about_ca_system_score_codex":0.0006235461,"about_ca_system_score_gemma":0.00041330507,"threshold_uncertainty_score":0.013271868},"labels":[],"label_agreement":null},{"id":"W4230977378","doi":"10.35940/ijeat.a2084.109119","title":"Visual Modeling of Data using Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Engineering and Advanced Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Normalization (sociology); Artificial intelligence; Artificial neural network; Feedforward neural network; Feed forward; Deep learning; Computer vision; Pattern recognition (psychology); Machine learning; Engineering","score_opus":0.0170601043596542,"score_gpt":0.28712093174950903,"score_spread":0.2700608273898548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230977378","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009197125,0.0006270844,0.98508203,0.0002544984,0.00006715247,0.000059019036,0.0006776366,0.0025059085,0.0015295102],"genre_scores_gemma":[0.572974,0.002758899,0.4120874,0.0002913261,0.00013583708,0.00026516416,0.0031392667,0.00042734906,0.007920701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996294,0.00006257208,0.000032192114,0.000111059475,0.00012274092,0.000042047985],"domain_scores_gemma":[0.99955064,0.00016545247,0.000052105937,0.000085375,0.00012722802,0.00001913174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006174551,0.0009277318,0.00065068033,0.0015227397,0.00025784975,0.0016223127,0.0014767613,0.0007794054,0.002492837],"category_scores_gemma":[0.0022204416,0.00056348945,0.0013584956,0.0015510995,0.00042323227,0.0016458029,0.0008507071,0.0013331452,0.000998449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011178876,0.00004601468,0.0010870267,0.00015743212,0.000096510026,0.00015213624,0.000076550285,0.83326924,0.006490348,0.010332053,0.0033251059,0.14485575],"study_design_scores_gemma":[0.0000014606316,0.000007504092,0.000121538265,0.000007498019,0.0000039721203,0.000017764685,0.000004610452,0.9955432,0.0008574787,0.0027240547,0.00070714776,0.0000037802315],"about_ca_topic_score_codex":0.021143092,"about_ca_topic_score_gemma":0.01708563,"teacher_disagreement_score":0.021143092,"about_ca_system_score_codex":0.0013198043,"about_ca_system_score_gemma":0.00064811687,"threshold_uncertainty_score":0.04204005},"labels":[],"label_agreement":null},{"id":"W4231081240","doi":"10.1017/cbo9781107447615","title":"Neuronal Dynamics","year":2014,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1482,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Field (mathematics); Graduate students; Ideal (ethics); Code (set theory); Computational neuroscience; Cognitive science; Dynamics (music); Artificial intelligence; Mathematics; Psychology; Epistemology; Programming language","score_opus":0.012616635981359666,"score_gpt":0.1844475031943825,"score_spread":0.17183086721302282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231081240","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005896434,0.031401422,0.1948262,0.007886889,0.0033547387,0.00007835161,0.0020343522,0.0013360796,0.7531855],"genre_scores_gemma":[0.14455456,0.037286256,0.06172194,0.0020027023,0.0013561142,0.00021853724,0.0023465343,0.00068572443,0.7498277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998728,0.00001256908,0.000005818447,0.000037375125,0.00006182348,0.000009587382],"domain_scores_gemma":[0.9998901,0.000026562806,0.00000914585,0.00001860256,0.00004189602,0.000013748833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013328662,0.0005058154,0.00039991646,0.0004948133,0.0004199048,0.0017803438,0.000615376,0.0009630499,0.034683276],"category_scores_gemma":[0.00074917974,0.0002390451,0.00037534663,0.00051081757,0.0006582312,0.0019269015,0.00073457183,0.0009945997,0.013453179],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022086995,0.00001726899,0.0003164847,0.0003366129,0.00003180503,0.0002008532,0.00024713177,0.011794728,0.004941222,0.67279273,0.14257157,0.16672757],"study_design_scores_gemma":[0.0000070623314,0.00001656074,0.0006311994,0.00018560873,0.00001234994,0.00046594435,0.00010770233,0.017326906,0.0012329746,0.3608702,0.61911815,0.000025413303],"about_ca_topic_score_codex":0.0014071467,"about_ca_topic_score_gemma":0.0014943271,"teacher_disagreement_score":0.034683276,"about_ca_system_score_codex":0.00079125323,"about_ca_system_score_gemma":0.0005460724,"threshold_uncertainty_score":0.11602718},"labels":[],"label_agreement":null},{"id":"W4231116606","doi":"10.1142/s0129065701000989","title":"INTRODUCTION BY GUEST EDITORS","year":2001,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Download; Library science; Computer science; Artificial neural network; Humanities; Artificial intelligence; World Wide Web; Art","score_opus":0.009682723924163528,"score_gpt":0.25297266836378396,"score_spread":0.24328994443962043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231116606","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006489407,0.016400391,0.0015993532,0.032760695,0.85126483,0.0001335223,0.00057000405,0.000569028,0.09605325],"genre_scores_gemma":[0.00361337,0.008943428,0.00081201765,0.013237822,0.27142808,0.00010639475,0.0006090394,0.00041756016,0.70083237],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990601,0.00010689347,0.000071811235,0.0002073202,0.00041371927,0.00014014827],"domain_scores_gemma":[0.99509805,0.0004142838,0.00030017906,0.00025504184,0.0023181057,0.0016144348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014868716,0.0013433114,0.001158553,0.002019972,0.0011174887,0.004310069,0.0013140986,0.0020918497,0.20015985],"category_scores_gemma":[0.0050573032,0.000438824,0.0005634417,0.0010470857,0.00040287484,0.0024920604,0.0023437422,0.0029077386,0.19208689],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003069658,0.000014033694,0.00006323965,0.00009818363,0.0000028171519,0.00003746175,0.000008364947,0.000027367038,0.00018925859,0.0005231311,0.968811,0.03019438],"study_design_scores_gemma":[0.000009371901,0.000017023018,0.0002061205,0.00005525476,0.0000050657472,0.00006298616,0.000014413558,0.000043784,0.00012598644,0.00045067573,0.9990044,0.0000048994734],"about_ca_topic_score_codex":0.00044986207,"about_ca_topic_score_gemma":0.00122517,"teacher_disagreement_score":0.20015985,"about_ca_system_score_codex":0.00069168094,"about_ca_system_score_gemma":0.0011152108,"threshold_uncertainty_score":0.66960144},"labels":[],"label_agreement":null},{"id":"W4231256044","doi":"10.32920/ryerson.14668260.v1","title":"Audiovisual Emotion Recognition Using Entropy-estimation-based Multimodal Information Fusion","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Emotion recognition; Entropy (arrow of time); Dimensionality reduction; Feature (linguistics); Affective computing; Fusion; Machine learning; Support vector machine","score_opus":0.03181814441833854,"score_gpt":0.2791304743149105,"score_spread":0.24731232989657198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231256044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033452537,0.0006919437,0.9631314,0.00015932167,0.00009129742,0.000070710164,0.0001280249,0.0007210816,0.0015535933],"genre_scores_gemma":[0.68610257,0.0012507288,0.30890575,0.00019102218,0.00020586283,0.00018157787,0.0006856191,0.0001437357,0.0023331707],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922407,0.00014713506,0.00005928663,0.00019622975,0.0002998608,0.00007348027],"domain_scores_gemma":[0.9994116,0.00018973726,0.000079977544,0.000062006824,0.0002274883,0.000029333447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091658003,0.0008925076,0.001031339,0.0018064427,0.00031806293,0.0010099582,0.0006566919,0.00049656385,0.0012410566],"category_scores_gemma":[0.0027596506,0.00022916036,0.0012240455,0.0010661723,0.00045260027,0.0016442853,0.0011871628,0.00069427304,0.00041526946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062263454,0.00015953885,0.0027598233,0.00022373369,0.00021306956,0.00021839215,0.00033490197,0.07194675,0.11140552,0.0067917,0.003145715,0.80217814],"study_design_scores_gemma":[0.00001617484,0.00011944296,0.004688431,0.000022732282,0.000107292944,0.00015087402,0.000095978765,0.956867,0.02980443,0.0062861815,0.0017868137,0.00005477547],"about_ca_topic_score_codex":0.0011820135,"about_ca_topic_score_gemma":0.0008010272,"teacher_disagreement_score":0.0018064427,"about_ca_system_score_codex":0.0005056797,"about_ca_system_score_gemma":0.00032575338,"threshold_uncertainty_score":0.0048474073},"labels":[],"label_agreement":null},{"id":"W4231423496","doi":"10.1007/978-0-387-36795-8_8","title":"Discretization Methods","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Discretization; Computer science; Mathematics; Applied mathematics; Mathematical analysis","score_opus":0.06255935864044568,"score_gpt":0.3600115837880712,"score_spread":0.2974522251476255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231423496","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014778993,0.0039718463,0.84239304,0.00062539417,0.0011327412,0.000076872464,0.00053077925,0.0013058571,0.14848554],"genre_scores_gemma":[0.063865125,0.0063856235,0.534636,0.00089267804,0.0007389852,0.00039070976,0.002545557,0.0019743443,0.38857085],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995597,0.00007845602,0.000021491478,0.00008880589,0.00022397345,0.000027549087],"domain_scores_gemma":[0.9996327,0.00010641397,0.000014675545,0.00012748699,0.00009944447,0.000019249585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047083924,0.0006858603,0.00086116896,0.00095015677,0.00055300683,0.0014223855,0.001135058,0.0008191742,0.047032908],"category_scores_gemma":[0.0013614187,0.00044174364,0.0006994195,0.0010631589,0.0007898148,0.0013570699,0.0012363984,0.0018590948,0.018411214],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043608736,0.000053295473,0.00017376117,0.00029914774,0.00003891802,0.0000440729,0.00009815503,0.018962508,0.0034771778,0.44529873,0.09534385,0.43616673],"study_design_scores_gemma":[0.000035343655,0.00003527947,0.0002974153,0.00018982905,0.000029866778,0.00021754025,0.000058674457,0.13694593,0.005781241,0.4157202,0.44065255,0.000036142766],"about_ca_topic_score_codex":0.0012563075,"about_ca_topic_score_gemma":0.0020878203,"teacher_disagreement_score":0.047032908,"about_ca_system_score_codex":0.000660607,"about_ca_system_score_gemma":0.0006453321,"threshold_uncertainty_score":0.15734082},"labels":[],"label_agreement":null},{"id":"W4231489951","doi":"10.1007/978-1-4939-7131-2_100509","title":"Information Fusion","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Fusion; Computer science; Philosophy; Linguistics","score_opus":0.014292246912216781,"score_gpt":0.216161220437653,"score_spread":0.20186897352543623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231489951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003270305,0.020262262,0.7211468,0.0020563963,0.0028930816,0.00015056657,0.00081406074,0.0025687958,0.24683776],"genre_scores_gemma":[0.18152712,0.031318147,0.37537074,0.0024940565,0.0031770556,0.0003477697,0.004266142,0.0011647466,0.4003342],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99929655,0.00007192886,0.00003766817,0.00016908546,0.00037789703,0.00004691046],"domain_scores_gemma":[0.9994821,0.00012496598,0.00002524316,0.00016231176,0.00018227514,0.000023104018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008927644,0.0010188746,0.0009748552,0.0019003665,0.000637962,0.002210484,0.0012604959,0.0014357065,0.03158012],"category_scores_gemma":[0.0018027168,0.00041803403,0.00073948206,0.0021155172,0.0010103856,0.0034151317,0.0022625173,0.0015426942,0.020462867],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007757955,0.000046386405,0.0001210038,0.000403267,0.00007517024,0.00007614639,0.00007706205,0.0065129786,0.011045301,0.12897986,0.063395634,0.7891897],"study_design_scores_gemma":[0.00001837594,0.00013077632,0.0007611196,0.00030387143,0.00011075253,0.0007018508,0.00010114288,0.069193065,0.04422827,0.30971435,0.5746353,0.00010109313],"about_ca_topic_score_codex":0.00064306566,"about_ca_topic_score_gemma":0.0005528592,"teacher_disagreement_score":0.03158012,"about_ca_system_score_codex":0.0007124392,"about_ca_system_score_gemma":0.00062455627,"threshold_uncertainty_score":0.105646014},"labels":[],"label_agreement":null},{"id":"W4231770681","doi":"10.1109/tit.2019.2910956","title":"IEEE Transactions on Information Theory publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Information theory; Computer science; Information retrieval; Theoretical computer science; Mathematics; Statistics","score_opus":0.007383270973087514,"score_gpt":0.21237916444984037,"score_spread":0.20499589347675284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231770681","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007685056,0.040465705,0.41808102,0.02506351,0.04612417,0.000768203,0.030053955,0.005549991,0.42620844],"genre_scores_gemma":[0.122801326,0.07575902,0.10311073,0.0047494355,0.012413274,0.0009117524,0.048232794,0.0021712712,0.6298504],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980856,0.0003394827,0.00015933822,0.00018997153,0.0010984318,0.00012714314],"domain_scores_gemma":[0.99481934,0.0017338797,0.00018284685,0.0012878221,0.0018059026,0.00017020266],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002010765,0.0012870186,0.002069679,0.0038233974,0.0009367815,0.0043012095,0.001149016,0.0020608127,0.18499774],"category_scores_gemma":[0.01094879,0.00057128386,0.000768873,0.0049888906,0.0008021027,0.004122502,0.0017218122,0.002442378,0.075375594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016672397,0.00012354781,0.0006889728,0.00089499366,0.0000646775,0.00014958144,0.00005247238,0.0033523254,0.0011527906,0.077194825,0.51723707,0.398922],"study_design_scores_gemma":[0.00006777776,0.00010850995,0.0013717826,0.00090496,0.00015986768,0.0005224925,0.00011307768,0.035070773,0.0030674974,0.18424289,0.77429456,0.00007579623],"about_ca_topic_score_codex":0.0014958134,"about_ca_topic_score_gemma":0.0012613396,"teacher_disagreement_score":0.81500226,"about_ca_system_score_codex":0.000997073,"about_ca_system_score_gemma":0.0021575359,"threshold_uncertainty_score":0.6188791},"labels":[],"label_agreement":null},{"id":"W4231953836","doi":"10.1109/date.2003.1253694","title":"Time domain multiplexed TAM: implementation and comparison","year":2003,"lang":"en","type":"article","venue":"2003 Design, Automation and Test in Europe Conference and Exhibition","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Reconfigurability; Scalability; Embedded system; Overhead (engineering); System on a chip; Multiplexing; Computer architecture; Testability; Flexibility (engineering); Domain (mathematical analysis); Engineering; Reliability engineering; Operating system; Telecommunications","score_opus":0.03343460545837553,"score_gpt":0.27887543341331245,"score_spread":0.2454408279549369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231953836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7027525,0.0034576813,0.25817466,0.00032711594,0.00025566402,0.00039354744,0.000524025,0.0044425037,0.029672287],"genre_scores_gemma":[0.93255126,0.0004981417,0.061653536,0.000056878143,0.000029035695,0.0000738461,0.00023409413,0.00007427071,0.004828988],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999476,0.0001420012,0.000030811032,0.00005561711,0.0002115854,0.000083916326],"domain_scores_gemma":[0.9989511,0.0003207046,0.00012394498,0.00016814403,0.00038661124,0.000049427043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067036966,0.00046492225,0.00029531363,0.0011386122,0.00018567719,0.0007002598,0.00096627773,0.00041241245,0.0052320766],"category_scores_gemma":[0.0017657998,0.00011977114,0.0002329783,0.0007190066,0.00020463232,0.0009113021,0.00024334049,0.00022505318,0.00062250573],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0049591474,0.00057357486,0.008339733,0.00088179647,0.00019922413,0.0005772548,0.0002677964,0.04757485,0.13913225,0.019757373,0.004610531,0.7731264],"study_design_scores_gemma":[0.000648699,0.008076085,0.009343818,0.00012207935,0.0003890483,0.0028531381,0.00033757117,0.5065947,0.42381927,0.0036732398,0.044034112,0.00010823514],"about_ca_topic_score_codex":0.00082536606,"about_ca_topic_score_gemma":0.0005298263,"teacher_disagreement_score":0.0052320766,"about_ca_system_score_codex":0.0005295363,"about_ca_system_score_gemma":0.000320433,"threshold_uncertainty_score":0.017503023},"labels":[],"label_agreement":null},{"id":"W4231997730","doi":"10.4018/9781591403128.ch016","title":"Continuous Attractor Neural Networks","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Attractor; Artificial neural network; Context (archaeology); Artificial intelligence; Information processing; Neural system; Cognitive science; Neuroscience; Biology; Psychology; Mathematics","score_opus":0.025206268434787828,"score_gpt":0.2349316961427592,"score_spread":0.20972542770797137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231997730","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011469685,0.06393137,0.7474629,0.0019578708,0.0014296521,0.00009805845,0.0008485847,0.0010398149,0.17176215],"genre_scores_gemma":[0.42376202,0.08782432,0.30755845,0.0012713065,0.0015237138,0.00047153863,0.0032424685,0.00042946122,0.1739167],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998679,0.000029638566,0.00000971201,0.000036313166,0.00004413161,0.000012384342],"domain_scores_gemma":[0.99988544,0.000055812827,0.000009585637,0.000011415568,0.000029823555,0.000008011973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002126139,0.0006048892,0.00045337,0.0006797603,0.00025089644,0.0013160601,0.0006954445,0.00091120787,0.0115397135],"category_scores_gemma":[0.00086982606,0.00018705345,0.0003217692,0.0009453479,0.0005203149,0.0012147669,0.0006829307,0.0008865142,0.003213891],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004864429,0.00003322663,0.0004277451,0.00074236916,0.000060608807,0.00017433452,0.00012262679,0.08667172,0.0049533374,0.538863,0.02398544,0.34391695],"study_design_scores_gemma":[0.00001629495,0.00005033367,0.0005580446,0.00022902453,0.000033742544,0.00042474244,0.000055223663,0.4474346,0.0021459514,0.37593102,0.17308317,0.000037879556],"about_ca_topic_score_codex":0.0009243679,"about_ca_topic_score_gemma":0.0012448344,"teacher_disagreement_score":0.0115397135,"about_ca_system_score_codex":0.00047705122,"about_ca_system_score_gemma":0.00029660255,"threshold_uncertainty_score":0.03860426},"labels":[],"label_agreement":null},{"id":"W4232255986","doi":"10.1109/tnnls.2017.2757801","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universidade de Pernambuco; Southeast University; Sichuan University; University of Stirling; Politechnika Warszawska; Euskal Herriko Unibertsitatea; University of Waterloo; Washington State University; University of Cincinnati; Universidade Federal de Pernambuco; Northwestern University; Fudan University; Missouri University of Science and Technology; Tsinghua University","keywords":"Computer science; Artificial neural network; Data science; Artificial intelligence; Information retrieval; World Wide Web","score_opus":0.015980655759344205,"score_gpt":0.23376861415394118,"score_spread":0.21778795839459697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232255986","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009403616,0.06576203,0.3204442,0.016823372,0.10853472,0.00056156615,0.015161496,0.0064402516,0.45686874],"genre_scores_gemma":[0.05088003,0.053422313,0.057726495,0.0020209507,0.00860953,0.0003178019,0.019473381,0.0017281646,0.8058214],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992273,0.00006299186,0.000068509,0.00007380356,0.00050943403,0.000057936817],"domain_scores_gemma":[0.99821943,0.00035867843,0.00006301751,0.00029033344,0.00096894265,0.00009947884],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010461779,0.00088507135,0.0014799872,0.0017325677,0.000541599,0.0024630693,0.0010116256,0.001537254,0.1928779],"category_scores_gemma":[0.0038532107,0.0003774178,0.00048725275,0.0019024126,0.0004635184,0.00242385,0.001188969,0.00159813,0.07467374],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099314275,0.000094099196,0.00049860805,0.00051193126,0.00002976412,0.000087297594,0.000023486802,0.003076865,0.001646329,0.015333843,0.5051719,0.47342658],"study_design_scores_gemma":[0.000029266284,0.00007864368,0.0014085425,0.00043428552,0.00006266159,0.00033258044,0.000051311006,0.030094247,0.0027692437,0.033876766,0.9308226,0.00003980343],"about_ca_topic_score_codex":0.0018277055,"about_ca_topic_score_gemma":0.0029667204,"teacher_disagreement_score":0.8071221,"about_ca_system_score_codex":0.0005472281,"about_ca_system_score_gemma":0.0017659772,"threshold_uncertainty_score":0.6452409},"labels":[],"label_agreement":null},{"id":"W4232291834","doi":"10.1007/978-1-4471-7452-3_4","title":"Perceptrons","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Perceptron; Computer science; Artificial neural network; Artificial intelligence; Multilayer perceptron; Machine learning; Pattern recognition (psychology)","score_opus":0.018550916800871755,"score_gpt":0.22183445694955717,"score_spread":0.2032835401486854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232291834","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020259782,0.01449976,0.4027599,0.0021334265,0.002874842,0.00009305404,0.0017030063,0.0052473354,0.56866264],"genre_scores_gemma":[0.027772771,0.010630454,0.103737384,0.00089741993,0.00086676114,0.00011845394,0.0027856864,0.0010212922,0.8521698],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999765,0.000024603687,0.000009527852,0.0000630076,0.00011668197,0.000021189488],"domain_scores_gemma":[0.9998,0.00006448836,0.000009106158,0.000049895567,0.000059166003,0.000017434564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025501224,0.0011118095,0.0005684897,0.000796763,0.00041631094,0.0019844312,0.0010968142,0.0010155714,0.106558874],"category_scores_gemma":[0.0009844055,0.00042713442,0.00042593695,0.0012572183,0.0005006581,0.0019640836,0.0010045948,0.001735051,0.06597111],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004016219,0.000043359716,0.00007622528,0.00032453646,0.00002161816,0.00003872684,0.000031757794,0.007996706,0.0023585449,0.098496065,0.19359148,0.6969809],"study_design_scores_gemma":[0.0000126354835,0.00003948184,0.00023422456,0.00022427883,0.000024390167,0.00019036856,0.000025877025,0.036490116,0.005484968,0.15652457,0.80072117,0.000027917846],"about_ca_topic_score_codex":0.0009166818,"about_ca_topic_score_gemma":0.0018461867,"teacher_disagreement_score":0.106558874,"about_ca_system_score_codex":0.00060215726,"about_ca_system_score_gemma":0.00065466017,"threshold_uncertainty_score":0.35647494},"labels":[],"label_agreement":null},{"id":"W4232345223","doi":"10.32920/ryerson.14656947.v1","title":"A bidirectional associative memory based on cortical spiking neurons using temporal coding","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Content-addressable memory; Spiking neural network; Coding (social sciences); Artificial neural network; Neural coding; Bidirectional associative memory; Associative property; Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.06236437656462041,"score_gpt":0.30557106010149193,"score_spread":0.24320668353687153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232345223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12833422,0.001544054,0.85245776,0.00057666225,0.00044594335,0.000080368496,0.00021957919,0.0016482597,0.01469323],"genre_scores_gemma":[0.9016061,0.0009974525,0.08892686,0.00025155125,0.00007158518,0.0000960968,0.00016764039,0.0000562838,0.007826407],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999899,0.000013384916,0.000008110338,0.000025075762,0.000030667936,0.000023793342],"domain_scores_gemma":[0.99981445,0.00004209004,0.00001718666,0.00003580088,0.00006778275,0.000022723245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002147161,0.00028530488,0.00044465877,0.00026383734,0.0003555081,0.0006210213,0.0010045035,0.00047421333,0.0026100047],"category_scores_gemma":[0.00051551673,0.0001562077,0.0004361032,0.00047800053,0.00037331504,0.0011715061,0.0005922469,0.0004926616,0.00042032675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006499144,0.0003148632,0.003051078,0.00066774274,0.00029028143,0.0007827947,0.00033165078,0.22560681,0.2359809,0.13143204,0.0055800807,0.39531174],"study_design_scores_gemma":[0.000030773823,0.00018741655,0.0005621036,0.000030141558,0.00009372378,0.00027027054,0.00003996369,0.92387503,0.040331643,0.028534034,0.0060114674,0.000033414293],"about_ca_topic_score_codex":0.0016262362,"about_ca_topic_score_gemma":0.0013503444,"teacher_disagreement_score":0.0026100047,"about_ca_system_score_codex":0.000282602,"about_ca_system_score_gemma":0.00042896063,"threshold_uncertainty_score":0.008731306},"labels":[],"label_agreement":null},{"id":"W4232532458","doi":"10.1086/380055","title":"<i>Advances in Neural Information Processing Systems <scp>14</scp></i>. Based on the annual Neural Information Processing Systems (NIPS) Conference, held in British Columbia, Canada, 3–8 December 2001. Volumes 1 and 2. Edited by Thomas G  Dietterich, Suzanna  Becker, and Zoubin  Ghahramani. A Bradford Book. <i>Cambridge (Massachusetts): MIT Press.</i> $90.00 (2‐volume set). (1) xxiii + 808 p; ill.; index of authors and keyword index. (2) xxiii + pp 809–1594; ill.; index of authors and keyword index. ISBN: 0‐262‐04206‐1 (Volume 1); 0‐262‐04207‐X (Volume 2); 0‐262‐04208‐8 (2‐volume set). 2002.","year":2003,"lang":"en","type":"review","venue":"The Quarterly Review of Biology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Library science; Volume (thermodynamics); Columbia university; Download; Computer science; Artificial intelligence; Information retrieval; History; Media studies; Sociology; World Wide Web","score_opus":0.012245004449997585,"score_gpt":0.24107035031546978,"score_spread":0.2288253458654722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232532458","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001279642,0.982427,0.001733029,0.0022270782,0.0019899341,0.000014834081,0.00010569605,0.00011294116,0.011261442],"genre_scores_gemma":[0.0014662775,0.96588737,0.001522137,0.0008928947,0.0017958374,0.000031481093,0.00024051765,0.000037441456,0.028126083],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960834,0.0000565332,0.000035371297,0.00005574396,0.000215256,0.000028842987],"domain_scores_gemma":[0.9993512,0.00022349216,0.00006452406,0.000027758544,0.0002666619,0.00006634461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061991456,0.001424161,0.001648381,0.0025120345,0.00033437772,0.0015734005,0.0012476132,0.0013656365,0.02520585],"category_scores_gemma":[0.0016061447,0.00057718495,0.00042222627,0.003865678,0.0009042899,0.0029839338,0.00080141774,0.0022283546,0.02472083],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003921421,0.000028765451,0.00015801306,0.003442713,0.00004780178,0.00009128539,0.000047492613,0.00036249304,0.0006909346,0.0040641627,0.3844076,0.60661954],"study_design_scores_gemma":[0.0000070469855,0.00003396427,0.00058649573,0.0009084851,0.000023205263,0.00047879486,0.000037272013,0.00017566246,0.00028178818,0.005445351,0.9920095,0.000012295354],"about_ca_topic_score_codex":0.0029386352,"about_ca_topic_score_gemma":0.0073016165,"teacher_disagreement_score":0.9970614,"about_ca_system_score_codex":0.0011161999,"about_ca_system_score_gemma":0.0016810514,"threshold_uncertainty_score":0.084321976},"labels":[],"label_agreement":null},{"id":"W4232553776","doi":"10.1109/tnnls.2020.3016807","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Syracuse University; Tencent; Euskal Herriko Unibertsitatea; University of Birmingham; College of Engineering, Michigan State University; University of Manitoba; Arizona State University","keywords":"Computer science; Artificial neural network; Artificial intelligence; Information retrieval; World Wide Web; Data science","score_opus":0.015987716786376855,"score_gpt":0.21670722049405713,"score_spread":0.20071950370768027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232553776","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0085761715,0.076698825,0.3297185,0.01450642,0.10285765,0.0005575825,0.013103406,0.00566726,0.44831422],"genre_scores_gemma":[0.04708451,0.059917793,0.058605425,0.0020181374,0.0077924347,0.00033739055,0.017914081,0.001476334,0.804854],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927086,0.00006628691,0.000060309238,0.00006927319,0.00048062616,0.000052675143],"domain_scores_gemma":[0.99844545,0.00032881377,0.0000544493,0.0002518332,0.0008327398,0.00008680569],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010211334,0.0008695145,0.0014611329,0.0016655496,0.00051198015,0.0022717533,0.0009440689,0.0014729509,0.17235099],"category_scores_gemma":[0.0033838958,0.00036509873,0.00046575707,0.0019373352,0.0004407287,0.0021492683,0.0010930956,0.0015128229,0.070227936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099547644,0.00008659512,0.000488883,0.00051558594,0.000030311943,0.00008042999,0.000023498118,0.0031102146,0.0016169639,0.014257364,0.49972716,0.4799634],"study_design_scores_gemma":[0.000027552762,0.000078212244,0.0014004629,0.00042337633,0.00006316967,0.00029664868,0.000045767134,0.028939035,0.0023299262,0.029093001,0.93726635,0.000036443565],"about_ca_topic_score_codex":0.0019519095,"about_ca_topic_score_gemma":0.0031764794,"teacher_disagreement_score":0.827649,"about_ca_system_score_codex":0.00050869177,"about_ca_system_score_gemma":0.0016292125,"threshold_uncertainty_score":0.57657146},"labels":[],"label_agreement":null},{"id":"W4232596258","doi":"10.1109/tit.2018.2881822","title":"IEEE Transactions on Information Theory publication information","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Information theory; Computer science; Mathematics; Statistics","score_opus":0.009859260653836669,"score_gpt":0.2267323195932553,"score_spread":0.21687305893941863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232596258","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075886752,0.03450608,0.37589628,0.028927293,0.052169286,0.00078925234,0.03879711,0.0064008934,0.45492515],"genre_scores_gemma":[0.11858974,0.06850063,0.090462565,0.0051360684,0.01373131,0.00089299027,0.056598403,0.0027871744,0.6433012],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99794954,0.00033785796,0.000169827,0.00021402955,0.0011793278,0.000149413],"domain_scores_gemma":[0.9937953,0.0020618648,0.00021209495,0.0015606398,0.002160756,0.00020940119],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021437379,0.0013837162,0.0021563147,0.0039232336,0.0009921466,0.0045337295,0.0012955151,0.0022782085,0.23127455],"category_scores_gemma":[0.012982578,0.00062062254,0.0008206204,0.0050654747,0.0008102244,0.0045962213,0.0018308015,0.0027652583,0.098597996],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001775029,0.00013199805,0.0006146807,0.000867521,0.00005735344,0.0001520026,0.00004537619,0.0031645668,0.0010952258,0.06849876,0.5728305,0.35236454],"study_design_scores_gemma":[0.00007642604,0.00010941409,0.001258478,0.0008973995,0.00015124984,0.00054662244,0.00010854275,0.034255814,0.0032187246,0.18546115,0.7738345,0.00008166401],"about_ca_topic_score_codex":0.0014729707,"about_ca_topic_score_gemma":0.0012031312,"teacher_disagreement_score":0.76872545,"about_ca_system_score_codex":0.0010227711,"about_ca_system_score_gemma":0.0023775187,"threshold_uncertainty_score":0.77369046},"labels":[],"label_agreement":null},{"id":"W4232873935","doi":"10.1109/tnnls.2020.3043903","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Computer science; Artificial neural network; Data science; Artificial intelligence; Information retrieval","score_opus":0.012733470139682094,"score_gpt":0.21942843061264394,"score_spread":0.20669496047296185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232873935","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008674992,0.07775329,0.3317228,0.014763441,0.10534082,0.00056262995,0.0132251615,0.0057110414,0.4422458],"genre_scores_gemma":[0.047799703,0.060790274,0.05947508,0.0020392463,0.007916191,0.00034253838,0.018183041,0.0014959566,0.80195796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992557,0.00006812914,0.000062097395,0.00007053413,0.0004896761,0.00005377804],"domain_scores_gemma":[0.9984035,0.00033720626,0.00005568766,0.00025708953,0.0008579105,0.00008860813],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010399217,0.00087971514,0.0014855702,0.0016763533,0.0005160981,0.0022983786,0.00094688445,0.0014829766,0.1712527],"category_scores_gemma":[0.0034331358,0.0003704023,0.00046931638,0.0019506597,0.00044431514,0.002172209,0.001100526,0.0015288608,0.06967644],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010125696,0.00008644695,0.00049349456,0.0005234777,0.000030718227,0.00008122713,0.000023858423,0.003108043,0.001625891,0.014201481,0.49946615,0.4802579],"study_design_scores_gemma":[0.000027818764,0.0000788564,0.0014199645,0.00042970068,0.000064212174,0.00029786731,0.000046496316,0.028729105,0.0023577304,0.028642723,0.9378687,0.00003680235],"about_ca_topic_score_codex":0.0019654583,"about_ca_topic_score_gemma":0.0031941677,"teacher_disagreement_score":0.8287473,"about_ca_system_score_codex":0.00051413826,"about_ca_system_score_gemma":0.0016375178,"threshold_uncertainty_score":0.5728973},"labels":[],"label_agreement":null},{"id":"W4233117633","doi":"10.1007/978-1-4614-6170-8_100090","title":"Belief Propagation","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Psychology","score_opus":0.015403809434965578,"score_gpt":0.21423714761577384,"score_spread":0.19883333818080826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233117633","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012530399,0.0068941717,0.79643935,0.0012887067,0.0008604103,0.000055810044,0.00045261983,0.0018774549,0.19087847],"genre_scores_gemma":[0.085483894,0.016598308,0.4021832,0.000785247,0.00084347365,0.00024175573,0.0018434643,0.0012940591,0.4907266],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996113,0.000066239845,0.000015424604,0.000086842745,0.00019387301,0.000026379064],"domain_scores_gemma":[0.9995043,0.00021757433,0.000019160578,0.00009157142,0.00014883353,0.000018462044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005055867,0.0012160909,0.000696444,0.0010017119,0.0004561326,0.0019780376,0.0012831765,0.0012745993,0.0350499],"category_scores_gemma":[0.0021896253,0.00052160793,0.0004931909,0.0013634325,0.00077780726,0.0021405935,0.0011553003,0.002019702,0.019042823],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035693392,0.00004768848,0.00011725331,0.00027494485,0.00004336739,0.000045460638,0.00007663056,0.03613305,0.0012961559,0.22256893,0.08804347,0.65131736],"study_design_scores_gemma":[0.000016196955,0.00003244664,0.00021198594,0.00025621595,0.000049450166,0.00017934336,0.000042702108,0.19257556,0.0048404816,0.45428348,0.34746552,0.000046538382],"about_ca_topic_score_codex":0.002269583,"about_ca_topic_score_gemma":0.003046172,"teacher_disagreement_score":0.0350499,"about_ca_system_score_codex":0.000814483,"about_ca_system_score_gemma":0.0008350581,"threshold_uncertainty_score":0.11725354},"labels":[],"label_agreement":null},{"id":"W4233140430","doi":"10.1007/978-1-4939-7131-2_100837","title":"Organismic Computing","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.018399407682779954,"score_gpt":0.22832103324222378,"score_spread":0.20992162555944383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233140430","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015758894,0.013277478,0.0396091,0.0031920606,0.0017458941,0.000026796683,0.00017753254,0.00027258348,0.9401227],"genre_scores_gemma":[0.060538013,0.018771833,0.028371405,0.0016602319,0.0016572688,0.0001403999,0.00064490776,0.0004323863,0.88778365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998642,0.000031945387,0.0000043253663,0.000031617383,0.00005445511,0.000013442578],"domain_scores_gemma":[0.999905,0.00002790801,0.0000041750322,0.000028636989,0.000018623286,0.000015683263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017845123,0.00061335234,0.00032309777,0.0006536716,0.00076309306,0.0024838557,0.00071014056,0.00071110093,0.037334803],"category_scores_gemma":[0.0005402592,0.00021227481,0.0002805879,0.0007826605,0.0020593354,0.0024010993,0.0015812764,0.0016109884,0.013904028],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049465025,0.000008785056,0.000058598187,0.000099708064,0.0000052861633,0.000020238173,0.00017519698,0.00072875904,0.0004255426,0.832869,0.06707487,0.09852912],"study_design_scores_gemma":[0.0000017302652,0.0000052387436,0.00010980156,0.00008497318,0.00000242337,0.000046299458,0.00005232306,0.00062936224,0.00022896857,0.3355898,0.6632447,0.0000043099517],"about_ca_topic_score_codex":0.0007792436,"about_ca_topic_score_gemma":0.0015163284,"teacher_disagreement_score":0.037334803,"about_ca_system_score_codex":0.001186719,"about_ca_system_score_gemma":0.0007916435,"threshold_uncertainty_score":0.12489736},"labels":[],"label_agreement":null},{"id":"W4233335867","doi":"10.1007/978-3-319-28099-8_781-1","title":"Neural Networks","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Geography; Geology","score_opus":0.027411890775791863,"score_gpt":0.24599489827421428,"score_spread":0.2185830074984224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233335867","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026398436,0.05902972,0.36307287,0.003583711,0.004837855,0.00008420437,0.0008277949,0.0019648366,0.5639591],"genre_scores_gemma":[0.04065741,0.04497441,0.09253951,0.001175733,0.0021438794,0.00018259489,0.0015879081,0.0007182847,0.81602025],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997942,0.000021029253,0.000009042784,0.000044127515,0.00011794436,0.000013606828],"domain_scores_gemma":[0.99982125,0.000057357316,0.000009575813,0.000036537054,0.00006499026,0.000010369022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021577405,0.00086948584,0.0005691965,0.00078680285,0.00031536954,0.0013674079,0.0009173978,0.0009984976,0.039689995],"category_scores_gemma":[0.0010325467,0.0003303933,0.00030350563,0.001032932,0.0005310362,0.0017947545,0.0010076074,0.0014268971,0.024024231],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017815515,0.000024877565,0.00010335782,0.000426649,0.000029948691,0.000044967914,0.00003977775,0.012267024,0.001971355,0.1261536,0.15022601,0.7086947],"study_design_scores_gemma":[0.0000067306933,0.000025228199,0.00032259128,0.00032374533,0.000025844036,0.00022168647,0.000029000146,0.035900034,0.0034011248,0.21350262,0.7462135,0.000027972219],"about_ca_topic_score_codex":0.0010309137,"about_ca_topic_score_gemma":0.0019405484,"teacher_disagreement_score":0.039689995,"about_ca_system_score_codex":0.00061812456,"about_ca_system_score_gemma":0.00054626056,"threshold_uncertainty_score":0.13277626},"labels":[],"label_agreement":null},{"id":"W4233499474","doi":"10.2307/3315880","title":"Bootstrapping Regression Models with BLUS Residuals","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bootstrapping (finance); Statistics; Regression analysis; Regression; Econometrics; Computer science; Mathematics","score_opus":0.022457621885627426,"score_gpt":0.2261719914402272,"score_spread":0.2037143695545998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233499474","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011659126,0.00024680837,0.9859443,0.0001351068,0.000060649512,0.00012277342,0.00013696653,0.000788112,0.00090634],"genre_scores_gemma":[0.32263622,0.00057098304,0.6699242,0.00038057007,0.00016819034,0.0012897488,0.0016672104,0.0006310341,0.0027318113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9825398,0.0137029365,0.0005812088,0.0012278613,0.001469193,0.00047914346],"domain_scores_gemma":[0.95740235,0.03238075,0.002465201,0.005185368,0.002277891,0.0002883243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022931619,0.001561263,0.0024725348,0.0026053924,0.0008602979,0.0019302994,0.0033867466,0.0021944384,0.0036467996],"category_scores_gemma":[0.12531537,0.0010370634,0.0020453823,0.0032616246,0.0018499821,0.0026804172,0.0028041159,0.0030136956,0.002043985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054632145,0.00023074108,0.013153426,0.00048329186,0.0005867854,0.000636036,0.00094300276,0.63946974,0.0020489048,0.15278183,0.0073599983,0.18175995],"study_design_scores_gemma":[0.000041204385,0.0000942641,0.001130286,0.00008108269,0.00004583409,0.00006566551,0.000076881464,0.9360702,0.00090014027,0.057681005,0.0037724597,0.000041038656],"about_ca_topic_score_codex":0.005905053,"about_ca_topic_score_gemma":0.0039641904,"teacher_disagreement_score":0.022931619,"about_ca_system_score_codex":0.0011183609,"about_ca_system_score_gemma":0.0012694703,"threshold_uncertainty_score":0.121275425},"labels":[],"label_agreement":null},{"id":"W4233673623","doi":"10.3410/f.733851525.793550281","title":"Faculty Opinions recommendation of Multi-dimensional Coding by Basolateral Amygdala Neurons.","year":2018,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Basolateral amygdala; Coding (social sciences); Amygdala; Neuroscience; Psychology; Computer science; Mathematics; Statistics","score_opus":0.03333521355605959,"score_gpt":0.33876262692988635,"score_spread":0.30542741337382673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233673623","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031147038,0.0005558337,0.0010269312,0.00077321107,0.00078415545,0.00014427511,0.9829912,0.0032173807,0.007392315],"genre_scores_gemma":[0.005643967,0.00019217863,0.0018542018,0.0002259786,0.000102204795,0.0001629752,0.98230743,0.00020261673,0.009308483],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99909997,0.00011645111,0.00006561036,0.0002532588,0.0002742403,0.00019041015],"domain_scores_gemma":[0.99836904,0.00022685212,0.00008642552,0.00046043657,0.0006414859,0.0002157997],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.000974931,0.0028865163,0.0014526077,0.0021708081,0.00070915394,0.002501034,0.0031205348,0.0029959634,0.07525637],"category_scores_gemma":[0.005437199,0.00049136556,0.0019998595,0.0023240834,0.0004389016,0.0011964029,0.0017852709,0.0018993919,0.10029827],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013061125,0.000068152316,0.00090981927,0.00031365285,0.000038507787,0.000022494898,0.000009329955,0.0002862039,0.00047134722,0.00016354847,0.9883543,0.009232025],"study_design_scores_gemma":[0.0007162573,0.00015595912,0.013901539,0.0004177458,0.00015458703,0.00025900235,0.00019489352,0.009928358,0.006332141,0.0030199678,0.96482253,0.00009709939],"about_ca_topic_score_codex":0.023940587,"about_ca_topic_score_gemma":0.073348515,"teacher_disagreement_score":0.99902505,"about_ca_system_score_codex":0.0013047988,"about_ca_system_score_gemma":0.002047858,"threshold_uncertainty_score":0.25175768},"labels":[],"label_agreement":null},{"id":"W4233727872","doi":"10.32920/ryerson.14657649","title":"Wavelet-based image compression using mathematical morphology and self organization feature map","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Image compression; Wavelet transform; Wavelet; Pattern recognition (psychology); Computer vision; JPEG; Computer science; Data compression; Feature (linguistics); Discrete wavelet transform; JPEG 2000; Mathematics; Image processing; Image (mathematics)","score_opus":0.015314719696674963,"score_gpt":0.25864875449189045,"score_spread":0.24333403479521548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233727872","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053242747,0.00063975254,0.941468,0.00016872851,0.0000629126,0.0000717194,0.00006258454,0.0007712055,0.003512296],"genre_scores_gemma":[0.4018387,0.0013003516,0.59219515,0.000055091255,0.00006537995,0.00009015423,0.00017310462,0.00009610139,0.004186033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998641,0.000015294312,0.000008437522,0.00001633619,0.00008768913,0.000008050679],"domain_scores_gemma":[0.9997949,0.00006796416,0.000029830015,0.000031585056,0.00006894483,0.0000068452373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021568972,0.00018937314,0.0002788611,0.0006804622,0.00016208799,0.0005310038,0.00027041006,0.00025625527,0.0010645924],"category_scores_gemma":[0.0007184956,0.00011947399,0.00033989912,0.00076920865,0.00029251346,0.0006658765,0.0002761284,0.00029592862,0.0003751674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013371138,0.000075770244,0.0009498612,0.0002070518,0.000055571047,0.00035084656,0.00016017967,0.06448814,0.271616,0.030138131,0.0021101697,0.6297146],"study_design_scores_gemma":[0.00002368043,0.0002449403,0.0034624066,0.000027832788,0.00003606938,0.00090284034,0.00005779811,0.8521717,0.12003259,0.012011095,0.010995187,0.000033856464],"about_ca_topic_score_codex":0.0004221347,"about_ca_topic_score_gemma":0.00041036218,"teacher_disagreement_score":0.0010645924,"about_ca_system_score_codex":0.00022428972,"about_ca_system_score_gemma":0.00021038199,"threshold_uncertainty_score":0.0035614371},"labels":[],"label_agreement":null},{"id":"W4233931959","doi":"10.1007/978-981-10-6113-4_12","title":"Object Classification","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; One-class classification; Object (grammar); Contextual image classification; Computer science; A priori and a posteriori; Class (philosophy); Statistical classification; Set (abstract data type); Mathematics; Image (mathematics); Classifier (UML)","score_opus":0.0586521882687756,"score_gpt":0.2741151282947065,"score_spread":0.21546294002593092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233931959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004443284,0.015681325,0.59795094,0.002082438,0.0028406791,0.00041803194,0.0037477415,0.009410479,0.36342514],"genre_scores_gemma":[0.034044523,0.015309432,0.33460283,0.0014511137,0.0014359041,0.00040908728,0.0148788355,0.0015030648,0.5963652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994386,0.000034619967,0.000029041355,0.00021229418,0.00024001452,0.000045323926],"domain_scores_gemma":[0.9996543,0.00006221751,0.00001692043,0.000119045624,0.00012649689,0.000021108526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058295915,0.0016162443,0.0010834592,0.0021029566,0.0007965937,0.0029425933,0.0022883955,0.0011101764,0.05886968],"category_scores_gemma":[0.0010668138,0.0005356603,0.0011872906,0.002544489,0.0006051874,0.003148995,0.0018206735,0.0016178194,0.06841598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030130106,0.00004151872,0.00019361112,0.00029758,0.00002903581,0.000037970443,0.00003724136,0.0013201879,0.0038280953,0.02624725,0.12261286,0.8453246],"study_design_scores_gemma":[0.000009151536,0.000047084915,0.0009525526,0.00024460728,0.000057864097,0.00061587174,0.0000763455,0.025563283,0.0155581245,0.083710976,0.87312305,0.000041149346],"about_ca_topic_score_codex":0.001588262,"about_ca_topic_score_gemma":0.0024080765,"teacher_disagreement_score":0.05886968,"about_ca_system_score_codex":0.0008764313,"about_ca_system_score_gemma":0.00093621854,"threshold_uncertainty_score":0.1969387},"labels":[],"label_agreement":null},{"id":"W4234184979","doi":"10.1109/tnnls.2020.2987661","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Syracuse University; Tencent; Euskal Herriko Unibertsitatea; University of Birmingham; College of Engineering, Michigan State University; University of Manitoba; Arizona State University","keywords":"Computer science; Artificial neural network; Information retrieval; World Wide Web; Artificial intelligence","score_opus":0.015987716786376855,"score_gpt":0.21670722049405713,"score_spread":0.20071950370768027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234184979","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0085761715,0.076698825,0.3297185,0.01450642,0.10285765,0.0005575825,0.013103406,0.00566726,0.44831422],"genre_scores_gemma":[0.04708451,0.059917793,0.058605425,0.0020181374,0.0077924347,0.00033739055,0.017914081,0.001476334,0.804854],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927086,0.00006628691,0.000060309238,0.00006927319,0.00048062616,0.000052675143],"domain_scores_gemma":[0.99844545,0.00032881377,0.0000544493,0.0002518332,0.0008327398,0.00008680569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010211334,0.0008695145,0.0014611329,0.0016655496,0.00051198015,0.0022717533,0.0009440689,0.0014729509,0.17235099],"category_scores_gemma":[0.0033838958,0.00036509873,0.00046575707,0.0019373352,0.0004407287,0.0021492683,0.0010930956,0.0015128229,0.070227936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099547644,0.00008659512,0.000488883,0.00051558594,0.000030311943,0.00008042999,0.000023498118,0.0031102146,0.0016169639,0.014257364,0.49972716,0.4799634],"study_design_scores_gemma":[0.000027552762,0.000078212244,0.0014004629,0.00042337633,0.00006316967,0.00029664868,0.000045767134,0.028939035,0.0023299262,0.029093001,0.93726635,0.000036443565],"about_ca_topic_score_codex":0.0019519095,"about_ca_topic_score_gemma":0.0031764794,"teacher_disagreement_score":0.17235099,"about_ca_system_score_codex":0.00050869177,"about_ca_system_score_gemma":0.0016292125,"threshold_uncertainty_score":0.57657146},"labels":[],"label_agreement":null},{"id":"W4234970792","doi":"10.4018/978-1-7998-0414-7.ch091","title":"Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction","year":2019,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Sigmoid function; Artificial neural network; Algorithm; Fidelity; Computer science; Nonlinear system; Artificial intelligence","score_opus":0.029523891158017117,"score_gpt":0.27883746613587,"score_spread":0.2493135749778529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234970792","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005112405,0.00020573364,0.9979133,0.000053476517,0.000019471849,0.000023798095,0.00001935399,0.0004060955,0.00084744464],"genre_scores_gemma":[0.007243112,0.0002728564,0.9901423,0.000060010098,0.000021628082,0.00006816208,0.00013037682,0.00010246167,0.001959002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916697,0.00024519107,0.00006639693,0.00019350463,0.000298667,0.000029405748],"domain_scores_gemma":[0.9977902,0.0011535787,0.00011388275,0.0002737622,0.00063607446,0.000032536474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001920554,0.00088543276,0.00095826166,0.0012918408,0.00034632822,0.0019478468,0.0018467639,0.0010930769,0.0054527363],"category_scores_gemma":[0.0056116646,0.00063366996,0.0009740192,0.0015731166,0.00061285653,0.0018829743,0.0010125943,0.0021082447,0.005064201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048060792,0.000051114603,0.00047761807,0.00032267242,0.00007927271,0.00013195655,0.00012609178,0.116756834,0.009351416,0.021915203,0.0047716442,0.8459681],"study_design_scores_gemma":[0.000008731783,0.000033157048,0.00015321419,0.000062630665,0.000014803741,0.00012463056,0.000020418467,0.9670963,0.0045876955,0.014455242,0.013429516,0.000013609494],"about_ca_topic_score_codex":0.0018628059,"about_ca_topic_score_gemma":0.0022121146,"teacher_disagreement_score":0.0054527363,"about_ca_system_score_codex":0.0005785209,"about_ca_system_score_gemma":0.0009073128,"threshold_uncertainty_score":0.018241167},"labels":[],"label_agreement":null},{"id":"W4235039285","doi":"10.1109/tnnls.2021.3089401","title":"IEEE Transactions on Neural Networks and Learning Systems Publication Information","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Computer science; Artificial neural network; Information retrieval; Artificial intelligence; World Wide Web","score_opus":0.012733470139682094,"score_gpt":0.21942843061264394,"score_spread":0.20669496047296185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235039285","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008674992,0.07775329,0.3317228,0.014763441,0.10534082,0.00056262995,0.0132251615,0.0057110414,0.4422458],"genre_scores_gemma":[0.047799703,0.060790274,0.05947508,0.0020392463,0.007916191,0.00034253838,0.018183041,0.0014959566,0.80195796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992557,0.00006812914,0.000062097395,0.00007053413,0.0004896761,0.00005377804],"domain_scores_gemma":[0.9984035,0.00033720626,0.00005568766,0.00025708953,0.0008579105,0.00008860813],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010399217,0.00087971514,0.0014855702,0.0016763533,0.0005160981,0.0022983786,0.00094688445,0.0014829766,0.1712527],"category_scores_gemma":[0.0034331358,0.0003704023,0.00046931638,0.0019506597,0.00044431514,0.002172209,0.001100526,0.0015288608,0.06967644],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010125696,0.00008644695,0.00049349456,0.0005234777,0.000030718227,0.00008122713,0.000023858423,0.003108043,0.001625891,0.014201481,0.49946615,0.4802579],"study_design_scores_gemma":[0.000027818764,0.0000788564,0.0014199645,0.00042970068,0.000064212174,0.00029786731,0.000046496316,0.028729105,0.0023577304,0.028642723,0.9378687,0.00003680235],"about_ca_topic_score_codex":0.0019654583,"about_ca_topic_score_gemma":0.0031941677,"teacher_disagreement_score":0.8287473,"about_ca_system_score_codex":0.00051413826,"about_ca_system_score_gemma":0.0016375178,"threshold_uncertainty_score":0.5728973},"labels":[],"label_agreement":null},{"id":"W4235276132","doi":"10.2139/ssrn.3809532","title":"Consistent Local Spectrum (Lcm) Inference for Predictive Return Regressions","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Inference; Econometrics; Spectrum (functional analysis); Predictive inference; Statistics; Computer science; Mathematics; Artificial intelligence; Frequentist inference; Bayesian inference; Bayesian probability; Physics","score_opus":0.013228211966641718,"score_gpt":0.264581896913095,"score_spread":0.2513536849464533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235276132","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022663442,0.0005151979,0.97413886,0.00051981915,0.000051121664,0.000028667579,0.00014821634,0.0006349898,0.0012997045],"genre_scores_gemma":[0.7393719,0.00046985518,0.2536368,0.00059754046,0.00037608136,0.00015340987,0.0007219581,0.0004798697,0.0041925586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969844,0.00189712,0.0000993607,0.0004832102,0.0004017655,0.00013419293],"domain_scores_gemma":[0.97649324,0.019248711,0.0010047476,0.0019713186,0.00092979445,0.0003523538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0086314585,0.00082368555,0.0018389104,0.0016861157,0.000751639,0.001563907,0.0030935393,0.0021064328,0.0044723824],"category_scores_gemma":[0.0395724,0.00093529094,0.0011046329,0.0014333976,0.0016149362,0.0028468298,0.0025268905,0.0032695138,0.00088403764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006853155,0.00036047114,0.007008201,0.00037837084,0.00050544896,0.00025160503,0.0002200429,0.45771512,0.0039371536,0.21324867,0.008073687,0.307616],"study_design_scores_gemma":[0.000027328737,0.000026296706,0.00035629034,0.000016880638,0.000018048262,0.000015350395,0.0000072889798,0.9165063,0.00042793265,0.082274064,0.00031558756,0.000008717562],"about_ca_topic_score_codex":0.0023014392,"about_ca_topic_score_gemma":0.003091837,"teacher_disagreement_score":0.0086314585,"about_ca_system_score_codex":0.00092365127,"about_ca_system_score_gemma":0.0014379183,"threshold_uncertainty_score":0.04564804},"labels":[],"label_agreement":null},{"id":"W4235287877","doi":"10.1139/cjpp7811cover","title":"Neural Strategies for Complex Motor Behaviours / Stratégies neurales pour les comportements moteurs complexes","year":2000,"lang":"fr","type":"article","venue":"Canadian Journal of Physiology and Pharmacology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psychology; Humanities; Philosophy","score_opus":0.07405107478129226,"score_gpt":0.3255372460797181,"score_spread":0.2514861712984259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235287877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44800216,0.002891191,0.49754384,0.0012276891,0.00028346982,0.00020394195,0.0002485466,0.00045524593,0.049143944],"genre_scores_gemma":[0.9377158,0.00070953247,0.049342792,0.00012342907,0.00003119886,0.00015662261,0.00009728129,0.000075686636,0.011747689],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99974126,0.000052766824,0.0000200943,0.00006542202,0.000080939804,0.000039465947],"domain_scores_gemma":[0.9995059,0.00019115646,0.00006521995,0.000035994974,0.00012771186,0.00007401589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005237537,0.00078896625,0.00035953434,0.000397508,0.00048460258,0.0018761693,0.00048713337,0.00077877217,0.005056195],"category_scores_gemma":[0.002120434,0.00029385768,0.0005583748,0.00032471653,0.0006817053,0.0010871305,0.000708071,0.0007826072,0.00065119436],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059878576,0.00019310026,0.009630849,0.00048738005,0.00034142277,0.0008262066,0.001094906,0.122408986,0.27103075,0.27313238,0.0022769768,0.3179782],"study_design_scores_gemma":[0.00016298232,0.000846723,0.055289637,0.00022708923,0.00018951048,0.0013804055,0.0011475832,0.69137025,0.069562614,0.16897273,0.010677426,0.00017303342],"about_ca_topic_score_codex":0.0051954524,"about_ca_topic_score_gemma":0.0054607196,"teacher_disagreement_score":0.0051954524,"about_ca_system_score_codex":0.00086033775,"about_ca_system_score_gemma":0.00093740347,"threshold_uncertainty_score":0.016914666},"labels":[],"label_agreement":null},{"id":"W4235348784","doi":"10.1109/tnnls.2021.3052668","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; World Wide Web","score_opus":0.012733470139682094,"score_gpt":0.21942843061264394,"score_spread":0.20669496047296185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235348784","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008674992,0.07775329,0.3317228,0.014763441,0.10534082,0.00056262995,0.0132251615,0.0057110414,0.4422458],"genre_scores_gemma":[0.047799703,0.060790274,0.05947508,0.0020392463,0.007916191,0.00034253838,0.018183041,0.0014959566,0.80195796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992557,0.00006812914,0.000062097395,0.00007053413,0.0004896761,0.00005377804],"domain_scores_gemma":[0.9984035,0.00033720626,0.00005568766,0.00025708953,0.0008579105,0.00008860813],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010399217,0.00087971514,0.0014855702,0.0016763533,0.0005160981,0.0022983786,0.00094688445,0.0014829766,0.1712527],"category_scores_gemma":[0.0034331358,0.0003704023,0.00046931638,0.0019506597,0.00044431514,0.002172209,0.001100526,0.0015288608,0.06967644],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010125696,0.00008644695,0.00049349456,0.0005234777,0.000030718227,0.00008122713,0.000023858423,0.003108043,0.001625891,0.014201481,0.49946615,0.4802579],"study_design_scores_gemma":[0.000027818764,0.0000788564,0.0014199645,0.00042970068,0.000064212174,0.00029786731,0.000046496316,0.028729105,0.0023577304,0.028642723,0.9378687,0.00003680235],"about_ca_topic_score_codex":0.0019654583,"about_ca_topic_score_gemma":0.0031941677,"teacher_disagreement_score":0.8287473,"about_ca_system_score_codex":0.00051413826,"about_ca_system_score_gemma":0.0016375178,"threshold_uncertainty_score":0.5728973},"labels":[],"label_agreement":null},{"id":"W4235353501","doi":"10.1016/b0-08-043076-7/00539-8","title":"Constraint Satisfaction Models","year":2001,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Complexity of constraint satisfaction; Constraint satisfaction; Constraint satisfaction problem; Local consistency; Constraint learning; Computer science; Constraint (computer-aided design); Constraint satisfaction dual problem; Artificial neural network; Schema (genetic algorithms); Cognitive dissonance; Artificial intelligence; Psychology; Machine learning; Mathematics; Social psychology","score_opus":0.026599826487790603,"score_gpt":0.23832880862759767,"score_spread":0.21172898213980706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235353501","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037726557,0.0039554765,0.7234977,0.0022203873,0.00039801802,0.00016771715,0.0035033803,0.0027469674,0.25973764],"genre_scores_gemma":[0.1518816,0.00927242,0.44859073,0.0012299691,0.0004794063,0.0008956203,0.018092675,0.002284043,0.36727348],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993137,0.00017798862,0.00003634347,0.00014030757,0.00027961782,0.00005210909],"domain_scores_gemma":[0.9991941,0.0003756052,0.000036139776,0.00021114157,0.00014670078,0.00003634155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006924229,0.0015232618,0.0011050717,0.0010668214,0.00079233333,0.0028935722,0.0020347047,0.0010430306,0.060833722],"category_scores_gemma":[0.003125214,0.0008371841,0.001196154,0.0031736633,0.00090625486,0.0033124322,0.0016063628,0.0025925967,0.013804418],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051444196,0.000101036865,0.00027609259,0.00032396102,0.000055538672,0.00010451816,0.00014238892,0.044844676,0.00073075644,0.48257172,0.13091934,0.33987847],"study_design_scores_gemma":[0.000029675528,0.000019407102,0.00016767769,0.0001368334,0.000032492382,0.000111691144,0.00007282534,0.17939267,0.001135861,0.6225401,0.19633803,0.00002272916],"about_ca_topic_score_codex":0.007163307,"about_ca_topic_score_gemma":0.011370116,"teacher_disagreement_score":0.060833722,"about_ca_system_score_codex":0.001257146,"about_ca_system_score_gemma":0.0016659502,"threshold_uncertainty_score":0.20350909},"labels":[],"label_agreement":null},{"id":"W4235367183","doi":"10.1007/978-1-4471-7452-3_1","title":"Introduction","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Cognitive science; Psychology","score_opus":0.012644390565875706,"score_gpt":0.20861109337136405,"score_spread":0.19596670280548834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235367183","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027828035,0.0038291567,0.006959318,0.0021706119,0.0047021545,0.0001227097,0.0016324201,0.0008728649,0.9794326],"genre_scores_gemma":[0.0007780071,0.0019378423,0.0013688565,0.00068069145,0.0006712551,0.000053474094,0.00097095856,0.00021787727,0.993321],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995353,0.000039021597,0.000014603878,0.000094391085,0.00026849387,0.000048169386],"domain_scores_gemma":[0.99938786,0.00008976892,0.000022102324,0.0000873383,0.00029729947,0.0001155632],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004815329,0.001002437,0.00058503926,0.0018209163,0.001084467,0.0035504422,0.0015332921,0.0015190829,0.5597133],"category_scores_gemma":[0.0018438613,0.00035958661,0.0005003814,0.0016950074,0.00054518,0.0029521035,0.0020858669,0.0017849853,0.49651346],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017179033,0.000030488498,0.000053035183,0.00016201996,0.0000018475718,0.000030110774,0.00006216065,0.00015083383,0.00044325044,0.028554052,0.7735246,0.19697052],"study_design_scores_gemma":[0.0000012326763,0.0000049096134,0.00004127232,0.000052371408,7.273539e-7,0.000028020044,0.000016483511,0.000028294317,0.00008204405,0.003638916,0.99610364,0.000002138654],"about_ca_topic_score_codex":0.0018338793,"about_ca_topic_score_gemma":0.003456612,"teacher_disagreement_score":0.4402867,"about_ca_system_score_codex":0.0010642841,"about_ca_system_score_gemma":0.0016078501,"threshold_uncertainty_score":0.6280155},"labels":[],"label_agreement":null},{"id":"W4235582107","doi":"10.4018/978-1-60566-170-4.ch021","title":"A Unified Approach to Fractal Dimensions","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Multifractal system; Fractal; Fractal dimension; Fractal analysis; Computer science; Mathematics; Theoretical computer science; Statistical physics; Fractal transform; Pattern recognition (psychology); Artificial intelligence; Algorithm; Image processing; Image compression; Mathematical analysis; Physics; Image (mathematics)","score_opus":0.033448543144707986,"score_gpt":0.24049637279144317,"score_spread":0.20704782964673518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235582107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007572335,0.107987076,0.62830657,0.011307484,0.003028765,0.00014079484,0.0005880674,0.00054999907,0.24051891],"genre_scores_gemma":[0.18883814,0.109246,0.6134276,0.00398703,0.006397931,0.00074384874,0.0009502174,0.00043005525,0.07597923],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99910384,0.00021311568,0.00008364223,0.00020517447,0.00032896252,0.00006530721],"domain_scores_gemma":[0.99914825,0.00045740098,0.0000515031,0.00009713099,0.00021128095,0.000034510405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013797935,0.0010566446,0.0009248627,0.0038370064,0.001237025,0.0038999133,0.0011415227,0.001643192,0.0045908988],"category_scores_gemma":[0.0021353217,0.00041528253,0.0008867013,0.002467482,0.0043456526,0.0055737323,0.0018313227,0.0034556936,0.0016887585],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023803404,0.000004142426,0.00004645502,0.000079183636,0.000004319033,0.000025151945,0.00025310746,0.0009865414,0.00030808654,0.97288847,0.004009859,0.021392286],"study_design_scores_gemma":[0.0000022411934,0.000010975618,0.00014711273,0.00010490537,0.0000041978437,0.00011536047,0.00008894156,0.002818799,0.00019884505,0.86911666,0.12737733,0.000014715293],"about_ca_topic_score_codex":0.0007870094,"about_ca_topic_score_gemma":0.00076325337,"teacher_disagreement_score":0.0045908988,"about_ca_system_score_codex":0.0020075066,"about_ca_system_score_gemma":0.000806411,"threshold_uncertainty_score":0.01535809},"labels":[],"label_agreement":null},{"id":"W4235673876","doi":"10.1109/tnnls.2020.3030508","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Syracuse University; Tencent; Euskal Herriko Unibertsitatea; University of Birmingham; College of Engineering, Michigan State University; University of Manitoba; Arizona State University","keywords":"Computer science; Artificial neural network; World Wide Web; Information retrieval; Data science; Artificial intelligence","score_opus":0.015987716786376855,"score_gpt":0.21670722049405713,"score_spread":0.20071950370768027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235673876","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0085761715,0.076698825,0.3297185,0.01450642,0.10285765,0.0005575825,0.013103406,0.00566726,0.44831422],"genre_scores_gemma":[0.04708451,0.059917793,0.058605425,0.0020181374,0.0077924347,0.00033739055,0.017914081,0.001476334,0.804854],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927086,0.00006628691,0.000060309238,0.00006927319,0.00048062616,0.000052675143],"domain_scores_gemma":[0.99844545,0.00032881377,0.0000544493,0.0002518332,0.0008327398,0.00008680569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010211334,0.0008695145,0.0014611329,0.0016655496,0.00051198015,0.0022717533,0.0009440689,0.0014729509,0.17235099],"category_scores_gemma":[0.0033838958,0.00036509873,0.00046575707,0.0019373352,0.0004407287,0.0021492683,0.0010930956,0.0015128229,0.070227936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099547644,0.00008659512,0.000488883,0.00051558594,0.000030311943,0.00008042999,0.000023498118,0.0031102146,0.0016169639,0.014257364,0.49972716,0.4799634],"study_design_scores_gemma":[0.000027552762,0.000078212244,0.0014004629,0.00042337633,0.00006316967,0.00029664868,0.000045767134,0.028939035,0.0023299262,0.029093001,0.93726635,0.000036443565],"about_ca_topic_score_codex":0.0019519095,"about_ca_topic_score_gemma":0.0031764794,"teacher_disagreement_score":0.17235099,"about_ca_system_score_codex":0.00050869177,"about_ca_system_score_gemma":0.0016292125,"threshold_uncertainty_score":0.57657146},"labels":[],"label_agreement":null},{"id":"W4235900849","doi":"10.1007/978-1-4899-7502-7_33-1","title":"Cascade-Correlation","year":2014,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Machine Learning and Data Mining","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Cascade; Engineering","score_opus":0.019180698113864504,"score_gpt":0.2622898245828162,"score_spread":0.2431091264689517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235900849","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071484544,0.003293018,0.85953635,0.0006726349,0.0014673985,0.0003499062,0.002108613,0.008201556,0.11722211],"genre_scores_gemma":[0.23145002,0.0038213457,0.53846365,0.0013300729,0.0012778218,0.0005987161,0.008712959,0.0033323409,0.21101306],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99883944,0.0001953429,0.00004928653,0.00037190743,0.0004118034,0.00013233845],"domain_scores_gemma":[0.99882454,0.0002245341,0.00006592953,0.0005214743,0.00029101272,0.00007246366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009967573,0.0014526506,0.0016373447,0.0015298051,0.0011872211,0.002269107,0.0020108607,0.0015748169,0.06326868],"category_scores_gemma":[0.003496173,0.0008335667,0.0011899875,0.0021114924,0.00079848716,0.002556671,0.0027711277,0.0021485342,0.0323737],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039211483,0.00018938132,0.0011386217,0.0005276492,0.00019002482,0.00041009168,0.00012657307,0.030979734,0.010525995,0.28361914,0.15853971,0.51336104],"study_design_scores_gemma":[0.00010104395,0.00024466318,0.0016255898,0.00020780132,0.0001584296,0.0014295671,0.0000588746,0.47709858,0.03091294,0.27347898,0.21452892,0.00015457754],"about_ca_topic_score_codex":0.0018888213,"about_ca_topic_score_gemma":0.0039963266,"teacher_disagreement_score":0.06326868,"about_ca_system_score_codex":0.00085905363,"about_ca_system_score_gemma":0.0016351513,"threshold_uncertainty_score":0.21165478},"labels":[],"label_agreement":null},{"id":"W4236093551","doi":"10.32920/ryerson.14647062.v1","title":"Study of estimation of distribution algorithms applied to neuroevolution","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Neuroevolution; Computer science; Algorithm; Modularity (biology); Bitwise operation; Encoding (memory); Network topology; Artificial neural network; Coding (social sciences); Estimation of distribution algorithm; Operator (biology); Function (biology); Multiplexer; Topology (electrical circuits); Artificial intelligence; Mathematics; Multiplexing","score_opus":0.025316484074031795,"score_gpt":0.2822057598110577,"score_spread":0.2568892757370259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236093551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014452347,0.0003409212,0.9825455,0.0002762057,0.000029432118,0.000042330703,0.000008956347,0.00010394456,0.0022003313],"genre_scores_gemma":[0.56243813,0.0007360617,0.43195173,0.0002599384,0.00014516155,0.000454654,0.00008276841,0.00025384926,0.0036778192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782336,0.0013901292,0.00006833006,0.00022675496,0.00038978975,0.00010175539],"domain_scores_gemma":[0.9801456,0.01715993,0.00074862404,0.0006845086,0.0010032946,0.00025802638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007353027,0.0009793288,0.0010711413,0.0012757939,0.000647922,0.0014425926,0.0014107414,0.0014812717,0.001971996],"category_scores_gemma":[0.036574617,0.00067721587,0.000781728,0.0007224848,0.0020704926,0.0022815545,0.002076903,0.0022080368,0.00023601788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043222146,0.00004738543,0.0017652268,0.00010278478,0.00007411795,0.000044124336,0.00015504299,0.7714873,0.0012059322,0.17215101,0.00060199253,0.052321848],"study_design_scores_gemma":[0.000006734347,0.000018971283,0.00007712537,0.0000128063375,0.000003635039,0.000011645733,0.00000608967,0.9823715,0.00037813987,0.016751403,0.00035776786,0.0000041736225],"about_ca_topic_score_codex":0.0020931102,"about_ca_topic_score_gemma":0.0013097186,"teacher_disagreement_score":0.007353027,"about_ca_system_score_codex":0.0019139688,"about_ca_system_score_gemma":0.0011903691,"threshold_uncertainty_score":0.038886964},"labels":[],"label_agreement":null},{"id":"W4236289726","doi":"10.1063/1.2405528","title":"New Books","year":2006,"lang":"en","type":"article","venue":"Physics Today","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Library science; Physics; Engineering physics; Computer science","score_opus":0.011336991290423074,"score_gpt":0.2243898634891577,"score_spread":0.21305287219873464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236289726","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000431281,0.020056248,0.003493626,0.0053828647,0.008614872,0.00012584834,0.005280438,0.0021628712,0.954452],"genre_scores_gemma":[0.0015262057,0.010556102,0.0022344072,0.0018291147,0.0015881445,0.00007590173,0.0036661595,0.0008422036,0.9776818],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99886,0.00009389649,0.00007424198,0.0003142385,0.00055475096,0.0001029404],"domain_scores_gemma":[0.9986909,0.00021750468,0.000057044785,0.00024693043,0.0004887848,0.00029878304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005986348,0.0014456097,0.0015348134,0.0032290772,0.0013812943,0.008701882,0.0023362204,0.002391987,0.69147545],"category_scores_gemma":[0.0031831115,0.00064251275,0.001021859,0.0038140537,0.0008087075,0.007529265,0.0037004873,0.003406327,0.65594786],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021109021,0.000030776366,0.00012338003,0.00032766772,0.0000066418556,0.00007982485,0.00008315818,0.00010158551,0.00026496965,0.013207559,0.809749,0.17600442],"study_design_scores_gemma":[0.0000017653488,0.000003389623,0.00007175501,0.00010356724,0.0000013188945,0.00007257951,0.000030368235,0.00002346201,0.000036181158,0.0015504794,0.9981018,0.00000345126],"about_ca_topic_score_codex":0.0018463649,"about_ca_topic_score_gemma":0.0036023022,"teacher_disagreement_score":0.69147545,"about_ca_system_score_codex":0.0015251087,"about_ca_system_score_gemma":0.0023599346,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4236311188","doi":"10.4018/9781605660608.ch188","title":"Adaptive Computation Paradigm in Knowledge Representation","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Computation; Representation (politics); Cognitive science; Artificial intelligence; Theoretical computer science; Psychology; Algorithm; Political science","score_opus":0.05455871920806379,"score_gpt":0.28966195591842653,"score_spread":0.23510323671036273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236311188","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005071186,0.03970891,0.7580247,0.0070996727,0.0008527728,0.00013241128,0.00019330288,0.00039609085,0.18852103],"genre_scores_gemma":[0.3526604,0.061885018,0.47482166,0.0035774617,0.0017852806,0.0009043092,0.0006925897,0.00019355942,0.103479736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99955755,0.00012643689,0.00002607623,0.00010640192,0.0001515753,0.00003193607],"domain_scores_gemma":[0.99975306,0.00013214722,0.000014997401,0.000048935945,0.000035902893,0.000014997787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051787606,0.00052988896,0.0005462007,0.0008023133,0.00045591372,0.0034453033,0.00143085,0.0013972138,0.006152362],"category_scores_gemma":[0.0011495387,0.00020506026,0.0006577496,0.0018467754,0.0024634383,0.0039717304,0.001329229,0.002304965,0.001626611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000985589,0.000012117334,0.0000725706,0.00019948951,0.000020614347,0.00007980437,0.00022593398,0.0059347637,0.0008364331,0.9215453,0.006238532,0.064824656],"study_design_scores_gemma":[0.0000073704623,0.000016647034,0.00011299619,0.00009010543,0.000013603847,0.0001652884,0.00010498518,0.03438151,0.0005072425,0.8955503,0.06903834,0.00001158207],"about_ca_topic_score_codex":0.0013044823,"about_ca_topic_score_gemma":0.0012680014,"teacher_disagreement_score":0.006152362,"about_ca_system_score_codex":0.0012210651,"about_ca_system_score_gemma":0.00082189514,"threshold_uncertainty_score":0.020581722},"labels":[],"label_agreement":null},{"id":"W4236315294","doi":"10.1109/tnnls.2020.3003488","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Syracuse University; Tencent; Euskal Herriko Unibertsitatea; University of Birmingham; College of Engineering, Michigan State University; University of Manitoba; Arizona State University","keywords":"Computer science; Artificial neural network; World Wide Web; Information retrieval; Artificial intelligence","score_opus":0.015987716786376855,"score_gpt":0.21670722049405713,"score_spread":0.20071950370768027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236315294","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0085761715,0.076698825,0.3297185,0.01450642,0.10285765,0.0005575825,0.013103406,0.00566726,0.44831422],"genre_scores_gemma":[0.04708451,0.059917793,0.058605425,0.0020181374,0.0077924347,0.00033739055,0.017914081,0.001476334,0.804854],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927086,0.00006628691,0.000060309238,0.00006927319,0.00048062616,0.000052675143],"domain_scores_gemma":[0.99844545,0.00032881377,0.0000544493,0.0002518332,0.0008327398,0.00008680569],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010211334,0.0008695145,0.0014611329,0.0016655496,0.00051198015,0.0022717533,0.0009440689,0.0014729509,0.17235099],"category_scores_gemma":[0.0033838958,0.00036509873,0.00046575707,0.0019373352,0.0004407287,0.0021492683,0.0010930956,0.0015128229,0.070227936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099547644,0.00008659512,0.000488883,0.00051558594,0.000030311943,0.00008042999,0.000023498118,0.0031102146,0.0016169639,0.014257364,0.49972716,0.4799634],"study_design_scores_gemma":[0.000027552762,0.000078212244,0.0014004629,0.00042337633,0.00006316967,0.00029664868,0.000045767134,0.028939035,0.0023299262,0.029093001,0.93726635,0.000036443565],"about_ca_topic_score_codex":0.0019519095,"about_ca_topic_score_gemma":0.0031764794,"teacher_disagreement_score":0.827649,"about_ca_system_score_codex":0.00050869177,"about_ca_system_score_gemma":0.0016292125,"threshold_uncertainty_score":0.57657146},"labels":[],"label_agreement":null},{"id":"W4236362309","doi":"10.1017/cbo9781107298019","title":"Understanding Machine Learning","year":2014,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2928,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Stability (learning theory); Algorithmic learning theory; Computational learning theory; Machine learning; Presentation (obstetrics); Stochastic gradient descent; Convexity; Online machine learning; Artificial neural network","score_opus":0.057622170100058795,"score_gpt":0.20464559742305238,"score_spread":0.1470234273229936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236362309","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037587222,0.12372326,0.30963585,0.037862796,0.0031214945,0.00012959325,0.0013364945,0.001203887,0.51922786],"genre_scores_gemma":[0.11413177,0.18815598,0.25599635,0.017927682,0.0066885334,0.0005446988,0.0050348043,0.00093180675,0.4105884],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994735,0.00011908859,0.000022437238,0.00010772176,0.00023703839,0.000040282364],"domain_scores_gemma":[0.99938405,0.00038717446,0.000028950015,0.00007290134,0.00009531405,0.000031642245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007156623,0.0009309,0.00058301754,0.0013701773,0.0007480362,0.004908076,0.0012556417,0.0018152006,0.018848026],"category_scores_gemma":[0.0033490432,0.0004254403,0.00059042196,0.0015037804,0.0021798874,0.007019379,0.0018340024,0.0028482096,0.009986304],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006949456,0.000016436401,0.00027072683,0.00042415631,0.000022430808,0.00008116355,0.0005601428,0.0026744471,0.000395047,0.74046576,0.11237594,0.14270675],"study_design_scores_gemma":[0.000001937129,0.0000072933644,0.00028231277,0.00027639623,0.0000041840826,0.00013755336,0.00014002551,0.0025203226,0.0001498121,0.56274575,0.43372568,0.000008683546],"about_ca_topic_score_codex":0.0012403494,"about_ca_topic_score_gemma":0.0014845937,"teacher_disagreement_score":0.018848026,"about_ca_system_score_codex":0.0013770688,"about_ca_system_score_gemma":0.0010022082,"threshold_uncertainty_score":0.06305295},"labels":[],"label_agreement":null},{"id":"W4236430162","doi":"10.1007/978-1-4939-7131-2_100331","title":"Ensemble of Classifiers","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning","score_opus":0.03164678610323419,"score_gpt":0.2388645136448956,"score_spread":0.20721772754166143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236430162","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008921463,0.0086599775,0.91551334,0.0008937507,0.0015526698,0.00011971122,0.00055630214,0.0018783767,0.061904408],"genre_scores_gemma":[0.21230581,0.012389101,0.61509573,0.0011116279,0.0029589324,0.000398885,0.0044219615,0.00082540774,0.15049261],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990258,0.00013189012,0.00005100067,0.00027207995,0.00044262994,0.00007652012],"domain_scores_gemma":[0.99918944,0.00022040194,0.00003157755,0.00024231571,0.00027659285,0.000039687315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012426955,0.0009831894,0.0017002417,0.0012830715,0.000589704,0.0017374024,0.0014246954,0.0012239534,0.009420257],"category_scores_gemma":[0.002391335,0.0005325548,0.0010088168,0.001665403,0.000376744,0.0027856354,0.001581964,0.0022279741,0.0069722026],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006873336,0.00006989249,0.00062349124,0.00019191873,0.00023211051,0.000054079963,0.000053983862,0.034451082,0.0045696646,0.03292438,0.036142085,0.89061844],"study_design_scores_gemma":[0.000020129275,0.00019113596,0.002072569,0.00025401713,0.0002904935,0.00056100247,0.00008643383,0.62764174,0.014632028,0.1544132,0.19976531,0.00007200244],"about_ca_topic_score_codex":0.00087424275,"about_ca_topic_score_gemma":0.001817733,"teacher_disagreement_score":0.009420257,"about_ca_system_score_codex":0.00053458964,"about_ca_system_score_gemma":0.0005952303,"threshold_uncertainty_score":0.03151387},"labels":[],"label_agreement":null},{"id":"W4236602569","doi":"10.3410/f.717970927.793469328","title":"Faculty Opinions recommendation of Lag normalization in an electrically coupled neural network.","year":2013,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Normalization (sociology); Lag; Artificial neural network; Time lag; Lag time; Computer science; Artificial intelligence; Biology; Computer network; Sociology; Biological system; Social science","score_opus":0.029043737599704047,"score_gpt":0.33110634884227375,"score_spread":0.3020626112425697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236602569","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004544403,0.00056243,0.0034874196,0.0008856834,0.0010221675,0.00023343967,0.975636,0.0067869932,0.0068415063],"genre_scores_gemma":[0.0067122043,0.00018210548,0.004799007,0.00020443003,0.00009721623,0.00024780043,0.97838014,0.00035155046,0.009025434],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986921,0.00023467976,0.00011237422,0.0003858365,0.0003786096,0.00019643918],"domain_scores_gemma":[0.9976816,0.00041038744,0.00011603433,0.00070524815,0.0009055868,0.00018110735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021830476,0.003203386,0.0014014508,0.00230926,0.0008188631,0.0025053846,0.0040295413,0.0029576172,0.04659807],"category_scores_gemma":[0.008338633,0.0006725101,0.0021807097,0.0023902706,0.00039578753,0.0014866409,0.0017179716,0.0019511099,0.0657669],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021148114,0.00012953374,0.001302647,0.0003246416,0.00006373227,0.000023104432,0.000006554657,0.0011459999,0.0002808363,0.0002568947,0.9816553,0.014599123],"study_design_scores_gemma":[0.0013952632,0.0002869349,0.01591349,0.00048022912,0.00024287257,0.00022502242,0.00016597287,0.040255673,0.008311155,0.0058244187,0.9267468,0.00015213412],"about_ca_topic_score_codex":0.03244993,"about_ca_topic_score_gemma":0.08969916,"teacher_disagreement_score":0.04659807,"about_ca_system_score_codex":0.0016364651,"about_ca_system_score_gemma":0.002825004,"threshold_uncertainty_score":0.15588611},"labels":[],"label_agreement":null},{"id":"W4236860863","doi":"10.1142/9789812701077_0004","title":"EDGE PRESERVED DENOISING IN MAGNETIC RESONANCE IMAGES AND THEIR APPLICATIONS","year":2005,"lang":"en","type":"book-chapter","venue":"World Scientific Publishing Company eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Noise reduction; Image denoising; Materials science; Enhanced Data Rates for GSM Evolution; Computer science; Artificial intelligence","score_opus":0.025092226929015804,"score_gpt":0.22706230025986673,"score_spread":0.20197007333085093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236860863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007903207,0.022406101,0.95244336,0.000513649,0.00051669654,0.00003406583,0.00018025926,0.00087144383,0.015131137],"genre_scores_gemma":[0.078092344,0.039248344,0.7938896,0.00038866303,0.0009623457,0.00007151532,0.0005820285,0.000591693,0.086173505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986327,0.000017435601,0.000008950359,0.000026698344,0.000074585645,0.000008996691],"domain_scores_gemma":[0.9996886,0.00013203238,0.000022495853,0.00004415914,0.000100165526,0.000012427443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034428752,0.00055005273,0.000673922,0.00073435454,0.00013550746,0.0007698095,0.00064728595,0.0009756535,0.005390549],"category_scores_gemma":[0.0008189201,0.00039786304,0.0003591065,0.0013960978,0.0004938836,0.00093048095,0.00038917345,0.0009125628,0.0025921182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090629386,0.0000877706,0.00021622606,0.00082463067,0.00004833996,0.00021067161,0.00011155486,0.0249744,0.06329807,0.047017127,0.021722354,0.8413982],"study_design_scores_gemma":[0.00003534677,0.0002633407,0.0027758158,0.00030507136,0.00016090219,0.0022502032,0.0001254363,0.5608485,0.11657081,0.123021856,0.19352983,0.00011285548],"about_ca_topic_score_codex":0.00043813433,"about_ca_topic_score_gemma":0.00075320277,"teacher_disagreement_score":0.005390549,"about_ca_system_score_codex":0.0001679774,"about_ca_system_score_gemma":0.00018542807,"threshold_uncertainty_score":0.018033206},"labels":[],"label_agreement":null},{"id":"W4236875335","doi":"10.4018/9781591408482.ch008","title":"Artificial Neural Networks in EEG Analysis","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Electroencephalography; Artificial neural network; Computer science; Neuroscience; Artificial intelligence; Psychology","score_opus":0.02711001686905869,"score_gpt":0.24557598300312888,"score_spread":0.21846596613407018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236875335","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034234906,0.5552904,0.29165906,0.0057604928,0.0037982091,0.0001147556,0.00052514975,0.00091759185,0.13851081],"genre_scores_gemma":[0.07190889,0.5351357,0.21352476,0.0025949404,0.0036829666,0.0003616448,0.0012379161,0.0004236104,0.17112972],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959034,0.00012828651,0.00003320422,0.00007014708,0.00015989026,0.000018030145],"domain_scores_gemma":[0.9996377,0.00021545785,0.00002702667,0.00002593989,0.00008147405,0.000012357108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053261017,0.0008571939,0.00065184664,0.0011810344,0.00025030773,0.0016564842,0.00076982024,0.001597015,0.012307115],"category_scores_gemma":[0.0013376335,0.0002959221,0.00037902038,0.0027747683,0.00063187274,0.0016522267,0.0007924221,0.0016685606,0.006005645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003829717,0.0000691929,0.0005892788,0.0020920567,0.00007342248,0.00032767197,0.00023761923,0.020514078,0.002321447,0.08746467,0.07190372,0.8143686],"study_design_scores_gemma":[0.000012632218,0.00008128303,0.0023843592,0.002142775,0.000045437122,0.0009699065,0.00024467247,0.07039904,0.0019335012,0.2012896,0.7204123,0.000084503255],"about_ca_topic_score_codex":0.0012805412,"about_ca_topic_score_gemma":0.0015051708,"teacher_disagreement_score":0.012307115,"about_ca_system_score_codex":0.00050874654,"about_ca_system_score_gemma":0.00041418365,"threshold_uncertainty_score":0.041171372},"labels":[],"label_agreement":null},{"id":"W4237303492","doi":"10.32920/ryerson.14654562","title":"AC-AC voltage regulation by switch mode PWM Cûk voltage controller with improved performance","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Dropout voltage; Voltage regulator; Low-dropout regulator; Voltage divider; Voltage; Control theory (sociology); Pulse-width modulation; Disturbance voltage; Voltage regulation; Voltage controller; Total harmonic distortion; Duty cycle; Waveform; Harmonics; Voltage reference; Regulator; Voltage droop; Engineering; Electrical engineering; Computer science; Control (management); Chemistry","score_opus":0.008592560898714715,"score_gpt":0.22364425906752977,"score_spread":0.21505169816881506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237303492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24483956,0.0021649252,0.7287709,0.00029609862,0.0002991424,0.0001396497,0.00012545534,0.004625588,0.018738626],"genre_scores_gemma":[0.96600133,0.00020755891,0.029901281,0.000058873167,0.000033994664,0.00003312087,0.000051866828,0.000042665135,0.0036692081],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972814,0.000033563105,0.000014129319,0.00006161095,0.0001407231,0.000021693768],"domain_scores_gemma":[0.9997719,0.000039874692,0.000031891665,0.000028794355,0.00011802072,0.00000949557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023194015,0.00022899304,0.0002611196,0.0003174955,0.00021167213,0.0006543919,0.0008116733,0.00038726657,0.0022397852],"category_scores_gemma":[0.0005075025,0.00008193706,0.00015708897,0.00040764266,0.00015003141,0.0004986804,0.00017802702,0.00031891977,0.00043086894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007831074,0.00032255435,0.0030433745,0.0006748149,0.00009701616,0.00035931898,0.00028072103,0.06665617,0.4770777,0.007047046,0.0047592698,0.4388989],"study_design_scores_gemma":[0.000094859985,0.00094380876,0.0036181062,0.000044274864,0.00006826477,0.00087219395,0.00006239084,0.80346185,0.17224576,0.0016995809,0.016850036,0.00003888831],"about_ca_topic_score_codex":0.0013722518,"about_ca_topic_score_gemma":0.0014581652,"teacher_disagreement_score":0.0022397852,"about_ca_system_score_codex":0.00029281926,"about_ca_system_score_gemma":0.00026628943,"threshold_uncertainty_score":0.0074928403},"labels":[],"label_agreement":null},{"id":"W4237505254","doi":"10.32920/ryerson.14656947","title":"A bidirectional associative memory based on cortical spiking neurons using temporal coding","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Content-addressable memory; Spiking neural network; Coding (social sciences); Associative property; Artificial neural network; Neural coding; Bidirectional associative memory; Artificial intelligence; Mathematics","score_opus":0.06236437656462041,"score_gpt":0.30557106010149193,"score_spread":0.24320668353687153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237505254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12833422,0.001544054,0.85245776,0.00057666225,0.00044594335,0.000080368496,0.00021957919,0.0016482597,0.01469323],"genre_scores_gemma":[0.9016061,0.0009974525,0.08892686,0.00025155125,0.00007158518,0.0000960968,0.00016764039,0.0000562838,0.007826407],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999899,0.000013384916,0.000008110338,0.000025075762,0.000030667936,0.000023793342],"domain_scores_gemma":[0.99981445,0.00004209004,0.00001718666,0.00003580088,0.00006778275,0.000022723245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002147161,0.00028530488,0.00044465877,0.00026383734,0.0003555081,0.0006210213,0.0010045035,0.00047421333,0.0026100047],"category_scores_gemma":[0.00051551673,0.0001562077,0.0004361032,0.00047800053,0.00037331504,0.0011715061,0.0005922469,0.0004926616,0.00042032675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006499144,0.0003148632,0.003051078,0.00066774274,0.00029028143,0.0007827947,0.00033165078,0.22560681,0.2359809,0.13143204,0.0055800807,0.39531174],"study_design_scores_gemma":[0.000030773823,0.00018741655,0.0005621036,0.000030141558,0.00009372378,0.00027027054,0.00003996369,0.92387503,0.040331643,0.028534034,0.0060114674,0.000033414293],"about_ca_topic_score_codex":0.0016262362,"about_ca_topic_score_gemma":0.0013503444,"teacher_disagreement_score":0.0026100047,"about_ca_system_score_codex":0.000282602,"about_ca_system_score_gemma":0.00042896063,"threshold_uncertainty_score":0.008731306},"labels":[],"label_agreement":null},{"id":"W4237507051","doi":"10.1109/tnnls.2020.2994916","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Euskal Herriko Unibertsitatea; University of Birmingham; Syracuse University; Tencent; Institute of Electrical and Electronics Engineers; College of Engineering, Michigan State University; University of Manitoba; Arizona State University","keywords":"Computer science; Artificial neural network; Information retrieval; World Wide Web; Artificial intelligence","score_opus":0.015987716786376855,"score_gpt":0.21670722049405713,"score_spread":0.20071950370768027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237507051","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0085761715,0.076698825,0.3297185,0.01450642,0.10285765,0.0005575825,0.013103406,0.00566726,0.44831422],"genre_scores_gemma":[0.04708451,0.059917793,0.058605425,0.0020181374,0.0077924347,0.00033739055,0.017914081,0.001476334,0.804854],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927086,0.00006628691,0.000060309238,0.00006927319,0.00048062616,0.000052675143],"domain_scores_gemma":[0.99844545,0.00032881377,0.0000544493,0.0002518332,0.0008327398,0.00008680569],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010211334,0.0008695145,0.0014611329,0.0016655496,0.00051198015,0.0022717533,0.0009440689,0.0014729509,0.17235099],"category_scores_gemma":[0.0033838958,0.00036509873,0.00046575707,0.0019373352,0.0004407287,0.0021492683,0.0010930956,0.0015128229,0.070227936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099547644,0.00008659512,0.000488883,0.00051558594,0.000030311943,0.00008042999,0.000023498118,0.0031102146,0.0016169639,0.014257364,0.49972716,0.4799634],"study_design_scores_gemma":[0.000027552762,0.000078212244,0.0014004629,0.00042337633,0.00006316967,0.00029664868,0.000045767134,0.028939035,0.0023299262,0.029093001,0.93726635,0.000036443565],"about_ca_topic_score_codex":0.0019519095,"about_ca_topic_score_gemma":0.0031764794,"teacher_disagreement_score":0.827649,"about_ca_system_score_codex":0.00050869177,"about_ca_system_score_gemma":0.0016292125,"threshold_uncertainty_score":0.57657146},"labels":[],"label_agreement":null},{"id":"W4237535683","doi":"10.1109/tit.2013.2285984","title":"IEEE Transactions on Information Theory publication information","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Information theory; Computer science; Information retrieval; Mathematics; Statistics","score_opus":0.008243812519496796,"score_gpt":0.21042254786751838,"score_spread":0.20217873534802158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237535683","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0079091275,0.03755039,0.42466298,0.025495855,0.042800706,0.00075192185,0.03140446,0.005755368,0.4236692],"genre_scores_gemma":[0.13355762,0.07424585,0.10541926,0.0047936672,0.012029835,0.0009198664,0.048963323,0.00227613,0.6177945],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803704,0.00033869187,0.00016374774,0.00019873674,0.001125958,0.00013587455],"domain_scores_gemma":[0.9945709,0.0017822967,0.00019389545,0.0014115663,0.0018734253,0.00016799562],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020160223,0.0013117128,0.0020686707,0.0038707089,0.0009730602,0.004412817,0.0011944221,0.0021146517,0.1867627],"category_scores_gemma":[0.01151466,0.0005862424,0.00079467165,0.0051745386,0.0008250657,0.0045223697,0.0017605267,0.0025047946,0.07623412],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017339068,0.0001256474,0.00070909905,0.00087392353,0.00006486732,0.00016038366,0.000052595475,0.0034882312,0.0011181703,0.081756964,0.5115632,0.3999136],"study_design_scores_gemma":[0.0000704554,0.0001089552,0.0013712166,0.000903883,0.00017071984,0.0005743178,0.000120662466,0.037477564,0.003340284,0.21059091,0.7451893,0.00008171901],"about_ca_topic_score_codex":0.0014628328,"about_ca_topic_score_gemma":0.0012368626,"teacher_disagreement_score":0.8132373,"about_ca_system_score_codex":0.0010147836,"about_ca_system_score_gemma":0.0021953448,"threshold_uncertainty_score":0.6247835},"labels":[],"label_agreement":null},{"id":"W4237798487","doi":"10.32920/ryerson.14645937.v1","title":"Growing self-organizing tree-based kernel smoother for machine learning and data mining","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Regression; Self-organizing map; Multivariate statistics; Decision tree; Semi-supervised learning; Supervised learning; Tree (set theory); Regression analysis; Data mining; Big data; Unsupervised learning; Kernel (algebra); Artificial neural network; Mathematics; Statistics","score_opus":0.04344977185646058,"score_gpt":0.2909828003612443,"score_spread":0.2475330285047837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237798487","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011407031,0.00017010774,0.9875749,0.00007121324,0.00002208639,0.000016107759,0.000023511062,0.00039201122,0.00032304245],"genre_scores_gemma":[0.4353769,0.0004168892,0.56105995,0.00007009463,0.00005148599,0.00015890639,0.00020684229,0.00018373842,0.002475092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954766,0.00017881402,0.000027685288,0.000071114424,0.0001401354,0.00003463243],"domain_scores_gemma":[0.9981489,0.0010077532,0.0001435589,0.0001870972,0.00046567005,0.000047040878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013271708,0.00044477958,0.0006834392,0.0006693119,0.00033480898,0.00055569626,0.0009212809,0.000743502,0.0011576649],"category_scores_gemma":[0.005572992,0.00029107687,0.00075206993,0.0010350696,0.00040805517,0.0011488731,0.0006635655,0.0011392599,0.000515911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015811234,0.00013304011,0.0018423143,0.00016328749,0.00010563818,0.00008227463,0.00018551291,0.74337524,0.009599529,0.02129876,0.002472628,0.22058372],"study_design_scores_gemma":[0.0000015636454,0.00000832789,0.00009402487,0.0000018030878,0.0000020113364,0.0000049236905,0.0000040274235,0.9977914,0.00036045138,0.0014736564,0.00025506038,0.0000026836763],"about_ca_topic_score_codex":0.003136675,"about_ca_topic_score_gemma":0.0029070543,"teacher_disagreement_score":0.003136675,"about_ca_system_score_codex":0.000514608,"about_ca_system_score_gemma":0.0007034886,"threshold_uncertainty_score":0.007018864},"labels":[],"label_agreement":null},{"id":"W4237883693","doi":"10.1002/0470018860.s00002","title":"Perceptron","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Cognitive Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.008173803214435053,"score_gpt":0.26791542490778003,"score_spread":0.25974162169334497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237883693","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025411049,0.0075141448,0.85535294,0.006814852,0.002606304,0.0003954915,0.009866914,0.01931499,0.07272327],"genre_scores_gemma":[0.4776044,0.0050563603,0.37319112,0.003289102,0.0013785108,0.0007755958,0.019349812,0.0018766646,0.11747843],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985361,0.00042775983,0.0000909748,0.00037186558,0.00041140435,0.00016188141],"domain_scores_gemma":[0.9981554,0.0007855102,0.00008846235,0.00038559662,0.00042336178,0.00016173838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016721572,0.0010245894,0.00120974,0.0011493788,0.0005768481,0.0027322515,0.0019279554,0.0014482415,0.04231244],"category_scores_gemma":[0.0073590856,0.0004941334,0.0012826499,0.0016633159,0.00068236823,0.0024465176,0.0011569727,0.0020012246,0.016604846],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006711192,0.00029937035,0.0023495774,0.0008099853,0.00035109927,0.00018817937,0.0000633316,0.17877436,0.0021374426,0.06927482,0.20446251,0.5406181],"study_design_scores_gemma":[0.00014444298,0.0001476591,0.00043680076,0.00014981114,0.00009315754,0.00016487541,0.000026412034,0.79330367,0.0035247612,0.1355872,0.06637913,0.000042149954],"about_ca_topic_score_codex":0.003181865,"about_ca_topic_score_gemma":0.004280919,"teacher_disagreement_score":0.04231244,"about_ca_system_score_codex":0.0010126425,"about_ca_system_score_gemma":0.0016313351,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4238134598","doi":"10.1109/wsc.2012.6464998","title":"Allocation of simulation effort for neural network vs. regression metamodels","year":2012,"lang":"en","type":"article","venue":"Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Metamodeling; Computer science; Artificial neural network; Point (geometry); Regression; Sample (material); Regression analysis; Machine learning; Data mining; Statistics; Mathematics","score_opus":0.04650105166041181,"score_gpt":0.30030865259902395,"score_spread":0.25380760093861215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238134598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106251165,0.00016026339,0.8860653,0.00049574726,0.000042780797,0.00025008814,0.00013136645,0.0005772033,0.006026044],"genre_scores_gemma":[0.77222174,0.00017055799,0.2248557,0.00012477134,0.000015820913,0.000595508,0.00016932255,0.00020995805,0.0016365441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966865,0.0022108362,0.00019196175,0.00034508115,0.00037420588,0.00019133375],"domain_scores_gemma":[0.990194,0.006411844,0.0006960554,0.0017955616,0.0006863747,0.00021609534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063938834,0.00073509704,0.0006968287,0.00080661225,0.0003245531,0.0010742121,0.0013403728,0.0009120396,0.003627656],"category_scores_gemma":[0.030932978,0.00045028876,0.000654044,0.0006779969,0.0005304243,0.0019456374,0.0013882251,0.001192806,0.00041150296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003543219,0.00012173556,0.0014214093,0.00007233658,0.0000427772,0.000031252654,0.00008391263,0.9413872,0.0022428948,0.019756446,0.00025509216,0.03423063],"study_design_scores_gemma":[0.000029250721,0.0000618013,0.00029083755,0.00001674975,0.000013505384,0.000008100399,0.000022225035,0.9899414,0.001444932,0.0075332094,0.0006305813,0.000007340258],"about_ca_topic_score_codex":0.002127193,"about_ca_topic_score_gemma":0.002728616,"teacher_disagreement_score":0.0063938834,"about_ca_system_score_codex":0.001559365,"about_ca_system_score_gemma":0.0012022834,"threshold_uncertainty_score":0.03381449},"labels":[],"label_agreement":null},{"id":"W4238781109","doi":"10.31234/osf.io/nesuq","title":"Encoding structure in holographic reduced representations","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Carleton University","funders":"","keywords":"Encoding (memory); Decoding methods; Content-addressable memory; Shuffling; ENCODE; Computer science; Associative property; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science; Algorithm; Mathematics; Artificial neural network; Biology; Pure mathematics","score_opus":0.02774963516039623,"score_gpt":0.2986585870542886,"score_spread":0.27090895189389236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238781109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18491249,0.000640113,0.7916477,0.0007854385,0.00012227273,0.000056585774,0.0003923021,0.0006069503,0.020836245],"genre_scores_gemma":[0.8617726,0.0006700257,0.12454434,0.00026019925,0.000110172135,0.00011200532,0.00042759255,0.00021909719,0.01188411],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959975,0.00014018576,0.000028864175,0.00007826233,0.00010708655,0.000045879227],"domain_scores_gemma":[0.99923396,0.00030444015,0.00012058084,0.00019068208,0.00011128796,0.000039081177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042354243,0.00029486883,0.00044285634,0.0006929451,0.00030625562,0.0015860006,0.00071480044,0.000616025,0.004350031],"category_scores_gemma":[0.002446108,0.00030074533,0.0004489787,0.0005366481,0.001636423,0.003559555,0.001147425,0.00090347516,0.00070364145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006292497,0.000019704754,0.0001397065,0.000072562194,0.000013503774,0.00007441668,0.00017494003,0.027628152,0.007221929,0.9330296,0.0010241681,0.030538436],"study_design_scores_gemma":[0.000016667713,0.0000428399,0.00014578384,0.000018576611,0.000006345608,0.00007106537,0.00006183482,0.195356,0.0035674646,0.7976338,0.0030600228,0.000019585626],"about_ca_topic_score_codex":0.0006757446,"about_ca_topic_score_gemma":0.000624464,"teacher_disagreement_score":0.004350031,"about_ca_system_score_codex":0.0005779477,"about_ca_system_score_gemma":0.00037568403,"threshold_uncertainty_score":0.014552295},"labels":[],"label_agreement":null},{"id":"W4238816956","doi":"10.1093/cercor/bhaa113","title":"Corrigendum: Dissociable Neural Information Dynamics of Perceptual Integration and Differentiation during Bistable Perception","year":2020,"lang":"en","type":"erratum","venue":"Cerebral Cortex","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Bistability; Perception; Dynamics (music); Cognitive psychology; Psychology; Neuroscience; Cognitive science; Neural correlates of consciousness; Computer science; Cognition; Physics","score_opus":0.011652474083612183,"score_gpt":0.2155413982888059,"score_spread":0.20388892420519372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238816956","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025253507,0.00070653454,0.002923084,0.02728448,0.9549768,0.00003062365,0.0016097213,0.0012642419,0.01095188],"genre_scores_gemma":[0.022101419,0.004647879,0.010031059,0.030744761,0.27911413,0.00026131968,0.0063118353,0.006124789,0.6406628],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961026,0.00045254154,0.0006712758,0.0006155477,0.0018921676,0.00026586856],"domain_scores_gemma":[0.9643716,0.00695055,0.00073253334,0.0029714636,0.02391872,0.0010549778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00280388,0.0023258107,0.0018763301,0.0039048642,0.004013136,0.0044598337,0.0031386632,0.0046845363,0.13635395],"category_scores_gemma":[0.047751997,0.0010631138,0.0016561593,0.0027847174,0.0019689624,0.0027244254,0.0022012093,0.0052808453,0.07620775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019957675,0.000004845203,0.000034731336,0.000060757364,0.0000061251385,0.000146902,0.000017365743,0.00004710847,0.000086218344,0.0009736544,0.99223197,0.0063703116],"study_design_scores_gemma":[0.000035875008,0.00003375465,0.0013825754,0.00018871485,0.00006266177,0.0010918349,0.000082963335,0.0011652365,0.0019479241,0.0060522775,0.9878805,0.00007569157],"about_ca_topic_score_codex":0.018023381,"about_ca_topic_score_gemma":0.02202969,"teacher_disagreement_score":0.13635395,"about_ca_system_score_codex":0.0045767785,"about_ca_system_score_gemma":0.0029122515,"threshold_uncertainty_score":0.4561494},"labels":[],"label_agreement":null},{"id":"W4238852676","doi":"10.32920/ryerson.14645937","title":"Growing self-organizing tree-based kernel smoother for machine learning and data mining","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Regression; Self-organizing map; Multivariate statistics; Decision tree; Tree (set theory); Semi-supervised learning; Supervised learning; Kernel (algebra); Regression analysis; Big data; Data mining; Range (aeronautics); Kernel regression; Unsupervised learning; Artificial neural network; Mathematics; Engineering; Statistics","score_opus":0.04344977185646058,"score_gpt":0.2909828003612443,"score_spread":0.2475330285047837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238852676","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011407031,0.00017010774,0.9875749,0.00007121324,0.00002208639,0.000016107759,0.000023511062,0.00039201122,0.00032304245],"genre_scores_gemma":[0.4353769,0.0004168892,0.56105995,0.00007009463,0.00005148599,0.00015890639,0.00020684229,0.00018373842,0.002475092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954766,0.00017881402,0.000027685288,0.000071114424,0.0001401354,0.00003463243],"domain_scores_gemma":[0.9981489,0.0010077532,0.0001435589,0.0001870972,0.00046567005,0.000047040878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013271708,0.00044477958,0.0006834392,0.0006693119,0.00033480898,0.00055569626,0.0009212809,0.000743502,0.0011576649],"category_scores_gemma":[0.005572992,0.00029107687,0.00075206993,0.0010350696,0.00040805517,0.0011488731,0.0006635655,0.0011392599,0.000515911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015811234,0.00013304011,0.0018423143,0.00016328749,0.00010563818,0.00008227463,0.00018551291,0.74337524,0.009599529,0.02129876,0.002472628,0.22058372],"study_design_scores_gemma":[0.0000015636454,0.00000832789,0.00009402487,0.0000018030878,0.0000020113364,0.0000049236905,0.0000040274235,0.9977914,0.00036045138,0.0014736564,0.00025506038,0.0000026836763],"about_ca_topic_score_codex":0.003136675,"about_ca_topic_score_gemma":0.0029070543,"teacher_disagreement_score":0.003136675,"about_ca_system_score_codex":0.000514608,"about_ca_system_score_gemma":0.0007034886,"threshold_uncertainty_score":0.007018864},"labels":[],"label_agreement":null},{"id":"W4238949319","doi":"10.1016/b978-012646490-0/50007-x","title":"Neural Networks: A Guided Tour","year":2000,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.020564856476648876,"score_gpt":0.2427030787629118,"score_spread":0.22213822228626293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238949319","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002843489,0.19629407,0.51277983,0.008241463,0.004528033,0.000094659204,0.00067996414,0.0019095254,0.272629],"genre_scores_gemma":[0.03925595,0.16319095,0.325975,0.0024196068,0.0051741856,0.00042973526,0.0012907024,0.0015642944,0.46069965],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999782,0.00005815298,0.000013002105,0.000045510093,0.00008933136,0.000011949759],"domain_scores_gemma":[0.9995357,0.00027697434,0.000015644851,0.000056363184,0.000088304645,0.000027161188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043329372,0.0015271046,0.0012977016,0.0014300109,0.00038525494,0.00200499,0.0011136646,0.0022232356,0.028924834],"category_scores_gemma":[0.0016593814,0.00097551354,0.00077947346,0.002530275,0.0008241821,0.0038505206,0.0014357618,0.002414617,0.01704761],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004316611,0.000073491,0.00019416239,0.0009735127,0.00009414598,0.000105289575,0.00009056149,0.02646107,0.00084222946,0.10320853,0.20163916,0.66627467],"study_design_scores_gemma":[0.0000343386,0.00005799512,0.00038478663,0.0009539181,0.000047760655,0.00030698325,0.00008608116,0.055238925,0.00094616757,0.354545,0.5873542,0.00004388967],"about_ca_topic_score_codex":0.0018912372,"about_ca_topic_score_gemma":0.0035187572,"teacher_disagreement_score":0.028924834,"about_ca_system_score_codex":0.00096226484,"about_ca_system_score_gemma":0.00072712236,"threshold_uncertainty_score":0.09676319},"labels":[],"label_agreement":null},{"id":"W4239238456","doi":"10.1109/intmag.2003.1230526","title":"Joint estimation of data and timing in the presence of inter-symbol interference","year":2004,"lang":"en","type":"article","venue":"Digest of INTERMAG 2003. International Magnetics Conference (Cat. No.03CH37401)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Joint (building); Interference (communication); Symbol (formal); Markov process; Channel (broadcasting); Hidden Markov model; Intersymbol interference; Speech recognition; Markov chain; Algorithm; Telecommunications; Statistics; Engineering; Mathematics; Machine learning","score_opus":0.05779219128632767,"score_gpt":0.2972289214307525,"score_spread":0.23943673014442485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239238456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09228906,0.00028563425,0.9061979,0.00018033378,0.000077579156,0.000023485542,0.00006517643,0.00023257209,0.00064821145],"genre_scores_gemma":[0.8889702,0.0003248627,0.109149106,0.000069420224,0.00009897496,0.000027042433,0.00018392713,0.0000646972,0.0011118073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977356,0.00059211056,0.00013634669,0.00032473213,0.00093600655,0.00027521208],"domain_scores_gemma":[0.9935633,0.0039100153,0.0007058892,0.0007160315,0.0009023941,0.0002023535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025380275,0.0007162416,0.0011023224,0.00061472296,0.00039216044,0.001137443,0.0007141533,0.00091240683,0.00043230518],"category_scores_gemma":[0.014700746,0.0005614137,0.0004296547,0.000778403,0.00085815246,0.00186688,0.0012480399,0.0014917452,0.00019357313],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018658687,0.00017283912,0.022715075,0.00026027538,0.00033677346,0.001528873,0.00045906886,0.686607,0.0850173,0.019824173,0.0006562173,0.1805566],"study_design_scores_gemma":[0.000026982052,0.00016494091,0.004883904,0.000016955599,0.00005690705,0.00049103866,0.000055926994,0.95150644,0.03454403,0.007308986,0.000896418,0.000047450365],"about_ca_topic_score_codex":0.0015877752,"about_ca_topic_score_gemma":0.001934704,"teacher_disagreement_score":0.0025380275,"about_ca_system_score_codex":0.00044283376,"about_ca_system_score_gemma":0.0011158367,"threshold_uncertainty_score":0.013422549},"labels":[],"label_agreement":null},{"id":"W4239333878","doi":"10.3410/f.726373905.793520843","title":"Faculty Opinions recommendation of A shared neural ensemble links distinct contextual memories encoded close in time.","year":2016,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Psychology","score_opus":0.028625120547021328,"score_gpt":0.3302195331607035,"score_spread":0.3015944126136822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239333878","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021593515,0.002765493,0.003150128,0.0017457803,0.0011411859,0.00026322025,0.9553376,0.005288564,0.008714572],"genre_scores_gemma":[0.026874566,0.0004749921,0.005173373,0.0002719335,0.00016659845,0.00016601717,0.9580093,0.00014487153,0.008718232],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988568,0.000201344,0.000104442544,0.0004230988,0.0002579464,0.0001564342],"domain_scores_gemma":[0.998111,0.0003354928,0.00013997202,0.00057821034,0.00063644646,0.00019893923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014917458,0.00263165,0.0012344028,0.0027106998,0.00066806754,0.0016084472,0.0031271367,0.00295649,0.015371127],"category_scores_gemma":[0.0073253857,0.00043350295,0.0019141682,0.0031275337,0.0004510412,0.0017554725,0.0014946857,0.0018335232,0.023960467],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071842375,0.00029546904,0.009747388,0.0007912148,0.0002584412,0.00010689307,0.000045794903,0.0029091309,0.001270123,0.00047381772,0.94403476,0.03934851],"study_design_scores_gemma":[0.001521584,0.0008030834,0.05219976,0.00095569174,0.0010174197,0.0009034538,0.0006331124,0.08127887,0.018482063,0.00821201,0.8336763,0.0003167158],"about_ca_topic_score_codex":0.037786543,"about_ca_topic_score_gemma":0.13438858,"teacher_disagreement_score":0.037786543,"about_ca_system_score_codex":0.0016305929,"about_ca_system_score_gemma":0.0025913923,"threshold_uncertainty_score":0.075133204},"labels":[],"label_agreement":null},{"id":"W4239346039","doi":"10.1002/1097-0193(200010)11:2<77::aid-hbm20>3.3.co;2-s","title":"Coupling of regional activations in a human brain during an object and face affect recognition task","year":2000,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"RIKEN","keywords":"Affect (linguistics); Task (project management); Psychology; Coupling (piping); Neuroscience; Face (sociological concept); Object (grammar); Human brain; Cognitive psychology; Computer science; Communication; Artificial intelligence; Materials science; Sociology; Engineering","score_opus":0.046639671381312066,"score_gpt":0.28572396570972197,"score_spread":0.2390842943284099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239346039","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936557,0.000055225777,0.0050383247,0.000031425974,0.00000706344,0.00003623944,0.00018492826,0.000056482117,0.00093454676],"genre_scores_gemma":[0.99583364,0.000030905252,0.00323367,0.000026902828,0.000012636283,0.000043913114,0.0001961463,0.000019868294,0.000602321],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998368,0.000031181997,0.0000068695517,0.00007055247,0.000033156994,0.000021428088],"domain_scores_gemma":[0.9996321,0.00023706019,0.000043832326,0.000026604366,0.000025820853,0.000034517376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027190277,0.00024091628,0.00032279053,0.00024399237,0.00012660876,0.0003170493,0.000101479054,0.00031981952,0.0012957569],"category_scores_gemma":[0.002076799,0.00016181527,0.00017532997,0.00016593433,0.0002476809,0.00022411573,0.00024973988,0.00015915469,0.0002035595],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021027727,0.00013852555,0.011832994,0.00009997986,0.0001492215,0.0003793,0.00097360625,0.0020345547,0.9431411,0.0003250257,0.00045061533,0.038372353],"study_design_scores_gemma":[0.00009057897,0.0011049748,0.9215328,0.000008651011,0.00011616891,0.0015218745,0.00021785722,0.025301032,0.04825325,0.0010995275,0.0007107893,0.000042551805],"about_ca_topic_score_codex":0.0008002513,"about_ca_topic_score_gemma":0.001112584,"teacher_disagreement_score":0.0012957569,"about_ca_system_score_codex":0.00015401668,"about_ca_system_score_gemma":0.00012059259,"threshold_uncertainty_score":0.004334688},"labels":[],"label_agreement":null},{"id":"W4239350229","doi":"10.4018/978-1-7998-3038-2.ch006","title":"Development of the Enhanced Piece-Wise Linear Neural Network Algorithm","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Sigmoid function; Artificial neural network; Algorithm; Computer science; Dual (grammatical number); Nonlinear system; Artificial intelligence","score_opus":0.024300955371944717,"score_gpt":0.2676632265708691,"score_spread":0.2433622711989244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239350229","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016008416,0.00012640515,0.99570024,0.0000429839,0.000038048624,0.000027650829,0.000027765593,0.0006035567,0.0018325652],"genre_scores_gemma":[0.03828582,0.00021784574,0.9550873,0.0000833393,0.000046657628,0.00011618394,0.00022863591,0.0001792186,0.005755072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994886,0.00010055834,0.00003136882,0.00012985496,0.0002170677,0.000032579643],"domain_scores_gemma":[0.9992028,0.00024315393,0.00004723501,0.000103639264,0.00038731613,0.000015825037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086791767,0.0006439252,0.00081746257,0.00060385064,0.00025389093,0.0008059496,0.0013155062,0.0007222907,0.004467922],"category_scores_gemma":[0.0025696675,0.0005286751,0.0006370756,0.00064097915,0.0003008526,0.0013564419,0.0008903056,0.0014296814,0.002771494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007641608,0.000049065027,0.0004087534,0.00013784191,0.000041547057,0.00010087327,0.000049482773,0.30312097,0.0120611135,0.015479031,0.004023242,0.66445166],"study_design_scores_gemma":[0.000004307082,0.000014469651,0.0000690637,0.0000072569073,0.0000045908932,0.000033779485,0.0000024802484,0.9924218,0.0022579322,0.0019384844,0.0032411397,0.000004638944],"about_ca_topic_score_codex":0.0020152759,"about_ca_topic_score_gemma":0.0022941902,"teacher_disagreement_score":0.004467922,"about_ca_system_score_codex":0.0004976694,"about_ca_system_score_gemma":0.0007624632,"threshold_uncertainty_score":0.014946759},"labels":[],"label_agreement":null},{"id":"W4239436589","doi":"10.1109/ijcnn.2006.1716725","title":"Search Space Analysis of Recurrent Spiking and Continuous-time Neural Networks","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; University of Guelph; University of Waterloo","funders":"","keywords":"Computer science; Recurrent neural network; Benchmark (surveying); Stochastic neural network; Artificial neural network; Inverted pendulum; Construct (python library); Artificial intelligence; Spiking neural network; Outcome (game theory); Machine learning; Mathematics","score_opus":0.029138410603138598,"score_gpt":0.2659151546236827,"score_spread":0.2367767440205441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239436589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26159334,0.0012741777,0.72113264,0.0011325597,0.00003384599,0.00007014965,0.00011988819,0.00020032891,0.0144430185],"genre_scores_gemma":[0.97596425,0.00031642322,0.020204302,0.00008215762,0.000016593911,0.000114670205,0.00007720084,0.000045801182,0.003178479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966276,0.00014106836,0.000015859901,0.00004526531,0.000088322195,0.00004680251],"domain_scores_gemma":[0.9974437,0.0018620867,0.0002740853,0.000067259345,0.00023853834,0.00011440458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00179522,0.0005332123,0.00064432394,0.0007552976,0.00041751435,0.0010244811,0.0008947039,0.0011249251,0.0020576946],"category_scores_gemma":[0.006989619,0.0004774807,0.00057780225,0.0004585406,0.0011501767,0.001324379,0.00082385587,0.000736339,0.00014382749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040212522,0.000014788636,0.00042699737,0.000041214924,0.000029027427,0.0000514667,0.00004333906,0.94561344,0.0007390549,0.049244765,0.0002609279,0.003494626],"study_design_scores_gemma":[0.0000034504094,0.000008711053,0.00006227335,0.000003019194,0.0000025548607,0.000005364948,0.0000053229082,0.98944855,0.00007361038,0.010328056,0.0000568032,0.000002274608],"about_ca_topic_score_codex":0.004036889,"about_ca_topic_score_gemma":0.002530253,"teacher_disagreement_score":0.004036889,"about_ca_system_score_codex":0.0012525067,"about_ca_system_score_gemma":0.00079377065,"threshold_uncertainty_score":0.009494126},"labels":[],"label_agreement":null},{"id":"W4239445517","doi":"10.1002/0471427950.index","title":"Index","year":2003,"lang":"en","type":"paratext","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Index (typography); Library science; Computer science; World Wide Web","score_opus":0.01638874316122828,"score_gpt":0.2603254869210205,"score_spread":0.24393674375979219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239445517","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00088399445,0.0036722464,0.011740379,0.002277648,0.0059597604,0.0003891507,0.018238198,0.0041684965,0.9526701],"genre_scores_gemma":[0.002830073,0.0026546286,0.0032840974,0.0006551757,0.0012453021,0.00020752364,0.010326493,0.0010246361,0.9777721],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994636,0.000074749914,0.000032652766,0.0000975396,0.00028913343,0.000042274765],"domain_scores_gemma":[0.99832565,0.000379275,0.000075396456,0.00025296482,0.0006419963,0.00032473364],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00056075485,0.0015734918,0.001908395,0.0045990297,0.0013830101,0.005596073,0.0019435246,0.0016291566,0.7522172],"category_scores_gemma":[0.0049063875,0.00037425954,0.00046524414,0.0063012107,0.0006299681,0.0040204804,0.0018128452,0.0012687081,0.7210552],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002271046,0.00004082253,0.00009572436,0.00021306248,0.0000043681034,0.000016275135,0.000014807715,0.0002596691,0.00024807494,0.004954856,0.87240285,0.1217268],"study_design_scores_gemma":[0.000013212566,0.000018023133,0.00027937265,0.00018508705,0.000007861343,0.000039113023,0.00002384596,0.0009311178,0.00028503523,0.008996785,0.98921007,0.00001046799],"about_ca_topic_score_codex":0.0041169105,"about_ca_topic_score_gemma":0.0067312927,"teacher_disagreement_score":0.24778283,"about_ca_system_score_codex":0.0014844828,"about_ca_system_score_gemma":0.0013735971,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4239506917","doi":"10.1142/9789812774583_0003","title":"HIGH-SPEED OVERSAMPLING ANALOG-TO-DIGITAL CONVERTERS","year":2006,"lang":"en","type":"book-chapter","venue":"Selected topics in electornics and systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Oversampling; Converters; Computer science; Electronic engineering; Electrical engineering; Engineering; Telecommunications; Bandwidth (computing); Voltage","score_opus":0.014834714704188708,"score_gpt":0.21466616356960663,"score_spread":0.19983144886541793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239506917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042024937,0.007544196,0.790463,0.00080169155,0.0013155178,0.0002703768,0.0004431167,0.0044896966,0.15264758],"genre_scores_gemma":[0.5332138,0.007167823,0.3340621,0.0009092255,0.0010218194,0.0001464565,0.0009285343,0.00039531602,0.12215502],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997043,0.000030151978,0.000013407757,0.000035471312,0.00019524299,0.000021347216],"domain_scores_gemma":[0.99975103,0.00009858942,0.000014825799,0.00004109023,0.000084165134,0.000010354321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023521026,0.0005291263,0.00030228499,0.00068498,0.00034650424,0.0011620318,0.00057801517,0.00047684397,0.01610066],"category_scores_gemma":[0.00069237273,0.0001897639,0.00014788416,0.0010186833,0.0001703121,0.0009579846,0.00037935472,0.00052465283,0.0035617205],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023227921,0.00010153839,0.0004896008,0.00034915344,0.000047016092,0.00018336375,0.0001378167,0.004762032,0.17786519,0.024570711,0.013576336,0.777685],"study_design_scores_gemma":[0.00013153629,0.00038143178,0.0032599384,0.000346311,0.00017808999,0.0024168808,0.00020853321,0.18638608,0.32760388,0.033990186,0.4450239,0.000073295436],"about_ca_topic_score_codex":0.0005105776,"about_ca_topic_score_gemma":0.0019003693,"teacher_disagreement_score":0.01610066,"about_ca_system_score_codex":0.00030317277,"about_ca_system_score_gemma":0.00024570565,"threshold_uncertainty_score":0.053862095},"labels":[],"label_agreement":null},{"id":"W4239514278","doi":"10.5194/hess-2020-128","title":"Technical note: Bit by bit: A practical and general approach for evaluating model computational complexity vs. model performance","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundação para a Ciência e a Tecnologia; Deutsche Forschungsgemeinschaft; Northern Arizona University","keywords":"Computer science; Computational model; Computational complexity theory; Occam's razor; Computational resource; Decision tree model; Theoretical computer science; Artificial intelligence; occam; Algorithm; Machine learning; Mathematics; Decision tree; Statistics","score_opus":0.19676606918564274,"score_gpt":0.3895960096817255,"score_spread":0.19282994049608274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239514278","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008223216,0.00030840075,0.9846794,0.0015636139,0.00018077665,0.00011352085,0.00033998673,0.0008099321,0.0037811215],"genre_scores_gemma":[0.24992879,0.0005828674,0.74392337,0.000979324,0.0003084673,0.00078709365,0.0007194827,0.000850536,0.0019200611],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9857174,0.0068784137,0.000901723,0.001259115,0.004838719,0.00040461452],"domain_scores_gemma":[0.9050325,0.05901595,0.007359106,0.02017968,0.007500592,0.00091216626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016490271,0.0018522139,0.0015686691,0.003335464,0.0015184294,0.004501631,0.0035849544,0.0026016172,0.009999976],"category_scores_gemma":[0.14322926,0.0007932792,0.0015627481,0.0055590593,0.0057102344,0.012462469,0.0064769154,0.005039484,0.001719414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052193075,0.00019048722,0.010982746,0.00056485884,0.0002594385,0.0003527823,0.0005877999,0.26029614,0.008139307,0.5615182,0.019222168,0.13736422],"study_design_scores_gemma":[0.000026721036,0.00019933122,0.0017780818,0.00011435805,0.000056349665,0.00042232135,0.00022323335,0.7236091,0.0063408413,0.2588045,0.008328772,0.00009629316],"about_ca_topic_score_codex":0.0024133723,"about_ca_topic_score_gemma":0.0023928815,"teacher_disagreement_score":0.016490271,"about_ca_system_score_codex":0.0020537593,"about_ca_system_score_gemma":0.0026747661,"threshold_uncertainty_score":0.08720988},"labels":[],"label_agreement":null},{"id":"W4239836826","doi":"10.1109/tit.2018.2876186","title":"IEEE Transactions on Information Theory publication information","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.009859260653836669,"score_gpt":0.2267323195932553,"score_spread":0.21687305893941863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239836826","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075886752,0.03450608,0.37589628,0.028927293,0.052169286,0.00078925234,0.03879711,0.0064008934,0.45492515],"genre_scores_gemma":[0.11858974,0.06850063,0.090462565,0.0051360684,0.01373131,0.00089299027,0.056598403,0.0027871744,0.6433012],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99794954,0.00033785796,0.000169827,0.00021402955,0.0011793278,0.000149413],"domain_scores_gemma":[0.9937953,0.0020618648,0.00021209495,0.0015606398,0.002160756,0.00020940119],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021437379,0.0013837162,0.0021563147,0.0039232336,0.0009921466,0.0045337295,0.0012955151,0.0022782085,0.23127455],"category_scores_gemma":[0.012982578,0.00062062254,0.0008206204,0.0050654747,0.0008102244,0.0045962213,0.0018308015,0.0027652583,0.098597996],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001775029,0.00013199805,0.0006146807,0.000867521,0.00005735344,0.0001520026,0.00004537619,0.0031645668,0.0010952258,0.06849876,0.5728305,0.35236454],"study_design_scores_gemma":[0.00007642604,0.00010941409,0.001258478,0.0008973995,0.00015124984,0.00054662244,0.00010854275,0.034255814,0.0032187246,0.18546115,0.7738345,0.00008166401],"about_ca_topic_score_codex":0.0014729707,"about_ca_topic_score_gemma":0.0012031312,"teacher_disagreement_score":0.76872545,"about_ca_system_score_codex":0.0010227711,"about_ca_system_score_gemma":0.0023775187,"threshold_uncertainty_score":0.77369046},"labels":[],"label_agreement":null},{"id":"W4239897362","doi":"10.4324/9781410604439-10","title":"Statistical and Neural Network Models for Speech Recognition","year":2001,"lang":"en","type":"book-chapter","venue":"Psychology Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Speech recognition; Artificial neural network; Computer science; Artificial intelligence","score_opus":0.11077006230439083,"score_gpt":0.32290842822436583,"score_spread":0.21213836591997498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239897362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0108192265,0.020965017,0.9451253,0.0023880491,0.0007983042,0.00004364843,0.00093950087,0.0020453471,0.016875606],"genre_scores_gemma":[0.53527135,0.025609644,0.31980896,0.0007383095,0.0022143011,0.00055252045,0.0049134856,0.0008148492,0.11007653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996693,0.00012431524,0.000023233706,0.00007441588,0.00007981489,0.000028877583],"domain_scores_gemma":[0.99890316,0.0008025695,0.00003964232,0.000096398064,0.00013698067,0.000021217955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010013154,0.0011961209,0.001115451,0.0007575506,0.0003638297,0.001344101,0.0015378519,0.0015668694,0.007852467],"category_scores_gemma":[0.0035403126,0.0006421718,0.00082259014,0.0013073307,0.0005470135,0.0021073502,0.00084965926,0.0018031045,0.0036586574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012176326,0.00006459795,0.00053772656,0.00015838996,0.00014691277,0.00010603479,0.00008256171,0.7024774,0.0010903561,0.07436386,0.01933629,0.20151411],"study_design_scores_gemma":[0.000005561525,0.0000060119514,0.00012347724,0.000011143419,0.000007728456,0.000021582788,0.0000059167414,0.9556801,0.00013586499,0.0410812,0.0029148147,0.0000065866298],"about_ca_topic_score_codex":0.010746299,"about_ca_topic_score_gemma":0.010179228,"teacher_disagreement_score":0.010746299,"about_ca_system_score_codex":0.00092920696,"about_ca_system_score_gemma":0.00059680105,"threshold_uncertainty_score":0.026269138},"labels":[],"label_agreement":null},{"id":"W4240395865","doi":"10.1007/978-1-4939-7131-2_100058","title":"Belief Propagation","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Psychology; Computer science","score_opus":0.02020495375408319,"score_gpt":0.22857227893478377,"score_spread":0.20836732518070059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240395865","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013436448,0.006587342,0.79546213,0.0014260325,0.000979191,0.00006107418,0.00052748667,0.0021392324,0.19147384],"genre_scores_gemma":[0.091040775,0.015948782,0.40828222,0.00084643444,0.0009374128,0.00025280533,0.0021543948,0.0014361008,0.4791011],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995919,0.00006868429,0.000016214015,0.00009470146,0.00020029857,0.000028216135],"domain_scores_gemma":[0.99943644,0.00024354643,0.000021393744,0.00010941772,0.00016721946,0.00002185891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005422793,0.0012318399,0.0006964258,0.0010059625,0.00047380372,0.0021468818,0.001331786,0.0013257944,0.037826095],"category_scores_gemma":[0.0024702472,0.0005187656,0.00051935064,0.0013636377,0.00078481063,0.00227493,0.0012071535,0.0021617108,0.021704873],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004146823,0.000054341002,0.00012616745,0.00029908112,0.000046101366,0.000047831665,0.00007744094,0.035972092,0.0014345492,0.23707502,0.0904438,0.63438207],"study_design_scores_gemma":[0.000017296634,0.000035090365,0.00020844363,0.00025238883,0.000050929542,0.00017123586,0.000040486982,0.19469348,0.004978811,0.45717177,0.34233528,0.000044741595],"about_ca_topic_score_codex":0.0021101295,"about_ca_topic_score_gemma":0.002645758,"teacher_disagreement_score":0.037826095,"about_ca_system_score_codex":0.0008306294,"about_ca_system_score_gemma":0.0008598574,"threshold_uncertainty_score":0.1265409},"labels":[],"label_agreement":null},{"id":"W4240514807","doi":"10.1017/s0021900200018568","title":"Convergence in distribution of the multi-dimensional Kohonen algorithm","year":2001,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Mathematics; Probability measure; Invariant measure; Measure (data warehouse); Markov chain; Self-organizing map; Rate of convergence; Probability distribution; Algorithm; Invariant (physics); Markov process; Applied mathematics; Cluster analysis; Discrete mathematics; Mathematical analysis; Statistics; Computer science; Data mining; Ergodic theory","score_opus":0.0191232212571621,"score_gpt":0.24076750282997614,"score_spread":0.22164428157281404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240514807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108673304,0.0006531993,0.8849975,0.0005934495,0.000062340536,0.000044093977,0.00003626885,0.00015198364,0.004787881],"genre_scores_gemma":[0.8978664,0.00054497435,0.0966733,0.00012349278,0.000040579373,0.000099099474,0.00005606221,0.00007392911,0.0045221825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995478,0.00013700007,0.000022070892,0.00008374023,0.00014475817,0.000064653184],"domain_scores_gemma":[0.9973937,0.0017396975,0.00016916278,0.00016227041,0.00041001124,0.00012518113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025585443,0.000325618,0.0005412231,0.0007532699,0.00045131365,0.0013311462,0.0011863856,0.0011347223,0.0014591049],"category_scores_gemma":[0.010476746,0.00033018328,0.0005152502,0.00046226324,0.0014453207,0.0022428338,0.001311335,0.0009700805,0.00022247978],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011209504,0.000042847794,0.002444896,0.00010544665,0.000038005022,0.0001591242,0.00022069759,0.6631552,0.004205485,0.3051414,0.0005218049,0.023853045],"study_design_scores_gemma":[0.0000034984598,0.000008835134,0.00012369186,0.0000062813215,0.0000019621716,0.000014957577,0.000013341279,0.9767586,0.00036898302,0.022549924,0.00014405353,0.0000058677474],"about_ca_topic_score_codex":0.0031834412,"about_ca_topic_score_gemma":0.0016415383,"teacher_disagreement_score":0.0031834412,"about_ca_system_score_codex":0.0011967529,"about_ca_system_score_gemma":0.0008883744,"threshold_uncertainty_score":0.013531029},"labels":[],"label_agreement":null},{"id":"W4240572102","doi":"10.4211/hs.ea4ccadf124b4bed86fe6fc7efd8c779","title":"2 - Discharge time series data","year":2020,"lang":"en","type":"dataset","venue":"HydroShare Resources","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Series (stratigraphy); Computer science; Geology","score_opus":0.028915129559250425,"score_gpt":0.2555711322209986,"score_spread":0.22665600266174815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240572102","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027847984,0.00007813985,0.00023918276,0.00011355368,0.00010548624,0.000035513796,0.99464995,0.0007160437,0.0012773328],"genre_scores_gemma":[0.0024943787,0.00004578893,0.00047355582,0.000052447227,0.000017536686,0.000072443676,0.9957313,0.000056312754,0.0010562218],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924964,0.00010079648,0.00007267289,0.00019958218,0.00021635536,0.00016102087],"domain_scores_gemma":[0.9987374,0.0001621531,0.000097339886,0.00037456842,0.00045229518,0.00017625102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008680848,0.0024446622,0.0011836834,0.002966114,0.0005710952,0.001210935,0.002668836,0.0023637917,0.027663467],"category_scores_gemma":[0.0030819585,0.00047225284,0.0015911115,0.0041738874,0.00052175415,0.00086756673,0.001358088,0.001403834,0.049308166],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029786228,0.00020168061,0.0026759815,0.00058503996,0.00006515214,0.00009211289,0.00003436936,0.0033632885,0.0007264664,0.00053102226,0.9812122,0.0102147125],"study_design_scores_gemma":[0.00082120823,0.00018632275,0.030042687,0.0003353683,0.00009028748,0.00031907347,0.000284724,0.009161018,0.0028817724,0.0024742782,0.95326596,0.00013731583],"about_ca_topic_score_codex":0.018870335,"about_ca_topic_score_gemma":0.02950258,"teacher_disagreement_score":0.027663467,"about_ca_system_score_codex":0.0008485512,"about_ca_system_score_gemma":0.0014947064,"threshold_uncertainty_score":0.09254354},"labels":[],"label_agreement":null},{"id":"W4240890094","doi":"10.1002/9781119302872.index","title":"INDEX","year":2018,"lang":"en","type":"paratext","venue":"Pattern Recognition","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Index (typography); Library science; Science Citation Index; Informatics; Technical university; Geography; Citation; Political science; Law; Computer science","score_opus":0.03496007632213096,"score_gpt":0.2714726398226687,"score_spread":0.23651256350053776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240890094","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020611107,0.017188646,0.08887659,0.010255665,0.029490698,0.0014066551,0.13032433,0.01973786,0.70065844],"genre_scores_gemma":[0.016698956,0.017185343,0.0825539,0.0050706672,0.014231566,0.0011649845,0.17983751,0.00914069,0.6741164],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963356,0.00035586013,0.00029738812,0.00050652353,0.002357547,0.00014703274],"domain_scores_gemma":[0.99299425,0.001563726,0.0002809002,0.0011410891,0.0036213815,0.00039857466],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016132833,0.0026043246,0.0018783448,0.007087755,0.0016121968,0.0066954223,0.00298368,0.001683284,0.5215352],"category_scores_gemma":[0.019593326,0.0005324165,0.000894391,0.011034512,0.0007274508,0.0054357294,0.0020354677,0.0015810347,0.43813327],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028245839,0.000035361823,0.00017609696,0.00028755213,0.000007570958,0.000010365538,0.000008448455,0.00039794558,0.00018518856,0.005390716,0.84123224,0.15224032],"study_design_scores_gemma":[0.00002691642,0.000050960643,0.0007504612,0.00033234715,0.0000134144575,0.00014899923,0.00005410214,0.0039687585,0.0007432436,0.023646599,0.9702355,0.000028598435],"about_ca_topic_score_codex":0.0056352406,"about_ca_topic_score_gemma":0.0064754975,"teacher_disagreement_score":0.47846478,"about_ca_system_score_codex":0.0028951203,"about_ca_system_score_gemma":0.002512241,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4241563497","doi":"10.1109/tnnls.2020.2973778","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Euskal Herriko Unibertsitatea; University of Birmingham; Syracuse University; Tencent; Institute of Electrical and Electronics Engineers; College of Engineering, Michigan State University; University of Manitoba; Arizona State University","keywords":"Computer science; Artificial neural network; Data science; Artificial intelligence; Information retrieval","score_opus":0.015987716786376855,"score_gpt":0.21670722049405713,"score_spread":0.20071950370768027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241563497","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0085761715,0.076698825,0.3297185,0.01450642,0.10285765,0.0005575825,0.013103406,0.00566726,0.44831422],"genre_scores_gemma":[0.04708451,0.059917793,0.058605425,0.0020181374,0.0077924347,0.00033739055,0.017914081,0.001476334,0.804854],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927086,0.00006628691,0.000060309238,0.00006927319,0.00048062616,0.000052675143],"domain_scores_gemma":[0.99844545,0.00032881377,0.0000544493,0.0002518332,0.0008327398,0.00008680569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010211334,0.0008695145,0.0014611329,0.0016655496,0.00051198015,0.0022717533,0.0009440689,0.0014729509,0.17235099],"category_scores_gemma":[0.0033838958,0.00036509873,0.00046575707,0.0019373352,0.0004407287,0.0021492683,0.0010930956,0.0015128229,0.070227936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099547644,0.00008659512,0.000488883,0.00051558594,0.000030311943,0.00008042999,0.000023498118,0.0031102146,0.0016169639,0.014257364,0.49972716,0.4799634],"study_design_scores_gemma":[0.000027552762,0.000078212244,0.0014004629,0.00042337633,0.00006316967,0.00029664868,0.000045767134,0.028939035,0.0023299262,0.029093001,0.93726635,0.000036443565],"about_ca_topic_score_codex":0.0019519095,"about_ca_topic_score_gemma":0.0031764794,"teacher_disagreement_score":0.17235099,"about_ca_system_score_codex":0.00050869177,"about_ca_system_score_gemma":0.0016292125,"threshold_uncertainty_score":0.57657146},"labels":[],"label_agreement":null},{"id":"W4241735485","doi":"10.1109/tit.2013.2281126","title":"IEEE Transactions on Information Theory publication information","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Information theory; Computer science; Computer security; Information retrieval; Mathematics; Statistics","score_opus":0.008243812519496796,"score_gpt":0.21042254786751838,"score_spread":0.20217873534802158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241735485","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0079091275,0.03755039,0.42466298,0.025495855,0.042800706,0.00075192185,0.03140446,0.005755368,0.4236692],"genre_scores_gemma":[0.13355762,0.07424585,0.10541926,0.0047936672,0.012029835,0.0009198664,0.048963323,0.00227613,0.6177945],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803704,0.00033869187,0.00016374774,0.00019873674,0.001125958,0.00013587455],"domain_scores_gemma":[0.9945709,0.0017822967,0.00019389545,0.0014115663,0.0018734253,0.00016799562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020160223,0.0013117128,0.0020686707,0.0038707089,0.0009730602,0.004412817,0.0011944221,0.0021146517,0.1867627],"category_scores_gemma":[0.01151466,0.0005862424,0.00079467165,0.0051745386,0.0008250657,0.0045223697,0.0017605267,0.0025047946,0.07623412],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017339068,0.0001256474,0.00070909905,0.00087392353,0.00006486732,0.00016038366,0.000052595475,0.0034882312,0.0011181703,0.081756964,0.5115632,0.3999136],"study_design_scores_gemma":[0.0000704554,0.0001089552,0.0013712166,0.000903883,0.00017071984,0.0005743178,0.000120662466,0.037477564,0.003340284,0.21059091,0.7451893,0.00008171901],"about_ca_topic_score_codex":0.0014628328,"about_ca_topic_score_gemma":0.0012368626,"teacher_disagreement_score":0.1867627,"about_ca_system_score_codex":0.0010147836,"about_ca_system_score_gemma":0.0021953448,"threshold_uncertainty_score":0.6247835},"labels":[],"label_agreement":null},{"id":"W4241790329","doi":"10.1007/978-3-319-59993-9_2","title":"Multivariable Calculus","year":2017,"lang":"en","type":"book-chapter","venue":"Springer optimization and its applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Multivariable calculus; Calculus (dental); Mathematics; Applied mathematics; Engineering; Control engineering; Medicine","score_opus":0.020732060782942714,"score_gpt":0.2459439870076457,"score_spread":0.22521192622470299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241790329","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043282006,0.009693368,0.19774948,0.004145269,0.0029086345,0.00004299552,0.00041536792,0.0004385221,0.78027815],"genre_scores_gemma":[0.21923468,0.014333269,0.034361646,0.0020228946,0.007124206,0.00016609159,0.0006340344,0.00066338753,0.7214598],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996625,0.00006729752,0.00001174689,0.000058549394,0.0001701599,0.000029693128],"domain_scores_gemma":[0.99986625,0.00002824973,0.000011946868,0.000028812732,0.000049303704,0.000015388434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032961872,0.0007722644,0.0007034463,0.0013336304,0.0009301202,0.002346566,0.000549978,0.0006884227,0.02942619],"category_scores_gemma":[0.00080594304,0.00026324333,0.0005523212,0.0011056355,0.0016888686,0.002131365,0.0014724967,0.0017975227,0.007909316],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017817201,0.00000400177,0.0000111869285,0.000022632381,0.0000028455954,0.000007966868,0.000027288723,0.00031870068,0.0001768332,0.97681457,0.010889496,0.011722813],"study_design_scores_gemma":[0.0000027713222,0.000005797863,0.00008514618,0.0000185833,0.0000046277437,0.000033874883,0.00002038712,0.0018239055,0.00017331216,0.8868998,0.11092602,0.000005870902],"about_ca_topic_score_codex":0.0010402204,"about_ca_topic_score_gemma":0.0009969914,"teacher_disagreement_score":0.02942619,"about_ca_system_score_codex":0.001237879,"about_ca_system_score_gemma":0.0006237877,"threshold_uncertainty_score":0.09844041},"labels":[],"label_agreement":null},{"id":"W4241824423","doi":"10.32920/ryerson.14664273","title":"Generative Modelling and Machine Learning Methods for Travel Behaviour Analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Generative grammar; Artificial intelligence; Generative model; Machine learning; Computer science; Inference; Bayesian inference; Artificial neural network; Representation (politics); Bayesian probability","score_opus":0.09234453186759631,"score_gpt":0.36443658523462175,"score_spread":0.27209205336702547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241824423","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022225317,0.0006571424,0.9933501,0.0006061462,0.00008051569,0.000081041544,0.00014622464,0.00011810043,0.002738229],"genre_scores_gemma":[0.2142209,0.0035928588,0.77012336,0.0008171774,0.0004869227,0.0016459239,0.0008059754,0.00031356816,0.007993381],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99176484,0.005618992,0.00035938405,0.0009836977,0.0010617147,0.00021134797],"domain_scores_gemma":[0.9652712,0.030539911,0.0011595946,0.002074987,0.0008072293,0.00014713922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010890893,0.0014260733,0.0015886963,0.0024294355,0.0006713338,0.003123068,0.0025985911,0.0022404012,0.007366683],"category_scores_gemma":[0.037239958,0.00094587094,0.0038606578,0.0028039305,0.0034240643,0.0035210135,0.0024310942,0.0049140253,0.001172904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002589294,0.000063701766,0.0012669497,0.00029673212,0.00020715746,0.00004603835,0.00038523163,0.14379945,0.00035349678,0.81574863,0.0017530068,0.036053784],"study_design_scores_gemma":[0.000019960386,0.000030805237,0.0005991395,0.00009689966,0.000027433103,0.00003635807,0.0000773884,0.36870068,0.00021099427,0.62494385,0.005218949,0.000037650687],"about_ca_topic_score_codex":0.005533816,"about_ca_topic_score_gemma":0.0038935444,"teacher_disagreement_score":0.010890893,"about_ca_system_score_codex":0.0030718222,"about_ca_system_score_gemma":0.0018748911,"threshold_uncertainty_score":0.05759722},"labels":[],"label_agreement":null},{"id":"W4241830781","doi":"10.32920/ryerson.14648343.v1","title":"Extended Kalman filter approach to electric arc furnace forecasting","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Kalman filter; Electric arc furnace; Computer science; Voltage; Fuzzy logic; Wavelet; Ensemble Kalman filter; Control theory (sociology); Algorithm; Extended Kalman filter; Artificial intelligence; Engineering; Materials science","score_opus":0.053861781691266764,"score_gpt":0.2631498647825134,"score_spread":0.20928808309124664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241830781","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006014603,0.00030111027,0.99251056,0.00006518503,0.00003275843,0.000006891489,0.00003275603,0.00023179472,0.00080439204],"genre_scores_gemma":[0.6842681,0.0019565627,0.30363712,0.00009862644,0.00018589478,0.00013750543,0.00022126813,0.00009403868,0.00940082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977225,0.000062409075,0.000016868997,0.00006110592,0.00006632846,0.000020960437],"domain_scores_gemma":[0.99968815,0.00017593491,0.000030014073,0.000023382385,0.00007500781,0.000007601407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046605416,0.0004143799,0.00071710045,0.0004355845,0.0002210696,0.00060324057,0.0006872382,0.0006862166,0.0014587166],"category_scores_gemma":[0.0013012319,0.0003022818,0.0004980626,0.00050591206,0.00026828478,0.0010024484,0.00033198114,0.00081541063,0.00032321518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035720783,0.000019975858,0.00051149534,0.00005704247,0.0000623429,0.000045156463,0.0000638654,0.9143083,0.002482797,0.015006462,0.00046192197,0.0669449],"study_design_scores_gemma":[0.000002221433,0.0000059858125,0.00008414098,0.000002815956,0.000004450387,0.0000037257428,0.0000030899396,0.9974226,0.00027959398,0.0017661509,0.00042149084,0.000003664519],"about_ca_topic_score_codex":0.016410066,"about_ca_topic_score_gemma":0.009480527,"teacher_disagreement_score":0.016410066,"about_ca_system_score_codex":0.0006268955,"about_ca_system_score_gemma":0.0006411945,"threshold_uncertainty_score":0.032629073},"labels":[],"label_agreement":null},{"id":"W4241974376","doi":"10.4018/978-1-7998-0414-7.ch006","title":"Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights","year":2019,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Ant colony optimization algorithms; Computer science; Artificial neural network; Benchmark (surveying); Adaptation (eye); Generalization; Exponential function; Artificial intelligence; Set (abstract data type); Algorithm; Mathematical optimization; Mathematics","score_opus":0.016504770688153255,"score_gpt":0.20900753726614074,"score_spread":0.19250276657798748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241974376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11203556,0.0011708138,0.8750384,0.0004626165,0.00023195923,0.00011033022,0.00018867635,0.002121415,0.008640152],"genre_scores_gemma":[0.49660563,0.0005273697,0.49260953,0.00017448993,0.00007200611,0.00015221468,0.00056169706,0.00028299497,0.009014036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999534,0.00016524647,0.000028485134,0.00010598834,0.00013593775,0.000030322673],"domain_scores_gemma":[0.9983308,0.0009748068,0.00011767382,0.00024500318,0.00028693586,0.000044740515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010775913,0.0009891937,0.0006170113,0.00040238243,0.00029386973,0.0006276482,0.0011623207,0.0008889557,0.0014505452],"category_scores_gemma":[0.0041113133,0.0003444571,0.00048240027,0.00070202985,0.00044885438,0.0007276779,0.0006781362,0.0013854398,0.00052231294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007231928,0.000048258073,0.00084967376,0.000086285996,0.00005210507,0.00008968936,0.000056095454,0.865301,0.003470066,0.002497198,0.0028481581,0.12462909],"study_design_scores_gemma":[0.0000028016068,0.0000164017,0.00009899381,0.0000028568963,0.0000029342814,0.000008438308,0.0000031605564,0.99806505,0.00075053365,0.0007082144,0.0003389033,0.000001791572],"about_ca_topic_score_codex":0.005327678,"about_ca_topic_score_gemma":0.00669848,"teacher_disagreement_score":0.005327678,"about_ca_system_score_codex":0.0005740145,"about_ca_system_score_gemma":0.0006404656,"threshold_uncertainty_score":0.010593295},"labels":[],"label_agreement":null},{"id":"W4242297044","doi":"10.1109/ijcnn.2006.1716433","title":"A Cooperative Recurrent Neural Network Algorithm for Parameter Estimation of Autoregressive Signals","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoregressive model; Computer science; Algorithm; Weighting; Artificial neural network; Convergence (economics); Gaussian; Recurrent neural network; Gaussian noise; Noise (video); Standard deviation; Mathematics; Artificial intelligence; Statistics","score_opus":0.042923676088890084,"score_gpt":0.2922119532105307,"score_spread":0.2492882771216406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242297044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021545594,0.0001730226,0.99700445,0.00002252848,0.000019606787,0.000012976486,0.000009096168,0.0002331013,0.00037053358],"genre_scores_gemma":[0.19799854,0.00043203117,0.7978235,0.000096318865,0.000074599855,0.00016733888,0.0001569259,0.00012489456,0.003125835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993799,0.00014626395,0.00004602979,0.00018789225,0.00020142081,0.000038459802],"domain_scores_gemma":[0.9992649,0.0002659232,0.00011735886,0.00009065081,0.0002405399,0.000020686228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012378196,0.00088311196,0.0008738938,0.00069206965,0.00032865195,0.00067608996,0.0016785149,0.0009864033,0.001229728],"category_scores_gemma":[0.0025469167,0.0004004851,0.0006694551,0.00071662467,0.00042853123,0.0013004477,0.00070907857,0.0011651104,0.00066829746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012474648,0.00008292712,0.00075173884,0.00014431747,0.00012464813,0.00011593237,0.0001506585,0.46249372,0.019408615,0.018816544,0.002255581,0.49553064],"study_design_scores_gemma":[0.0000076088086,0.000029383302,0.00008137477,0.0000052167156,0.000012698237,0.0000431697,0.000005071654,0.9947301,0.002274595,0.0017255781,0.0010747069,0.0000104611645],"about_ca_topic_score_codex":0.0030191306,"about_ca_topic_score_gemma":0.0030408925,"teacher_disagreement_score":0.0030191306,"about_ca_system_score_codex":0.00043120733,"about_ca_system_score_gemma":0.00057926157,"threshold_uncertainty_score":0.0065463185},"labels":[],"label_agreement":null},{"id":"W4242366467","doi":"10.1007/978-1-4471-7452-3_5","title":"Multilayer Perceptrons: Architecture and Error Backpropagation","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Backpropagation; Perceptron; Computer science; Speedup; Multilayer perceptron; Artificial neural network; Artificial intelligence; Multivariate statistics; Machine learning; Parallel computing","score_opus":0.019040802669287323,"score_gpt":0.23415216784836046,"score_spread":0.21511136517907314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242366467","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019542105,0.08383463,0.744906,0.0022841112,0.0034092115,0.00007515878,0.0011204431,0.0034902915,0.15892592],"genre_scores_gemma":[0.034253806,0.091185205,0.43074226,0.0008936841,0.0024386458,0.0002082826,0.0022267408,0.0019750702,0.43607625],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996854,0.000035356694,0.000019463017,0.000054686257,0.00018403426,0.00002118368],"domain_scores_gemma":[0.9997439,0.000100400495,0.00001779436,0.000037008795,0.000088921755,0.000012040541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041232086,0.001307342,0.0008290752,0.0010636734,0.0002716235,0.0019555013,0.001534525,0.0017342411,0.023333648],"category_scores_gemma":[0.001476123,0.00075886527,0.00048889965,0.0030648676,0.0006200452,0.0027919116,0.0009157887,0.002240722,0.022176353],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039806586,0.000050570983,0.00009278224,0.000682252,0.00003983203,0.00006162532,0.0000661904,0.026595918,0.002685614,0.123842105,0.12313369,0.7227096],"study_design_scores_gemma":[0.000014139087,0.000043353706,0.00045498493,0.0004733218,0.000042478576,0.00036207927,0.00003731472,0.13343436,0.005543631,0.21712416,0.6424059,0.000064253814],"about_ca_topic_score_codex":0.0014063697,"about_ca_topic_score_gemma":0.0026696331,"teacher_disagreement_score":0.023333648,"about_ca_system_score_codex":0.00078582205,"about_ca_system_score_gemma":0.00069898105,"threshold_uncertainty_score":0.07805878},"labels":[],"label_agreement":null},{"id":"W4242753539","doi":"10.1007/978-0-387-39940-9_3706","title":"Strong Memory Consistency","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Consistency (knowledge bases); Computer science; Artificial intelligence","score_opus":0.02052756756874706,"score_gpt":0.23918603052439627,"score_spread":0.21865846295564922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242753539","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027486572,0.009801393,0.5692721,0.0069740214,0.0034960995,0.00032746766,0.0027261132,0.00609723,0.37381905],"genre_scores_gemma":[0.5876747,0.0054177493,0.14719683,0.0046441164,0.0033759512,0.0006939363,0.0045403442,0.0033287061,0.24312775],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971744,0.00049552205,0.00023626431,0.00085221604,0.0008772058,0.00036434733],"domain_scores_gemma":[0.9920901,0.0023944965,0.000341129,0.0035573505,0.0013954405,0.00022157987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020163034,0.0010812852,0.001531856,0.001284636,0.0019352657,0.004380063,0.0031184708,0.0016842834,0.034540154],"category_scores_gemma":[0.013415741,0.0008943608,0.0013419994,0.001951227,0.0030326839,0.012057794,0.0047050733,0.004229107,0.009947227],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020509666,0.0000654004,0.00036021648,0.00035805907,0.000060910716,0.00012842138,0.00021631061,0.0021450818,0.002208783,0.83642346,0.048926014,0.10890226],"study_design_scores_gemma":[0.000032284697,0.00002536584,0.00013393932,0.000053799085,0.00003504227,0.00015274774,0.00003476678,0.0044808444,0.0031443024,0.9573016,0.03458393,0.000021461581],"about_ca_topic_score_codex":0.00096404686,"about_ca_topic_score_gemma":0.0009870755,"teacher_disagreement_score":0.034540154,"about_ca_system_score_codex":0.0012611516,"about_ca_system_score_gemma":0.0023529967,"threshold_uncertainty_score":0.11554831},"labels":[],"label_agreement":null},{"id":"W4243000033","doi":"10.32920/ryerson.14656347.v1","title":"Reinforcement learning using associative memory networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reinforcement learning; Computer science; Hebbian theory; Content-addressable memory; Leabra; Artificial neural network; Artificial intelligence; Bidirectional associative memory; Associative property; Associative learning; Machine learning; Mathematics; Wake-sleep algorithm","score_opus":0.033018703433173674,"score_gpt":0.27884994781943523,"score_spread":0.24583124438626155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243000033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04870537,0.00050760334,0.939556,0.0006664504,0.00010345745,0.000038343525,0.000044173215,0.00026716007,0.010111441],"genre_scores_gemma":[0.9416854,0.0004838206,0.051304754,0.00014290228,0.00007502064,0.00011320521,0.000042867905,0.000033781867,0.00611829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996691,0.00013716011,0.000018141922,0.00006764485,0.00006669266,0.00004127128],"domain_scores_gemma":[0.99869114,0.0008744705,0.00012723009,0.00010307112,0.00014103288,0.00006302607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079011364,0.0004859512,0.0006114187,0.00030817144,0.0003028186,0.0009263372,0.00091652723,0.0008366422,0.003138341],"category_scores_gemma":[0.0038959808,0.00023637098,0.00044389055,0.00036670786,0.0011946873,0.0012723518,0.0009284287,0.0012234342,0.00030279928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049249942,0.000044607354,0.0005359694,0.00005272271,0.000048498277,0.000098300705,0.000057851197,0.8285791,0.0012640817,0.13776578,0.00081038394,0.030693335],"study_design_scores_gemma":[0.000007919413,0.000013465158,0.000043299744,0.0000035609162,0.000004932244,0.000011943506,0.0000029701425,0.9528204,0.00021428807,0.046498172,0.0003756342,0.0000034634395],"about_ca_topic_score_codex":0.0025575599,"about_ca_topic_score_gemma":0.0018279003,"teacher_disagreement_score":0.003138341,"about_ca_system_score_codex":0.0007427899,"about_ca_system_score_gemma":0.0005819527,"threshold_uncertainty_score":0.010498762},"labels":[],"label_agreement":null},{"id":"W4243049722","doi":"10.36227/techrxiv.14593686","title":"Accelerating Convolutional Neural Network Using Discrete Orthogonal Transforms","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs","keywords":"Computer science; Parallel computing; Python (programming language); Convolutional neural network; Fast Fourier transform; Xeon Phi; Algorithm; Xeon; Benchmark (surveying); CUDA; Artificial neural network; Computational science; Artificial intelligence; Programming language","score_opus":0.05815903960112196,"score_gpt":0.29100115066199816,"score_spread":0.2328421110608762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243049722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10408789,0.0012230567,0.84171534,0.0006035156,0.00046614153,0.00012222416,0.0011359049,0.032966096,0.017679812],"genre_scores_gemma":[0.5048357,0.0005498195,0.47062597,0.0003362026,0.0000840535,0.00021121578,0.0042062355,0.0018105378,0.017340316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962854,0.00003899399,0.000016273476,0.000087708446,0.000165404,0.00006299901],"domain_scores_gemma":[0.9994759,0.00018039854,0.000031645097,0.00011895573,0.00015996424,0.000033073444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006563312,0.0009780867,0.0005936705,0.00064084044,0.00027273776,0.0007380138,0.001272724,0.000771702,0.0063756215],"category_scores_gemma":[0.0026822889,0.00034508063,0.00066492654,0.0008419557,0.00037251212,0.0012230293,0.001122838,0.0014870018,0.0027631551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018514307,0.000109794426,0.0013511056,0.00017396994,0.00012804785,0.00014767663,0.000080825645,0.7644469,0.009586605,0.009745878,0.020502593,0.19354141],"study_design_scores_gemma":[0.000007653445,0.00000943458,0.00012916164,0.000003329574,0.0000038468097,0.0000104756355,0.000004595553,0.996243,0.0016303655,0.00093039323,0.0010247525,0.0000029815471],"about_ca_topic_score_codex":0.021678763,"about_ca_topic_score_gemma":0.030018497,"teacher_disagreement_score":0.021678763,"about_ca_system_score_codex":0.0012998707,"about_ca_system_score_gemma":0.0014243114,"threshold_uncertainty_score":0.043105185},"labels":[],"label_agreement":null},{"id":"W4243065495","doi":"10.1007/978-1-4614-6170-8_100744","title":"Information Propagation","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Geography","score_opus":0.011190116152884106,"score_gpt":0.2024379439029758,"score_spread":0.1912478277500917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243065495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020651338,0.011644348,0.7132065,0.0015641575,0.0017776678,0.000103005885,0.00067302294,0.0025967804,0.26636946],"genre_scores_gemma":[0.07122448,0.019010283,0.27745754,0.0009839856,0.0012273655,0.00026264836,0.0020747972,0.0013492447,0.6264097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960953,0.000048197093,0.00001670346,0.00009554972,0.00020142694,0.00002866839],"domain_scores_gemma":[0.99956864,0.000147281,0.00001952393,0.00010344068,0.00014496027,0.000016094215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005272097,0.0012375885,0.0007447586,0.0013231667,0.0005475119,0.0018044723,0.0013224769,0.0013740511,0.037505977],"category_scores_gemma":[0.0016479973,0.0004823237,0.0005656543,0.0018288133,0.00082602503,0.0024686547,0.0011171817,0.0019076683,0.021607725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003270022,0.000045130735,0.0001019762,0.00028046253,0.0000404028,0.000047757105,0.000049341448,0.021519534,0.0023172433,0.1479535,0.10417367,0.7234384],"study_design_scores_gemma":[0.000017073316,0.000056646535,0.00033335667,0.00030985457,0.00006429124,0.0002575981,0.000032618245,0.16836864,0.012785595,0.3210951,0.49661675,0.00006245481],"about_ca_topic_score_codex":0.0014889629,"about_ca_topic_score_gemma":0.0019347408,"teacher_disagreement_score":0.037505977,"about_ca_system_score_codex":0.0008090841,"about_ca_system_score_gemma":0.0007141294,"threshold_uncertainty_score":0.12547004},"labels":[],"label_agreement":null},{"id":"W4243100646","doi":"10.1007/978-1-4614-6170-8_100685","title":"Data Extraction","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Extraction (chemistry); Computer science; Chromatography; Chemistry","score_opus":0.077033764363031,"score_gpt":0.2963008045023277,"score_spread":0.2192670401392967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243100646","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040516285,0.0056931623,0.7482409,0.0025545503,0.0020376858,0.002247659,0.038834188,0.021312684,0.17502758],"genre_scores_gemma":[0.024894116,0.00848391,0.68110484,0.0020050681,0.00077401433,0.0018994757,0.081871,0.0036660065,0.19530149],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99886763,0.00009269696,0.00014373501,0.00036159888,0.00046501958,0.00006943025],"domain_scores_gemma":[0.9981621,0.0004278627,0.00007609106,0.0005608397,0.0007139915,0.00005925159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013414628,0.0018327772,0.0013100392,0.0057536853,0.0013685601,0.0037467375,0.002227388,0.000856366,0.081967644],"category_scores_gemma":[0.0048475964,0.0007510486,0.001392888,0.0062187985,0.0006477275,0.0041683577,0.0034566447,0.0015625696,0.10102455],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007334996,0.000044457443,0.00040130626,0.0007799545,0.000028201415,0.000113980714,0.000113378905,0.0003831154,0.007178996,0.015464721,0.14270735,0.8327112],"study_design_scores_gemma":[0.000017118175,0.00004193987,0.0008632411,0.00037494887,0.00006496475,0.00063794455,0.00020571149,0.0041762427,0.029127179,0.030401217,0.93404835,0.000041143077],"about_ca_topic_score_codex":0.0012573771,"about_ca_topic_score_gemma":0.001601931,"teacher_disagreement_score":0.081967644,"about_ca_system_score_codex":0.0007010702,"about_ca_system_score_gemma":0.0019422828,"threshold_uncertainty_score":0.27420914},"labels":[],"label_agreement":null},{"id":"W4243162164","doi":"10.1007/978-1-4939-7131-2_100510","title":"Information Propagation","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.01441222718529515,"score_gpt":0.21500590034457753,"score_spread":0.2005936731592824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243162164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021597187,0.01116034,0.7058246,0.0017335549,0.0020053096,0.00011219972,0.000780837,0.0028583044,0.27336505],"genre_scores_gemma":[0.07623545,0.018669445,0.27728423,0.0010767751,0.0013861813,0.00027725278,0.0024394197,0.0014914491,0.62113976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995877,0.000051137453,0.000017535169,0.000104350955,0.00020847701,0.000030818355],"domain_scores_gemma":[0.9995098,0.00016456073,0.000021881267,0.0001233853,0.0001613155,0.000018972996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005724115,0.0012590309,0.0007296563,0.0013314689,0.0005641725,0.0019349065,0.0013712993,0.001418976,0.040610053],"category_scores_gemma":[0.001847112,0.00047103068,0.0005927422,0.0018445976,0.000838221,0.0026096646,0.0011542015,0.0020228298,0.024664309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037686597,0.000051288243,0.00010899136,0.00030591618,0.00004268147,0.000051050596,0.00004993849,0.020953245,0.0025436892,0.16191268,0.10930273,0.7046401],"study_design_scores_gemma":[0.000017947381,0.000059779442,0.00032445695,0.00030660682,0.00006503159,0.00024986232,0.000030723902,0.16478358,0.013003962,0.3263151,0.49478343,0.00005943813],"about_ca_topic_score_codex":0.0013562462,"about_ca_topic_score_gemma":0.0016426836,"teacher_disagreement_score":0.040610053,"about_ca_system_score_codex":0.0008184045,"about_ca_system_score_gemma":0.00073335547,"threshold_uncertainty_score":0.13585413},"labels":[],"label_agreement":null},{"id":"W4243264051","doi":"10.4018/9781591408369.ch005","title":"Modelling of Some Aspects of Skilled Locomotor Behaviour Using Artificial Neural Networks","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial neural network; Neurophysiology; Action (physics); Artificial intelligence; Control (management); Motor control; Cognitive science; Human–computer interaction; Neuroscience; Psychology","score_opus":0.053712343340169603,"score_gpt":0.25330310079863305,"score_spread":0.19959075745846344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243264051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02316524,0.009991503,0.8676665,0.0009358534,0.00022061712,0.000057838297,0.00031898558,0.0005219005,0.09712156],"genre_scores_gemma":[0.4873468,0.026696727,0.3727109,0.00025388162,0.00028515648,0.0003899887,0.0009794255,0.00020792773,0.11112913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999392,0.000014577282,0.000004408657,0.000014365086,0.000022315358,0.0000051083744],"domain_scores_gemma":[0.9999211,0.000044956465,0.000007964264,0.0000089551795,0.000013390472,0.0000036889282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015288666,0.0005891336,0.0003233372,0.00029642248,0.00020724445,0.0007895213,0.00078266236,0.00088467926,0.0037891692],"category_scores_gemma":[0.00036820894,0.00032233878,0.00049518206,0.00048980437,0.0004778493,0.0008114359,0.00029266046,0.00073478505,0.0009286697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021806096,0.000023678782,0.00030347367,0.0003130392,0.000029632765,0.00020778045,0.00013970329,0.8121529,0.005243788,0.12014716,0.0026633237,0.058753785],"study_design_scores_gemma":[0.000004306908,0.000017302895,0.00042109122,0.00007060433,0.000011643011,0.00010773125,0.000018167033,0.9095099,0.0013729959,0.06604136,0.02241218,0.000012736691],"about_ca_topic_score_codex":0.0023357715,"about_ca_topic_score_gemma":0.0035599861,"teacher_disagreement_score":0.0037891692,"about_ca_system_score_codex":0.00048991316,"about_ca_system_score_gemma":0.00032192664,"threshold_uncertainty_score":0.012676001},"labels":[],"label_agreement":null},{"id":"W4243484913","doi":"10.1007/978-1-4614-6170-8_100491","title":"Elastic Computing","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.0174881195315698,"score_gpt":0.2246816304220829,"score_spread":0.20719351089051308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243484913","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00422342,0.010432206,0.3331606,0.0038239204,0.0027891311,0.00011106544,0.0005951011,0.0026557685,0.64220876],"genre_scores_gemma":[0.08359807,0.014571109,0.11918088,0.0013047905,0.002376376,0.00020179574,0.0019063167,0.0016918884,0.77516884],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999634,0.00003249126,0.000016887847,0.00008147864,0.00019489182,0.000040240066],"domain_scores_gemma":[0.9996244,0.000104889274,0.000016867196,0.00015159453,0.00007629161,0.000025988445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003620718,0.0009115769,0.0005783863,0.0008036359,0.0007047102,0.0021080321,0.0010899231,0.00077439006,0.0624053],"category_scores_gemma":[0.0014671568,0.00043794306,0.00036055688,0.0018080523,0.00071285176,0.004003152,0.0017519464,0.0018128641,0.022193871],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054652566,0.00004541594,0.00011059554,0.00018789573,0.00002030472,0.000054070373,0.000056716948,0.010636595,0.00225049,0.28516194,0.14985095,0.55157024],"study_design_scores_gemma":[0.000009119992,0.000016470509,0.00026573616,0.000143685,0.000013723279,0.00020981103,0.000048362916,0.038415577,0.0036328877,0.37822345,0.57899517,0.000026042257],"about_ca_topic_score_codex":0.0009187926,"about_ca_topic_score_gemma":0.0012174676,"teacher_disagreement_score":0.0624053,"about_ca_system_score_codex":0.00075278484,"about_ca_system_score_gemma":0.0005953341,"threshold_uncertainty_score":0.20876652},"labels":[],"label_agreement":null},{"id":"W4243526850","doi":"10.1109/tit.2017.2763858","title":"IEEE Transactions on Information Theory publication information","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.012543147841475616,"score_gpt":0.23790893327885437,"score_spread":0.22536578543737876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243526850","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007630523,0.036449492,0.3967322,0.027924532,0.048095964,0.0007562309,0.03403828,0.00614095,0.44223198],"genre_scores_gemma":[0.12367172,0.0703073,0.09714296,0.005033089,0.013124351,0.0008795162,0.051890098,0.002578398,0.63537264],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979766,0.00033619188,0.0001684629,0.00020831136,0.001166879,0.00014360776],"domain_scores_gemma":[0.9941602,0.0019406211,0.00020380305,0.0014763966,0.0020270508,0.00019189817],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020985992,0.0013364143,0.002099609,0.003911759,0.0009687994,0.0045251613,0.0012487242,0.0022127526,0.20849873],"category_scores_gemma":[0.012298231,0.00060423237,0.00081521715,0.0050242073,0.000823775,0.0045335377,0.0018301125,0.002672678,0.08831038],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016826339,0.00012829731,0.0006506261,0.00086315366,0.000061345636,0.00015395603,0.00004772375,0.0032975161,0.0011016104,0.07365642,0.54315656,0.37671453],"study_design_scores_gemma":[0.00007064382,0.000105349965,0.0012704858,0.0009066583,0.00015354357,0.0005512421,0.00011218779,0.035644572,0.003219291,0.19575356,0.76213276,0.000079672325],"about_ca_topic_score_codex":0.0014660577,"about_ca_topic_score_gemma":0.0012081917,"teacher_disagreement_score":0.7915013,"about_ca_system_score_codex":0.001034928,"about_ca_system_score_gemma":0.0023356415,"threshold_uncertainty_score":0.6974977},"labels":[],"label_agreement":null},{"id":"W4243573209","doi":"10.32920/ryerson.14657649.v1","title":"Wavelet-based image compression using mathematical morphology and self organization feature map","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Wavelet transform; Image compression; Wavelet; Pattern recognition (psychology); Computer vision; JPEG; Computer science; Data compression; Discrete wavelet transform; Feature (linguistics); Second-generation wavelet transform; JPEG 2000; Stationary wavelet transform; Mathematics; Image processing; Image (mathematics)","score_opus":0.015314719696674963,"score_gpt":0.25864875449189045,"score_spread":0.24333403479521548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243573209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053242747,0.00063975254,0.941468,0.00016872851,0.0000629126,0.0000717194,0.00006258454,0.0007712055,0.003512296],"genre_scores_gemma":[0.4018387,0.0013003516,0.59219515,0.000055091255,0.00006537995,0.00009015423,0.00017310462,0.00009610139,0.004186033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998641,0.000015294312,0.000008437522,0.00001633619,0.00008768913,0.000008050679],"domain_scores_gemma":[0.9997949,0.00006796416,0.000029830015,0.000031585056,0.00006894483,0.0000068452373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021568972,0.00018937314,0.0002788611,0.0006804622,0.00016208799,0.0005310038,0.00027041006,0.00025625527,0.0010645924],"category_scores_gemma":[0.0007184956,0.00011947399,0.00033989912,0.00076920865,0.00029251346,0.0006658765,0.0002761284,0.00029592862,0.0003751674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013371138,0.000075770244,0.0009498612,0.0002070518,0.000055571047,0.00035084656,0.00016017967,0.06448814,0.271616,0.030138131,0.0021101697,0.6297146],"study_design_scores_gemma":[0.00002368043,0.0002449403,0.0034624066,0.000027832788,0.00003606938,0.00090284034,0.00005779811,0.8521717,0.12003259,0.012011095,0.010995187,0.000033856464],"about_ca_topic_score_codex":0.0004221347,"about_ca_topic_score_gemma":0.00041036218,"teacher_disagreement_score":0.0010645924,"about_ca_system_score_codex":0.00022428972,"about_ca_system_score_gemma":0.00021038199,"threshold_uncertainty_score":0.0035614371},"labels":[],"label_agreement":null},{"id":"W4243637876","doi":"10.1017/9781108938051","title":"Machine Learning Fundamentals","year":2021,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Transformer; Variety (cybernetics); Natural language processing; Engineering","score_opus":0.020269139951491988,"score_gpt":0.2025616822722167,"score_spread":0.1822925423207247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243637876","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009751257,0.1540558,0.23121543,0.012471513,0.007014858,0.00016620502,0.001918092,0.0029515564,0.5892315],"genre_scores_gemma":[0.021097604,0.15007254,0.1855903,0.009235598,0.0099892,0.00068474334,0.0032756247,0.0019288091,0.61812556],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990727,0.0001261155,0.000062907915,0.00018740614,0.00050314027,0.000047671878],"domain_scores_gemma":[0.9989466,0.00059475657,0.000037507176,0.0001344405,0.00022874377,0.000057979567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009957654,0.001805017,0.0010802113,0.0024025845,0.00078662665,0.0033176485,0.0014097817,0.0016719326,0.04106728],"category_scores_gemma":[0.0023448563,0.0007138525,0.00082368904,0.0031589726,0.0018454364,0.004270608,0.001480334,0.0044608773,0.048019905],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017977127,0.000039628114,0.00016036918,0.000916232,0.000020321468,0.000094944495,0.00022196893,0.0024159676,0.00092308014,0.31702414,0.38229063,0.29587474],"study_design_scores_gemma":[0.0000028718553,0.0000132444675,0.00017723099,0.00027747173,0.0000031837872,0.00016929669,0.00001970131,0.0015558869,0.00020614697,0.14795649,0.8496067,0.000011671305],"about_ca_topic_score_codex":0.0009874598,"about_ca_topic_score_gemma":0.0011368043,"teacher_disagreement_score":0.04106728,"about_ca_system_score_codex":0.0017051274,"about_ca_system_score_gemma":0.001043908,"threshold_uncertainty_score":0.1373837},"labels":[],"label_agreement":null},{"id":"W4243699570","doi":"10.1109/iemdc.1997.604145","title":"Direct neural adaptive control applied to synchronous generator","year":2002,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial neural network; Backpropagation; Controller (irrigation); Adaptive control; Complement (music); Control theory (sociology); Computation; Generator (circuit theory); Scheme (mathematics); Sign (mathematics); Control engineering; Control (management); Artificial intelligence; Algorithm; Engineering; Mathematics","score_opus":0.016200948016067657,"score_gpt":0.20734002562741863,"score_spread":0.19113907761135096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243699570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06668792,0.001241668,0.9139797,0.00023923175,0.00026396126,0.00005960114,0.000014225302,0.00048744527,0.017026166],"genre_scores_gemma":[0.9493929,0.0005677488,0.04238201,0.00005989321,0.000053388474,0.000040240706,0.000011487953,0.00001638189,0.0074758325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990463,0.000021531128,0.0000039499796,0.00002039446,0.00004189735,0.000007638694],"domain_scores_gemma":[0.9998858,0.000047565885,0.000015222047,0.000012071341,0.000032770433,0.000006460941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017513115,0.00020845115,0.00017143042,0.00011551317,0.00013279087,0.00023906468,0.0002865351,0.00028343848,0.0010217095],"category_scores_gemma":[0.0006540683,0.00008382473,0.00010398959,0.00014924876,0.00027780284,0.00019001812,0.0002628754,0.00027187582,0.00013725749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000989326,0.000058530666,0.00064668834,0.00016917054,0.000028350934,0.0002966826,0.000109415385,0.6108096,0.05134414,0.03802149,0.0012506329,0.29716635],"study_design_scores_gemma":[0.000009123518,0.000056053745,0.00018965125,0.0000045462393,0.0000047667722,0.000039703882,0.0000032640512,0.99002725,0.0043530106,0.0034215318,0.0018865541,0.00000448977],"about_ca_topic_score_codex":0.0020551777,"about_ca_topic_score_gemma":0.0021085788,"teacher_disagreement_score":0.0020551777,"about_ca_system_score_codex":0.00023687092,"about_ca_system_score_gemma":0.0002191719,"threshold_uncertainty_score":0.004086435},"labels":[],"label_agreement":null},{"id":"W4243776355","doi":"10.14430/arctic376","title":"Papers to Appear in ARCTIC","year":2009,"lang":"en","type":"article","venue":"ARCTIC","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arctic; The arctic; Oceanography; Geography; Environmental science; Physical geography; Geology","score_opus":0.00934234704598011,"score_gpt":0.2387138462647664,"score_spread":0.2293714992187863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243776355","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023940504,0.009908779,0.0032989173,0.00884322,0.10357158,0.00016280985,0.012754796,0.0013012752,0.85776454],"genre_scores_gemma":[0.0038945873,0.0033584505,0.00089449674,0.0005868561,0.0041882456,0.00002795598,0.003560367,0.0005412611,0.98294777],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99936074,0.00008341199,0.000036076857,0.00014804225,0.00027869703,0.00009311201],"domain_scores_gemma":[0.99799216,0.00018582921,0.00008250298,0.00024851237,0.00090353785,0.0005874828],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009360759,0.0011680984,0.0015832771,0.003363192,0.0030049316,0.0059484574,0.0013903023,0.0013414385,0.661076],"category_scores_gemma":[0.003458845,0.00033265437,0.0008498377,0.0040863655,0.0004754109,0.0022777978,0.001763516,0.0010812578,0.46655434],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008712006,0.00004244763,0.00014585971,0.00016705952,0.000012370578,0.000051168914,0.00003355439,0.0001884419,0.00023771747,0.004097665,0.93210113,0.06283541],"study_design_scores_gemma":[0.000008710842,0.000014159441,0.0003564009,0.000061116865,0.000009792977,0.000029197081,0.000056799498,0.00015251197,0.00016436013,0.0020173297,0.99712485,0.000004829868],"about_ca_topic_score_codex":0.008295121,"about_ca_topic_score_gemma":0.020076428,"teacher_disagreement_score":0.661076,"about_ca_system_score_codex":0.0016892136,"about_ca_system_score_gemma":0.0030565844,"threshold_uncertainty_score":0.4834339},"labels":[],"label_agreement":null},{"id":"W4244234736","doi":"10.1109/tnnls.2021.3073388","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence","score_opus":0.012733470139682094,"score_gpt":0.21942843061264394,"score_spread":0.20669496047296185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244234736","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008674992,0.07775329,0.3317228,0.014763441,0.10534082,0.00056262995,0.0132251615,0.0057110414,0.4422458],"genre_scores_gemma":[0.047799703,0.060790274,0.05947508,0.0020392463,0.007916191,0.00034253838,0.018183041,0.0014959566,0.80195796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992557,0.00006812914,0.000062097395,0.00007053413,0.0004896761,0.00005377804],"domain_scores_gemma":[0.9984035,0.00033720626,0.00005568766,0.00025708953,0.0008579105,0.00008860813],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010399217,0.00087971514,0.0014855702,0.0016763533,0.0005160981,0.0022983786,0.00094688445,0.0014829766,0.1712527],"category_scores_gemma":[0.0034331358,0.0003704023,0.00046931638,0.0019506597,0.00044431514,0.002172209,0.001100526,0.0015288608,0.06967644],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010125696,0.00008644695,0.00049349456,0.0005234777,0.000030718227,0.00008122713,0.000023858423,0.003108043,0.001625891,0.014201481,0.49946615,0.4802579],"study_design_scores_gemma":[0.000027818764,0.0000788564,0.0014199645,0.00042970068,0.000064212174,0.00029786731,0.000046496316,0.028729105,0.0023577304,0.028642723,0.9378687,0.00003680235],"about_ca_topic_score_codex":0.0019654583,"about_ca_topic_score_gemma":0.0031941677,"teacher_disagreement_score":0.8287473,"about_ca_system_score_codex":0.00051413826,"about_ca_system_score_gemma":0.0016375178,"threshold_uncertainty_score":0.5728973},"labels":[],"label_agreement":null},{"id":"W4244499503","doi":"10.1109/tnnls.2020.2968824","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Syracuse University; Tencent; Euskal Herriko Unibertsitatea; University of Birmingham; College of Engineering, Michigan State University; University of Manitoba; Arizona State University","keywords":"Computer science; Artificial neural network; World Wide Web; Information retrieval; Data science; Artificial intelligence","score_opus":0.015987716786376855,"score_gpt":0.21670722049405713,"score_spread":0.20071950370768027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244499503","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0085761715,0.076698825,0.3297185,0.01450642,0.10285765,0.0005575825,0.013103406,0.00566726,0.44831422],"genre_scores_gemma":[0.04708451,0.059917793,0.058605425,0.0020181374,0.0077924347,0.00033739055,0.017914081,0.001476334,0.804854],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927086,0.00006628691,0.000060309238,0.00006927319,0.00048062616,0.000052675143],"domain_scores_gemma":[0.99844545,0.00032881377,0.0000544493,0.0002518332,0.0008327398,0.00008680569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010211334,0.0008695145,0.0014611329,0.0016655496,0.00051198015,0.0022717533,0.0009440689,0.0014729509,0.17235099],"category_scores_gemma":[0.0033838958,0.00036509873,0.00046575707,0.0019373352,0.0004407287,0.0021492683,0.0010930956,0.0015128229,0.070227936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099547644,0.00008659512,0.000488883,0.00051558594,0.000030311943,0.00008042999,0.000023498118,0.0031102146,0.0016169639,0.014257364,0.49972716,0.4799634],"study_design_scores_gemma":[0.000027552762,0.000078212244,0.0014004629,0.00042337633,0.00006316967,0.00029664868,0.000045767134,0.028939035,0.0023299262,0.029093001,0.93726635,0.000036443565],"about_ca_topic_score_codex":0.0019519095,"about_ca_topic_score_gemma":0.0031764794,"teacher_disagreement_score":0.17235099,"about_ca_system_score_codex":0.00050869177,"about_ca_system_score_gemma":0.0016292125,"threshold_uncertainty_score":0.57657146},"labels":[],"label_agreement":null},{"id":"W4244697169","doi":"10.1007/978-1-4939-7131-2_100317","title":"Elastic Computing","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.022685548539883447,"score_gpt":0.23873743836489597,"score_spread":0.21605188982501253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244697169","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004147135,0.010346202,0.3218766,0.0043820343,0.0033421626,0.00012149198,0.0006912848,0.0028348998,0.65225816],"genre_scores_gemma":[0.08357399,0.015051344,0.12150749,0.0015237381,0.0028761881,0.00022015284,0.002252679,0.0019202229,0.7710742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996136,0.000034060053,0.000017991522,0.00008979913,0.00020183387,0.000042688866],"domain_scores_gemma":[0.99957126,0.00011892998,0.000018571995,0.0001771504,0.00008364115,0.00003037883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003890718,0.0009348454,0.0005665964,0.00080299407,0.00072323566,0.0022255299,0.0011270096,0.00079908856,0.06643447],"category_scores_gemma":[0.001671358,0.00043894054,0.00038859787,0.0018064616,0.00072495255,0.0042095473,0.001829811,0.0019700336,0.024976654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056835423,0.00004943116,0.00011090098,0.00020664056,0.00002128578,0.00005376079,0.000055461624,0.010047033,0.0021667262,0.3036677,0.16007055,0.5234937],"study_design_scores_gemma":[0.000008932491,0.000016258473,0.0002505854,0.00014342286,0.000013253718,0.00019701921,0.00004307502,0.033526845,0.0032255012,0.3804801,0.58207077,0.000024268387],"about_ca_topic_score_codex":0.0008535868,"about_ca_topic_score_gemma":0.0010854732,"teacher_disagreement_score":0.06643447,"about_ca_system_score_codex":0.0007695998,"about_ca_system_score_gemma":0.000621943,"threshold_uncertainty_score":0.22224545},"labels":[],"label_agreement":null},{"id":"W4245061725","doi":"10.1109/tnnls.2019.2892545","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"College of Engineering, Michigan State University; Hefei University of Technology; Hefei University; Guangdong University of Technology; Universidade de Pernambuco; Southeast University; Nanjing University; Univerza v Ljubljani; Royal Holloway, University of London; Nanjing University of Science and Technology; Zhejiang University; Dalian University of Technology; Huazhong University of Science and Technology; Fudan University; Concordia University; China University of Petroleum, Beijing; Chongqing University; Chinese Academy of Sciences; University of Cincinnati; Technische Universität Berlin; Nanyang Technological University; Indian Institute of Technology Kanpur; University of East Anglia; University of Western Sydney; Sichuan University; Michigan State University; National University of Singapore; KU Leuven; Beijing Institute of Technology; University of Texas at San Antonio; East China Normal University; South Dakota State University; Arizona State University; Universidade Federal de Pernambuco; Politechnika Warszawska; Università di Pisa; Euskal Herriko Unibertsitatea; Georgia Institute of Technology","keywords":"Artificial neural network; Computer science; Data science; Artificial intelligence; World Wide Web; Information retrieval","score_opus":0.010163785013374877,"score_gpt":0.21205169411808591,"score_spread":0.20188790910471105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245061725","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008899727,0.07329836,0.334508,0.014876888,0.10617927,0.0005726314,0.014310819,0.0058845654,0.4414696],"genre_scores_gemma":[0.04794887,0.057555623,0.05991698,0.0020019212,0.008071902,0.000339036,0.019448452,0.0015167624,0.80320036],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928135,0.0000636256,0.00006260795,0.000068180285,0.0004713992,0.00005282707],"domain_scores_gemma":[0.99839276,0.00033825744,0.0000569753,0.00025896134,0.00086459937,0.00008844862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010156212,0.00087232766,0.0014583672,0.001704815,0.0005097351,0.002243883,0.0009574125,0.0014773789,0.17766625],"category_scores_gemma":[0.0034237404,0.0003695549,0.00047246626,0.0019176643,0.00043690857,0.002146321,0.0010921526,0.0014882626,0.06931586],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010347157,0.00008929407,0.00047833225,0.0005399351,0.0000312507,0.00008295293,0.000023566216,0.0031701017,0.001729261,0.015105657,0.5023823,0.47626394],"study_design_scores_gemma":[0.000028916875,0.000079822865,0.001413968,0.0004273545,0.00006523208,0.00030156336,0.000044609227,0.028296227,0.0025551051,0.029404452,0.9373448,0.000038036185],"about_ca_topic_score_codex":0.0018966998,"about_ca_topic_score_gemma":0.0031056423,"teacher_disagreement_score":0.17766625,"about_ca_system_score_codex":0.0005167021,"about_ca_system_score_gemma":0.0016132678,"threshold_uncertainty_score":0.59435284},"labels":[],"label_agreement":null},{"id":"W4245243153","doi":"10.36227/techrxiv.12100713","title":"Capsule Networks: An Alternative Approach to Image Classification Using Convolutional Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Capsule; Artificial neural network; Geology","score_opus":0.09721064983910437,"score_gpt":0.3110722037266361,"score_spread":0.2138615538875317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245243153","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008282055,0.0005678195,0.9875031,0.00035971988,0.00012408111,0.00003682228,0.000084518135,0.00042340977,0.0026183971],"genre_scores_gemma":[0.44547868,0.002274098,0.5267873,0.0008596367,0.00054359034,0.00022411215,0.00083718443,0.0004340051,0.02256148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936634,0.00014736525,0.000035189616,0.00015649354,0.00021093241,0.00008373365],"domain_scores_gemma":[0.99906105,0.00038811454,0.000101349586,0.00017183185,0.00020687937,0.00007075098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011806553,0.0012762302,0.0009154149,0.0013061528,0.00042617923,0.0018538242,0.001858847,0.0014842255,0.0023903113],"category_scores_gemma":[0.0032939562,0.00059618556,0.0012204034,0.0014835529,0.0011434931,0.003272962,0.002284047,0.002522187,0.0007607692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003676448,0.00013416818,0.002151817,0.00036515377,0.00034957012,0.00021290906,0.00016186133,0.28058317,0.023005446,0.26348013,0.010399219,0.4187889],"study_design_scores_gemma":[0.000010103962,0.00007585413,0.00024543455,0.000019293104,0.000026605372,0.000047583602,0.000010495463,0.95520616,0.007364761,0.031372096,0.0056028008,0.000018871251],"about_ca_topic_score_codex":0.0036062736,"about_ca_topic_score_gemma":0.0027981515,"teacher_disagreement_score":0.0036062736,"about_ca_system_score_codex":0.00095440703,"about_ca_system_score_gemma":0.00083979,"threshold_uncertainty_score":0.00799644},"labels":[],"label_agreement":null},{"id":"W4245318270","doi":"10.14322/publons.r2610381","title":"10.14322/publons.r2610381","year":2000,"lang":"en","type":"dataset","venue":"Time to knit","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Current (fluid); Artificial neural network; Computer science; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.007003138386354326,"score_gpt":0.19309830925156082,"score_spread":0.1860951708652065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245318270","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008802072,0.00027729102,0.001698419,0.00025146492,0.00020818108,0.00009292551,0.9621512,0.018840402,0.015599905],"genre_scores_gemma":[0.0016895417,0.00017074744,0.0016839302,0.00015880012,0.000045348195,0.0001387402,0.9649819,0.001850172,0.029280856],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99881184,0.00014716359,0.00011958315,0.00042544556,0.00029926267,0.00019681474],"domain_scores_gemma":[0.998237,0.0002198075,0.00013272603,0.0007685635,0.00042677083,0.00021512763],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014943782,0.0030383999,0.0014750726,0.0033461545,0.00060215493,0.0026724504,0.0033240942,0.0016302763,0.5698347],"category_scores_gemma":[0.0040905923,0.0011625544,0.001067134,0.0050497986,0.000662843,0.0015535363,0.0025642677,0.0010914551,0.82661515],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001000759,0.000034566707,0.00032966005,0.00024102781,0.000015025369,0.000012989146,0.000008690893,0.00035202905,0.00027291957,0.0003090143,0.9842165,0.014107475],"study_design_scores_gemma":[0.00022525569,0.00007375421,0.0018700728,0.00012916335,0.000021227765,0.000068935035,0.000036856625,0.0019446774,0.0020905947,0.0012278425,0.99227285,0.00003889392],"about_ca_topic_score_codex":0.009163117,"about_ca_topic_score_gemma":0.012601681,"teacher_disagreement_score":0.4301653,"about_ca_system_score_codex":0.0011838693,"about_ca_system_score_gemma":0.0013306031,"threshold_uncertainty_score":0.61357856},"labels":[],"label_agreement":null},{"id":"W4245475860","doi":"10.1007/978-1-4939-7131-2_100237","title":"Data Extraction","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Extraction (chemistry); Computer science; Chromatography; Chemistry","score_opus":0.09749605329285443,"score_gpt":0.31264859371591214,"score_spread":0.2151525404230577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245475860","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042226445,0.0060790977,0.70972466,0.0028947233,0.002439156,0.0024780077,0.04783788,0.023634223,0.20068961],"genre_scores_gemma":[0.026853096,0.008857179,0.6565675,0.0022428408,0.00091175444,0.0021378,0.099847145,0.004142321,0.19844033],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987931,0.00010115132,0.000159801,0.00039435443,0.0004759824,0.000075551914],"domain_scores_gemma":[0.99792,0.00046991732,0.000084019564,0.00065851526,0.0007988647,0.00006874587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014350852,0.0018767838,0.0013058374,0.005999778,0.0014115013,0.0039222627,0.002247845,0.0008860455,0.09000699],"category_scores_gemma":[0.0053475183,0.0007613316,0.0014124406,0.006333208,0.0006542756,0.004317889,0.003592177,0.0016263105,0.11161137],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007947529,0.000049017646,0.00043753348,0.000883521,0.000030351315,0.00012286716,0.000118655924,0.00038352018,0.0071940455,0.01667085,0.1661345,0.8078956],"study_design_scores_gemma":[0.000016649943,0.00003827382,0.00079431984,0.00036297573,0.0000607643,0.0005722393,0.00018657307,0.0035905468,0.024898022,0.027940655,0.94150066,0.000038319915],"about_ca_topic_score_codex":0.0012079501,"about_ca_topic_score_gemma":0.0015470653,"teacher_disagreement_score":0.09000699,"about_ca_system_score_codex":0.00072137814,"about_ca_system_score_gemma":0.002054244,"threshold_uncertainty_score":0.3011034},"labels":[],"label_agreement":null},{"id":"W4245502891","doi":"10.1007/978-981-10-8624-3_2","title":"Electrical Laws","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Wheatstone bridge; Electrical engineering; Engineering; Electrical network; Voltage; Ohm; Electronic circuit; Law; Electronic engineering; Resistor; Political science","score_opus":0.020133830696120344,"score_gpt":0.23392530664775454,"score_spread":0.2137914759516342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245502891","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00087642984,0.004115065,0.024670677,0.002998292,0.0013581112,0.000025208197,0.00019610282,0.00017704807,0.965583],"genre_scores_gemma":[0.045731068,0.009203513,0.009024278,0.0016955923,0.0013942217,0.00013851532,0.00035337647,0.00029235985,0.932167],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998416,0.000024913428,0.0000053027566,0.000036092253,0.000079292215,0.000012846576],"domain_scores_gemma":[0.9998841,0.0000386065,0.0000059006175,0.00002998748,0.00003388839,0.000007457849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018482299,0.00063619093,0.00036288486,0.000767991,0.0006428258,0.0011982933,0.00055062736,0.0008763398,0.05987858],"category_scores_gemma":[0.0008189654,0.00024011757,0.000249171,0.00047771353,0.0019278161,0.002201932,0.000967709,0.0018990935,0.020338617],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042381353,0.000007763786,0.000022272414,0.000068149806,0.0000027676454,0.000018976045,0.00007356745,0.00044577123,0.0003246759,0.8980528,0.06127549,0.03970352],"study_design_scores_gemma":[0.00000349129,0.000005247559,0.00005664954,0.0000619013,0.0000024726633,0.000056542398,0.000026061609,0.00075238856,0.00036079984,0.4315813,0.567089,0.0000041060002],"about_ca_topic_score_codex":0.00064687,"about_ca_topic_score_gemma":0.00095508154,"teacher_disagreement_score":0.05987858,"about_ca_system_score_codex":0.00088342204,"about_ca_system_score_gemma":0.0005021112,"threshold_uncertainty_score":0.2003138},"labels":[],"label_agreement":null},{"id":"W4245729008","doi":"10.1007/978-1-4614-6170-8_100262","title":"Spectral Network Analysis","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.014082218283408561,"score_gpt":0.22236256394132622,"score_spread":0.20828034565791767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245729008","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024711266,0.0073510422,0.7878986,0.0011815586,0.0011056938,0.00003204127,0.00029337278,0.0012391636,0.19842753],"genre_scores_gemma":[0.12332377,0.01582945,0.2744126,0.00087021355,0.0023744304,0.0001778406,0.001594669,0.0020933622,0.57932365],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998241,0.00003188317,0.0000056998597,0.000045399564,0.00008249267,0.000010467442],"domain_scores_gemma":[0.999816,0.00005982911,0.000009268993,0.000039454182,0.000064914966,0.000010468825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030297687,0.0009043334,0.00055245677,0.0011677908,0.00042523103,0.0012865891,0.00066769106,0.00067587214,0.023837632],"category_scores_gemma":[0.0008694838,0.00028844964,0.00044039445,0.0009165748,0.00077985454,0.001556136,0.0009056385,0.0013738229,0.0135308765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001842113,0.00003071935,0.0001515773,0.00018176479,0.000043022,0.00004414105,0.00007350484,0.018014625,0.004308453,0.3999373,0.10151193,0.47568458],"study_design_scores_gemma":[0.0000035305643,0.000012743354,0.0004249602,0.000091074166,0.000022082555,0.00016806964,0.000047735357,0.108679764,0.0041992115,0.63626856,0.2500562,0.000026064012],"about_ca_topic_score_codex":0.0006739076,"about_ca_topic_score_gemma":0.0011492601,"teacher_disagreement_score":0.023837632,"about_ca_system_score_codex":0.0005813448,"about_ca_system_score_gemma":0.0003222171,"threshold_uncertainty_score":0.079744816},"labels":[],"label_agreement":null},{"id":"W4245823426","doi":"10.1109/tit.2019.2922560","title":"IEEE Transactions on Information Theory publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.007383270973087514,"score_gpt":0.21237916444984037,"score_spread":0.20499589347675284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245823426","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007685056,0.040465705,0.41808102,0.02506351,0.04612417,0.000768203,0.030053955,0.005549991,0.42620844],"genre_scores_gemma":[0.122801326,0.07575902,0.10311073,0.0047494355,0.012413274,0.0009117524,0.048232794,0.0021712712,0.6298504],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980856,0.0003394827,0.00015933822,0.00018997153,0.0010984318,0.00012714314],"domain_scores_gemma":[0.99481934,0.0017338797,0.00018284685,0.0012878221,0.0018059026,0.00017020266],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002010765,0.0012870186,0.002069679,0.0038233974,0.0009367815,0.0043012095,0.001149016,0.0020608127,0.18499774],"category_scores_gemma":[0.01094879,0.00057128386,0.000768873,0.0049888906,0.0008021027,0.004122502,0.0017218122,0.002442378,0.075375594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016672397,0.00012354781,0.0006889728,0.00089499366,0.0000646775,0.00014958144,0.00005247238,0.0033523254,0.0011527906,0.077194825,0.51723707,0.398922],"study_design_scores_gemma":[0.00006777776,0.00010850995,0.0013717826,0.00090496,0.00015986768,0.0005224925,0.00011307768,0.035070773,0.0030674974,0.18424289,0.77429456,0.00007579623],"about_ca_topic_score_codex":0.0014958134,"about_ca_topic_score_gemma":0.0012613396,"teacher_disagreement_score":0.81500226,"about_ca_system_score_codex":0.000997073,"about_ca_system_score_gemma":0.0021575359,"threshold_uncertainty_score":0.6188791},"labels":[],"label_agreement":null},{"id":"W4245923114","doi":"10.23952/jnva.5.2021.1.08","title":"Architecture self-attention mechanism: nonlinear optimization for neural architecture search","year":2021,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mechanism (biology); Architecture; Computer science; Computer architecture; Nonlinear system; Artificial intelligence","score_opus":0.010627369884156895,"score_gpt":0.2550664596421153,"score_spread":0.2444390897579584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245923114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022342792,0.00054042315,0.9732389,0.00022454736,0.00005862832,0.00006473165,0.000028848499,0.0007222705,0.0027787476],"genre_scores_gemma":[0.66348207,0.00043258452,0.32886317,0.00048149086,0.00013920016,0.00031997813,0.0001711987,0.00022160423,0.005888723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957496,0.00010649341,0.000026663296,0.00010871328,0.00012416442,0.0000589518],"domain_scores_gemma":[0.9994604,0.00019621121,0.00007219915,0.00008034158,0.00015059339,0.00004020002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011529669,0.0010634718,0.0009314575,0.00086370454,0.000545366,0.00060294085,0.001975165,0.0013103998,0.0031074937],"category_scores_gemma":[0.0022614305,0.0005073347,0.0007436207,0.0007648734,0.0007832966,0.0014111492,0.0013748006,0.0009964425,0.00042298358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013946433,0.00013194676,0.0018066555,0.00013840567,0.00012580294,0.00011384571,0.00012736893,0.69119316,0.012919083,0.023053145,0.00454052,0.26571047],"study_design_scores_gemma":[0.000011991118,0.000027594027,0.00011749229,0.000003716872,0.000010030665,0.000015683474,0.0000045874926,0.9960998,0.00073772355,0.002536139,0.0004309226,0.0000043238606],"about_ca_topic_score_codex":0.0041975235,"about_ca_topic_score_gemma":0.005627863,"teacher_disagreement_score":0.0041975235,"about_ca_system_score_codex":0.0008202904,"about_ca_system_score_gemma":0.001381118,"threshold_uncertainty_score":0.0103955865},"labels":[],"label_agreement":null},{"id":"W4246163720","doi":"10.32920/ryerson.14663880.v1","title":"Optimally weighted local discriminant bases : theory and applications in statistical signal and image processing","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Discriminant; Computer science; Pattern recognition (psychology); Wavelet; Feature extraction; Dimension (graph theory); Artificial intelligence; Feature (linguistics); Noise (video); Wavelet packet decomposition; Basis (linear algebra); Algorithm; Wavelet transform; Mathematics; Image (mathematics)","score_opus":0.011938082972056176,"score_gpt":0.26953465204181715,"score_spread":0.25759656906976097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246163720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031788768,0.0030277867,0.9916164,0.00019572664,0.00007678475,0.000015523074,0.000036099045,0.000083396684,0.0017694406],"genre_scores_gemma":[0.16426624,0.013142277,0.8132627,0.0002693799,0.00051235285,0.00022726243,0.0002806504,0.0001899776,0.007849106],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994516,0.00015756271,0.000032407912,0.00010185807,0.00022207235,0.00003453083],"domain_scores_gemma":[0.9993241,0.00033941952,0.00007255967,0.00007564573,0.00016670121,0.00002166712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097035686,0.00066024734,0.00086919643,0.0011389073,0.00030911187,0.001287682,0.00065101875,0.0008360748,0.001746771],"category_scores_gemma":[0.002454488,0.0003115171,0.0005882504,0.0021956896,0.00092145556,0.0011839957,0.0009572374,0.0016694791,0.0010544037],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008674057,0.0000643157,0.0008971239,0.0005499338,0.00008566537,0.0001259908,0.00016814665,0.13689642,0.022712635,0.30605072,0.005800252,0.52656204],"study_design_scores_gemma":[0.00001244964,0.000096640935,0.0006829359,0.000092691356,0.000029039846,0.00024624667,0.00005334236,0.81731707,0.0071741664,0.1531262,0.021125907,0.0000432988],"about_ca_topic_score_codex":0.0007691958,"about_ca_topic_score_gemma":0.0005731018,"teacher_disagreement_score":0.001746771,"about_ca_system_score_codex":0.0005368318,"about_ca_system_score_gemma":0.00052944466,"threshold_uncertainty_score":0.00584352},"labels":[],"label_agreement":null},{"id":"W4246236519","doi":"10.1109/tnnls.2018.2885887","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"College of Engineering, Michigan State University; Hefei University of Technology; Hefei University; Guangdong University of Technology; Universidade de Pernambuco; Southeast University; Nanjing University; Univerza v Ljubljani; Royal Holloway, University of London; Nanjing University of Science and Technology; Zhejiang University; Dalian University of Technology; Huazhong University of Science and Technology; Fudan University; Concordia University; China University of Petroleum, Beijing; Chongqing University; Chinese Academy of Sciences; University of Cincinnati; Technische Universität Berlin; Nanyang Technological University; Indian Institute of Technology Kanpur; University of East Anglia; University of Western Sydney; Sichuan University; Michigan State University; National University of Singapore; KU Leuven; Beijing Institute of Technology; University of Texas at San Antonio; East China Normal University; South Dakota State University; Arizona State University; Universidade Federal de Pernambuco; Politechnika Warszawska; Università di Pisa; Euskal Herriko Unibertsitatea; Georgia Institute of Technology","keywords":"Computer science; Artificial neural network; Artificial intelligence; Information retrieval; World Wide Web; Data science","score_opus":0.010163785013374877,"score_gpt":0.21205169411808591,"score_spread":0.20188790910471105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246236519","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008899732,0.07329838,0.33450884,0.014876881,0.10617912,0.0005726314,0.014310819,0.005884566,0.441469],"genre_scores_gemma":[0.047948938,0.057555676,0.059917103,0.0020019216,0.0080719,0.00033903655,0.019448467,0.0015167642,0.8032002],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928135,0.0000636256,0.00006260801,0.000068180285,0.0004713992,0.000052827017],"domain_scores_gemma":[0.99839276,0.00033825793,0.00005697541,0.0002589616,0.0008646001,0.00008844862],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010156222,0.00087232766,0.0014583672,0.001704815,0.00050973555,0.002243883,0.00095741346,0.0014773781,0.17766611],"category_scores_gemma":[0.0034237436,0.0003695553,0.0004724667,0.001917666,0.00043690918,0.002146321,0.0010921526,0.0014882634,0.0693158],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103471626,0.000089294124,0.00047833324,0.00053993537,0.000031250773,0.000082952974,0.000023566228,0.0031701159,0.0017292621,0.015105687,0.5023816,0.47626448],"study_design_scores_gemma":[0.000028916895,0.000079822996,0.0014139698,0.00042735483,0.000065232314,0.0003015636,0.000044609307,0.028296337,0.0025551107,0.029404538,0.9373446,0.00003803625],"about_ca_topic_score_codex":0.0018967035,"about_ca_topic_score_gemma":0.0031056437,"teacher_disagreement_score":0.8223339,"about_ca_system_score_codex":0.0005167023,"about_ca_system_score_gemma":0.0016132678,"threshold_uncertainty_score":0.59435236},"labels":[],"label_agreement":null},{"id":"W4246265124","doi":"10.32920/ryerson.14665509","title":"Analysis of psychometric data using statistical and machine learning methods","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Categorical variable; Binomial regression; Regression analysis; Linear regression; Logistic regression; Regression diagnostic; Computer science; Statistics; Proper linear model; Statistical model; Regression; Time series; Econometrics; Machine learning; Artificial intelligence; Mathematics; Polynomial regression","score_opus":0.21321749006583748,"score_gpt":0.4465798290900272,"score_spread":0.2333623390241897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246265124","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043641668,0.0011563126,0.948119,0.00089024846,0.0001526955,0.0004984919,0.0017915017,0.0010469632,0.0027030136],"genre_scores_gemma":[0.37439802,0.0020292029,0.61605346,0.00032882506,0.00029653247,0.0018959517,0.0033882337,0.00024655095,0.0013631871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9784889,0.013721133,0.001535257,0.0014292739,0.0044906526,0.00033481963],"domain_scores_gemma":[0.8859264,0.09231592,0.0071472013,0.0071773245,0.0069656656,0.00046747475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02183436,0.0013226194,0.0016702279,0.007929647,0.0005883387,0.003023855,0.0012089205,0.001024978,0.0032402186],"category_scores_gemma":[0.10397999,0.00040912553,0.0016899487,0.008468859,0.0012186,0.0025783645,0.001357088,0.0025135595,0.0012760777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000272277,0.000722922,0.10149822,0.0015808411,0.0013586741,0.0004624252,0.0010323934,0.11185772,0.002863343,0.033724137,0.007619135,0.7370079],"study_design_scores_gemma":[0.00007259983,0.000631926,0.081588596,0.00076997746,0.00023505594,0.00051691546,0.0016185377,0.8030232,0.004097336,0.0940247,0.013236766,0.00018433676],"about_ca_topic_score_codex":0.0019449643,"about_ca_topic_score_gemma":0.0012038362,"teacher_disagreement_score":0.02183436,"about_ca_system_score_codex":0.0008888782,"about_ca_system_score_gemma":0.0016929875,"threshold_uncertainty_score":0.115472496},"labels":[],"label_agreement":null},{"id":"W4246515325","doi":"10.1109/tnnls.2021.3058473","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.012733470139682094,"score_gpt":0.21942843061264394,"score_spread":0.20669496047296185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246515325","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008675005,0.077753305,0.33172244,0.014763446,0.10534092,0.00056262896,0.013225148,0.0057110363,0.44224605],"genre_scores_gemma":[0.04779971,0.060790252,0.059475042,0.002039246,0.007916198,0.00034253756,0.018183034,0.0014959542,0.8019579],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992557,0.00006812914,0.000062097395,0.000070534064,0.0004896763,0.000053777934],"domain_scores_gemma":[0.9984035,0.00033720545,0.000055687604,0.00025708906,0.00085790886,0.000088607965],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010399211,0.0008797143,0.0014855688,0.00167635,0.00051609735,0.0022983765,0.00094688387,0.0014829752,0.17125262],"category_scores_gemma":[0.0034331311,0.00037040195,0.00046931618,0.0019506569,0.00044431514,0.002172207,0.001100525,0.0015288587,0.069676355],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010125716,0.00008644678,0.0004934948,0.0005234777,0.000030718227,0.00008122721,0.000023858423,0.0031080372,0.0016258918,0.014201487,0.49946615,0.4802579],"study_design_scores_gemma":[0.00002781874,0.00007885641,0.0014199658,0.0004297005,0.000064212174,0.00029786734,0.000046496363,0.02872908,0.0023577304,0.028642712,0.9378688,0.000036802354],"about_ca_topic_score_codex":0.0019654573,"about_ca_topic_score_gemma":0.0031941647,"teacher_disagreement_score":0.8287474,"about_ca_system_score_codex":0.00051413826,"about_ca_system_score_gemma":0.0016375172,"threshold_uncertainty_score":0.5728971},"labels":[],"label_agreement":null},{"id":"W4246889709","doi":"10.14430/arctic4176","title":"Papers to Appear in &lt;i&gt;ARCTIC&lt;/i&gt;","year":2012,"lang":"en","type":"article","venue":"ARCTIC","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arctic; The arctic; Oceanography; Geography; Physical geography; Environmental science; Geology","score_opus":0.013615218883972487,"score_gpt":0.23802296936729095,"score_spread":0.22440775048331846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246889709","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023603698,0.009566712,0.008726317,0.018362207,0.25245628,0.00032097552,0.02008809,0.0023500747,0.68576896],"genre_scores_gemma":[0.004150692,0.0034499064,0.002011737,0.0013123547,0.010664445,0.000067983594,0.0059749642,0.0011074493,0.97126037],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991973,0.00006044898,0.000044858763,0.00018363172,0.0003970258,0.00011676495],"domain_scores_gemma":[0.99652797,0.00036484265,0.00010840369,0.000448309,0.0016830823,0.0008672904],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014176563,0.0014780149,0.002015416,0.00315475,0.0025676566,0.0067529073,0.0022852938,0.0019603588,0.6480496],"category_scores_gemma":[0.004728486,0.00048350904,0.0012183621,0.004604936,0.000644429,0.0035873658,0.0016958533,0.001616239,0.45632356],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007348849,0.000040263527,0.00010383075,0.0001680608,0.0000149114785,0.000030995343,0.000018612474,0.00014346406,0.00038047723,0.0028717243,0.9444312,0.051723022],"study_design_scores_gemma":[0.000018126202,0.00002656894,0.00039992548,0.000066646935,0.000020644722,0.000034137323,0.00003172658,0.00026195007,0.00041503558,0.0031028055,0.99561405,0.00000834727],"about_ca_topic_score_codex":0.0067796623,"about_ca_topic_score_gemma":0.013524552,"teacher_disagreement_score":0.3519504,"about_ca_system_score_codex":0.002104201,"about_ca_system_score_gemma":0.003192407,"threshold_uncertainty_score":0.5020145},"labels":[],"label_agreement":null},{"id":"W4246952677","doi":"10.4018/978-1-60566-170-4.ch002","title":"Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Entropy (arrow of time); Multifractal system; Information theory; Computer science; Transfer entropy; Fractal; Artificial intelligence; Cognition; Informatics; Statistical physics; Mathematics; Theoretical computer science; Data mining; Psychology; Principle of maximum entropy; Statistics; Physics; Mathematical analysis; Engineering","score_opus":0.0651667198535927,"score_gpt":0.28136462495385617,"score_spread":0.2161979051002635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246952677","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02412163,0.22473541,0.55454785,0.014666362,0.0036978684,0.00008472988,0.0009779336,0.0005619154,0.1766063],"genre_scores_gemma":[0.5030724,0.20050798,0.2342182,0.004862848,0.009354681,0.0003671705,0.0011584723,0.00051579246,0.04594253],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992685,0.00017328605,0.00006172457,0.00014509764,0.00031004797,0.0000414166],"domain_scores_gemma":[0.99879813,0.0008765369,0.00007226141,0.000106680425,0.00011826862,0.000028102138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013005016,0.00084607577,0.0009772959,0.002531498,0.00042703142,0.0028440105,0.00075148034,0.0010330077,0.0034661205],"category_scores_gemma":[0.0031372865,0.00028248644,0.0005216634,0.0022258235,0.003441553,0.005870084,0.0008957713,0.001361282,0.0014358527],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024363404,0.000017697172,0.0006052689,0.00065047696,0.000047232712,0.000110946254,0.00049133104,0.0026290894,0.0022238833,0.8405637,0.010352123,0.14228387],"study_design_scores_gemma":[0.000003837364,0.00003383623,0.0011432107,0.0002842995,0.000021452388,0.0003748057,0.00018406274,0.007883851,0.0013263302,0.893669,0.095028356,0.000046987076],"about_ca_topic_score_codex":0.0003647107,"about_ca_topic_score_gemma":0.0003546094,"teacher_disagreement_score":0.0034661205,"about_ca_system_score_codex":0.000807442,"about_ca_system_score_gemma":0.00037276684,"threshold_uncertainty_score":0.011595368},"labels":[],"label_agreement":null},{"id":"W4247297230","doi":"10.32920/ryerson.14650011.v1","title":"Effects of Parameter Change on Neural Network and Deep Neural Network","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Computation; Noise (video); Computer science; Gaussian noise; Decision boundary; Artificial intelligence; Simple (philosophy); MATLAB; Algorithm; Time delay neural network; Gaussian; Boundary (topology); Stochastic neural network; Separable space; Pattern recognition (psychology); Mathematics; Support vector machine; Physics","score_opus":0.026626199407397536,"score_gpt":0.2542359264108689,"score_spread":0.22760972700347137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247297230","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8332198,0.010670171,0.12614828,0.004089965,0.0027942802,0.0003139714,0.0018384773,0.0030932236,0.01783191],"genre_scores_gemma":[0.9745067,0.0011186936,0.020050513,0.00046627352,0.00009313397,0.00014276351,0.0004736978,0.0006293057,0.0025189614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997449,0.0006759566,0.00031466334,0.00067631283,0.00059153244,0.00029256294],"domain_scores_gemma":[0.9738435,0.019997295,0.0008960858,0.0029666831,0.001886716,0.00040965315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025392445,0.0012971602,0.0007689221,0.0008261711,0.0005546403,0.001502146,0.0011992349,0.001997204,0.005603858],"category_scores_gemma":[0.054527033,0.0004703807,0.0006389831,0.0011099047,0.0014122718,0.0031701473,0.0014424992,0.0026123433,0.00082759204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033711544,0.00073111657,0.011402261,0.0016538134,0.00041981632,0.001455466,0.0006485768,0.7095105,0.075616464,0.012239882,0.0063290517,0.17662193],"study_design_scores_gemma":[0.00034432596,0.0032432128,0.019948227,0.0006178557,0.00064966094,0.0017152898,0.001255101,0.66071635,0.25477037,0.025261896,0.031132469,0.00034523607],"about_ca_topic_score_codex":0.0029937853,"about_ca_topic_score_gemma":0.0020342497,"teacher_disagreement_score":0.005603858,"about_ca_system_score_codex":0.0009653033,"about_ca_system_score_gemma":0.0005292378,"threshold_uncertainty_score":0.018746734},"labels":[],"label_agreement":null},{"id":"W4247477371","doi":"10.31234/osf.io/xdgn9","title":"The same-different task as a tool to study unconscious processing","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Subliminal stimuli; Unconscious mind; Task (project management); Consciousness; Cognition; Computer science; Context (archaeology); Priming (agriculture); Cognitive psychology; Cognitive science; Information processing; Psychology; Neuroscience","score_opus":0.030344176276170604,"score_gpt":0.29721975693155245,"score_spread":0.26687558065538186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247477371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39892718,0.002222086,0.5458526,0.0017114402,0.0012317416,0.00073973,0.0006038157,0.0012717864,0.04743961],"genre_scores_gemma":[0.708972,0.0010118444,0.27628863,0.0015289604,0.0003051509,0.0012988128,0.00044736022,0.00087707915,0.0092702005],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934286,0.0002468837,0.00005465605,0.00018255155,0.00011493898,0.00005805962],"domain_scores_gemma":[0.9972018,0.0016001839,0.00024622135,0.0006982311,0.00012080716,0.00013283214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013056807,0.00089556404,0.00066563475,0.00039256908,0.00043956525,0.0008484186,0.00078003923,0.0010620357,0.0065340386],"category_scores_gemma":[0.0038023444,0.00024296052,0.00042932224,0.00040387327,0.0010839228,0.0021678808,0.0013217536,0.0018752731,0.0011238181],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017509843,0.0010627821,0.0015225407,0.0013127099,0.000058961577,0.00034374685,0.001441413,0.00090869504,0.79250664,0.11208755,0.0040085055,0.08299548],"study_design_scores_gemma":[0.0005979527,0.005136254,0.024990464,0.0002467755,0.0002699861,0.0043954435,0.00082129636,0.026005251,0.5997567,0.26002052,0.07750191,0.00025746977],"about_ca_topic_score_codex":0.00009030614,"about_ca_topic_score_gemma":0.00010122796,"teacher_disagreement_score":0.0065340386,"about_ca_system_score_codex":0.0002175494,"about_ca_system_score_gemma":0.00033477833,"threshold_uncertainty_score":0.021858513},"labels":[],"label_agreement":null},{"id":"W4247643452","doi":"10.1007/978-1-4939-7131-2_100643","title":"Maximum Entropy Random Networks","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Statistical physics; Mathematics; Computer science; Physics","score_opus":0.014202619889499711,"score_gpt":0.2190027799875106,"score_spread":0.20480016009801089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247643452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050571454,0.03332766,0.5989409,0.0042745196,0.0020481695,0.00006139482,0.0006461076,0.0010865619,0.3545575],"genre_scores_gemma":[0.24449521,0.04675108,0.2093635,0.0022295795,0.0053142197,0.00036969275,0.0018276562,0.0016612585,0.48798782],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99961805,0.000103148384,0.00001527165,0.00007979155,0.00016196241,0.000021783899],"domain_scores_gemma":[0.9993179,0.00043058212,0.000041524287,0.000109870765,0.00007696718,0.000023190678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007019158,0.0008484971,0.000869114,0.00085741974,0.000352295,0.0013327879,0.00078583066,0.0010990968,0.018760348],"category_scores_gemma":[0.0028549237,0.00042405023,0.00037166275,0.0010244782,0.0010560845,0.0023403084,0.001076953,0.0020218464,0.007169934],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030334402,0.000024080391,0.00009701549,0.00034469744,0.000040618404,0.000043433964,0.00003985922,0.036759883,0.0013677424,0.6923395,0.05206657,0.21684636],"study_design_scores_gemma":[0.000008037449,0.000016226326,0.00019465807,0.00014277024,0.000016658254,0.00009823325,0.000010528987,0.06301053,0.0014302916,0.83380145,0.10124754,0.000023161665],"about_ca_topic_score_codex":0.0002913595,"about_ca_topic_score_gemma":0.0004997308,"teacher_disagreement_score":0.018760348,"about_ca_system_score_codex":0.0007048908,"about_ca_system_score_gemma":0.00039157618,"threshold_uncertainty_score":0.06275964},"labels":[],"label_agreement":null},{"id":"W4247686494","doi":"10.31234/osf.io/ryvg2","title":"Holographic Declarative Memory: Distributional semantics as the architecture of memory","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Cognitive architecture; Semantics (computer science); Memory model; Theoretical computer science; Cognition; Programming language; Shared memory; Psychology","score_opus":0.016822280336190293,"score_gpt":0.26068130790151905,"score_spread":0.24385902756532876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247686494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12026556,0.00077933335,0.85779315,0.002318909,0.00013878201,0.000026846506,0.000233207,0.0003784316,0.018065684],"genre_scores_gemma":[0.9095374,0.00065675494,0.084127136,0.00033643655,0.00012557275,0.00005717155,0.00018533795,0.0000667559,0.0049074553],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997353,0.00007801002,0.000018353909,0.00006508479,0.00006567174,0.000037521255],"domain_scores_gemma":[0.9987728,0.00046867883,0.00017212241,0.00037894532,0.00012930205,0.000078138095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005254361,0.00035204992,0.00042469692,0.00042817643,0.00040790992,0.0023262934,0.0012379739,0.00072611443,0.0031092225],"category_scores_gemma":[0.0030768276,0.00021342126,0.0006423062,0.0004850927,0.0026123147,0.006155585,0.0017510576,0.0014231611,0.00050328975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003665762,0.00002778793,0.0007703783,0.00005031763,0.000022445633,0.000107225496,0.0002954513,0.012123886,0.0023561474,0.9627238,0.0009592447,0.02052676],"study_design_scores_gemma":[0.000007176999,0.0000238887,0.00027253266,0.000012313842,0.000007888559,0.000094518415,0.00004393411,0.073436394,0.0010313387,0.9231043,0.00195651,0.000009260425],"about_ca_topic_score_codex":0.0010091979,"about_ca_topic_score_gemma":0.000874025,"teacher_disagreement_score":0.0031092225,"about_ca_system_score_codex":0.00055981125,"about_ca_system_score_gemma":0.0005541355,"threshold_uncertainty_score":0.010401368},"labels":[],"label_agreement":null},{"id":"W4247879929","doi":"10.1109/.2001.981010","title":"Law of the iterated logarithm for a constant-gain linear stochastic gradient algorithm","year":2002,"lang":"en","type":"article","venue":"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Law of the iterated logarithm; Logarithm; Constant (computer programming); Iterated function; Iterated logarithm; Algorithm; Mathematics; Applied mathematics; Computer science; Mathematical analysis","score_opus":0.028218994873677482,"score_gpt":0.24443723839199122,"score_spread":0.21621824351831373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247879929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02990896,0.00034252836,0.9612624,0.00059423933,0.000054522585,0.000059417274,0.000052885887,0.000296139,0.0074288775],"genre_scores_gemma":[0.9148457,0.0007386348,0.07303215,0.00032880853,0.00011740208,0.00032345898,0.00009545131,0.0002053156,0.010313083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988172,0.00039205194,0.00006729354,0.0002136943,0.00037441266,0.00013537522],"domain_scores_gemma":[0.99039334,0.0065944595,0.0008019106,0.00046070604,0.0013242728,0.0004252249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003999668,0.00096595334,0.0011170731,0.0009039178,0.0005408221,0.0023775683,0.0017103194,0.0018333461,0.0048111975],"category_scores_gemma":[0.0260377,0.0003821119,0.00072003,0.00049315894,0.0031221672,0.0034306406,0.0016258562,0.0025411788,0.0007170858],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012519473,0.00009211177,0.0013110832,0.00028305626,0.00006619142,0.0003689944,0.00040706396,0.3370195,0.008529886,0.6254412,0.0019638801,0.024391927],"study_design_scores_gemma":[0.000015628202,0.00005598554,0.00016866441,0.000025610057,0.0000095744335,0.00008448841,0.000019782465,0.93702734,0.00086444296,0.061256375,0.00045248994,0.000019630594],"about_ca_topic_score_codex":0.0024552413,"about_ca_topic_score_gemma":0.0012741084,"teacher_disagreement_score":0.0048111975,"about_ca_system_score_codex":0.0015207212,"about_ca_system_score_gemma":0.0015833932,"threshold_uncertainty_score":0.021152496},"labels":[],"label_agreement":null},{"id":"W4247904712","doi":"10.1007/978-1-4471-7452-3_7","title":"Hopfield Networks, Simulated Annealing, and Chaotic Neural Networks","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Simulated annealing; Hopfield network; Chaotic; Artificial neural network; Metaheuristic; Computer science; Recurrent neural network; Artificial intelligence; Combinatorial optimization; Mathematical optimization; Algorithm; Mathematics","score_opus":0.0152390490052334,"score_gpt":0.22149642539471434,"score_spread":0.20625737638948094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247904712","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065524774,0.205488,0.23321767,0.0025280174,0.0026101288,0.00006720421,0.0004556278,0.00069581304,0.5483851],"genre_scores_gemma":[0.14858508,0.14866024,0.11282765,0.00082501664,0.002255755,0.00016592328,0.0008067887,0.0004586985,0.5854149],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998926,0.00001554902,0.0000048568595,0.000021539801,0.00005676909,0.000008692515],"domain_scores_gemma":[0.99989927,0.000055222725,0.0000075916223,0.000013751504,0.000018563796,0.0000054754505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001797894,0.00058178185,0.0005248609,0.00067520904,0.00033045188,0.001308478,0.0007459863,0.00090971583,0.015985435],"category_scores_gemma":[0.000697125,0.0003124305,0.00033783042,0.0019936773,0.00084029906,0.0014284424,0.00045061338,0.0009838964,0.0037226973],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047157027,0.000038990296,0.00017790445,0.0008642814,0.00004677644,0.000080937134,0.000115587136,0.06001797,0.0024738014,0.42071563,0.06904077,0.4463802],"study_design_scores_gemma":[0.000014707663,0.000035399673,0.00045726582,0.00024189799,0.000023224176,0.00016475201,0.00006464413,0.08075016,0.0018097972,0.5106536,0.40574983,0.00003475056],"about_ca_topic_score_codex":0.0024215956,"about_ca_topic_score_gemma":0.0040757195,"teacher_disagreement_score":0.015985435,"about_ca_system_score_codex":0.0007964631,"about_ca_system_score_gemma":0.0005185795,"threshold_uncertainty_score":0.053476572},"labels":[],"label_agreement":null},{"id":"W4248128758","doi":"10.1149/ma2012-02/53/3606","title":"An Arrhenius Argument to Explain Electrical Conductivity Maxima versus Temperature","year":2012,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Arrhenius equation; Argument (complex analysis); Maxima; Electrical resistivity and conductivity; Thermodynamics; Materials science; Conductivity; Activation energy; Statistical physics; Physics; Chemistry; History; Physical chemistry; Quantum mechanics; Art history","score_opus":0.026986329976246,"score_gpt":0.2817345670355798,"score_spread":0.2547482370593338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248128758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1126441,0.017495811,0.6197672,0.050156023,0.00697098,0.00018738079,0.0013867053,0.0054729944,0.18591891],"genre_scores_gemma":[0.8800427,0.0072625084,0.025927594,0.0044764653,0.0011524138,0.00023595446,0.0005126831,0.0008858539,0.079503834],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99979335,0.000028821625,0.000008478796,0.00005811089,0.00006651007,0.000044735225],"domain_scores_gemma":[0.9994904,0.0002423034,0.00005380421,0.000083284176,0.00010378296,0.000026394317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064123364,0.00056697836,0.000714919,0.00044593797,0.0004939135,0.00089081394,0.0017503668,0.0020001754,0.026787315],"category_scores_gemma":[0.0031228736,0.00034822297,0.00073586166,0.00046859065,0.00097011,0.002804772,0.0005627837,0.0027959584,0.0069121635],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003398615,0.00015088686,0.0008291231,0.0010249597,0.000064515196,0.00082547293,0.00016136501,0.017631266,0.1605476,0.7475842,0.036760237,0.03408047],"study_design_scores_gemma":[0.00015063862,0.00033139315,0.002364843,0.00023068237,0.00008191596,0.0012147664,0.00015469111,0.19198635,0.14418462,0.5856879,0.073497705,0.00011450674],"about_ca_topic_score_codex":0.00021441885,"about_ca_topic_score_gemma":0.00020873854,"teacher_disagreement_score":0.026787315,"about_ca_system_score_codex":0.00040914075,"about_ca_system_score_gemma":0.00025054938,"threshold_uncertainty_score":0.089612484},"labels":[],"label_agreement":null},{"id":"W4248255431","doi":"10.14430/arctic658","title":"Papers to Appear in ARCTIC","year":2010,"lang":"en","type":"article","venue":"ARCTIC","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arctic; The arctic; Geography; Oceanography; Physical geography; History; Geology","score_opus":0.007010902225926942,"score_gpt":0.23139669218928102,"score_spread":0.2243857899633541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248255431","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025529163,0.010768001,0.0033346515,0.009006341,0.10840385,0.00016315689,0.012496367,0.0012824817,0.8519922],"genre_scores_gemma":[0.004016444,0.0035266194,0.0008929336,0.00058172434,0.0044354233,0.000027788532,0.0034824014,0.0005325963,0.98250395],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993623,0.0000853087,0.00003563522,0.00014864175,0.00027617413,0.00009196516],"domain_scores_gemma":[0.9979796,0.00019263872,0.00008501902,0.00024189838,0.00089870155,0.0006021996],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00094480405,0.001194804,0.0016208228,0.0034201269,0.003060449,0.0059938403,0.0013810812,0.0013558393,0.6559593],"category_scores_gemma":[0.003463736,0.0003330301,0.00084312225,0.004225121,0.00048647675,0.002287948,0.0017213059,0.0010727972,0.45643923],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009767866,0.000045592016,0.0001552683,0.00018541598,0.000013415627,0.000053714375,0.00003546084,0.00020202655,0.00025565477,0.0042078635,0.9283103,0.06643768],"study_design_scores_gemma":[0.000009065692,0.000015216993,0.00036948285,0.000063630396,0.000010478364,0.000030234307,0.00005849905,0.00015806044,0.00017128672,0.0020420405,0.99706703,0.000004938473],"about_ca_topic_score_codex":0.008215737,"about_ca_topic_score_gemma":0.020061746,"teacher_disagreement_score":0.6559593,"about_ca_system_score_codex":0.0016609945,"about_ca_system_score_gemma":0.003080936,"threshold_uncertainty_score":0.49073225},"labels":[],"label_agreement":null},{"id":"W4248290806","doi":"10.1109/ijcnn.2006.1716763","title":"Improving the Convergence of Backpropagation by Opposite Transfer Functions","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backpropagation; Computer science; Convergence (economics); Benchmark (surveying); Perceptron; Artificial neural network; Rate of convergence; Artificial intelligence; Activation function; Multilayer perceptron; Machine learning; Algorithm; Key (lock)","score_opus":0.024682205241403854,"score_gpt":0.22773344666735623,"score_spread":0.20305124142595238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248290806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05065626,0.0003181325,0.9422725,0.00024646946,0.00020645127,0.00005981188,0.000019044817,0.00095436245,0.0052670105],"genre_scores_gemma":[0.5445768,0.00037871656,0.45030332,0.0002320449,0.00008901852,0.00016983788,0.00011392953,0.0003285922,0.0038077761],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881124,0.0003186696,0.000088518114,0.00012565182,0.00054710836,0.00010881229],"domain_scores_gemma":[0.99604404,0.0020388456,0.00026905804,0.00040705633,0.0011348566,0.00010611005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032839521,0.0012544158,0.0009308086,0.00095875154,0.00044417966,0.0010899288,0.0013447842,0.0019798495,0.0020011265],"category_scores_gemma":[0.01368438,0.00040639486,0.0006816783,0.00063330587,0.00092038023,0.0017931338,0.0013920028,0.0018892887,0.00096177036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047427195,0.00028093238,0.001454493,0.00027969494,0.00013347827,0.00025782033,0.00013340102,0.7106795,0.028389333,0.021091558,0.0026710986,0.2341545],"study_design_scores_gemma":[0.000029935905,0.00008775524,0.00016415895,0.000010777286,0.000014240461,0.00007433945,0.000008604177,0.9863239,0.008053888,0.0041555883,0.00106525,0.00001145192],"about_ca_topic_score_codex":0.0011605285,"about_ca_topic_score_gemma":0.00081956456,"teacher_disagreement_score":0.0032839521,"about_ca_system_score_codex":0.00049418194,"about_ca_system_score_gemma":0.0008076918,"threshold_uncertainty_score":0.017367363},"labels":[],"label_agreement":null},{"id":"W4248473500","doi":"10.1002/9781119093435.ch189","title":"Comparison of Linear Multivariable, Partial Least Square Regression, and Artificial Neural Network Analyses to Study the Effect of Different Parameters on Anode Properties","year":2015,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Chicoutimi","keywords":"Multivariable calculus; Artificial neural network; Linear regression; Partial least squares regression; Regression; Mathematics; Statistics; Regression analysis; Square (algebra); Biological system; Artificial intelligence; Computer science; Engineering; Biology; Control engineering","score_opus":0.11963180159726085,"score_gpt":0.37654603468406994,"score_spread":0.2569142330868091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248473500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6184759,0.0036016933,0.3659733,0.0002758361,0.00016841908,0.00018414123,0.0011793865,0.0015864607,0.008554923],"genre_scores_gemma":[0.93391055,0.0010008275,0.061782714,0.000045594963,0.000030316218,0.0001597669,0.0006659396,0.00011714899,0.0022870433],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877363,0.00064054015,0.00007827391,0.00014385149,0.0003184577,0.00004529083],"domain_scores_gemma":[0.99619484,0.0026217003,0.00026198744,0.00017099315,0.0007085591,0.000041849366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027155255,0.0007858269,0.00069337874,0.0017964758,0.00016996961,0.00061044743,0.00046483098,0.0003278677,0.0016744966],"category_scores_gemma":[0.0068508373,0.00014124572,0.00078247476,0.0017683483,0.0002060363,0.0006318854,0.00037597926,0.00046447993,0.0004019471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015635743,0.00079833943,0.08411674,0.0017348148,0.0013575586,0.00045206,0.00035324518,0.2206389,0.057500575,0.0033537988,0.0028886832,0.6252417],"study_design_scores_gemma":[0.000032651973,0.0008544136,0.06711789,0.000056079392,0.00044936434,0.00017909688,0.00020110438,0.8981055,0.027853161,0.0019938957,0.0030618366,0.000094952586],"about_ca_topic_score_codex":0.0018824734,"about_ca_topic_score_gemma":0.0033162928,"teacher_disagreement_score":0.0027155255,"about_ca_system_score_codex":0.00025241091,"about_ca_system_score_gemma":0.00043409865,"threshold_uncertainty_score":0.014361262},"labels":[],"label_agreement":null},{"id":"W4248611014","doi":"10.1109/tit.2018.2836451","title":"IEEE Transactions on Information Theory publication information","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.009859260653836669,"score_gpt":0.2267323195932553,"score_spread":0.21687305893941863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248611014","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075886752,0.03450608,0.37589628,0.028927293,0.052169286,0.00078925234,0.03879711,0.0064008934,0.45492515],"genre_scores_gemma":[0.11858974,0.06850063,0.090462565,0.0051360684,0.01373131,0.00089299027,0.056598403,0.0027871744,0.6433012],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99794954,0.00033785796,0.000169827,0.00021402955,0.0011793278,0.000149413],"domain_scores_gemma":[0.9937953,0.0020618648,0.00021209495,0.0015606398,0.002160756,0.00020940119],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021437379,0.0013837162,0.0021563147,0.0039232336,0.0009921466,0.0045337295,0.0012955151,0.0022782085,0.23127455],"category_scores_gemma":[0.012982578,0.00062062254,0.0008206204,0.0050654747,0.0008102244,0.0045962213,0.0018308015,0.0027652583,0.098597996],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001775029,0.00013199805,0.0006146807,0.000867521,0.00005735344,0.0001520026,0.00004537619,0.0031645668,0.0010952258,0.06849876,0.5728305,0.35236454],"study_design_scores_gemma":[0.00007642604,0.00010941409,0.001258478,0.0008973995,0.00015124984,0.00054662244,0.00010854275,0.034255814,0.0032187246,0.18546115,0.7738345,0.00008166401],"about_ca_topic_score_codex":0.0014729707,"about_ca_topic_score_gemma":0.0012031312,"teacher_disagreement_score":0.76872545,"about_ca_system_score_codex":0.0010227711,"about_ca_system_score_gemma":0.0023775187,"threshold_uncertainty_score":0.77369046},"labels":[],"label_agreement":null},{"id":"W4248743600","doi":"10.32920/ryerson.14649354","title":"Chaotic time series forecasting with residual analysis using synergy of ensemble neural networks and Taguchi's design of experiments","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Chaotic; Residual; Artificial neural network; Computer science; Series (stratigraphy); Embedding; Time series; Phase space; Algorithm; Artificial intelligence; Machine learning","score_opus":0.049014708877945734,"score_gpt":0.2585273144339447,"score_spread":0.209512605555999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248743600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13460317,0.00046403368,0.8621197,0.00005153177,0.000099096185,0.0010235019,0.00005634303,0.00028199662,0.0013007509],"genre_scores_gemma":[0.4873596,0.00043013535,0.50944626,0.000034229484,0.000019569065,0.0022104,0.00006301569,0.000022051288,0.000414759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99626356,0.0018216104,0.00035937518,0.00045480474,0.00091850705,0.00018214392],"domain_scores_gemma":[0.99676526,0.0019302876,0.00036954015,0.00034288774,0.0005340101,0.00005802535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008875763,0.0012155998,0.0015735907,0.0011152852,0.00046605527,0.0008692482,0.0010468154,0.0008712549,0.00072752923],"category_scores_gemma":[0.0067080907,0.00051412726,0.0016102539,0.0010067506,0.000531245,0.0007874618,0.0005522502,0.0009786143,0.00007680463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012035279,0.0010965386,0.0058343993,0.0014795432,0.00048876967,0.00012809527,0.00043798806,0.64058065,0.119340725,0.01019134,0.00031937353,0.21889904],"study_design_scores_gemma":[0.000114854025,0.0026008554,0.0029023937,0.000055549106,0.0002482634,0.000035862256,0.00007196518,0.9218667,0.06842422,0.0024190675,0.0011804753,0.00007970622],"about_ca_topic_score_codex":0.0013880731,"about_ca_topic_score_gemma":0.0014269272,"teacher_disagreement_score":0.008875763,"about_ca_system_score_codex":0.000806404,"about_ca_system_score_gemma":0.0010010836,"threshold_uncertainty_score":0.04694003},"labels":[],"label_agreement":null},{"id":"W4248786075","doi":"10.1109/tit.2013.2272495","title":"IEEE Transactions on Information Theory publication information","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Computer science; Information theory; Mathematics; Statistics","score_opus":0.008243812519496796,"score_gpt":0.21042254786751838,"score_spread":0.20217873534802158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248786075","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0079091275,0.03755039,0.42466298,0.025495855,0.042800706,0.00075192185,0.03140446,0.005755368,0.4236692],"genre_scores_gemma":[0.13355762,0.07424585,0.10541926,0.0047936672,0.012029835,0.0009198664,0.048963323,0.00227613,0.6177945],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803704,0.00033869187,0.00016374774,0.00019873674,0.001125958,0.00013587455],"domain_scores_gemma":[0.9945709,0.0017822967,0.00019389545,0.0014115663,0.0018734253,0.00016799562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020160223,0.0013117128,0.0020686707,0.0038707089,0.0009730602,0.004412817,0.0011944221,0.0021146517,0.1867627],"category_scores_gemma":[0.01151466,0.0005862424,0.00079467165,0.0051745386,0.0008250657,0.0045223697,0.0017605267,0.0025047946,0.07623412],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017339068,0.0001256474,0.00070909905,0.00087392353,0.00006486732,0.00016038366,0.000052595475,0.0034882312,0.0011181703,0.081756964,0.5115632,0.3999136],"study_design_scores_gemma":[0.0000704554,0.0001089552,0.0013712166,0.000903883,0.00017071984,0.0005743178,0.000120662466,0.037477564,0.003340284,0.21059091,0.7451893,0.00008171901],"about_ca_topic_score_codex":0.0014628328,"about_ca_topic_score_gemma":0.0012368626,"teacher_disagreement_score":0.1867627,"about_ca_system_score_codex":0.0010147836,"about_ca_system_score_gemma":0.0021953448,"threshold_uncertainty_score":0.6247835},"labels":[],"label_agreement":null},{"id":"W4248831422","doi":"10.21608/mjeer.2020.22756.1003","title":"Applying Hierarchal Clusters on Deep Reinforcement Learning Controlled Traffic Network","year":2020,"lang":"en","type":"article","venue":"Menoufia Journal of Electronic Engineering Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Reinforcement; Computer science; Artificial intelligence; Engineering; Structural engineering","score_opus":0.025028092571991387,"score_gpt":0.2800890059837788,"score_spread":0.2550609134117874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248831422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32756856,0.00033077493,0.66447335,0.00045465791,0.00011408224,0.00011880203,0.00011816947,0.0012263699,0.0055951853],"genre_scores_gemma":[0.9764033,0.000045992703,0.022125442,0.0000492342,0.000009890791,0.000032389766,0.00006304282,0.00001977616,0.0012508881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972576,0.00009052817,0.000011373445,0.00006810024,0.000040713596,0.00006355418],"domain_scores_gemma":[0.9993356,0.00027149988,0.000079776146,0.00007251284,0.000178423,0.00006209496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008417688,0.00052185636,0.00064835907,0.00051063305,0.00044808598,0.00054166117,0.001093868,0.00069075543,0.0013135787],"category_scores_gemma":[0.001695273,0.00030634704,0.00038733968,0.00036575715,0.0006250875,0.0005898738,0.000852105,0.0007554603,0.00014099221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024055957,0.000021877333,0.00030870747,0.0000075125704,0.000009045554,0.000013353069,0.000012626985,0.9908433,0.00036523287,0.0011209161,0.00021345686,0.0070599318],"study_design_scores_gemma":[0.000001000463,0.0000073028064,0.000030306526,3.9495922e-7,7.4308264e-7,7.8901377e-7,0.0000018007802,0.99955255,0.000054775235,0.00032668866,0.000022740594,7.673487e-7],"about_ca_topic_score_codex":0.025417808,"about_ca_topic_score_gemma":0.01975308,"teacher_disagreement_score":0.025417808,"about_ca_system_score_codex":0.0015768717,"about_ca_system_score_gemma":0.0009350228,"threshold_uncertainty_score":0.050539732},"labels":[],"label_agreement":null},{"id":"W4248855137","doi":"10.1109/tnnls.2019.2960913","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Syracuse University; Tencent; Euskal Herriko Unibertsitatea; University of Birmingham; College of Engineering, Michigan State University; University of Manitoba; Arizona State University","keywords":"Artificial neural network; Computer science; Data science; Artificial intelligence","score_opus":0.015987716786376855,"score_gpt":0.21670722049405713,"score_spread":0.20071950370768027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248855137","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00857617,0.076698765,0.3297187,0.01450642,0.10285747,0.00055758224,0.013103406,0.0056672622,0.44831422],"genre_scores_gemma":[0.047084533,0.059917737,0.05860546,0.0020181378,0.007792425,0.00033739046,0.017914077,0.0014763336,0.8048538],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927086,0.00006628704,0.000060309354,0.00006927319,0.0004806266,0.000052675143],"domain_scores_gemma":[0.99844545,0.00032881438,0.000054449454,0.0002518337,0.0008327406,0.00008680569],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010211344,0.0008695141,0.0014611343,0.0016655496,0.0005119804,0.0022717533,0.00094406976,0.0014729509,0.17235105],"category_scores_gemma":[0.0033839007,0.00036509839,0.0004657575,0.0019373373,0.00044072914,0.0021492725,0.0010930961,0.0015128239,0.070227936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000995476,0.00008659508,0.0004888837,0.0005155857,0.000030311987,0.00008042995,0.000023498107,0.003110216,0.0016169616,0.014257373,0.49972692,0.47996363],"study_design_scores_gemma":[0.000027552758,0.00007821224,0.0014004648,0.00042337668,0.00006316978,0.00029664894,0.000045767218,0.028939093,0.0023299237,0.029093038,0.93726623,0.000036443595],"about_ca_topic_score_codex":0.0019519103,"about_ca_topic_score_gemma":0.0031764808,"teacher_disagreement_score":0.82764894,"about_ca_system_score_codex":0.00050869223,"about_ca_system_score_gemma":0.0016292133,"threshold_uncertainty_score":0.5765717},"labels":[],"label_agreement":null},{"id":"W4248887991","doi":"10.1002/asmb.506","title":"N. E. Huang <i>et al</i>. Applications of Hilbert–Huang transform to non‐stationary financial time series analysis. <i>Applied Stochastic Models in Business and Industry</i> 2003; <b>19</b>(3): 245–268.","year":2003,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Series (stratigraphy); Time series; Econometrics; Hilbert–Huang transform; Computer science; Mathematics; Economics; Statistics; White noise","score_opus":0.014947157388137972,"score_gpt":0.23416620905267882,"score_spread":0.21921905166454084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248887991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025533885,0.06450758,0.7977853,0.039577384,0.029936975,0.000084333406,0.0011641224,0.0006300617,0.040780272],"genre_scores_gemma":[0.48772588,0.1260165,0.22962146,0.006868597,0.01430157,0.00015583857,0.002202061,0.0006315054,0.13247666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995272,0.000085852844,0.000029020563,0.00006931965,0.00025783954,0.000030742664],"domain_scores_gemma":[0.9985946,0.0006461845,0.00006488745,0.00007974589,0.0005746429,0.00004003876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011075494,0.0003965884,0.00046343863,0.00077303307,0.00033079388,0.0013635746,0.0006517184,0.0008292909,0.0056211557],"category_scores_gemma":[0.005986964,0.00017219532,0.0003210063,0.0011319881,0.0006899925,0.0016096092,0.0006011887,0.0013839343,0.0020359105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031298804,0.000064172455,0.0024817938,0.0007399082,0.000089273155,0.00085881166,0.0003363631,0.010324561,0.010019371,0.18431015,0.22892256,0.56154007],"study_design_scores_gemma":[0.0000612993,0.00016237953,0.008982314,0.00022167296,0.00014996783,0.0024148177,0.0003624625,0.19961315,0.03768674,0.26324025,0.4868335,0.00027131502],"about_ca_topic_score_codex":0.0021859019,"about_ca_topic_score_gemma":0.00358412,"teacher_disagreement_score":0.0056211557,"about_ca_system_score_codex":0.00054815575,"about_ca_system_score_gemma":0.0005764472,"threshold_uncertainty_score":0.01880467},"labels":[],"label_agreement":null},{"id":"W4249128234","doi":"10.32920/ryerson.14647062","title":"Study of estimation of distribution algorithms applied to neuroevolution","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Neuroevolution; Computer science; Algorithm; Estimation of distribution algorithm; Network topology; Bitwise operation; Modularity (biology); Encoding (memory); Artificial neural network; Coding (social sciences); Operator (biology); Topology (electrical circuits); Artificial intelligence; Mathematics","score_opus":0.025316484074031795,"score_gpt":0.2822057598110577,"score_spread":0.2568892757370259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249128234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014452347,0.0003409212,0.9825455,0.0002762057,0.000029432118,0.000042330703,0.000008956347,0.00010394456,0.0022003313],"genre_scores_gemma":[0.56243813,0.0007360617,0.43195173,0.0002599384,0.00014516155,0.000454654,0.00008276841,0.00025384926,0.0036778192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782336,0.0013901292,0.00006833006,0.00022675496,0.00038978975,0.00010175539],"domain_scores_gemma":[0.9801456,0.01715993,0.00074862404,0.0006845086,0.0010032946,0.00025802638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007353027,0.0009793288,0.0010711413,0.0012757939,0.000647922,0.0014425926,0.0014107414,0.0014812717,0.001971996],"category_scores_gemma":[0.036574617,0.00067721587,0.000781728,0.0007224848,0.0020704926,0.0022815545,0.002076903,0.0022080368,0.00023601788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043222146,0.00004738543,0.0017652268,0.00010278478,0.00007411795,0.000044124336,0.00015504299,0.7714873,0.0012059322,0.17215101,0.00060199253,0.052321848],"study_design_scores_gemma":[0.000006734347,0.000018971283,0.00007712537,0.0000128063375,0.000003635039,0.000011645733,0.00000608967,0.9823715,0.00037813987,0.016751403,0.00035776786,0.0000041736225],"about_ca_topic_score_codex":0.0020931102,"about_ca_topic_score_gemma":0.0013097186,"teacher_disagreement_score":0.007353027,"about_ca_system_score_codex":0.0019139688,"about_ca_system_score_gemma":0.0011903691,"threshold_uncertainty_score":0.038886964},"labels":[],"label_agreement":null},{"id":"W4249565363","doi":"10.32920/ryerson.14650011","title":"Effects of Parameter Change on Neural Network and Deep Neural Network","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Noise (video); Computation; Computer science; Gaussian noise; Decision boundary; Simple (philosophy); Artificial intelligence; MATLAB; Time delay neural network; Algorithm; Stochastic neural network; Boundary (topology); Gaussian; Pattern recognition (psychology); Mathematics; Support vector machine; Physics","score_opus":0.026626199407397536,"score_gpt":0.2542359264108689,"score_spread":0.22760972700347137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249565363","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8332198,0.010670171,0.12614828,0.004089965,0.0027942802,0.0003139714,0.0018384773,0.0030932236,0.01783191],"genre_scores_gemma":[0.9745067,0.0011186936,0.020050513,0.00046627352,0.00009313397,0.00014276351,0.0004736978,0.0006293057,0.0025189614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997449,0.0006759566,0.00031466334,0.00067631283,0.00059153244,0.00029256294],"domain_scores_gemma":[0.9738435,0.019997295,0.0008960858,0.0029666831,0.001886716,0.00040965315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025392445,0.0012971602,0.0007689221,0.0008261711,0.0005546403,0.001502146,0.0011992349,0.001997204,0.005603858],"category_scores_gemma":[0.054527033,0.0004703807,0.0006389831,0.0011099047,0.0014122718,0.0031701473,0.0014424992,0.0026123433,0.00082759204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033711544,0.00073111657,0.011402261,0.0016538134,0.00041981632,0.001455466,0.0006485768,0.7095105,0.075616464,0.012239882,0.0063290517,0.17662193],"study_design_scores_gemma":[0.00034432596,0.0032432128,0.019948227,0.0006178557,0.00064966094,0.0017152898,0.001255101,0.66071635,0.25477037,0.025261896,0.031132469,0.00034523607],"about_ca_topic_score_codex":0.0029937853,"about_ca_topic_score_gemma":0.0020342497,"teacher_disagreement_score":0.005603858,"about_ca_system_score_codex":0.0009653033,"about_ca_system_score_gemma":0.0005292378,"threshold_uncertainty_score":0.018746734},"labels":[],"label_agreement":null},{"id":"W4249604879","doi":"10.1139/cjp-79-5-857","title":"On an application of a generalization of the discrete Fourier transform to short time series","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Physics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Physics; Generalization; Series (stratigraphy); Fourier transform; Discrete-time Fourier transform; Discrete Fourier transform (general); Non-uniform discrete Fourier transform; Fourier series; Short-time Fourier transform; Mathematical analysis; Fourier analysis; Statistical physics; Quantum mechanics","score_opus":0.009737464074271042,"score_gpt":0.2257762933187581,"score_spread":0.21603882924448706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249604879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020811167,0.0018445778,0.953833,0.0012638943,0.0010459302,0.00004008774,0.00008452707,0.00015412862,0.020922806],"genre_scores_gemma":[0.45824566,0.010800069,0.47437894,0.0014138458,0.0034627118,0.00014604625,0.0003786528,0.0005227218,0.050651312],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996495,0.00011274754,0.000026683345,0.00006202241,0.000121581164,0.000027494185],"domain_scores_gemma":[0.9984036,0.0010393586,0.0000796071,0.00020314353,0.00020413827,0.00007011287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013137608,0.00062465004,0.00079038815,0.0011210308,0.0006269123,0.001028423,0.0010059424,0.0010556248,0.004905406],"category_scores_gemma":[0.006204043,0.00020859172,0.0012728942,0.0012903805,0.0013541972,0.002762343,0.001716734,0.0017235375,0.00071389705],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087702785,0.00007663075,0.0004536302,0.00018113785,0.000057944184,0.00047199917,0.0002473845,0.037956357,0.0086033745,0.854419,0.0043657054,0.09307916],"study_design_scores_gemma":[0.000015479525,0.00006375438,0.00054009276,0.000033101653,0.000034818368,0.0005073477,0.00007849405,0.456298,0.0015990001,0.5256651,0.0151362,0.000028696968],"about_ca_topic_score_codex":0.0018009173,"about_ca_topic_score_gemma":0.0019909514,"teacher_disagreement_score":0.004905406,"about_ca_system_score_codex":0.0004052731,"about_ca_system_score_gemma":0.00042700942,"threshold_uncertainty_score":0.016410232},"labels":[],"label_agreement":null},{"id":"W4249808116","doi":"10.32920/ryerson.14665509.v1","title":"Analysis of psychometric data using statistical and machine learning methods","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Categorical variable; Binomial regression; Regression analysis; Linear regression; Regression diagnostic; Logistic regression; Statistics; Computer science; Proper linear model; Regression; Statistical model; Econometrics; Machine learning; Artificial intelligence; Mathematics; Polynomial regression","score_opus":0.21321749006583748,"score_gpt":0.4465798290900272,"score_spread":0.2333623390241897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249808116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043641668,0.0011563126,0.948119,0.00089024846,0.0001526955,0.0004984919,0.0017915017,0.0010469632,0.0027030136],"genre_scores_gemma":[0.37439802,0.0020292029,0.61605346,0.00032882506,0.00029653247,0.0018959517,0.0033882337,0.00024655095,0.0013631871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9784889,0.013721133,0.001535257,0.0014292739,0.0044906526,0.00033481963],"domain_scores_gemma":[0.8859264,0.09231592,0.0071472013,0.0071773245,0.0069656656,0.00046747475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02183436,0.0013226194,0.0016702279,0.007929647,0.0005883387,0.003023855,0.0012089205,0.001024978,0.0032402186],"category_scores_gemma":[0.10397999,0.00040912553,0.0016899487,0.008468859,0.0012186,0.0025783645,0.001357088,0.0025135595,0.0012760777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000272277,0.000722922,0.10149822,0.0015808411,0.0013586741,0.0004624252,0.0010323934,0.11185772,0.002863343,0.033724137,0.007619135,0.7370079],"study_design_scores_gemma":[0.00007259983,0.000631926,0.081588596,0.00076997746,0.00023505594,0.00051691546,0.0016185377,0.8030232,0.004097336,0.0940247,0.013236766,0.00018433676],"about_ca_topic_score_codex":0.0019449643,"about_ca_topic_score_gemma":0.0012038362,"teacher_disagreement_score":0.02183436,"about_ca_system_score_codex":0.0008888782,"about_ca_system_score_gemma":0.0016929875,"threshold_uncertainty_score":0.115472496},"labels":[],"label_agreement":null},{"id":"W4250057196","doi":"10.32920/ryerson.14663658","title":"Convolutional neural network for image classification based on transfer learning technique","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Transfer of learning; Artificial neural network; Deep learning; Contextual image classification; Machine learning; Interface (matter); Network architecture; MATLAB; Time delay neural network; Pattern recognition (psychology); Image (mathematics)","score_opus":0.03467951178793252,"score_gpt":0.2771902292258709,"score_spread":0.24251071743793837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250057196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004064761,0.0010243229,0.98892283,0.00019783573,0.00012414414,0.000049804414,0.00010264778,0.0014198069,0.0040938156],"genre_scores_gemma":[0.2729371,0.0028337399,0.7039545,0.00027275912,0.0002505846,0.00029407407,0.0007992916,0.0005317607,0.018126171],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960655,0.00005621408,0.00002397285,0.0000934717,0.00017616333,0.00004371486],"domain_scores_gemma":[0.99968946,0.000098753386,0.000031250274,0.00007086371,0.00009378824,0.000015975407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006017181,0.00070914207,0.00058206473,0.0008514066,0.0002981983,0.0007925991,0.0010811259,0.0009431666,0.0045510386],"category_scores_gemma":[0.0016185812,0.00029307316,0.0007992058,0.0013577226,0.00066238706,0.0016417322,0.0009517895,0.0019345834,0.002767626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012691265,0.00011016829,0.00073198724,0.00040859764,0.00013843036,0.00021833999,0.00010376957,0.26577097,0.041477665,0.10434285,0.013690479,0.5728799],"study_design_scores_gemma":[0.0000050082285,0.000026128118,0.0003378413,0.000023898847,0.00001471673,0.00009347349,0.000007013481,0.9567667,0.010397386,0.023119206,0.009194064,0.000014497928],"about_ca_topic_score_codex":0.003008518,"about_ca_topic_score_gemma":0.0025179333,"teacher_disagreement_score":0.0045510386,"about_ca_system_score_codex":0.00099783,"about_ca_system_score_gemma":0.0006973356,"threshold_uncertainty_score":0.015224814},"labels":[],"label_agreement":null},{"id":"W4250117935","doi":"10.32920/ryerson.14663880","title":"Optimally weighted local discriminant bases : theory and applications in statistical signal and image processing","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Discriminant; Computer science; Feature extraction; Pattern recognition (psychology); Wavelet; Dimension (graph theory); Artificial intelligence; Feature (linguistics); Noise (video); Wavelet packet decomposition; Linear discriminant analysis; Basis (linear algebra); Algorithm; Wavelet transform; Mathematics; Image (mathematics)","score_opus":0.011938082972056176,"score_gpt":0.26953465204181715,"score_spread":0.25759656906976097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250117935","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031788768,0.0030277867,0.9916164,0.00019572664,0.00007678475,0.000015523074,0.000036099045,0.000083396684,0.0017694406],"genre_scores_gemma":[0.16426624,0.013142277,0.8132627,0.0002693799,0.00051235285,0.00022726243,0.0002806504,0.0001899776,0.007849106],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994516,0.00015756271,0.000032407912,0.00010185807,0.00022207235,0.00003453083],"domain_scores_gemma":[0.9993241,0.00033941952,0.00007255967,0.00007564573,0.00016670121,0.00002166712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097035686,0.00066024734,0.00086919643,0.0011389073,0.00030911187,0.001287682,0.00065101875,0.0008360748,0.001746771],"category_scores_gemma":[0.002454488,0.0003115171,0.0005882504,0.0021956896,0.00092145556,0.0011839957,0.0009572374,0.0016694791,0.0010544037],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008674057,0.0000643157,0.0008971239,0.0005499338,0.00008566537,0.0001259908,0.00016814665,0.13689642,0.022712635,0.30605072,0.005800252,0.52656204],"study_design_scores_gemma":[0.00001244964,0.000096640935,0.0006829359,0.000092691356,0.000029039846,0.00024624667,0.00005334236,0.81731707,0.0071741664,0.1531262,0.021125907,0.0000432988],"about_ca_topic_score_codex":0.0007691958,"about_ca_topic_score_gemma":0.0005731018,"teacher_disagreement_score":0.001746771,"about_ca_system_score_codex":0.0005368318,"about_ca_system_score_gemma":0.00052944466,"threshold_uncertainty_score":0.00584352},"labels":[],"label_agreement":null},{"id":"W4250450916","doi":"10.1109/tnnls.2020.2982918","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Euskal Herriko Unibertsitatea; University of Birmingham; Syracuse University; Tencent; Institute of Electrical and Electronics Engineers; College of Engineering, Michigan State University; University of Manitoba; Arizona State University","keywords":"Computer science; Artificial neural network; Data science; Artificial intelligence","score_opus":0.015987716786376855,"score_gpt":0.21670722049405713,"score_spread":0.20071950370768027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250450916","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00857617,0.076698765,0.3297187,0.01450642,0.10285747,0.00055758224,0.013103406,0.0056672622,0.44831422],"genre_scores_gemma":[0.047084533,0.059917737,0.05860546,0.0020181378,0.007792425,0.00033739046,0.017914077,0.0014763336,0.8048538],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927086,0.00006628704,0.000060309354,0.00006927319,0.0004806266,0.000052675143],"domain_scores_gemma":[0.99844545,0.00032881438,0.000054449454,0.0002518337,0.0008327406,0.00008680569],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010211344,0.0008695141,0.0014611343,0.0016655496,0.0005119804,0.0022717533,0.00094406976,0.0014729509,0.17235105],"category_scores_gemma":[0.0033839007,0.00036509839,0.0004657575,0.0019373373,0.00044072914,0.0021492725,0.0010930961,0.0015128239,0.070227936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000995476,0.00008659508,0.0004888837,0.0005155857,0.000030311987,0.00008042995,0.000023498107,0.003110216,0.0016169616,0.014257373,0.49972692,0.47996363],"study_design_scores_gemma":[0.000027552758,0.00007821224,0.0014004648,0.00042337668,0.00006316978,0.00029664894,0.000045767218,0.028939093,0.0023299237,0.029093038,0.93726623,0.000036443595],"about_ca_topic_score_codex":0.0019519103,"about_ca_topic_score_gemma":0.0031764808,"teacher_disagreement_score":0.82764894,"about_ca_system_score_codex":0.00050869223,"about_ca_system_score_gemma":0.0016292133,"threshold_uncertainty_score":0.5765717},"labels":[],"label_agreement":null},{"id":"W4250965548","doi":"10.1109/tit.2019.2926694","title":"IEEE Transactions on Information Theory publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.007383270973087514,"score_gpt":0.21237916444984037,"score_spread":0.20499589347675284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250965548","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007685056,0.040465705,0.41808102,0.02506351,0.04612417,0.000768203,0.030053955,0.005549991,0.42620844],"genre_scores_gemma":[0.122801326,0.07575902,0.10311073,0.0047494355,0.012413274,0.0009117524,0.048232794,0.0021712712,0.6298504],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980856,0.0003394827,0.00015933822,0.00018997153,0.0010984318,0.00012714314],"domain_scores_gemma":[0.99481934,0.0017338797,0.00018284685,0.0012878221,0.0018059026,0.00017020266],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002010765,0.0012870186,0.002069679,0.0038233974,0.0009367815,0.0043012095,0.001149016,0.0020608127,0.18499774],"category_scores_gemma":[0.01094879,0.00057128386,0.000768873,0.0049888906,0.0008021027,0.004122502,0.0017218122,0.002442378,0.075375594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016672397,0.00012354781,0.0006889728,0.00089499366,0.0000646775,0.00014958144,0.00005247238,0.0033523254,0.0011527906,0.077194825,0.51723707,0.398922],"study_design_scores_gemma":[0.00006777776,0.00010850995,0.0013717826,0.00090496,0.00015986768,0.0005224925,0.00011307768,0.035070773,0.0030674974,0.18424289,0.77429456,0.00007579623],"about_ca_topic_score_codex":0.0014958134,"about_ca_topic_score_gemma":0.0012613396,"teacher_disagreement_score":0.81500226,"about_ca_system_score_codex":0.000997073,"about_ca_system_score_gemma":0.0021575359,"threshold_uncertainty_score":0.6188791},"labels":[],"label_agreement":null},{"id":"W4251080075","doi":"10.32920/ryerson.14656347","title":"Reinforcement learning using associative memory networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reinforcement learning; Computer science; Hebbian theory; Content-addressable memory; Leabra; Artificial neural network; Artificial intelligence; Bidirectional associative memory; Associative property; Associative learning; Machine learning; Mathematics","score_opus":0.033018703433173674,"score_gpt":0.27884994781943523,"score_spread":0.24583124438626155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251080075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04870537,0.00050760334,0.939556,0.0006664504,0.00010345745,0.000038343525,0.000044173215,0.00026716007,0.010111441],"genre_scores_gemma":[0.9416854,0.0004838206,0.051304754,0.00014290228,0.00007502064,0.00011320521,0.000042867905,0.000033781867,0.00611829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996691,0.00013716011,0.000018141922,0.00006764485,0.00006669266,0.00004127128],"domain_scores_gemma":[0.99869114,0.0008744705,0.00012723009,0.00010307112,0.00014103288,0.00006302607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079011364,0.0004859512,0.0006114187,0.00030817144,0.0003028186,0.0009263372,0.00091652723,0.0008366422,0.003138341],"category_scores_gemma":[0.0038959808,0.00023637098,0.00044389055,0.00036670786,0.0011946873,0.0012723518,0.0009284287,0.0012234342,0.00030279928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049249942,0.000044607354,0.0005359694,0.00005272271,0.000048498277,0.000098300705,0.000057851197,0.8285791,0.0012640817,0.13776578,0.00081038394,0.030693335],"study_design_scores_gemma":[0.000007919413,0.000013465158,0.000043299744,0.0000035609162,0.000004932244,0.000011943506,0.0000029701425,0.9528204,0.00021428807,0.046498172,0.0003756342,0.0000034634395],"about_ca_topic_score_codex":0.0025575599,"about_ca_topic_score_gemma":0.0018279003,"teacher_disagreement_score":0.003138341,"about_ca_system_score_codex":0.0007427899,"about_ca_system_score_gemma":0.0005819527,"threshold_uncertainty_score":0.010498762},"labels":[],"label_agreement":null},{"id":"W4251132810","doi":"10.1007/978-1-4614-6170-8_100249","title":"Information Fusion","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Information fusion; Computer science; Fusion; Artificial intelligence; Linguistics; Philosophy","score_opus":0.011024015379012564,"score_gpt":0.20323514709499751,"score_spread":0.19221113171598495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251132810","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030521401,0.021080224,0.71879417,0.0018624231,0.0025416643,0.00013631562,0.0006916119,0.0022860237,0.24955553],"genre_scores_gemma":[0.16342837,0.031370305,0.3689446,0.0022020366,0.0027441178,0.00032000965,0.0035787264,0.0010472253,0.42636457],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993345,0.00006828152,0.000035324803,0.00015261398,0.00036691426,0.000042308104],"domain_scores_gemma":[0.99955076,0.000111176414,0.000021946284,0.00013757616,0.00015921338,0.000019344083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008126256,0.0009809294,0.0009795957,0.0018514477,0.00062189973,0.002099555,0.0012166377,0.001374779,0.030385626],"category_scores_gemma":[0.0015922079,0.00041888672,0.0006948754,0.0020949456,0.0010070072,0.0032622046,0.0021692624,0.0014628405,0.01874779],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006477657,0.00004034512,0.000105411935,0.00036137208,0.0000666324,0.00006891072,0.0000748936,0.006203891,0.009631881,0.124117,0.060777858,0.79848707],"study_design_scores_gemma":[0.000016469952,0.00011707488,0.0007322821,0.00029547987,0.00010123922,0.0006991364,0.000103360304,0.065840155,0.040440228,0.3214397,0.5701168,0.0000980998],"about_ca_topic_score_codex":0.0006628617,"about_ca_topic_score_gemma":0.0006124726,"teacher_disagreement_score":0.030385626,"about_ca_system_score_codex":0.00069559435,"about_ca_system_score_gemma":0.00059105427,"threshold_uncertainty_score":0.10165},"labels":[],"label_agreement":null},{"id":"W4251355121","doi":"10.21611/qirt.2010.048","title":"An Implicit Finite-Difference model for Thermal Processes Simulation and Surveys","year":2010,"lang":"en","type":"article","venue":"Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Finite difference method; Thermal; Simulation; Mathematics; Meteorology; Physics; Mathematical analysis","score_opus":0.0633533015733858,"score_gpt":0.33122103374150125,"score_spread":0.26786773216811544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251355121","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005090894,0.00032462343,0.99041593,0.00013551781,0.000079638776,0.000043991156,0.00011013328,0.00018254742,0.0036166974],"genre_scores_gemma":[0.3948956,0.0014199414,0.5814309,0.00022334939,0.00010351907,0.0008934222,0.0005532446,0.00031161652,0.020168412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997745,0.00006580961,0.000012627036,0.000027072472,0.0001002939,0.000019619567],"domain_scores_gemma":[0.9996942,0.00014090256,0.000023940564,0.00003714014,0.00008207088,0.000021653843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046963117,0.0004249087,0.0006986993,0.00035110756,0.0004561202,0.00065055327,0.0015338236,0.0011418256,0.002656112],"category_scores_gemma":[0.0013162595,0.00032938877,0.0006350018,0.0006223898,0.000498682,0.0007919284,0.00066203054,0.0013188112,0.00070471514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030898893,0.000038891147,0.000348207,0.00009029081,0.000014557362,0.000054536224,0.000063311236,0.897504,0.0076214885,0.07513217,0.0009204618,0.01818128],"study_design_scores_gemma":[0.000003404554,0.00000658608,0.000027953638,0.0000033443953,0.0000016343394,0.000008971576,0.000002442834,0.99426997,0.00037337458,0.003572411,0.0017260854,0.0000037922923],"about_ca_topic_score_codex":0.0044642095,"about_ca_topic_score_gemma":0.0034759422,"teacher_disagreement_score":0.0044642095,"about_ca_system_score_codex":0.0006657385,"about_ca_system_score_gemma":0.0011198967,"threshold_uncertainty_score":0.008885562},"labels":[],"label_agreement":null},{"id":"W4251392571","doi":"10.1149/ma2018-03/5/275","title":"(Invited) Diffusion Map Analysis of Multi-Channel Time Series Data","year":2018,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Trajectory; Phase space; Series (stratigraphy); Curse of dimensionality; Dynamical systems theory; Position (finance); Channel (broadcasting); Time series; Computer science; Diffusion; Manifold (fluid mechanics); Algorithm; State space; Dynamical system (definition); Phase (matter); Mathematics; Physics; Artificial intelligence; Statistics","score_opus":0.03575708167319097,"score_gpt":0.27585745021351005,"score_spread":0.24010036854031908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251392571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054521784,0.00049749046,0.93916947,0.0005907256,0.00025407173,0.00008087348,0.00069516,0.001507646,0.0026828616],"genre_scores_gemma":[0.70494604,0.00093797134,0.28338784,0.00016516121,0.00024093046,0.0001340783,0.0008449489,0.00026119009,0.009081983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970645,0.000030720184,0.00001442696,0.000096783835,0.00011439804,0.000037230428],"domain_scores_gemma":[0.9995757,0.00012572613,0.000065612425,0.00006063859,0.00014093195,0.000031387834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050973415,0.00048669265,0.00036059864,0.0012986566,0.00034410524,0.0007334051,0.000499342,0.0006320815,0.002432988],"category_scores_gemma":[0.0022713004,0.00020905903,0.000658801,0.0011878688,0.00034706446,0.00097514415,0.0005801611,0.0008081152,0.00063693535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045909337,0.00017690953,0.011270472,0.0006194372,0.000284985,0.0016107757,0.0008489042,0.20244062,0.12515831,0.075854115,0.014090156,0.56718624],"study_design_scores_gemma":[0.000012142683,0.00005762095,0.006974972,0.000022132088,0.000024157132,0.0005532141,0.00012172069,0.9443688,0.015788088,0.020711593,0.011314038,0.000051470233],"about_ca_topic_score_codex":0.0029247496,"about_ca_topic_score_gemma":0.0016502646,"teacher_disagreement_score":0.0029247496,"about_ca_system_score_codex":0.00034904623,"about_ca_system_score_gemma":0.00038755868,"threshold_uncertainty_score":0.008139193},"labels":[],"label_agreement":null},{"id":"W4251773358","doi":"10.1007/978-1-4615-0377-4_11","title":"Data Fitting","year":2003,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TRIUMF","funders":"","keywords":"Extrapolation; Square (algebra); Chi-square test; Interpolation (computer graphics); Sample (material); Statistics; Class (philosophy); Computer science; Mathematics; Applied mathematics; Algorithm; Artificial intelligence; Physics","score_opus":0.08718023477483175,"score_gpt":0.2761007893029504,"score_spread":0.18892055452811865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251773358","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017085035,0.000870847,0.9505892,0.0004901561,0.00048760258,0.00022008027,0.0015914034,0.0074639386,0.036578327],"genre_scores_gemma":[0.04699233,0.0020876923,0.7844321,0.0008873233,0.00039324013,0.00061553007,0.010715438,0.004916489,0.1489599],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99881136,0.00014487117,0.00007460872,0.0004025,0.0005077013,0.000058930927],"domain_scores_gemma":[0.99831533,0.00036824023,0.000050007882,0.00073423074,0.00048319928,0.00004909249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017618296,0.0015136595,0.0013850064,0.0018146649,0.00086368935,0.0025716592,0.0031638667,0.0016205095,0.08192296],"category_scores_gemma":[0.0073158974,0.00078457553,0.0013516878,0.0025844933,0.00066874566,0.002910181,0.0029103996,0.0022133547,0.06551116],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011231132,0.00008400657,0.00043188452,0.00038523693,0.000051748088,0.0000918798,0.000096959586,0.017426103,0.0074637374,0.044159897,0.07612852,0.85356766],"study_design_scores_gemma":[0.000044648994,0.000089335335,0.00083839195,0.000255291,0.00006483522,0.00061584654,0.00020213253,0.3502897,0.04452525,0.11479128,0.4881924,0.00009095443],"about_ca_topic_score_codex":0.0015975183,"about_ca_topic_score_gemma":0.0013066051,"teacher_disagreement_score":0.08192296,"about_ca_system_score_codex":0.00060551305,"about_ca_system_score_gemma":0.0010430893,"threshold_uncertainty_score":0.2740596},"labels":[],"label_agreement":null},{"id":"W4251824869","doi":"10.32920/ryerson.14668260","title":"Audiovisual Emotion Recognition Using Entropy-estimation-based Multimodal Information Fusion","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Emotion recognition; Dimensionality reduction; Entropy (arrow of time); Feature (linguistics); Affective computing; Fusion; Machine learning; Support vector machine","score_opus":0.03181814441833854,"score_gpt":0.2791304743149105,"score_spread":0.24731232989657198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251824869","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033452537,0.0006919437,0.9631314,0.00015932167,0.00009129742,0.000070710164,0.0001280249,0.0007210816,0.0015535933],"genre_scores_gemma":[0.68610257,0.0012507288,0.30890575,0.00019102218,0.00020586283,0.00018157787,0.0006856191,0.0001437357,0.0023331707],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922407,0.00014713506,0.00005928663,0.00019622975,0.0002998608,0.00007348027],"domain_scores_gemma":[0.9994116,0.00018973726,0.000079977544,0.000062006824,0.0002274883,0.000029333447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091658003,0.0008925076,0.001031339,0.0018064427,0.00031806293,0.0010099582,0.0006566919,0.00049656385,0.0012410566],"category_scores_gemma":[0.0027596506,0.00022916036,0.0012240455,0.0010661723,0.00045260027,0.0016442853,0.0011871628,0.00069427304,0.00041526946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062263454,0.00015953885,0.0027598233,0.00022373369,0.00021306956,0.00021839215,0.00033490197,0.07194675,0.11140552,0.0067917,0.003145715,0.80217814],"study_design_scores_gemma":[0.00001617484,0.00011944296,0.004688431,0.000022732282,0.000107292944,0.00015087402,0.000095978765,0.956867,0.02980443,0.0062861815,0.0017868137,0.00005477547],"about_ca_topic_score_codex":0.0011820135,"about_ca_topic_score_gemma":0.0008010272,"teacher_disagreement_score":0.0018064427,"about_ca_system_score_codex":0.0005056797,"about_ca_system_score_gemma":0.00032575338,"threshold_uncertainty_score":0.0048474073},"labels":[],"label_agreement":null},{"id":"W4251962500","doi":"10.3410/f.1029473.345425","title":"Faculty Opinions recommendation of Encoding a temporally structured stimulus with a temporally structured neural representation.","year":2005,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Stimulus (psychology); Computer science; Psychology; Cognitive psychology","score_opus":0.03167930269712188,"score_gpt":0.34162841263537264,"score_spread":0.30994910993825076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251962500","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027119275,0.00027890483,0.0014059818,0.0006146539,0.0006639691,0.00024858981,0.98165303,0.003872768,0.008550146],"genre_scores_gemma":[0.0037772602,0.00011974875,0.0027354565,0.00014010532,0.000046867684,0.0001824798,0.98526055,0.00015452558,0.007582941],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992442,0.00012966743,0.000072239105,0.0002073414,0.00022573871,0.00012087579],"domain_scores_gemma":[0.99808383,0.0003197726,0.0000921241,0.0006439695,0.0006415871,0.0002188201],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0013104541,0.0030794104,0.0013149731,0.0021390296,0.0007267534,0.0026244973,0.0032986638,0.0025526006,0.08412853],"category_scores_gemma":[0.0076017347,0.00068174134,0.0017942445,0.0024578632,0.00040334195,0.0014497656,0.0014177503,0.0019176003,0.08435518],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017910196,0.00010232316,0.0006145831,0.00035977951,0.000036360027,0.000021895858,0.000008516401,0.00043148347,0.00041728804,0.0002382377,0.9870608,0.010529786],"study_design_scores_gemma":[0.00084020913,0.00018561789,0.008303825,0.0003070296,0.00014967239,0.00018278217,0.00011933252,0.013761708,0.007297038,0.0023434134,0.9664158,0.000093645176],"about_ca_topic_score_codex":0.025773764,"about_ca_topic_score_gemma":0.058020696,"teacher_disagreement_score":0.99868953,"about_ca_system_score_codex":0.0012360587,"about_ca_system_score_gemma":0.002517611,"threshold_uncertainty_score":0.281438},"labels":[],"label_agreement":null},{"id":"W4252157525","doi":"10.1007/978-1-4614-6170-8_100177","title":"Eigenvalue and Eigenvector = Characteristic Value and Characteristic Vector","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Eigenvalues and eigenvectors; Mathematics; Value (mathematics); Mathematical analysis; Physics; Statistics","score_opus":0.011371325589602342,"score_gpt":0.2033101347782734,"score_spread":0.19193880918867104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252157525","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041403254,0.023744125,0.44270787,0.0030194602,0.004018597,0.000055295037,0.00056390394,0.00134266,0.52040786],"genre_scores_gemma":[0.06424658,0.022447754,0.14822188,0.001038393,0.0031385773,0.00017972759,0.0007248333,0.0015077057,0.7584947],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997254,0.00003779829,0.000011488712,0.000068914196,0.0001405213,0.000015892054],"domain_scores_gemma":[0.99975795,0.000109191446,0.000019330535,0.000028599155,0.000071636816,0.000013375372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024623965,0.0009483904,0.0006663431,0.0013412475,0.00044748452,0.0021973806,0.00055162306,0.000756599,0.032652922],"category_scores_gemma":[0.00097340136,0.00029134226,0.00027687868,0.0020217106,0.0016153827,0.0027888638,0.00073967944,0.0013662995,0.01652414],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015474892,0.000015184498,0.00012759362,0.00029226462,0.000010447256,0.00004735412,0.00014078009,0.00202194,0.0021952128,0.50813156,0.10376618,0.38323602],"study_design_scores_gemma":[0.000004375104,0.000018078455,0.00038461445,0.00012913493,0.000008893112,0.00038110802,0.00008529399,0.00581022,0.0022629022,0.5455678,0.4453198,0.000027759306],"about_ca_topic_score_codex":0.00047406007,"about_ca_topic_score_gemma":0.0007850242,"teacher_disagreement_score":0.032652922,"about_ca_system_score_codex":0.000586736,"about_ca_system_score_gemma":0.00052865344,"threshold_uncertainty_score":0.10923487},"labels":[],"label_agreement":null},{"id":"W4252198796","doi":"10.20944/preprints201904.0076.v4","title":"Crystal Period Vectors under External Stress in Statistical Physics","year":2020,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University; Albert-Ludwigs-Universität Freiburg; Weizmann Institute of Science; Shanxi University; Northeast Normal University; Uniwersytet Marii Curie-Skłodowskiej; Temple University; University of Wisconsin-Madison; University of Southern California","keywords":"Period (music); Physics; Statistical physics; Crystal (programming language); Classical physics; Stress (linguistics); Harmonic; Quantum; Theoretical physics; Quantum mechanics; Computer science","score_opus":0.11642651467468725,"score_gpt":0.3466182661795589,"score_spread":0.23019175150487164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252198796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19550058,0.005279497,0.76011854,0.0018135174,0.0007571845,0.000061808154,0.00045847913,0.0002175537,0.035792872],"genre_scores_gemma":[0.8639652,0.006562828,0.10512888,0.0005524509,0.00095071195,0.00017836248,0.00047159084,0.00028036174,0.021909585],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997491,0.00006868566,0.00001356702,0.00006644246,0.00007008841,0.00003209726],"domain_scores_gemma":[0.9996482,0.00011859549,0.00005785563,0.000055476452,0.00008885763,0.00003101411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000737255,0.00035003707,0.00031264027,0.000753107,0.00047694714,0.0010874128,0.0006428861,0.0008822095,0.0020019533],"category_scores_gemma":[0.0019651495,0.0002664248,0.00041781674,0.0006964762,0.0021335566,0.002782335,0.00086255313,0.0013417944,0.00045466534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015985848,0.000008834591,0.00032059968,0.00006152309,0.0000063799243,0.000044907876,0.000094572264,0.020383565,0.004279986,0.9602824,0.0009098626,0.013591548],"study_design_scores_gemma":[0.0000055022633,0.000025032765,0.0006019242,0.00001726695,0.00000486034,0.000066308814,0.00003959911,0.13460925,0.0013825218,0.85945386,0.0037661996,0.00002781929],"about_ca_topic_score_codex":0.0008476873,"about_ca_topic_score_gemma":0.00053512165,"teacher_disagreement_score":0.0020019533,"about_ca_system_score_codex":0.0005422542,"about_ca_system_score_gemma":0.00048429894,"threshold_uncertainty_score":0.006697178},"labels":[],"label_agreement":null},{"id":"W4252222626","doi":"10.1561/1500000061","title":"An Introduction to Neural Information Retrieval","year":2018,"lang":"en","type":"article","venue":"Foundations and Trends® in Information Retrieval","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":185,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Information retrieval; Computer science","score_opus":0.011782348446887527,"score_gpt":0.2774988290535456,"score_spread":0.2657164806066581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252222626","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030552058,0.35609493,0.48276412,0.009927071,0.004173568,0.0002948803,0.0029567962,0.0020180354,0.13871539],"genre_scores_gemma":[0.063902065,0.4164096,0.3647512,0.0075260433,0.0110290665,0.0010057205,0.005128279,0.00055145053,0.12969665],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995184,0.00008407802,0.000060909544,0.00011522106,0.00018997103,0.00003145019],"domain_scores_gemma":[0.99935657,0.0003523553,0.00003417518,0.00007112077,0.00015894399,0.000026898852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006372951,0.0008031991,0.0006907333,0.0017141203,0.00038941248,0.0018895504,0.0012487546,0.0017740383,0.01766115],"category_scores_gemma":[0.00253508,0.0004814645,0.00062513695,0.0029890807,0.0007971985,0.004004165,0.00083672436,0.0023638648,0.009646631],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006146009,0.00012010736,0.0007616327,0.0025339199,0.000079986174,0.00023874463,0.00019521265,0.010879063,0.0032208539,0.26955518,0.1332736,0.5790802],"study_design_scores_gemma":[0.000010656618,0.00007946482,0.00068274495,0.0007113385,0.000030041074,0.0006145736,0.000041168758,0.019477826,0.0009428491,0.22359338,0.75376487,0.000051068557],"about_ca_topic_score_codex":0.0020362758,"about_ca_topic_score_gemma":0.00223346,"teacher_disagreement_score":0.01766115,"about_ca_system_score_codex":0.0011549923,"about_ca_system_score_gemma":0.0008561131,"threshold_uncertainty_score":0.05908245},"labels":[],"label_agreement":null},{"id":"W4252394700","doi":"10.1109/tnnls.2020.3009707","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pontifícia Universidade Católica do Rio de Janeiro; Syracuse University; Tencent; Euskal Herriko Unibertsitatea; University of Birmingham; College of Engineering, Michigan State University; University of Manitoba; Arizona State University","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.015987716786376855,"score_gpt":0.21670722049405713,"score_spread":0.20071950370768027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252394700","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0085761715,0.076698825,0.3297185,0.01450642,0.10285765,0.0005575825,0.013103406,0.00566726,0.44831422],"genre_scores_gemma":[0.04708451,0.059917793,0.058605425,0.0020181374,0.0077924347,0.00033739055,0.017914081,0.001476334,0.804854],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927086,0.00006628691,0.000060309238,0.00006927319,0.00048062616,0.000052675143],"domain_scores_gemma":[0.99844545,0.00032881377,0.0000544493,0.0002518332,0.0008327398,0.00008680569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010211334,0.0008695145,0.0014611329,0.0016655496,0.00051198015,0.0022717533,0.0009440689,0.0014729509,0.17235099],"category_scores_gemma":[0.0033838958,0.00036509873,0.00046575707,0.0019373352,0.0004407287,0.0021492683,0.0010930956,0.0015128229,0.070227936],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099547644,0.00008659512,0.000488883,0.00051558594,0.000030311943,0.00008042999,0.000023498118,0.0031102146,0.0016169639,0.014257364,0.49972716,0.4799634],"study_design_scores_gemma":[0.000027552762,0.000078212244,0.0014004629,0.00042337633,0.00006316967,0.00029664868,0.000045767134,0.028939035,0.0023299262,0.029093001,0.93726635,0.000036443565],"about_ca_topic_score_codex":0.0019519095,"about_ca_topic_score_gemma":0.0031764794,"teacher_disagreement_score":0.17235099,"about_ca_system_score_codex":0.00050869177,"about_ca_system_score_gemma":0.0016292125,"threshold_uncertainty_score":0.57657146},"labels":[],"label_agreement":null},{"id":"W4252414370","doi":"10.1109/tit.2019.2893007","title":"IEEE Transactions on Information Theory publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Information theory; Computer science; Information retrieval; Mathematics; Statistics","score_opus":0.007383270973087514,"score_gpt":0.21237916444984037,"score_spread":0.20499589347675284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252414370","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007685056,0.040465705,0.41808102,0.02506351,0.04612417,0.000768203,0.030053955,0.005549991,0.42620844],"genre_scores_gemma":[0.122801326,0.07575902,0.10311073,0.0047494355,0.012413274,0.0009117524,0.048232794,0.0021712712,0.6298504],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980856,0.0003394827,0.00015933822,0.00018997153,0.0010984318,0.00012714314],"domain_scores_gemma":[0.99481934,0.0017338797,0.00018284685,0.0012878221,0.0018059026,0.00017020266],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002010765,0.0012870186,0.002069679,0.0038233974,0.0009367815,0.0043012095,0.001149016,0.0020608127,0.18499774],"category_scores_gemma":[0.01094879,0.00057128386,0.000768873,0.0049888906,0.0008021027,0.004122502,0.0017218122,0.002442378,0.075375594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016672397,0.00012354781,0.0006889728,0.00089499366,0.0000646775,0.00014958144,0.00005247238,0.0033523254,0.0011527906,0.077194825,0.51723707,0.398922],"study_design_scores_gemma":[0.00006777776,0.00010850995,0.0013717826,0.00090496,0.00015986768,0.0005224925,0.00011307768,0.035070773,0.0030674974,0.18424289,0.77429456,0.00007579623],"about_ca_topic_score_codex":0.0014958134,"about_ca_topic_score_gemma":0.0012613396,"teacher_disagreement_score":0.81500226,"about_ca_system_score_codex":0.000997073,"about_ca_system_score_gemma":0.0021575359,"threshold_uncertainty_score":0.6188791},"labels":[],"label_agreement":null},{"id":"W4252449808","doi":"10.1016/s0129-0657(01)00055-2","title":"Stochastic Radial Basis Functions","year":2001,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Gaussian; Bernoulli's principle; Binary number; Transfer function; Basis (linear algebra); Function (biology); Mathematics; Bernoulli distribution; Radial basis function; Applied mathematics; Computer science; Algorithm; Artificial neural network; Control theory (sociology); Random variable; Statistics; Artificial intelligence; Control (management); Physics","score_opus":0.022738930733472655,"score_gpt":0.26634129567638726,"score_spread":0.2436023649429146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252449808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037418758,0.0009815531,0.98636985,0.00042260275,0.00016300123,0.000052639603,0.0002168347,0.0010745213,0.006977037],"genre_scores_gemma":[0.27868685,0.0036423511,0.68629754,0.00067521253,0.00028327308,0.00052962016,0.0011712931,0.00067448436,0.028039372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911505,0.00029850967,0.000041989093,0.00010897833,0.00035444912,0.00008091989],"domain_scores_gemma":[0.9988269,0.0003327667,0.00008092186,0.00018537372,0.00051151117,0.00006246625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015638169,0.0011569114,0.0010629066,0.00078836316,0.0004527222,0.0016416954,0.0017001,0.0019903698,0.007616],"category_scores_gemma":[0.0060176235,0.0004872613,0.0008347589,0.001300408,0.0005943431,0.0015474107,0.0008208437,0.0014599513,0.006644171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021737786,0.000121320954,0.0010099831,0.00039463135,0.00015005161,0.0001639498,0.000081701386,0.46184587,0.007331824,0.2160055,0.025228145,0.28744963],"study_design_scores_gemma":[0.000013899093,0.000017173646,0.00017949073,0.00002693952,0.0000085533675,0.00004485886,0.00000767832,0.9674087,0.0013978606,0.021749424,0.0091210045,0.000024355684],"about_ca_topic_score_codex":0.004684274,"about_ca_topic_score_gemma":0.0032546313,"teacher_disagreement_score":0.007616,"about_ca_system_score_codex":0.0008253708,"about_ca_system_score_gemma":0.0010642598,"threshold_uncertainty_score":0.025478125},"labels":[],"label_agreement":null},{"id":"W4252731466","doi":"10.1109/tnnls.2018.2833239","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Région Normandie; Hefei University of Technology; Hefei University; Guangdong University of Technology; Southeast University; Univerza v Ljubljani; Nanjing University of Science and Technology; Sichuan University; Nanjing University; University of Waterloo; Universidade Federal de Pernambuco; Politechnika Warszawska; East China Normal University; National University of Singapore; University of Surrey; Fudan University; Oxford Brookes University; Huazhong University of Science and Technology; Dalian University of Technology; China University of Petroleum, Beijing; Sun Yat-sen University; Chongqing University; Chinese Academy of Sciences; Universidade de Pernambuco; Universität Hamburg; University of Birmingham; KU Leuven; University of Western Sydney; University of East Anglia; Technische Universität Berlin; Nanyang Technological University; Nanjing University of Posts and Telecommunications; Missouri University of Science and Technology; Washington State University; University of Cincinnati; City University of Hong Kong; Arizona State University; Euskal Herriko Unibertsitatea; Georgia Institute of Technology","keywords":"Artificial neural network; Computer science; Data science; Artificial intelligence","score_opus":0.013021081999415327,"score_gpt":0.2243338633400051,"score_spread":0.21131278134058978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252731466","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009213545,0.061501946,0.3069996,0.01684767,0.11479788,0.0005867063,0.016452728,0.006501109,0.46709886],"genre_scores_gemma":[0.049583063,0.051060986,0.05564453,0.002009387,0.008756634,0.0003296282,0.0202791,0.0017576851,0.8105789],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992143,0.000064191845,0.00006917407,0.00007610063,0.000516527,0.000059704504],"domain_scores_gemma":[0.99813145,0.0003725935,0.000064726046,0.00030443797,0.0010228313,0.000103946135],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010596319,0.0009020276,0.0014842114,0.0016890591,0.0005466708,0.0024275698,0.0010224574,0.0015323383,0.20238681],"category_scores_gemma":[0.0040087625,0.00037333553,0.0004804495,0.0018794385,0.00045100274,0.0024154994,0.0011601703,0.001623644,0.0786392],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010187591,0.0000950979,0.00047943412,0.00050550565,0.000027789029,0.000086634864,0.00002296454,0.002930951,0.001639971,0.014637775,0.5276888,0.45178315],"study_design_scores_gemma":[0.000030155757,0.00007992901,0.0013904821,0.0004111302,0.000059263974,0.00032151665,0.00004929113,0.028267676,0.0026766418,0.03168962,0.9349855,0.000038814112],"about_ca_topic_score_codex":0.0018344345,"about_ca_topic_score_gemma":0.00295973,"teacher_disagreement_score":0.7976132,"about_ca_system_score_codex":0.0005366205,"about_ca_system_score_gemma":0.001748881,"threshold_uncertainty_score":0.6770513},"labels":[],"label_agreement":null},{"id":"W4253371817","doi":"10.32920/ryerson.14664273.v1","title":"Generative Modelling and Machine Learning Methods for Travel Behaviour Analysis","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Generative grammar; Generative model; Artificial intelligence; Machine learning; Computer science; Inference; Bayesian inference; Representation (politics); Artificial neural network; Process (computing); Bayesian probability","score_opus":0.09234453186759631,"score_gpt":0.36443658523462175,"score_spread":0.27209205336702547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253371817","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022225317,0.0006571424,0.9933501,0.0006061462,0.00008051569,0.000081041544,0.00014622464,0.00011810043,0.002738229],"genre_scores_gemma":[0.2142209,0.0035928588,0.77012336,0.0008171774,0.0004869227,0.0016459239,0.0008059754,0.00031356816,0.007993381],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99176484,0.005618992,0.00035938405,0.0009836977,0.0010617147,0.00021134797],"domain_scores_gemma":[0.9652712,0.030539911,0.0011595946,0.002074987,0.0008072293,0.00014713922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010890893,0.0014260733,0.0015886963,0.0024294355,0.0006713338,0.003123068,0.0025985911,0.0022404012,0.007366683],"category_scores_gemma":[0.037239958,0.00094587094,0.0038606578,0.0028039305,0.0034240643,0.0035210135,0.0024310942,0.0049140253,0.001172904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002589294,0.000063701766,0.0012669497,0.00029673212,0.00020715746,0.00004603835,0.00038523163,0.14379945,0.00035349678,0.81574863,0.0017530068,0.036053784],"study_design_scores_gemma":[0.000019960386,0.000030805237,0.0005991395,0.00009689966,0.000027433103,0.00003635807,0.0000773884,0.36870068,0.00021099427,0.62494385,0.005218949,0.000037650687],"about_ca_topic_score_codex":0.005533816,"about_ca_topic_score_gemma":0.0038935444,"teacher_disagreement_score":0.010890893,"about_ca_system_score_codex":0.0030718222,"about_ca_system_score_gemma":0.0018748911,"threshold_uncertainty_score":0.05759722},"labels":[],"label_agreement":null},{"id":"W4253393619","doi":"10.1109/tit.2019.2898915","title":"IEEE Transactions on Information Theory publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.007383270973087514,"score_gpt":0.21237916444984037,"score_spread":0.20499589347675284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253393619","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007685056,0.040465705,0.41808102,0.02506351,0.04612417,0.000768203,0.030053955,0.005549991,0.42620844],"genre_scores_gemma":[0.122801326,0.07575902,0.10311073,0.0047494355,0.012413274,0.0009117524,0.048232794,0.0021712712,0.6298504],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980856,0.0003394827,0.00015933822,0.00018997153,0.0010984318,0.00012714314],"domain_scores_gemma":[0.99481934,0.0017338797,0.00018284685,0.0012878221,0.0018059026,0.00017020266],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002010765,0.0012870186,0.002069679,0.0038233974,0.0009367815,0.0043012095,0.001149016,0.0020608127,0.18499774],"category_scores_gemma":[0.01094879,0.00057128386,0.000768873,0.0049888906,0.0008021027,0.004122502,0.0017218122,0.002442378,0.075375594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016672397,0.00012354781,0.0006889728,0.00089499366,0.0000646775,0.00014958144,0.00005247238,0.0033523254,0.0011527906,0.077194825,0.51723707,0.398922],"study_design_scores_gemma":[0.00006777776,0.00010850995,0.0013717826,0.00090496,0.00015986768,0.0005224925,0.00011307768,0.035070773,0.0030674974,0.18424289,0.77429456,0.00007579623],"about_ca_topic_score_codex":0.0014958134,"about_ca_topic_score_gemma":0.0012613396,"teacher_disagreement_score":0.81500226,"about_ca_system_score_codex":0.000997073,"about_ca_system_score_gemma":0.0021575359,"threshold_uncertainty_score":0.6188791},"labels":[],"label_agreement":null},{"id":"W4253749315","doi":"10.1007/978-1-4614-6170-8_100536","title":"Maximum Entropy Random Networks","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Statistical physics; Principle of maximum entropy; Computer science; Mathematics; Statistics; Physics","score_opus":0.011350797891454802,"score_gpt":0.20763172688082715,"score_spread":0.19628092898937236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253749315","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049370187,0.033653926,0.606983,0.0037901446,0.001695574,0.000057249064,0.0005631162,0.0009847848,0.3473353],"genre_scores_gemma":[0.23839535,0.04697489,0.21433862,0.0019743417,0.004569455,0.0003499711,0.0015636324,0.0014735836,0.4903601],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99964774,0.00009554299,0.000014158316,0.000070727074,0.00015190938,0.000019884641],"domain_scores_gemma":[0.999403,0.0003761419,0.00003741254,0.000094940624,0.00006905227,0.000019423704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006465935,0.0008036221,0.0008457993,0.00085550925,0.0003439114,0.0012161556,0.00074180093,0.0010160712,0.017671982],"category_scores_gemma":[0.0024720517,0.00042436528,0.00034867457,0.0010424575,0.0010332277,0.0021507987,0.0009974878,0.0018602092,0.0061491006],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002770437,0.0000231881,0.000099256504,0.00032262583,0.00003918562,0.00004465537,0.000041717893,0.039588146,0.0014099642,0.66331434,0.052381232,0.24270804],"study_design_scores_gemma":[0.00000790674,0.00001628369,0.00021234584,0.0001436871,0.000016534632,0.000107212545,0.0000115628545,0.06877597,0.0015657012,0.8240869,0.10503118,0.000024763425],"about_ca_topic_score_codex":0.00032537532,"about_ca_topic_score_gemma":0.0005992519,"teacher_disagreement_score":0.017671982,"about_ca_system_score_codex":0.00068234553,"about_ca_system_score_gemma":0.0003794303,"threshold_uncertainty_score":0.05911863},"labels":[],"label_agreement":null},{"id":"W4254024741","doi":"10.1109/tit.2019.2905061","title":"IEEE Transactions on Information Theory publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Information theory; Computer science; Information retrieval; Mathematics; Statistics","score_opus":0.007383270973087514,"score_gpt":0.21237916444984037,"score_spread":0.20499589347675284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254024741","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007685056,0.040465705,0.41808102,0.02506351,0.04612417,0.000768203,0.030053955,0.005549991,0.42620844],"genre_scores_gemma":[0.122801326,0.07575902,0.10311073,0.0047494355,0.012413274,0.0009117524,0.048232794,0.0021712712,0.6298504],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980856,0.0003394827,0.00015933822,0.00018997153,0.0010984318,0.00012714314],"domain_scores_gemma":[0.99481934,0.0017338797,0.00018284685,0.0012878221,0.0018059026,0.00017020266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002010765,0.0012870186,0.002069679,0.0038233974,0.0009367815,0.0043012095,0.001149016,0.0020608127,0.18499774],"category_scores_gemma":[0.01094879,0.00057128386,0.000768873,0.0049888906,0.0008021027,0.004122502,0.0017218122,0.002442378,0.075375594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016672397,0.00012354781,0.0006889728,0.00089499366,0.0000646775,0.00014958144,0.00005247238,0.0033523254,0.0011527906,0.077194825,0.51723707,0.398922],"study_design_scores_gemma":[0.00006777776,0.00010850995,0.0013717826,0.00090496,0.00015986768,0.0005224925,0.00011307768,0.035070773,0.0030674974,0.18424289,0.77429456,0.00007579623],"about_ca_topic_score_codex":0.0014958134,"about_ca_topic_score_gemma":0.0012613396,"teacher_disagreement_score":0.18499774,"about_ca_system_score_codex":0.000997073,"about_ca_system_score_gemma":0.0021575359,"threshold_uncertainty_score":0.6188791},"labels":[],"label_agreement":null},{"id":"W4254025234","doi":"10.1007/978-1-4939-7131-2_101254","title":"Spectral Network Analysis","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Spectral analysis; Computer science; Physics; Astronomy; Spectroscopy","score_opus":0.017771253672756324,"score_gpt":0.23468753884807886,"score_spread":0.21691628517532252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254025234","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025756557,0.0068358267,0.79303795,0.00131261,0.0012786378,0.0000351694,0.00033613792,0.001349854,0.19323808],"genre_scores_gemma":[0.13402863,0.015770217,0.2848046,0.0009847287,0.0028411823,0.00019269105,0.0019065955,0.002358357,0.557113],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999808,0.0000346795,0.0000063313273,0.00005128813,0.00008814459,0.000011516209],"domain_scores_gemma":[0.99978393,0.00006885799,0.000010903741,0.000047357957,0.00007651905,0.00001251758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033759186,0.0009279532,0.000548844,0.0012107977,0.00043794705,0.001404645,0.0006971296,0.0007090046,0.024935521],"category_scores_gemma":[0.0010234105,0.0002858783,0.00046856562,0.00091204693,0.0007848234,0.001678618,0.0009574659,0.0014383462,0.015206341],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020890773,0.000033732096,0.00016242947,0.00019722189,0.000046840596,0.00004604088,0.00007393,0.017631022,0.0046972386,0.4222833,0.10152557,0.45328176],"study_design_scores_gemma":[0.0000036124904,0.000013285535,0.00043155358,0.00009112412,0.000023007487,0.00016324985,0.0000464093,0.110043354,0.004311866,0.64073986,0.24410711,0.000025523204],"about_ca_topic_score_codex":0.0006271298,"about_ca_topic_score_gemma":0.0009763885,"teacher_disagreement_score":0.024935521,"about_ca_system_score_codex":0.00059803063,"about_ca_system_score_gemma":0.0003339222,"threshold_uncertainty_score":0.083417594},"labels":[],"label_agreement":null},{"id":"W4254079413","doi":"10.1109/ijcnn.2006.1716653","title":"A Novel Cooperative Neural Learning Algorithm for Data Fusion","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Algorithm; Robustness (evolution); Computer science; Least absolute deviations; Artificial neural network; Modular design; Fusion; Sensor fusion; Gaussian; Image fusion; Variance (accounting); Artificial intelligence; Image (mathematics); Mathematics; Estimator","score_opus":0.07620864815218481,"score_gpt":0.2962242574833673,"score_spread":0.22001560933118247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254079413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013153706,0.00006631945,0.99801046,0.000040436476,0.0000149777015,0.000015831285,0.0000046773944,0.00014920368,0.00038284232],"genre_scores_gemma":[0.13982978,0.0001496225,0.8570968,0.00018664397,0.00006304423,0.00025936955,0.00007411493,0.000079534264,0.0022610698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989969,0.00021624372,0.000056244928,0.00025110727,0.00041156088,0.00006790382],"domain_scores_gemma":[0.99917644,0.00026351665,0.0001155685,0.00009765964,0.00031199865,0.000034887733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015930883,0.00070744375,0.0007222384,0.00078553753,0.00060508004,0.0008590459,0.002000699,0.0012125815,0.0015854639],"category_scores_gemma":[0.0031132144,0.0003860877,0.0005234296,0.0009538907,0.0008191141,0.0017021593,0.0019637295,0.0012789,0.00065851986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015041325,0.000091814865,0.00070263864,0.0001278912,0.00008173569,0.000097138545,0.0002507783,0.25616884,0.022011358,0.031160558,0.003203366,0.6859535],"study_design_scores_gemma":[0.00001443664,0.00003450224,0.0000702104,0.00000536476,0.0000067767655,0.000041416668,0.000009080898,0.99165547,0.0030516789,0.0033242088,0.0017759777,0.000010911709],"about_ca_topic_score_codex":0.0023380872,"about_ca_topic_score_gemma":0.0019416201,"teacher_disagreement_score":0.0023380872,"about_ca_system_score_codex":0.0008513396,"about_ca_system_score_gemma":0.0010990733,"threshold_uncertainty_score":0.008425117},"labels":[],"label_agreement":null},{"id":"W4254576220","doi":"10.21236/ada466341","title":"Key Establishment in Heterogeneous Self-Organized Networks","year":2007,"lang":"en","type":"report","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Army Research Laboratory","keywords":"Key (lock); Computer science; Computer security","score_opus":0.021535734829129533,"score_gpt":0.2758287993455296,"score_spread":0.2542930645164001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254576220","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.218033,0.0013516393,0.7271261,0.0021604605,0.00043371558,0.00023797134,0.00030882098,0.0007772244,0.04957108],"genre_scores_gemma":[0.96957445,0.00063442223,0.018291872,0.00019990664,0.00009387189,0.0001546172,0.00014292229,0.000055249522,0.0108526815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996014,0.00008321245,0.0000209365,0.00010220967,0.00009500604,0.000097185795],"domain_scores_gemma":[0.9977132,0.0009071238,0.00036614662,0.00030864717,0.00030726244,0.0003976199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008053063,0.0002620004,0.0006197858,0.0007252663,0.0011419685,0.0016032753,0.0016633327,0.0010615106,0.005679635],"category_scores_gemma":[0.0057471963,0.0004110897,0.0003606416,0.0005260545,0.0013834896,0.0034592326,0.002323661,0.0010878091,0.00095644064],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030364934,0.00006086497,0.0013551287,0.00028885735,0.00007171003,0.0006577774,0.0006145717,0.1123813,0.01499523,0.8282335,0.006709333,0.03432818],"study_design_scores_gemma":[0.00008615043,0.00011061818,0.00074351736,0.000057690802,0.00004556951,0.00022123309,0.000278914,0.5188968,0.004100155,0.46685126,0.008574095,0.00003392703],"about_ca_topic_score_codex":0.0010573459,"about_ca_topic_score_gemma":0.0008172741,"teacher_disagreement_score":0.005679635,"about_ca_system_score_codex":0.0006874622,"about_ca_system_score_gemma":0.000555432,"threshold_uncertainty_score":0.019000292},"labels":[],"label_agreement":null},{"id":"W4254621010","doi":"10.1002/0471708607.ch8","title":"Directional Clustering","year":2005,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Dependency (UML); Computer science; Data mining; Algorithm; Artificial intelligence","score_opus":0.01097461571631888,"score_gpt":0.23984748755644927,"score_spread":0.2288728718401304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254621010","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073069106,0.0005441349,0.9746875,0.00028572377,0.00021657857,0.00030144068,0.0009636562,0.0027076623,0.012986328],"genre_scores_gemma":[0.108931184,0.00088643655,0.86161274,0.00024376424,0.00011346938,0.0003758984,0.0040535457,0.0008205151,0.022962438],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976457,0.00046653426,0.00013917101,0.00068974483,0.0008544434,0.00020436493],"domain_scores_gemma":[0.9976617,0.00030340854,0.00014943031,0.0008968846,0.0008883957,0.000100212994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019601274,0.0014847812,0.0013534809,0.0053046593,0.002287839,0.0033971192,0.0017386326,0.0013384663,0.011867779],"category_scores_gemma":[0.0054647527,0.0006860083,0.0015110371,0.0068368716,0.00094067445,0.0018114875,0.0023394222,0.0015051184,0.009190884],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025834906,0.00013808148,0.005203426,0.00044879332,0.00021874777,0.00011598477,0.00044957598,0.022750264,0.017926827,0.10409534,0.044413447,0.80398107],"study_design_scores_gemma":[0.0000836514,0.00019631264,0.007799567,0.00022947267,0.0003193862,0.0015813799,0.0010611488,0.501242,0.042164657,0.17567599,0.26939347,0.00025299593],"about_ca_topic_score_codex":0.0035055487,"about_ca_topic_score_gemma":0.006820753,"teacher_disagreement_score":0.011867779,"about_ca_system_score_codex":0.0009422299,"about_ca_system_score_gemma":0.0018862727,"threshold_uncertainty_score":0.0397017},"labels":[],"label_agreement":null},{"id":"W4254641693","doi":"10.2307/3647906","title":"Fractals, Graphs, and Fields","year":2003,"lang":"en","type":"article","venue":"American Mathematical Monthly","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geography; Mathematics; Computer science","score_opus":0.009195713767686001,"score_gpt":0.23652343941055626,"score_spread":0.22732772564287027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254641693","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087109864,0.5284908,0.07881959,0.08117115,0.007084664,0.000043793825,0.0012257134,0.0005914363,0.21546309],"genre_scores_gemma":[0.7636387,0.15541957,0.027685935,0.004014663,0.010594556,0.00011485566,0.00082454795,0.00011855663,0.03758865],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996642,0.000110619585,0.000022508993,0.00006213866,0.000114755756,0.000025814494],"domain_scores_gemma":[0.99836725,0.000929349,0.00021051105,0.00017828905,0.0001753516,0.00013927525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081871223,0.00056464213,0.00088949373,0.0037001583,0.0010361755,0.004702037,0.000404261,0.0017401866,0.006071488],"category_scores_gemma":[0.004147445,0.00031649484,0.00037786065,0.0031565875,0.004101719,0.005353108,0.0008365083,0.002125108,0.00084479817],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000103709335,0.000005973472,0.00022806396,0.00005728037,0.000008404367,0.00001887532,0.00009774562,0.00070307887,0.000098331104,0.97422904,0.0114899995,0.013052932],"study_design_scores_gemma":[0.000004669454,0.0000045382376,0.0002725885,0.000020359485,0.0000043552827,0.000053117546,0.00006477781,0.0009125932,0.000025690426,0.97351044,0.025121525,0.000005313262],"about_ca_topic_score_codex":0.0019863124,"about_ca_topic_score_gemma":0.0018156533,"teacher_disagreement_score":0.006071488,"about_ca_system_score_codex":0.001589387,"about_ca_system_score_gemma":0.0006815854,"threshold_uncertainty_score":0.020311117},"labels":[],"label_agreement":null},{"id":"W4254841080","doi":"10.32920/ryerson.14648343","title":"Extended Kalman filter approach to electric arc furnace forecasting","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Kalman filter; Electric arc furnace; Computer science; Voltage; Fuzzy logic; Wavelet; Ensemble Kalman filter; Control theory (sociology); Algorithm; Extended Kalman filter; Artificial intelligence; Engineering; Materials science","score_opus":0.053861781691266764,"score_gpt":0.2631498647825134,"score_spread":0.20928808309124664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254841080","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006014603,0.00030111027,0.99251056,0.00006518503,0.00003275843,0.000006891489,0.00003275603,0.00023179472,0.00080439204],"genre_scores_gemma":[0.6842681,0.0019565627,0.30363712,0.00009862644,0.00018589478,0.00013750543,0.00022126813,0.00009403868,0.00940082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977225,0.000062409075,0.000016868997,0.00006110592,0.00006632846,0.000020960437],"domain_scores_gemma":[0.99968815,0.00017593491,0.000030014073,0.000023382385,0.00007500781,0.000007601407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046605416,0.0004143799,0.00071710045,0.0004355845,0.0002210696,0.00060324057,0.0006872382,0.0006862166,0.0014587166],"category_scores_gemma":[0.0013012319,0.0003022818,0.0004980626,0.00050591206,0.00026828478,0.0010024484,0.00033198114,0.00081541063,0.00032321518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035720783,0.000019975858,0.00051149534,0.00005704247,0.0000623429,0.000045156463,0.0000638654,0.9143083,0.002482797,0.015006462,0.00046192197,0.0669449],"study_design_scores_gemma":[0.000002221433,0.0000059858125,0.00008414098,0.000002815956,0.000004450387,0.0000037257428,0.0000030899396,0.9974226,0.00027959398,0.0017661509,0.00042149084,0.000003664519],"about_ca_topic_score_codex":0.016410066,"about_ca_topic_score_gemma":0.009480527,"teacher_disagreement_score":0.016410066,"about_ca_system_score_codex":0.0006268955,"about_ca_system_score_gemma":0.0006411945,"threshold_uncertainty_score":0.032629073},"labels":[],"label_agreement":null},{"id":"W4255249119","doi":"10.1016/b978-008045405-4.00159-2","title":"Hopfield Network","year":2008,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hopfield network; Boltzmann machine; Artificial neural network; Restricted Boltzmann machine; Artificial intelligence; Computer science; Feedforward neural network; Types of artificial neural networks; Leabra; Backpropagation; Unsupervised learning; Recurrent neural network; Wake-sleep algorithm","score_opus":0.018347384891412945,"score_gpt":0.2232971561452513,"score_spread":0.20494977125383837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255249119","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016640166,0.010767494,0.52875155,0.0012079758,0.0011213499,0.000106303945,0.00063585775,0.0023074232,0.4384618],"genre_scores_gemma":[0.30053598,0.010646569,0.13674788,0.00044103956,0.00052600744,0.00017824724,0.0013560782,0.00035008352,0.54921806],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99991536,0.000010744996,0.0000040519662,0.000022781176,0.000036651898,0.000010420786],"domain_scores_gemma":[0.9999027,0.00002741968,0.000005558079,0.000020042862,0.000034997065,0.000009175144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016910153,0.00050130376,0.00041837312,0.00059246004,0.00040450617,0.0009835674,0.00061319506,0.0006928219,0.026313195],"category_scores_gemma":[0.0006278163,0.0001894311,0.0002689999,0.00065771444,0.00033066302,0.0011526737,0.0005799173,0.0005558136,0.0063161086],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069534624,0.00004590868,0.000345883,0.00022581188,0.000044461773,0.00010353665,0.00005928963,0.066426516,0.0058633154,0.14177075,0.037466146,0.74757886],"study_design_scores_gemma":[0.000032076612,0.000078152756,0.000915892,0.00016825889,0.00007792754,0.00030076958,0.000058423404,0.45137244,0.011359681,0.31297058,0.22260737,0.000058436715],"about_ca_topic_score_codex":0.0030669458,"about_ca_topic_score_gemma":0.0031983517,"teacher_disagreement_score":0.026313195,"about_ca_system_score_codex":0.00054006797,"about_ca_system_score_gemma":0.0005429806,"threshold_uncertainty_score":0.088026404},"labels":[],"label_agreement":null},{"id":"W4255425592","doi":"10.1109/tit.2018.2826784","title":"IEEE Transactions on Information Theory publication information","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Computer security; Mathematics; Statistics","score_opus":0.009859260653836669,"score_gpt":0.2267323195932553,"score_spread":0.21687305893941863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255425592","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075886752,0.03450608,0.37589628,0.028927293,0.052169286,0.00078925234,0.03879711,0.0064008934,0.45492515],"genre_scores_gemma":[0.11858974,0.06850063,0.090462565,0.0051360684,0.01373131,0.00089299027,0.056598403,0.0027871744,0.6433012],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99794954,0.00033785796,0.000169827,0.00021402955,0.0011793278,0.000149413],"domain_scores_gemma":[0.9937953,0.0020618648,0.00021209495,0.0015606398,0.002160756,0.00020940119],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021437379,0.0013837162,0.0021563147,0.0039232336,0.0009921466,0.0045337295,0.0012955151,0.0022782085,0.23127455],"category_scores_gemma":[0.012982578,0.00062062254,0.0008206204,0.0050654747,0.0008102244,0.0045962213,0.0018308015,0.0027652583,0.098597996],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001775029,0.00013199805,0.0006146807,0.000867521,0.00005735344,0.0001520026,0.00004537619,0.0031645668,0.0010952258,0.06849876,0.5728305,0.35236454],"study_design_scores_gemma":[0.00007642604,0.00010941409,0.001258478,0.0008973995,0.00015124984,0.00054662244,0.00010854275,0.034255814,0.0032187246,0.18546115,0.7738345,0.00008166401],"about_ca_topic_score_codex":0.0014729707,"about_ca_topic_score_gemma":0.0012031312,"teacher_disagreement_score":0.76872545,"about_ca_system_score_codex":0.0010227711,"about_ca_system_score_gemma":0.0023775187,"threshold_uncertainty_score":0.77369046},"labels":[],"label_agreement":null},{"id":"W4255427831","doi":"10.1007/978-3-030-17519-1","title":"Matrices, Statistics and Big Data","year":2019,"lang":"en","type":"book","venue":"Contributions to statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Statistics; Big data; Data science; Computer science; Mathematics; Data mining","score_opus":0.03385533044696285,"score_gpt":0.3001139516347039,"score_spread":0.266258621187741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255427831","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012653823,0.42537975,0.32833642,0.01598403,0.021920897,0.00007660595,0.0026271492,0.003279521,0.20113029],"genre_scores_gemma":[0.028914385,0.31958294,0.19493227,0.006505076,0.045699347,0.0003218318,0.0031587286,0.0024306206,0.3984548],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989655,0.00017259963,0.00006291749,0.00015865346,0.00061054697,0.000029821702],"domain_scores_gemma":[0.99696535,0.00214063,0.000104982246,0.0002640618,0.0004118571,0.00011325119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011231658,0.0018228403,0.0012735575,0.003573357,0.00068903936,0.0040493943,0.0008585823,0.0011436184,0.022081569],"category_scores_gemma":[0.0057410747,0.0006874452,0.0005834408,0.00761648,0.0022675195,0.0034399813,0.0012235461,0.0031916606,0.015294334],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021776417,0.000030423822,0.00017508568,0.0005792758,0.00003671613,0.000047267793,0.00010050366,0.0026547192,0.00048974424,0.21335804,0.5396063,0.24290022],"study_design_scores_gemma":[0.00000866883,0.000012831936,0.00029870338,0.00019180191,0.000012443084,0.00016050856,0.00003286141,0.004236505,0.00020748642,0.3679527,0.62686056,0.000024933788],"about_ca_topic_score_codex":0.0018439218,"about_ca_topic_score_gemma":0.0032766215,"teacher_disagreement_score":0.022081569,"about_ca_system_score_codex":0.0010732863,"about_ca_system_score_gemma":0.0020568313,"threshold_uncertainty_score":0.07387024},"labels":[],"label_agreement":null},{"id":"W4255430775","doi":"10.1109/tnnls.2021.3078592","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Artificial neural network; Computer science; Data science; World Wide Web; Information retrieval; Artificial intelligence","score_opus":0.012733470139682094,"score_gpt":0.21942843061264394,"score_spread":0.20669496047296185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255430775","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008674992,0.07775329,0.3317228,0.014763441,0.10534082,0.00056262995,0.0132251615,0.0057110414,0.4422458],"genre_scores_gemma":[0.047799703,0.060790274,0.05947508,0.0020392463,0.007916191,0.00034253838,0.018183041,0.0014959566,0.80195796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992557,0.00006812914,0.000062097395,0.00007053413,0.0004896761,0.00005377804],"domain_scores_gemma":[0.9984035,0.00033720626,0.00005568766,0.00025708953,0.0008579105,0.00008860813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010399217,0.00087971514,0.0014855702,0.0016763533,0.0005160981,0.0022983786,0.00094688445,0.0014829766,0.1712527],"category_scores_gemma":[0.0034331358,0.0003704023,0.00046931638,0.0019506597,0.00044431514,0.002172209,0.001100526,0.0015288608,0.06967644],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010125696,0.00008644695,0.00049349456,0.0005234777,0.000030718227,0.00008122713,0.000023858423,0.003108043,0.001625891,0.014201481,0.49946615,0.4802579],"study_design_scores_gemma":[0.000027818764,0.0000788564,0.0014199645,0.00042970068,0.000064212174,0.00029786731,0.000046496316,0.028729105,0.0023577304,0.028642723,0.9378687,0.00003680235],"about_ca_topic_score_codex":0.0019654583,"about_ca_topic_score_gemma":0.0031941677,"teacher_disagreement_score":0.1712527,"about_ca_system_score_codex":0.00051413826,"about_ca_system_score_gemma":0.0016375178,"threshold_uncertainty_score":0.5728973},"labels":[],"label_agreement":null},{"id":"W4255597828","doi":"10.4108/icst.autonomics2007.2126","title":"Localization Applying An Efficient Neural Network Mapping","year":2007,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Communications Research Centre Canada","funders":"","keywords":"Computer science; Node (physics); Curvilinear coordinates; Scheme (mathematics); Position (finance); Component (thermodynamics); Artificial neural network; Artificial intelligence; Data mining; Distributed computing; Mathematics; Engineering","score_opus":0.023024652064410126,"score_gpt":0.258986376120616,"score_spread":0.23596172405620586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255597828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015347329,0.000081708866,0.98222685,0.000104946484,0.000028083114,0.000026141115,0.000018905244,0.0005233381,0.0016427574],"genre_scores_gemma":[0.60053635,0.0002244907,0.39496613,0.00005950065,0.000043860644,0.00009670997,0.00007399187,0.00010327733,0.0038957247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979323,0.000059122336,0.0000068993477,0.00004319044,0.00007647658,0.000021039545],"domain_scores_gemma":[0.99953866,0.00016170989,0.000053872634,0.000094348914,0.00013801789,0.000013470484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004310612,0.00049909623,0.00032029155,0.0004872922,0.00036520112,0.0004944043,0.00055950263,0.00042442168,0.001412904],"category_scores_gemma":[0.0020874846,0.00016064841,0.00021495332,0.00056164496,0.00045658654,0.0008841622,0.00085640524,0.0004410746,0.00039052457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018002256,0.000055734447,0.0013725874,0.00008136248,0.000043910604,0.000075154974,0.00009715838,0.52437526,0.050826754,0.021404425,0.0018224574,0.39966506],"study_design_scores_gemma":[0.000005822283,0.000024666422,0.0002806366,0.0000027575848,0.0000044413555,0.000023834464,0.000007499997,0.9909033,0.005257796,0.0024708922,0.0010111241,0.0000072424114],"about_ca_topic_score_codex":0.0061062262,"about_ca_topic_score_gemma":0.0066131307,"teacher_disagreement_score":0.0061062262,"about_ca_system_score_codex":0.000549586,"about_ca_system_score_gemma":0.0007007689,"threshold_uncertainty_score":0.012141347},"labels":[],"label_agreement":null},{"id":"W4255868613","doi":"10.1515/iupac.79.1397","title":"Henderson–Hasselbalch Equation","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Hazard; Toxicology; Chemistry; Philosophy; Biology; Linguistics; Organic chemistry","score_opus":0.030287760023545177,"score_gpt":0.39821046392283227,"score_spread":0.3679227038992871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255868613","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015480683,0.009967005,0.09056495,0.004615296,0.002008455,0.00087116763,0.805637,0.0042648157,0.06659067],"genre_scores_gemma":[0.122128874,0.007610139,0.082532294,0.0030288484,0.0011594789,0.0019503388,0.6349154,0.0018252658,0.14484939],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984823,0.0002725231,0.0001753382,0.0005786875,0.0003234789,0.00016771992],"domain_scores_gemma":[0.99819595,0.0008844165,0.00009421579,0.0002753498,0.0004918317,0.000058304922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021920893,0.0010728682,0.0018421735,0.0027577353,0.00063767174,0.0025915883,0.0036357362,0.002048201,0.08152562],"category_scores_gemma":[0.013274188,0.0005029267,0.0018194658,0.0033090666,0.0003893751,0.0028186243,0.0010076397,0.0027515457,0.07354555],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001672772,0.00011293817,0.009972174,0.0006473552,0.00020922018,0.00014960165,0.00006105889,0.035530247,0.00021451469,0.02738291,0.77670676,0.14884596],"study_design_scores_gemma":[0.00033897973,0.000098156,0.010854792,0.00068625004,0.00021539722,0.00071224704,0.00017126303,0.27367306,0.00088879326,0.09386704,0.618363,0.0001310257],"about_ca_topic_score_codex":0.036699045,"about_ca_topic_score_gemma":0.038005173,"teacher_disagreement_score":0.08152562,"about_ca_system_score_codex":0.001810528,"about_ca_system_score_gemma":0.002350645,"threshold_uncertainty_score":0.2727304},"labels":[],"label_agreement":null},{"id":"W4255983590","doi":"10.1007/978-0-387-09724-4_13","title":"Stability and KAM Theory","year":2008,"lang":"en","type":"book-chapter","venue":"Applied mathematical sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Kolmogorov–Arnold–Moser theorem; Stability (learning theory); Computer science; Mathematics; Pure mathematics","score_opus":0.042620088707667876,"score_gpt":0.2444373638377944,"score_spread":0.20181727513012654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255983590","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010180496,0.021047171,0.120705195,0.0036242777,0.0020292022,0.000042184485,0.00012888217,0.00042162108,0.8418209],"genre_scores_gemma":[0.27331197,0.017362192,0.028215652,0.00164388,0.0031673869,0.00020347828,0.0002511402,0.0006329821,0.6752113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998312,0.00004059707,0.0000066293064,0.000030207731,0.00007259033,0.000018647788],"domain_scores_gemma":[0.9998764,0.00004530349,0.00001060247,0.000024778097,0.00003281375,0.000010161795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031367893,0.00073402456,0.0007491062,0.0012313166,0.0010879507,0.0017615956,0.00055677595,0.00085464853,0.01567179],"category_scores_gemma":[0.0006853666,0.00030107872,0.00048834126,0.0009266952,0.0022725025,0.0027780728,0.0011456738,0.0021189617,0.0053910073],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005285378,0.000006317461,0.00001856189,0.00004570082,0.0000048151,0.000016282222,0.000093537186,0.00066335266,0.0005001202,0.9680635,0.011702379,0.018880123],"study_design_scores_gemma":[0.0000027802319,0.000007703793,0.00006342078,0.000025234987,0.000003716177,0.00005520966,0.000036709793,0.001954666,0.00036821325,0.9379559,0.059519902,0.0000064071846],"about_ca_topic_score_codex":0.00035624174,"about_ca_topic_score_gemma":0.00040369353,"teacher_disagreement_score":0.01567179,"about_ca_system_score_codex":0.00093047693,"about_ca_system_score_gemma":0.00046136766,"threshold_uncertainty_score":0.05242735},"labels":[],"label_agreement":null},{"id":"W4255988759","doi":"10.1109/ijcnn.2006.1716617","title":"The Self-Organising Hierarchical Variance Map","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hebbian theory; Self-organizing map; Computer science; Cluster analysis; Variance (accounting); Artificial neural network; Artificial intelligence; Unsupervised learning; Competitive learning; Neural gas; Network topology; Topology (electrical circuits); Pattern recognition (psychology); Mathematics; Recurrent neural network","score_opus":0.02392029913870507,"score_gpt":0.24832798346208407,"score_spread":0.224407684323379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255988759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014977689,0.00039157504,0.9807694,0.00013312383,0.000040587118,0.000032051434,0.00008249033,0.0005698546,0.0030031882],"genre_scores_gemma":[0.5619016,0.00057381135,0.42953908,0.00014018946,0.00009418096,0.00013325569,0.00035530576,0.00023923018,0.007023344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964833,0.00008258309,0.000012800719,0.00008879801,0.00012976653,0.000037700407],"domain_scores_gemma":[0.9995289,0.00017157149,0.00005144068,0.00008456348,0.00013419338,0.00002930926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005074153,0.00032101915,0.0004496681,0.0007868768,0.00046240233,0.0010223775,0.0011465144,0.0006747687,0.0015191827],"category_scores_gemma":[0.0023434546,0.00028608716,0.00056941947,0.0007478847,0.0006791588,0.0013763714,0.0009469512,0.0005990106,0.0005374943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014058696,0.00006199134,0.0023283986,0.00024537058,0.00014245653,0.00023282609,0.00041628824,0.42185467,0.026199281,0.21160702,0.00754217,0.32922897],"study_design_scores_gemma":[0.000008021482,0.000028861603,0.00057663955,0.000010986749,0.000012191462,0.000101059726,0.00002624055,0.94416237,0.0025461232,0.04784342,0.004666066,0.000018093766],"about_ca_topic_score_codex":0.0019572647,"about_ca_topic_score_gemma":0.0018117393,"teacher_disagreement_score":0.0019572647,"about_ca_system_score_codex":0.0005315249,"about_ca_system_score_gemma":0.0008003089,"threshold_uncertainty_score":0.0050821304},"labels":[],"label_agreement":null},{"id":"W4256207213","doi":"10.1109/tit.2018.2886695","title":"IEEE Transactions on Information Theory publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.007383270973087514,"score_gpt":0.21237916444984037,"score_spread":0.20499589347675284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256207213","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007685056,0.040465705,0.41808102,0.02506351,0.04612417,0.000768203,0.030053955,0.005549991,0.42620844],"genre_scores_gemma":[0.122801326,0.07575902,0.10311073,0.0047494355,0.012413274,0.0009117524,0.048232794,0.0021712712,0.6298504],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980856,0.0003394827,0.00015933822,0.00018997153,0.0010984318,0.00012714314],"domain_scores_gemma":[0.99481934,0.0017338797,0.00018284685,0.0012878221,0.0018059026,0.00017020266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002010765,0.0012870186,0.002069679,0.0038233974,0.0009367815,0.0043012095,0.001149016,0.0020608127,0.18499774],"category_scores_gemma":[0.01094879,0.00057128386,0.000768873,0.0049888906,0.0008021027,0.004122502,0.0017218122,0.002442378,0.075375594],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016672397,0.00012354781,0.0006889728,0.00089499366,0.0000646775,0.00014958144,0.00005247238,0.0033523254,0.0011527906,0.077194825,0.51723707,0.398922],"study_design_scores_gemma":[0.00006777776,0.00010850995,0.0013717826,0.00090496,0.00015986768,0.0005224925,0.00011307768,0.035070773,0.0030674974,0.18424289,0.77429456,0.00007579623],"about_ca_topic_score_codex":0.0014958134,"about_ca_topic_score_gemma":0.0012613396,"teacher_disagreement_score":0.18499774,"about_ca_system_score_codex":0.000997073,"about_ca_system_score_gemma":0.0021575359,"threshold_uncertainty_score":0.6188791},"labels":[],"label_agreement":null},{"id":"W4256235498","doi":"10.1109/tnnls.2021.3065512","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Standards Association","funders":"","keywords":"Computer science; Artificial neural network; World Wide Web; Information retrieval; Artificial intelligence","score_opus":0.012733470139682094,"score_gpt":0.21942843061264394,"score_spread":0.20669496047296185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256235498","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008674992,0.07775329,0.3317228,0.014763441,0.10534082,0.00056262995,0.0132251615,0.0057110414,0.4422458],"genre_scores_gemma":[0.047799703,0.060790274,0.05947508,0.0020392463,0.007916191,0.00034253838,0.018183041,0.0014959566,0.80195796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992557,0.00006812914,0.000062097395,0.00007053413,0.0004896761,0.00005377804],"domain_scores_gemma":[0.9984035,0.00033720626,0.00005568766,0.00025708953,0.0008579105,0.00008860813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010399217,0.00087971514,0.0014855702,0.0016763533,0.0005160981,0.0022983786,0.00094688445,0.0014829766,0.1712527],"category_scores_gemma":[0.0034331358,0.0003704023,0.00046931638,0.0019506597,0.00044431514,0.002172209,0.001100526,0.0015288608,0.06967644],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010125696,0.00008644695,0.00049349456,0.0005234777,0.000030718227,0.00008122713,0.000023858423,0.003108043,0.001625891,0.014201481,0.49946615,0.4802579],"study_design_scores_gemma":[0.000027818764,0.0000788564,0.0014199645,0.00042970068,0.000064212174,0.00029786731,0.000046496316,0.028729105,0.0023577304,0.028642723,0.9378687,0.00003680235],"about_ca_topic_score_codex":0.0019654583,"about_ca_topic_score_gemma":0.0031941677,"teacher_disagreement_score":0.1712527,"about_ca_system_score_codex":0.00051413826,"about_ca_system_score_gemma":0.0016375178,"threshold_uncertainty_score":0.5728973},"labels":[],"label_agreement":null},{"id":"W4280566838","doi":"10.1016/j.eswa.2022.117585","title":"Associative reasoning-based interpretable continuous decision making in industrial production process","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Associative property; Production (economics); Process (computing); Artificial intelligence; Machine learning; Mathematics","score_opus":0.015480408047705826,"score_gpt":0.2773489672760624,"score_spread":0.26186855922835656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280566838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31467143,0.0004843488,0.6780327,0.0005024217,0.00010416511,0.000047554244,0.000105565894,0.00039951215,0.005652317],"genre_scores_gemma":[0.969775,0.000089278175,0.029401526,0.000030479776,0.000017473709,0.000020599511,0.000038500664,0.000016825503,0.0006102418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989335,0.00038048948,0.00009264854,0.00024212684,0.00025398936,0.00009717959],"domain_scores_gemma":[0.99581146,0.0030567304,0.00036429518,0.00021914566,0.00043119534,0.00011717723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024068546,0.0004742047,0.00076757575,0.0006637391,0.00059436465,0.002180689,0.0010975971,0.00096098037,0.0022911439],"category_scores_gemma":[0.009080921,0.0003965202,0.0006893097,0.000613676,0.0012775584,0.0028192059,0.0009175421,0.0012582889,0.00018264313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046855048,0.00023083338,0.0025403507,0.00017806012,0.0001467507,0.0005359851,0.0005566285,0.821865,0.0068879924,0.08904272,0.000605295,0.07694171],"study_design_scores_gemma":[0.000008139496,0.000019811283,0.00033896006,0.0000075282196,0.000012887773,0.000017587045,0.000022663182,0.96205765,0.0010359292,0.036369313,0.00010105426,0.00000852358],"about_ca_topic_score_codex":0.004581919,"about_ca_topic_score_gemma":0.0029725176,"teacher_disagreement_score":0.004581919,"about_ca_system_score_codex":0.0010569284,"about_ca_system_score_gemma":0.0011261324,"threshold_uncertainty_score":0.01272881},"labels":[],"label_agreement":null},{"id":"W4280632894","doi":"10.1109/tit.2022.3166330","title":"IEEE Transactions on Information Theory publication information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Information theory; Computer science; Computer security; Mathematics; Statistics","score_opus":0.009156959172592861,"score_gpt":0.21540977404581182,"score_spread":0.20625281487321898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280632894","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073632444,0.041065272,0.4078097,0.025557896,0.04692853,0.0007379434,0.02859816,0.005462615,0.43647674],"genre_scores_gemma":[0.12103247,0.07721286,0.099727295,0.0049459753,0.012654195,0.0008991606,0.04606473,0.002194734,0.63526857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980058,0.0003531626,0.00016168886,0.00020072536,0.0011487462,0.00012988472],"domain_scores_gemma":[0.9946779,0.0017836735,0.0001828315,0.0013241188,0.0018538217,0.0001776851],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0020620713,0.0012995908,0.0020753695,0.0038026476,0.00096002803,0.004441838,0.0011496176,0.0020986642,0.18658403],"category_scores_gemma":[0.011094833,0.00058022304,0.0007657712,0.005049121,0.00082062976,0.0042192605,0.0017576806,0.0025192474,0.07861321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016382568,0.00012273522,0.0006897795,0.00085827295,0.00006272511,0.00014853044,0.00005241059,0.0032309913,0.0011106146,0.07595082,0.524427,0.3931823],"study_design_scores_gemma":[0.00006565506,0.00010452829,0.0013115945,0.0008803995,0.00015325854,0.00051396637,0.00011317045,0.033855367,0.0028811428,0.1821534,0.77789456,0.00007305823],"about_ca_topic_score_codex":0.0014865134,"about_ca_topic_score_gemma":0.0012459535,"teacher_disagreement_score":0.99619734,"about_ca_system_score_codex":0.001008677,"about_ca_system_score_gemma":0.0021853258,"threshold_uncertainty_score":0.62418574},"labels":[],"label_agreement":null},{"id":"W4280650547","doi":"10.5281/zenodo.6564285","title":"D3.7 Technical report on information extraction from heterogeneous data using TDM","year":2022,"lang":"en","type":"report","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canarie","funders":"European Commission","keywords":"Extraction (chemistry); Computer science; Data extraction; Information retrieval; Data science; Data mining; Chromatography; Chemistry; MEDLINE","score_opus":0.13108834635572558,"score_gpt":0.3304839633735825,"score_spread":0.1993956170178569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280650547","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037244773,0.0008578237,0.84591615,0.0029791342,0.0010749314,0.0021572914,0.056181196,0.0655255,0.021583498],"genre_scores_gemma":[0.014560088,0.0013571394,0.7429399,0.0010383552,0.00041422973,0.002919238,0.19295365,0.01559744,0.028220007],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98337895,0.003330227,0.0019573157,0.0025093823,0.008096207,0.00072788494],"domain_scores_gemma":[0.9711604,0.008738071,0.0009158079,0.0093608685,0.008947285,0.00087757304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016053842,0.0020579414,0.0015983118,0.0054905578,0.0016680182,0.00850716,0.0036310474,0.0018237014,0.055537548],"category_scores_gemma":[0.044647157,0.001729752,0.0034468903,0.0048090885,0.0011731884,0.007702809,0.0095826965,0.003843159,0.08480541],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043660894,0.0003728443,0.002210993,0.003179051,0.00033607983,0.0009838117,0.0017257379,0.005696891,0.025411699,0.027904348,0.38448974,0.5472522],"study_design_scores_gemma":[0.00017125308,0.00018471933,0.002413185,0.00080165145,0.00011182903,0.0006860119,0.0008924107,0.034213092,0.049436014,0.028338328,0.8825685,0.0001830612],"about_ca_topic_score_codex":0.008900067,"about_ca_topic_score_gemma":0.0050895773,"teacher_disagreement_score":0.055537548,"about_ca_system_score_codex":0.0020333717,"about_ca_system_score_gemma":0.007178319,"threshold_uncertainty_score":0.18579161},"labels":[],"label_agreement":null},{"id":"W4281646271","doi":"10.25046/aj070304","title":"Encompassing Chaos in Brain-inspired Neural Network Models for Substance Identification and Breast Cancer Detection","year":2022,"lang":"en","type":"article","venue":"Advances in Science Technology and Engineering Systems Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Direction Générale de la Recherche Scientifique et du Développement Technologique; Université du Québec à Montréal; University of Houston","keywords":"CHAOS (operating system); Identification (biology); Breast cancer; Artificial neural network; Neuroscience; Computer science; Artificial intelligence; Cognitive science; Computational biology; Cancer; Psychology; Medicine; Biology; Computer security; Internal medicine","score_opus":0.009192333305381044,"score_gpt":0.2466620565571461,"score_spread":0.23746972325176505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281646271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1861322,0.002194348,0.7943698,0.00083280914,0.00018293053,0.000072305564,0.00018511934,0.000333052,0.015697382],"genre_scores_gemma":[0.9601217,0.0008692599,0.030998735,0.00009023068,0.000044408585,0.000103285165,0.0000768306,0.000027521193,0.00766801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999915,0.00002694069,0.000003890584,0.000018306875,0.000023903665,0.000011877651],"domain_scores_gemma":[0.9998683,0.00006548233,0.000020928574,0.000009671373,0.000029514806,0.000005981344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002412825,0.00034071718,0.00031732116,0.00034914407,0.00020819453,0.0005315745,0.0004933155,0.0006238799,0.0007695617],"category_scores_gemma":[0.0007176453,0.00014995903,0.00040751504,0.00026492274,0.0003496889,0.00053894613,0.0003940473,0.00056072837,0.00017118492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004817823,0.000030398007,0.0017183721,0.00008371359,0.000046632882,0.00013984536,0.000113099115,0.9466799,0.0075486577,0.026226422,0.00048549287,0.01687936],"study_design_scores_gemma":[9.507558e-7,0.000009616077,0.00014339949,0.000003435991,0.000004048185,0.000020508798,0.0000047130256,0.99628973,0.0003372452,0.0028626423,0.0003203206,0.0000033066362],"about_ca_topic_score_codex":0.0024283957,"about_ca_topic_score_gemma":0.0023845297,"teacher_disagreement_score":0.0024283957,"about_ca_system_score_codex":0.00034450984,"about_ca_system_score_gemma":0.00034886674,"threshold_uncertainty_score":0.0048285723},"labels":[],"label_agreement":null},{"id":"W4281707786","doi":"10.48550/arxiv.2206.06779","title":"Benchmarking Bayesian neural networks and evaluation metrics for\\n regression tasks","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Bayesian probability; Artificial neural network; Computer science; Artificial intelligence; Deep neural networks; Bayesian network; Machine learning; Econometrics; Mathematics","score_opus":0.09183349694236233,"score_gpt":0.23703819404442092,"score_spread":0.14520469710205858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281707786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20017885,0.021625213,0.7381148,0.003580765,0.0008948013,0.00074871536,0.007397654,0.009947263,0.017511968],"genre_scores_gemma":[0.7051817,0.00426836,0.2604923,0.0007400476,0.0004079011,0.00091976224,0.022437485,0.0013301042,0.004222401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9844215,0.007941404,0.0011954681,0.0021583692,0.0036993776,0.00058381516],"domain_scores_gemma":[0.9757867,0.015495238,0.0017516158,0.0024197507,0.0038533802,0.00069326686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017720597,0.003135569,0.001698686,0.0052601956,0.00078995706,0.0027305253,0.0035679229,0.004417942,0.0029175805],"category_scores_gemma":[0.066637725,0.0005602206,0.0014027095,0.003378895,0.0012793324,0.0034999128,0.00298326,0.0031501465,0.0013921055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008912608,0.00048275734,0.008943657,0.00092849764,0.0006579396,0.000087006985,0.000084905434,0.801436,0.0016758229,0.0113868555,0.012629495,0.16079588],"study_design_scores_gemma":[0.000031558644,0.00017321852,0.001377379,0.00010640444,0.00003998703,0.00004971197,0.000027589513,0.99019766,0.0015617211,0.005223112,0.0011902824,0.000021322512],"about_ca_topic_score_codex":0.014087489,"about_ca_topic_score_gemma":0.0132747255,"teacher_disagreement_score":0.017720597,"about_ca_system_score_codex":0.0034527096,"about_ca_system_score_gemma":0.0023795145,"threshold_uncertainty_score":0.09371656},"labels":[],"label_agreement":null},{"id":"W4281726948","doi":"10.1016/j.neunet.2022.06.007","title":"Neural network interpolation operators optimized by Lagrange polynomial","year":2022,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Zhejiang University; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Mathematics; Applied mathematics; Lemma (botany); Interpolation (computer graphics); Function approximation; Converse; Artificial neural network; Modulus of continuity; Type (biology); Pure mathematics; Computer science","score_opus":0.009678215468537272,"score_gpt":0.22499573567420902,"score_spread":0.21531752020567174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281726948","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010197429,0.00027852188,0.9857971,0.00018171551,0.00010996751,0.000029972121,0.00003805366,0.00015797671,0.00320923],"genre_scores_gemma":[0.37502447,0.0006741373,0.610391,0.00021277061,0.00014496445,0.00022140419,0.00018379847,0.00036738437,0.012779944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997018,0.0001234768,0.000012976139,0.000054673594,0.00007317259,0.00003389061],"domain_scores_gemma":[0.99941134,0.00030521725,0.000045596815,0.000066304834,0.00013564475,0.000035796686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011840267,0.00054844655,0.00073484425,0.0005369715,0.00035943545,0.0005773005,0.0008835357,0.0009658713,0.0040228222],"category_scores_gemma":[0.0035357266,0.0003153392,0.00046124432,0.00096230744,0.0007232219,0.0010835511,0.0011148178,0.001647046,0.00076879316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023816209,0.000083707106,0.0003210413,0.00020814649,0.000043674543,0.00006455578,0.00007820641,0.7207023,0.0071019656,0.11519392,0.004528716,0.15143557],"study_design_scores_gemma":[0.000007842176,0.000014204328,0.000026444133,0.0000071956915,0.0000033750143,0.000009375058,0.0000043857203,0.991443,0.0007230335,0.0069451104,0.0008121643,0.0000038649587],"about_ca_topic_score_codex":0.0024364418,"about_ca_topic_score_gemma":0.002908419,"teacher_disagreement_score":0.0040228222,"about_ca_system_score_codex":0.0006632699,"about_ca_system_score_gemma":0.0011442793,"threshold_uncertainty_score":0.0134577155},"labels":[],"label_agreement":null},{"id":"W4283034429","doi":"10.1109/tit.2022.3178828","title":"IEEE Transactions on Information Theory publication information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Theoretical computer science; Information retrieval; Mathematics; Statistics","score_opus":0.009156959172592861,"score_gpt":0.21540977404581182,"score_spread":0.20625281487321898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283034429","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073632444,0.041065272,0.4078097,0.025557896,0.04692853,0.0007379434,0.02859816,0.005462615,0.43647674],"genre_scores_gemma":[0.12103247,0.07721286,0.099727295,0.0049459753,0.012654195,0.0008991606,0.04606473,0.002194734,0.63526857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980058,0.0003531626,0.00016168886,0.00020072536,0.0011487462,0.00012988472],"domain_scores_gemma":[0.9946779,0.0017836735,0.0001828315,0.0013241188,0.0018538217,0.0001776851],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020620713,0.0012995908,0.0020753695,0.0038026476,0.00096002803,0.004441838,0.0011496176,0.0020986642,0.18658403],"category_scores_gemma":[0.011094833,0.00058022304,0.0007657712,0.005049121,0.00082062976,0.0042192605,0.0017576806,0.0025192474,0.07861321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016382568,0.00012273522,0.0006897795,0.00085827295,0.00006272511,0.00014853044,0.00005241059,0.0032309913,0.0011106146,0.07595082,0.524427,0.3931823],"study_design_scores_gemma":[0.00006565506,0.00010452829,0.0013115945,0.0008803995,0.00015325854,0.00051396637,0.00011317045,0.033855367,0.0028811428,0.1821534,0.77789456,0.00007305823],"about_ca_topic_score_codex":0.0014865134,"about_ca_topic_score_gemma":0.0012459535,"teacher_disagreement_score":0.813416,"about_ca_system_score_codex":0.001008677,"about_ca_system_score_gemma":0.0021853258,"threshold_uncertainty_score":0.62418574},"labels":[],"label_agreement":null},{"id":"W4283079959","doi":"10.1016/j.eswa.2022.117904","title":"Design of fuzzy rule-based models with fuzzy relational factorization","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canada First Research Excellence Fund; University of Alberta","keywords":"Interpretability; Factorization; Fuzzy rule; Fuzzy logic; Computer science; Fuzzy number; Fuzzy classification; Data mining; Fuzzy set operations; Relational database; Defuzzification; Fuzzy associative matrix; Curse of dimensionality; Artificial intelligence; Fuzzy set; Mathematics; Algorithm","score_opus":0.03439008955923596,"score_gpt":0.2346934231085828,"score_spread":0.20030333354934682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283079959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005475675,0.00013696245,0.99275726,0.00008104331,0.000017495584,0.00006677832,0.00006494134,0.0002583625,0.0011415738],"genre_scores_gemma":[0.43805262,0.00037014842,0.55807745,0.000114033144,0.000039883074,0.00045200236,0.00037201482,0.000073577256,0.0024482647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989297,0.00035065738,0.00007183624,0.00025833474,0.00029393256,0.00009559991],"domain_scores_gemma":[0.99900025,0.0004766249,0.000119554046,0.000093166964,0.00026719816,0.000043162112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001618716,0.00076495286,0.0014671072,0.00064070214,0.0006125657,0.0018022816,0.0017590967,0.0014248705,0.0027151094],"category_scores_gemma":[0.004104219,0.0009142195,0.0015444498,0.0005851137,0.0006239777,0.001526236,0.0011275031,0.0011733214,0.0010385578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011674557,0.00006743724,0.00038445203,0.00015477873,0.00009863436,0.0001596231,0.00016900094,0.9063016,0.0034611756,0.029012984,0.00089347165,0.05918009],"study_design_scores_gemma":[0.000011733184,0.000023203958,0.000031089443,0.000010261463,0.00001943358,0.000017610771,0.0000090881695,0.9919463,0.00065471756,0.0067117536,0.00055884174,0.00000595552],"about_ca_topic_score_codex":0.0057267724,"about_ca_topic_score_gemma":0.006294455,"teacher_disagreement_score":0.0057267724,"about_ca_system_score_codex":0.0008524367,"about_ca_system_score_gemma":0.0016325488,"threshold_uncertainty_score":0.011386931},"labels":[],"label_agreement":null},{"id":"W4283325543","doi":"10.7554/elife.77115.sa0","title":"Editor's evaluation: Sparse genetically defined neurons refine the canonical role of periaqueductal gray columnar organization","year":2022,"lang":"en","type":"peer-review","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Gray (unit); Periaqueductal gray; Neuroscience; Biology; Computer science; Artificial intelligence; Medicine; Midbrain; Central nervous system","score_opus":0.0214522007025685,"score_gpt":0.2782491047514798,"score_spread":0.2567969040489113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283325543","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004393475,0.001973558,0.0011930272,0.15572323,0.83602226,0.00008391325,0.00026030527,0.00026539175,0.0040389695],"genre_scores_gemma":[0.007058815,0.0037716406,0.002551922,0.08196749,0.82434523,0.00014377463,0.0002605412,0.00032230027,0.079578236],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932585,0.0009892798,0.00084764586,0.00081676594,0.0035118617,0.00057594443],"domain_scores_gemma":[0.9473474,0.009804713,0.0027150756,0.0017182362,0.033026654,0.0053878445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010501591,0.0018464852,0.0024516468,0.0024724302,0.0026479368,0.0059018913,0.0039598434,0.011688173,0.03244311],"category_scores_gemma":[0.048681263,0.0009318743,0.0010818637,0.0015855309,0.0020233172,0.0021602518,0.0016688324,0.0082252845,0.016469719],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006267096,0.000017209732,0.00011731804,0.000113387156,0.000015437021,0.00009411661,0.000011885687,0.000054576743,0.0001682962,0.00042586474,0.98760915,0.011310178],"study_design_scores_gemma":[0.00015871905,0.000051878273,0.002074475,0.00026297962,0.000072196264,0.00041697331,0.00009987161,0.0019004238,0.0010985333,0.0015155314,0.9922909,0.000057589914],"about_ca_topic_score_codex":0.0027252613,"about_ca_topic_score_gemma":0.008787416,"teacher_disagreement_score":0.03244311,"about_ca_system_score_codex":0.0024633515,"about_ca_system_score_gemma":0.0049385335,"threshold_uncertainty_score":0.108533025},"labels":[],"label_agreement":null},{"id":"W4283794072","doi":"10.1609/aaai.v36i11.21679","title":"NeuralArTS: Structuring Neural Architecture Search with Type Theory (Student Abstract)","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Structuring; Computer science; Architecture; Set (abstract data type); Convolutional neural network; Artificial neural network; Task (project management); Artificial intelligence; Scratch; Space (punctuation); Type (biology); Machine learning; Theoretical computer science; Engineering; Programming language","score_opus":0.052022085327000415,"score_gpt":0.29159257454656373,"score_spread":0.2395704892195633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283794072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005697976,0.00026672287,0.9844892,0.0004021978,0.00013731734,0.000080977064,0.00010793808,0.0014111175,0.0074064406],"genre_scores_gemma":[0.15692236,0.00039931832,0.83075505,0.00053179165,0.00022314444,0.0004015407,0.00044748557,0.00093832746,0.009380962],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99829656,0.0007046647,0.00015324715,0.00029390908,0.0004197545,0.000131925],"domain_scores_gemma":[0.9965738,0.0018380996,0.00020419502,0.000828579,0.0004019585,0.00015347355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032624092,0.00060124707,0.00092566654,0.0011763239,0.0010125489,0.0031825078,0.0024496594,0.0014532434,0.011925435],"category_scores_gemma":[0.010046182,0.00061441894,0.0020044118,0.0011469121,0.0033100448,0.004421889,0.0035795625,0.002730437,0.002791937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018436706,0.00008552126,0.0010462872,0.00018307315,0.000069229565,0.00014066146,0.0002196419,0.09903857,0.0022994587,0.7432684,0.011929886,0.14153488],"study_design_scores_gemma":[0.000044908644,0.000073943964,0.000093254974,0.000062778236,0.000022702534,0.00009797646,0.000042769083,0.42577022,0.003143687,0.55612713,0.014493582,0.000027116497],"about_ca_topic_score_codex":0.0017020458,"about_ca_topic_score_gemma":0.002493003,"teacher_disagreement_score":0.011925435,"about_ca_system_score_codex":0.0013124133,"about_ca_system_score_gemma":0.0015010142,"threshold_uncertainty_score":0.03989452},"labels":[],"label_agreement":null},{"id":"W4285082555","doi":"10.1109/tit.2022.3186602","title":"IEEE Transactions on Information Theory publication information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.009156959172592861,"score_gpt":0.21540977404581182,"score_spread":0.20625281487321898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285082555","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073632444,0.041065272,0.4078097,0.025557896,0.04692853,0.0007379434,0.02859816,0.005462615,0.43647674],"genre_scores_gemma":[0.12103247,0.07721286,0.099727295,0.0049459753,0.012654195,0.0008991606,0.04606473,0.002194734,0.63526857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980058,0.0003531626,0.00016168886,0.00020072536,0.0011487462,0.00012988472],"domain_scores_gemma":[0.9946779,0.0017836735,0.0001828315,0.0013241188,0.0018538217,0.0001776851],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020620713,0.0012995908,0.0020753695,0.0038026476,0.00096002803,0.004441838,0.0011496176,0.0020986642,0.18658403],"category_scores_gemma":[0.011094833,0.00058022304,0.0007657712,0.005049121,0.00082062976,0.0042192605,0.0017576806,0.0025192474,0.07861321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016382568,0.00012273522,0.0006897795,0.00085827295,0.00006272511,0.00014853044,0.00005241059,0.0032309913,0.0011106146,0.07595082,0.524427,0.3931823],"study_design_scores_gemma":[0.00006565506,0.00010452829,0.0013115945,0.0008803995,0.00015325854,0.00051396637,0.00011317045,0.033855367,0.0028811428,0.1821534,0.77789456,0.00007305823],"about_ca_topic_score_codex":0.0014865134,"about_ca_topic_score_gemma":0.0012459535,"teacher_disagreement_score":0.813416,"about_ca_system_score_codex":0.001008677,"about_ca_system_score_gemma":0.0021853258,"threshold_uncertainty_score":0.62418574},"labels":[],"label_agreement":null},{"id":"W4285242070","doi":"10.23952/jano.4.2022.2.05","title":"Point-to-set distance functions for output-constrained neural networks","year":2022,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Set (abstract data type); Artificial neural network; Point (geometry); Set point; Computer science; Mathematics; Artificial intelligence; Geometry; Engineering; Control engineering","score_opus":0.011135720018041528,"score_gpt":0.22566582963416587,"score_spread":0.21453010961612434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285242070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020656658,0.0001609405,0.99621385,0.00017509157,0.000020884045,0.00004138998,0.000043460543,0.00010561794,0.0011730684],"genre_scores_gemma":[0.13852754,0.00062251196,0.8535547,0.00028637174,0.00006270417,0.00082462275,0.00045735415,0.00052634365,0.0051378426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99871314,0.0005164287,0.00008620403,0.00019698775,0.00041373083,0.00007348479],"domain_scores_gemma":[0.99739885,0.0015518499,0.000212876,0.0002174235,0.0005019846,0.000117102965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029544355,0.0018143022,0.0010590461,0.0009763351,0.0006230346,0.0016935623,0.0019372625,0.0026313448,0.0038666017],"category_scores_gemma":[0.011153635,0.00067078864,0.0010154615,0.0010566299,0.0019800428,0.0029359118,0.003164858,0.0046716076,0.0011112092],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007200083,0.000074284544,0.00035654366,0.00024606427,0.000042606465,0.000077164914,0.00009376003,0.766657,0.003249661,0.15435965,0.0032176252,0.0715536],"study_design_scores_gemma":[0.000005049183,0.000019686267,0.00005395681,0.00002301931,0.0000027210363,0.000020653868,0.000006447357,0.9677086,0.00087700906,0.029934965,0.0013382762,0.000009584462],"about_ca_topic_score_codex":0.0025193526,"about_ca_topic_score_gemma":0.0026269262,"teacher_disagreement_score":0.0038666017,"about_ca_system_score_codex":0.002315742,"about_ca_system_score_gemma":0.0018927592,"threshold_uncertainty_score":0.016801953},"labels":[],"label_agreement":null},{"id":"W4285256993","doi":"10.1007/978-3-031-09282-4","title":"Pattern Recognition and Artificial Intelligence","year":2022,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.04235816556134437,"score_gpt":0.2634067356421219,"score_spread":0.22104857008077755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285256993","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028009976,0.11198661,0.48755607,0.0028495383,0.0078074713,0.00023274394,0.0012810676,0.005013177,0.38047242],"genre_scores_gemma":[0.027305216,0.062665686,0.15872806,0.00134914,0.0028062207,0.00033093564,0.0022028305,0.0010586582,0.74355334],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996331,0.000041627285,0.000025209561,0.00008272403,0.00019866049,0.000018600405],"domain_scores_gemma":[0.9996352,0.00011426276,0.000021537051,0.00012470367,0.00008079094,0.000023442679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031697558,0.0014147836,0.0020283754,0.001452024,0.00033154906,0.0023419878,0.001190368,0.00087501074,0.07343648],"category_scores_gemma":[0.0010360734,0.0005089714,0.0005805439,0.003120222,0.00084595685,0.002718202,0.0012359256,0.0013436319,0.0591524],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031653828,0.000032887634,0.00011200968,0.000815048,0.00005043398,0.00008084762,0.000048325972,0.002145305,0.0030950755,0.0525353,0.14451261,0.79654044],"study_design_scores_gemma":[0.00001966146,0.000079633326,0.00072873896,0.00031281562,0.000047383874,0.00077648653,0.000050059374,0.012576054,0.0027244145,0.1458525,0.8368081,0.000024161694],"about_ca_topic_score_codex":0.00052861014,"about_ca_topic_score_gemma":0.0006455247,"teacher_disagreement_score":0.07343648,"about_ca_system_score_codex":0.00037953397,"about_ca_system_score_gemma":0.00045564066,"threshold_uncertainty_score":0.24566948},"labels":[],"label_agreement":null},{"id":"W4285259144","doi":"10.1007/978-3-031-09037-0","title":"Pattern Recognition and Artificial Intelligence","year":2022,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":159,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.04235816556134437,"score_gpt":0.2634067356421219,"score_spread":0.22104857008077755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285259144","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028009976,0.11198661,0.48755607,0.0028495383,0.0078074713,0.00023274394,0.0012810676,0.005013177,0.38047242],"genre_scores_gemma":[0.027305216,0.062665686,0.15872806,0.00134914,0.0028062207,0.00033093564,0.0022028305,0.0010586582,0.74355334],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996331,0.000041627285,0.000025209561,0.00008272403,0.00019866049,0.000018600405],"domain_scores_gemma":[0.9996352,0.00011426276,0.000021537051,0.00012470367,0.00008079094,0.000023442679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031697558,0.0014147836,0.0020283754,0.001452024,0.00033154906,0.0023419878,0.001190368,0.00087501074,0.07343648],"category_scores_gemma":[0.0010360734,0.0005089714,0.0005805439,0.003120222,0.00084595685,0.002718202,0.0012359256,0.0013436319,0.0591524],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031653828,0.000032887634,0.00011200968,0.000815048,0.00005043398,0.00008084762,0.000048325972,0.002145305,0.0030950755,0.0525353,0.14451261,0.79654044],"study_design_scores_gemma":[0.00001966146,0.000079633326,0.00072873896,0.00031281562,0.000047383874,0.00077648653,0.000050059374,0.012576054,0.0027244145,0.1458525,0.8368081,0.000024161694],"about_ca_topic_score_codex":0.00052861014,"about_ca_topic_score_gemma":0.0006455247,"teacher_disagreement_score":0.07343648,"about_ca_system_score_codex":0.00037953397,"about_ca_system_score_gemma":0.00045564066,"threshold_uncertainty_score":0.24566948},"labels":[],"label_agreement":null},{"id":"W4285446353","doi":"10.7554/elife.65361.sa2","title":"Author response: Competition between parallel sensorimotor learning systems","year":2021,"lang":"en","type":"peer-review","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Competition (biology); Computer science; Neuroscience; Psychology; Cognitive psychology; Cognitive science; Biology; Ecology","score_opus":0.06642938383288112,"score_gpt":0.33652126441913194,"score_spread":0.27009188058625083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285446353","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006205875,0.0019998748,0.0043530157,0.73112565,0.16480838,0.00027820017,0.0010953927,0.00087000383,0.08926359],"genre_scores_gemma":[0.07508387,0.0022123288,0.002685785,0.21326584,0.061558194,0.00043950925,0.0011251994,0.000625476,0.6430038],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9914272,0.0023166419,0.00046862682,0.000954805,0.0040003387,0.0008324793],"domain_scores_gemma":[0.9115536,0.037711058,0.0028419648,0.0041245385,0.03799565,0.0057732374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0091323275,0.00063601177,0.0010337697,0.0011612213,0.002216222,0.0034832372,0.0015185847,0.01038378,0.19841424],"category_scores_gemma":[0.09631857,0.00034230307,0.00065222045,0.0007425938,0.0011865501,0.0016780491,0.0018250175,0.0050862827,0.05814728],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023695441,0.000026151272,0.0003735245,0.00010593134,0.000017851347,0.00013722738,0.00006666921,0.00010546735,0.0003424435,0.0036878467,0.97580916,0.01909077],"study_design_scores_gemma":[0.00015591996,0.00008408693,0.0021386477,0.00012526146,0.000027588338,0.00032895253,0.00034770367,0.0010795828,0.001105682,0.009523203,0.9850412,0.000042067113],"about_ca_topic_score_codex":0.00228421,"about_ca_topic_score_gemma":0.0066358615,"teacher_disagreement_score":0.19841424,"about_ca_system_score_codex":0.002391168,"about_ca_system_score_gemma":0.003964712,"threshold_uncertainty_score":0.66376173},"labels":[],"label_agreement":null},{"id":"W4285464805","doi":"10.32920/ryerson.14644767.v1","title":"Developing pseudo random number generator based on neural networks and neurofuzzy systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"NIST; Pseudorandom number generator; Measure (data warehouse); Artificial neural network; Generator (circuit theory); Computer science; Random number generation; Fuzzy logic; Artificial intelligence; Algorithm; Data mining; Natural language processing; Physics","score_opus":0.025446158553933642,"score_gpt":0.258351408001954,"score_spread":0.23290524944802035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285464805","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019425284,0.0005936622,0.97637033,0.0001719727,0.00007174491,0.00009393502,0.000032740503,0.00034748222,0.0028927843],"genre_scores_gemma":[0.5937012,0.0009576597,0.40095586,0.00015598918,0.00008385832,0.0001748993,0.00012275661,0.000055651275,0.0037920992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999423,0.00020925529,0.000032314183,0.00009064787,0.00020851861,0.00003625612],"domain_scores_gemma":[0.9992531,0.00037407235,0.0000749786,0.00009088718,0.00018368015,0.000023264533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008555627,0.00032495745,0.00030599764,0.00051770627,0.0002453379,0.0004400061,0.00054300856,0.00057325047,0.0010548221],"category_scores_gemma":[0.0026320682,0.00016548621,0.0003579209,0.00041101404,0.00061922957,0.0012062197,0.00044417585,0.0005070657,0.00027047607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018162956,0.000091139926,0.0023424646,0.0003687571,0.00009889004,0.00048460884,0.00019736282,0.48805055,0.048325885,0.18344885,0.0026562798,0.27375352],"study_design_scores_gemma":[0.000011836667,0.000061041814,0.00024215072,0.000014345497,0.000010956339,0.00020049515,0.000009064805,0.9703306,0.009761992,0.016142359,0.0031992306,0.000015996555],"about_ca_topic_score_codex":0.00089682866,"about_ca_topic_score_gemma":0.00088966504,"teacher_disagreement_score":0.0010548221,"about_ca_system_score_codex":0.00044940604,"about_ca_system_score_gemma":0.0005205036,"threshold_uncertainty_score":0.004524708},"labels":[],"label_agreement":null},{"id":"W4286449751","doi":"10.18280/isi.270301","title":"Inference Control in Aggregation Query Processing Based on Supervised Learning Models","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Inference; Computer science; Artificial intelligence; Machine learning; Control (management)","score_opus":0.021111253163623986,"score_gpt":0.23143559572014197,"score_spread":0.210324342556518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286449751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06748874,0.00022002014,0.929254,0.00030827455,0.000034591634,0.00009017947,0.000103753286,0.0014915388,0.0010088504],"genre_scores_gemma":[0.915463,0.00011347273,0.0828351,0.00012524663,0.000070814785,0.00011875474,0.00020838012,0.00008519196,0.0009801383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946101,0.0017526349,0.00043242157,0.0013713551,0.0013519194,0.00048158705],"domain_scores_gemma":[0.98267245,0.011496012,0.0016670376,0.0014740006,0.0023873597,0.00030303645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007859067,0.0006776058,0.001421952,0.0010473542,0.0009553231,0.0023825646,0.001888831,0.0009093362,0.0012330567],"category_scores_gemma":[0.017660463,0.0005610676,0.0008839382,0.000996912,0.001375458,0.0027715508,0.0012332323,0.0019608373,0.000250769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000575416,0.0003151685,0.0052646315,0.000109385976,0.00015080199,0.00012122668,0.0002788268,0.86428195,0.0025899059,0.019713556,0.0013623767,0.1052368],"study_design_scores_gemma":[0.000004171347,0.000010229801,0.00009105209,0.0000013344344,0.0000035284497,0.0000046293676,0.000004579511,0.9973726,0.0003367712,0.002115845,0.000053304204,0.0000018299841],"about_ca_topic_score_codex":0.017002603,"about_ca_topic_score_gemma":0.012179208,"teacher_disagreement_score":0.017002603,"about_ca_system_score_codex":0.0023234012,"about_ca_system_score_gemma":0.0027793425,"threshold_uncertainty_score":0.041563213},"labels":[],"label_agreement":null},{"id":"W4286774223","doi":"","title":"Integrating and reporting full multi-view supervised learning experiments using SuMMIT","year":2021,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre hospitalier de l'Université Laval; Université Laval","funders":"","keywords":"Summit; Computer science; Artificial intelligence; Machine learning; Geography; Cartography","score_opus":0.06068588380669114,"score_gpt":0.2975914759832826,"score_spread":0.23690559217659146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286774223","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10582557,0.004889037,0.5063685,0.0023336315,0.004621216,0.0020169911,0.11887157,0.2393238,0.015749574],"genre_scores_gemma":[0.3439061,0.00060022576,0.40615928,0.0012204598,0.0007609275,0.0023738698,0.21723908,0.01570926,0.012030766],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.97348624,0.010066716,0.0022258267,0.0063147596,0.006656622,0.0012498145],"domain_scores_gemma":[0.8961111,0.03535977,0.003503533,0.041687828,0.020734824,0.0026030235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02635714,0.003775347,0.0031534845,0.004805192,0.002265793,0.006008184,0.0044061057,0.0051056743,0.012611415],"category_scores_gemma":[0.08118191,0.0015357938,0.003223181,0.002289038,0.0020114942,0.005668005,0.0059361686,0.003356132,0.012866818],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006591844,0.0025101146,0.031605773,0.005043809,0.0040183445,0.0010345902,0.00077183073,0.06234043,0.10197849,0.004903599,0.40910065,0.3701006],"study_design_scores_gemma":[0.0008065451,0.0020883072,0.038903262,0.0005087544,0.0015115066,0.0010807969,0.00078255346,0.60761714,0.20317869,0.026209097,0.11652904,0.0007843174],"about_ca_topic_score_codex":0.008548654,"about_ca_topic_score_gemma":0.02022402,"teacher_disagreement_score":0.02635714,"about_ca_system_score_codex":0.0015398564,"about_ca_system_score_gemma":0.004024926,"threshold_uncertainty_score":0.13939154},"labels":[],"label_agreement":null},{"id":"W4287231569","doi":"10.5281/zenodo.4678963","title":"The Shallow Gibbs Network, Double Backpropagation and Differential Machine learning","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Backpropagation; Computer science; Differential (mechanical device); Artificial intelligence; Machine learning; Artificial neural network; Physics; Thermodynamics","score_opus":0.026787506619207337,"score_gpt":0.2277462460944936,"score_spread":0.20095873947528625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287231569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007994704,0.00036374477,0.98877233,0.00029793594,0.000052738334,0.00002791147,0.000065368054,0.00034621244,0.0020789711],"genre_scores_gemma":[0.43997085,0.0010519619,0.5467377,0.00045676474,0.00012896954,0.00023464128,0.0003977373,0.00034233596,0.010679122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950767,0.00014225482,0.000025163381,0.00012958923,0.00013600069,0.00005938252],"domain_scores_gemma":[0.9994506,0.0002441417,0.000053771495,0.00008582317,0.00011276995,0.000052834053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011703718,0.0008112399,0.00087621924,0.00081662176,0.00047689848,0.0009694536,0.0017705972,0.0013013242,0.0023252966],"category_scores_gemma":[0.0030173755,0.0006225473,0.0007452186,0.00095920387,0.0014692268,0.002423648,0.0015526747,0.0017434708,0.0005933591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007047359,0.00004354154,0.0010357193,0.00009454947,0.000055212742,0.00006992609,0.000082417515,0.74802226,0.0026010112,0.13802937,0.002392014,0.107503556],"study_design_scores_gemma":[0.000004280707,0.0000102634485,0.00007413032,0.0000057652173,0.0000042057445,0.000012783256,0.0000024623585,0.9707899,0.0005504605,0.027540613,0.0009980608,0.000007082458],"about_ca_topic_score_codex":0.009995162,"about_ca_topic_score_gemma":0.011489044,"teacher_disagreement_score":0.009995162,"about_ca_system_score_codex":0.0014379396,"about_ca_system_score_gemma":0.0014682254,"threshold_uncertainty_score":0.019873977},"labels":[],"label_agreement":null},{"id":"W4287545976","doi":"10.48550/arxiv.2012.15458","title":"Differentiable Programming à la Moreau","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Differentiable function; Computer science; Envelope (radar); Automatic differentiation; Artificial intelligence; Mathematical optimization; Theoretical computer science; Programming language; Mathematics; Computation; Pure mathematics","score_opus":0.08405163039584398,"score_gpt":0.19028510605480758,"score_spread":0.1062334756589636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287545976","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008407845,0.004967913,0.88824594,0.006249107,0.0006577616,0.000037339152,0.00028673225,0.0004080981,0.09073934],"genre_scores_gemma":[0.47072127,0.009318966,0.43314508,0.0043715783,0.0021089118,0.00044913232,0.00046342265,0.0011516161,0.07827011],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99899477,0.00031275873,0.000041805564,0.00022131685,0.00034197365,0.00008735168],"domain_scores_gemma":[0.9991153,0.0004511235,0.00006223759,0.0001447665,0.00016821835,0.000058332804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00196084,0.00094860815,0.0008505815,0.00091001537,0.0010035182,0.0037984143,0.00070826575,0.0016233681,0.0070998794],"category_scores_gemma":[0.0041703633,0.0004928304,0.0014642376,0.0012645423,0.0042722407,0.0046900865,0.002175616,0.0055370205,0.0026437645],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006454218,0.000005837616,0.00003366744,0.000021810176,0.000004534181,0.000015555548,0.000042980566,0.0022087146,0.00015910601,0.98926765,0.0016579552,0.006575654],"study_design_scores_gemma":[0.000007917722,0.000012225945,0.000052239677,0.000033877954,0.000004623575,0.00003255852,0.000014544792,0.01928855,0.00029545987,0.9569566,0.023291497,0.000009939283],"about_ca_topic_score_codex":0.0024990921,"about_ca_topic_score_gemma":0.0014660695,"teacher_disagreement_score":0.0070998794,"about_ca_system_score_codex":0.0022838393,"about_ca_system_score_gemma":0.0013536576,"threshold_uncertainty_score":0.023751438},"labels":[],"label_agreement":null},{"id":"W4287645654","doi":"10.5281/zenodo.4067799","title":"DEEP LEARNING NEURAL NETWORK APPROACHES TO LAND USE-DEMOGRAPHIC- TEMPORAL BASED TRAFFIC PREDICTION","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Deep learning; Artificial neural network; Computer science; Artificial intelligence; Machine learning; Geography","score_opus":0.09321691412561013,"score_gpt":0.21998898222627775,"score_spread":0.1267720681006676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287645654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.115563266,0.0049399994,0.8645423,0.0022430066,0.00031848327,0.000068750385,0.0019245056,0.0015501777,0.0088495305],"genre_scores_gemma":[0.87699574,0.0024523586,0.10561219,0.00026214132,0.00023685834,0.00010683743,0.001838465,0.00006542922,0.012430047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998672,0.000036684192,0.000010179655,0.00003877506,0.000024590767,0.000022641292],"domain_scores_gemma":[0.9996536,0.00014708098,0.000047402384,0.000020786249,0.00011481255,0.00001632979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058779726,0.0006229895,0.00036954452,0.00078074646,0.00021174135,0.00070650416,0.00076688523,0.0007333401,0.001615182],"category_scores_gemma":[0.0012865707,0.0002616881,0.00032308855,0.0011262965,0.00024839936,0.0007338837,0.0003996243,0.0007496657,0.00040802485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048578357,0.00007962471,0.0038983107,0.000079103826,0.0000776908,0.000079282974,0.000034589757,0.8647189,0.001560195,0.005583327,0.0032632262,0.120577164],"study_design_scores_gemma":[0.0000011553244,0.0000046742875,0.0005231353,0.000004350019,0.0000039061056,0.000004324609,0.0000049591017,0.9965384,0.0002910883,0.0022409712,0.00038051192,0.0000025234892],"about_ca_topic_score_codex":0.029794678,"about_ca_topic_score_gemma":0.03490866,"teacher_disagreement_score":0.029794678,"about_ca_system_score_codex":0.0010275268,"about_ca_system_score_gemma":0.0006213949,"threshold_uncertainty_score":0.059242547},"labels":[],"label_agreement":null},{"id":"W4287728238","doi":"","title":"Neumann networks: differential programming for supervised learning with missing values","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Differential (mechanical device); Computer science; Artificial intelligence; Missing data; Supervised learning; Machine learning; Semi-supervised learning; Artificial neural network; Engineering","score_opus":0.020407226262286984,"score_gpt":0.23581272699906855,"score_spread":0.21540550073678155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287728238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023653125,0.00022862482,0.9961825,0.00030782146,0.000057361085,0.000034063,0.00006329512,0.000080248275,0.0006807706],"genre_scores_gemma":[0.26440507,0.0013307144,0.71613264,0.00073720637,0.00049327593,0.0011793047,0.000827956,0.00042907018,0.014464833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970674,0.001727787,0.00012129661,0.00046039178,0.00048810145,0.00013495621],"domain_scores_gemma":[0.99132454,0.006955748,0.00029193328,0.00040406457,0.0007047582,0.00031891093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006152738,0.0014202921,0.0021910435,0.0008925914,0.0006918405,0.0019916128,0.0033654002,0.0029588435,0.003401256],"category_scores_gemma":[0.018934775,0.00165337,0.0011993245,0.0012915209,0.002571587,0.0034124174,0.0052451524,0.0056683845,0.0008865601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023470518,0.00017781585,0.0006126225,0.0004125626,0.00016627612,0.00014126215,0.0002371917,0.56601864,0.0015076427,0.3446942,0.0057070954,0.08009007],"study_design_scores_gemma":[0.000010917115,0.000014909721,0.00002366879,0.000015022648,0.000006152249,0.000010976715,0.0000074328573,0.88819337,0.00023778147,0.110915266,0.00055706577,0.0000073757874],"about_ca_topic_score_codex":0.0021123928,"about_ca_topic_score_gemma":0.0025555186,"teacher_disagreement_score":0.006152738,"about_ca_system_score_codex":0.001408289,"about_ca_system_score_gemma":0.0022350322,"threshold_uncertainty_score":0.03253913},"labels":[],"label_agreement":null},{"id":"W4288877484","doi":"10.1007/978-1-4471-7452-3","title":"Neural Networks and Statistical Learning","year":2019,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":204,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Deep learning; Statistical learning; Big data; Deep neural networks; Machine learning; Neural coding; Unsupervised learning; Cloud computing; Data science; Data mining","score_opus":0.009845208218876219,"score_gpt":0.22687394926050938,"score_spread":0.21702874104163317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288877484","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013715021,0.17260481,0.23867111,0.007328593,0.009351391,0.00009456714,0.0014702146,0.0025572816,0.56655055],"genre_scores_gemma":[0.012064239,0.06554434,0.050360452,0.0016703159,0.0043479893,0.00017438126,0.0013966323,0.0010021866,0.86343944],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995665,0.00004986593,0.000021196374,0.00006884892,0.0002751581,0.000018462839],"domain_scores_gemma":[0.9994159,0.00030283644,0.000035998837,0.00006913303,0.00014559284,0.00003063263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040404734,0.0014958277,0.0012893743,0.0019956336,0.00038732472,0.0020554634,0.00097419607,0.0010488469,0.0515291],"category_scores_gemma":[0.0020100484,0.0005514378,0.00039600153,0.0035608353,0.00089879957,0.0030340683,0.0011773395,0.0023068278,0.041420545],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014862975,0.000025044857,0.00011646702,0.0004905255,0.000023784512,0.000032919746,0.000037507612,0.0035535023,0.0006360761,0.08822136,0.33959764,0.5672503],"study_design_scores_gemma":[0.000004809661,0.000018966153,0.00042254262,0.00029808213,0.000014780648,0.00015827459,0.00002322366,0.009347762,0.0004939583,0.1522281,0.83696735,0.000022140968],"about_ca_topic_score_codex":0.0015050869,"about_ca_topic_score_gemma":0.0036184194,"teacher_disagreement_score":0.0515291,"about_ca_system_score_codex":0.0008612026,"about_ca_system_score_gemma":0.00081970944,"threshold_uncertainty_score":0.172382},"labels":[],"label_agreement":null},{"id":"W4292070296","doi":"10.1109/newcas52662.2022.9842248","title":"Neural Network-Based Signal Translation with Application to the ECG","year":2022,"lang":"en","type":"article","venue":"2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Translation (biology); SIGNAL (programming language); Speech recognition; Artificial intelligence; Programming language","score_opus":0.042877860591061266,"score_gpt":0.2638744234602459,"score_spread":0.22099656286918465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292070296","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024202473,0.0004812637,0.97099227,0.00022295013,0.000106455955,0.000049844628,0.00008595755,0.0010923214,0.0027664418],"genre_scores_gemma":[0.6514549,0.0007139089,0.33851123,0.0002684581,0.00010487354,0.00011184298,0.00042352162,0.00021885225,0.008192442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998369,0.0000472494,0.0000070310502,0.00004314918,0.000049345064,0.000016324097],"domain_scores_gemma":[0.9998228,0.000084920226,0.000014973907,0.000032272867,0.0000383969,0.000006619661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003572922,0.00051649474,0.0002643384,0.00017080811,0.00011412858,0.00031731997,0.00041593882,0.0004641252,0.0016551331],"category_scores_gemma":[0.00091833755,0.00017080322,0.0003338292,0.00026319013,0.00028939667,0.0003663382,0.00045468225,0.0008516183,0.0005189671],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019440977,0.00007571368,0.00065026875,0.0001367099,0.000082167455,0.0002640513,0.00007478878,0.5970296,0.05560239,0.007738285,0.002390701,0.33576095],"study_design_scores_gemma":[0.0000047754183,0.00006559582,0.00026789613,0.0000071428335,0.0000083610985,0.00009672593,0.0000052379432,0.98322845,0.013211019,0.0014175652,0.0016803216,0.0000068874906],"about_ca_topic_score_codex":0.0013971559,"about_ca_topic_score_gemma":0.0013630522,"teacher_disagreement_score":0.0016551331,"about_ca_system_score_codex":0.00025522118,"about_ca_system_score_gemma":0.0002528411,"threshold_uncertainty_score":0.0055369735},"labels":[],"label_agreement":null},{"id":"W4292261520","doi":"10.1109/tit.2022.3195652","title":"IEEE Transactions on Information Theory publication information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Information theory; Computer science; Theoretical computer science; Information retrieval; Mathematics; Statistics","score_opus":0.009156959172592861,"score_gpt":0.21540977404581182,"score_spread":0.20625281487321898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292261520","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073632444,0.041065272,0.4078097,0.025557896,0.04692853,0.0007379434,0.02859816,0.005462615,0.43647674],"genre_scores_gemma":[0.12103247,0.07721286,0.099727295,0.0049459753,0.012654195,0.0008991606,0.04606473,0.002194734,0.63526857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980058,0.0003531626,0.00016168886,0.00020072536,0.0011487462,0.00012988472],"domain_scores_gemma":[0.9946779,0.0017836735,0.0001828315,0.0013241188,0.0018538217,0.0001776851],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020620713,0.0012995908,0.0020753695,0.0038026476,0.00096002803,0.004441838,0.0011496176,0.0020986642,0.18658403],"category_scores_gemma":[0.011094833,0.00058022304,0.0007657712,0.005049121,0.00082062976,0.0042192605,0.0017576806,0.0025192474,0.07861321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016382568,0.00012273522,0.0006897795,0.00085827295,0.00006272511,0.00014853044,0.00005241059,0.0032309913,0.0011106146,0.07595082,0.524427,0.3931823],"study_design_scores_gemma":[0.00006565506,0.00010452829,0.0013115945,0.0008803995,0.00015325854,0.00051396637,0.00011317045,0.033855367,0.0028811428,0.1821534,0.77789456,0.00007305823],"about_ca_topic_score_codex":0.0014865134,"about_ca_topic_score_gemma":0.0012459535,"teacher_disagreement_score":0.813416,"about_ca_system_score_codex":0.001008677,"about_ca_system_score_gemma":0.0021853258,"threshold_uncertainty_score":0.62418574},"labels":[],"label_agreement":null},{"id":"W4292862258","doi":"10.1109/mwscas54063.2022.9859428","title":"FPGA-Based Architectures for Random Forest Acceleration","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Random forest; Field-programmable gate array; Software; Acceleration; Implementation; Software implementation; Embedded system; Computer engineering; Parallel computing; Computer architecture; Real-time computing; Machine learning; Operating system; Software engineering","score_opus":0.025611232203403205,"score_gpt":0.2598958673849199,"score_spread":0.23428463518151668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292862258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.111581035,0.003946882,0.8028921,0.0006011259,0.0010205444,0.0003512045,0.0011236819,0.025906008,0.052577376],"genre_scores_gemma":[0.66040105,0.00092302996,0.31684095,0.00040566572,0.00014207259,0.0002561489,0.001499838,0.00030537823,0.019225862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978286,0.000031805692,0.0000120800705,0.00004132052,0.00007133829,0.0000606485],"domain_scores_gemma":[0.9998086,0.000050799426,0.000019162428,0.000033805263,0.000074517586,0.000013181741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024314196,0.0006046904,0.000253738,0.00067498034,0.00021873224,0.0004963249,0.0010290311,0.00030277963,0.020843673],"category_scores_gemma":[0.0005518042,0.00020612883,0.00025570006,0.00061808573,0.00010016062,0.0006037358,0.0002485536,0.0003684479,0.0054655233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090364116,0.00021834431,0.0028707716,0.00061526115,0.00010195629,0.00040607832,0.00009624872,0.03822053,0.062102087,0.011574044,0.04106569,0.84182537],"study_design_scores_gemma":[0.0003957507,0.0018735828,0.0071936324,0.00025002484,0.00019057382,0.001816306,0.00013573989,0.64814454,0.15696141,0.00713615,0.17577884,0.00012351363],"about_ca_topic_score_codex":0.0021654582,"about_ca_topic_score_gemma":0.00362082,"teacher_disagreement_score":0.020843673,"about_ca_system_score_codex":0.00036672095,"about_ca_system_score_gemma":0.000444183,"threshold_uncertainty_score":0.06972909},"labels":[],"label_agreement":null},{"id":"W4293060484","doi":"10.21203/rs.3.rs-1789791/v1","title":"Rotated Digit Recognition by Variational Autoencoders with Fixed Output Distributions","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MNIST database; Autoencoder; Numerical digit; Dimension (graph theory); Pattern recognition (psychology); Computer science; Artificial intelligence; Representation (politics); Mathematics; Algorithm; Arithmetic; Artificial neural network; Combinatorics","score_opus":0.07097462891165235,"score_gpt":0.3503070347588132,"score_spread":0.27933240584716085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293060484","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05652533,0.00017640773,0.94176334,0.00009802711,0.000032487354,0.000018537803,0.00006375133,0.00027109962,0.0010510306],"genre_scores_gemma":[0.6628,0.00018122645,0.33128923,0.00009745585,0.000028203023,0.000043712414,0.00026114547,0.00011711777,0.0051817996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968827,0.00012045735,0.00001608368,0.000084251726,0.00005676588,0.00003422397],"domain_scores_gemma":[0.9994748,0.00028903774,0.0000487954,0.00009464415,0.000072634524,0.00001995852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010351702,0.00046516297,0.00048139202,0.0002697861,0.00014299442,0.0006622974,0.0007234163,0.00055469293,0.0011630733],"category_scores_gemma":[0.0023638401,0.00038053715,0.0005962666,0.0003165908,0.00064320577,0.00077280775,0.0006112882,0.000789463,0.00034111761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013607951,0.000030498131,0.00097410614,0.000047272755,0.000069771115,0.00006364715,0.00006391455,0.85424095,0.014489279,0.023067934,0.00071709865,0.10609937],"study_design_scores_gemma":[0.000001701844,0.0000059377307,0.00007974048,0.0000018751481,0.0000018090378,0.000007830364,0.0000019328568,0.99641305,0.001323116,0.0020380202,0.00012182963,0.0000030896747],"about_ca_topic_score_codex":0.0035045592,"about_ca_topic_score_gemma":0.003880624,"teacher_disagreement_score":0.0035045592,"about_ca_system_score_codex":0.00061988475,"about_ca_system_score_gemma":0.0005078347,"threshold_uncertainty_score":0.0069683194},"labels":[],"label_agreement":null},{"id":"W4293084263","doi":"10.5539/mer.v10n1p1","title":"Processing Structures for Computer Simulation of Human Movement","year":2022,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ohio State University","keywords":"Object (grammar); Computer science; Movement (music); Human–computer interaction; Artificial intelligence; Sensory system; Computer vision; Neuroscience; Psychology","score_opus":0.06963851237815535,"score_gpt":0.36798796440944614,"score_spread":0.2983494520312908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293084263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00588052,0.001017493,0.9559982,0.0011239519,0.0003248619,0.0001198267,0.00072398747,0.0014101268,0.0334011],"genre_scores_gemma":[0.29145765,0.0021039988,0.6735128,0.00040156438,0.00029506892,0.0019497976,0.0017431373,0.00072561443,0.027810296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998553,0.000048738802,0.000008746473,0.000025937174,0.00005101814,0.000010353661],"domain_scores_gemma":[0.9997261,0.00014980137,0.000014943559,0.00004346081,0.00005235305,0.000013387809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003222595,0.0006988811,0.0005882291,0.00044117155,0.00044350603,0.0011241013,0.0010035249,0.0012211662,0.018783087],"category_scores_gemma":[0.0019462762,0.00028166297,0.0006228966,0.0005179459,0.00070623966,0.0010914338,0.000870416,0.0012175959,0.0035502699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030932773,0.000022479724,0.0002544805,0.00014598397,0.00002954577,0.0000701331,0.00012351687,0.58275515,0.001676195,0.37518916,0.008244764,0.031457692],"study_design_scores_gemma":[0.000012973956,0.0000073391266,0.000065480024,0.000020141691,0.0000034524755,0.000013548722,0.00001626321,0.8755147,0.00025343103,0.107742734,0.016345203,0.000004806704],"about_ca_topic_score_codex":0.003975522,"about_ca_topic_score_gemma":0.0028185986,"teacher_disagreement_score":0.018783087,"about_ca_system_score_codex":0.000832774,"about_ca_system_score_gemma":0.00072439655,"threshold_uncertainty_score":0.06283569},"labels":[],"label_agreement":null},{"id":"W4293116017","doi":"10.11159/htff22.175","title":"Prediction Accuracy of Artificial Neural Networks in Thermal Management Applications Subject to Neural Network Architectures","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; U.S. Department of Energy; National Science Foundation","keywords":"Artificial neural network; Computer science; Physical neural network; Artificial intelligence; Nervous system network models; Subject (documents); Time delay neural network; Types of artificial neural networks; Machine learning","score_opus":0.008343658647139917,"score_gpt":0.20832948305540108,"score_spread":0.19998582440826115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293116017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8173244,0.002659673,0.17291155,0.0007609851,0.00016287794,0.000068278925,0.00027477831,0.00080224796,0.00503513],"genre_scores_gemma":[0.98787284,0.00036221306,0.010681299,0.000039576018,0.000024315897,0.000026940772,0.00020357511,0.00003164255,0.0007577335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976622,0.0011141326,0.00018385862,0.00032125902,0.00053272076,0.00018589146],"domain_scores_gemma":[0.97794193,0.018130416,0.0009926067,0.00087690935,0.0019212186,0.00013690887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006212419,0.0010948792,0.0007304226,0.000911927,0.0003782113,0.0010965947,0.0005414328,0.0010759234,0.00073097687],"category_scores_gemma":[0.031157885,0.00039702118,0.00044199638,0.0008071724,0.00049546297,0.0012354925,0.0006468666,0.0010846711,0.00024566302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001871913,0.00004913588,0.011157213,0.000060552742,0.00007718402,0.000046681795,0.00003073654,0.95664895,0.00088710425,0.00042302653,0.00028569245,0.030146524],"study_design_scores_gemma":[0.0000022754364,0.000036256228,0.0021453039,0.000010249249,0.000009257942,0.000010352928,0.000009870848,0.9964168,0.0009463764,0.0003445838,0.000062989435,0.000005609455],"about_ca_topic_score_codex":0.008195604,"about_ca_topic_score_gemma":0.0060065472,"teacher_disagreement_score":0.008195604,"about_ca_system_score_codex":0.0008923099,"about_ca_system_score_gemma":0.0006478513,"threshold_uncertainty_score":0.032854795},"labels":[],"label_agreement":null},{"id":"W4293118218","doi":"10.1007/978-3-031-01588-5_5","title":"The Graph Neural Network Model","year":2020,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on artificial intelligence and machine learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"","keywords":"Computer science; Embedding; Theoretical computer science; Graph; Graph embedding; Formalism (music); Artificial neural network; Encoder; Artificial intelligence","score_opus":0.049115176673314796,"score_gpt":0.25899285095070285,"score_spread":0.20987767427738804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293118218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018843647,0.013161078,0.65216005,0.006680369,0.0015654891,0.0000674145,0.0013695152,0.0010989467,0.30505353],"genre_scores_gemma":[0.5699947,0.014758291,0.13883862,0.0017819807,0.0012346351,0.00027966863,0.0020744863,0.0006333575,0.27040428],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998863,0.000033826756,0.0000028537772,0.00003733203,0.000027617152,0.000012094947],"domain_scores_gemma":[0.9997782,0.00010030174,0.000015119588,0.000039447164,0.00004868859,0.000018222683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021143269,0.00052132417,0.00045594087,0.0006038659,0.0003640885,0.0011033908,0.0008910907,0.001100179,0.013769679],"category_scores_gemma":[0.0015223577,0.0002406197,0.0003828647,0.0008555635,0.00085340824,0.0015514031,0.0005635134,0.0014321506,0.003777552],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023019278,0.00001703949,0.00018875762,0.00007880318,0.000025294801,0.000055288252,0.000038376023,0.080053285,0.0006850125,0.8322499,0.026903575,0.059681635],"study_design_scores_gemma":[0.000005369161,0.0000066186317,0.00012165654,0.00002348114,0.0000092641885,0.00003304882,0.000010450015,0.18988743,0.00022599829,0.7869036,0.022763545,0.0000095790165],"about_ca_topic_score_codex":0.0044715926,"about_ca_topic_score_gemma":0.0032052458,"teacher_disagreement_score":0.013769679,"about_ca_system_score_codex":0.00071763335,"about_ca_system_score_gemma":0.0005718335,"threshold_uncertainty_score":0.046064198},"labels":[],"label_agreement":null},{"id":"W4293202069","doi":"10.1561/2600000027","title":"Adaptive Internal Models in Neuroscience","year":2022,"lang":"en","type":"article","venue":"Foundations and Trends® in Systems and Control","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Neuroscience; Computational neuroscience; Cognitive science; Psychology; Computer science","score_opus":0.028079005567730284,"score_gpt":0.2550144430613092,"score_spread":0.22693543749357892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293202069","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008526666,0.050964586,0.71699095,0.02425366,0.0025463386,0.00006446683,0.0003461318,0.00083072623,0.19547646],"genre_scores_gemma":[0.6844355,0.057960197,0.18430746,0.007514565,0.006779268,0.00087689474,0.0007673625,0.000499823,0.05685896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993591,0.00029990874,0.000033469605,0.000115865536,0.00014427498,0.000047419744],"domain_scores_gemma":[0.9989403,0.0007273178,0.00005489109,0.0001359087,0.0001068673,0.000034834044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012792678,0.0008002132,0.00082934776,0.00086970796,0.0005517927,0.002763121,0.0012700423,0.0023557327,0.006205887],"category_scores_gemma":[0.0030012997,0.00028451983,0.0009813327,0.0006199022,0.0035931587,0.0037236894,0.0016118216,0.0030568417,0.0013624149],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028894663,0.0000041781286,0.00005978587,0.000058311216,0.0000102730455,0.000019829255,0.00007397116,0.005934749,0.000086183565,0.98454696,0.0028971555,0.0063055507],"study_design_scores_gemma":[0.0000030368046,0.000005533685,0.000050424314,0.000027940692,0.0000033578606,0.000019931536,0.000019259534,0.0140245585,0.000037225862,0.9731767,0.012626647,0.000005366757],"about_ca_topic_score_codex":0.0014209524,"about_ca_topic_score_gemma":0.0008026272,"teacher_disagreement_score":0.006205887,"about_ca_system_score_codex":0.0018502392,"about_ca_system_score_gemma":0.00093073625,"threshold_uncertainty_score":0.020760775},"labels":[],"label_agreement":null},{"id":"W4293252697","doi":"10.5267/j.ijiec.2022.5.003","title":"Optimizing the learning process of multi-layer perceptrons using a hybrid algorithm based on MVO and SA","year":2022,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Computer science; Simulated annealing; Perceptron; Artificial intelligence; Machine learning; Process (computing); Heuristic; Algorithm; Convergence (economics); Data mining","score_opus":0.04562637255635546,"score_gpt":0.2973455510961347,"score_spread":0.2517191785397792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293252697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07370802,0.0008468395,0.919729,0.00019066186,0.000092908056,0.00012239433,0.000050565188,0.00069110625,0.0045684436],"genre_scores_gemma":[0.701745,0.00035052025,0.29494676,0.00012768582,0.000038244132,0.00029289522,0.0001317449,0.000060599275,0.0023066276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995602,0.0001370896,0.000042086318,0.00009120038,0.00010424493,0.00006520868],"domain_scores_gemma":[0.99929225,0.0003862063,0.00008863501,0.000045252076,0.00015395561,0.00003373954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001245317,0.0011536864,0.001227422,0.0007507442,0.00053047034,0.001132055,0.0012527019,0.001583611,0.0012116878],"category_scores_gemma":[0.001865338,0.00067957403,0.0012187022,0.00078286155,0.00062651717,0.00071163377,0.0006716301,0.0009352766,0.00022020644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005429507,0.000030104211,0.00052114896,0.000060346927,0.00004907359,0.00003594974,0.000031850646,0.9708191,0.001417873,0.0008439731,0.00015926431,0.025977118],"study_design_scores_gemma":[0.0000056494805,0.00003102853,0.00007842357,0.000006189304,0.0000067098263,0.0000063934144,0.0000056795866,0.9991014,0.00040237274,0.00023760842,0.000115944866,0.000002660045],"about_ca_topic_score_codex":0.006112909,"about_ca_topic_score_gemma":0.0051321974,"teacher_disagreement_score":0.006112909,"about_ca_system_score_codex":0.0006700848,"about_ca_system_score_gemma":0.0012141579,"threshold_uncertainty_score":0.012154639},"labels":[],"label_agreement":null},{"id":"W4293859060","doi":"10.1037/cp2007014","title":"Introduction to functional data analysis.","year":2007,"lang":"en","type":"article","venue":"Canadian Psychology/Psychologie canadienne","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Functional data analysis; Functional principal component analysis; Computer science; Set (abstract data type); Data set; Ask price; Data science; Series (stratigraphy); Data mining; Machine learning; Artificial intelligence; Biology","score_opus":0.06787281756869973,"score_gpt":0.3253437489068723,"score_spread":0.25747093133817256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293859060","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00054220547,0.03331962,0.93562627,0.0056511913,0.0015568536,0.00025931036,0.0023192873,0.002070835,0.018654447],"genre_scores_gemma":[0.02508699,0.04929538,0.8938466,0.004088572,0.0045776414,0.0018351709,0.0029504753,0.0011185549,0.01720065],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974153,0.00086619623,0.0002600004,0.00057425856,0.00079151907,0.00009269212],"domain_scores_gemma":[0.9944564,0.00360363,0.00025056576,0.0007396125,0.0007761007,0.0001735999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047904705,0.0019877818,0.0012431731,0.0039706174,0.0007851394,0.0034425096,0.002281777,0.0034773266,0.026496286],"category_scores_gemma":[0.011536477,0.0009270174,0.0018328121,0.00399809,0.0029726713,0.0037529864,0.002152489,0.0048248973,0.009811666],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058830406,0.000055564382,0.0011014089,0.002469368,0.00021796803,0.000438149,0.00044549428,0.008619552,0.0032342228,0.3886647,0.15973859,0.4349561],"study_design_scores_gemma":[0.000020978136,0.000047443064,0.0012182354,0.0009689444,0.000048066624,0.0014635941,0.00008621492,0.01794504,0.0010292986,0.40127042,0.5758158,0.00008596074],"about_ca_topic_score_codex":0.002820021,"about_ca_topic_score_gemma":0.0017760372,"teacher_disagreement_score":0.026496286,"about_ca_system_score_codex":0.0012611715,"about_ca_system_score_gemma":0.0018891006,"threshold_uncertainty_score":0.0886389},"labels":[],"label_agreement":null},{"id":"W4296131057","doi":"10.1109/tit.2022.3202450","title":"IEEE Transactions on Information Theory publication information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Information theory; Computer science; Theoretical computer science; Information retrieval; Mathematics; Statistics","score_opus":0.009156959172592861,"score_gpt":0.21540977404581182,"score_spread":0.20625281487321898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296131057","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073632444,0.041065272,0.4078097,0.025557896,0.04692853,0.0007379434,0.02859816,0.005462615,0.43647674],"genre_scores_gemma":[0.12103247,0.07721286,0.099727295,0.0049459753,0.012654195,0.0008991606,0.04606473,0.002194734,0.63526857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980058,0.0003531626,0.00016168886,0.00020072536,0.0011487462,0.00012988472],"domain_scores_gemma":[0.9946779,0.0017836735,0.0001828315,0.0013241188,0.0018538217,0.0001776851],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020620713,0.0012995908,0.0020753695,0.0038026476,0.00096002803,0.004441838,0.0011496176,0.0020986642,0.18658403],"category_scores_gemma":[0.011094833,0.00058022304,0.0007657712,0.005049121,0.00082062976,0.0042192605,0.0017576806,0.0025192474,0.07861321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016382568,0.00012273522,0.0006897795,0.00085827295,0.00006272511,0.00014853044,0.00005241059,0.0032309913,0.0011106146,0.07595082,0.524427,0.3931823],"study_design_scores_gemma":[0.00006565506,0.00010452829,0.0013115945,0.0008803995,0.00015325854,0.00051396637,0.00011317045,0.033855367,0.0028811428,0.1821534,0.77789456,0.00007305823],"about_ca_topic_score_codex":0.0014865134,"about_ca_topic_score_gemma":0.0012459535,"teacher_disagreement_score":0.813416,"about_ca_system_score_codex":0.001008677,"about_ca_system_score_gemma":0.0021853258,"threshold_uncertainty_score":0.62418574},"labels":[],"label_agreement":null},{"id":"W4296887698","doi":"10.18280/isi.270419","title":"Speech Coding Using Discrete Cosine Transform and Chaotic Map","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Discrete cosine transform; Computer science; Speech coding; Data compression; Coding (social sciences); Speech recognition; Chaotic; Compression ratio; Algorithm; Artificial intelligence; Image (mathematics); Mathematics","score_opus":0.018403393913244383,"score_gpt":0.23843748705692894,"score_spread":0.22003409314368455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296887698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1596026,0.0036053192,0.8201524,0.00075378735,0.0007433054,0.00031878884,0.0015973962,0.0030551078,0.010171376],"genre_scores_gemma":[0.66770005,0.0022915662,0.3167481,0.00016767846,0.00022186835,0.00024805294,0.0035643587,0.00012758288,0.008930787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996406,0.000046551122,0.000028289776,0.000066750756,0.00019315249,0.00002474288],"domain_scores_gemma":[0.9996885,0.00007325589,0.000023854212,0.00005070437,0.00015131524,0.000012426873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003260722,0.00048744178,0.000444755,0.0013393761,0.00022664886,0.00047705078,0.00036077294,0.00034822346,0.0015210776],"category_scores_gemma":[0.0012829944,0.0001084784,0.00036314825,0.001231414,0.00026972,0.0006452608,0.0003991383,0.00037210344,0.00070171984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063331827,0.00013965528,0.0013331141,0.00037229955,0.00006371205,0.00039957243,0.00009830326,0.09055522,0.11711495,0.008779326,0.008198643,0.7723119],"study_design_scores_gemma":[0.00005204113,0.00022138297,0.0028060435,0.000037535654,0.00004041847,0.0004746945,0.000059250106,0.8789933,0.10184946,0.0028975713,0.012519955,0.000048413527],"about_ca_topic_score_codex":0.004695129,"about_ca_topic_score_gemma":0.003530301,"teacher_disagreement_score":0.004695129,"about_ca_system_score_codex":0.0003391868,"about_ca_system_score_gemma":0.00054547336,"threshold_uncertainty_score":0.0093355775},"labels":[],"label_agreement":null},{"id":"W4297616741","doi":"10.48550/arxiv.1412.6567","title":"Classifier with Hierarchical Topographical Maps as Internal\\n Representation","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Classifier (UML); Computer science; Artificial intelligence; Representation (politics); Deep learning; Architecture; Machine learning; Geography","score_opus":0.054720535968531034,"score_gpt":0.2057596956385731,"score_spread":0.15103915967004206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297616741","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062906206,0.00087424397,0.92359763,0.0009529073,0.00024955167,0.00010688995,0.00078618317,0.0026297648,0.007896502],"genre_scores_gemma":[0.7928249,0.00048205836,0.19467022,0.000504384,0.00016379778,0.0001321781,0.0014895678,0.00013502799,0.009597893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972147,0.000046529058,0.000012879046,0.00008253166,0.000080303435,0.0000562021],"domain_scores_gemma":[0.9996313,0.00010928887,0.00003543866,0.00009621796,0.00009697742,0.00003083175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004776619,0.00048433727,0.0004883887,0.0006429303,0.00028162726,0.0011846585,0.0012951178,0.0010321165,0.0031260636],"category_scores_gemma":[0.001666576,0.00019610915,0.0006003061,0.0007622866,0.00041549973,0.0019315014,0.0012015988,0.0012119227,0.001128316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032722042,0.00020437734,0.0043252143,0.0002428847,0.0001301548,0.0002290671,0.00015789292,0.23791216,0.022276184,0.06313341,0.019330489,0.65173095],"study_design_scores_gemma":[0.000011508425,0.0000426404,0.00060499716,0.000015226061,0.00002244811,0.00005352816,0.000028911876,0.9715039,0.0041133603,0.02066458,0.0029284167,0.000010542089],"about_ca_topic_score_codex":0.003781004,"about_ca_topic_score_gemma":0.005042353,"teacher_disagreement_score":0.003781004,"about_ca_system_score_codex":0.0006204853,"about_ca_system_score_gemma":0.0007114602,"threshold_uncertainty_score":0.0104576945},"labels":[],"label_agreement":null},{"id":"W4297841482","doi":"10.21437/interspeech.2022-760","title":"Low-bit Shift Network for End-to-End Spoken Language Understanding","year":2022,"lang":"en","type":"article","venue":"Interspeech 2022","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; End-to-end principle; Computer network","score_opus":0.02692692012041936,"score_gpt":0.27559052982646537,"score_spread":0.248663609706046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297841482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061559536,0.0020336756,0.9123696,0.0006290958,0.00025122042,0.00015481403,0.0006523278,0.014413146,0.007936485],"genre_scores_gemma":[0.6684049,0.0007289307,0.31107658,0.00059197925,0.00008003786,0.00022177109,0.0018475682,0.0003586963,0.0166896],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970955,0.00004635135,0.000019825047,0.00008316289,0.000108256936,0.000032891134],"domain_scores_gemma":[0.9997552,0.000080384525,0.00002065056,0.000045245764,0.00008631579,0.000012218306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045603956,0.0006837757,0.00033060438,0.00034706082,0.0002692783,0.0006331495,0.0010607733,0.00077955436,0.008531108],"category_scores_gemma":[0.0012337709,0.00020595477,0.00022966869,0.00037661777,0.00034682298,0.0016473087,0.00082145596,0.0012057463,0.002697292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005370138,0.00018196506,0.00056396367,0.0002196752,0.000051620034,0.00017577202,0.00016572216,0.070016235,0.08528774,0.007320559,0.012796641,0.8226831],"study_design_scores_gemma":[0.000032138687,0.00018610888,0.00045587175,0.000033381708,0.000025934061,0.00010166279,0.000070407266,0.93076336,0.05141743,0.008600439,0.008285872,0.000027423559],"about_ca_topic_score_codex":0.005031242,"about_ca_topic_score_gemma":0.011058245,"teacher_disagreement_score":0.008531108,"about_ca_system_score_codex":0.00055050774,"about_ca_system_score_gemma":0.0008983724,"threshold_uncertainty_score":0.02853936},"labels":[],"label_agreement":null},{"id":"W4298003195","doi":"10.11591/eei.v11i6.4287","title":"Multi-feature stacking order impact on speech emotion recognition performance","year":2022,"lang":"en","type":"article","venue":"Bulletin of Electrical Engineering and Informatics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Binus University","keywords":"Mel-frequency cepstrum; Spectrogram; Computer science; Convolutional neural network; Speech recognition; Contrast (vision); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Emotion recognition; Benchmarking; Feature extraction","score_opus":0.009297901819786389,"score_gpt":0.20985950939704004,"score_spread":0.20056160757725366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298003195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89442176,0.0060184677,0.07688335,0.0009766016,0.0011809951,0.00014703898,0.0011410321,0.0084419185,0.010788773],"genre_scores_gemma":[0.9766391,0.00078229565,0.016789144,0.00019105074,0.00005241394,0.000043639437,0.0017961948,0.00020794348,0.0034983656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988667,0.00019924578,0.000111490706,0.00033135328,0.00026180025,0.00022942148],"domain_scores_gemma":[0.9981773,0.00088001287,0.0000852125,0.00035901918,0.00040723546,0.00009132504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020070272,0.0019038372,0.0011520408,0.0004902154,0.00040149497,0.0015251341,0.00077806,0.0010363897,0.0030075223],"category_scores_gemma":[0.005495471,0.0004049016,0.001063294,0.00032368448,0.00040518385,0.0024209265,0.0011256858,0.0016812651,0.001959966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026617646,0.00083113415,0.019725688,0.00045105035,0.00044965002,0.0006549401,0.0001527132,0.3310627,0.059166938,0.00083320506,0.007102677,0.5769075],"study_design_scores_gemma":[0.00004502803,0.00097486295,0.0084027825,0.00006383425,0.00021560381,0.00031710332,0.00019747631,0.9215772,0.06456358,0.0008598111,0.0027200151,0.000062734835],"about_ca_topic_score_codex":0.007332431,"about_ca_topic_score_gemma":0.006284763,"teacher_disagreement_score":0.007332431,"about_ca_system_score_codex":0.00065957877,"about_ca_system_score_gemma":0.0009073045,"threshold_uncertainty_score":0.0145795345},"labels":[],"label_agreement":null},{"id":"W4298016074","doi":"","title":"Stochastic Multiple Stream Decoding of Cortex Codes","year":2011,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Decoding methods; Cortex (anatomy); Computer science; Neural decoding; Neuroscience; Algorithm; Psychology","score_opus":0.0434902604558877,"score_gpt":0.26851030009623605,"score_spread":0.22502003964034836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298016074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09103999,0.0010048135,0.8877015,0.0021694184,0.0005481953,0.000052340973,0.0008562784,0.0011076385,0.015519824],"genre_scores_gemma":[0.8432718,0.0013514096,0.12094456,0.00053731113,0.00076604454,0.00010757328,0.0010965925,0.00057334814,0.031351443],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99955505,0.0001322084,0.000025456884,0.00008494526,0.00013706484,0.00006524648],"domain_scores_gemma":[0.9978721,0.0012508809,0.00013539036,0.0002091738,0.00041050982,0.00012192857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006715514,0.00064532505,0.0008246786,0.0005975096,0.00033656962,0.0013659995,0.0006572339,0.0012743227,0.0067698695],"category_scores_gemma":[0.006820879,0.0003208227,0.0005418763,0.0008405581,0.00058706803,0.0013155262,0.00094650284,0.0013478512,0.0015122462],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066315045,0.00008827733,0.0021534488,0.00039610514,0.0001254973,0.0004671521,0.00024213803,0.35920984,0.026398847,0.40096208,0.017028783,0.1922647],"study_design_scores_gemma":[0.000016012777,0.000018803657,0.00030241522,0.00002458365,0.000009279909,0.00007742818,0.000015327352,0.88406134,0.0055175577,0.10858356,0.0013586484,0.000015040902],"about_ca_topic_score_codex":0.0021461924,"about_ca_topic_score_gemma":0.0029207298,"teacher_disagreement_score":0.0067698695,"about_ca_system_score_codex":0.0007223544,"about_ca_system_score_gemma":0.0010778885,"threshold_uncertainty_score":0.02264744},"labels":[],"label_agreement":null},{"id":"W4298051788","doi":"","title":"Algorithm and Implementation of an Associative Memory for Oriented Edge Detection Using Improved Clustered Neural Networks","year":2015,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Content-addressable memory; Associative property; Artificial neural network; Bidirectional associative memory; Enhanced Data Rates for GSM Evolution; Content-addressable storage; Artificial intelligence; Parallel computing; Mathematics","score_opus":0.019324689051926965,"score_gpt":0.26859153205516245,"score_spread":0.24926684300323548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298051788","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022478074,0.0001319739,0.9726452,0.00009528723,0.00008314461,0.00012223564,0.00010116899,0.0025443353,0.0017985005],"genre_scores_gemma":[0.17501412,0.00008347063,0.8205455,0.000092331466,0.000032024076,0.00024030956,0.00031092588,0.00009850357,0.0035828175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997962,0.000015722557,0.000015977997,0.000059061425,0.000079777,0.00003330011],"domain_scores_gemma":[0.9995407,0.00008129757,0.000028484128,0.00007189094,0.00025104106,0.000026596861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029536994,0.0004996479,0.00066356105,0.00080122874,0.00048904045,0.00069096807,0.0027156104,0.00081041316,0.005897383],"category_scores_gemma":[0.000958542,0.0003634045,0.00041065196,0.00070545706,0.0002756623,0.00065917126,0.0008444171,0.00057810935,0.0012561167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036135173,0.00016756615,0.0011076408,0.00019509811,0.00012105971,0.0001378714,0.00008262684,0.16591044,0.045356926,0.008345023,0.00515034,0.7730641],"study_design_scores_gemma":[0.000038181613,0.00008330681,0.0003859621,0.000009908211,0.0000326148,0.00007414282,0.000019669946,0.97768265,0.01799487,0.0018948775,0.0017677831,0.000016048874],"about_ca_topic_score_codex":0.009400592,"about_ca_topic_score_gemma":0.012606933,"teacher_disagreement_score":0.009400592,"about_ca_system_score_codex":0.0006158668,"about_ca_system_score_gemma":0.0013933736,"threshold_uncertainty_score":0.01972866},"labels":[],"label_agreement":null},{"id":"W4299973565","doi":"10.1007/978-3-642-31254-0","title":"Image and Signal Processing","year":2012,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Image processing; Signal processing; SIGNAL (programming language); Digital image processing; Computer graphics (images); Image (mathematics); Computer vision; Artificial intelligence; Speech recognition; Digital signal processing; Computer hardware; Programming language","score_opus":0.013724523955110677,"score_gpt":0.25123269566719025,"score_spread":0.23750817171207958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299973565","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029958698,0.053326976,0.336527,0.0017873538,0.0074094986,0.00020730431,0.0013504745,0.005182544,0.59121305],"genre_scores_gemma":[0.010945131,0.020669028,0.043646358,0.0006390565,0.0018835204,0.000092987626,0.001163597,0.0005981105,0.9203622],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996965,0.000016233875,0.000011518461,0.00006390896,0.00019398978,0.000017871167],"domain_scores_gemma":[0.9997397,0.000045503697,0.000011772886,0.00007586869,0.00009485549,0.000032302454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002663062,0.0013995722,0.0011554417,0.0017806244,0.00042221154,0.0017653898,0.0009848366,0.0010751181,0.1156675],"category_scores_gemma":[0.0005888026,0.00041068997,0.0004595216,0.002049725,0.0006842072,0.0016562153,0.0012998626,0.001255087,0.09575808],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004870633,0.000039485705,0.000068495276,0.0005394892,0.000029442319,0.00008785821,0.00004319012,0.0010618925,0.014355734,0.021702098,0.1854143,0.7766094],"study_design_scores_gemma":[0.000010128633,0.000091575945,0.0006687885,0.00013619786,0.000028724748,0.0006668995,0.000037820893,0.0057911403,0.009537765,0.019495606,0.9635109,0.00002448976],"about_ca_topic_score_codex":0.00052831345,"about_ca_topic_score_gemma":0.0011454718,"teacher_disagreement_score":0.1156675,"about_ca_system_score_codex":0.00036497344,"about_ca_system_score_gemma":0.000410167,"threshold_uncertainty_score":0.38694632},"labels":[],"label_agreement":null},{"id":"W4300006072","doi":"","title":"Complex Self-reproducing Systems","year":2013,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science","score_opus":0.04664805402205304,"score_gpt":0.2665097582244451,"score_spread":0.21986170420239207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300006072","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5046993,0.00095152855,0.43400455,0.0012259918,0.00020247883,0.00009410417,0.00027847814,0.0004818382,0.058061793],"genre_scores_gemma":[0.98408407,0.00014091513,0.011365046,0.0001041254,0.0000488905,0.000042108597,0.00007947313,0.000024757343,0.004110584],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996019,0.00011277777,0.000020260648,0.00007916351,0.00013598173,0.00005000714],"domain_scores_gemma":[0.99886525,0.00043909237,0.00023790433,0.00015055093,0.00019553975,0.000111705966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003865167,0.00026210834,0.0003921483,0.00044367628,0.00041608,0.0011889056,0.00045110827,0.00080167473,0.0027210345],"category_scores_gemma":[0.002106965,0.0001512913,0.00036923288,0.0002446101,0.0017393974,0.00086433435,0.0007703376,0.0006349885,0.00032658892],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024662753,0.000029342147,0.0012559976,0.000054334407,0.000028258246,0.00025063765,0.00026530362,0.06957929,0.0065736715,0.91490406,0.0013008219,0.005733556],"study_design_scores_gemma":[0.00002186118,0.000031137242,0.00064422045,0.000010753726,0.000009311838,0.00022159635,0.000056405388,0.598846,0.0011839652,0.39442974,0.0045241816,0.00002085919],"about_ca_topic_score_codex":0.0011262096,"about_ca_topic_score_gemma":0.0006432572,"teacher_disagreement_score":0.0027210345,"about_ca_system_score_codex":0.0007547408,"about_ca_system_score_gemma":0.0003204058,"threshold_uncertainty_score":0.009102762},"labels":[],"label_agreement":null},{"id":"W4302071234","doi":"","title":"Statistics versus Machine Learning","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Statistics; Computer science; Artificial intelligence; Machine learning; Mathematics","score_opus":0.020584658674390688,"score_gpt":0.24759710871730142,"score_spread":0.22701245004291074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302071234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017335597,0.23391412,0.3441588,0.17808826,0.03026021,0.000067116336,0.0014704489,0.0017035581,0.19300193],"genre_scores_gemma":[0.46688715,0.13778056,0.070137225,0.021641793,0.11219377,0.00035855095,0.0018794809,0.0018272668,0.18729416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976127,0.0012546508,0.00012968568,0.00044999854,0.00049195846,0.00006106768],"domain_scores_gemma":[0.97762865,0.018573767,0.0006966772,0.0008619638,0.0018802806,0.0003586108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028915012,0.0010334054,0.0019403765,0.0029135274,0.0007743011,0.004886414,0.00074818445,0.0028071762,0.017620403],"category_scores_gemma":[0.021492984,0.0003842038,0.0004078024,0.0037635774,0.003249268,0.0052877106,0.0010896849,0.0037699454,0.007424085],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051469033,0.00002936115,0.0005889005,0.00036743976,0.00003351445,0.000059299906,0.00009181405,0.0028542064,0.000564317,0.8276283,0.059506293,0.108225],"study_design_scores_gemma":[0.000012718581,0.00004497559,0.0007008058,0.00023989554,0.000020297311,0.00011436238,0.000058053447,0.01541217,0.00051408575,0.87037516,0.112483844,0.00002372487],"about_ca_topic_score_codex":0.0010668107,"about_ca_topic_score_gemma":0.0007060476,"teacher_disagreement_score":0.017620403,"about_ca_system_score_codex":0.0016739052,"about_ca_system_score_gemma":0.0011639505,"threshold_uncertainty_score":0.058946133},"labels":[],"label_agreement":null},{"id":"W4302423171","doi":"10.1007/978-3-540-69939-2","title":"Artificial Neural Networks in Pattern Recognition","year":2008,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.026350020297131842,"score_gpt":0.24492401224480026,"score_spread":0.2185739919476684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302423171","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003195043,0.12310945,0.7785996,0.0018167499,0.007421737,0.00009138255,0.00060781144,0.002340095,0.08281804],"genre_scores_gemma":[0.049442243,0.10929703,0.46253145,0.0011966167,0.0039038649,0.0003031478,0.0018938418,0.0010572178,0.37037456],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99971145,0.000041032217,0.000029494027,0.000056638874,0.00014412697,0.000017328759],"domain_scores_gemma":[0.9996481,0.00014590219,0.000029382783,0.00007059172,0.00009191964,0.000014098484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039087096,0.0009578162,0.0013442072,0.00097540254,0.00020590627,0.0014146611,0.0012001535,0.0010484183,0.019680176],"category_scores_gemma":[0.0011995574,0.0005232819,0.00044807376,0.0026155044,0.00054141035,0.002340893,0.00077224197,0.001231532,0.013147898],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003780344,0.0000330809,0.0001769759,0.0010705991,0.000060496663,0.000092626164,0.000044496417,0.014134435,0.003406254,0.040524215,0.07502509,0.8653939],"study_design_scores_gemma":[0.00002818539,0.00010942563,0.0013031822,0.0007546036,0.00013005067,0.0009441815,0.000076606775,0.16518317,0.00858712,0.15969977,0.6631118,0.00007189365],"about_ca_topic_score_codex":0.0008390793,"about_ca_topic_score_gemma":0.0011315315,"teacher_disagreement_score":0.019680176,"about_ca_system_score_codex":0.000297915,"about_ca_system_score_gemma":0.00036287794,"threshold_uncertainty_score":0.06583673},"labels":[],"label_agreement":null},{"id":"W4304465621","doi":"10.48550/arxiv.2206.01685","title":"Toward a realistic model of speech processing in the brain with self-supervised learning","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Speech recognition; Artificial intelligence; Speech processing; Natural language processing","score_opus":0.09502923280498451,"score_gpt":0.20357425131308315,"score_spread":0.10854501850809864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304465621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07648612,0.00020066746,0.9191864,0.00092691113,0.000044081255,0.000047289668,0.00017455537,0.00045598633,0.0024779951],"genre_scores_gemma":[0.834943,0.00033983213,0.15710503,0.00035071687,0.00010689168,0.00027964963,0.0003201313,0.0001251107,0.006429693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984455,0.00005282308,0.0000051262687,0.000049813058,0.00003091657,0.000016770491],"domain_scores_gemma":[0.9996006,0.00022002107,0.000043063428,0.000041886087,0.00007247994,0.000022013639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052160025,0.00037429627,0.00041050813,0.00022096382,0.00018825459,0.00065516884,0.001186948,0.0009725773,0.0009450661],"category_scores_gemma":[0.0013468002,0.00037311064,0.00066673214,0.00022114128,0.0007888869,0.0010295165,0.00054003834,0.0013155851,0.0003101063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003710261,0.000033810546,0.0004791232,0.00003765183,0.000028490183,0.00003551065,0.000067747314,0.97369236,0.0034743054,0.013100806,0.00068321265,0.008329781],"study_design_scores_gemma":[0.0000017837159,0.000004617531,0.000055335044,9.657462e-7,0.0000010668367,0.0000033273327,0.0000014356664,0.9969795,0.000119694916,0.0027404202,0.00009084737,0.000001062333],"about_ca_topic_score_codex":0.0036802776,"about_ca_topic_score_gemma":0.0044731232,"teacher_disagreement_score":0.0036802776,"about_ca_system_score_codex":0.0007526295,"about_ca_system_score_gemma":0.0007289755,"threshold_uncertainty_score":0.007317722},"labels":[],"label_agreement":null},{"id":"W4306407734","doi":"10.29169/1927-5129.2022.18.08","title":"Prediction of Surface Roughness of U71Mn Steel Milling Based on RBF Neural Network","year":2022,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Radial basis function; Surface roughness; MATLAB; Surface finish; Surface (topology); Materials science; Computer science; Artificial intelligence; Composite material; Mathematics; Geometry","score_opus":0.03337338871362785,"score_gpt":0.2441077518572853,"score_spread":0.21073436314365745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306407734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8367624,0.00031208314,0.15965354,0.0001102227,0.000049723116,0.00003811175,0.00015826934,0.00092804653,0.0019876163],"genre_scores_gemma":[0.9891026,0.00007645444,0.010081339,0.000008758704,0.0000026850867,0.00001431292,0.00007950427,0.000012147842,0.00062220567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969137,0.00003170914,0.000014162689,0.00007102263,0.00015525009,0.00003651524],"domain_scores_gemma":[0.99973255,0.00007891672,0.00003271954,0.00002598043,0.0001192248,0.000010574833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003641977,0.00053037325,0.00042852393,0.000337041,0.00019290925,0.0003069894,0.0004083019,0.00050567824,0.00049732224],"category_scores_gemma":[0.00075874705,0.0001931115,0.0004964167,0.00027331224,0.0001490953,0.00035816475,0.00016654874,0.00037119022,0.00016307487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007142093,0.00024145425,0.018105341,0.00034703338,0.000075250406,0.00026058577,0.00016747943,0.54601157,0.2680794,0.0005172627,0.0012007545,0.1642797],"study_design_scores_gemma":[0.000008998773,0.00012513969,0.007609007,0.0000036875003,0.00001276857,0.000019017476,0.000024042381,0.96866983,0.023270493,0.00006365809,0.00017597308,0.00001732927],"about_ca_topic_score_codex":0.008717316,"about_ca_topic_score_gemma":0.007670225,"teacher_disagreement_score":0.008717316,"about_ca_system_score_codex":0.0003272219,"about_ca_system_score_gemma":0.0003474253,"threshold_uncertainty_score":0.01733315},"labels":[],"label_agreement":null},{"id":"W4306846726","doi":"10.1038/s42256-022-00565-6","title":"Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Compression (physics); Artificial neural network; Artificial intelligence; Materials science; Composite material","score_opus":0.011574333500157998,"score_gpt":0.28090654632412493,"score_spread":0.2693322128239669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306846726","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008592804,0.0008703019,0.00863404,0.039710257,0.94073874,0.00002937863,0.002434557,0.0025851973,0.0041382057],"genre_scores_gemma":[0.1372137,0.00700022,0.04706954,0.052380897,0.32886317,0.0004125795,0.008730116,0.010073829,0.408256],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99453497,0.00071244873,0.00086721417,0.0011668797,0.0022615287,0.00045694006],"domain_scores_gemma":[0.9280338,0.013549229,0.0027343621,0.0064919987,0.04729564,0.0018949903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036909042,0.0018481524,0.0023212745,0.0029670745,0.0028998787,0.0040209177,0.0037455396,0.0068958886,0.14194067],"category_scores_gemma":[0.10487274,0.001137352,0.0013394329,0.0034526817,0.0021602612,0.003168249,0.0026292696,0.0070809564,0.07714516],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103376165,0.000010803133,0.00029845122,0.00029893062,0.00004537579,0.0004323176,0.00008377639,0.0003297586,0.00041389902,0.0035886648,0.97669005,0.017704604],"study_design_scores_gemma":[0.00015693613,0.000078816694,0.002037895,0.0005117173,0.00014885959,0.002133146,0.00029151712,0.005797427,0.0074386266,0.01502636,0.96620953,0.00016909298],"about_ca_topic_score_codex":0.004313205,"about_ca_topic_score_gemma":0.007156545,"teacher_disagreement_score":0.14194067,"about_ca_system_score_codex":0.002305658,"about_ca_system_score_gemma":0.003776461,"threshold_uncertainty_score":0.47483885},"labels":[],"label_agreement":null},{"id":"W4307081142","doi":"","title":"Unsupervised Learning with Ferroelectric Synapses","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Ferroelectricity; Unsupervised learning; Computer science; Artificial intelligence; Materials science; Optoelectronics; Dielectric","score_opus":0.012553717483067942,"score_gpt":0.21662576000745484,"score_spread":0.2040720425243869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307081142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110473566,0.0009790942,0.8779396,0.000989225,0.00021650418,0.000045098845,0.00019381306,0.001153716,0.0080093425],"genre_scores_gemma":[0.8527118,0.00056286267,0.12923867,0.00023439903,0.00023529105,0.00007471892,0.0003523494,0.00015073843,0.016439097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998196,0.000052415697,0.000010207156,0.000059919264,0.00003872266,0.000019160225],"domain_scores_gemma":[0.99884534,0.00068492023,0.000086607135,0.000152426,0.00017774274,0.000052965563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046477202,0.00032452372,0.0006671161,0.00034469113,0.00026635875,0.00057786616,0.00092100794,0.00085466966,0.002719177],"category_scores_gemma":[0.002820068,0.00032020028,0.00040145312,0.00036019474,0.00045095882,0.00079486024,0.0007696229,0.00088746834,0.0005409845],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002672949,0.00014552216,0.0022639984,0.00028646202,0.00016268811,0.00022838908,0.00008413796,0.5710526,0.026520144,0.051911354,0.0071588475,0.33991855],"study_design_scores_gemma":[0.000007708815,0.00001488622,0.00022200285,0.000005360956,0.0000056010927,0.00003021334,0.0000043708137,0.9840943,0.0024500971,0.012620303,0.00054186874,0.0000032744936],"about_ca_topic_score_codex":0.0010379639,"about_ca_topic_score_gemma":0.002067729,"teacher_disagreement_score":0.002719177,"about_ca_system_score_codex":0.0003904088,"about_ca_system_score_gemma":0.0004039372,"threshold_uncertainty_score":0.009096503},"labels":[],"label_agreement":null},{"id":"W4308468268","doi":"10.1109/tit.2022.3211169","title":"IEEE Transactions on Information Theory Publication Information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Theoretical computer science; Mathematics; Statistics","score_opus":0.009156959172592861,"score_gpt":0.21540977404581182,"score_spread":0.20625281487321898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308468268","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073632444,0.041065272,0.4078097,0.025557896,0.04692853,0.0007379434,0.02859816,0.005462615,0.43647674],"genre_scores_gemma":[0.12103247,0.07721286,0.099727295,0.0049459753,0.012654195,0.0008991606,0.04606473,0.002194734,0.63526857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980058,0.0003531626,0.00016168886,0.00020072536,0.0011487462,0.00012988472],"domain_scores_gemma":[0.9946779,0.0017836735,0.0001828315,0.0013241188,0.0018538217,0.0001776851],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020620713,0.0012995908,0.0020753695,0.0038026476,0.00096002803,0.004441838,0.0011496176,0.0020986642,0.18658403],"category_scores_gemma":[0.011094833,0.00058022304,0.0007657712,0.005049121,0.00082062976,0.0042192605,0.0017576806,0.0025192474,0.07861321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016382568,0.00012273522,0.0006897795,0.00085827295,0.00006272511,0.00014853044,0.00005241059,0.0032309913,0.0011106146,0.07595082,0.524427,0.3931823],"study_design_scores_gemma":[0.00006565506,0.00010452829,0.0013115945,0.0008803995,0.00015325854,0.00051396637,0.00011317045,0.033855367,0.0028811428,0.1821534,0.77789456,0.00007305823],"about_ca_topic_score_codex":0.0014865134,"about_ca_topic_score_gemma":0.0012459535,"teacher_disagreement_score":0.813416,"about_ca_system_score_codex":0.001008677,"about_ca_system_score_gemma":0.0021853258,"threshold_uncertainty_score":0.62418574},"labels":[],"label_agreement":null},{"id":"W4308579676","doi":"10.47004/wp.cem.2022.2022","title":"Graphical model inference with external network data","year":2022,"lang":"en","type":"report","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Agencia Estatal de Investigación; Banco Bilbao Vizcaya Argentaria; European Commission; China Scholarship Council; Fundación BBVA","keywords":"Graphical model; Inference; Computer science; Artificial intelligence","score_opus":0.11955321687513985,"score_gpt":0.34060541200908273,"score_spread":0.22105219513394286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308579676","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004502859,0.00007460959,0.99340916,0.0003825919,0.00002273258,0.000025518691,0.0003801308,0.00046281086,0.0007396074],"genre_scores_gemma":[0.33672428,0.000542052,0.6495386,0.0007910139,0.00025365714,0.0005673962,0.004935301,0.0007367508,0.005911005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961824,0.0024039815,0.00011569511,0.0007127182,0.0004197565,0.00016547088],"domain_scores_gemma":[0.9738487,0.02167275,0.0010408631,0.0022658263,0.0008655804,0.00030622075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062542707,0.0013896914,0.0013888063,0.0019778418,0.00095996686,0.0022497515,0.0026967598,0.0019382704,0.0071781115],"category_scores_gemma":[0.04263262,0.001227633,0.0019341755,0.0020319587,0.0017425963,0.0040629962,0.0031114584,0.005737151,0.0014051173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007724197,0.000056369656,0.0029253066,0.0001104017,0.00009972887,0.00015442671,0.0001351441,0.82146704,0.00040198132,0.13484879,0.0052157557,0.034507774],"study_design_scores_gemma":[0.000012784442,0.000004443649,0.00013888482,0.000013905837,0.0000080757145,0.000015897709,0.000010716084,0.90272975,0.000109265915,0.096134216,0.00081663975,0.000005438131],"about_ca_topic_score_codex":0.012330686,"about_ca_topic_score_gemma":0.020179464,"teacher_disagreement_score":0.012330686,"about_ca_system_score_codex":0.0016309959,"about_ca_system_score_gemma":0.001697913,"threshold_uncertainty_score":0.033076167},"labels":[],"label_agreement":null},{"id":"W4309963732","doi":"","title":"COMPARISON BETWEEN EULERIAN AND LAGRANGIAN DESCRIPTION OF MOTION APPLICATION TO EDDY CURRENTS COMPUTATION IN CIRCUIT BREAKERS ELECTRODES","year":2003,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Diagnosis and Research on Alzheimer's Disease","funders":"","keywords":"Computation; Circuit breaker; Mechanics; Eulerian path; Electrode; Motion (physics); Eddy current; Lagrangian; Physics; Classical mechanics; Computer science; Electrical engineering; Acoustics; Engineering; Algorithm; Theoretical physics","score_opus":0.026417886308726175,"score_gpt":0.2644177460884884,"score_spread":0.2379998597797622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309963732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24789591,0.0014448353,0.73864967,0.0007254456,0.00021204876,0.000086970125,0.00015165734,0.0006936959,0.0101397615],"genre_scores_gemma":[0.9311293,0.00093907706,0.062495623,0.0000787059,0.00007334,0.00005634532,0.0001363664,0.00021112527,0.0048800474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988127,0.000046593825,0.000011239708,0.000011313806,0.00003766883,0.000011911655],"domain_scores_gemma":[0.9993968,0.00028506483,0.000037602673,0.00006327145,0.00018088572,0.000036338657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046941638,0.00038588,0.00060346455,0.0004922267,0.00036203602,0.0009932185,0.00051283586,0.0006613431,0.00202674],"category_scores_gemma":[0.001814474,0.00021841151,0.00038105025,0.00051826023,0.00038457793,0.0008197778,0.00042258986,0.00036088994,0.0003295587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017594833,0.00008861661,0.0018151276,0.00016802338,0.000031131334,0.0001311103,0.00010900642,0.9251512,0.009748863,0.0140578635,0.0007281236,0.047795013],"study_design_scores_gemma":[0.000004076151,0.000010069242,0.00015682894,0.0000031516324,0.0000019333736,0.00000597979,0.0000086129985,0.99846125,0.00056547683,0.00054310757,0.00023739955,0.0000020715283],"about_ca_topic_score_codex":0.0058349357,"about_ca_topic_score_gemma":0.0045042923,"teacher_disagreement_score":0.0058349357,"about_ca_system_score_codex":0.0004888864,"about_ca_system_score_gemma":0.00048601284,"threshold_uncertainty_score":0.011601925},"labels":[],"label_agreement":null},{"id":"W4311350769","doi":"10.3390/math10244730","title":"Perceptron: Learning, Generalization, Model Selection, Fault Tolerance, and Role in the Deep Learning Era","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Perceptron; Artificial intelligence; Artificial neural network; Computer science; Generalization; Machine learning; Multilayer perceptron; Deep learning; Backpropagation; Activation function; Model selection; Mathematics","score_opus":0.010373327228341835,"score_gpt":0.22964424953081194,"score_spread":0.2192709223024701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311350769","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007311457,0.29751635,0.6472505,0.012904172,0.002993529,0.00009707338,0.0005178296,0.0015957602,0.029813444],"genre_scores_gemma":[0.2139737,0.44577336,0.296744,0.004200716,0.010732722,0.00039514233,0.001292571,0.00063988706,0.026247844],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99799997,0.00065546273,0.0001490754,0.00039392835,0.0007023731,0.00009920556],"domain_scores_gemma":[0.9969138,0.0022070203,0.0001643443,0.00023829796,0.00038495174,0.00009160432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027525274,0.0017592064,0.0017155928,0.0018678852,0.0006188358,0.0031998693,0.0019392208,0.002976129,0.0038773296],"category_scores_gemma":[0.0072164196,0.00071158155,0.00083381886,0.004327952,0.0032184874,0.0059359493,0.001761588,0.0049564024,0.0018684842],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012884455,0.000121841185,0.0019037515,0.002229635,0.00016611538,0.0002524177,0.000326063,0.04802587,0.0020618255,0.2832391,0.031929526,0.62961495],"study_design_scores_gemma":[0.00002820537,0.00021354065,0.0016835836,0.0011649422,0.000098413184,0.000638157,0.0001694441,0.18807639,0.0026661584,0.60674345,0.19838288,0.00013483944],"about_ca_topic_score_codex":0.0026855334,"about_ca_topic_score_gemma":0.0015942856,"teacher_disagreement_score":0.0038773296,"about_ca_system_score_codex":0.0017422753,"about_ca_system_score_gemma":0.0015239379,"threshold_uncertainty_score":0.014556944},"labels":[],"label_agreement":null},{"id":"W4311569772","doi":"10.5539/mer.v10n1p13","title":"Tools and Computational Machinery for Movement","year":2022,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sensory system; Redundancy (engineering); Computer science; Human–computer interaction; Basketball; Motor system; Neuroscience; Artificial intelligence; Psychology; Geography","score_opus":0.06649070469643008,"score_gpt":0.3297038198508327,"score_spread":0.2632131151544026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311569772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008130561,0.02820665,0.64667106,0.01850976,0.0023608175,0.00009410981,0.0006389569,0.0011527974,0.29423523],"genre_scores_gemma":[0.5197201,0.02930636,0.33667994,0.0051077325,0.0050793835,0.0010368793,0.0012776993,0.00062348443,0.10116856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999159,0.00022767168,0.000070541566,0.0002417214,0.00021622145,0.00008495437],"domain_scores_gemma":[0.9990771,0.0004040518,0.000068624235,0.00029593598,0.00010108643,0.00005321412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010475871,0.0008243537,0.00075894734,0.0015986003,0.0012112332,0.00390362,0.0014918497,0.0018623237,0.012965161],"category_scores_gemma":[0.0027799068,0.00030629386,0.0010808829,0.0011354209,0.0094194915,0.0058673904,0.00240282,0.002866762,0.003821364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021807532,0.0000016163659,0.000030868898,0.000023745279,0.0000037041702,0.000023180892,0.000063696556,0.0006970422,0.00007527173,0.9931901,0.0012943685,0.004594049],"study_design_scores_gemma":[0.000003848964,0.0000042275988,0.00007324073,0.000027814189,0.0000026400542,0.00004251119,0.000026572268,0.0016933272,0.000058594098,0.95692444,0.04113671,0.000006007919],"about_ca_topic_score_codex":0.001852517,"about_ca_topic_score_gemma":0.0010453814,"teacher_disagreement_score":0.012965161,"about_ca_system_score_codex":0.0013529081,"about_ca_system_score_gemma":0.0010064379,"threshold_uncertainty_score":0.04337281},"labels":[],"label_agreement":null},{"id":"W4312001641","doi":"10.5121/csit.2022.122204","title":"A Memory Based Approach for Digital Implementation of Tanh using LUT and RALUT","year":2022,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hyperbolic function; Lookup table; Computer science; Field-programmable gate array; Table (database); Artificial neural network; Activation function; Function (biology); Differentiable function; Range (aeronautics); Schematic; Backpropagation; Algorithm; Arithmetic; Parallel computing; Computer engineering; Computer hardware; Artificial intelligence; Mathematics; Electronic engineering; Data mining","score_opus":0.032178888474857795,"score_gpt":0.2908276819422849,"score_spread":0.25864879346742714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312001641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03876211,0.0042304774,0.9219361,0.00037532276,0.0005340004,0.00022489214,0.00036355667,0.0073097693,0.026263779],"genre_scores_gemma":[0.5275925,0.0016153066,0.4482835,0.00046115348,0.00012459935,0.00018594235,0.00042892733,0.0002198046,0.021088336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998179,0.000029070115,0.0000224708,0.0000363846,0.00007065699,0.000023440749],"domain_scores_gemma":[0.9997644,0.000058534926,0.000030359874,0.00006440592,0.00007649049,0.0000057722223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022159885,0.0004599375,0.00023741426,0.00063845015,0.00025149927,0.00086445955,0.0012090822,0.00032668578,0.010613735],"category_scores_gemma":[0.00059461535,0.00019507244,0.00025401614,0.0006888565,0.00021345483,0.0008280034,0.00025525113,0.0004989435,0.0020707815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032800384,0.00009735395,0.00093184644,0.0012376918,0.00009719642,0.00052470405,0.0002549398,0.01192196,0.23421918,0.04212619,0.008002258,0.7002587],"study_design_scores_gemma":[0.000120889315,0.0018314283,0.0021782669,0.00028408327,0.00023437891,0.0046281964,0.00022970118,0.15451209,0.6219385,0.008499565,0.20543255,0.000110287954],"about_ca_topic_score_codex":0.0008184244,"about_ca_topic_score_gemma":0.0018755498,"teacher_disagreement_score":0.010613735,"about_ca_system_score_codex":0.00037204917,"about_ca_system_score_gemma":0.00042167716,"threshold_uncertainty_score":0.035506487},"labels":[],"label_agreement":null},{"id":"W4312537438","doi":"10.1007/978-3-031-16719-5_9","title":"Grid-Based DNN PCGML","year":2022,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on games and computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Grid; Computer science; Transformer; Artificial neural network; Representation (politics); Dimension (graph theory); Convolutional neural network; Artificial intelligence; Deep learning; Focus (optics); Sequence (biology); Theoretical computer science; Mathematics; Engineering; Pure mathematics; Electrical engineering","score_opus":0.02314186573572086,"score_gpt":0.24801730010465312,"score_spread":0.22487543436893226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312537438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009456491,0.0007457315,0.94280016,0.00033113497,0.0004039711,0.00011120823,0.0014063563,0.01356087,0.031184064],"genre_scores_gemma":[0.29410782,0.0009789519,0.6395683,0.00079645164,0.00016895674,0.0003413712,0.005214904,0.0014382864,0.05738495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998318,0.000022204571,0.000011089763,0.00006479267,0.0000433223,0.00002680588],"domain_scores_gemma":[0.99977595,0.000048478916,0.000008658343,0.000059520717,0.000089409994,0.000017940727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030365586,0.0005467987,0.0005596213,0.0004197799,0.0003202155,0.00089416996,0.0017115045,0.0007600906,0.025294064],"category_scores_gemma":[0.0007202255,0.0003346571,0.00050310296,0.00065231556,0.0002671795,0.0009784307,0.000859855,0.0010577695,0.008578715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020895951,0.000092367154,0.00036997456,0.00020337671,0.00006540792,0.00010470374,0.000033541077,0.23544239,0.007814385,0.02491394,0.03866709,0.6920839],"study_design_scores_gemma":[0.000021046302,0.000029000941,0.000105499304,0.000017082823,0.000013275585,0.000029974193,0.000011837822,0.9701459,0.0043061804,0.014020636,0.011293199,0.000006387708],"about_ca_topic_score_codex":0.01661032,"about_ca_topic_score_gemma":0.028916439,"teacher_disagreement_score":0.025294064,"about_ca_system_score_codex":0.00090077234,"about_ca_system_score_gemma":0.00092134014,"threshold_uncertainty_score":0.08461708},"labels":[],"label_agreement":null},{"id":"W4312666301","doi":"10.23977/acss.2022.060612","title":"A Survey on Bayesian Neural Networks","year":2022,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Variable-order Bayesian network; Markov chain Monte Carlo; Bayesian inference; Artificial intelligence; Computer science; Bayesian probability; Artificial neural network; Inference; Machine learning; Bayesian hierarchical modeling; Bayesian experimental design; Posterior probability; Bayesian statistics; Bayesian network; Bayesian average","score_opus":0.02135796399218402,"score_gpt":0.26594085482042173,"score_spread":0.24458289082823773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312666301","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018183971,0.48668084,0.43542832,0.008884453,0.0019942382,0.00011721508,0.0013628871,0.00058865605,0.06312507],"genre_scores_gemma":[0.042463742,0.75008386,0.17929117,0.0027418965,0.0054842923,0.0003205397,0.0021160464,0.00036205602,0.017136397],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974873,0.000859102,0.00021642038,0.00038952212,0.00094156404,0.00010607729],"domain_scores_gemma":[0.9962535,0.002441234,0.00017587657,0.00023136912,0.00079033925,0.00010758279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034809655,0.0015436324,0.0018132973,0.004070283,0.0009399662,0.0035480405,0.0024423248,0.0028403979,0.010414706],"category_scores_gemma":[0.00956949,0.001194425,0.0012209202,0.007022115,0.0014642641,0.0048862076,0.0017131282,0.0029062775,0.0047579063],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005099544,0.00007839308,0.0018473747,0.0028015978,0.00015990478,0.00018934334,0.00019380206,0.022228299,0.00042286023,0.39604786,0.05205305,0.52392656],"study_design_scores_gemma":[0.00001577401,0.00004066633,0.001096513,0.002182116,0.00008255766,0.00053640385,0.000083846644,0.041750234,0.00045278072,0.4377179,0.51595724,0.00008394994],"about_ca_topic_score_codex":0.0089286,"about_ca_topic_score_gemma":0.0066199666,"teacher_disagreement_score":0.010414706,"about_ca_system_score_codex":0.0026279495,"about_ca_system_score_gemma":0.0033270828,"threshold_uncertainty_score":0.034840703},"labels":[],"label_agreement":null},{"id":"W4312681734","doi":"10.1109/tfuzz.2022.3226250","title":"Design of Distributed Rule-Based Models in the Presence of Large Data","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Weighting; Data mining; Curse of dimensionality; Boosting (machine learning); Machine learning; Data modeling; Artificial intelligence; Database","score_opus":0.06823746302586862,"score_gpt":0.2744964670999681,"score_spread":0.20625900407409947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312681734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010015229,0.000100905876,0.9886968,0.00006969424,0.000018432282,0.000084200256,0.000027401751,0.0004112392,0.0005761879],"genre_scores_gemma":[0.540701,0.00025134592,0.45675668,0.00011638167,0.000043742213,0.00051089696,0.00018025217,0.00007427054,0.001365364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970831,0.0009431827,0.00023405018,0.000795122,0.00076462165,0.00017990351],"domain_scores_gemma":[0.99489814,0.002715706,0.00045516918,0.0007016249,0.0010754135,0.00015401993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004311565,0.0009694189,0.0018068788,0.00053348916,0.0005965798,0.002705344,0.0023487045,0.001763751,0.001368809],"category_scores_gemma":[0.009085797,0.000881046,0.001229739,0.000662674,0.00089936715,0.0019520089,0.0014295868,0.0019958331,0.00067614083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010757479,0.000084187486,0.0005197641,0.000084470084,0.00006679407,0.00012113174,0.00015065489,0.94013655,0.0030748742,0.0069793514,0.00029236675,0.048382297],"study_design_scores_gemma":[0.000011693075,0.000046097168,0.00005198263,0.000005975651,0.000012875442,0.00001978463,0.000013914464,0.99515694,0.0011792431,0.0031389075,0.00035748884,0.000005085238],"about_ca_topic_score_codex":0.0022226993,"about_ca_topic_score_gemma":0.001880304,"teacher_disagreement_score":0.004311565,"about_ca_system_score_codex":0.00093936734,"about_ca_system_score_gemma":0.001544858,"threshold_uncertainty_score":0.022801995},"labels":[],"label_agreement":null},{"id":"W4313016919","doi":"10.1109/ijcnn55064.2022.9892810","title":"E-LSTM: An extension to the LSTM architecture for incorporating long lag dependencies","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Compute Canada","keywords":"Computer science; Extension (predicate logic); Lag; Process (computing); Recurrent neural network; Long short term memory; Architecture; Artificial intelligence; Transmission (telecommunications); Artificial neural network; Pattern recognition (psychology); Machine learning","score_opus":0.04506472041204799,"score_gpt":0.27954777963322164,"score_spread":0.23448305922117366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313016919","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01598133,0.0006975806,0.97440004,0.0003076332,0.00023370357,0.000071884475,0.00049712637,0.0033616265,0.0044490383],"genre_scores_gemma":[0.40938252,0.001179333,0.5720125,0.00052400026,0.00019954953,0.00022099727,0.0015029957,0.00042162606,0.014556403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998342,0.00003246481,0.000015571974,0.000048465332,0.000044397624,0.000024926456],"domain_scores_gemma":[0.99976844,0.00008398516,0.000022206044,0.00004009936,0.0000717495,0.000013595479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043282044,0.0007101101,0.00036282244,0.00030068058,0.0002470194,0.00053524994,0.0012328124,0.00095672446,0.005826331],"category_scores_gemma":[0.0014502524,0.00029624053,0.00046034958,0.0004982983,0.00023819171,0.0015950805,0.0007280348,0.0011765726,0.0014850158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031288664,0.00019564379,0.0015792706,0.00037688782,0.00022855544,0.00059449393,0.00017784662,0.20352003,0.061764542,0.017033268,0.016824605,0.697392],"study_design_scores_gemma":[0.00001577153,0.00006643469,0.00055642775,0.000029365197,0.000035934892,0.00017243421,0.00001774164,0.969815,0.011920092,0.008026388,0.009324448,0.000019935407],"about_ca_topic_score_codex":0.0030644974,"about_ca_topic_score_gemma":0.007059559,"teacher_disagreement_score":0.005826331,"about_ca_system_score_codex":0.00031509277,"about_ca_system_score_gemma":0.00074595545,"threshold_uncertainty_score":0.019491017},"labels":[],"label_agreement":null},{"id":"W4313140576","doi":"10.1007/978-3-030-95995-1_10","title":"Discriminant-Based Classification","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discriminant; Discriminant function analysis; Classifier (UML); Linear discriminant analysis; Notation; Pattern recognition (psychology); Mathematics; Artificial intelligence; Equivalence (formal languages); Combinatorics; Computer science; Discrete mathematics; Statistics; Arithmetic","score_opus":0.05299555007099396,"score_gpt":0.2519158818732957,"score_spread":0.19892033180230176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313140576","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043592975,0.008771915,0.8268319,0.0009444196,0.001434793,0.00012453653,0.0010689502,0.0043812394,0.15208302],"genre_scores_gemma":[0.06360238,0.008533345,0.4732603,0.0009011482,0.000994106,0.00022269615,0.0048658177,0.0012320362,0.44638827],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99961793,0.00004132943,0.000015528421,0.000113562586,0.00018352954,0.00002816176],"domain_scores_gemma":[0.9996991,0.00007927517,0.000014407414,0.00007894103,0.000113657516,0.000014582081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051376695,0.001141868,0.0009054086,0.0016719954,0.00064368406,0.0017587392,0.0012752697,0.0008309579,0.034898676],"category_scores_gemma":[0.0009959774,0.00041694462,0.0006081918,0.0019883069,0.00057555066,0.0016286154,0.0011688648,0.0015617032,0.0355077],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004001763,0.0000409296,0.0002477663,0.00014184993,0.000023532017,0.000025535577,0.000029911487,0.0038240012,0.0060790232,0.03237493,0.0847408,0.87243164],"study_design_scores_gemma":[0.00001810246,0.00009801117,0.0017909674,0.00018265253,0.00007103105,0.00078960997,0.00010161085,0.2042157,0.023941558,0.14441481,0.62429833,0.00007758484],"about_ca_topic_score_codex":0.0010714154,"about_ca_topic_score_gemma":0.002081763,"teacher_disagreement_score":0.034898676,"about_ca_system_score_codex":0.00058176887,"about_ca_system_score_gemma":0.00045948566,"threshold_uncertainty_score":0.11674768},"labels":[],"label_agreement":null},{"id":"W4313555251","doi":"10.3389/fnins.2022.1070337","title":"Editorial: Advanced analytic techniques in developmental neuroscience","year":2023,"lang":"en","type":"editorial","venue":"Frontiers in Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Health Authority; St. Francis Xavier University","funders":"National Institutes of Health","keywords":"Neuroscience; Psychology; Cognitive science; Developmental cognitive neuroscience; Neuroimaging; Functional neuroimaging","score_opus":0.013971980225679482,"score_gpt":0.2691466015827406,"score_spread":0.25517462135706115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313555251","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003879493,0.0020538445,0.00044500476,0.014578501,0.9821406,0.000020680603,0.000056777506,0.00010564518,0.0005602136],"genre_scores_gemma":[0.00028121582,0.0019464806,0.00021949747,0.009301002,0.98550886,0.000024237987,0.000032378255,0.00006527047,0.0026211035],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99166405,0.001429291,0.0012598481,0.0010880576,0.004049337,0.0005094533],"domain_scores_gemma":[0.9442134,0.02619801,0.0028421753,0.001706414,0.02082047,0.00421951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009626399,0.004549665,0.0032545219,0.0061517996,0.0028460515,0.00843006,0.004531296,0.012089839,0.017952072],"category_scores_gemma":[0.0460128,0.001296954,0.0046825893,0.002066814,0.0035656434,0.0049263565,0.0016769288,0.017344063,0.01487365],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006615886,0.000023967754,0.000044733224,0.00035581784,0.00003198797,0.00012390327,0.00001629542,0.00006110419,0.00013845626,0.0005253432,0.9919497,0.006662492],"study_design_scores_gemma":[0.000067557325,0.000069609225,0.0003461342,0.00057063444,0.0000627553,0.00036949583,0.000037390055,0.00040894217,0.00039184047,0.0019253865,0.9957094,0.00004086667],"about_ca_topic_score_codex":0.0007135597,"about_ca_topic_score_gemma":0.0012342193,"teacher_disagreement_score":0.017952072,"about_ca_system_score_codex":0.0030480798,"about_ca_system_score_gemma":0.00301225,"threshold_uncertainty_score":0.060055733},"labels":[],"label_agreement":null},{"id":"W4313908188","doi":"10.1111/cogs.13233","title":"How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox","year":2023,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Musical form; Similarity (geometry); Fourier transform; Artificial neural network; Computer science; Cognition; Artificial intelligence; Set (abstract data type); Pitch (Music); Pattern recognition (psychology); Musical; Mathematics; Psychology","score_opus":0.09587030205864186,"score_gpt":0.34744733227431673,"score_spread":0.2515770302156749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313908188","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7631835,0.004646531,0.048583318,0.08066231,0.00052238087,0.00013031678,0.00020866292,0.00035308712,0.1017099],"genre_scores_gemma":[0.98700047,0.0004208276,0.0075612166,0.0015976022,0.00010337156,0.00003899202,0.000038508875,0.000046766963,0.0031920979],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981205,0.0011812865,0.00008266024,0.00020178703,0.0002873664,0.00012632387],"domain_scores_gemma":[0.9887321,0.008781987,0.00047082323,0.0007581207,0.0007783189,0.00047867474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068517746,0.00042089284,0.0008135375,0.0016610024,0.0037292659,0.002508553,0.0010257859,0.0035996996,0.0032982219],"category_scores_gemma":[0.03563783,0.00017077618,0.0004304621,0.001746333,0.0047071236,0.0067698443,0.0016815811,0.002859109,0.0005055862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008698136,0.0003433048,0.03485613,0.00027125547,0.00012519117,0.0075050625,0.018717816,0.016902877,0.001392017,0.7167309,0.024842456,0.17744324],"study_design_scores_gemma":[0.00020747946,0.00011365922,0.011833729,0.00015047884,0.00004195585,0.0033508022,0.008977104,0.09095606,0.0013059179,0.8369014,0.046092667,0.00006879561],"about_ca_topic_score_codex":0.0054740906,"about_ca_topic_score_gemma":0.004361699,"teacher_disagreement_score":0.0068517746,"about_ca_system_score_codex":0.0021467071,"about_ca_system_score_gemma":0.0006764392,"threshold_uncertainty_score":0.036236107},"labels":[],"label_agreement":null},{"id":"W4315490211","doi":"10.1038/s41592-022-01747-1","title":"Neural networks primer","year":2023,"lang":"es","type":"article","venue":"Nature Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Primer (cosmetics); Computational biology; Computer science; Biology; Artificial intelligence; Chemistry","score_opus":0.029578340118643222,"score_gpt":0.4047613696741572,"score_spread":0.37518302955551397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315490211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009079529,0.026778754,0.47340667,0.028900348,0.016902907,0.00020425259,0.0037132388,0.006822531,0.44236335],"genre_scores_gemma":[0.014296523,0.025141219,0.18840618,0.012968474,0.0062184003,0.00066927774,0.004046575,0.0024414633,0.7458118],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958724,0.00009573732,0.000024397592,0.0000722708,0.0001950757,0.000025281426],"domain_scores_gemma":[0.99889034,0.00050601695,0.000046278485,0.00014991027,0.00035117823,0.00005623791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008914257,0.0006991949,0.00042476333,0.0013074372,0.00038848954,0.0019024062,0.0013674097,0.0018930105,0.08626586],"category_scores_gemma":[0.0036627478,0.0004620875,0.00054224854,0.0008268046,0.000473895,0.0023894543,0.0010233739,0.002062763,0.062263343],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002997401,0.00003243588,0.00012882658,0.00029983342,0.000016451506,0.0000629108,0.000042724896,0.0018449833,0.0010080311,0.11735081,0.58070135,0.2984817],"study_design_scores_gemma":[0.0000064660817,0.000008225017,0.00013090845,0.0001829488,0.000006978498,0.0001279217,0.0000138339865,0.0040280824,0.0010925962,0.066190615,0.9281995,0.000011841292],"about_ca_topic_score_codex":0.0016512382,"about_ca_topic_score_gemma":0.00254461,"teacher_disagreement_score":0.08626586,"about_ca_system_score_codex":0.0007407124,"about_ca_system_score_gemma":0.00086259627,"threshold_uncertainty_score":0.28858805},"labels":[],"label_agreement":null},{"id":"W4315701650","doi":"10.3390/math11020400","title":"Input-Output Selection for LSTM-Based Reduced-Order State Estimator Design","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Computer science; Recurrent neural network; Kalman filter; Sensitivity (control systems); State (computer science); Selection (genetic algorithm); Artificial neural network; Model selection; Construct (python library); Control theory (sociology); Algorithm; Artificial intelligence; Machine learning; Mathematics; Engineering","score_opus":0.061856177712229815,"score_gpt":0.2988225033633919,"score_spread":0.23696632565116207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315701650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031142994,0.00006600269,0.9960866,0.00004197131,0.000009845027,0.00002534166,0.000022262433,0.00024994393,0.0003836977],"genre_scores_gemma":[0.5643686,0.00027773523,0.4318184,0.00024040497,0.00005343721,0.00059583643,0.00037142742,0.00019591028,0.0020782875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999102,0.00040328095,0.000060430186,0.00018380798,0.00019149698,0.00005898384],"domain_scores_gemma":[0.9977309,0.0015149243,0.00016728263,0.00012182856,0.00042538327,0.00003981246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025007247,0.0011923896,0.0010037166,0.0005003719,0.00037539206,0.0008222512,0.0009415052,0.0013445516,0.0025812872],"category_scores_gemma":[0.007875261,0.00085466966,0.00087132753,0.00037146246,0.0006515532,0.0010676715,0.00092665624,0.0013163157,0.0007826284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017281115,0.000084986794,0.0012127938,0.00023225913,0.00012577903,0.00013254503,0.00014560585,0.87741804,0.013118619,0.016017638,0.0009064981,0.09043236],"study_design_scores_gemma":[0.0000044949875,0.00002092319,0.000100998,0.0000075604944,0.0000071038703,0.000011702409,0.0000026375824,0.9957652,0.0018785562,0.0019266396,0.00026836112,0.000005878553],"about_ca_topic_score_codex":0.0026361886,"about_ca_topic_score_gemma":0.0025521563,"teacher_disagreement_score":0.0026361886,"about_ca_system_score_codex":0.00078535196,"about_ca_system_score_gemma":0.0012328881,"threshold_uncertainty_score":0.013225257},"labels":[],"label_agreement":null},{"id":"W4316083097","doi":"10.1049/icp.2022.2425","title":"Extended state observer-based neural networks with super-twisting sliding mode control feedback stabilizer","year":2022,"lang":"en","type":"article","venue":"IET conference proceedings.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; École de Technologie Supérieure","funders":"","keywords":"Control theory (sociology); Stabilizer (aeronautics); Sliding mode control; Artificial neural network; Computer science; Observer (physics); State observer; Feedback control; Mode (computer interface); State (computer science); Control (management); Control engineering; Artificial intelligence; Engineering; Physics; Structural engineering; Algorithm; Nonlinear system","score_opus":0.0221990984688938,"score_gpt":0.23395580093696428,"score_spread":0.2117567024680705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316083097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013648544,0.000155227,0.9834477,0.000054396038,0.00008843488,0.000027426539,0.000016045888,0.0004170452,0.002145192],"genre_scores_gemma":[0.8945502,0.00022899912,0.10028506,0.00006385779,0.000053024956,0.00012257964,0.00007558283,0.000028162229,0.0045925197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972266,0.000049033024,0.00002186365,0.00006323935,0.00010756914,0.000035605637],"domain_scores_gemma":[0.9996693,0.000080615835,0.00007046038,0.000044178003,0.000117674834,0.000017822482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050848455,0.0005183712,0.0006205353,0.00022737765,0.00024945196,0.0004957864,0.0008229292,0.0005432402,0.001173513],"category_scores_gemma":[0.00080658,0.00023195232,0.00039402887,0.00019989068,0.00044870103,0.0006290319,0.0005962927,0.00085499993,0.00024633805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029170752,0.00006359203,0.0008984504,0.00019765408,0.00006800564,0.00022952823,0.0001574179,0.7917117,0.040614687,0.014888451,0.0011908239,0.14968795],"study_design_scores_gemma":[0.00000729301,0.000059378304,0.00010838418,0.000004311465,0.000005894884,0.000014945303,0.0000027580484,0.996837,0.0018545708,0.0005433513,0.0005571887,0.000004827286],"about_ca_topic_score_codex":0.003284928,"about_ca_topic_score_gemma":0.0038603353,"teacher_disagreement_score":0.003284928,"about_ca_system_score_codex":0.00037762494,"about_ca_system_score_gemma":0.00051477284,"threshold_uncertainty_score":0.006531596},"labels":[],"label_agreement":null},{"id":"W4317780736","doi":"10.1080/19420889.2022.2163131","title":"Biologically-inspired neuronal adaptation improves learning in neural networks","year":2023,"lang":"en","type":"article","venue":"Communicative & Integrative Biology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University; University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Compute Canada","keywords":"Hebbian theory; Computer science; Artificial intelligence; MNIST database; Adaptation (eye); Artificial neural network; Perceptron; Machine learning; Backpropagation; Stability (learning theory); Convolutional neural network; Pattern recognition (psychology); Neuroscience; Psychology","score_opus":0.048794003989580434,"score_gpt":0.3076739811272455,"score_spread":0.25887997713766503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317780736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47150704,0.0020286732,0.5098202,0.0013371409,0.00039782116,0.00005361131,0.00008715213,0.003248766,0.011519469],"genre_scores_gemma":[0.9622072,0.00036937685,0.03507342,0.00020763246,0.000054645858,0.00002408844,0.000053157375,0.00014052598,0.0018698972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997602,0.00005490142,0.000015308351,0.00007556386,0.000061877145,0.00003220908],"domain_scores_gemma":[0.9991571,0.0004072109,0.00011128461,0.0001607269,0.00011882811,0.000044822147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006662008,0.00053136656,0.000297737,0.00025270638,0.0002243937,0.0005453684,0.0007790015,0.0006465772,0.0017151845],"category_scores_gemma":[0.004433361,0.00019367559,0.0002687802,0.0002811934,0.0005793899,0.0011882354,0.00090207823,0.0011437159,0.00039329316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019353951,0.00022064512,0.003418491,0.0002152843,0.0001498637,0.00018823956,0.00020713978,0.57931334,0.16226521,0.02525018,0.0025257121,0.22605243],"study_design_scores_gemma":[0.000015626634,0.00015969809,0.002334731,0.000016741891,0.00003246271,0.000067966124,0.00002275716,0.9396875,0.030680181,0.024191627,0.0027689084,0.000021852895],"about_ca_topic_score_codex":0.0012749668,"about_ca_topic_score_gemma":0.0012315208,"teacher_disagreement_score":0.0017151845,"about_ca_system_score_codex":0.0005247319,"about_ca_system_score_gemma":0.00034931352,"threshold_uncertainty_score":0.005737841},"labels":[],"label_agreement":null},{"id":"W4318977067","doi":"10.1007/978-3-031-10602-6_3","title":"Background on Kernels","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Rigged Hilbert space; Infinite-dimensional vector function; Hilbert space; Mathematics; Hilbert manifold; Euclidean space; Reproducing kernel Hilbert space; Space (punctuation); Pure mathematics; Curse of dimensionality; Quotient space (topology); Field (mathematics); Banach space; Mathematical analysis; Banach manifold; Lp space; Computer science","score_opus":0.0727648152024449,"score_gpt":0.27357298787886664,"score_spread":0.20080817267642173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318977067","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017131886,0.06135595,0.37761968,0.0026999176,0.0024366858,0.000077784294,0.0009571706,0.0027685033,0.5503711],"genre_scores_gemma":[0.028697735,0.053488478,0.12861742,0.001885239,0.0027067044,0.00013386672,0.0019529958,0.002037683,0.7804798],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99972945,0.000024757781,0.000014900205,0.00007542489,0.00013357573,0.000021810152],"domain_scores_gemma":[0.9996325,0.00013734488,0.00001494109,0.0000703348,0.0001194963,0.000025371794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032014746,0.0010223999,0.00072428683,0.0016134183,0.000546246,0.0021062354,0.0008825164,0.0009940034,0.05434969],"category_scores_gemma":[0.0012235694,0.00057185395,0.00054474856,0.0025501347,0.00074159703,0.0035013915,0.0010213135,0.0020545998,0.040132686],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016304419,0.000053616466,0.000078204874,0.00047694138,0.000013413823,0.000054347176,0.000107610394,0.00335257,0.002075526,0.33275935,0.17673841,0.48427367],"study_design_scores_gemma":[0.0000030052292,0.00001579386,0.00017890583,0.00019060544,0.000008676667,0.00022170563,0.000020530199,0.0052813985,0.0012323137,0.26814058,0.72468805,0.0000183797],"about_ca_topic_score_codex":0.001349922,"about_ca_topic_score_gemma":0.0020479476,"teacher_disagreement_score":0.05434969,"about_ca_system_score_codex":0.00082140265,"about_ca_system_score_gemma":0.0006266682,"threshold_uncertainty_score":0.18181783},"labels":[],"label_agreement":null},{"id":"W4318977988","doi":"10.1007/978-3-031-10602-6_20","title":"Variational Autoencoders","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoencoder; Discriminative model; Inference; Generative model; Latent variable; Upper and lower bounds; Expectation–maximization algorithm; Artificial intelligence; Maximization; Computer science; Bayesian inference; Pattern recognition (psychology); Backpropagation; Artificial neural network; Generative grammar; Algorithm; Mathematics; Bayesian probability; Maximum likelihood; Mathematical optimization; Statistics","score_opus":0.020322794138062785,"score_gpt":0.22046189803644525,"score_spread":0.20013910389838246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318977988","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024823484,0.008589852,0.8220379,0.0010952352,0.0008640087,0.00002848629,0.00042593997,0.0008602308,0.16361594],"genre_scores_gemma":[0.112880774,0.013862622,0.25827226,0.0007854937,0.0011367211,0.00014468597,0.001827425,0.0015559664,0.609534],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998522,0.000028243729,0.0000049274877,0.000035479334,0.00006921666,0.000009994186],"domain_scores_gemma":[0.99984765,0.00006712761,0.000006267931,0.000037953403,0.000033338183,0.00000755339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003263698,0.00076501415,0.00061561447,0.00035900314,0.00024890364,0.00096736755,0.0006860057,0.0007957185,0.019736188],"category_scores_gemma":[0.0010165336,0.00043838393,0.00034797587,0.00055836124,0.00062799104,0.0012014557,0.0009725036,0.0015049321,0.008134995],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021380933,0.00002674371,0.00014012314,0.00017084915,0.00004721308,0.000033838667,0.000054539214,0.07731862,0.0023424444,0.41578954,0.08861268,0.41544208],"study_design_scores_gemma":[0.0000065736826,0.000022031967,0.00038464527,0.0001040196,0.000023870496,0.00012197142,0.000023099874,0.32070178,0.0036246404,0.41687346,0.25808644,0.000027466049],"about_ca_topic_score_codex":0.0016962336,"about_ca_topic_score_gemma":0.0035269493,"teacher_disagreement_score":0.019736188,"about_ca_system_score_codex":0.0005403089,"about_ca_system_score_gemma":0.00042098836,"threshold_uncertainty_score":0.066024184},"labels":[],"label_agreement":null},{"id":"W4319989526","doi":"10.1007/978-3-031-10602-6","title":"Elements of Dimensionality Reduction and Manifold Learning","year":2023,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Nonlinear dimensionality reduction; Dimensionality reduction; Manifold (fluid mechanics); Reduction (mathematics); Curse of dimensionality; Manifold alignment; Mathematics; Artificial intelligence; Computer science; Geometry; Engineering; Mechanical engineering","score_opus":0.024112318421841434,"score_gpt":0.2572668252153844,"score_spread":0.23315450679354296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319989526","genre_codex":"review","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017472836,0.42702082,0.37583405,0.01103487,0.008307323,0.00013359035,0.00062609225,0.0019816377,0.17331435],"genre_scores_gemma":[0.02410883,0.42556578,0.32994202,0.0045310133,0.010326468,0.00034899727,0.0021835996,0.0011146547,0.20187867],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99880934,0.00016648081,0.00006604811,0.00014227262,0.0007723732,0.00004341785],"domain_scores_gemma":[0.99918646,0.00043956336,0.000038658745,0.00010666166,0.00018264244,0.00004599701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086710026,0.0013501889,0.0014806625,0.0026644862,0.00078885467,0.0034708194,0.0010846106,0.00091418717,0.012397508],"category_scores_gemma":[0.0026632017,0.00073167105,0.001020989,0.0048915828,0.0029233552,0.004858418,0.0018864162,0.0039684437,0.009466019],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002336072,0.000029173232,0.0002457331,0.0013846883,0.000063068415,0.00008462455,0.00033824315,0.0028445087,0.0015209059,0.24670075,0.19945203,0.5473129],"study_design_scores_gemma":[0.0000037467746,0.000025768195,0.00041637165,0.0003600281,0.0000139771355,0.00048030258,0.000088953486,0.0024296069,0.0006134092,0.1746659,0.8208735,0.00002842048],"about_ca_topic_score_codex":0.0010486242,"about_ca_topic_score_gemma":0.001758612,"teacher_disagreement_score":0.012397508,"about_ca_system_score_codex":0.00089216704,"about_ca_system_score_gemma":0.0010381349,"threshold_uncertainty_score":0.041473746},"labels":[],"label_agreement":null},{"id":"W4319989718","doi":"10.1007/978-3-031-10602-6_9","title":"Laplacian-Based Dimensionality Reduction","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Dimensionality reduction; Reduction (mathematics); Laplace operator; Computer science; Mathematics; Artificial intelligence; Mathematical analysis; Geometry","score_opus":0.03938717824527691,"score_gpt":0.24969612747355607,"score_spread":0.21030894922827917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319989718","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055645667,0.0024372784,0.9563258,0.00067083794,0.0004500371,0.000048660128,0.00044987362,0.0019128937,0.032140028],"genre_scores_gemma":[0.123974115,0.0052112076,0.7512525,0.0006458914,0.00059520977,0.0002133746,0.0029452438,0.0011119664,0.11405064],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997043,0.000046853576,0.000013953114,0.000058048114,0.00015398707,0.000022839446],"domain_scores_gemma":[0.99978596,0.000045576726,0.000009372911,0.000068925205,0.00007795039,0.000012222027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028726133,0.0006114126,0.00076094107,0.0007608417,0.00043702498,0.00115041,0.0008033499,0.00046620079,0.009024794],"category_scores_gemma":[0.0009415501,0.0002587785,0.0007270163,0.0014229375,0.0004847151,0.0012571173,0.0013484053,0.0012378154,0.005172244],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046782396,0.000060801118,0.00016912668,0.00016610509,0.000049078102,0.00004839118,0.000086630185,0.026670406,0.013847974,0.08031373,0.06818434,0.8103567],"study_design_scores_gemma":[0.00001631775,0.00007647418,0.0008762287,0.000054113618,0.00004817089,0.00040021827,0.000109602086,0.67782754,0.019647565,0.19302239,0.10785102,0.00007029065],"about_ca_topic_score_codex":0.0011945952,"about_ca_topic_score_gemma":0.002206002,"teacher_disagreement_score":0.009024794,"about_ca_system_score_codex":0.0003407503,"about_ca_system_score_gemma":0.0005121205,"threshold_uncertainty_score":0.030190945},"labels":[],"label_agreement":null},{"id":"W4320018809","doi":"10.1007/978-3-031-23492-7_31","title":"Convergence of RBF Networks Regression Function Estimates and Classifiers","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Convergence (economics); Computer science; Regression; Artificial intelligence; Artificial neural network; Dimension (graph theory); Regression analysis; Radial basis function; Machine learning; Function (biology); Algorithm; Statistics; Mathematics","score_opus":0.022897502510000334,"score_gpt":0.2510630144253859,"score_spread":0.2281655119153856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320018809","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076603885,0.002626884,0.9810667,0.0003433513,0.0001474599,0.000026978763,0.000054710286,0.00020263194,0.007870907],"genre_scores_gemma":[0.36444455,0.007161825,0.5914,0.00030482968,0.00068711536,0.00044663585,0.00054831157,0.0011930935,0.033813745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811363,0.00084689364,0.000095897325,0.000277268,0.0005763626,0.000089948815],"domain_scores_gemma":[0.9869119,0.009766206,0.00044483025,0.00055643194,0.0021860967,0.00013442879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00558087,0.0010728295,0.0013360585,0.0016656194,0.00050314923,0.0021184382,0.0018469548,0.0021705942,0.004093006],"category_scores_gemma":[0.037701134,0.001084137,0.0010847641,0.0012758826,0.0016557131,0.003095278,0.002093635,0.0034253546,0.0018911302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030783933,0.00006660747,0.0008778843,0.00060778775,0.00008967623,0.00009170142,0.0003713697,0.5079282,0.0077756774,0.24455744,0.008118828,0.22920702],"study_design_scores_gemma":[0.0000051715024,0.000018579958,0.00020876116,0.00005883832,0.000009413493,0.000034980578,0.000017494032,0.95857173,0.0013822656,0.037941694,0.0017366172,0.000014578625],"about_ca_topic_score_codex":0.0034505217,"about_ca_topic_score_gemma":0.0017321158,"teacher_disagreement_score":0.00558087,"about_ca_system_score_codex":0.0015587058,"about_ca_system_score_gemma":0.0007899375,"threshold_uncertainty_score":0.02951485},"labels":[],"label_agreement":null},{"id":"W4323083077","doi":"10.5281/zenodo.7697428","title":"Eight Types Of Bears In Contrast - Identification Information","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Contrast (vision); Artificial intelligence; Psychology; Computer science; Biology; Ecology","score_opus":0.02417135037119053,"score_gpt":0.23806615818364404,"score_spread":0.2138948078124535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323083077","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79792327,0.00081148365,0.010013794,0.0005670587,0.000292806,0.0004640684,0.017426366,0.00044281717,0.17205837],"genre_scores_gemma":[0.9030902,0.00039481177,0.011361923,0.00029864465,0.00010334761,0.00027006268,0.015778342,0.00007128459,0.0686314],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994605,0.000044567525,0.000049251234,0.00012410557,0.00020836959,0.00011318847],"domain_scores_gemma":[0.9994186,0.00010599501,0.00013100974,0.000128453,0.0001600016,0.000055891145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038416003,0.0003845609,0.00022228893,0.0012073658,0.0009673905,0.00065699895,0.00059693045,0.00036701353,0.024526333],"category_scores_gemma":[0.0010501677,0.00016174109,0.0002951259,0.0011752307,0.00032870646,0.0006843171,0.0008637897,0.00027196473,0.0045559816],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032552269,0.0005936318,0.46725821,0.0006866819,0.00015737314,0.00438104,0.0053297356,0.0014176491,0.024522657,0.030041194,0.049913537,0.41244307],"study_design_scores_gemma":[0.00014823274,0.00059519487,0.5274878,0.00023189856,0.00031369712,0.008755668,0.0044975234,0.0039413245,0.02596684,0.008737838,0.41917375,0.00015017514],"about_ca_topic_score_codex":0.0035421147,"about_ca_topic_score_gemma":0.007377832,"teacher_disagreement_score":0.024526333,"about_ca_system_score_codex":0.00042261663,"about_ca_system_score_gemma":0.00041704773,"threshold_uncertainty_score":0.082048714},"labels":[],"label_agreement":null},{"id":"W4323240500","doi":"10.5220/0011807600003411","title":"Invertible Neural Network-Based Video Compression","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Data compression; Artificial neural network; Compression (physics); Invertible matrix; Artificial intelligence; Computer vision; Mathematics","score_opus":0.02979714891683327,"score_gpt":0.2640973178329724,"score_spread":0.23430016891613914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323240500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056614984,0.0011924756,0.92852074,0.00044186186,0.00029134043,0.000067369496,0.0001814579,0.0009427178,0.0117470855],"genre_scores_gemma":[0.6839679,0.0012299186,0.29588917,0.00025895247,0.00022310519,0.00007659009,0.00038869772,0.00011821082,0.017847506],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998282,0.000028615463,0.000009376884,0.00003217044,0.00008252929,0.000019047942],"domain_scores_gemma":[0.99978393,0.00009488167,0.00001599515,0.00004227787,0.000052589672,0.000010417051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023867002,0.00031361406,0.0002662267,0.0004311064,0.00015542752,0.00035059682,0.0004918489,0.0004516037,0.0037743894],"category_scores_gemma":[0.00113776,0.00011008862,0.00016907042,0.00049636065,0.00030867365,0.0006777198,0.00054430636,0.00064945925,0.0005690473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004921879,0.00012446583,0.00039438886,0.00016834993,0.000035734414,0.00024560103,0.00007189955,0.09864904,0.11509666,0.042924996,0.0044410275,0.7373557],"study_design_scores_gemma":[0.000026485786,0.00006638338,0.0003258177,0.000019317044,0.000013569273,0.00024132621,0.000014940186,0.94950813,0.038666733,0.00807188,0.0030339619,0.000011434529],"about_ca_topic_score_codex":0.0014540858,"about_ca_topic_score_gemma":0.0019897826,"teacher_disagreement_score":0.0037743894,"about_ca_system_score_codex":0.00024780186,"about_ca_system_score_gemma":0.00025918765,"threshold_uncertainty_score":0.012626529},"labels":[],"label_agreement":null},{"id":"W4323649708","doi":"10.23977/acss.2023.070110","title":"Time series regression based on Bayesian model averaging and principal component analysis","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Principal component regression; Lasso (programming language); Bayesian probability; Computer science; Time series; Bayesian information criterion; Regression; Pattern recognition (psychology); Artificial intelligence; Regression analysis; Series (stratigraphy); Projection pursuit; Projection (relational algebra); Mathematics; Data mining; Statistics; Machine learning; Algorithm","score_opus":0.015973326989112187,"score_gpt":0.2671875189295735,"score_spread":0.2512141919404613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323649708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025956826,0.0002478928,0.9965403,0.00007325356,0.000030607473,0.000009333436,0.000017470342,0.0001441353,0.0003413552],"genre_scores_gemma":[0.47562963,0.0024772263,0.5151598,0.0002469545,0.0005139378,0.000276992,0.0004484225,0.0002911793,0.0049558748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825424,0.0005883358,0.00009646921,0.00040754696,0.0005458108,0.00010754162],"domain_scores_gemma":[0.99902415,0.00047706152,0.00012636364,0.000078264566,0.00026244926,0.00003171192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022928917,0.001065092,0.0016123787,0.0009204942,0.00042982795,0.00088153814,0.0014009612,0.00079157675,0.00076963287],"category_scores_gemma":[0.004469736,0.0005072859,0.0014308344,0.0014520019,0.0005733737,0.0016347031,0.0008048402,0.0018545364,0.00032138475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061293664,0.00007354735,0.0015104256,0.00015142908,0.00032153647,0.00011587916,0.00012639385,0.7558315,0.0059934757,0.0562593,0.0022856693,0.17726962],"study_design_scores_gemma":[0.0000020320306,0.000013535938,0.00017032272,0.0000033825231,0.000013433444,0.000015044759,0.0000022423135,0.9941871,0.0003612032,0.004690187,0.00053153466,0.0000100616935],"about_ca_topic_score_codex":0.0065488038,"about_ca_topic_score_gemma":0.0050504194,"teacher_disagreement_score":0.0065488038,"about_ca_system_score_codex":0.00052718684,"about_ca_system_score_gemma":0.0012416891,"threshold_uncertainty_score":0.01302135},"labels":[],"label_agreement":null},{"id":"W4323844359","doi":"10.18280/isi.280128","title":"Design of Dynamic Network for Parallel Processing on a Distributed System","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Mosul","keywords":"Computer science; Distributed computing; Parallel computing","score_opus":0.02258614098620354,"score_gpt":0.24998923945985674,"score_spread":0.2274030984736532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323844359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027640332,0.00022343735,0.95808053,0.0002347625,0.00009443427,0.00018065094,0.000044890447,0.0012522595,0.012248707],"genre_scores_gemma":[0.58811474,0.00041168704,0.39983273,0.00013705186,0.00006652902,0.00067355204,0.00016368942,0.0001847484,0.010415276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996668,0.000097985176,0.000020272808,0.00007386124,0.00010368136,0.000037456404],"domain_scores_gemma":[0.9997832,0.000047649854,0.000018428138,0.000035706475,0.00008838145,0.000026489919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034628663,0.0003032092,0.0003115147,0.00034367267,0.00066552724,0.0008402434,0.00096689654,0.00036118506,0.0029114876],"category_scores_gemma":[0.0006102019,0.00023083,0.00027735106,0.00030815703,0.000363601,0.00075072946,0.00057511276,0.00038709474,0.0006419996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004631669,0.00027153146,0.0030182842,0.00047866828,0.00013965511,0.00067119143,0.0005530825,0.5220114,0.10389955,0.15698749,0.008063394,0.20344259],"study_design_scores_gemma":[0.000078798366,0.00023591936,0.0005200737,0.000033043318,0.000053399723,0.00023891003,0.00008430965,0.9240912,0.018990783,0.018728163,0.03691663,0.000028753522],"about_ca_topic_score_codex":0.001132882,"about_ca_topic_score_gemma":0.001337991,"teacher_disagreement_score":0.0029114876,"about_ca_system_score_codex":0.00062418496,"about_ca_system_score_gemma":0.0007492633,"threshold_uncertainty_score":0.009739876},"labels":[],"label_agreement":null},{"id":"W4323922154","doi":"10.5281/zenodo.7720006","title":"8 Types Of Bears In Contrast - Identification Information","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Contrast (vision); Identification (biology); Computer science; Artificial intelligence; Biology","score_opus":0.025837480285006837,"score_gpt":0.24209935269709998,"score_spread":0.21626187241209313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323922154","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49464643,0.0024537803,0.015233369,0.0014348497,0.0009581848,0.00045128973,0.077522114,0.0012141318,0.4060858],"genre_scores_gemma":[0.7784844,0.0011553124,0.01713622,0.0008043795,0.00045353663,0.0003462787,0.06915768,0.00022573242,0.13223638],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99930274,0.00006194558,0.00007163917,0.00017439119,0.00026158654,0.00012769479],"domain_scores_gemma":[0.99918634,0.00018492967,0.00017039038,0.00021464293,0.0001869674,0.000056656543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044410155,0.00040918132,0.00028715015,0.0015263553,0.00080595457,0.0006846449,0.0006554383,0.00046038398,0.049212642],"category_scores_gemma":[0.0013430242,0.00017757592,0.00034790754,0.0015723971,0.0002251603,0.00096772786,0.0008960463,0.0003133666,0.011609487],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020549623,0.000457338,0.3236872,0.00085141265,0.00014806767,0.0039428365,0.0032900712,0.0016577645,0.015024615,0.043717902,0.16136786,0.4438],"study_design_scores_gemma":[0.00010170879,0.00027497305,0.26513398,0.00039311664,0.00021941087,0.007448616,0.002237186,0.0031992225,0.013963736,0.010847878,0.6960503,0.00012993478],"about_ca_topic_score_codex":0.0031172065,"about_ca_topic_score_gemma":0.0050023533,"teacher_disagreement_score":0.049212642,"about_ca_system_score_codex":0.0003828842,"about_ca_system_score_gemma":0.00034555764,"threshold_uncertainty_score":0.16463268},"labels":[],"label_agreement":null},{"id":"W4324099566","doi":"10.28924/2291-8639-21-2023-20","title":"Symbolic Algorithm for Inverting General k-Tridiagonal Interval Matrices","year":2023,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tridiagonal matrix; Mathematics; Interval (graph theory); Tridiagonal matrix algorithm; Matrix (chemical analysis); Algorithm; Algebra over a field; Inverse; Factorization; Applied mathematics; Interval arithmetic; Pure mathematics; Combinatorics; Mathematical analysis; Eigenvalues and eigenvectors; Geometry","score_opus":0.015400248287535436,"score_gpt":0.3056229826179495,"score_spread":0.29022273433041407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324099566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018628174,0.00007300981,0.9959019,0.000026079066,0.000025065192,0.000016926766,0.000029328836,0.00041649793,0.0016484506],"genre_scores_gemma":[0.10409867,0.00024912413,0.89196473,0.00005623297,0.000049659746,0.00014223963,0.00030509883,0.00021118508,0.0029230427],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996457,0.00006420064,0.000030201638,0.000061830244,0.00015456845,0.000043501394],"domain_scores_gemma":[0.999469,0.00017987436,0.00004683942,0.00008836177,0.00019268352,0.000023266539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004049599,0.000593666,0.00064603984,0.0008685527,0.0005553682,0.00087572966,0.0008981563,0.00046031974,0.0077368235],"category_scores_gemma":[0.0021977967,0.00019532109,0.00043791698,0.0009912467,0.00063220615,0.0010250957,0.00095555483,0.001043139,0.002601126],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017662198,0.000063969215,0.00059996423,0.00030970376,0.000033513876,0.00024339197,0.0003831192,0.1259555,0.020749124,0.14319365,0.0064006965,0.7018908],"study_design_scores_gemma":[0.000046441324,0.000110549874,0.00019723157,0.00004972178,0.000017280461,0.0002990488,0.00010697871,0.87969273,0.011058299,0.09414211,0.014243161,0.000036464695],"about_ca_topic_score_codex":0.0014629519,"about_ca_topic_score_gemma":0.0020042497,"teacher_disagreement_score":0.0077368235,"about_ca_system_score_codex":0.00036678882,"about_ca_system_score_gemma":0.000998528,"threshold_uncertainty_score":0.025882304},"labels":[],"label_agreement":null},{"id":"W4327528274","doi":"10.5281/zenodo.7741698","title":"Eight Types Of Bears In Contrast - Identification Information","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Contrast (vision); Identification (biology); Artificial intelligence; Computer science; Biology; Ecology","score_opus":0.02417135037119053,"score_gpt":0.23806615818364404,"score_spread":0.2138948078124535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327528274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8287622,0.0007141334,0.0076830518,0.0004910599,0.00023636941,0.00038627797,0.01496995,0.0003315421,0.14642562],"genre_scores_gemma":[0.91566306,0.00033838602,0.008489104,0.00026180482,0.000086356966,0.00023745529,0.013385577,0.00005463675,0.06148375],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952877,0.000041003044,0.000043458364,0.00010887125,0.00018169529,0.000096195436],"domain_scores_gemma":[0.99946064,0.000104434446,0.00012744378,0.00011114329,0.0001455623,0.000050716022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035462817,0.0003571863,0.00020588959,0.0010998328,0.0008832482,0.0005658042,0.0005354952,0.00033685402,0.0246898],"category_scores_gemma":[0.0010177532,0.00014883635,0.00026800638,0.0010448765,0.00027473818,0.0005964564,0.00076320174,0.00023929821,0.0041431477],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028158466,0.00052562926,0.53220767,0.000573276,0.00014801913,0.003899567,0.004626231,0.0012201957,0.021385979,0.020935306,0.042849228,0.36881313],"study_design_scores_gemma":[0.00013919795,0.00055853615,0.61933297,0.00020180861,0.000293306,0.008504084,0.00413691,0.0033190614,0.020707667,0.006981859,0.33570102,0.00012357165],"about_ca_topic_score_codex":0.0035887943,"about_ca_topic_score_gemma":0.00791058,"teacher_disagreement_score":0.0246898,"about_ca_system_score_codex":0.00038646866,"about_ca_system_score_gemma":0.00038001273,"threshold_uncertainty_score":0.08259559},"labels":[],"label_agreement":null},{"id":"W4327534007","doi":"10.5281/zenodo.7741745","title":"Eight Types Of Bears In Contrast - Identification Information","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Contrast (vision); Geography; Artificial intelligence; Psychology; Computer science; Biology; Ecology","score_opus":0.02417135037119053,"score_gpt":0.23806615818364404,"score_spread":0.2138948078124535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327534007","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8328703,0.0006740975,0.01034224,0.0004553066,0.00024493612,0.00042919524,0.011864415,0.00040121397,0.14271826],"genre_scores_gemma":[0.926557,0.00029879712,0.009748623,0.00025499286,0.00008493616,0.00022201816,0.010312852,0.000058716912,0.052462105],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994723,0.000044526038,0.00004595167,0.000118722965,0.00020845234,0.0001099906],"domain_scores_gemma":[0.99938893,0.00011654713,0.0001396083,0.0001394473,0.00015983624,0.000055593384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039142973,0.0003598059,0.0002179526,0.0010808825,0.00094711763,0.00064821076,0.0005763008,0.00034927373,0.020728081],"category_scores_gemma":[0.0011297179,0.00015921281,0.0002843951,0.0010598893,0.0003297853,0.0006926143,0.0008814533,0.0002588244,0.0035518222],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032896386,0.0006090253,0.4902067,0.00061687344,0.00016165414,0.004393494,0.0052748155,0.001384594,0.025728494,0.030396106,0.040975116,0.39696348],"study_design_scores_gemma":[0.00016002244,0.0006648737,0.58406734,0.00024303758,0.00036459393,0.009959804,0.0046180175,0.0051457803,0.030145667,0.009532986,0.3549412,0.00015660842],"about_ca_topic_score_codex":0.003340782,"about_ca_topic_score_gemma":0.006825839,"teacher_disagreement_score":0.020728081,"about_ca_system_score_codex":0.0003988804,"about_ca_system_score_gemma":0.00040384365,"threshold_uncertainty_score":0.069342315},"labels":[],"label_agreement":null},{"id":"W4327850327","doi":"10.1007/978-981-19-6553-1_2","title":"Basics of SVM Method and Least Squares SVM","year":2023,"lang":"en","type":"book-chapter","venue":"Industrial and applied mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Karush–Kuhn–Tucker conditions; Support vector machine; Least squares support vector machine; Quadratic programming; Mathematical optimization; Computer science; Process (computing); Least-squares function approximation; Convex optimization; Regular polygon; Mathematics; Artificial intelligence","score_opus":0.09253394175284425,"score_gpt":0.27082150938023725,"score_spread":0.178287567627393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327850327","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012087136,0.020789765,0.911841,0.0012931926,0.0021757325,0.00006861949,0.00042843854,0.0008834447,0.061311167],"genre_scores_gemma":[0.062329225,0.03540637,0.7764842,0.0016431451,0.00581298,0.0004168241,0.0018300129,0.00094436517,0.11513297],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99942243,0.000091971306,0.000047300757,0.00014286955,0.00026603971,0.000029469364],"domain_scores_gemma":[0.9996222,0.0001442677,0.000022004397,0.00004700341,0.00014599308,0.000018405677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005978269,0.0007808216,0.0008580184,0.0014769646,0.0004963069,0.0018064616,0.0011454714,0.0015177571,0.012718174],"category_scores_gemma":[0.001481927,0.00041322017,0.00067039876,0.0021504438,0.0009501016,0.0027617102,0.0007176248,0.0026295534,0.012057277],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046864927,0.0000732604,0.00038913966,0.00077046786,0.000039409017,0.00015489628,0.00019526329,0.008030531,0.008878126,0.4349132,0.061615117,0.4848937],"study_design_scores_gemma":[0.000010996958,0.00008352082,0.0008852178,0.00019236158,0.000023661973,0.0010673733,0.000059057053,0.08198726,0.0045220735,0.42511553,0.4859922,0.00006084288],"about_ca_topic_score_codex":0.0007028388,"about_ca_topic_score_gemma":0.0006027004,"teacher_disagreement_score":0.012718174,"about_ca_system_score_codex":0.00044664033,"about_ca_system_score_gemma":0.0005605676,"threshold_uncertainty_score":0.04254651},"labels":[],"label_agreement":null},{"id":"W4353031136","doi":"10.1017/9781107588493.016","title":"Deep Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Deep learning; Convolutional neural network; Artificial intelligence; Encoder; Recurrent neural network; Artificial neural network; Layer (electronics); Pattern recognition (psychology)","score_opus":0.025687247652628288,"score_gpt":0.1995958482688319,"score_spread":0.1739086006162036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353031136","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038195166,0.01725928,0.80818164,0.0023276736,0.0010739353,0.000115793366,0.001982883,0.0049494677,0.16028978],"genre_scores_gemma":[0.11454681,0.045823414,0.49010947,0.0025977883,0.0009910539,0.00038659194,0.012051695,0.0020986372,0.33139458],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996375,0.000038226335,0.00002018178,0.00008984573,0.00017935221,0.000034999328],"domain_scores_gemma":[0.99967897,0.000102786325,0.000020073645,0.00007079278,0.00010729754,0.000020226069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005302939,0.0011724567,0.0006703049,0.0007909977,0.00040645275,0.0019516207,0.0016961616,0.0011421208,0.036329478],"category_scores_gemma":[0.0016313826,0.00037123164,0.0006265494,0.0012266809,0.00045731614,0.0023283958,0.0014287457,0.0016304424,0.021854443],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032306132,0.0000447815,0.00044191012,0.00039294333,0.00006244785,0.00007785848,0.000048414713,0.039543573,0.0024448354,0.06465927,0.09436654,0.7978852],"study_design_scores_gemma":[0.00001579122,0.000051309016,0.00060348643,0.0004106914,0.000043216456,0.00037046854,0.00005836311,0.19756359,0.005889521,0.2016676,0.5932774,0.00004852853],"about_ca_topic_score_codex":0.002010896,"about_ca_topic_score_gemma":0.0032670593,"teacher_disagreement_score":0.036329478,"about_ca_system_score_codex":0.00082371687,"about_ca_system_score_gemma":0.0008058732,"threshold_uncertainty_score":0.12153423},"labels":[],"label_agreement":null},{"id":"W4353031166","doi":"10.1017/9781107588493.011","title":"Unsupervised Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cluster analysis; Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Hierarchical clustering; Computer science; Artificial neural network; Unsupervised learning; Canonical correlation; Conceptual clustering; Single-linkage clustering; Correlation clustering; CURE data clustering algorithm","score_opus":0.031504651063648276,"score_gpt":0.19941479150092156,"score_spread":0.1679101404372733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353031166","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029556863,0.006724054,0.915543,0.002871519,0.000779905,0.0003497537,0.0018119909,0.0030400306,0.06592407],"genre_scores_gemma":[0.09554612,0.018795867,0.82144636,0.0026442981,0.0018672383,0.0011850138,0.008192639,0.0015736084,0.04874884],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9946721,0.001581613,0.00031421706,0.001668967,0.0015988501,0.00016418866],"domain_scores_gemma":[0.9951443,0.0025056435,0.0003944544,0.0009895826,0.00082163815,0.00014430117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034414302,0.0016409219,0.0017844755,0.0032947217,0.0010898462,0.005457985,0.003027308,0.0016932809,0.017348543],"category_scores_gemma":[0.012375972,0.00060522545,0.0014181181,0.0038894743,0.0026351092,0.0042311656,0.0036967902,0.0021598611,0.012833055],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059960017,0.00007370399,0.002876691,0.0018712291,0.00047444564,0.0002649092,0.0005916518,0.021961339,0.0019282401,0.28155452,0.095197655,0.59314567],"study_design_scores_gemma":[0.000025421336,0.000050895185,0.0018985642,0.0006633336,0.00007735407,0.00071234355,0.00036558998,0.06922095,0.0027842764,0.56522155,0.35888943,0.00009031377],"about_ca_topic_score_codex":0.0011445333,"about_ca_topic_score_gemma":0.0016279869,"teacher_disagreement_score":0.017348543,"about_ca_system_score_codex":0.0013997917,"about_ca_system_score_gemma":0.0023349905,"threshold_uncertainty_score":0.058036685},"labels":[],"label_agreement":null},{"id":"W4353031475","doi":"10.1017/9781107588493.015","title":"Decision Trees, Random Forests and Boosting","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Boosting (machine learning); Random forest; Decision tree; Machine learning; Gradient boosting; Artificial intelligence; Computer science; Tree (set theory); Cart; Mathematics; Geography; Combinatorics","score_opus":0.02587227570765598,"score_gpt":0.20988803366697073,"score_spread":0.18401575795931474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353031475","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035909184,0.20815618,0.5394084,0.0062164487,0.0034348667,0.00024737208,0.0013734594,0.002107552,0.23546489],"genre_scores_gemma":[0.09092319,0.2074917,0.52053666,0.0039532227,0.0065742712,0.00053483754,0.0047355075,0.0014622379,0.16378838],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985287,0.00039075725,0.00006794051,0.00021872105,0.0007035506,0.000090236375],"domain_scores_gemma":[0.9988286,0.0007355724,0.0000690517,0.00010710919,0.00019657944,0.00006311998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019892913,0.0015748154,0.0013451204,0.0023468158,0.0006570475,0.0027950902,0.0015356103,0.0017235292,0.015048239],"category_scores_gemma":[0.005198489,0.0006619554,0.0009237952,0.004844508,0.0014630007,0.0037659148,0.0014819136,0.0025098226,0.0124969],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000572377,0.00007024167,0.00042053248,0.0009874916,0.000076213255,0.000115010334,0.00017234299,0.022445612,0.00041864158,0.31214112,0.13500491,0.5280906],"study_design_scores_gemma":[0.00001879701,0.000032671865,0.0003575907,0.00050192024,0.00002075944,0.0002683592,0.000060040456,0.015923856,0.0003950784,0.53163594,0.4507497,0.000035245543],"about_ca_topic_score_codex":0.0012479478,"about_ca_topic_score_gemma":0.002170873,"teacher_disagreement_score":0.015048239,"about_ca_system_score_codex":0.0010127791,"about_ca_system_score_gemma":0.0010950941,"threshold_uncertainty_score":0.050341368},"labels":[],"label_agreement":null},{"id":"W4353031490","doi":"10.1017/9781107588493.013","title":"Classification","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Categorical variable; Artificial intelligence; Naive Bayes classifier; Bayes error rate; Discriminant function analysis; Pattern recognition (psychology); Perceptron; Bayes classifier; Logistic regression; Linear discriminant analysis; Quadratic classifier; Classifier (UML); Machine learning; Computer science; Linear classifier; Multilayer perceptron; Mathematics; Support vector machine; Artificial neural network","score_opus":0.04969631314967261,"score_gpt":0.21635234531983774,"score_spread":0.16665603217016514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353031490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075641964,0.019651363,0.48842207,0.01061825,0.010741704,0.0015878774,0.0382821,0.017588405,0.40554404],"genre_scores_gemma":[0.0708404,0.023760905,0.36756355,0.0058552977,0.005737872,0.001758589,0.10518037,0.002841142,0.41646194],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954665,0.00062465935,0.0003260437,0.0010206498,0.0022768169,0.0002852854],"domain_scores_gemma":[0.99606663,0.0009455725,0.00030012755,0.0009560582,0.0015553124,0.00017626904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002653095,0.0016309859,0.0015415553,0.0039364058,0.001127912,0.0058794995,0.0032770622,0.001961555,0.07856948],"category_scores_gemma":[0.00883979,0.00032567236,0.0012916022,0.004635604,0.00082796946,0.0039539607,0.002980045,0.0019087992,0.102320395],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078223726,0.000042879456,0.0009639919,0.0005999772,0.00005226007,0.00011053512,0.00010031585,0.001398086,0.0011160459,0.0334564,0.38908508,0.57299614],"study_design_scores_gemma":[0.000019221996,0.000048207316,0.001241996,0.0004840897,0.0000343141,0.0004765843,0.00020853413,0.009883678,0.002755072,0.05834678,0.9264562,0.0000452438],"about_ca_topic_score_codex":0.0013840221,"about_ca_topic_score_gemma":0.001299292,"teacher_disagreement_score":0.07856948,"about_ca_system_score_codex":0.0012493428,"about_ca_system_score_gemma":0.0016672671,"threshold_uncertainty_score":0.26284105},"labels":[],"label_agreement":null},{"id":"W4353031518","doi":"10.1017/9781107588493.007","title":"Neural Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial neural network; Computer science; Extreme learning machine; Maxima and minima; Artificial intelligence; Perceptron; Machine learning; Multilayer perceptron; Mathematics","score_opus":0.028078650915506223,"score_gpt":0.20053421619561565,"score_spread":0.1724555652801094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353031518","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004464308,0.07164438,0.5636251,0.0060509616,0.0057703285,0.00018033486,0.0019192365,0.0030079214,0.3433374],"genre_scores_gemma":[0.13019238,0.10646572,0.2894584,0.0033037625,0.0024793881,0.0005821314,0.0056230794,0.00082765013,0.46106747],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99962676,0.000059484824,0.000026304759,0.00008441696,0.00017809107,0.000024909692],"domain_scores_gemma":[0.99972516,0.00009533122,0.000019105248,0.000042964064,0.00010190306,0.000015580968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033601787,0.0009347742,0.00069059443,0.0007621911,0.00042963072,0.0021192913,0.001538352,0.0016060033,0.032829348],"category_scores_gemma":[0.0017610891,0.00032408035,0.00041887874,0.0012809492,0.00057277223,0.0018119196,0.0011133644,0.0016203175,0.015407579],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053522846,0.000039202765,0.00036845927,0.00078751036,0.00008016026,0.00017651418,0.000084561194,0.045699466,0.0030007255,0.20150876,0.11856688,0.6296342],"study_design_scores_gemma":[0.000016252894,0.00004339172,0.00040806833,0.00043909464,0.000030112225,0.00036259304,0.000059476937,0.07932917,0.001915251,0.20955375,0.7078032,0.000039720286],"about_ca_topic_score_codex":0.0015623851,"about_ca_topic_score_gemma":0.0020735739,"teacher_disagreement_score":0.032829348,"about_ca_system_score_codex":0.0007340582,"about_ca_system_score_gemma":0.00057241996,"threshold_uncertainty_score":0.109825075},"labels":[],"label_agreement":null},{"id":"W4353046000","doi":"10.1017/9781107588493.014","title":"Kernel Methods","year":2023,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Kernel principal component analysis; Kernel Fisher discriminant analysis; Principal component regression; Kernel (algebra); Kernel method; Polynomial kernel; Principal component analysis; Artificial intelligence; Linear discriminant analysis; Mathematics; Pattern recognition (psychology); Kernel embedding of distributions; Radial basis function kernel; Computer science; Variable kernel density estimation; Applied mathematics; Support vector machine; Discrete mathematics","score_opus":0.04880679406989833,"score_gpt":0.26062088024657915,"score_spread":0.21181408617668082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353046000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011210433,0.0051240544,0.9421039,0.0007756185,0.00084161677,0.00016724264,0.0011052316,0.004086928,0.04467439],"genre_scores_gemma":[0.07101999,0.015488968,0.7219762,0.0012684906,0.0015503789,0.00066357624,0.008754522,0.004487039,0.1747908],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99668956,0.00069474377,0.00022482601,0.00063068536,0.0015786077,0.00018156819],"domain_scores_gemma":[0.996747,0.00091925554,0.0002060969,0.0008134159,0.0012042208,0.00010988605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020143162,0.001926552,0.0016800915,0.002943574,0.0008940958,0.004773573,0.0029163046,0.0023758565,0.058106825],"category_scores_gemma":[0.010367139,0.00073619204,0.0015462656,0.0032234648,0.000852057,0.0039024297,0.0029455828,0.0024092807,0.05043112],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009443662,0.00009022306,0.0006973617,0.0007266388,0.0001704523,0.00013849429,0.00014065769,0.022036437,0.0024916043,0.17233802,0.12668282,0.6743928],"study_design_scores_gemma":[0.000046959267,0.00006819377,0.0009188941,0.0003598987,0.00008181246,0.0006625107,0.00012876383,0.14943072,0.004813522,0.1956016,0.6477828,0.00010440798],"about_ca_topic_score_codex":0.0019061799,"about_ca_topic_score_gemma":0.0013025283,"teacher_disagreement_score":0.058106825,"about_ca_system_score_codex":0.0010024197,"about_ca_system_score_gemma":0.0014393933,"threshold_uncertainty_score":0.19438672},"labels":[],"label_agreement":null},{"id":"W4360801527","doi":"10.5281/zenodo.7765343","title":"8 Types Of Bears In Contrast - Identification Information","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Contrast (vision); Identification (biology); Computer science; Artificial intelligence; Biology; Ecology","score_opus":0.025837480285006837,"score_gpt":0.24209935269709998,"score_spread":0.21626187241209313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360801527","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5750261,0.002071561,0.013087518,0.001111412,0.0007233896,0.00039462073,0.05772272,0.0009852576,0.3488775],"genre_scores_gemma":[0.8332707,0.0009147167,0.013050012,0.0006197785,0.00037518656,0.00026814756,0.04661531,0.00017300792,0.10471308],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993864,0.0000575639,0.00006140029,0.00014953512,0.00023256955,0.000112489135],"domain_scores_gemma":[0.9992411,0.0001859195,0.0001649888,0.00019548576,0.00016049258,0.00005210789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040480957,0.00038229738,0.00026221783,0.001432727,0.00075130776,0.0006084757,0.00059158937,0.00041020982,0.04547816],"category_scores_gemma":[0.0012721192,0.00016824077,0.0003075448,0.0014160196,0.00021816466,0.0008821133,0.00083353167,0.0002747822,0.009243408],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021821123,0.00045126711,0.36353207,0.0007622519,0.00015073894,0.0038766162,0.0033264945,0.0015395326,0.016566155,0.0380244,0.1327919,0.43679646],"study_design_scores_gemma":[0.000108711574,0.00032593185,0.35048908,0.0003962274,0.00025321555,0.008738829,0.002465791,0.0037469687,0.016588097,0.010700207,0.60605407,0.00013282681],"about_ca_topic_score_codex":0.0026548542,"about_ca_topic_score_gemma":0.00452343,"teacher_disagreement_score":0.04547816,"about_ca_system_score_codex":0.00032655156,"about_ca_system_score_gemma":0.00028352064,"threshold_uncertainty_score":0.1521396},"labels":[],"label_agreement":null},{"id":"W4360895003","doi":"10.1016/c2020-0-04555-x","title":"The Interdisciplinary Handbook of Perceptual Control Theory, Volume II","year":2023,"lang":"en","type":"book","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Volume (thermodynamics); Perception; Cognitive science; Psychology; Control (management); Computer science; Artificial intelligence; Neuroscience; Physics; Thermodynamics","score_opus":0.010304082926949606,"score_gpt":0.24860956514976018,"score_spread":0.23830548222281056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360895003","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012404339,0.39878654,0.17675361,0.00316225,0.006809803,0.00013150282,0.0021489994,0.0025011662,0.40846568],"genre_scores_gemma":[0.025588347,0.24887362,0.11387024,0.00258279,0.0044611935,0.00039537024,0.0027932958,0.0016143362,0.5998208],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997278,0.000028164908,0.000019682011,0.000054563025,0.00015360568,0.000016179778],"domain_scores_gemma":[0.9995258,0.0002309015,0.000021807953,0.00005100707,0.00013950164,0.000030950217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034152856,0.001503522,0.0014541515,0.0021785963,0.00046169234,0.0036242139,0.0016251395,0.001215175,0.084202975],"category_scores_gemma":[0.0014432974,0.0008100866,0.00055997935,0.0027142847,0.0011073606,0.003026488,0.000809369,0.0020929917,0.037481595],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033059874,0.000060235365,0.0001267196,0.0013332536,0.000035627196,0.00006111291,0.00016838465,0.0015704333,0.0022762963,0.08306736,0.38738522,0.5238823],"study_design_scores_gemma":[0.00000841384,0.000031618605,0.0006560509,0.0005992775,0.000019144873,0.0004277677,0.000086486514,0.0026409167,0.0006005536,0.11850869,0.876393,0.000027979959],"about_ca_topic_score_codex":0.0018354092,"about_ca_topic_score_gemma":0.002326426,"teacher_disagreement_score":0.084202975,"about_ca_system_score_codex":0.0007358417,"about_ca_system_score_gemma":0.0011270628,"threshold_uncertainty_score":0.28168702},"labels":[],"label_agreement":null},{"id":"W4360936498","doi":"10.5281/zenodo.7769887","title":"Eight Types Of Bears In Contrast - Identification Information","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Contrast (vision); Identification (biology); Computer science; Artificial intelligence; Biology; Ecology","score_opus":0.02417135037119053,"score_gpt":0.23806615818364404,"score_spread":0.2138948078124535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360936498","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7994295,0.00078796694,0.010305336,0.00052877114,0.00027678726,0.00048330185,0.016478697,0.00042609585,0.17128365],"genre_scores_gemma":[0.9051552,0.00038188125,0.011544117,0.00028898576,0.000100634024,0.00028829536,0.014687108,0.00007088049,0.06748291],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994636,0.000045983496,0.000049135506,0.000121248384,0.00020538621,0.000114658134],"domain_scores_gemma":[0.9994331,0.0001048776,0.0001266642,0.00012673694,0.00015347578,0.000055202243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003970995,0.00039232237,0.00022670375,0.0011746058,0.0009486376,0.00065158575,0.0005899506,0.00036836922,0.024729295],"category_scores_gemma":[0.0010628611,0.000159659,0.00029244376,0.001163578,0.0003376189,0.00066126406,0.0008692825,0.00027862992,0.004620663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034979563,0.00061636814,0.46443173,0.00070253294,0.00016186081,0.0043182084,0.0056480723,0.0014644335,0.02536099,0.03121235,0.047947604,0.41463795],"study_design_scores_gemma":[0.00015789748,0.0006389947,0.5293117,0.00023522784,0.00032447206,0.0086396,0.004522064,0.004005551,0.026327962,0.009110433,0.41657367,0.000152411],"about_ca_topic_score_codex":0.0033811955,"about_ca_topic_score_gemma":0.0070013218,"teacher_disagreement_score":0.024729295,"about_ca_system_score_codex":0.0004068218,"about_ca_system_score_gemma":0.00040046158,"threshold_uncertainty_score":0.08272767},"labels":[],"label_agreement":null},{"id":"W4361987383","doi":"10.1109/access.2023.3263483","title":"Design and Development of a Machine Learning-Based Task Orchestrator for Intelligent Systems on Edge Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Enhanced Data Rates for GSM Evolution; Artificial intelligence; Computer architecture; Human–computer interaction; Systems engineering; Engineering","score_opus":0.0907443257159869,"score_gpt":0.3173898770783858,"score_spread":0.2266455513623989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361987383","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018846052,0.00011801749,0.97335535,0.00018159435,0.00007689907,0.0002344955,0.000059302525,0.0021251205,0.005003162],"genre_scores_gemma":[0.59546846,0.00019963551,0.39659545,0.00026033493,0.000051128405,0.00046055275,0.00023236063,0.00023335883,0.006498689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947697,0.00008609622,0.00003359886,0.00014123866,0.00014900515,0.00011306292],"domain_scores_gemma":[0.99952686,0.00010896286,0.000045544846,0.00006913773,0.00017516963,0.00007441124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057505164,0.0006311027,0.0005472685,0.00028514254,0.00045681468,0.0009729216,0.0021026235,0.00071462203,0.0032069602],"category_scores_gemma":[0.001083262,0.00039376624,0.0004891551,0.0002249288,0.00034910528,0.00090720685,0.00083029026,0.000976422,0.0012725034],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021081076,0.00029576247,0.0023064758,0.00017130279,0.00007077274,0.00026395766,0.000122022444,0.83805335,0.026464902,0.014927908,0.003967811,0.11314501],"study_design_scores_gemma":[0.0000066262533,0.000041399082,0.00008423726,0.00000435709,0.0000049951755,0.000015149705,0.000008392625,0.9947619,0.0027625288,0.00060945924,0.0016965806,0.0000043840196],"about_ca_topic_score_codex":0.003748573,"about_ca_topic_score_gemma":0.003702664,"teacher_disagreement_score":0.003748573,"about_ca_system_score_codex":0.0009117338,"about_ca_system_score_gemma":0.001902131,"threshold_uncertainty_score":0.010728359},"labels":[],"label_agreement":null},{"id":"W4362714877","doi":"10.2316/j.2023.206-0817","title":"CHAINED-CENTER-TRACKER: AN EFFICIENT END-TO-END NEURAL NETWORK FOR AUTOMATED MULTI-OBJECT TRACKING, 306-316.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tracking (education); Computer science; Computer vision; End-to-end principle; Object (grammar); Artificial intelligence; Artificial neural network; Center (category theory); Video tracking; Psychology","score_opus":0.028756320506081585,"score_gpt":0.3163884747570456,"score_spread":0.28763215425096406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362714877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004204625,0.00047488048,0.9897668,0.00007704305,0.00009148643,0.00004149677,0.00019985961,0.004259057,0.0008848154],"genre_scores_gemma":[0.11457261,0.00056956476,0.8707414,0.00015280032,0.0000761541,0.00013361842,0.0011903585,0.0006792489,0.011884212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956053,0.000062765044,0.000023460609,0.00012693867,0.00018283317,0.00004346041],"domain_scores_gemma":[0.99922764,0.00022375752,0.0000538608,0.0001337253,0.0003065589,0.000054459175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012384146,0.0010333847,0.00090268144,0.0007729333,0.00059780496,0.0008675101,0.0021434838,0.0017708029,0.0059375726],"category_scores_gemma":[0.0026173438,0.0008842899,0.0004358127,0.0011891163,0.00051562954,0.0018588196,0.0012723792,0.0014088692,0.0037527208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058687635,0.00018246823,0.001169063,0.000132564,0.00012095625,0.00013028494,0.00005502819,0.1380042,0.01907219,0.0044207037,0.02809315,0.8080326],"study_design_scores_gemma":[0.000023977194,0.000041801504,0.00032829389,0.0000075007315,0.00001413546,0.000050816347,0.000007667074,0.98575956,0.008423096,0.0023356155,0.0029954638,0.000011961331],"about_ca_topic_score_codex":0.014415298,"about_ca_topic_score_gemma":0.028463466,"teacher_disagreement_score":0.014415298,"about_ca_system_score_codex":0.00074769236,"about_ca_system_score_gemma":0.001438786,"threshold_uncertainty_score":0.0286628},"labels":[],"label_agreement":null},{"id":"W4365393228","doi":"10.1007/978-3-031-30108-7_4","title":"Lagrange Programming Neural Networks for Sparse Portfolio Design","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial neural network; Mathematical optimization; Portfolio; Weighting; Differentiable function; Project portfolio management; Norm (philosophy); Artificial intelligence; Mathematics; Project management","score_opus":0.046896514652502434,"score_gpt":0.26820468186828,"score_spread":0.22130816721577756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365393228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019394298,0.00052470603,0.9939569,0.00018361617,0.000047438356,0.00001790316,0.000028785937,0.00007772418,0.0032234464],"genre_scores_gemma":[0.29766956,0.0028388328,0.6522697,0.0004353057,0.00045732403,0.00052726775,0.00034515295,0.00030978562,0.04514707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996412,0.00018680454,0.000013940342,0.000045555567,0.00008760949,0.000024894189],"domain_scores_gemma":[0.9988951,0.0008369657,0.00006181906,0.000059501126,0.000117212214,0.000029480823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013387034,0.00082320103,0.0009599952,0.00042657103,0.00022257291,0.0009029722,0.0009729883,0.001342066,0.007203684],"category_scores_gemma":[0.0049688322,0.00068947946,0.00042653547,0.0008738475,0.00055829796,0.0012608634,0.0013756604,0.0020596508,0.0011754144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060562754,0.000048924034,0.00019314376,0.00012518807,0.000046433346,0.000038549373,0.000029585313,0.7445427,0.0011482739,0.08793688,0.0058890586,0.1599407],"study_design_scores_gemma":[0.000005191955,0.0000110719775,0.000022735738,0.000009440481,0.0000033426077,0.0000087746575,0.0000018163636,0.97985214,0.00014908703,0.019075269,0.0008583496,0.000002872199],"about_ca_topic_score_codex":0.0009793255,"about_ca_topic_score_gemma":0.0014447991,"teacher_disagreement_score":0.007203684,"about_ca_system_score_codex":0.00052708917,"about_ca_system_score_gemma":0.00054512464,"threshold_uncertainty_score":0.024098754},"labels":[],"label_agreement":null},{"id":"W4366283734","doi":"10.3390/math11081903","title":"Approximation by the Extended Neural Network Operators of Kantorovich Type","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Artificial neural network; Operator (biology); Normalization (sociology); Mathematics; Type (biology); Modulus of continuity; Extension (predicate logic); Approximation error; Applied mathematics; Mathematical analysis; Algorithm; Computer science; Artificial intelligence","score_opus":0.02315640373001029,"score_gpt":0.26354683244865335,"score_spread":0.24039042871864305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366283734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012329857,0.00027210108,0.98554903,0.00006975297,0.00004491058,0.000010168679,0.000019790386,0.000055265988,0.0016490984],"genre_scores_gemma":[0.6221443,0.00094788027,0.3695373,0.00014423409,0.0001247585,0.000121285506,0.00012976432,0.00007177977,0.0067786924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993635,0.00022513568,0.00003805605,0.00011029487,0.00020391424,0.0000591638],"domain_scores_gemma":[0.99938,0.00024952693,0.00008832635,0.00005590807,0.00019306199,0.000033097243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013988061,0.0006283207,0.00064742693,0.0005173506,0.00027412307,0.0008268775,0.0009967777,0.0007667877,0.0010921484],"category_scores_gemma":[0.0026934762,0.0002195469,0.0007175923,0.00073509145,0.0009878023,0.0019356092,0.0011063099,0.0013571817,0.00020849588],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024596349,0.000055016677,0.00082260923,0.00023196044,0.000093193026,0.00016450277,0.00015954497,0.6256292,0.01594486,0.27865228,0.0015230606,0.07647793],"study_design_scores_gemma":[0.0000038329904,0.000015573272,0.000059964117,0.0000045130037,0.0000035079565,0.0000259492,0.0000049767427,0.9819333,0.0008338972,0.016805321,0.00030153937,0.000007643069],"about_ca_topic_score_codex":0.0022882484,"about_ca_topic_score_gemma":0.0015551902,"teacher_disagreement_score":0.0022882484,"about_ca_system_score_codex":0.0007424049,"about_ca_system_score_gemma":0.0007496811,"threshold_uncertainty_score":0.0073976517},"labels":[],"label_agreement":null},{"id":"W4366550287","doi":"10.1007/s10203-023-00394-1","title":"Correction: Revisiting the 1/N-strategy: a neural network framework for optimal strategies","year":2023,"lang":"en","type":"article","venue":"Decisions in Economics and Finance","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.03335991019540121,"score_gpt":0.2873819606539199,"score_spread":0.2540220504585187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366550287","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00052381936,0.0033356498,0.010793389,0.19504057,0.77627486,0.000034275465,0.0035064656,0.00053443376,0.009956478],"genre_scores_gemma":[0.097709276,0.010104599,0.021717934,0.12012887,0.50930625,0.00022022377,0.002289143,0.002094692,0.23642905],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99633104,0.001137251,0.000510416,0.00058721466,0.0011679612,0.00026611652],"domain_scores_gemma":[0.96442974,0.014055405,0.0015730868,0.0027606478,0.016250253,0.0009307919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047954335,0.0018862572,0.0013461149,0.002312218,0.0017119062,0.0039215065,0.003210039,0.007410702,0.048693422],"category_scores_gemma":[0.09834065,0.0006513089,0.0011199649,0.0024170936,0.0029086513,0.0036183386,0.0013962813,0.011090167,0.019728431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033871493,0.000003433519,0.000083729996,0.00014366959,0.000019598196,0.00011716559,0.00006385047,0.00027904703,0.000037111615,0.018558748,0.972735,0.007924691],"study_design_scores_gemma":[0.00010334736,0.00002068016,0.00097323366,0.00044787154,0.00006428891,0.00049980986,0.00010740616,0.0037620468,0.00052271184,0.059516687,0.93390006,0.00008176003],"about_ca_topic_score_codex":0.018502146,"about_ca_topic_score_gemma":0.015641086,"teacher_disagreement_score":0.048693422,"about_ca_system_score_codex":0.0038761124,"about_ca_system_score_gemma":0.00433331,"threshold_uncertainty_score":0.16289574},"labels":[],"label_agreement":null},{"id":"W4366703111","doi":"10.2139/ssrn.4425685","title":"An Electrophysiological Investigation of Referential Communication","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Brock University","funders":"","keywords":"Electrophysiology; Psychology; Computer science; Communication; Neuroscience","score_opus":0.03550592401949002,"score_gpt":0.2875592456324385,"score_spread":0.25205332161294847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366703111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9041885,0.0037482549,0.06669468,0.00075352227,0.00027162474,0.000170218,0.0007283079,0.000117860145,0.023326933],"genre_scores_gemma":[0.9865708,0.00086986704,0.010056405,0.00023745748,0.000108156186,0.00005915662,0.00012732165,0.000034681594,0.0019361853],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998833,0.000027250791,0.000008675855,0.000038457933,0.000025299069,0.00001697053],"domain_scores_gemma":[0.99947244,0.00031429715,0.00006276065,0.000060194514,0.00004765894,0.00004260459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002885129,0.00023292635,0.00012737588,0.00037609416,0.00026819017,0.00067967933,0.00037455594,0.00069860945,0.0038454195],"category_scores_gemma":[0.0023174358,0.0001313984,0.00017237125,0.00051641255,0.0005157025,0.0011225471,0.00048734352,0.0006515619,0.00046721692],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006350771,0.00017484084,0.0057354136,0.00032502116,0.00006013468,0.0012382888,0.0010677252,0.0004389069,0.88316864,0.016811138,0.0007163294,0.08962847],"study_design_scores_gemma":[0.00043957704,0.0026338403,0.5002157,0.000330091,0.00035429507,0.011841605,0.0031471336,0.025247704,0.30057815,0.13573417,0.01937841,0.00009929341],"about_ca_topic_score_codex":0.00027996264,"about_ca_topic_score_gemma":0.0002529366,"teacher_disagreement_score":0.0038454195,"about_ca_system_score_codex":0.000107712134,"about_ca_system_score_gemma":0.0001460669,"threshold_uncertainty_score":0.012864232},"labels":[],"label_agreement":null},{"id":"W4366724921","doi":"10.1109/iccicc57084.2022.10101500","title":"Adaptive Chaotic Injection to Reduce Overfitting in Artificial Neural Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Overfitting; Chaotic; Artificial neural network; Dropout (neural networks); Computer science; Benchmark (surveying); Artificial intelligence; Machine learning; Noise (video)","score_opus":0.030346917124846867,"score_gpt":0.2610239997660555,"score_spread":0.23067708264120865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366724921","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27159467,0.0011805536,0.72015756,0.00041930584,0.00015965967,0.00013175874,0.00008348087,0.0027749138,0.003498095],"genre_scores_gemma":[0.9327918,0.00019600758,0.065605745,0.0001254037,0.000016658116,0.00007897309,0.00008165537,0.00011208327,0.0009915638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967134,0.000079849924,0.000036481095,0.00005631345,0.00011249084,0.000043523134],"domain_scores_gemma":[0.998166,0.0009595158,0.00023418308,0.00022608839,0.0003643236,0.000049914644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011745963,0.0006701752,0.0007120296,0.00056432985,0.00029091982,0.0006154144,0.0007543187,0.000663317,0.00088023336],"category_scores_gemma":[0.005352291,0.0003381375,0.00051268435,0.00042425466,0.00053906173,0.0009282809,0.00074593414,0.0010403245,0.00018696858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018591792,0.00018608305,0.0038527185,0.00017738584,0.00007923583,0.00023238256,0.00021486198,0.8670826,0.021698872,0.004182398,0.0011185159,0.100989066],"study_design_scores_gemma":[0.000006509982,0.00006296043,0.00031739046,0.000009750345,0.000007692758,0.000027795679,0.000008479014,0.9916156,0.0066737277,0.0009605912,0.00030307422,0.0000064518103],"about_ca_topic_score_codex":0.0016350681,"about_ca_topic_score_gemma":0.0017335998,"teacher_disagreement_score":0.0016350681,"about_ca_system_score_codex":0.00062311685,"about_ca_system_score_gemma":0.00052125705,"threshold_uncertainty_score":0.0062119365},"labels":[],"label_agreement":null},{"id":"W4366967237","doi":"10.1109/wi-iat55865.2022.00069","title":"Oscillation Patterns of A Complex Exponential Neural Network","year":2022,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Artificial neural network; Oscillation (cell signaling); Exponential function; Computation; Nonlinear system; Computer science; Differential equation; Applied mathematics; Representation (politics); Dynamical systems theory; Exponential growth; Complex system; Control theory (sociology); Mathematics; Algorithm; Artificial intelligence; Mathematical analysis; Physics","score_opus":0.03148105548158315,"score_gpt":0.24905607285212109,"score_spread":0.21757501737053794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366967237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24546067,0.00031569766,0.74140346,0.00050137856,0.00006697369,0.00008827789,0.00009525873,0.00019378334,0.011874491],"genre_scores_gemma":[0.9669356,0.00014553356,0.029984102,0.000049667447,0.000007897494,0.000054085096,0.000039575163,0.000028174867,0.0027553164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980336,0.00006250239,0.00001037019,0.000045473756,0.000057633628,0.00002065433],"domain_scores_gemma":[0.99944526,0.00023760316,0.0000619829,0.000035113342,0.00019330232,0.000026766222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008902602,0.00036800362,0.00023668217,0.00034149192,0.00021066019,0.0005591159,0.0005576956,0.00056894694,0.0015291418],"category_scores_gemma":[0.0029154448,0.00018068113,0.00023142723,0.0002407404,0.00046321916,0.00081293576,0.00032784362,0.00044237045,0.00019549677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020873577,0.0000642956,0.0029427495,0.00010852838,0.000037572743,0.00019787316,0.00019102989,0.8666546,0.036956094,0.05592241,0.0010449666,0.035671093],"study_design_scores_gemma":[0.0000034934678,0.000016584749,0.00021192411,0.000003787548,0.000002695077,0.000017593227,0.0000061267847,0.99571735,0.00092240004,0.002905226,0.00018863342,0.0000041540156],"about_ca_topic_score_codex":0.001694534,"about_ca_topic_score_gemma":0.0011355127,"teacher_disagreement_score":0.001694534,"about_ca_system_score_codex":0.0005934343,"about_ca_system_score_gemma":0.00035558402,"threshold_uncertainty_score":0.0051154494},"labels":[],"label_agreement":null},{"id":"W4372055609","doi":"10.1007/978-3-031-05071-8_3","title":"From Machine Learning to Deep Learning","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université de Sherbrooke","funders":"LabEx PRIMES; Agence Nationale de la Recherche","keywords":"Artificial intelligence; Computer science; Machine learning; Classifier (UML); Python (programming language); Deep learning; Perceptron; Artificial neural network; Gradient descent; Convolutional neural network; Multiclass classification; Support vector machine","score_opus":0.02392627152258936,"score_gpt":0.24068436508186164,"score_spread":0.21675809355927228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372055609","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018446399,0.12025628,0.20043185,0.0072042686,0.005368114,0.000050925195,0.00080029824,0.0019343491,0.6621092],"genre_scores_gemma":[0.029995285,0.09089315,0.08311381,0.0026609905,0.003129613,0.000115408766,0.0010161203,0.0010766358,0.7879989],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99987733,0.00001346124,0.0000042820493,0.00001845763,0.00007574206,0.000010707599],"domain_scores_gemma":[0.9997538,0.00014005126,0.000009412667,0.000031423562,0.000047787427,0.00001759155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018456024,0.0006839494,0.00046429955,0.0008583218,0.0003332108,0.0018641903,0.0007459067,0.00070702046,0.05200414],"category_scores_gemma":[0.0010665216,0.00033003188,0.0002684872,0.0016407829,0.00077781954,0.0025876642,0.0010476391,0.0021072528,0.016693546],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017639135,0.000030849187,0.000059844308,0.0004964516,0.000009559632,0.00003113889,0.00006447994,0.0029348796,0.0010208199,0.24439867,0.22156988,0.5293657],"study_design_scores_gemma":[0.0000048463753,0.000011229358,0.00013576301,0.0002847531,0.000005315493,0.00010541609,0.000039189785,0.005883839,0.00081537233,0.34527957,0.6474233,0.0000113837095],"about_ca_topic_score_codex":0.001164647,"about_ca_topic_score_gemma":0.0025770247,"teacher_disagreement_score":0.05200414,"about_ca_system_score_codex":0.00076070934,"about_ca_system_score_gemma":0.00068147795,"threshold_uncertainty_score":0.17397118},"labels":[],"label_agreement":null},{"id":"W4375824959","doi":"10.1007/978-3-031-28332-1_7","title":"Tuning Neural Networks for Superior Accuracy on Resource-Constrained Edge Microcontrollers","year":2023,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Verafin (Canada)","funders":"","keywords":"Microcontroller; Computer science; Pruning; Artificial neural network; Quantization (signal processing); Enhanced Data Rates for GSM Evolution; Context (archaeology); Embedded system; Artificial intelligence; Computer engineering; Machine learning; Algorithm","score_opus":0.027196780576840177,"score_gpt":0.27648306431948766,"score_spread":0.2492862837426475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375824959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11178084,0.0035294278,0.83234006,0.0005960654,0.00060911174,0.00008541228,0.00017816716,0.003213357,0.047667563],"genre_scores_gemma":[0.8340721,0.0006120379,0.1475059,0.00033872918,0.00009537781,0.000058582627,0.00012706137,0.00028852024,0.016901704],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976975,0.000033469725,0.0000151512,0.00006354328,0.00008021316,0.000037879756],"domain_scores_gemma":[0.99965084,0.00016263158,0.000031381336,0.000058180343,0.000085436506,0.000011570273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025990925,0.00048160128,0.00042414002,0.0002519255,0.00027233397,0.0007409885,0.0013329345,0.0006334405,0.00776069],"category_scores_gemma":[0.001456495,0.00021172885,0.0001717868,0.00040669463,0.00020620333,0.00096126867,0.00045507032,0.00090737327,0.0013530118],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041765833,0.00014161543,0.0009979057,0.0004001975,0.00008130846,0.00014123265,0.000103483835,0.26923054,0.10594513,0.01951489,0.010785222,0.5922408],"study_design_scores_gemma":[0.000026467433,0.0001559292,0.00069482106,0.00005400783,0.000034758148,0.00009496955,0.00003259495,0.9375132,0.041172333,0.008893565,0.011309795,0.000017677396],"about_ca_topic_score_codex":0.0013914257,"about_ca_topic_score_gemma":0.0041547203,"teacher_disagreement_score":0.00776069,"about_ca_system_score_codex":0.0004973834,"about_ca_system_score_gemma":0.00028303586,"threshold_uncertainty_score":0.025962114},"labels":[],"label_agreement":null},{"id":"W4375858688","doi":"10.32473/flairs.36.133140","title":"Using Bidirectional Associative Memory Neural Networks to Solve the N-bit Task","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Content-addressable memory; Task (project management); Bidirectional associative memory; Identifier; Associative property; Artificial intelligence; Arithmetic; Mathematics","score_opus":0.21907951948329998,"score_gpt":0.3981573109817071,"score_spread":0.1790777914984071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375858688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24941003,0.0004754482,0.73470163,0.0006467171,0.00020751714,0.00015282354,0.0001661344,0.002417079,0.011822614],"genre_scores_gemma":[0.8100836,0.00029534881,0.18030454,0.00016029974,0.00003671396,0.00017137516,0.00020369847,0.00009571495,0.008648668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998578,0.000028176953,0.000011047499,0.000042852807,0.00002534336,0.00003467719],"domain_scores_gemma":[0.9996172,0.00014905505,0.00005275796,0.000089200774,0.000064269814,0.000027520184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005048992,0.0007159017,0.00042568182,0.00023627837,0.00033694765,0.00069185,0.0010343022,0.0007904713,0.0033546172],"category_scores_gemma":[0.0014805327,0.00026791543,0.00042344836,0.0002970188,0.00040161482,0.0019034585,0.00090345583,0.0010951333,0.00076320104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060827454,0.000497013,0.0033007285,0.0003470873,0.00013136354,0.00027058463,0.00034446572,0.51595813,0.07136797,0.028636549,0.003935838,0.37460193],"study_design_scores_gemma":[0.000018643068,0.00010621249,0.00022778889,0.000011353916,0.000021113401,0.000041179734,0.000028126282,0.973119,0.01318177,0.011761553,0.0014710432,0.000012249075],"about_ca_topic_score_codex":0.003680617,"about_ca_topic_score_gemma":0.0055126846,"teacher_disagreement_score":0.003680617,"about_ca_system_score_codex":0.00043689398,"about_ca_system_score_gemma":0.0008268055,"threshold_uncertainty_score":0.011222303},"labels":[],"label_agreement":null},{"id":"W4375858784","doi":"10.32473/flairs.36.133365","title":"Towards binary encoding in Bidirectional Associative Memories","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Encoding (memory); Computer science; Associative property; Binary number; Cognition; Bidirectional associative memory; Task (project management); Recall; Artificial intelligence; Content-addressable memory; Transmission (telecommunications); Function (biology); Artificial neural network; Pattern recognition (psychology); Cognitive psychology; Psychology; Neuroscience; Arithmetic; Mathematics; Biology; Engineering","score_opus":0.1746696167332556,"score_gpt":0.3893567369444212,"score_spread":0.2146871202111656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375858784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26574928,0.0011768341,0.7206918,0.00051478966,0.00016848995,0.000049709346,0.00014221059,0.0005254441,0.010981463],"genre_scores_gemma":[0.8932109,0.00065924606,0.10366499,0.00012041228,0.000053833577,0.000044311862,0.00011253208,0.000040474668,0.002093295],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998054,0.000071850685,0.000015494908,0.00003093854,0.000050013645,0.000026296091],"domain_scores_gemma":[0.9989625,0.0004976651,0.000144714,0.00015763046,0.00017195639,0.000065542605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057141396,0.0002775807,0.00028656816,0.00035823986,0.00018673875,0.0010641499,0.0005672038,0.00045886086,0.0020005286],"category_scores_gemma":[0.002530163,0.00016701405,0.00018431139,0.00037119517,0.0005143206,0.002241202,0.00069687224,0.0007523112,0.0003658675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009456838,0.0002863344,0.0015817721,0.0005189283,0.000060092145,0.00027322833,0.0004028331,0.086691946,0.109624065,0.52047503,0.0021206012,0.27701956],"study_design_scores_gemma":[0.000052665982,0.00021584918,0.0006420069,0.000059736914,0.000031944262,0.00025848675,0.0001037714,0.5953139,0.030436132,0.36840966,0.004445422,0.00003048114],"about_ca_topic_score_codex":0.0003774187,"about_ca_topic_score_gemma":0.00024746257,"teacher_disagreement_score":0.0020005286,"about_ca_system_score_codex":0.00027133123,"about_ca_system_score_gemma":0.00028850025,"threshold_uncertainty_score":0.006692469},"labels":[],"label_agreement":null},{"id":"W4376619788","doi":"10.1016/j.neucom.2023.126328","title":"Time series prediction with granular neural networks","year":2023,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Foundation for Innovative Research Groups of the National Natural Science Foundation of China; National Natural Science Foundation of China","keywords":"Artificial neural network; Computer science; Granularity; Generalization; Robustness (evolution); Time series; Series (stratigraphy); Vagueness; Interval (graph theory); Artificial intelligence; Data mining; Granular computing; Machine learning; Algorithm; Mathematics; Fuzzy logic; Rough set","score_opus":0.00905265314911462,"score_gpt":0.2076042507202718,"score_spread":0.19855159757115717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376619788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2101029,0.0019147219,0.7811783,0.00075384794,0.0005945336,0.00008225179,0.00039122844,0.00151475,0.0034675847],"genre_scores_gemma":[0.9523705,0.0003854401,0.04536122,0.00007734096,0.0001127371,0.000045668738,0.00022683978,0.000037962167,0.001382298],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959797,0.00009258152,0.000051204934,0.00010040691,0.00009151522,0.00006636403],"domain_scores_gemma":[0.99768114,0.0013271924,0.00026145313,0.00034850725,0.0002564211,0.00012532831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014837352,0.000537533,0.0012453063,0.0008500285,0.00042160985,0.001594624,0.0008688395,0.0009280334,0.0016532093],"category_scores_gemma":[0.0065100556,0.00052576396,0.00069782895,0.0010454078,0.0006225191,0.0020538913,0.0012611696,0.0015564767,0.0002821438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006142575,0.00013502767,0.0039468226,0.000118650074,0.00016622995,0.00021330692,0.00005363249,0.8677829,0.0016955285,0.017430538,0.0018323307,0.106010824],"study_design_scores_gemma":[0.0000051285374,0.000007867977,0.00015606737,0.000003973153,0.0000053272643,0.000004575536,0.0000023380385,0.99501884,0.00010688234,0.0046350798,0.000051533432,0.0000024011379],"about_ca_topic_score_codex":0.0074177906,"about_ca_topic_score_gemma":0.004527735,"teacher_disagreement_score":0.0074177906,"about_ca_system_score_codex":0.0007559778,"about_ca_system_score_gemma":0.0004929427,"threshold_uncertainty_score":0.014749229},"labels":[],"label_agreement":null},{"id":"W4377141943","doi":"10.5121/csit.2023.130701","title":"Efficient Implementation of Tanh: A Comparative Study of New Results","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Advances","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Hyperbolic function; CORDIC; Activation function; Computer science; Field-programmable gate array; Exponential function; Artificial neural network; Function (biology); Division (mathematics); Tangent; Algorithm; Computational science; Arithmetic; Mathematics; Artificial intelligence; Computer hardware; Mathematical analysis","score_opus":0.13442696053128572,"score_gpt":0.42798047802995975,"score_spread":0.29355351749867403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377141943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1143923,0.029764049,0.81480783,0.0005340514,0.00071737444,0.00011191785,0.000114031776,0.001673057,0.03788533],"genre_scores_gemma":[0.65307486,0.017597849,0.3144975,0.0001683869,0.00037210848,0.000105815714,0.00030001724,0.00044365902,0.013439825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987508,0.00030368648,0.000105976214,0.00012273366,0.0006275749,0.00008922084],"domain_scores_gemma":[0.9979679,0.00087623805,0.000118533295,0.00044072818,0.00054959883,0.000046997462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013109203,0.0005685878,0.00058779307,0.0010371647,0.00019945603,0.0011893027,0.0010661412,0.0004376159,0.006290647],"category_scores_gemma":[0.004611025,0.0002525007,0.0004062653,0.0013248723,0.0003902886,0.0024118791,0.0005204014,0.0005678997,0.0010664032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074892945,0.00017847268,0.0017668888,0.00093930843,0.00012570924,0.00027756003,0.00022308592,0.066389754,0.019827016,0.061877176,0.0031000446,0.84454614],"study_design_scores_gemma":[0.00015055075,0.00216487,0.0033345018,0.0003190532,0.00022161905,0.0019105712,0.00036659552,0.81313705,0.09103025,0.0181941,0.069074795,0.00009606923],"about_ca_topic_score_codex":0.00061495,"about_ca_topic_score_gemma":0.0005142059,"teacher_disagreement_score":0.006290647,"about_ca_system_score_codex":0.0005407742,"about_ca_system_score_gemma":0.00048724393,"threshold_uncertainty_score":0.021044314},"labels":[],"label_agreement":null},{"id":"W4378232116","doi":"10.1002/adts.202300148","title":"Chaotic Time Series Prediction of Multi‐Dimensional Nonlinear System Based on Bidirectional LSTM Model","year":2023,"lang":"en","type":"article","venue":"Advanced Theory and Simulations","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Xiamen University; Xiamen University of Technology; University of Regina","keywords":"Computer science; Chaotic; Generalization; Nonlinear system; Convolutional neural network; Noise (video); Artificial intelligence; Recurrent neural network; Stability (learning theory); Term (time); Artificial neural network; Machine learning; Algorithm; Speech recognition; Pattern recognition (psychology); Mathematics","score_opus":0.013919865242990213,"score_gpt":0.24826760888959237,"score_spread":0.23434774364660216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378232116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34359106,0.00080918346,0.6494583,0.0003784874,0.00015082753,0.000033129472,0.00028842257,0.0011083039,0.004182346],"genre_scores_gemma":[0.98580045,0.00013391064,0.012835134,0.000023391729,0.000011354604,0.000017192733,0.0001036309,0.000018395678,0.0010564799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999298,0.000011081323,0.0000050969293,0.000023953491,0.000019131805,0.000010861033],"domain_scores_gemma":[0.99986374,0.000048889306,0.000022398655,0.00001254358,0.000042935408,0.000009537193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024639655,0.00042460987,0.00032168088,0.00022951745,0.00015255246,0.000334241,0.00045233028,0.00036465263,0.0009520529],"category_scores_gemma":[0.0006727743,0.00016307706,0.0003598927,0.00023483626,0.00017309793,0.00052559335,0.00030843125,0.000509024,0.00015817718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008273041,0.000037170466,0.0027317237,0.00008319072,0.000057587935,0.000114603215,0.000049165817,0.93814546,0.011477297,0.002966316,0.00069617905,0.043558568],"study_design_scores_gemma":[4.9805226e-7,0.0000034056247,0.00009684551,0.0000010008041,0.0000014242673,0.00000265202,9.5317876e-7,0.99936515,0.00032493356,0.00017250935,0.000029744542,9.585206e-7],"about_ca_topic_score_codex":0.007691799,"about_ca_topic_score_gemma":0.006039831,"teacher_disagreement_score":0.007691799,"about_ca_system_score_codex":0.00033946388,"about_ca_system_score_gemma":0.0004111963,"threshold_uncertainty_score":0.015294075},"labels":[],"label_agreement":null},{"id":"W4378418323","doi":"10.18280/ria.370201","title":"Analogical Study of Activation Concept in Neural Networks with Neat- Python Module","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Python (programming language); Artificial neural network; Computer science; Programming language; Artificial intelligence; Cognitive science; Psychology","score_opus":0.049578969368920124,"score_gpt":0.2859712844846365,"score_spread":0.23639231511571637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378418323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1160189,0.00050399953,0.8563464,0.00058405177,0.00019620663,0.00008019534,0.00016272525,0.00075812417,0.025349377],"genre_scores_gemma":[0.847085,0.00032264995,0.14646217,0.00012674976,0.000047909292,0.00014841883,0.000080251484,0.00018322578,0.005543577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998276,0.000055150613,0.000008587426,0.00003893664,0.000046122314,0.0000235174],"domain_scores_gemma":[0.9994991,0.0003016214,0.000049870752,0.00004962973,0.00006686414,0.000032865806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063506264,0.00031607147,0.000217183,0.0005479849,0.0003604632,0.00069967716,0.00089863304,0.00056373066,0.007060622],"category_scores_gemma":[0.0026296696,0.00018054181,0.000505274,0.00033040304,0.0009282995,0.0013464566,0.0006999676,0.0010679965,0.0003081976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010207328,0.00009515955,0.0020923128,0.00024697863,0.000050654085,0.00032059065,0.00036625072,0.57773095,0.008039739,0.36035,0.002566952,0.048038315],"study_design_scores_gemma":[0.0000062653157,0.00002508137,0.0002952475,0.000010627071,0.0000041567896,0.00006378712,0.000030036397,0.9533868,0.00093145686,0.0441332,0.0011063038,0.0000069653233],"about_ca_topic_score_codex":0.002046866,"about_ca_topic_score_gemma":0.0015605467,"teacher_disagreement_score":0.007060622,"about_ca_system_score_codex":0.00049472327,"about_ca_system_score_gemma":0.000513355,"threshold_uncertainty_score":0.023620129},"labels":[],"label_agreement":null},{"id":"W4379164732","doi":"10.1007/978-1-4842-9367-6_4","title":"Core Generative AI Technology","year":2023,"lang":"en","type":"book-chapter","venue":"Apress eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Generative grammar; Core (optical fiber); Artificial intelligence; Computer science; Library science; Mathematics education; Psychology; Telecommunications","score_opus":0.07740852184352764,"score_gpt":0.2841523681502863,"score_spread":0.20674384630675868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379164732","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014819484,0.010045967,0.076782,0.0031090518,0.00060444983,0.000041006817,0.00015772483,0.0007695814,0.90700835],"genre_scores_gemma":[0.09689921,0.015512854,0.064731225,0.0019522866,0.00077470974,0.00019308929,0.0007353617,0.0012719563,0.8179294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99963295,0.00009882449,0.00001487181,0.000078398945,0.00014107248,0.0000338717],"domain_scores_gemma":[0.99951375,0.00022020093,0.000013982528,0.00014048055,0.00006680737,0.000044825665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000561978,0.00059797964,0.00027909916,0.0013247752,0.0009755131,0.004797803,0.001182691,0.0013379692,0.04000829],"category_scores_gemma":[0.0014655336,0.00045338034,0.00042232242,0.0010754295,0.0031871463,0.0045927824,0.0021005364,0.0022013267,0.017448459],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060452826,0.000009975098,0.00006258885,0.00010685732,0.0000040344953,0.000035524983,0.0004821681,0.00047342593,0.0004085901,0.90412915,0.034475584,0.059805956],"study_design_scores_gemma":[0.0000034698858,0.000004917847,0.00009006248,0.00013977954,0.0000027405245,0.00017181982,0.00008524497,0.0015781432,0.00061693863,0.27993554,0.717366,0.000005328319],"about_ca_topic_score_codex":0.0009976337,"about_ca_topic_score_gemma":0.0017716957,"teacher_disagreement_score":0.04000829,"about_ca_system_score_codex":0.0026803191,"about_ca_system_score_gemma":0.0012318334,"threshold_uncertainty_score":0.13384104},"labels":[],"label_agreement":null},{"id":"W4379522721","doi":"10.21428/594757db.4cec07db","title":"PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial neural network; Binary number; Generalization; Probabilistic logic; Computer science; Differentiable function; Bayesian network; Expression (computer science); Artificial intelligence; Stochastic neural network; Probability distribution; Binary classification; Recurrent neural network; Algorithm; Mathematics","score_opus":0.014676119825229681,"score_gpt":0.25464401514813406,"score_spread":0.23996789532290438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379522721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009076751,0.0001302494,0.98902076,0.0002192272,0.000017358654,0.000022701399,0.000044689106,0.00017963687,0.0012886988],"genre_scores_gemma":[0.6650202,0.00060886075,0.3237733,0.00040631497,0.00016378936,0.0003485782,0.0005216317,0.0003237986,0.008833575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986463,0.00049512513,0.000061666484,0.00032289303,0.00032968225,0.00014435053],"domain_scores_gemma":[0.99564505,0.0028775437,0.0004241852,0.00034078746,0.000541934,0.00017053263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028372393,0.0013465842,0.0015769762,0.00087614823,0.0005491763,0.0019630454,0.002286862,0.0018044516,0.00319167],"category_scores_gemma":[0.014291718,0.0011561987,0.0007741768,0.0011150637,0.0018241291,0.00462267,0.0027150267,0.003176215,0.00058617454],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007895783,0.00004582498,0.0005065062,0.000098407996,0.00005286317,0.0000691215,0.00011572638,0.81126213,0.0013970479,0.1334823,0.0014722939,0.051418696],"study_design_scores_gemma":[0.0000029916466,0.000008842941,0.000050212893,0.000009702701,0.0000045401066,0.000008318193,0.0000037816403,0.9539,0.00032673607,0.04546401,0.00021688877,0.000003920025],"about_ca_topic_score_codex":0.0036877338,"about_ca_topic_score_gemma":0.0048721754,"teacher_disagreement_score":0.0036877338,"about_ca_system_score_codex":0.0019685426,"about_ca_system_score_gemma":0.0015708588,"threshold_uncertainty_score":0.015004933},"labels":[],"label_agreement":null},{"id":"W4379983184","doi":"10.32920/23466977","title":"An Application Of Machine Learning Methods On Complicated Large Scale Dataset","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Gradient boosting; Online machine learning; Artificial neural network; Transfer of learning; Deep learning; Implementation; Convolutional neural network; Supervised learning; Random forest","score_opus":0.06463533124041808,"score_gpt":0.3981730079527594,"score_spread":0.3335376767123413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379983184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09987992,0.0019504417,0.8688225,0.0038955673,0.0012426336,0.00043275187,0.0068871686,0.00932579,0.0075632604],"genre_scores_gemma":[0.32113782,0.00078025233,0.6608775,0.0005102999,0.0004112786,0.00039727648,0.011410839,0.0004475622,0.0040271534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769187,0.0009134921,0.00013453505,0.0006471831,0.0005029003,0.000110077635],"domain_scores_gemma":[0.9951853,0.0021445495,0.0001930157,0.0016470987,0.0007059433,0.00012404913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032152554,0.0011153389,0.00081730133,0.0018580261,0.00084468065,0.0016359547,0.0013491675,0.0014772585,0.0027864473],"category_scores_gemma":[0.012556172,0.00037149026,0.0010825379,0.0026736546,0.00058635196,0.0022157854,0.0014325127,0.0018889866,0.0015659037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031589138,0.0004906403,0.009987022,0.0005589785,0.0005091834,0.0006820597,0.00019932512,0.209497,0.010040515,0.019089427,0.07682659,0.6718034],"study_design_scores_gemma":[0.000022052613,0.00005412981,0.0025979572,0.000026800288,0.000016710317,0.00011841679,0.00009870146,0.9641267,0.0034555786,0.020887336,0.008573872,0.000021798392],"about_ca_topic_score_codex":0.004482389,"about_ca_topic_score_gemma":0.0055149645,"teacher_disagreement_score":0.004482389,"about_ca_system_score_codex":0.0008213092,"about_ca_system_score_gemma":0.0010680234,"threshold_uncertainty_score":0.017004073},"labels":[],"label_agreement":null},{"id":"W4379984785","doi":"10.32920/23466977.v1","title":"An Application Of Machine Learning Methods On Complicated Large Scale Dataset","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Machine learning; Artificial intelligence; Computer science; Gradient boosting; Online machine learning; Artificial neural network; Transfer of learning; Deep learning; Implementation; Supervised learning; Convolutional neural network; Random forest","score_opus":0.06463533124041808,"score_gpt":0.3981730079527594,"score_spread":0.3335376767123413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379984785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09987992,0.0019504417,0.8688225,0.0038955673,0.0012426336,0.00043275187,0.0068871686,0.00932579,0.0075632604],"genre_scores_gemma":[0.32113782,0.00078025233,0.6608775,0.0005102999,0.0004112786,0.00039727648,0.011410839,0.0004475622,0.0040271534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769187,0.0009134921,0.00013453505,0.0006471831,0.0005029003,0.000110077635],"domain_scores_gemma":[0.9951853,0.0021445495,0.0001930157,0.0016470987,0.0007059433,0.00012404913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032152554,0.0011153389,0.00081730133,0.0018580261,0.00084468065,0.0016359547,0.0013491675,0.0014772585,0.0027864473],"category_scores_gemma":[0.012556172,0.00037149026,0.0010825379,0.0026736546,0.00058635196,0.0022157854,0.0014325127,0.0018889866,0.0015659037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031589138,0.0004906403,0.009987022,0.0005589785,0.0005091834,0.0006820597,0.00019932512,0.209497,0.010040515,0.019089427,0.07682659,0.6718034],"study_design_scores_gemma":[0.000022052613,0.00005412981,0.0025979572,0.000026800288,0.000016710317,0.00011841679,0.00009870146,0.9641267,0.0034555786,0.020887336,0.008573872,0.000021798392],"about_ca_topic_score_codex":0.004482389,"about_ca_topic_score_gemma":0.0055149645,"teacher_disagreement_score":0.004482389,"about_ca_system_score_codex":0.0008213092,"about_ca_system_score_gemma":0.0010680234,"threshold_uncertainty_score":0.017004073},"labels":[],"label_agreement":null},{"id":"W4380480075","doi":"10.1007/978-3-031-32095-8","title":"Advancements in Knowledge Distillation: Towards New Horizons of Intelligent Systems","year":2023,"lang":"en","type":"book","venue":"Studies in computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Distillation; Computer science; New horizons; Artificial intelligence; Engineering; Chemistry; Chromatography; Aerospace engineering","score_opus":0.1571477562996914,"score_gpt":0.40694522741617717,"score_spread":0.24979747111648576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380480075","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072359703,0.42740792,0.1529526,0.03494028,0.0046209777,0.000064029424,0.000384845,0.0006661312,0.3717272],"genre_scores_gemma":[0.16092834,0.49563193,0.15433666,0.0046911077,0.0068922075,0.00011325397,0.0008696246,0.00048580545,0.17605111],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99966574,0.00006399849,0.00002056005,0.000043594835,0.00018058396,0.000025563055],"domain_scores_gemma":[0.9989268,0.0006689906,0.00004284109,0.0001618674,0.00013571378,0.00006366061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010739926,0.0006644223,0.00059888104,0.0011788674,0.000515613,0.0038986744,0.0009999479,0.0010336624,0.010260479],"category_scores_gemma":[0.0022151025,0.00034794767,0.0003973371,0.0025643774,0.0028918202,0.012722833,0.0020808338,0.0030792172,0.0034811937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026423602,0.00003320108,0.000081928236,0.00061437854,0.000016216167,0.000027977316,0.00013434967,0.002132796,0.00070400396,0.70967525,0.04186975,0.24468365],"study_design_scores_gemma":[0.0000074068803,0.000016938033,0.0001063541,0.00029271725,0.000009600364,0.00007432045,0.00007943183,0.0054809223,0.00069099065,0.6772637,0.3159663,0.00001123404],"about_ca_topic_score_codex":0.0006901658,"about_ca_topic_score_gemma":0.0012257409,"teacher_disagreement_score":0.010260479,"about_ca_system_score_codex":0.00097269914,"about_ca_system_score_gemma":0.001548904,"threshold_uncertainty_score":0.034324765},"labels":[],"label_agreement":null},{"id":"W4380686930","doi":"10.1109/tetci.2023.3285932","title":"An Ensemble Semi-Supervised Adaptive Resonance Theory Model With Explanation Capability for Pattern Classification","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Queen's University","funders":"","keywords":"Adaptive resonance theory; Computer science; Benchmark (surveying); Artificial intelligence; Scheme (mathematics); Class (philosophy); Fuzzy logic; Machine learning; Ensemble learning; Pattern recognition (psychology); Data mining; Mathematics","score_opus":0.07577648699880708,"score_gpt":0.323253981571798,"score_spread":0.24747749457299095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380686930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027246958,0.0002997151,0.9702185,0.00021834159,0.000027941433,0.00003577767,0.00005615835,0.00035277096,0.0015437187],"genre_scores_gemma":[0.83326864,0.0002815245,0.16159002,0.0002403832,0.000078864214,0.00019197653,0.0002466062,0.000056706715,0.0040452536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933857,0.00022067546,0.000026707527,0.00020637612,0.00015320857,0.00005446257],"domain_scores_gemma":[0.9990395,0.00045134686,0.00011104052,0.000097818964,0.0002670841,0.000033167948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011820715,0.00069089927,0.0008200774,0.0007804456,0.0004099911,0.0007878588,0.0017580655,0.0010846846,0.0013613935],"category_scores_gemma":[0.0025865322,0.00037477008,0.0009551197,0.0006110162,0.0007144677,0.001639659,0.00078065234,0.0011541932,0.00035657795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009777371,0.00007072751,0.0017587328,0.00007168718,0.00010197367,0.00014155239,0.00023076222,0.86015856,0.0037408322,0.015613454,0.0016447941,0.11636912],"study_design_scores_gemma":[0.0000014295356,0.000011285744,0.00006786782,0.0000017670869,0.0000045536476,0.000009584172,0.000004376674,0.99713886,0.00015851973,0.0024795027,0.00011949574,0.0000027604126],"about_ca_topic_score_codex":0.003536414,"about_ca_topic_score_gemma":0.0036080044,"teacher_disagreement_score":0.003536414,"about_ca_system_score_codex":0.00062833016,"about_ca_system_score_gemma":0.00057312613,"threshold_uncertainty_score":0.0070316195},"labels":[],"label_agreement":null},{"id":"W4381686902","doi":"10.21817/indjcse/2023/v14i3/231403023","title":"DETECTION OF HUMAN PSYCHOLOGICAL STRESS WITH DEEP CONVOLUTIONAL NEURAL NETWORK USING DIFFERENT CRITERIAS FOR FEATURE SELECTION ON BASIS OF CONFIDENCE VALUE OF PAIRED t-TEST","year":2023,"lang":"en","type":"article","venue":"Indian Journal of Computer Science and Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Basis (linear algebra); Value (mathematics); Artificial intelligence; Feature selection; Selection (genetic algorithm); Pattern recognition (psychology); Feature (linguistics); Computer science; Test (biology); Stress (linguistics); Psychology; Machine learning; Mathematics; Geology","score_opus":0.02132834938774875,"score_gpt":0.26124396133603034,"score_spread":0.2399156119482816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381686902","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72884345,0.00044390396,0.26679534,0.00025286022,0.00012121957,0.00015788166,0.00042093504,0.0009990254,0.0019653349],"genre_scores_gemma":[0.9705458,0.000062333296,0.027990155,0.000042759493,0.000017572223,0.00007216639,0.00043130692,0.00001612761,0.0008217851],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995921,0.000066983084,0.000044464454,0.00010902226,0.00009959551,0.00008783223],"domain_scores_gemma":[0.9992084,0.00033845534,0.000086650754,0.000038771173,0.00027755307,0.000050297807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009864847,0.00060223934,0.00060257205,0.0007895168,0.00023974286,0.0006011377,0.00044551265,0.00054220465,0.001396564],"category_scores_gemma":[0.0028156745,0.00011811119,0.00039416808,0.00035467852,0.00022877175,0.00033368953,0.0004540122,0.00039464352,0.00017242518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0041983947,0.00084959815,0.0911505,0.00027374408,0.00037089287,0.00063380745,0.00019368141,0.1207321,0.05901669,0.0010077313,0.004673352,0.7168996],"study_design_scores_gemma":[0.00003875512,0.00047187737,0.030123865,0.000020072823,0.000053986154,0.00017184897,0.00007738353,0.94916165,0.018935207,0.00058689754,0.00033320402,0.000025252562],"about_ca_topic_score_codex":0.0028475418,"about_ca_topic_score_gemma":0.0028663068,"teacher_disagreement_score":0.0028475418,"about_ca_system_score_codex":0.00036423033,"about_ca_system_score_gemma":0.00044867749,"threshold_uncertainty_score":0.0056619644},"labels":[],"label_agreement":null},{"id":"W4382204135","doi":"10.1109/saci58269.2023.10158543","title":"A Combined Finite State Machine and PlantUML Approach to Machine Learning Applications","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Finite-state machine; Computer science; State (computer science); Machine learning; Artificial intelligence; Algorithm","score_opus":0.017593082294871637,"score_gpt":0.23508668588246026,"score_spread":0.21749360358758862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382204135","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026725656,0.0002609735,0.98859686,0.00042102978,0.00004780901,0.000052449523,0.00011914646,0.00204854,0.0057805926],"genre_scores_gemma":[0.29261905,0.0007995918,0.69274557,0.00042924474,0.00012380276,0.0005282528,0.00048733244,0.00032885972,0.011938315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989378,0.0003366226,0.00007209206,0.00022840257,0.00035377857,0.00007135675],"domain_scores_gemma":[0.99878746,0.00067274855,0.00007134526,0.0001926561,0.0002316943,0.000044125394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011150292,0.0005296476,0.0005346442,0.00092564104,0.0007043348,0.0023901786,0.0018617661,0.0012674973,0.007840294],"category_scores_gemma":[0.0032713541,0.00041839742,0.00106529,0.0007880597,0.0014109261,0.002200642,0.0017903834,0.0020376502,0.001983253],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011168954,0.00014098363,0.0012869145,0.00043169223,0.00006481521,0.00040025724,0.0004829645,0.38370734,0.006877559,0.40450123,0.0052780374,0.19671653],"study_design_scores_gemma":[0.000009432208,0.000040456674,0.00013507067,0.000051090257,0.00001606365,0.00005123014,0.000024974554,0.8850451,0.0026335418,0.09338451,0.018593617,0.0000149315165],"about_ca_topic_score_codex":0.0035907147,"about_ca_topic_score_gemma":0.0043726917,"teacher_disagreement_score":0.007840294,"about_ca_system_score_codex":0.0013227578,"about_ca_system_score_gemma":0.0014939678,"threshold_uncertainty_score":0.026228428},"labels":[],"label_agreement":null},{"id":"W4382317589","doi":"10.1609/aaai.v37i8.26101","title":"AIO-P: Expanding Neural Performance Predictors beyond Image Classification","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"","keywords":"Image (mathematics); Artificial intelligence; Computer science; Pattern recognition (psychology)","score_opus":0.08262618844954439,"score_gpt":0.3043537326942959,"score_spread":0.2217275442447515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382317589","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07409608,0.0009561217,0.9057358,0.0005426945,0.00016905722,0.00020568621,0.00040081132,0.01209216,0.0058016],"genre_scores_gemma":[0.67192954,0.0006046061,0.31662738,0.0004662469,0.00015089316,0.0005279527,0.0016154035,0.0009933641,0.0070845876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914443,0.00014874489,0.00004076236,0.00022698307,0.00030670152,0.00013243455],"domain_scores_gemma":[0.99568814,0.0019420261,0.00041855633,0.00078046793,0.0010038891,0.00016692233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002194876,0.0021156874,0.00096039317,0.000957479,0.00048173653,0.0012003096,0.0022050561,0.0010581566,0.0033384839],"category_scores_gemma":[0.009648759,0.0006721712,0.000502909,0.0010195305,0.0006343993,0.0030047272,0.0019255329,0.003248904,0.0016590947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035286168,0.00042114314,0.008125302,0.00019013934,0.000107743006,0.000086793254,0.00008465762,0.36438242,0.009872144,0.0048097665,0.010335434,0.6012315],"study_design_scores_gemma":[0.000016719088,0.00012912237,0.0008700322,0.00001797856,0.000020665646,0.000020545835,0.000014544454,0.9874764,0.0063843084,0.0037625523,0.0012720468,0.000015114698],"about_ca_topic_score_codex":0.0037043109,"about_ca_topic_score_gemma":0.007854045,"teacher_disagreement_score":0.0037043109,"about_ca_system_score_codex":0.0006326836,"about_ca_system_score_gemma":0.0021622982,"threshold_uncertainty_score":0.011607766},"labels":[],"label_agreement":null},{"id":"W4382400210","doi":"10.58286/28285","title":"Highlighting Anomalies in Ultrasonic Scan Data by Shannon Information Processing","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Ultrasonic sensor; Computer science; Artificial intelligence; Acoustics; Physics","score_opus":0.055563525885353016,"score_gpt":0.2931421397977171,"score_spread":0.23757861391236407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382400210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025051935,0.0001606697,0.9727666,0.00015189477,0.000031901607,0.000042874908,0.00013980077,0.00078995613,0.00086435024],"genre_scores_gemma":[0.28655908,0.00045654224,0.710543,0.000116489624,0.0000994067,0.00013776124,0.00049167644,0.00013856869,0.0014575276],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992823,0.00015703158,0.000059230355,0.00010235424,0.0003463463,0.000052888874],"domain_scores_gemma":[0.9975593,0.0014514297,0.0002533711,0.0003212921,0.00035369274,0.000060871607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013097759,0.00062893145,0.00042700412,0.0021377942,0.000315436,0.0013896987,0.0006561445,0.0007319082,0.0013499317],"category_scores_gemma":[0.0061065354,0.0003114173,0.00049552956,0.001965215,0.0009388555,0.00171005,0.0011607009,0.000879478,0.00061055506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058169715,0.00012450204,0.0028341098,0.00023462826,0.00005633581,0.00043434143,0.00055040274,0.094229616,0.12857594,0.04160871,0.002262484,0.72850734],"study_design_scores_gemma":[0.000012816692,0.0002149556,0.0032784003,0.000034566543,0.000026369167,0.00037866746,0.00011927022,0.91610026,0.053566076,0.022597397,0.0036073397,0.000063971296],"about_ca_topic_score_codex":0.0011077927,"about_ca_topic_score_gemma":0.0014231049,"teacher_disagreement_score":0.0021377942,"about_ca_system_score_codex":0.00061044627,"about_ca_system_score_gemma":0.00081808283,"threshold_uncertainty_score":0.006926894},"labels":[],"label_agreement":null},{"id":"W4382703295","doi":"10.1007/978-3-031-36021-3_26","title":"Learning Shape-Preserving Autoencoder for the Reconstruction of Functional Data from Noisy Observations","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Autoencoder; Computer science; Monotonic function; Akaike information criterion; Algorithm; Convexity; Artificial intelligence; A priori and a posteriori; Pattern recognition (psychology); Feature (linguistics); Computational complexity theory; Applied mathematics; Mathematics; Machine learning; Deep learning","score_opus":0.11454102913106962,"score_gpt":0.2810166620462866,"score_spread":0.166475632915217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382703295","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008063567,0.00017716002,0.9912288,0.000051631632,0.000019307101,0.000006992168,0.000030228688,0.00017202964,0.00025029713],"genre_scores_gemma":[0.33092535,0.001089627,0.6620332,0.00017908763,0.00009863398,0.00010701441,0.0005804786,0.00019297752,0.0047936966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998288,0.00004182447,0.000011546933,0.000047503563,0.00004844395,0.000021868975],"domain_scores_gemma":[0.9994702,0.00031146567,0.00004273247,0.00007628822,0.000076357,0.000022904716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081335916,0.00063605484,0.0009221555,0.0003144862,0.0001704996,0.00052133703,0.00083546597,0.0011940838,0.0009605061],"category_scores_gemma":[0.0019436623,0.0005693335,0.0009211084,0.0005893779,0.0007224273,0.00078245095,0.0009040526,0.0013121857,0.0004897556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014884115,0.00006907895,0.0006618706,0.00016423056,0.00010070233,0.00012500271,0.00008983366,0.56527483,0.028554104,0.015725939,0.0020786235,0.38700697],"study_design_scores_gemma":[0.0000020480911,0.000012905459,0.00009929335,0.000004418088,0.0000052673154,0.000025226298,0.00000250785,0.9959495,0.001706166,0.0019659828,0.00022264433,0.000004024035],"about_ca_topic_score_codex":0.0024406128,"about_ca_topic_score_gemma":0.003153053,"teacher_disagreement_score":0.0024406128,"about_ca_system_score_codex":0.00036044227,"about_ca_system_score_gemma":0.0005631958,"threshold_uncertainty_score":0.0048528314},"labels":[],"label_agreement":null},{"id":"W4382986740","doi":"10.1017/apr.2023.3","title":"-Stable convergence of heavy-/light-tailed infinitely wide neural networks","year":2023,"lang":"en","type":"article","venue":"Advances in Applied Probability","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Mathematics; Independent and identically distributed random variables; Artificial neural network; Perceptron; Heavy-tailed distribution; Limit (mathematics); Distribution (mathematics); Stable distribution; Convergence (economics); Series (stratigraphy); Applied mathematics; Random variable; Statistical physics; Artificial intelligence; Mathematical analysis; Statistics; Computer science; Physics","score_opus":0.01662740427759366,"score_gpt":0.2582175846380347,"score_spread":0.24159018036044103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382986740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1472194,0.001381564,0.839458,0.0013579447,0.00014405414,0.000059254715,0.00017360113,0.0004543865,0.009751831],"genre_scores_gemma":[0.9464617,0.00074454193,0.04409587,0.00052146614,0.00011597362,0.00017872312,0.00022104409,0.00018036345,0.0074803964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981888,0.0007268847,0.000090619105,0.000369293,0.0004194259,0.00020489433],"domain_scores_gemma":[0.9849978,0.010049922,0.0011869047,0.0007786906,0.0021790385,0.0008075797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006083727,0.0008654188,0.0013203198,0.0015004162,0.00078446785,0.0013770957,0.001964353,0.0016195979,0.0028479293],"category_scores_gemma":[0.034911882,0.00071105076,0.0013884063,0.00055302895,0.0041419026,0.0035051454,0.003253793,0.0032131409,0.00052132114],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019662917,0.00006322537,0.0033955788,0.00020323745,0.00012993571,0.0003629646,0.0003259504,0.62173206,0.0042817513,0.3525176,0.0020285125,0.014762558],"study_design_scores_gemma":[0.0000086134005,0.000024165613,0.0003515166,0.000031509786,0.00000775784,0.00003046092,0.000020401503,0.9335126,0.0004494019,0.065290295,0.00025897834,0.000014220626],"about_ca_topic_score_codex":0.0045005884,"about_ca_topic_score_gemma":0.0030939395,"teacher_disagreement_score":0.006083727,"about_ca_system_score_codex":0.0017351924,"about_ca_system_score_gemma":0.0011734545,"threshold_uncertainty_score":0.03217423},"labels":[],"label_agreement":null},{"id":"W4384074436","doi":"10.33103/uot.ijccce.23.1.4","title":"Architecture of Deep Learning and Its Applications","year":2023,"lang":"en","type":"article","venue":"Iraqi Journal of Computer Communication Control and System Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Artificial intelligence; Computer science; Deep learning; Machine learning; Recurrent neural network; Convolutional neural network; Artificial neural network; Inference; Deep belief network; Feature (linguistics)","score_opus":0.006914713668938221,"score_gpt":0.21080940112108193,"score_spread":0.2038946874521437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384074436","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013006326,0.016758671,0.9403826,0.0016367037,0.00022470117,0.000062871004,0.00031658311,0.00152689,0.026084619],"genre_scores_gemma":[0.60222244,0.03083967,0.34036592,0.00077737425,0.00032431132,0.00026447087,0.0008308097,0.00024498987,0.024130011],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997731,0.00004381443,0.000016963822,0.000055414264,0.00008274776,0.000028095921],"domain_scores_gemma":[0.99984396,0.000043412496,0.000011027452,0.000019419638,0.000070408,0.000011704611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042472544,0.00049546885,0.000273574,0.0005112876,0.00022112884,0.0008150883,0.0007702462,0.0007635218,0.0029492655],"category_scores_gemma":[0.0007413494,0.00027349472,0.0003150387,0.00073187606,0.00045362982,0.0010342071,0.00067319145,0.0010598545,0.0010874469],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011125951,0.000068447145,0.0020688903,0.0005659171,0.00007557328,0.00019145358,0.0001215101,0.23282602,0.015862804,0.1330019,0.010624824,0.60448146],"study_design_scores_gemma":[0.000011143069,0.00007859543,0.00094069116,0.00012567722,0.000031497308,0.00025885826,0.000034792796,0.8545494,0.009977605,0.073470704,0.06049331,0.00002775919],"about_ca_topic_score_codex":0.0028608157,"about_ca_topic_score_gemma":0.0019618857,"teacher_disagreement_score":0.0029492655,"about_ca_system_score_codex":0.0007926939,"about_ca_system_score_gemma":0.0006553905,"threshold_uncertainty_score":0.009866297},"labels":[],"label_agreement":null},{"id":"W4384159338","doi":"10.5954/icarob.2023.os25-2","title":"Multi Chaotic Flow Direction Algorithm for Feature Selection","year":2023,"lang":"en","type":"article","venue":"Proceedings of International Conference on Artificial Life and Robotics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Algorithm; Feature selection; Chaotic; Feature (linguistics); Computer science; Selection (genetic algorithm); Flow (mathematics); Pattern recognition (psychology); Artificial intelligence; Mathematics; Geometry","score_opus":0.05668885262158308,"score_gpt":0.3010376655827179,"score_spread":0.2443488129611348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384159338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019537061,0.00046471143,0.97705805,0.00015584123,0.000070478534,0.0001655457,0.00011589401,0.00060912914,0.0018232507],"genre_scores_gemma":[0.23781368,0.00034329537,0.75605494,0.00014171458,0.00006053626,0.0007210614,0.00062978256,0.0000841117,0.0041508353],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957234,0.00008451942,0.0000382578,0.000095334544,0.00016372412,0.000045795092],"domain_scores_gemma":[0.99960595,0.0001343906,0.000040343373,0.000032588814,0.0001701076,0.000016473161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072980195,0.00067088066,0.00074841833,0.001407971,0.00047799852,0.00075033505,0.00081153406,0.00078092323,0.0025853873],"category_scores_gemma":[0.001763725,0.00025863445,0.0007269117,0.0012308488,0.0003015596,0.0006857321,0.0005297031,0.0006518777,0.00057949877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021324746,0.000111148125,0.002044646,0.00017133573,0.000086082386,0.00010200664,0.00011312446,0.16758606,0.017740143,0.008005539,0.0058596153,0.7979671],"study_design_scores_gemma":[0.00004925567,0.00010353385,0.0008562544,0.0000127866315,0.000016043154,0.000108254375,0.000027523467,0.9848867,0.0056400294,0.0029144303,0.0053670537,0.000018082412],"about_ca_topic_score_codex":0.0025127456,"about_ca_topic_score_gemma":0.0020823157,"teacher_disagreement_score":0.0025853873,"about_ca_system_score_codex":0.00042700738,"about_ca_system_score_gemma":0.0010595531,"threshold_uncertainty_score":0.008648992},"labels":[],"label_agreement":null},{"id":"W4384575280","doi":"10.23952/jnva.7.2023.4.08","title":"Semi-implicit back propagation","year":2023,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"MNIST database; Backpropagation; Artificial neural network; Computer science; Convergence (economics); Gradient descent; Algorithm; Stochastic gradient descent; Artificial intelligence; Deep learning; Deep neural networks; Point (geometry); Training (meteorology); Machine learning; Mathematics","score_opus":0.01743801014657475,"score_gpt":0.2699292944734698,"score_spread":0.25249128432689505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384575280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033554272,0.00038698432,0.9930206,0.00014101663,0.00010075958,0.00004538321,0.00009052605,0.0007959342,0.002063367],"genre_scores_gemma":[0.2419177,0.0012556917,0.73974705,0.00042301547,0.00022042343,0.00038469705,0.00079944567,0.00061434094,0.0146375885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999033,0.00023735232,0.000069040856,0.00014883997,0.0004308356,0.00008089072],"domain_scores_gemma":[0.9976683,0.0009939506,0.00014294861,0.0003055809,0.0008121298,0.000076978395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016882425,0.0014185706,0.0014460215,0.0006481833,0.0004654169,0.001125513,0.0023999321,0.0018253408,0.004125692],"category_scores_gemma":[0.005898347,0.0006273689,0.00093870383,0.00076366885,0.00094522454,0.0016776082,0.0018842663,0.0023725596,0.0024626204],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020415256,0.00008431507,0.00055832457,0.0003683841,0.00009499446,0.00015767475,0.00014162828,0.7725648,0.006292735,0.026242224,0.008167897,0.18512282],"study_design_scores_gemma":[0.0000084481935,0.000014746549,0.000036103098,0.00001134485,0.000005154132,0.000023840126,0.000004195586,0.99389315,0.0011163375,0.0034157254,0.0014653925,0.000005621912],"about_ca_topic_score_codex":0.004475991,"about_ca_topic_score_gemma":0.0042533306,"teacher_disagreement_score":0.004475991,"about_ca_system_score_codex":0.0007122651,"about_ca_system_score_gemma":0.0016372631,"threshold_uncertainty_score":0.013801813},"labels":[],"label_agreement":null},{"id":"W4385061094","doi":"10.3934/jimo.2023094","title":"Variance estimation in high-dimensional linear regression via adaptive elastic-net","year":2023,"lang":"en","type":"article","venue":"Journal of Industrial and Management Optimization","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Elastic net regularization; Parameterized complexity; Variance (accounting); Computer science; Regression; Range (aeronautics); Regularization (linguistics); Estimation theory; Polynomial regression; Linear regression; Mathematics; Regression analysis; Mathematical optimization; Algorithm; Statistics; Artificial intelligence; Machine learning","score_opus":0.030503252530282472,"score_gpt":0.25558883979221053,"score_spread":0.22508558726192807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385061094","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008393695,0.00013645498,0.9905505,0.0001283143,0.00001586698,0.000013612179,0.000023262575,0.00013332312,0.0006048952],"genre_scores_gemma":[0.62917787,0.000559922,0.36243373,0.0004587651,0.00012163011,0.0002575472,0.00046688766,0.00027227163,0.006251326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985903,0.0006066551,0.00007447584,0.00028135508,0.00033271674,0.00011448127],"domain_scores_gemma":[0.99723744,0.0017790507,0.0002533988,0.00020770734,0.00044722384,0.00007509752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003580665,0.001119855,0.0013051447,0.0008189747,0.00060299085,0.0010873392,0.0018084534,0.001521079,0.0014538912],"category_scores_gemma":[0.007914945,0.00082277437,0.0010400862,0.00092578266,0.0013601261,0.0023562878,0.0022341604,0.0019592058,0.00040404964],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001240113,0.000050928662,0.0014686316,0.00007803392,0.00007676435,0.00009101598,0.000048214057,0.93300563,0.0015368216,0.017867928,0.00091590104,0.044736188],"study_design_scores_gemma":[0.0000032557443,0.0000061261403,0.00006763507,0.0000035726473,0.000003583895,0.0000085858055,0.0000027157243,0.9964228,0.00018471795,0.00318766,0.00010485439,0.000004347793],"about_ca_topic_score_codex":0.004220954,"about_ca_topic_score_gemma":0.0038951666,"teacher_disagreement_score":0.004220954,"about_ca_system_score_codex":0.0008338991,"about_ca_system_score_gemma":0.0012119083,"threshold_uncertainty_score":0.018936574},"labels":[],"label_agreement":null},{"id":"W4385076203","doi":"10.1016/j.fss.2023.108656","title":"Design of progressive fuzzy polynomial neural networks through gated recurrent unit structure and correlation/probabilistic selection strategies","year":2023,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Science, ICT and Future Planning; National Research Foundation of Korea; Ministry of Education","keywords":"Overfitting; Probabilistic logic; Computer science; Artificial intelligence; Fuzzy logic; Artificial neural network; Machine learning; Polynomial; Polynomial regression; Mathematical optimization; Mathematics; Regression analysis","score_opus":0.03444713472503099,"score_gpt":0.2742526431602098,"score_spread":0.2398055084351788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385076203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017393893,0.000135513,0.98047,0.000050604147,0.00002004859,0.000044188346,0.00001656017,0.00014183774,0.0017273426],"genre_scores_gemma":[0.81183535,0.000202685,0.18531929,0.00006413649,0.000026481448,0.00015958467,0.000046368452,0.000040120132,0.002305865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997836,0.00004540339,0.000011967136,0.000065461296,0.000053780077,0.000039881474],"domain_scores_gemma":[0.9996772,0.0001033537,0.000058586113,0.000022874923,0.00011117287,0.000026809636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006347455,0.0005864657,0.000698104,0.00027899246,0.00038631595,0.00059427455,0.0013851329,0.0008666174,0.0014463093],"category_scores_gemma":[0.001238717,0.0003853947,0.00044557697,0.0003297523,0.0004886975,0.0006992306,0.00075363717,0.0006727272,0.00034297988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011664333,0.00006754714,0.00031076482,0.00011581918,0.000038530936,0.00009206345,0.000074174124,0.85242283,0.01622099,0.024412695,0.0007571358,0.10537074],"study_design_scores_gemma":[0.0000060346615,0.000034999848,0.000038569255,0.0000036384727,0.0000065833738,0.000011702112,0.0000027889264,0.9970065,0.0012512156,0.0014384273,0.0001957263,0.0000038511344],"about_ca_topic_score_codex":0.0025555696,"about_ca_topic_score_gemma":0.004814055,"teacher_disagreement_score":0.0025555696,"about_ca_system_score_codex":0.00066998485,"about_ca_system_score_gemma":0.00086901296,"threshold_uncertainty_score":0.0050813556},"labels":[],"label_agreement":null},{"id":"W4385078302","doi":"10.18280/isi.280306","title":"Neural Network-Directed Detection and Localization of Faults in Railway Track Circuits: An Application of Dempster-Shafer Theory","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dempster–Shafer theory; Track (disk drive); Artificial neural network; Artificial intelligence; Computer science; Track circuit; Electronic circuit; Pattern recognition (psychology); Engineering; Electrical engineering","score_opus":0.012607619704389924,"score_gpt":0.23473318534285006,"score_spread":0.22212556563846014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385078302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06451611,0.0007869035,0.930005,0.00046800633,0.000065787404,0.000031831652,0.00003814261,0.00028728077,0.003800877],"genre_scores_gemma":[0.9148439,0.00042573427,0.08248877,0.00012730938,0.00003695513,0.000036057747,0.000048882765,0.000015526046,0.0019769003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998272,0.00004451726,0.000008531392,0.000037722337,0.00006352924,0.000018448522],"domain_scores_gemma":[0.99933535,0.00037083094,0.00010152805,0.00005761456,0.00011199763,0.000022664493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043910835,0.00041230652,0.0003202653,0.00062645896,0.0002384985,0.00048028256,0.0006935767,0.0008538638,0.0006253708],"category_scores_gemma":[0.002256169,0.0001430566,0.0002850645,0.00046214127,0.0006196414,0.001057693,0.00063222006,0.0006021658,0.00011745998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009652962,0.000056171848,0.0015221636,0.00009427356,0.00005425185,0.0001252991,0.00008939731,0.82284534,0.008351084,0.022396334,0.0011929541,0.14317624],"study_design_scores_gemma":[0.0000020143214,0.000014480574,0.00013859526,0.000002935293,0.0000034811721,0.000018183538,0.000004570289,0.9952625,0.0012572538,0.0031458756,0.00014665574,0.0000034377454],"about_ca_topic_score_codex":0.0032274474,"about_ca_topic_score_gemma":0.002659149,"teacher_disagreement_score":0.0032274474,"about_ca_system_score_codex":0.00077869755,"about_ca_system_score_gemma":0.00044405536,"threshold_uncertainty_score":0.006417334},"labels":[],"label_agreement":null},{"id":"W4385223049","doi":"10.1145/3583133.3590745","title":"Evolving Better Initializations For Neural Networks With Pruning","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Pruning; Computer science; Artificial intelligence; Artificial neural network; Machine learning; Biology","score_opus":0.025058196408119467,"score_gpt":0.26238739222266083,"score_spread":0.23732919581454137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385223049","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07716471,0.0008481397,0.91444707,0.00044156195,0.00016519945,0.00016301435,0.0000730564,0.0013828563,0.005314348],"genre_scores_gemma":[0.52190644,0.0004138042,0.47302976,0.00043854903,0.00005664305,0.00041854483,0.00024699268,0.0004842207,0.0030051558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993325,0.00023559245,0.00004890808,0.00012951848,0.0001691943,0.00008427068],"domain_scores_gemma":[0.9975448,0.0013100272,0.0002580743,0.00041639304,0.00037379627,0.00009688082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020451136,0.0014003229,0.001180114,0.0013393965,0.00071660685,0.0009455862,0.0017723123,0.0018703403,0.0028715378],"category_scores_gemma":[0.013083241,0.0009784717,0.0007812492,0.0006917129,0.001092434,0.0017786337,0.0014359991,0.002042615,0.00072704407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011875209,0.00013641508,0.0018269847,0.000096019,0.00008481069,0.00024262651,0.00021502405,0.8884831,0.00947863,0.018357348,0.0022869478,0.07867328],"study_design_scores_gemma":[0.000021676835,0.00004006419,0.00017259724,0.000029921906,0.000016299176,0.00004788612,0.000016428672,0.98988825,0.001986544,0.0067343726,0.0010374017,0.000008716188],"about_ca_topic_score_codex":0.002248229,"about_ca_topic_score_gemma":0.004906551,"teacher_disagreement_score":0.0028715378,"about_ca_system_score_codex":0.001027824,"about_ca_system_score_gemma":0.00078624865,"threshold_uncertainty_score":0.01081568},"labels":[],"label_agreement":null},{"id":"W4385285537","doi":"10.2139/ssrn.4509501","title":"Information-Theory Analysis of Mouse String-Pulling Agrees with Fitts's Law Increasing Task Difficulty Engages Multiple Sensorimotor Modalities in a Dual Oscillator Behavior","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Fitts's law; Modalities; Task (project management); Dual (grammatical number); String (physics); Psychology; Computer science; Cognitive psychology; Human–computer interaction; Physics; Economics; Sociology; Theoretical physics; Philosophy; Management","score_opus":0.015038946633454935,"score_gpt":0.24130174102420068,"score_spread":0.22626279439074576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385285537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67785007,0.00018994424,0.30330518,0.0015983238,0.00008003455,0.00007385972,0.00049592677,0.0006116139,0.015795149],"genre_scores_gemma":[0.9874577,0.00004949382,0.009587833,0.000112510475,0.000021830607,0.00004234723,0.00009596897,0.000084214065,0.0025482802],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997217,0.00003342362,0.000016220174,0.00008833894,0.00009156977,0.000048730108],"domain_scores_gemma":[0.9972019,0.0015820233,0.00037941034,0.00034058746,0.00027798518,0.00021810445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005400443,0.0003568672,0.00062156486,0.00069111853,0.0003017359,0.0008919303,0.0009242217,0.0006822983,0.009011375],"category_scores_gemma":[0.005879524,0.00028747777,0.0008940387,0.00034499212,0.00061019964,0.001370176,0.0006505046,0.0012053973,0.00068031444],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096948456,0.0006229526,0.019747443,0.000415235,0.00025064358,0.0008610941,0.0007988678,0.071666084,0.5502304,0.26293308,0.005116209,0.086388595],"study_design_scores_gemma":[0.000059583443,0.00025088678,0.053314447,0.0000277658,0.00006606939,0.00037036676,0.00012218117,0.808912,0.02014047,0.115979366,0.0006946821,0.00006223828],"about_ca_topic_score_codex":0.001207284,"about_ca_topic_score_gemma":0.001104183,"teacher_disagreement_score":0.009011375,"about_ca_system_score_codex":0.000375657,"about_ca_system_score_gemma":0.00039009162,"threshold_uncertainty_score":0.030146122},"labels":[],"label_agreement":null},{"id":"W4385327615","doi":"10.1063/5.0167771","title":"Induced synchronization by endogenous noise modulation in finite-size random neural networks: A stochastic mean-field study","year":2023,"lang":"en","type":"preprint","venue":"Chaos An Interdisciplinary Journal of Nonlinear Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Krembil Foundation; University Health Network; University of Toronto; Wilfrid Laurier University; Ontario Brain Institute; University of Ottawa; Canada Research Chairs","funders":"Natural Sciences and Engineering Research Council of Canada; Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Modulation (music); Noise (video); Artificial neural network; Field (mathematics); Computer science; Statistical physics; Physics; Acoustics; Mathematics; Artificial intelligence","score_opus":0.04539515345860542,"score_gpt":0.3271241198956682,"score_spread":0.2817289664370628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385327615","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5549394,0.0016086693,0.4337816,0.0010969617,0.00010584502,0.000057250636,0.00008842671,0.00015016922,0.00817173],"genre_scores_gemma":[0.99294925,0.00036117723,0.0056578284,0.00005410069,0.00004156617,0.000026445165,0.000017114222,0.000016655358,0.0008759432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998196,0.00009387894,0.0000070003925,0.000031555366,0.000028580771,0.000019387558],"domain_scores_gemma":[0.99882823,0.00076072826,0.00020162761,0.000062537496,0.00008021478,0.00006666484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007177253,0.0003356113,0.00042553904,0.0003393393,0.00026554152,0.00041572534,0.0006099089,0.000638264,0.0008273677],"category_scores_gemma":[0.002385822,0.00020399841,0.0006269463,0.0002406385,0.0010055682,0.0009283132,0.00048643272,0.00056804914,0.00006780283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014732358,0.00009626214,0.0025735612,0.00020058373,0.0001469052,0.00053009077,0.00023661512,0.7098225,0.044415537,0.23345459,0.00097203656,0.0074040703],"study_design_scores_gemma":[0.000009658211,0.000018045754,0.00043959907,0.0000042005977,0.000010215385,0.000029976163,0.000008613677,0.98842293,0.00046195742,0.010478132,0.00010917026,0.000007440879],"about_ca_topic_score_codex":0.00094732165,"about_ca_topic_score_gemma":0.00058748556,"teacher_disagreement_score":0.00094732165,"about_ca_system_score_codex":0.00042627595,"about_ca_system_score_gemma":0.0002326773,"threshold_uncertainty_score":0.003795743},"labels":[],"label_agreement":null},{"id":"W4385452551","doi":"10.1109/sds57534.2023.00015","title":"A Deep Discriminant Fractional-order Canonical Correlation Analysis For Information Fusion","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"MNIST database; Computer science; Canonical correlation; Artificial intelligence; Linear discriminant analysis; Sensor fusion; Artificial neural network; Discriminant; Deep learning; Pattern recognition (psychology); Machine learning; Data mining","score_opus":0.01894618471896022,"score_gpt":0.2750391088741005,"score_spread":0.2560929241551403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385452551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006965722,0.00048525806,0.9908568,0.00018013194,0.00008081354,0.000035951394,0.00008388729,0.00036520386,0.0009462564],"genre_scores_gemma":[0.31749058,0.0011819596,0.67546374,0.0003102268,0.00019585567,0.00015846209,0.00075502566,0.00015274943,0.0042913794],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991217,0.00021435738,0.000049412294,0.00020066772,0.0003292245,0.000084633575],"domain_scores_gemma":[0.9990902,0.00020662672,0.00006550587,0.00014468958,0.00043734364,0.000055689034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015209385,0.0009697173,0.0009651125,0.0018297891,0.0007581612,0.0011112727,0.0010046709,0.00086024584,0.002142452],"category_scores_gemma":[0.003185942,0.0003247453,0.0012930589,0.002053983,0.0007301689,0.0016651596,0.0013523006,0.0015107292,0.0006688843],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024104952,0.00016442445,0.0021599184,0.00017626413,0.0002365092,0.00018255632,0.00018615213,0.16586454,0.030194607,0.040847532,0.011485598,0.74826086],"study_design_scores_gemma":[0.0000052395153,0.000029732693,0.0004186044,0.000009744963,0.00002323989,0.000055670436,0.000014352268,0.9868929,0.004556831,0.0059621832,0.0020089878,0.000022470871],"about_ca_topic_score_codex":0.008973781,"about_ca_topic_score_gemma":0.008705428,"teacher_disagreement_score":0.008973781,"about_ca_system_score_codex":0.0009681345,"about_ca_system_score_gemma":0.002005681,"threshold_uncertainty_score":0.017843068},"labels":[],"label_agreement":null},{"id":"W4385488877","doi":"10.1109/ijcnn54540.2023.10191268","title":"Leaky-Integrate-and-Fire Neuron-Like Long-Short-Term-Memory Units as Model System in Computational Biology","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Spiking neural network; Artificial neural network; ENCODE; Artificial intelligence; Encoding (memory); Recurrent neural network; Biological neuron model; Series (stratigraphy); Binary number; Biological network; Machine learning; Biology","score_opus":0.041763261921637214,"score_gpt":0.2836579555849497,"score_spread":0.2418946936633125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385488877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3067057,0.0021815908,0.67769676,0.0009075535,0.00014034341,0.00006775758,0.00028201166,0.00052759115,0.011490662],"genre_scores_gemma":[0.96159273,0.0009365453,0.03277881,0.00010772253,0.00002296375,0.0000777491,0.00006667888,0.000041452113,0.00437534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999168,0.00002177024,0.0000038530643,0.00002300929,0.000016602584,0.000018016986],"domain_scores_gemma":[0.9998759,0.000047799556,0.000023414535,0.000018618592,0.000019978908,0.000014291605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025984927,0.00037490303,0.000466484,0.00023390779,0.00024592315,0.0006128764,0.0011693655,0.0011027913,0.001217887],"category_scores_gemma":[0.00076937466,0.00020145411,0.00042951808,0.00034156832,0.0006770578,0.0018359885,0.0004896788,0.000978474,0.00024959922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002003205,0.00009089647,0.0015925004,0.0002834701,0.000082018574,0.00058973226,0.0002975683,0.6480882,0.055426262,0.27301568,0.0018829303,0.018450458],"study_design_scores_gemma":[0.000008595988,0.000031319043,0.00021146455,0.000008652006,0.000011746478,0.000070838636,0.000015930253,0.9530774,0.0023312313,0.043519557,0.000703113,0.000010080598],"about_ca_topic_score_codex":0.0017765083,"about_ca_topic_score_gemma":0.0012718182,"teacher_disagreement_score":0.0017765083,"about_ca_system_score_codex":0.0004987441,"about_ca_system_score_gemma":0.00034134433,"threshold_uncertainty_score":0.0040742755},"labels":[],"label_agreement":null},{"id":"W4385489627","doi":"10.1007/978-3-031-33390-3_14","title":"Neural Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Backpropagation; Artificial neural network; Normalization (sociology); Regularization (linguistics); Gradient descent; Stochastic gradient descent; Computer science; Artificial intelligence; Feedforward neural network; Nonlinear system; Dropout (neural networks); Feed forward; Algorithm; Mathematics; Pattern recognition (psychology); Machine learning","score_opus":0.02097023155890035,"score_gpt":0.25225664645191254,"score_spread":0.2312864148930122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385489627","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066094934,0.01819362,0.66314566,0.002377307,0.002635745,0.00011141347,0.0011836338,0.0025589736,0.30318412],"genre_scores_gemma":[0.18522874,0.019065551,0.2273573,0.0013002469,0.0013784749,0.00031912854,0.002794788,0.00071540626,0.5618404],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99977964,0.000029744167,0.000010618099,0.000058798447,0.000103832994,0.000017373031],"domain_scores_gemma":[0.9997769,0.00006536149,0.000014038164,0.000050290775,0.000083011124,0.000010284321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027361105,0.00065379526,0.0005068755,0.00062654965,0.0003145273,0.0014743578,0.0009222092,0.0010116844,0.027682083],"category_scores_gemma":[0.0013300279,0.0002931022,0.00029954806,0.0008008624,0.0004233866,0.001430955,0.00070606644,0.0010500231,0.0123584345],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039912095,0.00003453557,0.0003162027,0.00026930377,0.00005321874,0.00007107152,0.00003700966,0.061835054,0.0032159796,0.15510641,0.06662439,0.7123969],"study_design_scores_gemma":[0.000016355581,0.000053758056,0.0006491442,0.00021125839,0.000058651134,0.00026671521,0.000046750236,0.42783505,0.007026293,0.25706238,0.30673617,0.000037487636],"about_ca_topic_score_codex":0.0018159697,"about_ca_topic_score_gemma":0.0025706138,"teacher_disagreement_score":0.027682083,"about_ca_system_score_codex":0.0004815368,"about_ca_system_score_gemma":0.000482152,"threshold_uncertainty_score":0.09260583},"labels":[],"label_agreement":null},{"id":"W4385584406","doi":"10.3233/faia230133","title":"Chapter 1. The Roles of Symbols in Neural-Based AI: They Are Not What You Think!","year":2023,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Cognitive science; Computer science; Artificial intelligence; Architecture; Artificial neural network; Cognitive architecture; Cognition; Psychology; Neuroscience","score_opus":0.04859777358942753,"score_gpt":0.27220783718997404,"score_spread":0.22361006360054653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385584406","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025559398,0.1196195,0.048077032,0.013305759,0.004270453,0.000074315685,0.00031480973,0.00047065137,0.8113115],"genre_scores_gemma":[0.057345003,0.08895233,0.036851395,0.0049489425,0.0031445452,0.00020020598,0.0005285571,0.00040159788,0.80762744],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999876,0.000025344065,0.0000046175487,0.000026413116,0.00005709244,0.000010486251],"domain_scores_gemma":[0.9998178,0.00009757891,0.000009783885,0.00001638786,0.000039381626,0.000019155039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021374281,0.0007195025,0.00029564212,0.0006306674,0.0010183967,0.0027441566,0.00072564615,0.0011591673,0.017713327],"category_scores_gemma":[0.0005416811,0.00024165015,0.0002696372,0.0010534539,0.00260813,0.0037630051,0.0008237687,0.002861047,0.007945587],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019668902,0.000023795199,0.00013200652,0.00042902268,0.0000064543997,0.00006911244,0.0009519115,0.0010454239,0.001038132,0.75328195,0.1305121,0.112490356],"study_design_scores_gemma":[0.0000023167447,0.000012300262,0.00017442637,0.00023778224,0.0000044123917,0.00018492663,0.00016137834,0.0008775055,0.00038458486,0.22253452,0.77541775,0.000008098142],"about_ca_topic_score_codex":0.0017257804,"about_ca_topic_score_gemma":0.0028193307,"teacher_disagreement_score":0.017713327,"about_ca_system_score_codex":0.0012945012,"about_ca_system_score_gemma":0.0011135871,"threshold_uncertainty_score":0.05925703},"labels":[],"label_agreement":null},{"id":"W4385806666","doi":"10.1007/s00521-023-08895-1","title":"A novel varying-parameter periodic rhythm neural network for solving time-varying matrix equation in finite energy noise environment and its application to robot arm","year":2023,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Artificial neural network; Noise (video); Computer science; Convergence (economics); Matrix (chemical analysis); Energy (signal processing); Control theory (sociology); Computational Science and Engineering; Interference (communication); Applied mathematics; Mathematics; Artificial intelligence; Control (management); Machine learning","score_opus":0.02511860058315816,"score_gpt":0.26308634090447464,"score_spread":0.23796774032131648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385806666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022356154,0.0003785445,0.9736476,0.00010479837,0.00010270594,0.00003573527,0.000025408,0.00022755208,0.003121498],"genre_scores_gemma":[0.56728745,0.00053367624,0.42445937,0.00015452517,0.000090425165,0.00016124074,0.00011080254,0.000059858543,0.0071426746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999151,0.000016972366,0.0000058632786,0.000025762014,0.000026355745,0.000009920744],"domain_scores_gemma":[0.99989855,0.000033159482,0.000009507239,0.00001044413,0.00003862208,0.000009678392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002961242,0.00037973718,0.00039133625,0.00015782616,0.00031445504,0.0002789132,0.00086882454,0.00088135083,0.0012584542],"category_scores_gemma":[0.0004542324,0.00022389744,0.0003033318,0.00031315186,0.0002612354,0.00047316012,0.00046722384,0.0004813734,0.00024475987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021366533,0.00012766816,0.0013924998,0.00025350373,0.00012919045,0.00030447452,0.00016390791,0.652283,0.05932537,0.017960422,0.0027302539,0.26511607],"study_design_scores_gemma":[0.000004153716,0.000019077814,0.000050846913,0.000001947342,0.0000047218064,0.00001432549,0.0000026982336,0.9986953,0.00057033595,0.00035348447,0.00028001753,0.0000030426877],"about_ca_topic_score_codex":0.003954596,"about_ca_topic_score_gemma":0.005361628,"teacher_disagreement_score":0.003954596,"about_ca_system_score_codex":0.00019342109,"about_ca_system_score_gemma":0.00038087522,"threshold_uncertainty_score":0.007863164},"labels":[],"label_agreement":null},{"id":"W4386002746","doi":"10.1016/j.neunet.2023.08.018","title":"A multilayered bidirectional associative memory model for learning nonlinear tasks","year":2023,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Government of Ontario","keywords":"Computer science; Bidirectional associative memory; Nonlinear system; Associative property; Artificial neural network; Content-addressable memory; Artificial intelligence; Set (abstract data type); Task (project management); Feature (linguistics); Pattern recognition (psychology); Layer (electronics); Mathematics","score_opus":0.035710211378483826,"score_gpt":0.28835291109186056,"score_spread":0.2526426997133767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386002746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06377964,0.00145588,0.920364,0.0006118991,0.00029798583,0.00011870976,0.0005474114,0.0012714673,0.011553101],"genre_scores_gemma":[0.8445642,0.0013893846,0.13591613,0.00023725923,0.000113193026,0.00042802337,0.00043181362,0.00006752904,0.016852435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998399,0.000030293504,0.000013681386,0.000041591924,0.00003685255,0.000037597933],"domain_scores_gemma":[0.9997819,0.00006357315,0.000034209228,0.000030176241,0.00006989759,0.000020262152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043769914,0.0006590538,0.000694333,0.0004268403,0.00035510145,0.0007719447,0.0019538938,0.00088673295,0.004351541],"category_scores_gemma":[0.0008356968,0.00033334838,0.000730933,0.0005524889,0.0004155197,0.0016248527,0.00058272283,0.0011240612,0.0007900553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023975426,0.00021146217,0.0021878842,0.00033255646,0.00024480704,0.0003022945,0.000185184,0.7709481,0.015906423,0.06873151,0.003712075,0.13699795],"study_design_scores_gemma":[0.000015361378,0.000068155794,0.00032054007,0.000013091255,0.00002849302,0.00006555686,0.000009318801,0.9793577,0.0011411173,0.017259417,0.0017078817,0.000013449052],"about_ca_topic_score_codex":0.0046832757,"about_ca_topic_score_gemma":0.0051396624,"teacher_disagreement_score":0.0046832757,"about_ca_system_score_codex":0.000586148,"about_ca_system_score_gemma":0.00081055536,"threshold_uncertainty_score":0.014557302},"labels":[],"label_agreement":null},{"id":"W4386148491","doi":"10.48550/arxiv.2308.12075","title":"Stabilizing RNN Gradients through Pre-training","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Compute Canada; CHIST-ERA; Nvidia","keywords":"Initialization; Computer science; Stability (learning theory); Recurrent neural network; Exponential stability; Artificial neural network; Simple (philosophy); Differentiable function; Artificial intelligence; Field (mathematics); State space; Deep learning; Mathematical optimization; Mathematics; Machine learning; Nonlinear system","score_opus":0.21439442841942596,"score_gpt":0.22963800864175152,"score_spread":0.015243580222325553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386148491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030249784,0.00034872704,0.9648644,0.00023715306,0.00006661363,0.000065727465,0.000058573114,0.0016920052,0.0024169686],"genre_scores_gemma":[0.61892325,0.0003650012,0.3756405,0.00031719654,0.00006496796,0.0002976125,0.00025116577,0.00062881154,0.0035114095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992374,0.00021418786,0.000062745006,0.00020196276,0.00020322711,0.00008041572],"domain_scores_gemma":[0.99626994,0.001972426,0.00039417666,0.0007037726,0.000561458,0.00009829009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001999358,0.0012745381,0.00078136387,0.0005821033,0.00045811635,0.0009875834,0.0013552475,0.0010073377,0.002501009],"category_scores_gemma":[0.01569069,0.0006992609,0.000421466,0.00038395153,0.001174491,0.0025093046,0.0019033988,0.0022193212,0.0010884316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025834996,0.00010605837,0.0022070035,0.00027155032,0.00006422498,0.00013457044,0.00026236483,0.70168877,0.019575683,0.04920796,0.0039304374,0.22229302],"study_design_scores_gemma":[0.000010568924,0.000058442198,0.00019565596,0.000030646046,0.00000746782,0.000025196108,0.0000145258555,0.97717184,0.0065979096,0.014904083,0.0009752394,0.000008440347],"about_ca_topic_score_codex":0.0018305331,"about_ca_topic_score_gemma":0.0042874324,"teacher_disagreement_score":0.002501009,"about_ca_system_score_codex":0.00096268795,"about_ca_system_score_gemma":0.0011990486,"threshold_uncertainty_score":0.010573745},"labels":[],"label_agreement":null},{"id":"W4386156211","doi":"10.1080/10705511.2023.2234086","title":"Deep Learning Generalized Structured Component Analysis: An Interpretable Artificial Neural Network Model with Composite Indexes","year":2023,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Component (thermodynamics); Component analysis; Computer science; Artificial intelligence; Function (biology); Flexibility (engineering); Artificial neural network; Independent component analysis; Predictive power; Deep learning; Multivariate statistics; Machine learning; Power (physics); Mathematics; Statistics; Biology","score_opus":0.04006853568200957,"score_gpt":0.2990695866781405,"score_spread":0.2590010509961309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386156211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022564078,0.00033815883,0.9743853,0.0005853787,0.00006564386,0.00006444225,0.00040895314,0.00041852923,0.0011694841],"genre_scores_gemma":[0.65624243,0.0008513656,0.3331594,0.00035325516,0.00016907329,0.00048621342,0.0014706983,0.00021016643,0.0070573627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998971,0.00053253805,0.000034942586,0.00025044475,0.00013556273,0.0000756128],"domain_scores_gemma":[0.9980667,0.0010895575,0.00020884858,0.0001882067,0.00038022368,0.00006644842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023786589,0.0012786369,0.0010103269,0.0010344505,0.0004020059,0.0015091462,0.0017891319,0.0010726413,0.0019963174],"category_scores_gemma":[0.0071802805,0.0005349824,0.0011157703,0.001440897,0.0011509379,0.0014893429,0.0014210532,0.0027239968,0.0004474101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109756045,0.00006936898,0.003274702,0.00008262531,0.00018197471,0.000098501296,0.000118989985,0.8666793,0.00086844026,0.05313249,0.004153022,0.071230926],"study_design_scores_gemma":[0.000003012197,0.0000064145456,0.00020316399,0.0000049564787,0.0000073119604,0.0000053685517,0.0000033553786,0.9856118,0.00007943908,0.013718213,0.00035175774,0.000005204386],"about_ca_topic_score_codex":0.014467889,"about_ca_topic_score_gemma":0.014685697,"teacher_disagreement_score":0.014467889,"about_ca_system_score_codex":0.0012820514,"about_ca_system_score_gemma":0.0019274877,"threshold_uncertainty_score":0.028767347},"labels":[],"label_agreement":null},{"id":"W4386173251","doi":"","title":"A Laplacian Approach to Locate Source of Forced Oscillations Under Resonance Conditions Based on Energy-Driven Multilateral Interactive Pattern","year":2022,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Open source; Energy (signal processing); Laplace operator; Resonance (particle physics); Physics; Computer science; Acoustics; Atomic physics; Quantum mechanics","score_opus":0.16682847722299302,"score_gpt":0.46875659978176293,"score_spread":0.3019281225587699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386173251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024260148,0.00004021387,0.97369903,0.00004729846,0.000011791867,0.000027406377,0.00004179631,0.00024626867,0.0016260259],"genre_scores_gemma":[0.7597671,0.000111234316,0.23675844,0.00004598333,0.000021421996,0.00009163735,0.00015823822,0.00008385571,0.0029620477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998006,0.000042778942,0.000008859366,0.000053095457,0.000073509975,0.000021193295],"domain_scores_gemma":[0.99962485,0.0001504169,0.000057240813,0.00003418703,0.00011378377,0.000019571578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027812854,0.0004901988,0.0003160597,0.001148366,0.00027369583,0.00044677616,0.0007067382,0.00038774716,0.0018682062],"category_scores_gemma":[0.001218914,0.00018260046,0.00041089754,0.0007413621,0.00041789122,0.00088533066,0.00049909216,0.0003548923,0.00025926897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020042228,0.00014001259,0.0048530255,0.00025180046,0.00008745253,0.0006849985,0.00041310754,0.6011127,0.0828468,0.047777865,0.0018430453,0.25978872],"study_design_scores_gemma":[0.0000029732669,0.00002433947,0.0005412786,0.0000031820414,0.0000048553197,0.00006008597,0.000026895437,0.99123436,0.0023511914,0.005415616,0.0003267174,0.0000085518],"about_ca_topic_score_codex":0.0017533038,"about_ca_topic_score_gemma":0.0019407165,"teacher_disagreement_score":0.0018682062,"about_ca_system_score_codex":0.00032356355,"about_ca_system_score_gemma":0.0003565161,"threshold_uncertainty_score":0.0062497854},"labels":[],"label_agreement":null},{"id":"W4386178914","doi":"10.2316/j.2023.201-0359","title":"TASK ALLOCATION IN A MULTI-AGENT SYSTEM USING HUMAN-BASED INTERACTION AS AN MDP, 143-151.","year":2023,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Task (project management); Computer science; Multi-agent system; Human–computer interaction; Distributed computing; Artificial intelligence; Engineering; Systems engineering","score_opus":0.04052972039842219,"score_gpt":0.30431736838779283,"score_spread":0.26378764798937065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386178914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15280528,0.0012912005,0.81301385,0.002241937,0.00021369212,0.00032709254,0.00020486409,0.00036401005,0.029538108],"genre_scores_gemma":[0.8984348,0.00033660646,0.09208907,0.00011369296,0.000031700434,0.0002610743,0.00005843021,0.000039789244,0.008634804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956566,0.00021063452,0.000025861547,0.000069378075,0.00005757011,0.00007093228],"domain_scores_gemma":[0.99937004,0.00038138122,0.000053464868,0.000036609625,0.000072713956,0.000085868895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011938125,0.00059746834,0.00076762075,0.00038738345,0.0011269335,0.0016168784,0.0009823653,0.0012178429,0.0043130983],"category_scores_gemma":[0.0023856699,0.0005086396,0.0004463592,0.0003573302,0.0010242537,0.0019566948,0.0013642067,0.00079649274,0.00045494712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030750906,0.00015915566,0.0008932581,0.000092017144,0.000049290913,0.00025015857,0.00020967037,0.9281779,0.003372058,0.037691828,0.0015094337,0.027287843],"study_design_scores_gemma":[0.0000395893,0.000056960118,0.00027522753,0.000009846017,0.000015097939,0.000033423355,0.00008291174,0.9787997,0.0007418826,0.01853836,0.0013956941,0.000011281756],"about_ca_topic_score_codex":0.008004751,"about_ca_topic_score_gemma":0.010249932,"teacher_disagreement_score":0.008004751,"about_ca_system_score_codex":0.0012268475,"about_ca_system_score_gemma":0.0018428436,"threshold_uncertainty_score":0.015916348},"labels":[],"label_agreement":null},{"id":"W4386240950","doi":"10.18280/mmep.100425","title":"Leveraging Neighbourhood Component Analysis for Optimizing Multilayer Feed-Forward Neural Networks in Heart Disease Prediction","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Neighbourhood (mathematics); Artificial neural network; Component (thermodynamics); Computer science; Artificial intelligence; Pattern recognition (psychology); Mathematics; Physics","score_opus":0.03210063982787564,"score_gpt":0.23107143170518935,"score_spread":0.1989707918773137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386240950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073586054,0.00057448517,0.9237046,0.00018848386,0.00006472037,0.00004692191,0.000064666325,0.00071394385,0.0010560884],"genre_scores_gemma":[0.87501055,0.00031246193,0.12266999,0.000085183165,0.000037050588,0.00011158025,0.00017470766,0.000071630304,0.0015268873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996933,0.00010612185,0.000022534306,0.00005704244,0.00007955652,0.00004149178],"domain_scores_gemma":[0.99919707,0.00046147656,0.0000699465,0.000046530204,0.00020301552,0.00002208323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012118898,0.00085291726,0.0008036064,0.000639023,0.00033503058,0.0006364314,0.0007659201,0.0008438355,0.0006544156],"category_scores_gemma":[0.0040898076,0.00040831396,0.0006012938,0.0005330543,0.00036237494,0.000790781,0.00073428,0.0008107751,0.00022975454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005181929,0.000031721112,0.0012826071,0.000023481207,0.000032169286,0.000030282157,0.000020011008,0.9448266,0.0015016181,0.00085693836,0.000365979,0.05097682],"study_design_scores_gemma":[7.6132096e-7,0.00000560106,0.000102343045,0.0000013887004,0.0000023080104,0.0000019435934,0.0000010030737,0.9993826,0.00017196294,0.00029569352,0.00003266233,0.0000016545968],"about_ca_topic_score_codex":0.011892341,"about_ca_topic_score_gemma":0.012028053,"teacher_disagreement_score":0.011892341,"about_ca_system_score_codex":0.00057135924,"about_ca_system_score_gemma":0.00087929156,"threshold_uncertainty_score":0.023646235},"labels":[],"label_agreement":null},{"id":"W4386250241","doi":"10.32920/24050733.v1","title":"Optimum Regularization Parameter C in Support Vector Machine (SVM) Binary Classification","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Overfitting; Support vector machine; Binary classification; Regularization (linguistics); Binary number; Artificial intelligence; Computer science; Pattern recognition (psychology); Separable space; Mathematics; Algorithm; Artificial neural network","score_opus":0.06140162478640203,"score_gpt":0.29463526621778063,"score_spread":0.2332336414313786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386250241","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02274653,0.002385819,0.9718292,0.0004474606,0.00009717029,0.000051513573,0.00005787194,0.00060717826,0.0017772573],"genre_scores_gemma":[0.553159,0.0024366153,0.4408166,0.00037539727,0.00020753164,0.0003057744,0.0003401064,0.0003764929,0.0019825152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99634284,0.0012613913,0.00028361593,0.0007829958,0.001114082,0.0002150311],"domain_scores_gemma":[0.9935592,0.0038662395,0.00060944556,0.00041929886,0.0014142945,0.00013155026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005233293,0.0009462188,0.0016730282,0.0012242707,0.000665808,0.0020468233,0.001444339,0.0024855672,0.0007812745],"category_scores_gemma":[0.019135816,0.00057986035,0.00067180936,0.0012524758,0.001291048,0.0023020238,0.0010854432,0.002533836,0.0005991085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005525322,0.00019424877,0.004062313,0.00096329604,0.00016915573,0.00033731258,0.0003789315,0.49645552,0.038583037,0.07422224,0.008764134,0.3753173],"study_design_scores_gemma":[0.000009590808,0.00006212204,0.00051546923,0.00008227745,0.000015287704,0.00008975234,0.000026394666,0.9788123,0.0052374406,0.013322004,0.00179753,0.000029889112],"about_ca_topic_score_codex":0.0012530443,"about_ca_topic_score_gemma":0.0006128171,"teacher_disagreement_score":0.005233293,"about_ca_system_score_codex":0.0009871522,"about_ca_system_score_gemma":0.0011929236,"threshold_uncertainty_score":0.027676582},"labels":[],"label_agreement":null},{"id":"W4386256752","doi":"10.32920/24050733","title":"Optimum Regularization Parameter C in Support Vector Machine (SVM) Binary Classification","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Overfitting; Support vector machine; Regularization (linguistics); Binary classification; Binary number; Artificial intelligence; Computer science; Pattern recognition (psychology); Separable space; Mathematics; Algorithm; Artificial neural network","score_opus":0.06140162478640203,"score_gpt":0.29463526621778063,"score_spread":0.2332336414313786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386256752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02274653,0.002385819,0.9718292,0.0004474606,0.00009717029,0.000051513573,0.00005787194,0.00060717826,0.0017772573],"genre_scores_gemma":[0.553159,0.0024366153,0.4408166,0.00037539727,0.00020753164,0.0003057744,0.0003401064,0.0003764929,0.0019825152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99634284,0.0012613913,0.00028361593,0.0007829958,0.001114082,0.0002150311],"domain_scores_gemma":[0.9935592,0.0038662395,0.00060944556,0.00041929886,0.0014142945,0.00013155026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005233293,0.0009462188,0.0016730282,0.0012242707,0.000665808,0.0020468233,0.001444339,0.0024855672,0.0007812745],"category_scores_gemma":[0.019135816,0.00057986035,0.00067180936,0.0012524758,0.001291048,0.0023020238,0.0010854432,0.002533836,0.0005991085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005525322,0.00019424877,0.004062313,0.00096329604,0.00016915573,0.00033731258,0.0003789315,0.49645552,0.038583037,0.07422224,0.008764134,0.3753173],"study_design_scores_gemma":[0.000009590808,0.00006212204,0.00051546923,0.00008227745,0.000015287704,0.00008975234,0.000026394666,0.9788123,0.0052374406,0.013322004,0.00179753,0.000029889112],"about_ca_topic_score_codex":0.0012530443,"about_ca_topic_score_gemma":0.0006128171,"teacher_disagreement_score":0.005233293,"about_ca_system_score_codex":0.0009871522,"about_ca_system_score_gemma":0.0011929236,"threshold_uncertainty_score":0.027676582},"labels":[],"label_agreement":null},{"id":"W4386256899","doi":"10.32920/24050760","title":"Signal and System Minimum Mismatch Modeling (3M) with Order Selection Analysis","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Akaike information criterion; Bayesian information criterion; Autoregressive model; Model selection; Mean squared error; Mathematics; Gaussian; Minimum description length; Algorithm; Information Criteria; Selection (genetic algorithm); Mathematical optimization; Computer science; Applied mathematics; Statistics; Artificial intelligence","score_opus":0.029491346500735083,"score_gpt":0.24756110724645503,"score_spread":0.21806976074571993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386256899","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026409081,0.00011778168,0.9960461,0.000049714385,0.000017632401,0.000012579528,0.00003281048,0.00023226553,0.00085019623],"genre_scores_gemma":[0.40060234,0.00063162396,0.5894561,0.00015195347,0.00010804164,0.0002462251,0.00049200165,0.00024476953,0.008066976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953246,0.00015554727,0.000025011075,0.00008795607,0.00016741299,0.000031576372],"domain_scores_gemma":[0.99962425,0.00017776314,0.00006606179,0.000050354836,0.00007092981,0.00001065743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006791309,0.0008705955,0.0006721747,0.00047851686,0.00034798932,0.00062373537,0.000651732,0.0007078383,0.0022525694],"category_scores_gemma":[0.001448674,0.0003408988,0.0009949318,0.00054937886,0.00038997602,0.00078341935,0.00072508416,0.00086385506,0.0006316494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001369386,0.00004126873,0.0008949523,0.00019958091,0.00009572398,0.0001411278,0.0001488368,0.763755,0.011300491,0.04331726,0.0019020268,0.17806678],"study_design_scores_gemma":[0.0000024871408,0.000026569049,0.00012772766,0.0000072746034,0.000007332106,0.000018268565,0.0000046397627,0.9930607,0.0014535338,0.0037755447,0.0015098744,0.0000059786207],"about_ca_topic_score_codex":0.0027470668,"about_ca_topic_score_gemma":0.0027146433,"teacher_disagreement_score":0.0027470668,"about_ca_system_score_codex":0.00045860655,"about_ca_system_score_gemma":0.0007672734,"threshold_uncertainty_score":0.0075356364},"labels":[],"label_agreement":null},{"id":"W4386264723","doi":"10.32920/24050760.v1","title":"Signal and System Minimum Mismatch Modeling (3M) with Order Selection Analysis","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Akaike information criterion; Bayesian information criterion; Autoregressive model; Model selection; Mathematics; Minimum description length; Mean squared error; Gaussian; Information Criteria; Algorithm; Selection (genetic algorithm); Mathematical optimization; Computer science; Applied mathematics; Statistics; Artificial intelligence","score_opus":0.029491346500735083,"score_gpt":0.24756110724645503,"score_spread":0.21806976074571993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386264723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026409081,0.00011778168,0.9960461,0.000049714385,0.000017632401,0.000012579528,0.00003281048,0.00023226553,0.00085019623],"genre_scores_gemma":[0.40060234,0.00063162396,0.5894561,0.00015195347,0.00010804164,0.0002462251,0.00049200165,0.00024476953,0.008066976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953246,0.00015554727,0.000025011075,0.00008795607,0.00016741299,0.000031576372],"domain_scores_gemma":[0.99962425,0.00017776314,0.00006606179,0.000050354836,0.00007092981,0.00001065743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006791309,0.0008705955,0.0006721747,0.00047851686,0.00034798932,0.00062373537,0.000651732,0.0007078383,0.0022525694],"category_scores_gemma":[0.001448674,0.0003408988,0.0009949318,0.00054937886,0.00038997602,0.00078341935,0.00072508416,0.00086385506,0.0006316494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001369386,0.00004126873,0.0008949523,0.00019958091,0.00009572398,0.0001411278,0.0001488368,0.763755,0.011300491,0.04331726,0.0019020268,0.17806678],"study_design_scores_gemma":[0.0000024871408,0.000026569049,0.00012772766,0.0000072746034,0.000007332106,0.000018268565,0.0000046397627,0.9930607,0.0014535338,0.0037755447,0.0015098744,0.0000059786207],"about_ca_topic_score_codex":0.0027470668,"about_ca_topic_score_gemma":0.0027146433,"teacher_disagreement_score":0.0027470668,"about_ca_system_score_codex":0.00045860655,"about_ca_system_score_gemma":0.0007672734,"threshold_uncertainty_score":0.0075356364},"labels":[],"label_agreement":null},{"id":"W4386325454","doi":"10.18280/ts.400414","title":"Neural Correlate-Based E-Learning Validation and Classification Using Convolutional and Long Short-Term Memory Networks","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Term (time); Convolutional neural network; Long short term memory; Computer science; Artificial intelligence; Artificial neural network; Machine learning; Pattern recognition (psychology); Recurrent neural network","score_opus":0.04510478692287224,"score_gpt":0.2679803290419787,"score_spread":0.22287554211910648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386325454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48405117,0.0016547637,0.5017153,0.0009260729,0.00029256346,0.00022051818,0.00071827683,0.004187385,0.006233921],"genre_scores_gemma":[0.9607194,0.00026346146,0.034574833,0.00014124702,0.00003425723,0.00008177643,0.00069786404,0.000045245644,0.0034418544],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99936825,0.00016772342,0.000050784893,0.0001696895,0.0001314429,0.000112237634],"domain_scores_gemma":[0.9982331,0.00078751554,0.00019385052,0.00021922548,0.00050146505,0.000064840446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001653684,0.0009640032,0.00054875505,0.0009122429,0.00031989056,0.0010749804,0.0011626114,0.0010541626,0.0012731937],"category_scores_gemma":[0.005434706,0.00029174756,0.0005677928,0.0006214159,0.00037846,0.001216983,0.0010627006,0.0013947231,0.0008166618],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005681682,0.0006870851,0.021494849,0.00014139635,0.00022155594,0.0002640483,0.00017353611,0.27193344,0.00975067,0.0018641896,0.003924064,0.6889769],"study_design_scores_gemma":[0.000005780671,0.000057300957,0.0017811275,0.000015281472,0.0000150095575,0.000023324626,0.000021728216,0.99217975,0.0048288107,0.000759656,0.00030382272,0.000008462618],"about_ca_topic_score_codex":0.008029127,"about_ca_topic_score_gemma":0.009139778,"teacher_disagreement_score":0.008029127,"about_ca_system_score_codex":0.0009297561,"about_ca_system_score_gemma":0.00081064296,"threshold_uncertainty_score":0.015964806},"labels":[],"label_agreement":null},{"id":"W4386415715","doi":"10.31449/inf.v47i8.4767","title":"LP SVM with A Novel Similarity function Outperforms Powerful LP-QP-Kernel-SVM Considering Efficient Classification","year":2023,"lang":"en","type":"article","venue":"Informatica","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Radial basis function kernel; Artificial intelligence; Support vector machine; Kernel (algebra); Classifier (UML); Similarity (geometry); Pattern recognition (psychology); Kernel method; Computer science; Polynomial kernel; Tree kernel; Machine learning; Generalization; Mathematics; Algorithm; Discrete mathematics","score_opus":0.03161360970452603,"score_gpt":0.24442180178319567,"score_spread":0.21280819207866963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386415715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36552328,0.0035408349,0.6124512,0.0010369297,0.0005393179,0.00014592278,0.0002971794,0.005249033,0.011216429],"genre_scores_gemma":[0.95952743,0.00020844663,0.037079643,0.000112025504,0.00011436549,0.000032800246,0.0005200197,0.00010236084,0.0023029104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986873,0.0002945658,0.00011970367,0.00028023915,0.00048352967,0.0001347664],"domain_scores_gemma":[0.9983907,0.00047908875,0.0001581378,0.00030525643,0.00055408775,0.00011272977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015090205,0.0006695039,0.0010089071,0.00065186963,0.00034365055,0.001330396,0.0009847999,0.0010812177,0.0026855154],"category_scores_gemma":[0.0037530311,0.0001399085,0.00048637012,0.0005836288,0.0004099364,0.001854323,0.00091554533,0.0012058371,0.0011119541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016416665,0.0009123079,0.007353717,0.0005201112,0.00020894948,0.00019175214,0.00007214479,0.29408735,0.032683335,0.008311382,0.014033688,0.63998365],"study_design_scores_gemma":[0.000011814254,0.00016741703,0.0009347422,0.000005705769,0.000015799362,0.00003570387,0.000012260301,0.9932642,0.0037879471,0.0010714814,0.00068711623,0.000005835571],"about_ca_topic_score_codex":0.0014383107,"about_ca_topic_score_gemma":0.0009178027,"teacher_disagreement_score":0.0026855154,"about_ca_system_score_codex":0.0005548206,"about_ca_system_score_gemma":0.0007585171,"threshold_uncertainty_score":0.00898397},"labels":[],"label_agreement":null},{"id":"W4386473069","doi":"10.32470/ccn.2023.1620-0","title":"Characterising the spatiotemporal profiles of neural object representations using implicit and explicit similarity judgement tasks","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Biotechnology and Biological Sciences Research Council","keywords":"Judgement; Similarity (geometry); Artificial intelligence; Computer science; Object (grammar); Pattern recognition (psychology); Natural language processing; Image (mathematics); Epistemology","score_opus":0.05749338431963872,"score_gpt":0.3195424767121744,"score_spread":0.26204909239253565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386473069","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9580782,0.00016969784,0.03961027,0.00008785397,0.000016199236,0.000032093736,0.00041933657,0.00010477278,0.0014815247],"genre_scores_gemma":[0.99352574,0.00007284117,0.0053140153,0.00001514725,0.0000069151706,0.00001394632,0.00043521635,0.000030644987,0.0005856157],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996501,0.000083130755,0.000036196707,0.000100837075,0.000078794634,0.000050959537],"domain_scores_gemma":[0.9961584,0.0023232077,0.00048862316,0.0004950644,0.00034222324,0.00019247507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013544777,0.00028097394,0.00038496236,0.00071914634,0.00013354767,0.0016916541,0.0004912819,0.00066144805,0.0018123491],"category_scores_gemma":[0.0155248195,0.0002197207,0.00029169774,0.0008964946,0.00031665087,0.0027414632,0.0010040001,0.00089619274,0.00038256004],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046170554,0.0007503324,0.11751974,0.00070062705,0.00040713244,0.00025605992,0.0018594582,0.03126987,0.37270427,0.009416555,0.0019775487,0.45852146],"study_design_scores_gemma":[0.00013247509,0.000876878,0.3282151,0.00007923043,0.00017618218,0.0005487909,0.0007694071,0.5764891,0.06513631,0.025907522,0.0015399025,0.00012909126],"about_ca_topic_score_codex":0.0016794014,"about_ca_topic_score_gemma":0.0020143623,"teacher_disagreement_score":0.0018123491,"about_ca_system_score_codex":0.00028749942,"about_ca_system_score_gemma":0.00033852813,"threshold_uncertainty_score":0.0071632266},"labels":[],"label_agreement":null},{"id":"W4386548534","doi":"10.3997/2214-4609.2023628018","title":"Improving the 4D Signal Inside the SOA at Eldfisk Using RTM Pseudo-Offset Gathers","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ConocoPhillips (Canada)","funders":"","keywords":"Offset (computer science); Computer science; Signal processing; Computer hardware; Digital signal processing","score_opus":0.035980544375077035,"score_gpt":0.257930669332602,"score_spread":0.22195012495752497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386548534","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9340651,0.00016049527,0.051976155,0.00041474245,0.00018229734,0.000046900146,0.0023290839,0.003663828,0.0071613966],"genre_scores_gemma":[0.92093104,0.00008826286,0.07382717,0.000051184543,0.000049054564,0.00003068653,0.0024933675,0.00034646224,0.0021827535],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997826,0.00002089814,0.000009088579,0.00003970107,0.000104864164,0.00004284137],"domain_scores_gemma":[0.99963045,0.000058733967,0.000032007618,0.000083519044,0.00015319018,0.000042020692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004434724,0.0006029331,0.00035999756,0.0007213442,0.00033124356,0.0008847399,0.00045334708,0.00047910836,0.0028294579],"category_scores_gemma":[0.00094369793,0.00028362096,0.0005003318,0.00052976876,0.0002963572,0.00070805853,0.00066483405,0.0005774169,0.00078156195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001914836,0.00033320443,0.053492017,0.00043348485,0.00021217254,0.0011475278,0.001604051,0.17198932,0.50944394,0.0014291644,0.006811543,0.25118878],"study_design_scores_gemma":[0.00016670798,0.00061030965,0.18836567,0.00009188096,0.00016755353,0.00063872134,0.0010975542,0.61427903,0.17156197,0.0012252021,0.021526111,0.00026937874],"about_ca_topic_score_codex":0.0061754617,"about_ca_topic_score_gemma":0.020453451,"teacher_disagreement_score":0.0061754617,"about_ca_system_score_codex":0.00038467292,"about_ca_system_score_gemma":0.0005623826,"threshold_uncertainty_score":0.012279034},"labels":[],"label_agreement":null},{"id":"W4386570240","doi":"10.1016/j.patcog.2023.109954","title":"Training feedforward neural nets in Hopfield-energy-based configuration: A two-step approach","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"Computer science; Artificial intelligence; Artificial neural network; Cluster analysis; Minification; Entropy (arrow of time); Feed forward; Unsupervised learning; Intuition; Machine learning; Hopfield network; Feedforward neural network; Energy minimization; Energy (signal processing); Pattern recognition (psychology); Mathematics","score_opus":0.08004302281945373,"score_gpt":0.27550466821605174,"score_spread":0.195461645396598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386570240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044807516,0.00034886927,0.9487363,0.00013774387,0.00006330912,0.00016046096,0.000069623966,0.0008379896,0.0048381086],"genre_scores_gemma":[0.5550409,0.00019535367,0.43747506,0.00017558415,0.000048035003,0.0003515221,0.00027924805,0.00012315363,0.0063112588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997645,0.000060396946,0.000023072933,0.000050936895,0.00006263977,0.000038341605],"domain_scores_gemma":[0.9994079,0.0003137549,0.000025576428,0.000072090705,0.00015088145,0.000029793768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073212234,0.000741331,0.0008154025,0.00046882455,0.00045227815,0.0007351713,0.001785688,0.0019008621,0.0050994167],"category_scores_gemma":[0.0019878375,0.00065319176,0.0005170462,0.0004715282,0.00041847106,0.001031765,0.0011579148,0.0010845456,0.0009147906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024268749,0.00015210955,0.0009603092,0.0001538317,0.000091970534,0.00017246448,0.000068075686,0.74978745,0.010011074,0.004857135,0.0011430477,0.23235977],"study_design_scores_gemma":[0.000012539186,0.000052427895,0.00012644229,0.000010805744,0.0000126322075,0.000021800699,0.000009767419,0.9960062,0.0019451871,0.0015860817,0.00020947252,0.0000066923635],"about_ca_topic_score_codex":0.0036636635,"about_ca_topic_score_gemma":0.0063843117,"teacher_disagreement_score":0.0050994167,"about_ca_system_score_codex":0.00043662463,"about_ca_system_score_gemma":0.00082338694,"threshold_uncertainty_score":0.017059267},"labels":[],"label_agreement":null},{"id":"W4386620658","doi":"10.3386/w31656","title":"Composite Sorting","year":2023,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Composite number; Sorting; Computer science; Mathematics; Algorithm","score_opus":0.5717358651164912,"score_gpt":0.5663309175480495,"score_spread":0.005404947568441676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386620658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06106633,0.0006020468,0.8082632,0.002271984,0.0002750641,0.00025070732,0.0013453437,0.0007539899,0.1251713],"genre_scores_gemma":[0.6784322,0.00082622917,0.24825451,0.0012651671,0.00030263522,0.00047092116,0.0015541327,0.00035887072,0.06853531],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756455,0.0005438158,0.00012288449,0.00062690605,0.00065571745,0.0004861949],"domain_scores_gemma":[0.99641776,0.00094356056,0.0004674306,0.0010382296,0.00083717704,0.00029581526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021574558,0.00077089976,0.0010139584,0.0022011895,0.0016473798,0.0031943347,0.0025372754,0.0016664872,0.03194233],"category_scores_gemma":[0.0068087443,0.0003756654,0.0013054296,0.0033682704,0.0021433095,0.004625084,0.0024422575,0.0016109723,0.0037681356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005556973,0.00006787831,0.0026291597,0.00007511195,0.000026523796,0.000051579693,0.00014052031,0.018332377,0.00051451393,0.9202235,0.006565648,0.051317602],"study_design_scores_gemma":[0.000034787186,0.000065383916,0.0015975909,0.00004051549,0.00002275934,0.00017027314,0.0001553553,0.068918936,0.00061997987,0.89450073,0.03384774,0.000025990694],"about_ca_topic_score_codex":0.0036271475,"about_ca_topic_score_gemma":0.0042017014,"teacher_disagreement_score":0.03194233,"about_ca_system_score_codex":0.0026205396,"about_ca_system_score_gemma":0.0027571674,"threshold_uncertainty_score":0.10685778},"labels":[],"label_agreement":null},{"id":"W4387019753","doi":"10.3390/electronics12194027","title":"Efficient Hyperbolic Perceptron for Image Classification","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Perceptron; Convolutional neural network; Pattern recognition (psychology); Feature vector; Multilayer perceptron; Feature (linguistics); Flexibility (engineering); Euclidean geometry; Hyperbolic space; Euclidean space; Mathematics; Geometry","score_opus":0.022600864086574884,"score_gpt":0.28096646451430535,"score_spread":0.2583656004277305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387019753","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010628261,0.0012774064,0.9808195,0.00034385765,0.00012331361,0.0000442251,0.00022684845,0.0029079693,0.003628696],"genre_scores_gemma":[0.42552134,0.0016698027,0.542321,0.0006569863,0.00019902167,0.00020646385,0.0016345539,0.00041806142,0.02737281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948275,0.0001086361,0.000038905546,0.00014068293,0.0001748258,0.000054112552],"domain_scores_gemma":[0.999613,0.000114083065,0.0000357881,0.000107244225,0.00010550396,0.000024536594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008716871,0.0008683048,0.00076643593,0.0005632594,0.00026482448,0.00086437346,0.0015735682,0.0009468014,0.0060914215],"category_scores_gemma":[0.0018333364,0.0004334203,0.0006388406,0.00095281703,0.00057201745,0.0016877715,0.0011494254,0.0017422455,0.0034813243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030956315,0.00011601901,0.00087629305,0.0002747874,0.00011691323,0.00014575392,0.00007664974,0.25459313,0.01721077,0.047653075,0.019361692,0.6592654],"study_design_scores_gemma":[0.00000773848,0.000024378267,0.00012249895,0.000008421209,0.000007211819,0.000031257274,0.000006573096,0.9839559,0.0026984257,0.010800485,0.0023292785,0.000007758055],"about_ca_topic_score_codex":0.0024179467,"about_ca_topic_score_gemma":0.0032624388,"teacher_disagreement_score":0.0060914215,"about_ca_system_score_codex":0.00083462766,"about_ca_system_score_gemma":0.00084238825,"threshold_uncertainty_score":0.020377815},"labels":[],"label_agreement":null},{"id":"W4387031816","doi":"","title":"Thermal information processing using phase change materials","year":2023,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Phase change; Computer science; Phase-change material; Phase (matter); Thermal; Engineering physics; Engineering; Thermodynamics; Physics","score_opus":0.05546312796602796,"score_gpt":0.2838112305630976,"score_spread":0.22834810259706964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387031816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15185505,0.0033629632,0.8018647,0.0013127566,0.0009959204,0.00015730859,0.0002481429,0.0017223889,0.038480755],"genre_scores_gemma":[0.83610266,0.0014538632,0.13465944,0.00031726263,0.0002497303,0.00009285196,0.00020934272,0.00023582554,0.026678963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999416,0.000008019241,0.0000023182135,0.00001752611,0.000024488849,0.000006159711],"domain_scores_gemma":[0.999861,0.000061272935,0.000013284632,0.000020625503,0.000035733443,0.000008060703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012142778,0.00025610873,0.00023088856,0.00020832894,0.0001688241,0.00063934043,0.00036818278,0.0003669679,0.008364799],"category_scores_gemma":[0.00039248043,0.00014282326,0.00018277709,0.00030721218,0.00029746175,0.0006452792,0.00023008382,0.0004139644,0.0009871179],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038709433,0.00012392706,0.00034506788,0.00045896153,0.000039140392,0.00014142177,0.00009191245,0.04148077,0.71761405,0.026749276,0.004828294,0.2077401],"study_design_scores_gemma":[0.000037367827,0.000286852,0.0008218491,0.000028275312,0.00003272938,0.00016409275,0.00004585524,0.5662173,0.38984036,0.020630213,0.021864789,0.000030286215],"about_ca_topic_score_codex":0.00018210127,"about_ca_topic_score_gemma":0.0002804435,"teacher_disagreement_score":0.008364799,"about_ca_system_score_codex":0.00025946877,"about_ca_system_score_gemma":0.00012959697,"threshold_uncertainty_score":0.02798301},"labels":[],"label_agreement":null},{"id":"W4387185352","doi":"10.3233/faia230273","title":"Resource-Constrained Knowledge Diffusion Processes Inspired by Human Peer Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Nanjing Institute of Technology; Institute for Catastrophic Loss Reduction; National Science Foundation","keywords":"Computer science; Overfitting; Artificial intelligence; Context (archaeology); Process (computing); Population; Modular design; Knowledge management; Artificial neural network; Machine learning","score_opus":0.041885873240576216,"score_gpt":0.2814090126414429,"score_spread":0.2395231394008667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387185352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100556925,0.001389958,0.8764973,0.0018840471,0.00013580258,0.000110277164,0.00012743766,0.00018168424,0.019116586],"genre_scores_gemma":[0.90947235,0.0010950244,0.076504774,0.00024860018,0.00013960828,0.00022364066,0.000082648694,0.000059018945,0.012174285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945885,0.0002366972,0.000018269351,0.00012562325,0.00009249919,0.00006813142],"domain_scores_gemma":[0.99728715,0.0018951976,0.00028778083,0.0001566075,0.00017060275,0.00020261238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015430812,0.00086240564,0.0010626343,0.00049573387,0.00052805064,0.0017194368,0.0020775779,0.0021065641,0.0032539184],"category_scores_gemma":[0.008142958,0.00037952673,0.0007506119,0.0007302399,0.0020813658,0.0028843938,0.0015817839,0.0018438594,0.0004371079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005076601,0.000059427603,0.0005658923,0.00010619597,0.000060609764,0.00019366348,0.0002779335,0.73530143,0.001479706,0.24565929,0.0013417472,0.014903296],"study_design_scores_gemma":[0.000013573843,0.000028877777,0.00010944541,0.0000105240315,0.000009904447,0.000030766245,0.000021121004,0.89403546,0.00021793797,0.10463587,0.00087616546,0.000010417495],"about_ca_topic_score_codex":0.0021075467,"about_ca_topic_score_gemma":0.0014049856,"teacher_disagreement_score":0.0032539184,"about_ca_system_score_codex":0.0011385308,"about_ca_system_score_gemma":0.00080471986,"threshold_uncertainty_score":0.010885417},"labels":[],"label_agreement":null},{"id":"W4387226913","doi":"10.1007/978-3-031-19568-6","title":"Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing","year":2023,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Indiana University Bloomington; University of California, Irvine; Karl-Franzens-Universität Graz; National University of Sciences and Technology; Turun Yliopisto; Università degli Studi di Padova; Queen's University; Technische Universität Wien; Universität Wien; Technische Universiteit Eindhoven; National University of Singapore; Radboud Universiteit; Yale University; Seoul National University; Vysoké Učení Technické v Brně; Technische Universiteit Delft; Universiteit van Amsterdam; Washington State University; Keio University; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; York University; National Science Foundation; Politecnico di Torino; Queen's University Belfast; New York University Abu Dhabi; Colorado State University; University of Pittsburgh; Drexel University; Technische Universitat Wien; University of Wisconsin-Madison; University of Notre Dame; Tamkeen; University of Cyprus; Massachusetts Institute of Technology; George Mason University","keywords":"Internet of Things; Edge computing; Cyber-physical system; Computer science; Enhanced Data Rates for GSM Evolution; Computer security; Human–computer interaction; Artificial intelligence; Operating system","score_opus":0.024573685880735618,"score_gpt":0.27700625111682936,"score_spread":0.25243256523609375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387226913","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039928686,0.06262835,0.43194604,0.0045674588,0.0066632847,0.0001721487,0.0015920919,0.005852626,0.4825851],"genre_scores_gemma":[0.018344013,0.028346658,0.09836959,0.001063228,0.0008619715,0.00012331267,0.001358378,0.0010643536,0.8504686],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998374,0.000009830878,0.000004727761,0.000025339026,0.00011148438,0.00001117707],"domain_scores_gemma":[0.99982363,0.000078359175,0.000008229245,0.0000238157,0.000050701165,0.000015365285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015094792,0.0008891828,0.0006030992,0.00059236406,0.00034963904,0.0018626403,0.0009651064,0.000707522,0.051419165],"category_scores_gemma":[0.0006812568,0.00048600585,0.0004983442,0.0012354825,0.00034581107,0.0020281784,0.0007132772,0.0016375531,0.024566218],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048548758,0.000056026878,0.00015448817,0.00050791615,0.000033068754,0.00008896605,0.00006514304,0.014296333,0.0060073594,0.0886966,0.34667042,0.5433752],"study_design_scores_gemma":[0.000008850509,0.00003441065,0.00040173632,0.00020611056,0.000013089389,0.0002523531,0.000026131696,0.037906032,0.002590733,0.053656556,0.9048811,0.000022864686],"about_ca_topic_score_codex":0.000852113,"about_ca_topic_score_gemma":0.0020303058,"teacher_disagreement_score":0.051419165,"about_ca_system_score_codex":0.00048416603,"about_ca_system_score_gemma":0.0005314129,"threshold_uncertainty_score":0.17201424},"labels":[],"label_agreement":null},{"id":"W4387252773","doi":"10.36227/techrxiv.24187617","title":"An Algorithm for Constructing Random Inverses of Non-Square Matrices Across Arbitrary Fields","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Square (algebra); Finite field; Inverse; Square matrix; Mathematics; Construct (python library); Inversion (geology); Matrix (chemical analysis); Linear algebra; Algebra over a field; Algorithm; Discrete mathematics; Computer science; Pure mathematics; Symmetric matrix; Geometry; Physics; Quantum mechanics; Eigenvalues and eigenvectors","score_opus":0.036672468325821134,"score_gpt":0.32929513696859675,"score_spread":0.29262266864277564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387252773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021987709,0.0000462964,0.99380857,0.00010867673,0.000045351186,0.00004315781,0.000058560596,0.001253646,0.0024370565],"genre_scores_gemma":[0.043177098,0.00009856317,0.94998693,0.00011571535,0.00004426359,0.00020993207,0.00023945449,0.00039430268,0.005733684],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994286,0.00010681303,0.000050099756,0.00012471975,0.00023049668,0.000059201804],"domain_scores_gemma":[0.9988356,0.0005288495,0.00010459293,0.0002117814,0.0002564293,0.00006270063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007746661,0.0008279422,0.00071450416,0.0009973381,0.0007025362,0.0011333703,0.0013335833,0.0009782619,0.012600542],"category_scores_gemma":[0.0046495455,0.00047922874,0.00097305275,0.00083818345,0.0010120171,0.001510665,0.0021137826,0.0013010249,0.004896908],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027507462,0.00014460536,0.0011133989,0.00031562854,0.00011029285,0.0005540225,0.00042726906,0.12832686,0.017885698,0.25630754,0.020421559,0.57411814],"study_design_scores_gemma":[0.00013242032,0.00012447331,0.00032706055,0.000076962955,0.00003314542,0.0005581807,0.00009002413,0.7876413,0.015731245,0.17379913,0.021430561,0.00005554827],"about_ca_topic_score_codex":0.0016774861,"about_ca_topic_score_gemma":0.0031282073,"teacher_disagreement_score":0.012600542,"about_ca_system_score_codex":0.0005939147,"about_ca_system_score_gemma":0.0012813348,"threshold_uncertainty_score":0.042153},"labels":[],"label_agreement":null},{"id":"W4387266495","doi":"10.36227/techrxiv.24187617.v1","title":"An Algorithm for Constructing Random Inverses of Non-Square Matrices Across Arbitrary Fields","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Finite field; Inverse; Square (algebra); Square matrix; Construct (python library); Inversion (geology); Mathematics; Matrix (chemical analysis); Linear algebra; Algebra over a field; Field (mathematics); Algorithm; Discrete mathematics; Computer science; Pure mathematics; Symmetric matrix; Geometry; Physics; Eigenvalues and eigenvectors; Quantum mechanics","score_opus":0.036672468325821134,"score_gpt":0.32929513696859675,"score_spread":0.29262266864277564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387266495","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028255235,0.0000538157,0.99393725,0.00011333442,0.000041607764,0.000039240498,0.000042621286,0.00088581716,0.0020607547],"genre_scores_gemma":[0.05859369,0.00012335317,0.935423,0.0001311691,0.000050007253,0.00020002673,0.00019172244,0.00030757362,0.004979527],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99942434,0.000119397264,0.000046547357,0.00012392213,0.00022932749,0.00005645777],"domain_scores_gemma":[0.99880314,0.00055543403,0.000114378636,0.00021448101,0.0002494788,0.00006303229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008374933,0.000721359,0.0006716682,0.00088315527,0.00063325063,0.0010003782,0.0012262504,0.0009561449,0.007976637],"category_scores_gemma":[0.0048369784,0.0004282717,0.0008631845,0.000723898,0.0010309843,0.0014881592,0.0018673838,0.0012522032,0.0030891527],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026063554,0.00014038675,0.001108157,0.0002833714,0.0001080447,0.0004698604,0.00041126835,0.16276558,0.017876301,0.28974482,0.0136327185,0.5131989],"study_design_scores_gemma":[0.00010490314,0.000117163145,0.00028566227,0.00006673686,0.000030377854,0.00047059456,0.000071934104,0.79352885,0.013711869,0.17644179,0.015122841,0.0000472886],"about_ca_topic_score_codex":0.001370471,"about_ca_topic_score_gemma":0.0026720325,"teacher_disagreement_score":0.007976637,"about_ca_system_score_codex":0.0005788628,"about_ca_system_score_gemma":0.0012039806,"threshold_uncertainty_score":0.026684582},"labels":[],"label_agreement":null},{"id":"W4387405241","doi":"10.1142/s021812662450124x","title":"Cascade AdaBoost Neural Network Classifier: Analysis and Design","year":2023,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"AdaBoost; Artificial intelligence; Computer science; Pattern recognition (psychology); Cascade; Classifier (UML); Artificial neural network; Machine learning; Particle swarm optimization; Data mining; Engineering","score_opus":0.0335456159664674,"score_gpt":0.2461191429266601,"score_spread":0.2125735269601927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387405241","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007561074,0.0008368028,0.98777467,0.0001425902,0.00015093727,0.00013580086,0.000043188822,0.000779231,0.0025757907],"genre_scores_gemma":[0.49830624,0.0015661935,0.48816946,0.00032569078,0.00024634533,0.00075938425,0.00039380183,0.00015342647,0.010079495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989774,0.00014880885,0.000056418477,0.00022543795,0.0005025653,0.00008932375],"domain_scores_gemma":[0.99910164,0.00018639168,0.00005001444,0.00004094138,0.0005851166,0.000035851044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013942888,0.0007983532,0.0011052801,0.0011130425,0.0006448867,0.0009248336,0.0016378983,0.0014603512,0.0024049492],"category_scores_gemma":[0.001911622,0.00055476936,0.0007131314,0.0008851286,0.00050355366,0.001312342,0.0004975292,0.001278414,0.0010142492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046466794,0.00023835237,0.0035014634,0.00030506583,0.00018546992,0.00016446224,0.00008343695,0.31808507,0.019224782,0.01231695,0.0067045335,0.63872576],"study_design_scores_gemma":[0.00000742697,0.000057424768,0.00035799175,0.000008074972,0.000017219692,0.000061816,0.0000057122033,0.99428564,0.002817572,0.00092804956,0.0014409185,0.000012118203],"about_ca_topic_score_codex":0.0072966176,"about_ca_topic_score_gemma":0.0054763206,"teacher_disagreement_score":0.0072966176,"about_ca_system_score_codex":0.0011266796,"about_ca_system_score_gemma":0.001410337,"threshold_uncertainty_score":0.014508307},"labels":[],"label_agreement":null},{"id":"W4387418770","doi":"10.1007/978-3-031-40677-5","title":"Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing","year":2023,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Indiana University Bloomington; University of Illinois at Urbana-Champaign; Karl-Franzens-Universität Graz; Queen's University; Technische Universität Wien; Universität Wien; Tamkeen; Technische Universität Dresden; Eidgenössische Technische Hochschule Zürich; Università degli Studi di Padova; Technische Universiteit Eindhoven; Radboud Universiteit; Technische Universiteit Delft; New York University Abu Dhabi; Queen's University Belfast; Drexel University; Arizona State University; Colorado State University; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; York University; Technische Universitat Wien; National University of Sciences and Technology; Purdue University; Yale University; National Science Foundation","keywords":"Cyber-physical system; Computer science; Internet of Things; Enhanced Data Rates for GSM Evolution; Distributed computing; Edge computing; Resource (disambiguation); Artificial intelligence; Software engineering; Data science; Computer architecture; Human–computer interaction; Embedded system; Operating system; Computer network","score_opus":0.024573685880735618,"score_gpt":0.27700625111682936,"score_spread":0.25243256523609375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387418770","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039928686,0.06262835,0.43194604,0.0045674588,0.0066632847,0.0001721487,0.0015920919,0.005852626,0.4825851],"genre_scores_gemma":[0.018344013,0.028346658,0.09836959,0.001063228,0.0008619715,0.00012331267,0.001358378,0.0010643536,0.8504686],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998374,0.000009830878,0.000004727761,0.000025339026,0.00011148438,0.00001117707],"domain_scores_gemma":[0.99982363,0.000078359175,0.000008229245,0.0000238157,0.000050701165,0.000015365285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015094792,0.0008891828,0.0006030992,0.00059236406,0.00034963904,0.0018626403,0.0009651064,0.000707522,0.051419165],"category_scores_gemma":[0.0006812568,0.00048600585,0.0004983442,0.0012354825,0.00034581107,0.0020281784,0.0007132772,0.0016375531,0.024566218],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048548758,0.000056026878,0.00015448817,0.00050791615,0.000033068754,0.00008896605,0.00006514304,0.014296333,0.0060073594,0.0886966,0.34667042,0.5433752],"study_design_scores_gemma":[0.000008850509,0.00003441065,0.00040173632,0.00020611056,0.000013089389,0.0002523531,0.000026131696,0.037906032,0.002590733,0.053656556,0.9048811,0.000022864686],"about_ca_topic_score_codex":0.000852113,"about_ca_topic_score_gemma":0.0020303058,"teacher_disagreement_score":0.051419165,"about_ca_system_score_codex":0.00048416603,"about_ca_system_score_gemma":0.0005314129,"threshold_uncertainty_score":0.17201424},"labels":[],"label_agreement":null},{"id":"W4387730810","doi":"10.36227/techrxiv.24314065.v1","title":"Explainable Fault Diagnosis Using Invertible Neural Networks-Part I: A Left Manifold-based Solution","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Invertible matrix; Manifold (fluid mechanics); Artificial neural network; Fault (geology); Series (stratigraphy); Nonlinear system; Control theory (sociology); Control (management); Computer science; Mathematics; Pure mathematics; Artificial intelligence; Engineering; Physics; Mechanical engineering; Geology","score_opus":0.08069093488015534,"score_gpt":0.28702423863818094,"score_spread":0.2063333037580256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387730810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012266189,0.00021882563,0.9835218,0.0009962162,0.000071479124,0.000015668807,0.000031573116,0.0000932117,0.002785094],"genre_scores_gemma":[0.75433016,0.0007816483,0.23617534,0.00035896082,0.0003280714,0.000077025026,0.00013692449,0.00007831599,0.0077335676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998405,0.000035239704,0.00000896295,0.00005082043,0.000044546647,0.000019904299],"domain_scores_gemma":[0.99969316,0.00016233783,0.000039737362,0.000041199637,0.000047891288,0.000015749492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035134077,0.00039291897,0.0003549615,0.00035312522,0.00025725111,0.00054307404,0.000562593,0.0009274119,0.0020717394],"category_scores_gemma":[0.0015925749,0.0001424563,0.000508079,0.00024248377,0.00087929075,0.0009140579,0.0009400057,0.0012169303,0.00017745742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005705315,0.00003034859,0.0009633552,0.00014896544,0.000043837044,0.00031085016,0.00020156072,0.5503088,0.008329745,0.34597358,0.0025669755,0.09106484],"study_design_scores_gemma":[0.0000045424104,0.00001795914,0.00014814173,0.000008871329,0.0000060220505,0.000058203932,0.000020372956,0.86267793,0.0014238073,0.13370498,0.00192224,0.0000068195254],"about_ca_topic_score_codex":0.0017535139,"about_ca_topic_score_gemma":0.0015972499,"teacher_disagreement_score":0.0020717394,"about_ca_system_score_codex":0.0006343757,"about_ca_system_score_gemma":0.00044181023,"threshold_uncertainty_score":0.0069306493},"labels":[],"label_agreement":null},{"id":"W4387730822","doi":"10.36227/techrxiv.24314065","title":"Explainable Fault Diagnosis Using Invertible Neural Networks-Part I: A Left Manifold-based Solution","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Invertible matrix; Manifold (fluid mechanics); Artificial neural network; Fault (geology); Series (stratigraphy); Control theory (sociology); Nonlinear system; Control (management); Computer science; Mathematics; Artificial intelligence; Pure mathematics; Engineering; Physics; Mechanical engineering","score_opus":0.08069093488015534,"score_gpt":0.28702423863818094,"score_spread":0.2063333037580256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387730822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013640189,0.00024227357,0.98233676,0.0010565842,0.00006742704,0.000016170608,0.000036471767,0.00012073777,0.0024833712],"genre_scores_gemma":[0.7675211,0.0007227868,0.22295535,0.0003638052,0.00029703195,0.00007126381,0.00015709132,0.000082605206,0.007828979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998505,0.000035792033,0.000008096583,0.000049163293,0.000037500682,0.00001888388],"domain_scores_gemma":[0.99965036,0.00020150722,0.000044921708,0.000040109844,0.00004696096,0.000016191105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033853814,0.00038097653,0.00036511142,0.00036391086,0.00024606433,0.0005456528,0.0005582769,0.00090499496,0.0022402934],"category_scores_gemma":[0.0016837319,0.00015647663,0.0005268114,0.00025956528,0.00084308407,0.0009093994,0.00089059153,0.0012103636,0.00018030406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006235218,0.000031818385,0.00095373444,0.00013706343,0.000049813476,0.00027768864,0.00018756339,0.6396782,0.007691437,0.24282733,0.0027243001,0.10537866],"study_design_scores_gemma":[0.000004170212,0.00001404273,0.00014139846,0.0000066153375,0.000005486536,0.000036873047,0.000016908392,0.9102422,0.0010099458,0.087279335,0.0012371785,0.0000059067415],"about_ca_topic_score_codex":0.0022583618,"about_ca_topic_score_gemma":0.0019329239,"teacher_disagreement_score":0.0022583618,"about_ca_system_score_codex":0.0006143683,"about_ca_system_score_gemma":0.00045914855,"threshold_uncertainty_score":0.007494569},"labels":[],"label_agreement":null},{"id":"W4387773665","doi":"10.21065/advengtech.7.1","title":"AWEIGHTED BINOMIALLINDLEYDISTRIBUTIONFORDISCRETEDATA","year":2022,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Psychology","score_opus":0.009898624740869505,"score_gpt":0.2096907730227151,"score_spread":0.1997921482818456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387773665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009222124,0.0018890736,0.9259734,0.00089539593,0.0037324748,0.004400615,0.029148815,0.014401285,0.010336813],"genre_scores_gemma":[0.06805435,0.0025368393,0.74004626,0.00096476544,0.0010096625,0.015187514,0.04700062,0.011854144,0.11334581],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97263867,0.009660924,0.0023183995,0.005850344,0.0075745042,0.0019571378],"domain_scores_gemma":[0.9322361,0.03201348,0.0025538926,0.022793174,0.0090975305,0.0013058697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03216474,0.003483416,0.0053499024,0.00698135,0.003074551,0.0061287093,0.007077762,0.0031522408,0.22426653],"category_scores_gemma":[0.09684029,0.002652985,0.0053590266,0.009886953,0.0039986027,0.005441499,0.004845766,0.008511508,0.056441028],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002381639,0.00066787744,0.012863791,0.0031639412,0.0013939077,0.0010053532,0.0007241781,0.017553255,0.0025666875,0.080326155,0.26959425,0.607759],"study_design_scores_gemma":[0.0009741342,0.00079332653,0.01522112,0.0019471456,0.0008251144,0.0017407374,0.00072047894,0.18400313,0.008032064,0.13474192,0.6504081,0.0005927171],"about_ca_topic_score_codex":0.031732466,"about_ca_topic_score_gemma":0.03911136,"teacher_disagreement_score":0.22426653,"about_ca_system_score_codex":0.0049060527,"about_ca_system_score_gemma":0.012210617,"threshold_uncertainty_score":0.7502463},"labels":[],"label_agreement":null},{"id":"W4387820447","doi":"10.31219/osf.io/z7c98","title":"A Neuro-Mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Cisco Systems","keywords":"Generative grammar; Computer science; Artificial intelligence; Hebbian theory; Cognitive science; Field (mathematics); Cognition; Generative model; Realization (probability); Artificial neural network; Cognitive architecture; Representation (politics); Generative Design; Psychology; Engineering; Mathematics","score_opus":0.03132563661859662,"score_gpt":0.24911612508410605,"score_spread":0.2177904884655094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387820447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025693085,0.0005633522,0.94945157,0.0020592355,0.00007690901,0.000022071017,0.000046471836,0.00020639943,0.021880774],"genre_scores_gemma":[0.819873,0.0006860274,0.1671589,0.00036960645,0.00011250074,0.000092973605,0.00005459614,0.000117335454,0.011535033],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99964714,0.00014692456,0.000011949262,0.000076356315,0.000086265485,0.0000313577],"domain_scores_gemma":[0.9996069,0.00019138664,0.000037692305,0.00008034456,0.000045072135,0.000038629674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081677706,0.00035690106,0.0005521644,0.0004236238,0.00056094385,0.0012776216,0.0017726886,0.0014771804,0.0037416504],"category_scores_gemma":[0.0025569447,0.0003030283,0.00056206214,0.0004087209,0.003289876,0.0025040216,0.0014339895,0.0017567129,0.00046586007],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002029426,0.000016216884,0.00017005128,0.00003638826,0.000028952047,0.000054998054,0.00015195357,0.12378629,0.0017776869,0.86115295,0.00085310824,0.011951147],"study_design_scores_gemma":[0.000007425357,0.000013798005,0.00010907176,0.0000073215547,0.000005702109,0.000031095886,0.000014739131,0.33765748,0.00042322584,0.66022646,0.0014939292,0.000009812639],"about_ca_topic_score_codex":0.0025176061,"about_ca_topic_score_gemma":0.0029716585,"teacher_disagreement_score":0.0037416504,"about_ca_system_score_codex":0.0010385084,"about_ca_system_score_gemma":0.0007963732,"threshold_uncertainty_score":0.012517095},"labels":[],"label_agreement":null},{"id":"W4387951222","doi":"10.1109/ccece58730.2023.10288905","title":"An Efficient VHDL Implementation of two Artificial Neural Networks on Zynq-7000 FPGA","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Field-programmable gate array; VHDL; MATLAB; Artificial neural network; Computer science; Network topology; Embedded system; Artificial intelligence; Computer hardware; Operating system","score_opus":0.031246932074126394,"score_gpt":0.3429055601024421,"score_spread":0.31165862802831573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12594184,0.0006107399,0.8206892,0.00022877872,0.0002607846,0.00024433396,0.0008291741,0.017429674,0.033765532],"genre_scores_gemma":[0.6968162,0.00029005247,0.2847243,0.0001448498,0.000028835502,0.00027342868,0.0011585858,0.00045446306,0.016109249],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979347,0.00003434273,0.000019817902,0.000029964436,0.00008059217,0.000041922387],"domain_scores_gemma":[0.9998485,0.00003801726,0.000013811275,0.000022247783,0.00006845745,0.000008929824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020882176,0.0005950584,0.00023192202,0.00034066636,0.00020394358,0.00061595516,0.0007102124,0.00022896816,0.0073267627],"category_scores_gemma":[0.00041164126,0.0002187631,0.00019548094,0.0002616629,0.00012234072,0.00039430018,0.00020001785,0.0004036208,0.0014459898],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013286048,0.00026054346,0.0026164763,0.0012763526,0.00016795917,0.001110381,0.00040491536,0.24326684,0.16809179,0.025917597,0.01687646,0.5386822],"study_design_scores_gemma":[0.00030102945,0.00064879464,0.0023345493,0.00011607624,0.00007966854,0.0007571641,0.00010561456,0.70107466,0.2367459,0.0041078376,0.053665448,0.000063234926],"about_ca_topic_score_codex":0.002030957,"about_ca_topic_score_gemma":0.002276856,"teacher_disagreement_score":0.0073267627,"about_ca_system_score_codex":0.0004861743,"about_ca_system_score_gemma":0.000562001,"threshold_uncertainty_score":0.024510443},"labels":[],"label_agreement":null},{"id":"W4388107886","doi":"10.1109/tim.2023.3328685","title":"Identification of Cellular Measurements: A Neural Network Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Allen-Vanguard (Canada); Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation of Korea","keywords":"Computer science; Bandwidth (computing); Cellular network; Artificial neural network; Wireless; Radio spectrum; Electronic engineering; Convolutional neural network; Frequency band; Identification (biology); Spectral density; Artificial intelligence; Telecommunications; Engineering","score_opus":0.06649960039320796,"score_gpt":0.26052501379407994,"score_spread":0.19402541340087198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388107886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074244075,0.0013397068,0.9190683,0.0004085869,0.00011809376,0.00006678428,0.00033867324,0.00065035565,0.0037654722],"genre_scores_gemma":[0.9158277,0.00079862645,0.077129826,0.0001665537,0.000114065086,0.00012017975,0.0005788318,0.000032840348,0.0052314005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996935,0.000057180798,0.000024239529,0.00010752334,0.000067547095,0.00005008456],"domain_scores_gemma":[0.99955446,0.0002094805,0.000055278808,0.000038432237,0.00012873992,0.000013661941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005407275,0.0007884913,0.00050315785,0.00085081614,0.00024973086,0.0007144242,0.0008439409,0.0010636831,0.0009270219],"category_scores_gemma":[0.0017645344,0.00028133206,0.0004003452,0.00080719584,0.00032019935,0.000685044,0.00051686534,0.00094137073,0.00033494053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116933246,0.00008992762,0.0057799993,0.00008866422,0.000091010574,0.00016238661,0.00006300776,0.7781476,0.0044230255,0.0033736003,0.0010283935,0.20663552],"study_design_scores_gemma":[0.0000012257059,0.000012087379,0.0007339348,0.0000067060646,0.0000065922695,0.000018998913,0.000009982197,0.9972499,0.0006601549,0.0010546877,0.00024126252,0.0000044453577],"about_ca_topic_score_codex":0.0110406075,"about_ca_topic_score_gemma":0.007748447,"teacher_disagreement_score":0.0110406075,"about_ca_system_score_codex":0.0006176762,"about_ca_system_score_gemma":0.00042228782,"threshold_uncertainty_score":0.021952689},"labels":[],"label_agreement":null},{"id":"W4388272969","doi":"10.1016/j.jcjd.2023.10.309","title":"EXTERNAL VALIDATION OF AI PRIMARY GRAFT DYSFUNCTION PREDICTION CALCULATOR","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Diabetes","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Public Health","funders":"","keywords":"Medicine; Calculator; Incidence (geometry); Multicenter study; Population; Internal medicine; Intensive care medicine; Environmental health","score_opus":0.01118125592029004,"score_gpt":0.2119566501347582,"score_spread":0.20077539421446816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388272969","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92684776,0.0007907221,0.05326358,0.00043636825,0.00053354044,0.00061893696,0.006719824,0.0026351146,0.008154217],"genre_scores_gemma":[0.9723697,0.00008143883,0.014568194,0.00023357007,0.00010995548,0.00043556202,0.009036678,0.00019751144,0.0029672936],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9909689,0.004671657,0.0005538944,0.0018646806,0.0015675514,0.0003733327],"domain_scores_gemma":[0.9649031,0.01819297,0.0014369,0.005933533,0.008796834,0.0007366974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019241933,0.0012024101,0.000901588,0.0016933333,0.0008195087,0.0019167901,0.0017749987,0.0016774286,0.004066857],"category_scores_gemma":[0.04174542,0.00032113178,0.0009830573,0.0009773052,0.0010317409,0.0007974367,0.0018730343,0.0013320753,0.0034836475],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0137964515,0.0032469693,0.602653,0.0006270924,0.0030458688,0.00055648305,0.0008653797,0.06738199,0.017197624,0.0028935135,0.027382132,0.26035336],"study_design_scores_gemma":[0.0018559232,0.0029301539,0.33651787,0.00033860528,0.0018474858,0.0011608778,0.00036440592,0.58011085,0.04650349,0.0035576492,0.02464939,0.00016327522],"about_ca_topic_score_codex":0.0032859317,"about_ca_topic_score_gemma":0.0024318,"teacher_disagreement_score":0.019241933,"about_ca_system_score_codex":0.00068296515,"about_ca_system_score_gemma":0.0014540753,"threshold_uncertainty_score":0.101762235},"labels":[],"label_agreement":null},{"id":"W4388469871","doi":"10.1137/1.9781611977509.ch7","title":"CHAPTER SEVEN: CHAOS GAME REPRESENTATION","year":2023,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Spurious relationship; Computer science; Representation (politics); Path (computing); Artificial intelligence; Cognitive science; Psychology; Machine learning; Programming language","score_opus":0.11362085571924797,"score_gpt":0.2727704026849453,"score_spread":0.15914954696569733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388469871","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029172956,0.011079898,0.11146354,0.004858319,0.0019165097,0.00014760248,0.0006681332,0.0009091834,0.8660396],"genre_scores_gemma":[0.07645627,0.014013742,0.047131334,0.0012581835,0.0010191223,0.00027596668,0.0010925389,0.0006017233,0.8581512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999175,0.000016100414,0.000003952936,0.000017179582,0.00003543708,0.000009860456],"domain_scores_gemma":[0.9999244,0.000030851294,0.0000035382468,0.0000110905885,0.000018947623,0.000011119947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001271463,0.0005744009,0.00030667082,0.00041495723,0.00064767164,0.0017302486,0.0006609027,0.00073483825,0.0644842],"category_scores_gemma":[0.00059294613,0.00021257864,0.0003615885,0.0004966134,0.0006670054,0.0015243443,0.0006804363,0.0015816657,0.013911783],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021109652,0.00004921885,0.00009326175,0.00019275435,0.000008241006,0.00007169609,0.00024113842,0.006478401,0.0008099581,0.6887698,0.1892639,0.11400059],"study_design_scores_gemma":[0.0000062281083,0.000020097143,0.00013144988,0.00020811259,0.0000045286474,0.00016669343,0.000094143346,0.009277665,0.00075229723,0.27055776,0.7187682,0.000012972573],"about_ca_topic_score_codex":0.0014815129,"about_ca_topic_score_gemma":0.0024522382,"teacher_disagreement_score":0.0644842,"about_ca_system_score_codex":0.0011738524,"about_ca_system_score_gemma":0.000534119,"threshold_uncertainty_score":0.21572119},"labels":[],"label_agreement":null},{"id":"W4388477310","doi":"10.18280/ria.370501","title":"Developed a Hybrid Bipolar Sigmoid-Recurrent Neural Network with Karush-Kuhn-Tucker- Arithmetic Optimization Algorithm to Predict the Heart Disease","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sigmoid function; Artificial neural network; Karush–Kuhn–Tucker conditions; Algorithm; Arithmetic; Mathematics; Computer science; Artificial intelligence; Mathematical optimization","score_opus":0.030548006150309323,"score_gpt":0.26297295633798684,"score_spread":0.2324249501876775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388477310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06581365,0.0012473196,0.9278178,0.00023485463,0.00013960188,0.00009970048,0.00020996132,0.0010728897,0.0033642065],"genre_scores_gemma":[0.76663476,0.000734336,0.22414072,0.00020156469,0.000071215465,0.000252179,0.00075545954,0.00007412006,0.007135606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997862,0.000044230048,0.000024906776,0.000058440823,0.00005626667,0.000029964702],"domain_scores_gemma":[0.9997805,0.00008088642,0.000018333747,0.000012420125,0.00009545448,0.0000123851605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006759056,0.00086361193,0.0006986773,0.00047824075,0.00031793985,0.000495905,0.00086559413,0.0008129168,0.0011069915],"category_scores_gemma":[0.0011642172,0.00033371276,0.0007684858,0.0004377369,0.00018653143,0.00059293455,0.00048421748,0.00089070265,0.00043283837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020123721,0.00015178062,0.005123547,0.00009369294,0.00023174768,0.00017438082,0.00007773272,0.6936451,0.0057966164,0.0034422725,0.0022580044,0.2888038],"study_design_scores_gemma":[0.000004710098,0.000032384924,0.00023280682,0.0000032653877,0.000011277676,0.00001193359,0.000003301328,0.99851686,0.00062409404,0.00031797137,0.00023684133,0.000004441728],"about_ca_topic_score_codex":0.01594787,"about_ca_topic_score_gemma":0.013442823,"teacher_disagreement_score":0.01594787,"about_ca_system_score_codex":0.00050529616,"about_ca_system_score_gemma":0.0007859288,"threshold_uncertainty_score":0.03171009},"labels":[],"label_agreement":null},{"id":"W4388481381","doi":"10.48550/arxiv.2106.06401","title":"Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"Asynchronous communication; Computer science; Asynchronous learning; Greedy algorithm; Artificial intelligence; Machine learning; Algorithm; Computer network; Mathematics; Synchronous learning; Cooperative learning; Mathematics education; Teaching method","score_opus":0.03723864034099048,"score_gpt":0.19020645619835277,"score_spread":0.1529678158573623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388481381","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026635202,0.00028971815,0.96842176,0.0003330283,0.00007055011,0.00006775395,0.00008547969,0.0012440004,0.0028525123],"genre_scores_gemma":[0.73044777,0.00021960742,0.26153597,0.0003247129,0.00009684493,0.00033130587,0.00025070866,0.00025504685,0.00653814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999373,0.00015463274,0.000030918585,0.00019493385,0.00014223024,0.000104193015],"domain_scores_gemma":[0.99895763,0.0005058424,0.00009456775,0.00021116993,0.00014562132,0.000085175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013931546,0.0009946114,0.0009010281,0.00039450303,0.00042434322,0.0009484713,0.0021426945,0.001232645,0.0036884241],"category_scores_gemma":[0.0042228163,0.00055961247,0.000515923,0.00051576813,0.0011278029,0.0018843559,0.0017883615,0.0013793075,0.0006720351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026485097,0.00010086247,0.0006460485,0.000091502196,0.000049464372,0.00011053284,0.00007818034,0.8825871,0.0049735364,0.032465167,0.002920482,0.075712286],"study_design_scores_gemma":[0.000018909976,0.00001581326,0.0000366741,0.00000258768,0.0000035191538,0.0000082003935,0.000004366117,0.9901905,0.0005846217,0.008850714,0.00028150252,0.0000025766135],"about_ca_topic_score_codex":0.0037131468,"about_ca_topic_score_gemma":0.007221785,"teacher_disagreement_score":0.0037131468,"about_ca_system_score_codex":0.0013481048,"about_ca_system_score_gemma":0.0017679681,"threshold_uncertainty_score":0.012338996},"labels":[],"label_agreement":null},{"id":"W4388508710","doi":"10.36227/techrxiv.24495202","title":"Subspace Rotation Algorithm for Implementing Restricted Hopfield Network as an Auto-Associative Memory","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Bidirectional associative memory; Subspace topology; Associative property; Content-addressable memory; Hopfield network; Computer science; Algorithm; Rotation (mathematics); Artificial intelligence; Artificial neural network; Arithmetic; Mathematics; Pure mathematics","score_opus":0.04923299765983189,"score_gpt":0.32939237635707264,"score_spread":0.2801593786972407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388508710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010830327,0.00037617964,0.98356736,0.000119972385,0.000109532266,0.00003374106,0.000052398216,0.0008273194,0.004083185],"genre_scores_gemma":[0.39411345,0.0007127295,0.5914625,0.00019217444,0.00012031348,0.00020812388,0.0002987937,0.00018221555,0.012709668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998863,0.000030884352,0.0000078846215,0.000024858437,0.00003310956,0.00001690734],"domain_scores_gemma":[0.99990773,0.000016492535,0.000006921557,0.000024437906,0.00003497572,0.000009544201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022095202,0.0003051522,0.00032813378,0.00023456574,0.00026660113,0.00041322567,0.00065310276,0.0003670018,0.0056034643],"category_scores_gemma":[0.00043155928,0.00013414523,0.00030281226,0.00039970796,0.00033720394,0.00093037373,0.00046281706,0.00056161574,0.0012754318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002888741,0.00008329541,0.0004723833,0.00016589902,0.0000964259,0.00009830015,0.00009544985,0.2393156,0.03911469,0.11580262,0.008839046,0.5956274],"study_design_scores_gemma":[0.000032232558,0.00009940302,0.00014325645,0.000011525185,0.000016586931,0.00006997699,0.000016372564,0.95131695,0.014396945,0.026747629,0.007130179,0.000019011897],"about_ca_topic_score_codex":0.0025971308,"about_ca_topic_score_gemma":0.002645642,"teacher_disagreement_score":0.0056034643,"about_ca_system_score_codex":0.00024101461,"about_ca_system_score_gemma":0.0005169548,"threshold_uncertainty_score":0.018745422},"labels":[],"label_agreement":null},{"id":"W4388509231","doi":"10.36227/techrxiv.24495202.v1","title":"Subspace Rotation Algorithm for Training Restricted Hopfield Network","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Subspace topology; Content-addressable memory; Rotation (mathematics); Bidirectional associative memory; Associative property; Algorithm; Hopfield network; Computer science; Arithmetic; Artificial intelligence; Artificial neural network; Mathematics; Pure mathematics","score_opus":0.09104006936496757,"score_gpt":0.30912251416571096,"score_spread":0.2180824448007434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388509231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012713663,0.0003229942,0.9841022,0.00006397313,0.000041723742,0.000056656194,0.000058315676,0.0007779069,0.0018627065],"genre_scores_gemma":[0.43549708,0.0005492602,0.55745083,0.000114987706,0.00004704581,0.00038752804,0.0005695951,0.00019004228,0.005193726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997297,0.00007883684,0.000021642576,0.000068246794,0.000065072,0.00003650675],"domain_scores_gemma":[0.9996191,0.00012375908,0.000032041153,0.00005823768,0.00014879584,0.000017995928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006315606,0.0007730953,0.0006090077,0.0005490897,0.00039531998,0.00047165112,0.0008566407,0.00063218054,0.0034609905],"category_scores_gemma":[0.0018769759,0.000308779,0.00056536,0.0006324044,0.00043261726,0.00094434194,0.0005449675,0.00085815456,0.000856526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081370104,0.00004526683,0.00083076226,0.00009669365,0.000059233986,0.0000498862,0.000083674124,0.6453601,0.007817631,0.008532131,0.0019867003,0.33505657],"study_design_scores_gemma":[0.000006521444,0.000024085146,0.00008766984,0.0000050569633,0.0000048643615,0.000015133573,0.0000061131163,0.9953719,0.001639799,0.0020653228,0.0007680879,0.000005465942],"about_ca_topic_score_codex":0.008461703,"about_ca_topic_score_gemma":0.0067047174,"teacher_disagreement_score":0.008461703,"about_ca_system_score_codex":0.0004290383,"about_ca_system_score_gemma":0.0010562143,"threshold_uncertainty_score":0.016824901},"labels":[],"label_agreement":null},{"id":"W4388522656","doi":"10.1088/978-0-7503-5097-6ch14","title":"Other neural codes","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science","score_opus":0.04364591997154346,"score_gpt":0.25365807302420945,"score_spread":0.210012153052666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388522656","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003139818,0.018237999,0.07347505,0.0020119015,0.0021884374,0.000057867906,0.0006968559,0.0005935987,0.8995984],"genre_scores_gemma":[0.085865885,0.026860839,0.06943262,0.0031310099,0.0017505331,0.00022399281,0.0013380703,0.00075809186,0.81063896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997708,0.000021447771,0.000009302846,0.000050108592,0.00012434268,0.000024070128],"domain_scores_gemma":[0.99978405,0.00008185766,0.000009869196,0.00004262946,0.000066158354,0.000015460999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017877447,0.00057320675,0.00037362732,0.0011782101,0.0007748743,0.002051672,0.00069641153,0.0010999402,0.0421588],"category_scores_gemma":[0.001024004,0.00020574144,0.0004516149,0.0010922238,0.0013046564,0.0024888122,0.00084847194,0.0015400815,0.01492718],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013749005,0.000016661257,0.00006195097,0.00020375845,0.0000092193495,0.000064970765,0.0001315172,0.0015379937,0.0013971629,0.83255,0.045183394,0.11882964],"study_design_scores_gemma":[0.0000048876714,0.000010835049,0.000100377256,0.00012442238,0.0000045704196,0.00021725244,0.00003666897,0.0021249733,0.0013328578,0.35154998,0.64447945,0.000013695753],"about_ca_topic_score_codex":0.0012051471,"about_ca_topic_score_gemma":0.0018039759,"teacher_disagreement_score":0.0421588,"about_ca_system_score_codex":0.0012440041,"about_ca_system_score_gemma":0.00054422027,"threshold_uncertainty_score":0.14103526},"labels":[],"label_agreement":null},{"id":"W4388823921","doi":"10.1103/physrevx.13.041033","title":"Learning Interacting Theories from Data","year":2023,"lang":"en","type":"article","venue":"Physical Review X","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Jülich Supercomputing Centre, Forschungszentrum Jülich","keywords":"Computer science; Artificial neural network; Work (physics); Complex system; Data science; Statistical physics; Artificial intelligence; Physics; Quantum mechanics","score_opus":0.06544884570926958,"score_gpt":0.3846265364239375,"score_spread":0.31917769071466795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388823921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053202394,0.0037393775,0.927547,0.0055944207,0.00031796243,0.0002826761,0.0025896686,0.0017786061,0.0049478128],"genre_scores_gemma":[0.6383407,0.004935556,0.33675852,0.0020638602,0.0010422353,0.0010323431,0.012427271,0.0003774802,0.0030219893],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963404,0.0017077866,0.00025825234,0.00073734665,0.00083759986,0.000118695345],"domain_scores_gemma":[0.9752104,0.020478196,0.0006737594,0.0025434203,0.0007626209,0.00033154158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050797747,0.0016256975,0.0019617567,0.004594132,0.0008440687,0.003530269,0.003027658,0.0022243105,0.0044102725],"category_scores_gemma":[0.039302155,0.0011901463,0.0021716685,0.0028774051,0.0025642554,0.008062823,0.0045931004,0.0050544557,0.001449039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065084477,0.00064721546,0.016616717,0.0023183336,0.0013385662,0.0011461643,0.001359215,0.23965256,0.002563341,0.37905598,0.021050781,0.3336003],"study_design_scores_gemma":[0.000060457132,0.000043924192,0.00057902216,0.00014700452,0.000076598124,0.00009379979,0.00013722913,0.42246157,0.000529047,0.57165486,0.004188423,0.00002808238],"about_ca_topic_score_codex":0.0016029578,"about_ca_topic_score_gemma":0.0021631564,"teacher_disagreement_score":0.0050797747,"about_ca_system_score_codex":0.001155237,"about_ca_system_score_gemma":0.0013178946,"threshold_uncertainty_score":0.026864767},"labels":[],"label_agreement":null},{"id":"W4388854508","doi":"10.1109/access.2023.3335841","title":"On the Effectiveness of Activation Noise in Both Training and Inference for Generative Classifiers","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); Queen's University","funders":"Semnan University; Huawei Technologies; Queen's University; Amirkabir University of Technology; Yonsei University; Korea Advanced Institute of Science and Technology; Harvard University","keywords":"Inference; Computer science; Noise (video); Artificial intelligence; Autoencoder; Regularization (linguistics); Machine learning; Pattern recognition (psychology); Artificial neural network","score_opus":0.09950281199325181,"score_gpt":0.35303688163328606,"score_spread":0.2535340696400342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388854508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07813894,0.0015148856,0.9127091,0.0017052469,0.00007729353,0.0000882528,0.00007129944,0.0006537643,0.0050412007],"genre_scores_gemma":[0.81432164,0.0009979641,0.18045673,0.00084264274,0.00015712154,0.00019993268,0.00025890808,0.00032047593,0.0024444922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934029,0.0036468825,0.00032930612,0.0011786451,0.0010329621,0.00040922975],"domain_scores_gemma":[0.95398235,0.039910465,0.0011603681,0.003016891,0.0014437371,0.0004862303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0149038015,0.0019358202,0.0013657218,0.0009192598,0.0012501832,0.0022455468,0.0020517665,0.003190338,0.0020293728],"category_scores_gemma":[0.06811221,0.0010281007,0.0010918482,0.0006233741,0.0038344937,0.004820032,0.003807497,0.0042994004,0.0006270246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076999096,0.00022268202,0.0062347623,0.00018640359,0.0002050109,0.00014241989,0.00036369596,0.8022367,0.007169016,0.057400785,0.0010604312,0.12400812],"study_design_scores_gemma":[0.000018388457,0.00013296236,0.0007236643,0.00004900255,0.000030517072,0.000062133,0.00002765351,0.97844636,0.004176993,0.015937082,0.00037826013,0.000016935848],"about_ca_topic_score_codex":0.005304478,"about_ca_topic_score_gemma":0.0053984215,"teacher_disagreement_score":0.0149038015,"about_ca_system_score_codex":0.0017331402,"about_ca_system_score_gemma":0.002354207,"threshold_uncertainty_score":0.07881975},"labels":[],"label_agreement":null},{"id":"W4389072212","doi":"10.48550/arxiv.2311.13799","title":"Thermodynamics based on Neural Networks","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; Bergische Universität Wuppertal","keywords":"TRACE (psycholinguistics); Artificial neural network; Operator (biology); Quantum; Statistical physics; Lattice (music); Imaginary time; Computer science; Physics; Quantum mechanics; Artificial intelligence; Chemistry; Quantum statistical mechanics","score_opus":0.07581459053627741,"score_gpt":0.190135511792981,"score_spread":0.11432092125670358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389072212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05139891,0.0010009507,0.9308589,0.0006364244,0.00014809007,0.0000470713,0.00019966923,0.0003981841,0.0153118875],"genre_scores_gemma":[0.88303185,0.0011100649,0.103446074,0.00024658337,0.00018737312,0.0002187597,0.00027159424,0.0002727285,0.011214974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996668,0.000108657034,0.000015832144,0.00005501993,0.00011491738,0.000038753904],"domain_scores_gemma":[0.9992275,0.00041828008,0.000062344385,0.00008775901,0.00015753804,0.000046544494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060313713,0.00047839235,0.0006924724,0.0009583553,0.0005501238,0.0011590162,0.0013308979,0.00084436836,0.0029688242],"category_scores_gemma":[0.0034842223,0.0004047918,0.000745398,0.00070461846,0.001171976,0.00257509,0.0012349802,0.0011919269,0.00046206327],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022919452,0.000023585779,0.00050898496,0.00006269025,0.000032660344,0.00004009934,0.000040740913,0.78498584,0.0013007454,0.1965093,0.0006706662,0.015801826],"study_design_scores_gemma":[0.0000012750759,0.000002052346,0.00006292064,0.0000037198572,0.0000017774689,0.0000052397645,0.0000019784331,0.9536846,0.0002448175,0.045730144,0.00025713234,0.0000043300574],"about_ca_topic_score_codex":0.0043894346,"about_ca_topic_score_gemma":0.004023768,"teacher_disagreement_score":0.0043894346,"about_ca_system_score_codex":0.0014066121,"about_ca_system_score_gemma":0.00077701715,"threshold_uncertainty_score":0.010205746},"labels":[],"label_agreement":null},{"id":"W4389339766","doi":"10.1137/22m1542313","title":"Deep Neural Networks Pruning via the Structured Perspective Regularization","year":2023,"lang":"en","type":"article","venue":"SIAM Journal on Mathematics of Data Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regularization (linguistics); Artificial neural network; Artificial intelligence; Deep neural networks; Pruning; Perspective (graphical); Computer science; Mathematics; Machine learning; Biology; Botany","score_opus":0.03930905499830477,"score_gpt":0.3110639967382346,"score_spread":0.2717549417399298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389339766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012590347,0.00048247582,0.9839903,0.000309297,0.000040503906,0.00003588187,0.00006349314,0.00021221557,0.0022755435],"genre_scores_gemma":[0.3776569,0.0013396174,0.6124099,0.00072372815,0.0002594839,0.00031398213,0.0006948833,0.00037390066,0.006227709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907756,0.00034145138,0.000037571885,0.000117750176,0.0003394141,0.000086227104],"domain_scores_gemma":[0.9987851,0.00064849464,0.00015281342,0.0001456109,0.00020860045,0.00005951188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015191054,0.0013400976,0.0010288801,0.0007273247,0.00035767426,0.00090642885,0.001452557,0.001330337,0.0019993484],"category_scores_gemma":[0.0039122063,0.00038045336,0.0010712658,0.00074673566,0.0011219617,0.001645907,0.0016145625,0.002753843,0.00042133042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002115665,0.00016898337,0.0009892101,0.00024309795,0.00011681338,0.0003740362,0.00017637336,0.6382097,0.013500898,0.15692456,0.01046801,0.17861678],"study_design_scores_gemma":[0.000018900304,0.000043864216,0.000103754,0.000024661322,0.000011494782,0.000050537506,0.000011118474,0.9697777,0.0019115731,0.026720213,0.0013191149,0.0000071484574],"about_ca_topic_score_codex":0.0015009664,"about_ca_topic_score_gemma":0.0023544838,"teacher_disagreement_score":0.0019993484,"about_ca_system_score_codex":0.00081075967,"about_ca_system_score_gemma":0.0010912982,"threshold_uncertainty_score":0.008033872},"labels":[],"label_agreement":null},{"id":"W4389443298","doi":"10.1007/s00422-023-00979-4","title":"Four attributes of intelligence, a thousand questions","year":2023,"lang":"en","type":"editorial","venue":"Biological Cybernetics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"Mount Royal University","keywords":"Artificial intelligence; Simple (philosophy); Computer science; Cognitive science; Human intelligence; Complex system; Machine learning; Psychology; Epistemology; Philosophy","score_opus":0.0583374873585234,"score_gpt":0.30117075083323447,"score_spread":0.24283326347471107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389443298","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000044179043,0.018043458,0.00024310971,0.17776774,0.801593,0.0000062545837,0.00003283803,0.00002818233,0.0022412234],"genre_scores_gemma":[0.0009573834,0.00908809,0.00019835195,0.057486895,0.9225544,0.000023843275,0.000027456452,0.000038850296,0.009624844],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932672,0.0015407829,0.000729658,0.00057926617,0.0035070595,0.00037613613],"domain_scores_gemma":[0.9574126,0.026693419,0.0011134829,0.0011329349,0.010113689,0.0035338188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010359329,0.002151025,0.002663123,0.004034884,0.0041114734,0.012542451,0.0028014025,0.01750307,0.007586125],"category_scores_gemma":[0.040615767,0.001095919,0.0014961183,0.0021051653,0.0088266935,0.008937785,0.0022024265,0.030143527,0.0047199572],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019019935,0.000006840833,0.00001609623,0.00010785274,0.000011781927,0.000042654206,0.000024119607,0.00002220996,0.000022785276,0.0030250747,0.9898051,0.006896538],"study_design_scores_gemma":[0.000051438456,0.000012331767,0.00018996642,0.00053798384,0.00003147496,0.00014961629,0.00010946266,0.00021277745,0.000060772178,0.019700158,0.9789123,0.000031768323],"about_ca_topic_score_codex":0.002963843,"about_ca_topic_score_gemma":0.0072041317,"teacher_disagreement_score":0.01750307,"about_ca_system_score_codex":0.005060776,"about_ca_system_score_gemma":0.0053225886,"threshold_uncertainty_score":0.054786026},"labels":[],"label_agreement":null},{"id":"W4389584840","doi":"10.17118/11143/21033","title":"Physics-informed neural network-based modeling of the staticreconfiguration of a plate under fluid flow","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Control reconfiguration; Fluid dynamics; Artificial neural network; Flow (mathematics); Physics; Computer science; Mechanics; Artificial intelligence; Embedded system","score_opus":0.04041936900913492,"score_gpt":0.2637058355629237,"score_spread":0.22328646655378878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389584840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13618602,0.00047195682,0.8501664,0.0003111069,0.00009618544,0.000050928677,0.00017454647,0.00040982192,0.012132975],"genre_scores_gemma":[0.9573403,0.00028482068,0.035245072,0.00007065213,0.000020822998,0.00010158947,0.00014146519,0.000036559686,0.0067588785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999262,0.000014647453,0.0000032918997,0.00002104154,0.000023917684,0.000010843866],"domain_scores_gemma":[0.9999037,0.000039188682,0.000017356406,0.000008406355,0.000021897868,0.000009413585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020004665,0.00043803224,0.0003341693,0.0002907446,0.00018791888,0.00047456875,0.0007906156,0.0008609753,0.0014523724],"category_scores_gemma":[0.00044354898,0.00029811048,0.00043011733,0.00021705133,0.0006128699,0.00067038595,0.0005208813,0.0006030745,0.0001838908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000878938,0.000006538307,0.00011392113,0.000010537324,0.000004978628,0.000022299708,0.00000780781,0.9947113,0.0014607348,0.0011273988,0.000053583928,0.0024721362],"study_design_scores_gemma":[3.890857e-7,0.0000025758145,0.000028995055,5.737759e-7,6.4999034e-7,0.0000017583346,7.5709096e-7,0.9996625,0.00010799427,0.00014927119,0.000043776425,7.4405085e-7],"about_ca_topic_score_codex":0.006265243,"about_ca_topic_score_gemma":0.004452643,"teacher_disagreement_score":0.006265243,"about_ca_system_score_codex":0.0006261644,"about_ca_system_score_gemma":0.0006333979,"threshold_uncertainty_score":0.01245755},"labels":[],"label_agreement":null},{"id":"W4389633780","doi":"10.1109/access.2023.3342063","title":"Universal Approximation and the Topological Neural Network","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada; University of Alberta","funders":"","keywords":"Artificial neural network; Generalization; Tychonoff space; Metric space; Metric (unit); Topological space; Mathematics; Range (aeronautics); Computer science; Space (punctuation); Corollary; Topology (electrical circuits); Discrete mathematics; Artificial intelligence; Combinatorics; Mathematical analysis","score_opus":0.03764927736205181,"score_gpt":0.2916804474211412,"score_spread":0.2540311700590894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389633780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055278983,0.0009961814,0.93403524,0.0011366338,0.000107718595,0.000012844164,0.000111148765,0.00020853158,0.008112685],"genre_scores_gemma":[0.9053582,0.0010505742,0.0886153,0.00020887068,0.00016273878,0.000054124175,0.00018911833,0.00006213866,0.0042987955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999424,0.00019376884,0.00003052579,0.00013336145,0.00015203116,0.00006640679],"domain_scores_gemma":[0.9984565,0.00080737285,0.00020367108,0.00020222916,0.00021185917,0.000118374606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010937486,0.00042235263,0.0004715453,0.0010734127,0.00042366763,0.0012157689,0.00084041356,0.0008493743,0.0014776897],"category_scores_gemma":[0.0072501027,0.00020859212,0.00054475584,0.0007595464,0.0025599734,0.002673465,0.00178765,0.0015329754,0.00017991765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032203134,0.000009324869,0.00055102794,0.00005219284,0.000015727608,0.000057484063,0.00006569803,0.071617834,0.0012845482,0.9107614,0.0005486152,0.015003989],"study_design_scores_gemma":[0.0000040467135,0.00002734726,0.00025287707,0.000017542721,0.000005940206,0.000075107455,0.000028953966,0.43871805,0.00055234216,0.55846125,0.0018469598,0.000009579197],"about_ca_topic_score_codex":0.0016947153,"about_ca_topic_score_gemma":0.00097304204,"teacher_disagreement_score":0.0016947153,"about_ca_system_score_codex":0.001265783,"about_ca_system_score_gemma":0.00048681008,"threshold_uncertainty_score":0.009183884},"labels":[],"label_agreement":null},{"id":"W4389666329","doi":"10.1109/nssmicrtsd49126.2023.10338406","title":"A Versatile Edge Machine Learning Test Bench for High Bandwidth Instrumentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Canada Foundation for Innovation","keywords":"Test bench; Debugging; Computer science; Field-programmable gate array; Modular design; Instrumentation (computer programming); Data acquisition; Computer hardware; Embedded system; Automatic test equipment; Data compression; Enhanced Data Rates for GSM Evolution; Test data; Artificial intelligence; Engineering; Operating system; Reliability engineering","score_opus":0.017763394133781748,"score_gpt":0.25451668016782536,"score_spread":0.2367532860340436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389666329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27243832,0.0005838242,0.6907093,0.00048920844,0.0005320329,0.0011326311,0.0017910055,0.02138843,0.010935179],"genre_scores_gemma":[0.76009023,0.00025444475,0.22338152,0.00077849627,0.00011102816,0.0011126692,0.0016768853,0.0011617008,0.011433065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990044,0.00014810666,0.000068481306,0.00016101744,0.0004893448,0.00012861373],"domain_scores_gemma":[0.9975188,0.0008547308,0.0002182853,0.00043604322,0.000803347,0.00016881492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097523566,0.00064267236,0.00046539045,0.0009804434,0.0004934408,0.0007811219,0.0019520425,0.0009571704,0.0104935365],"category_scores_gemma":[0.002611343,0.0002722875,0.0002458507,0.0005548973,0.00041752748,0.0009388734,0.0007016693,0.0008454645,0.0021297217],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017557398,0.0010921288,0.007922302,0.0009109479,0.000116493844,0.0017319963,0.00036366488,0.020075556,0.71594715,0.008676184,0.018444479,0.22296335],"study_design_scores_gemma":[0.00017786316,0.00345631,0.010211631,0.000094994146,0.000053313728,0.0015374428,0.000096502416,0.09633406,0.84501004,0.0016211831,0.041290388,0.0001162964],"about_ca_topic_score_codex":0.00042749854,"about_ca_topic_score_gemma":0.00038498602,"teacher_disagreement_score":0.0104935365,"about_ca_system_score_codex":0.0004566956,"about_ca_system_score_gemma":0.0005222223,"threshold_uncertainty_score":0.035104394},"labels":[],"label_agreement":null},{"id":"W4389785425","doi":"10.3390/e25121661","title":"Recent Advances in Statistical Theory and Applications","year":2023,"lang":"en","type":"editorial","venue":"Entropy","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistical physics; Computer science; Mathematical economics; Mathematics; Physics","score_opus":0.0067494958161686035,"score_gpt":0.28671010675921815,"score_spread":0.2799606109430495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389785425","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012077464,0.20269814,0.0036077301,0.0773634,0.7096172,0.000020592604,0.000094643714,0.00019488276,0.0062826993],"genre_scores_gemma":[0.0014225831,0.10419011,0.0011109179,0.011567769,0.8751061,0.000020685862,0.000051925403,0.0000786992,0.0064511686],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99489766,0.0010172176,0.00044575072,0.00049542857,0.0029640452,0.00017987248],"domain_scores_gemma":[0.9483788,0.0351277,0.0013550122,0.0010987314,0.011425058,0.002614658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008381737,0.0021634488,0.0023914,0.004051526,0.0013520258,0.0054270877,0.0022229892,0.005582143,0.010682293],"category_scores_gemma":[0.033938266,0.00086502347,0.0011879627,0.0024790636,0.00455935,0.004068443,0.0019095669,0.014102419,0.008251587],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037336413,0.000013125152,0.000046456214,0.0007357738,0.000039521256,0.0000798742,0.000028789942,0.00016073522,0.0001333649,0.009224095,0.92521054,0.06429049],"study_design_scores_gemma":[0.0000116402025,0.000020836134,0.000104191604,0.00023458275,0.000015173869,0.00016602267,0.000020374733,0.00028518363,0.00007542833,0.014710463,0.9843389,0.000017147724],"about_ca_topic_score_codex":0.00067061826,"about_ca_topic_score_gemma":0.0015509179,"teacher_disagreement_score":0.010682293,"about_ca_system_score_codex":0.0017172497,"about_ca_system_score_gemma":0.0024976663,"threshold_uncertainty_score":0.04432738},"labels":[],"label_agreement":null},{"id":"W4389882853","doi":"10.32920/24625155.v1","title":"Performance Review of Popular Classification Models","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Variety (cybernetics); Hyperparameter; Club; Logistic regression; Computer science; Artificial neural network; Loan; Artificial intelligence; Machine learning; Econometrics; Mathematics; Economics; Finance","score_opus":0.1493015235050507,"score_gpt":0.3163122419405477,"score_spread":0.167010718435497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389882853","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02847076,0.7797494,0.14208925,0.021202324,0.006200456,0.00015784916,0.0029568467,0.0023604576,0.016812654],"genre_scores_gemma":[0.39495265,0.43431857,0.117297046,0.0056608277,0.014494713,0.00037673244,0.013327499,0.0017370877,0.017834948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98779184,0.0041504675,0.0010122253,0.00236263,0.0041760122,0.00050676294],"domain_scores_gemma":[0.9591094,0.025641235,0.0012921453,0.0023112835,0.01113746,0.0005084647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022166764,0.0029788439,0.0033433535,0.006826887,0.0011658652,0.0046284855,0.0045730546,0.0022935802,0.0034465904],"category_scores_gemma":[0.04015121,0.0010174371,0.0025776497,0.007925825,0.0013280362,0.005079818,0.0017948219,0.004050385,0.0036505677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053255,0.00018203969,0.008046017,0.0024854932,0.00081343297,0.000051256822,0.00010818562,0.040763773,0.0004480049,0.014658861,0.07804316,0.85386723],"study_design_scores_gemma":[0.00017423385,0.0014323605,0.016471174,0.0038614736,0.0013330865,0.00065172993,0.0004996434,0.6822973,0.00576871,0.045208156,0.24191348,0.00038857642],"about_ca_topic_score_codex":0.015529878,"about_ca_topic_score_gemma":0.008862039,"teacher_disagreement_score":0.022166764,"about_ca_system_score_codex":0.003747462,"about_ca_system_score_gemma":0.0030825562,"threshold_uncertainty_score":0.117230415},"labels":[],"label_agreement":null},{"id":"W4389883007","doi":"10.32920/24625155","title":"Performance Review of Popular Classification Models","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Variety (cybernetics); Hyperparameter; Club; Computer science; Logistic regression; Artificial neural network; Machine learning; Artificial intelligence; Loan; Econometrics; Economics; Finance","score_opus":0.1493015235050507,"score_gpt":0.3163122419405477,"score_spread":0.167010718435497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389883007","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02847076,0.7797494,0.14208925,0.021202324,0.006200456,0.00015784916,0.0029568467,0.0023604576,0.016812654],"genre_scores_gemma":[0.39495265,0.43431857,0.117297046,0.0056608277,0.014494713,0.00037673244,0.013327499,0.0017370877,0.017834948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98779184,0.0041504675,0.0010122253,0.00236263,0.0041760122,0.00050676294],"domain_scores_gemma":[0.9591094,0.025641235,0.0012921453,0.0023112835,0.01113746,0.0005084647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022166764,0.0029788439,0.0033433535,0.006826887,0.0011658652,0.0046284855,0.0045730546,0.0022935802,0.0034465904],"category_scores_gemma":[0.04015121,0.0010174371,0.0025776497,0.007925825,0.0013280362,0.005079818,0.0017948219,0.004050385,0.0036505677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053255,0.00018203969,0.008046017,0.0024854932,0.00081343297,0.000051256822,0.00010818562,0.040763773,0.0004480049,0.014658861,0.07804316,0.85386723],"study_design_scores_gemma":[0.00017423385,0.0014323605,0.016471174,0.0038614736,0.0013330865,0.00065172993,0.0004996434,0.6822973,0.00576871,0.045208156,0.24191348,0.00038857642],"about_ca_topic_score_codex":0.015529878,"about_ca_topic_score_gemma":0.008862039,"teacher_disagreement_score":0.022166764,"about_ca_system_score_codex":0.003747462,"about_ca_system_score_gemma":0.0030825562,"threshold_uncertainty_score":0.117230415},"labels":[],"label_agreement":null},{"id":"W4390044952","doi":"10.1109/icdacai59742.2023.00138","title":"A Review of Various Dimension Reduction Methods","year":2023,"lang":"en","type":"review","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Dimensionality reduction; Dimension (graph theory); Reduction (mathematics); Computer science; Simple (philosophy); Intrinsic dimension; Data processing; Data reduction; Curse of dimensionality; Data mining; Artificial intelligence; Mathematics; Database","score_opus":0.14709744647401465,"score_gpt":0.46287606855321767,"score_spread":0.31577862207920304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390044952","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024606916,0.9909787,0.003109394,0.0004811285,0.00045501144,0.000019086125,0.000099845274,0.000048948015,0.0045617954],"genre_scores_gemma":[0.0013698613,0.99106604,0.004205577,0.00035577815,0.00060373364,0.000030381338,0.00017160898,0.000016679416,0.0021803367],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995023,0.00007524992,0.000073427094,0.00008918271,0.00022707108,0.000032806704],"domain_scores_gemma":[0.9991284,0.00044726455,0.00006893002,0.00003842685,0.0002773927,0.000039605453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009324535,0.001276612,0.0013444909,0.0038575213,0.00049553195,0.0013041022,0.0012634811,0.0010885197,0.008003343],"category_scores_gemma":[0.0018631057,0.0005711994,0.00090070884,0.005049641,0.00051572017,0.002050803,0.00072171475,0.0015559248,0.0054150815],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051327366,0.00010149424,0.00028091314,0.016004309,0.0001004393,0.00012103143,0.0000785799,0.0010432871,0.0014623306,0.013609397,0.059519716,0.90762734],"study_design_scores_gemma":[0.000010154087,0.00008706419,0.0007283694,0.0035518818,0.00009860981,0.0005755752,0.000043769942,0.0005835742,0.0008833372,0.005023365,0.9883711,0.0000432794],"about_ca_topic_score_codex":0.0016912443,"about_ca_topic_score_gemma":0.002001399,"teacher_disagreement_score":0.008003343,"about_ca_system_score_codex":0.0007106738,"about_ca_system_score_gemma":0.0014207588,"threshold_uncertainty_score":0.02677387},"labels":[],"label_agreement":null},{"id":"W4390090557","doi":"10.1007/s42979-023-02394-9","title":"DPDR: A Novel Machine Learning Method for the Decision Process for Dimensionality Reduction","year":2023,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Artificial intelligence; Machine learning; Computer science; Interpretability; Process (computing); Context (archaeology); Feature selection; Curse of dimensionality; Artificial neural network; Random forest; Multilayer perceptron; Data mining; Principal component analysis; Supervised learning; Pattern recognition (psychology)","score_opus":0.061719260430364874,"score_gpt":0.3754124143394524,"score_spread":0.31369315390908753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390090557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000741648,0.00016122092,0.9982913,0.00008210426,0.00007498169,0.000030432166,0.000061850784,0.00034710093,0.00020936686],"genre_scores_gemma":[0.033352993,0.00028204688,0.96321416,0.00025035674,0.00018640008,0.00026668896,0.00039219172,0.0001838725,0.0018711812],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971853,0.0011078778,0.0002124054,0.0005228627,0.00082546927,0.00014607378],"domain_scores_gemma":[0.99779046,0.0011315171,0.00013186605,0.00038368712,0.0004645201,0.00009799143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027621936,0.0011104415,0.0018402603,0.0014318383,0.0007242754,0.0017401576,0.0023016634,0.0016666275,0.0054735537],"category_scores_gemma":[0.0075869863,0.000592667,0.0014024917,0.0017102822,0.00092069904,0.0019246698,0.0024434566,0.00337119,0.0025220874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030553405,0.00017177523,0.00065112935,0.00039209428,0.00016037696,0.00011397642,0.00012327527,0.071566336,0.008656141,0.04597032,0.013115403,0.8587736],"study_design_scores_gemma":[0.000042809686,0.00009164377,0.00019027806,0.00002399366,0.000026171057,0.0001023196,0.000014599236,0.96113616,0.0046490156,0.025422083,0.008270869,0.000030030686],"about_ca_topic_score_codex":0.0015901959,"about_ca_topic_score_gemma":0.001559959,"teacher_disagreement_score":0.0054735537,"about_ca_system_score_codex":0.0005985416,"about_ca_system_score_gemma":0.0015901861,"threshold_uncertainty_score":0.018310845},"labels":[],"label_agreement":null},{"id":"W4390450078","doi":"10.1142/s0218127423501973","title":"Infinitely Many Coexisting Attractors and Scrolls in a Fractional-Order Discrete Neuron Map","year":2023,"lang":"en","type":"article","venue":"International Journal of Bifurcation and Chaos","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Professional Engineers Ontario","funders":"National Natural Science Foundation of China","keywords":"Attractor; Realizability; Artificial neural network; Nonlinear system; Dynamical systems theory; Chaotic; Mathematics; Fractional calculus; Dynamical system (definition); Computer science; Applied mathematics; Topology (electrical circuits); Algorithm; Artificial intelligence; Mathematical analysis; Physics","score_opus":0.02505111171230728,"score_gpt":0.30480975775573826,"score_spread":0.279758646043431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390450078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81007916,0.00052269595,0.18139577,0.00022278396,0.0000485986,0.000019648814,0.000051267136,0.00027374516,0.0073863314],"genre_scores_gemma":[0.9916442,0.00008165415,0.007157286,0.000013454132,0.0000052119967,0.00000897233,0.000012690331,0.000012581466,0.0010639143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992776,0.000017718003,0.0000051666193,0.00001471576,0.000019878376,0.000014824799],"domain_scores_gemma":[0.9998877,0.000035190922,0.000027850205,0.00001541043,0.00001617371,0.000017567048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016216807,0.00027443556,0.0003168417,0.0005575797,0.00047585135,0.00051080127,0.0003240411,0.000520303,0.00085232663],"category_scores_gemma":[0.00047734487,0.00017427343,0.00040293572,0.00024203454,0.0007851573,0.00070388056,0.0004906324,0.00028230902,0.00008657557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031199947,0.00007721457,0.004859234,0.0002677855,0.00013453714,0.0026144073,0.00088454754,0.6337613,0.13416857,0.18614705,0.0010732827,0.035700083],"study_design_scores_gemma":[0.000008861913,0.000030734634,0.00050751265,0.0000058842743,0.000010054205,0.000198368,0.00004246082,0.9758529,0.0036427276,0.019206753,0.00047825548,0.000015509477],"about_ca_topic_score_codex":0.0010310281,"about_ca_topic_score_gemma":0.00061328016,"teacher_disagreement_score":0.0010310281,"about_ca_system_score_codex":0.00031834262,"about_ca_system_score_gemma":0.00019330734,"threshold_uncertainty_score":0.0028513074},"labels":[],"label_agreement":null},{"id":"W4390496627","doi":"10.17504/protocols.io.kxygx34jdg8j/v1","title":"[PEER REVIEWED] Generating Non-English IATs and Collecting Offline Samples v1","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Flexibility (engineering); Psychology; Protocol (science); Computer science; Cognitive psychology; Mathematics; Statistics; Medicine","score_opus":0.08980624709348774,"score_gpt":0.313647439964621,"score_spread":0.2238411928711333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390496627","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012411785,0.00036323853,0.21362174,0.0033570677,0.0040863478,0.0127721885,0.35067612,0.16685079,0.2358608],"genre_scores_gemma":[0.07328533,0.0005426227,0.19757782,0.002481264,0.0014747103,0.019354204,0.3786209,0.06124291,0.26542017],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9922559,0.002704956,0.00086078176,0.0013088823,0.0023745988,0.0004949302],"domain_scores_gemma":[0.9493419,0.0108168665,0.0010965433,0.019774424,0.0175754,0.0013949437],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0089860745,0.0014413244,0.0013486814,0.0024832427,0.0018199023,0.0039684153,0.0019639127,0.0015350949,0.44627666],"category_scores_gemma":[0.05411083,0.0010608035,0.00064661575,0.0024991932,0.0008260428,0.0027844156,0.0048974957,0.0019126938,0.44688582],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061236083,0.000115804454,0.0018414643,0.000521346,0.000034493107,0.00019806622,0.00051086384,0.0005214296,0.005114481,0.010415277,0.8792298,0.10088465],"study_design_scores_gemma":[0.00017378252,0.000099829944,0.002290436,0.00017852128,0.000018668154,0.0002114045,0.00037064255,0.0036535384,0.0072783176,0.010008299,0.9756168,0.00009962451],"about_ca_topic_score_codex":0.0021868225,"about_ca_topic_score_gemma":0.0033712476,"teacher_disagreement_score":0.44627666,"about_ca_system_score_codex":0.00070846017,"about_ca_system_score_gemma":0.004027603,"threshold_uncertainty_score":0.7898191},"labels":[],"label_agreement":null},{"id":"W4390574613","doi":"10.1088/1741-2552/ad1ac1","title":"EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing","year":2024,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Modality (human–computer interaction); Sensory system; Electroencephalography; Sensory processing; Computer science; Stimulus modality; Speech recognition; Psychology; Artificial intelligence; Cognitive psychology; Neuroscience","score_opus":0.04929912999634299,"score_gpt":0.2938924454504926,"score_spread":0.24459331545414958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390574613","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99499655,0.000076925135,0.0037163743,0.000018627514,0.000007279184,0.000053111424,0.00036771304,0.000036866128,0.0007266266],"genre_scores_gemma":[0.9972155,0.000048976992,0.0022284638,0.000009352139,0.000007268986,0.000031448486,0.00022281554,0.000004681748,0.0002314027],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998217,0.000041123476,0.000016074662,0.000053817632,0.0000386077,0.00002869313],"domain_scores_gemma":[0.9988212,0.00057956146,0.00017627625,0.000066148365,0.0002483443,0.00010842988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003777328,0.00034068892,0.0002330805,0.000781486,0.000094088464,0.00046895663,0.00013294087,0.00032442596,0.0018206042],"category_scores_gemma":[0.003295688,0.000109710105,0.00016671652,0.0003825502,0.00015088891,0.00034083656,0.00035141397,0.00019568084,0.00029369272],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045490908,0.00052611734,0.5686345,0.00063370384,0.0003612639,0.00025816052,0.0013275101,0.003985055,0.24397957,0.00024679254,0.0009850664,0.17451315],"study_design_scores_gemma":[0.000035435052,0.00055353355,0.98053426,0.000020742063,0.00007443762,0.00018511301,0.00039602388,0.013078969,0.004682573,0.0002031177,0.00021422695,0.000021534588],"about_ca_topic_score_codex":0.0010705991,"about_ca_topic_score_gemma":0.0018550131,"teacher_disagreement_score":0.0018206042,"about_ca_system_score_codex":0.00010117681,"about_ca_system_score_gemma":0.00008483183,"threshold_uncertainty_score":0.006090522},"labels":[],"label_agreement":null},{"id":"W4390604836","doi":"10.1109/mei.2024.10381671","title":"Creativity Is the Essence of Innovation: A Conversation with Mélanie Lévesque","year":2024,"lang":"en","type":"article","venue":"IEEE Electrical Insulation Magazine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Conversation; Creativity; Multidisciplinary approach; Engineering; Root cause; Work (physics); Sociology; Engineering ethics; Computer science; Psychology; Mechanical engineering; Social science; Social psychology; Reliability engineering; Communication","score_opus":0.02165022344910862,"score_gpt":0.271716274240193,"score_spread":0.2500660507910844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390604836","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003290424,0.009770767,0.0006718578,0.9721529,0.0053531695,0.000011823167,0.0000099550225,0.000021094409,0.008718],"genre_scores_gemma":[0.18345058,0.02085744,0.0029495521,0.7543542,0.010939859,0.00012807189,0.000018658524,0.0002710891,0.027030487],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97999805,0.013513947,0.0003457642,0.0013743979,0.003091002,0.0016768889],"domain_scores_gemma":[0.95271784,0.03083465,0.0012361371,0.00084969803,0.005358646,0.009003065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023872321,0.001104254,0.0015413798,0.0016427181,0.020449605,0.0144340545,0.0023235604,0.013238323,0.0028578914],"category_scores_gemma":[0.0407947,0.0007300656,0.0010265782,0.001146291,0.02464356,0.017918099,0.0053229267,0.042716935,0.0009207171],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006774902,0.00013795975,0.00071900385,0.00018555523,0.00005422279,0.0021708123,0.25094515,0.00026071517,0.00069562113,0.113986485,0.61189926,0.018877376],"study_design_scores_gemma":[0.000023910374,0.000053555956,0.0005152153,0.00064520934,0.000009753004,0.0014619883,0.12183992,0.00039180563,0.00021694716,0.01928998,0.8554433,0.00010834852],"about_ca_topic_score_codex":0.027563758,"about_ca_topic_score_gemma":0.03239264,"teacher_disagreement_score":0.027563758,"about_ca_system_score_codex":0.010060206,"about_ca_system_score_gemma":0.010036395,"threshold_uncertainty_score":0.12625039},"labels":[],"label_agreement":null},{"id":"W4390841924","doi":"10.1007/978-3-031-42478-6","title":"Design and Applications of Emerging Computer Systems","year":2024,"lang":"en","type":"book","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Human–computer interaction","score_opus":0.01885859393961796,"score_gpt":0.24043430518783424,"score_spread":0.2215757112482163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390841924","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057494645,0.106056675,0.2952948,0.0036599303,0.004187508,0.00019191373,0.00042550545,0.0017139955,0.5827202],"genre_scores_gemma":[0.07619646,0.11050253,0.16599351,0.0016362795,0.0013858366,0.00043063215,0.0008288826,0.0006375062,0.64238834],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998192,0.000019284065,0.0000071897152,0.000023711618,0.000115810384,0.000014811531],"domain_scores_gemma":[0.9998847,0.000035322282,0.0000067799892,0.000020454514,0.000038965798,0.000013675203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019132,0.0006004096,0.0004502261,0.0005510319,0.00034342078,0.0017461816,0.0009913255,0.00059967296,0.025248727],"category_scores_gemma":[0.00041882743,0.0004005786,0.00043592817,0.00059350336,0.0005855687,0.0012537797,0.00073931535,0.0011634711,0.009825754],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003395648,0.000045695182,0.00021986295,0.0009225067,0.00004582213,0.00020471252,0.00019269918,0.0117214015,0.023923736,0.4343627,0.13929617,0.38903078],"study_design_scores_gemma":[0.000008563352,0.000040184434,0.0001994651,0.00018273963,0.000012490977,0.0003243159,0.000048217873,0.01630407,0.0038920986,0.10297404,0.87599885,0.000014849763],"about_ca_topic_score_codex":0.00021962603,"about_ca_topic_score_gemma":0.00044688222,"teacher_disagreement_score":0.025248727,"about_ca_system_score_codex":0.0004361088,"about_ca_system_score_gemma":0.0003039571,"threshold_uncertainty_score":0.084465444},"labels":[],"label_agreement":null},{"id":"W4390846387","doi":"10.1007/978-3-031-42478-6_10","title":"Applications of Ising Models Based on Stochastic Computing","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Ising model; Computer science; Statistical physics; Distributed computing; Physics","score_opus":0.04088913659630693,"score_gpt":0.2583446360669864,"score_spread":0.21745549947067944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390846387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010528078,0.05808027,0.54970455,0.0032279866,0.0036620935,0.000074892385,0.00034196518,0.0014263847,0.3729538],"genre_scores_gemma":[0.36351153,0.1036508,0.23192494,0.0012538676,0.003889774,0.00019003921,0.0008400906,0.001008897,0.29373002],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997867,0.000041546184,0.00001013112,0.000026451033,0.00011903516,0.000016204282],"domain_scores_gemma":[0.99967635,0.00019191483,0.000015835225,0.000047052876,0.000051659877,0.000017240853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002954319,0.0008188799,0.0009576748,0.0008687687,0.00045441595,0.0015417887,0.0010870099,0.0010562486,0.010066466],"category_scores_gemma":[0.0011591447,0.00038476533,0.00083107944,0.0015947714,0.00084612984,0.0015887604,0.00077770604,0.0018272253,0.0023032825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016852011,0.000034417862,0.00010839469,0.00025090599,0.000034191304,0.000066895984,0.000050877654,0.04708015,0.0017548932,0.8633808,0.019680392,0.06754123],"study_design_scores_gemma":[0.0000044864323,0.000013742353,0.00011899761,0.00007639831,0.000013461408,0.00012482992,0.00001418007,0.17420267,0.000982752,0.7710047,0.05342767,0.000016147636],"about_ca_topic_score_codex":0.001105879,"about_ca_topic_score_gemma":0.0013799118,"teacher_disagreement_score":0.010066466,"about_ca_system_score_codex":0.00091599766,"about_ca_system_score_gemma":0.0006756912,"threshold_uncertainty_score":0.03367573},"labels":[],"label_agreement":null},{"id":"W4390899191","doi":"","title":"Escaping unknown discontinuous regions inblackbox optimization","year":2022,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science","score_opus":0.0107500441254365,"score_gpt":0.2063414954397243,"score_spread":0.1955914513142878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390899191","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04498043,0.0009587473,0.94656706,0.00039406263,0.00015928478,0.00007726405,0.000067178305,0.00045459936,0.0063413912],"genre_scores_gemma":[0.57251054,0.00044865583,0.41039088,0.00052553474,0.00013060166,0.0003912781,0.00020370985,0.00072852225,0.014670258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948776,0.00023956072,0.000017832012,0.00006946275,0.000121375684,0.00006396476],"domain_scores_gemma":[0.99733955,0.0020942816,0.00014811302,0.00012632512,0.00016333083,0.00012832094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016971615,0.0010791797,0.002026986,0.0007962127,0.00067988597,0.0014227391,0.0016997649,0.002854906,0.0056124893],"category_scores_gemma":[0.006423076,0.0013306355,0.0007508529,0.0006416542,0.0015070345,0.0011442624,0.0032222823,0.0017423251,0.0005479742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001633579,0.000043445165,0.00026737538,0.00011483911,0.000046730787,0.000058660902,0.00005376927,0.96634555,0.0012494895,0.014187712,0.001417366,0.016051713],"study_design_scores_gemma":[0.0000114568975,0.000011145465,0.000015084703,0.000006512125,0.0000028235052,0.0000049918526,0.0000035162757,0.9973345,0.00015847012,0.0022694897,0.00018006578,0.0000020328648],"about_ca_topic_score_codex":0.0049967766,"about_ca_topic_score_gemma":0.004814041,"teacher_disagreement_score":0.0056124893,"about_ca_system_score_codex":0.0008570428,"about_ca_system_score_gemma":0.0009115543,"threshold_uncertainty_score":0.018775642},"labels":[],"label_agreement":null},{"id":"W4391058311","doi":"10.55708/js0211001","title":"Neural Synchrony-Based State Representation in Liquid State Machines, an Exploratory Study","year":2023,"lang":"en","type":"article","venue":"Journal of Engineering Research and Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Québec Science (Canada); Université du Québec à Montréal","funders":"","keywords":"State (computer science); Representation (politics); Computer science; Exploratory research; Artificial intelligence; Political science; Algorithm; Sociology","score_opus":0.09638182046164694,"score_gpt":0.3843647491174313,"score_spread":0.28798292865578434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391058311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29461986,0.00086758507,0.700864,0.0006141035,0.00004263658,0.00004268219,0.00014391357,0.00017740103,0.0026278074],"genre_scores_gemma":[0.9820899,0.00020085555,0.017077649,0.000023318311,0.000024563225,0.000026711077,0.00006711963,0.000025507488,0.0004642642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994954,0.00020137551,0.00003073333,0.00015143787,0.00007804287,0.000043049455],"domain_scores_gemma":[0.99454147,0.004079752,0.00067092554,0.00036394317,0.00023973531,0.00010422394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018635385,0.00038146842,0.0005531911,0.00060545705,0.00030579406,0.0012653484,0.00065636,0.00064016966,0.0013752445],"category_scores_gemma":[0.010536814,0.00024061235,0.00058224285,0.0005665238,0.0012232274,0.002738387,0.0007431623,0.00093165645,0.00018985367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004186473,0.00015106011,0.012310663,0.00036084562,0.00015013576,0.00017273074,0.0005693307,0.6848866,0.025961814,0.14981247,0.000686931,0.12451889],"study_design_scores_gemma":[0.0000027828612,0.000055022334,0.0010079289,0.000009437716,0.000009531242,0.000036117824,0.000022697277,0.96954614,0.0023100742,0.02682261,0.00016748674,0.000010175877],"about_ca_topic_score_codex":0.0007130543,"about_ca_topic_score_gemma":0.00044992403,"teacher_disagreement_score":0.0018635385,"about_ca_system_score_codex":0.00071502593,"about_ca_system_score_gemma":0.00043704498,"threshold_uncertainty_score":0.009855449},"labels":[],"label_agreement":null},{"id":"W4391068768","doi":"10.1007/s11868-023-00580-0","title":"A new approach to neural networks using pseudo-differential operators","year":2024,"lang":"en","type":"article","venue":"Journal of Pseudo-Differential Operators and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Operator (biology); Convolution (computer science); Artificial neural network; Convolutional neural network; Convergence (economics); Field (mathematics); Differential (mechanical device); Architecture; Artificial intelligence; Differential operator; Theoretical computer science; Mathematics; Engineering","score_opus":0.01971556891466011,"score_gpt":0.2727343025943305,"score_spread":0.2530187336796704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391068768","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018497513,0.00040169863,0.995404,0.00014298181,0.00018686565,0.00001705529,0.000015456179,0.000040717954,0.0019414515],"genre_scores_gemma":[0.17950068,0.0025327955,0.79580957,0.0005929685,0.0009470333,0.00032352572,0.00011145744,0.00014849396,0.020033523],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999569,0.00011801027,0.00003759829,0.00009322693,0.00015938506,0.000022693286],"domain_scores_gemma":[0.9994349,0.00025069228,0.000037091544,0.000055306464,0.00018261815,0.000039345454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008504153,0.0007673396,0.00080158236,0.0009764001,0.00051108346,0.0013056458,0.0018083268,0.0011951501,0.002356487],"category_scores_gemma":[0.002003834,0.00044358513,0.0010072533,0.0008696814,0.0010620484,0.0024199572,0.0021623361,0.0017586958,0.0005175331],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039061262,0.00006986532,0.000399055,0.000308791,0.00013717188,0.00019699849,0.00014776416,0.18026371,0.0096176285,0.68305373,0.0025794958,0.12318677],"study_design_scores_gemma":[0.0000071357967,0.000033952136,0.00007049351,0.000017124712,0.000020024998,0.00007003083,0.000011969647,0.8613727,0.00094579725,0.13362254,0.0038147466,0.000013522472],"about_ca_topic_score_codex":0.0013066821,"about_ca_topic_score_gemma":0.0019564629,"teacher_disagreement_score":0.002356487,"about_ca_system_score_codex":0.0006532569,"about_ca_system_score_gemma":0.00073807465,"threshold_uncertainty_score":0.007883251},"labels":[],"label_agreement":null},{"id":"W4391093842","doi":"10.1109/tit.2024.3352313","title":"IEEE Transactions on Information Theory publication information","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information theory; Information retrieval; Mathematics; Statistics","score_opus":0.009183430584671939,"score_gpt":0.22731554049427508,"score_spread":0.21813210990960313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391093842","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007465111,0.040694565,0.4111116,0.025479345,0.046122044,0.0007379445,0.02862868,0.005439965,0.43432075],"genre_scores_gemma":[0.12235066,0.07658969,0.10002279,0.004883814,0.012572103,0.0008984794,0.046208892,0.0021821207,0.6342914],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800307,0.00035269064,0.00016154909,0.0002000584,0.0011526959,0.00012988028],"domain_scores_gemma":[0.99467635,0.0017890986,0.00018396124,0.0013248847,0.0018494271,0.00017639961],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020602734,0.0012924075,0.0020678246,0.003797362,0.0009579358,0.0044236397,0.0011441201,0.002092893,0.1851079],"category_scores_gemma":[0.011168395,0.0005802313,0.000767064,0.0050276644,0.00081973674,0.0042114784,0.0017536718,0.0025113106,0.077406555],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016456856,0.00012274027,0.00069976837,0.0008617737,0.00006320876,0.00014873999,0.000052727944,0.0032664898,0.001126435,0.07650185,0.5205435,0.39644817],"study_design_scores_gemma":[0.0000663768,0.00010562416,0.0013464479,0.0008884408,0.00015490796,0.00051709265,0.00011438701,0.034595083,0.0029237047,0.18493888,0.77427524,0.00007376357],"about_ca_topic_score_codex":0.0015025839,"about_ca_topic_score_gemma":0.0012613864,"teacher_disagreement_score":0.8148921,"about_ca_system_score_codex":0.0010109864,"about_ca_system_score_gemma":0.002182975,"threshold_uncertainty_score":0.6192476},"labels":[],"label_agreement":null},{"id":"W4391116674","doi":"10.1609/aaaiss.v2i1.27702","title":"A Neuro-Mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Cisco Systems","keywords":"Generative grammar; Computer science; Artificial intelligence; Cognitive science; Hebbian theory; Field (mathematics); Realization (probability); Cognition; Artificial neural network; Generative model; Representation (politics); Cognitive architecture; Psychology; Mathematics","score_opus":0.007504798073150291,"score_gpt":0.2027236544554505,"score_spread":0.19521885638230022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391116674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026328975,0.0005965884,0.9459552,0.0023745422,0.00008928091,0.00002629765,0.000050086637,0.00023469664,0.024344284],"genre_scores_gemma":[0.8135201,0.0006583001,0.1740681,0.00040764306,0.00011658848,0.00010588647,0.000056300865,0.00012866777,0.010938482],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996284,0.00014992291,0.000013462112,0.0000817499,0.00009192765,0.000034559802],"domain_scores_gemma":[0.99954385,0.00021897432,0.000042484615,0.000095824595,0.00005143998,0.00004732329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089701626,0.0003708164,0.00061524456,0.000464056,0.0006417677,0.0014252756,0.002053763,0.0015313319,0.0041201436],"category_scores_gemma":[0.002861364,0.00033329971,0.00062700035,0.00042130632,0.0035760473,0.00278042,0.0016065271,0.0018794084,0.0005200811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002376161,0.000018893654,0.0001835067,0.000039129933,0.000033064185,0.000063499916,0.00018388791,0.13471591,0.0018808825,0.84929764,0.0009070186,0.01265283],"study_design_scores_gemma":[0.000008656948,0.000016033604,0.00010937528,0.000007637982,0.0000064909873,0.000032768054,0.000015955398,0.33369657,0.0003959885,0.66416204,0.0015371253,0.000011392939],"about_ca_topic_score_codex":0.0026813224,"about_ca_topic_score_gemma":0.0032027282,"teacher_disagreement_score":0.0041201436,"about_ca_system_score_codex":0.0011053239,"about_ca_system_score_gemma":0.0008631163,"threshold_uncertainty_score":0.0137832165},"labels":[],"label_agreement":null},{"id":"W4391117034","doi":"10.1609/aaaiss.v2i1.27686","title":"Bridging Cognitive Architectures and Generative Models with Vector Symbolic Algebras","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Generative grammar; Bridging (networking); Computer science; Cognition; Cognitive science; Cognitive architecture; Cognitive model; Artificial intelligence; Set (abstract data type); Generative model; Theoretical computer science; Psychology","score_opus":0.008378286763685062,"score_gpt":0.2084268212199097,"score_spread":0.20004853445622464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391117034","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015079158,0.00087709597,0.96920425,0.0019232187,0.00006153795,0.000037360656,0.00013405777,0.00022486581,0.012458459],"genre_scores_gemma":[0.65904534,0.0019033686,0.32876638,0.0007889751,0.0004096318,0.00033305227,0.00044788644,0.0002552422,0.008050125],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749714,0.0014548119,0.00014537542,0.000324363,0.00039396482,0.0001842881],"domain_scores_gemma":[0.99213505,0.005397291,0.00056532567,0.0011244168,0.00041974653,0.0003581111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00391028,0.0008575403,0.0008912558,0.0022068291,0.0011503696,0.005291768,0.0020423934,0.0016024093,0.006159743],"category_scores_gemma":[0.012649992,0.0007145038,0.0022141163,0.0018593505,0.0077726482,0.009741972,0.0036705905,0.0039573503,0.0009446132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005362367,0.000005671188,0.000089886336,0.000015892367,0.0000078904195,0.000019641697,0.00015407751,0.0056840344,0.000074262854,0.9909882,0.00014463632,0.0028105695],"study_design_scores_gemma":[0.000003068835,0.0000034908196,0.000028482968,0.000008548761,0.0000022252339,0.000012421164,0.000023486542,0.023986815,0.00005064933,0.97502893,0.000846755,0.000005081603],"about_ca_topic_score_codex":0.003113047,"about_ca_topic_score_gemma":0.003008113,"teacher_disagreement_score":0.006159743,"about_ca_system_score_codex":0.00253856,"about_ca_system_score_gemma":0.0014231984,"threshold_uncertainty_score":0.020679772},"labels":[],"label_agreement":null},{"id":"W4391227736","doi":"10.1784/cm2023.5d2","title":"Simulation of the behaviour of engines in their current state of wear","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Condition Monitoring and Asset Management","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Computer science; Exploit; Upload; State (computer science); Machine learning; Artificial intelligence; Systems engineering; Engineering; Computer security; World Wide Web","score_opus":0.04226895201591698,"score_gpt":0.31167465054487903,"score_spread":0.26940569852896207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391227736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8622462,0.0001897514,0.12648007,0.00034870513,0.00016276706,0.00007090681,0.002673791,0.0010491732,0.006778572],"genre_scores_gemma":[0.9901264,0.00007712132,0.007396865,0.000023842955,0.0000086637465,0.000039878934,0.0011662233,0.000033174878,0.0011278996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985445,0.000029610903,0.000011731153,0.000036120575,0.00004397926,0.000024085182],"domain_scores_gemma":[0.99941134,0.00032975458,0.00005287694,0.000088380264,0.00008241137,0.000035152894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003989605,0.0004214148,0.00035006463,0.0004200794,0.00023935048,0.00043036524,0.0007242796,0.0007917122,0.0022240598],"category_scores_gemma":[0.0016574741,0.00020059463,0.0006498202,0.00038932057,0.00040727635,0.00050267606,0.00037536182,0.00069314125,0.0002915001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000803914,0.000050850103,0.0062193153,0.00003684091,0.000028886443,0.000059617058,0.000048498794,0.9859555,0.0020615135,0.0011129057,0.000516784,0.0038288673],"study_design_scores_gemma":[0.0000053884205,0.000019628802,0.001744077,0.0000032412565,0.0000072626017,0.000013287175,0.00001198175,0.9967037,0.0007089991,0.0005321948,0.00024457637,0.0000057002035],"about_ca_topic_score_codex":0.016472802,"about_ca_topic_score_gemma":0.016465364,"teacher_disagreement_score":0.016472802,"about_ca_system_score_codex":0.00048466955,"about_ca_system_score_gemma":0.000513636,"threshold_uncertainty_score":0.032753885},"labels":[],"label_agreement":null},{"id":"W4391326329","doi":"10.1016/j.fss.2024.108887","title":"Simplification of ANFIS based on importance-confidence-similarity measures","year":2024,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Adaptive neuro fuzzy inference system; Mathematics; Defuzzification; Fuzzy logic; Fuzzy rule; Neuro-fuzzy; Data mining; Fuzzy number; Fuzzy set; Fuzzy set operations; Fuzzy control system; Artificial intelligence; Mathematical optimization; Algorithm; Computer science","score_opus":0.03835811487471629,"score_gpt":0.27410706785157773,"score_spread":0.23574895297686144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391326329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017063912,0.00029112637,0.975249,0.00006689313,0.000078236495,0.000065691514,0.00007058271,0.00038804873,0.0067265476],"genre_scores_gemma":[0.7356494,0.0004821477,0.25787556,0.000057195797,0.00011779433,0.00022657907,0.00030417883,0.00012643827,0.0051606228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947566,0.00010219895,0.0000479656,0.00006899278,0.00026403132,0.00004112705],"domain_scores_gemma":[0.9993956,0.00029105085,0.00004148156,0.00009238702,0.0001632982,0.00001616797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006853617,0.00061557215,0.001120349,0.0006770865,0.00044465833,0.0010215937,0.0008392704,0.00059669575,0.0027718025],"category_scores_gemma":[0.0021614702,0.00032248723,0.0010073743,0.00047802468,0.0003856926,0.00079081487,0.0005687878,0.0009742359,0.00056420907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014613742,0.000079839396,0.0004469991,0.0002427282,0.00009155553,0.00020281182,0.00015221938,0.73115396,0.020195423,0.022815697,0.0012844473,0.22318813],"study_design_scores_gemma":[0.0000049438477,0.000024242743,0.00018188459,0.0000095364985,0.000018807199,0.000026569514,0.000009532952,0.99430156,0.0019404483,0.00242822,0.0010494963,0.000004711378],"about_ca_topic_score_codex":0.0039467597,"about_ca_topic_score_gemma":0.0031789942,"teacher_disagreement_score":0.0039467597,"about_ca_system_score_codex":0.00049252354,"about_ca_system_score_gemma":0.0005727794,"threshold_uncertainty_score":0.009272575},"labels":[],"label_agreement":null},{"id":"W4391506137","doi":"10.48550/arxiv.2402.00645","title":"Spectrally Transformed Kernel Regression","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Defense Advanced Research Projects Agency; Institute for Catastrophic Loss Reduction; National Science Foundation","keywords":"Kernel regression; Regression; Kernel (algebra); Mathematics; Statistics; Computer science; Artificial intelligence; Econometrics; Discrete mathematics","score_opus":0.05881918905207582,"score_gpt":0.1967955850008797,"score_spread":0.1379763959488039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391506137","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044227503,0.000107599786,0.9939955,0.00013367507,0.000021564127,0.000015875812,0.00005838907,0.00037845087,0.0008661876],"genre_scores_gemma":[0.47646323,0.00079849944,0.5112642,0.00046357504,0.0002359165,0.00016535188,0.001182746,0.00070347305,0.00872292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979832,0.0007186804,0.00007266071,0.00048040936,0.00061378593,0.00013117463],"domain_scores_gemma":[0.99500704,0.0021251282,0.00043433925,0.0013868192,0.00091502094,0.00013169057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024251624,0.00086957833,0.0009833018,0.0008183826,0.0004385633,0.0013071527,0.0013287909,0.001171626,0.0027602753],"category_scores_gemma":[0.014138821,0.0004741478,0.00081960595,0.0012264036,0.0016963652,0.0031752174,0.0021115558,0.0027438616,0.001810548],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020412584,0.00013936425,0.0019082098,0.00024863254,0.00011302281,0.00015960431,0.0001993447,0.4995986,0.013701391,0.252491,0.008655469,0.2225813],"study_design_scores_gemma":[0.000007731814,0.000019936113,0.00022954763,0.000010013004,0.0000062072145,0.00004357496,0.000014706557,0.95646006,0.0019412343,0.03935772,0.0018970867,0.000012260304],"about_ca_topic_score_codex":0.0018457692,"about_ca_topic_score_gemma":0.0018168757,"teacher_disagreement_score":0.0027602753,"about_ca_system_score_codex":0.0007289203,"about_ca_system_score_gemma":0.00118117,"threshold_uncertainty_score":0.012825668},"labels":[],"label_agreement":null},{"id":"W4391506243","doi":"10.48550/arxiv.2402.00751","title":"Fast Exact Unlearning for In-Context Learning Data for LLMs","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; Government of Canada; Canadian Institute for Advanced Research; Alfred P. Sloan Foundation","keywords":"Computer science; Context (archaeology); Algorithm; Artificial intelligence; Machine learning; History; Archaeology","score_opus":0.1292539069615907,"score_gpt":0.23393262536886275,"score_spread":0.10467871840727205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391506243","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040266953,0.0005619736,0.9426411,0.00044658227,0.0001662787,0.00010397747,0.00024349276,0.013846664,0.0017229451],"genre_scores_gemma":[0.46305895,0.0002255031,0.53104645,0.0005575475,0.00006460217,0.00022353772,0.0008962596,0.0015999877,0.0023271604],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981103,0.0005592484,0.00016552853,0.0005380186,0.00041781162,0.00020913634],"domain_scores_gemma":[0.99504745,0.0018362727,0.00025593964,0.0022688957,0.00042179567,0.00016959479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021571706,0.0013249132,0.0013701629,0.0006746729,0.0008861206,0.0017589419,0.0031536901,0.001794752,0.005746886],"category_scores_gemma":[0.016685227,0.0007919522,0.0010120899,0.00081895606,0.0016634332,0.0058287843,0.005262052,0.0045366976,0.0023278254],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008883637,0.00036520747,0.00457675,0.00038844987,0.000099189885,0.00024504916,0.0004577012,0.30918467,0.019098785,0.033125695,0.011112513,0.6204577],"study_design_scores_gemma":[0.000039896437,0.00008410605,0.00030467333,0.000030472784,0.00001110194,0.00008029428,0.000091396774,0.94373345,0.014392219,0.038331877,0.0028793907,0.000021037864],"about_ca_topic_score_codex":0.0038476835,"about_ca_topic_score_gemma":0.008179124,"teacher_disagreement_score":0.005746886,"about_ca_system_score_codex":0.0016839677,"about_ca_system_score_gemma":0.00151173,"threshold_uncertainty_score":0.01922518},"labels":[],"label_agreement":null},{"id":"W4391538449","doi":"10.1016/j.knosys.2024.111481","title":"A self-organizing deep network architecture designed based on LSTM network via elitism-driven roulette-wheel selection for time-series forecasting","year":2024,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Shandong Province; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Artificial neural network; Cluster analysis; Fuzzy logic; Polynomial; Selection (genetic algorithm); Time series; Series (stratigraphy); Data mining; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.013701654325939826,"score_gpt":0.22656213513035142,"score_spread":0.21286048080441158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391538449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030578762,0.0006109442,0.9630767,0.0002351037,0.000239469,0.000051119543,0.00014007308,0.0020422833,0.003025609],"genre_scores_gemma":[0.7414012,0.00040392336,0.25001913,0.00031998864,0.00011240534,0.0001917797,0.00047878746,0.00015670304,0.0069161034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998554,0.000023913733,0.000009900549,0.000050756575,0.00003474788,0.000025257068],"domain_scores_gemma":[0.99984455,0.000034174267,0.000012198743,0.000018424384,0.000076788405,0.0000138952055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041628588,0.0005066567,0.0006173698,0.00031939842,0.00039590188,0.000498286,0.0014064943,0.0008598787,0.0021228935],"category_scores_gemma":[0.00062573364,0.00034415544,0.0004788739,0.000429338,0.00027350514,0.0007320459,0.00052190834,0.0010653686,0.0006828184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015842622,0.00015488177,0.0011048594,0.000118576456,0.00013971921,0.00013225056,0.00007510737,0.6407644,0.020285793,0.0061090547,0.006367074,0.32459],"study_design_scores_gemma":[0.000003815838,0.000014911299,0.000074121206,0.000002682093,0.0000080408445,0.000009702514,0.000001998705,0.99767166,0.0012912016,0.0006025273,0.0003157585,0.000003591194],"about_ca_topic_score_codex":0.008310075,"about_ca_topic_score_gemma":0.010425592,"teacher_disagreement_score":0.008310075,"about_ca_system_score_codex":0.0005240356,"about_ca_system_score_gemma":0.00083272427,"threshold_uncertainty_score":0.01652342},"labels":[],"label_agreement":null},{"id":"W4391667652","doi":"10.48550/arxiv.2402.03460","title":"Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Curse of dimensionality; Curse; Computation; Computer science; Artificial neural network; Artificial intelligence; Sociology; Algorithm","score_opus":0.052119055066705604,"score_gpt":0.20555170056699615,"score_spread":0.15343264550029054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391667652","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028181477,0.004964937,0.9508677,0.0031118277,0.00017635664,0.00010690698,0.0005178059,0.0015951005,0.010477904],"genre_scores_gemma":[0.6162776,0.005252583,0.35626978,0.0027035943,0.0008028071,0.00097298116,0.002238308,0.0016678742,0.013814385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964908,0.001180057,0.00017252578,0.0006854958,0.0008570291,0.00061412883],"domain_scores_gemma":[0.97658336,0.01713064,0.0010754341,0.0023198626,0.0020261381,0.00086457876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008680938,0.0038349272,0.0025804169,0.0022084801,0.0013797791,0.0035060565,0.004577724,0.004458056,0.005627076],"category_scores_gemma":[0.04156408,0.001597373,0.0026197024,0.0016752436,0.0034514316,0.008062922,0.007817949,0.0104099745,0.002017528],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005282825,0.0001672692,0.002566468,0.0004234852,0.00016647555,0.00016824543,0.00029826493,0.76379544,0.0027100842,0.16373338,0.010413429,0.0550292],"study_design_scores_gemma":[0.000008006461,0.000029815494,0.00014754118,0.000047913953,0.000016921318,0.000044424447,0.000019956333,0.9682524,0.00072930363,0.030033262,0.00065750227,0.000012914081],"about_ca_topic_score_codex":0.0062112384,"about_ca_topic_score_gemma":0.0065353904,"teacher_disagreement_score":0.008680938,"about_ca_system_score_codex":0.0059569674,"about_ca_system_score_gemma":0.0024723727,"threshold_uncertainty_score":0.045909762},"labels":[],"label_agreement":null},{"id":"W4391769641","doi":"10.1109/itsc57777.2023.10422604","title":"eMARLIN+: Overcoming Partial-Observability Caused by Sensor Limitations and Short Detection Ranges in Traffic Signal Control","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Observability; SIGNAL (programming language); Computer science; Traffic signal; Control (management); Real-time computing; Artificial intelligence; Mathematics","score_opus":0.04011405445822895,"score_gpt":0.2545791297255782,"score_spread":0.21446507526734926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391769641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13478833,0.00029209084,0.8589197,0.00022189789,0.000054324097,0.00008676223,0.00007895008,0.0019139944,0.003644013],"genre_scores_gemma":[0.95919627,0.000053814387,0.0396639,0.0000738278,0.000015072502,0.00006461589,0.000051112842,0.000040495444,0.0008407596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993259,0.00018566487,0.000036212303,0.00020612667,0.00014936586,0.0000967887],"domain_scores_gemma":[0.998382,0.0007045546,0.00026428976,0.00030259532,0.00026109462,0.000085421365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001259714,0.0007186829,0.00072532636,0.00028154993,0.00042255459,0.00054387725,0.0013873205,0.0005988177,0.0012317806],"category_scores_gemma":[0.002873905,0.0002641152,0.0002763528,0.00018218048,0.0007125716,0.0009585844,0.0014616004,0.000908546,0.00020594476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026448732,0.000112367125,0.0013965843,0.00013415393,0.000033312794,0.00016390128,0.00011792067,0.90741,0.018258408,0.00679732,0.0011438853,0.06416764],"study_design_scores_gemma":[0.000028836432,0.00024306934,0.0003122037,0.0000070647716,0.000010945837,0.00005416188,0.000020291132,0.9904046,0.0060872906,0.001891374,0.00093158556,0.000008660026],"about_ca_topic_score_codex":0.0019706704,"about_ca_topic_score_gemma":0.0027252664,"teacher_disagreement_score":0.0019706704,"about_ca_system_score_codex":0.00045712353,"about_ca_system_score_gemma":0.0008725412,"threshold_uncertainty_score":0.0066620708},"labels":[],"label_agreement":null},{"id":"W4391777660","doi":"10.1214/23-ejs2207","title":"Analysis of the rate of convergence of two regression estimates defined by neural features which are easy to implement","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Regression; Rate of convergence; Statistics; Artificial neural network; Regression analysis; Convergence (economics); Applied mathematics; Econometrics; Artificial intelligence; Computer science; Key (lock)","score_opus":0.009441366941634942,"score_gpt":0.29667994641069206,"score_spread":0.2872385794690571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391777660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032609854,0.0021124918,0.961501,0.0005816692,0.00010094236,0.00011334608,0.00009752331,0.0003725037,0.002510648],"genre_scores_gemma":[0.5457305,0.0028893542,0.44104528,0.00043762915,0.0002513275,0.0009690185,0.000916537,0.0010657156,0.0066946503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99108106,0.0050079967,0.00037060713,0.0010676081,0.001994392,0.0004782904],"domain_scores_gemma":[0.8320113,0.14543082,0.0065258485,0.00672522,0.008059116,0.0012477067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025993371,0.0026095107,0.0019541858,0.0021388636,0.0006968696,0.0021289333,0.002468684,0.0030807091,0.0020331242],"category_scores_gemma":[0.16027139,0.001021402,0.0014548544,0.0011993919,0.0037409323,0.0049997596,0.004310501,0.0048882393,0.00059026835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012303563,0.00024631823,0.009381027,0.0011937522,0.0005244366,0.0005182325,0.0007521605,0.6295446,0.012673388,0.22592586,0.0029250542,0.115084834],"study_design_scores_gemma":[0.000037952155,0.00016353848,0.0014346516,0.00009364931,0.000048820984,0.00020016507,0.00005268045,0.9694605,0.00532538,0.022131205,0.0009896779,0.00006176008],"about_ca_topic_score_codex":0.0021013801,"about_ca_topic_score_gemma":0.00093772775,"teacher_disagreement_score":0.025993371,"about_ca_system_score_codex":0.0019936764,"about_ca_system_score_gemma":0.0014418509,"threshold_uncertainty_score":0.13746774},"labels":[],"label_agreement":null},{"id":"W4391800034","doi":"10.1007/978-3-031-50381-8_79","title":"Are Associations All You Need to Solve the Dimension Change Card Sort and N-bit Parity Task","year":2024,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"sort; Bit (key); Arithmetic; Computer science; Parity (physics); Dimension (graph theory); Task (project management); Card sorting; Theoretical computer science; Mathematics; Computer security; Economics; Combinatorics","score_opus":0.1802884950765724,"score_gpt":0.3695140874342669,"score_spread":0.18922559235769448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391800034","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17920487,0.004282919,0.34643382,0.038390897,0.0069222185,0.0002470765,0.0032515554,0.005173382,0.4160932],"genre_scores_gemma":[0.49232116,0.0037084846,0.2835438,0.005472376,0.0010325597,0.00022158484,0.0042273253,0.0009144025,0.20855823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978656,0.000033468405,0.00001666487,0.0000655639,0.00006008345,0.000037723392],"domain_scores_gemma":[0.99926585,0.0003032129,0.000096525255,0.000151715,0.00008845306,0.00009423272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058194576,0.00058087095,0.00046787164,0.00023416975,0.0005269746,0.0018591806,0.0008394459,0.0015868782,0.041385796],"category_scores_gemma":[0.0029604265,0.00041233323,0.0004664237,0.00050380215,0.00081545924,0.007980214,0.00073978305,0.0019164198,0.020281322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009114877,0.00032131013,0.00553292,0.0005096557,0.00007600785,0.00024439607,0.000342865,0.0023961416,0.021530049,0.09379624,0.14517058,0.72916836],"study_design_scores_gemma":[0.000252809,0.0004243929,0.009650635,0.0002443651,0.000100089266,0.00127625,0.0013279231,0.02699878,0.033960897,0.6471328,0.278494,0.00013692927],"about_ca_topic_score_codex":0.0010126705,"about_ca_topic_score_gemma":0.0015327695,"teacher_disagreement_score":0.041385796,"about_ca_system_score_codex":0.0002559215,"about_ca_system_score_gemma":0.00061179197,"threshold_uncertainty_score":0.13844931},"labels":[],"label_agreement":null},{"id":"W4391800639","doi":"10.48550/arxiv.2402.06935","title":"Taxonomic classification with maximal exact matches in KATKA kernels and minimizer digests","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Gruppo Nazionale per il Calcolo Scientifico; Agencia Nacional de Investigación y Desarrollo; Ministero della Salute; Istituto Nazionale di Alta Matematica \"Francesco Severi\"; National Institutes of Health; National Science Foundation","keywords":"Mathematics; Statistics; Combinatorics","score_opus":0.07483692217326347,"score_gpt":0.1863372144139413,"score_spread":0.11150029224067783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391800639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4386636,0.00063566986,0.55105066,0.0004470963,0.00008157065,0.00018666974,0.0009140853,0.0065598707,0.0014608407],"genre_scores_gemma":[0.67437303,0.0001312912,0.32038546,0.00014132907,0.00003200161,0.00020804601,0.002642233,0.00034818827,0.0017383717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976394,0.00072237465,0.0003191855,0.00057379133,0.00047610028,0.00026919745],"domain_scores_gemma":[0.99299765,0.0035811306,0.00078407704,0.0014969877,0.0009233541,0.00021682461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045039523,0.0009871032,0.0011299589,0.0019716432,0.00069251,0.0019471516,0.0015530125,0.0017777408,0.0014708457],"category_scores_gemma":[0.021601068,0.0005531363,0.0014290342,0.0014052464,0.0008646709,0.00434745,0.0020296702,0.0023355682,0.0012328235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003079261,0.000866938,0.038715556,0.0005799297,0.00042278593,0.00034951512,0.0013689762,0.3802819,0.028291369,0.019656805,0.007798949,0.518588],"study_design_scores_gemma":[0.000029281413,0.00011011986,0.0021735844,0.000022046519,0.000025280822,0.00009173872,0.00018228598,0.9825606,0.0043166494,0.009630388,0.0008259801,0.000032011638],"about_ca_topic_score_codex":0.004872057,"about_ca_topic_score_gemma":0.0041852635,"teacher_disagreement_score":0.004872057,"about_ca_system_score_codex":0.001332739,"about_ca_system_score_gemma":0.001476956,"threshold_uncertainty_score":0.023819447},"labels":[],"label_agreement":null},{"id":"W4391836026","doi":"10.48550/arxiv.2402.06957","title":"Architectural Neural Backdoors from First Principles","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science","score_opus":0.07969150709225993,"score_gpt":0.18432325940414343,"score_spread":0.1046317523118835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391836026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03078509,0.000691802,0.95597106,0.0017473646,0.00010605334,0.000101841884,0.00011753755,0.0028817512,0.0075975275],"genre_scores_gemma":[0.706787,0.0010331501,0.28338563,0.0010863547,0.000080805155,0.00034769232,0.0003117901,0.0012384431,0.005729245],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.994472,0.0015711752,0.0003631703,0.0012460257,0.0017351928,0.00061249407],"domain_scores_gemma":[0.98433924,0.0057619386,0.0011490724,0.0074784653,0.000984224,0.00028704572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005543071,0.002061776,0.0011519014,0.0015724135,0.001520622,0.00583623,0.003665216,0.003073305,0.0056231716],"category_scores_gemma":[0.028899884,0.0022709148,0.0020254238,0.00060839765,0.009909159,0.012886267,0.007758374,0.007770699,0.0011235725],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029471077,0.00010447452,0.0049278038,0.0006519155,0.00031061607,0.00057175674,0.0012603662,0.19461949,0.014499407,0.6307466,0.005160297,0.14685266],"study_design_scores_gemma":[0.000030686184,0.00013068551,0.0005563648,0.00019787178,0.00007778453,0.00044595855,0.00016884903,0.33504987,0.016137905,0.63546103,0.011678108,0.00006480128],"about_ca_topic_score_codex":0.00278292,"about_ca_topic_score_gemma":0.004383285,"teacher_disagreement_score":0.00583623,"about_ca_system_score_codex":0.0028827419,"about_ca_system_score_gemma":0.00194903,"threshold_uncertainty_score":0.029314876},"labels":[],"label_agreement":null},{"id":"W4391839143","doi":"10.1007/s10479-024-05834-8","title":"The impact of sparsity and entropy criteria on neural network performance","year":2024,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Artificial neural network; Theory of computation; Computer science; Entropy (arrow of time); Artificial intelligence; Machine learning; Algorithm; Thermodynamics; Physics","score_opus":0.18792104234497645,"score_gpt":0.4748126505737744,"score_spread":0.286891608228798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391839143","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8423574,0.003128746,0.13693672,0.004441193,0.000310135,0.000042668238,0.0005659001,0.00025461937,0.011962644],"genre_scores_gemma":[0.99394953,0.00030438817,0.0049650995,0.000064906024,0.00012862487,0.000008405268,0.000102991406,0.00004242553,0.00043352647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968731,0.0017085016,0.00021418848,0.00021740817,0.0007304986,0.00025637352],"domain_scores_gemma":[0.89337015,0.09592375,0.0024983983,0.002825012,0.004264207,0.001118447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00779547,0.00053423765,0.001016799,0.00088275346,0.0004571562,0.0015574677,0.0006372793,0.0014463953,0.001607632],"category_scores_gemma":[0.080414236,0.00026857323,0.0002508735,0.00070863805,0.0013883971,0.003979846,0.0014424854,0.001219137,0.00022922066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025396193,0.00047903793,0.02706025,0.00030149033,0.00025343592,0.00033956647,0.0001284052,0.81143725,0.01002381,0.04623313,0.0036726273,0.09753138],"study_design_scores_gemma":[0.000058504524,0.0004196966,0.0067545273,0.000037245092,0.000034485853,0.00016497639,0.00006689167,0.95382106,0.0043480126,0.034029234,0.00023113162,0.000034330053],"about_ca_topic_score_codex":0.0011568879,"about_ca_topic_score_gemma":0.0016282658,"teacher_disagreement_score":0.00779547,"about_ca_system_score_codex":0.00058506493,"about_ca_system_score_gemma":0.0010241171,"threshold_uncertainty_score":0.041226864},"labels":[],"label_agreement":null},{"id":"W4391940682","doi":"10.48550/arxiv.2402.10339","title":"What to Do When Your Discrete Optimization Is the Size of a Neural Network?","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Artificial neural network; Computer science; Mathematics; Artificial intelligence","score_opus":0.0517181833803865,"score_gpt":0.20596423953420262,"score_spread":0.15424605615381612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391940682","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015192223,0.020290324,0.70204383,0.20537245,0.0068298643,0.00018100272,0.0011432064,0.0019604284,0.046986755],"genre_scores_gemma":[0.37296358,0.018771688,0.5468891,0.026072113,0.005969175,0.0006448848,0.00068982336,0.0036615161,0.024338108],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945226,0.0019559884,0.0003712056,0.0011310809,0.0017473106,0.0002718791],"domain_scores_gemma":[0.9670502,0.02371054,0.0018236673,0.0034748125,0.00285866,0.0010822522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006907269,0.0014187727,0.0021940188,0.001390683,0.0014960065,0.007010312,0.003254882,0.0052289483,0.012722684],"category_scores_gemma":[0.1009952,0.0009598817,0.0011073474,0.0014644997,0.0087546855,0.022087505,0.0024776398,0.009156804,0.005159117],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044316493,0.0002175931,0.005982008,0.0013343465,0.00020885492,0.00043363572,0.00091712794,0.03153157,0.004415882,0.54110247,0.09834023,0.31507304],"study_design_scores_gemma":[0.00006643645,0.00009171606,0.0015940415,0.00089674926,0.00005845911,0.00056888274,0.0007330811,0.055277187,0.0033654184,0.87232196,0.064902954,0.00012314209],"about_ca_topic_score_codex":0.003527665,"about_ca_topic_score_gemma":0.0030329854,"teacher_disagreement_score":0.012722684,"about_ca_system_score_codex":0.002342818,"about_ca_system_score_gemma":0.0017975116,"threshold_uncertainty_score":0.04256159},"labels":[],"label_agreement":null},{"id":"W4391951736","doi":"10.2139/ssrn.4733295","title":"Integrating Uncertainty Propagation Models in Autonomous Vehicle Perception with Machine Learning Components","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Perception; Computer science; Artificial intelligence; Machine learning; Psychology","score_opus":0.014428836701191713,"score_gpt":0.24247216333337226,"score_spread":0.22804332663218055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391951736","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017580358,0.000225341,0.98038447,0.00019982336,0.00005662677,0.000014657927,0.000022557331,0.00023520169,0.0012808609],"genre_scores_gemma":[0.8848097,0.00038841204,0.11179928,0.0000998544,0.000084367704,0.000056597033,0.00006884926,0.00009756696,0.002595409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975806,0.00005317057,0.000013816177,0.000057446094,0.000089057445,0.000028513048],"domain_scores_gemma":[0.99925154,0.00042113566,0.00007819866,0.00006497163,0.00015011846,0.000034035736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006894729,0.00071156013,0.00075757154,0.0004432759,0.00038491006,0.0014010686,0.0011530678,0.0011307605,0.0015186497],"category_scores_gemma":[0.0029431009,0.00064095255,0.00063868816,0.00062653504,0.0005821313,0.0023611586,0.0012016834,0.001274055,0.00034481406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000419907,0.000031029926,0.0003346378,0.00003189309,0.000039781327,0.000029257033,0.000038642975,0.94978493,0.0022917532,0.011035504,0.00024332976,0.03609727],"study_design_scores_gemma":[0.0000010238912,0.0000063953858,0.000047089186,0.0000015172594,0.000002958436,0.0000035838548,0.000001961967,0.99511445,0.00029232248,0.0044383253,0.000087507266,0.0000028487302],"about_ca_topic_score_codex":0.0076879566,"about_ca_topic_score_gemma":0.004259796,"teacher_disagreement_score":0.0076879566,"about_ca_system_score_codex":0.0006595181,"about_ca_system_score_gemma":0.000811987,"threshold_uncertainty_score":0.015286386},"labels":[],"label_agreement":null},{"id":"W4391971355","doi":"10.1088/1742-5468/ad0a8c","title":"A simple probabilistic neural network for machine understanding","year":2024,"lang":"en","type":"article","venue":"Journal of Statistical Mechanics Theory and Experiment","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Particle Physics","funders":"","keywords":"Simple (philosophy); Probabilistic logic; Artificial neural network; Computer science; Probabilistic neural network; Artificial intelligence; Machine learning; Time delay neural network","score_opus":0.03366818495435355,"score_gpt":0.3100963615772229,"score_spread":0.2764281766228694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391971355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09292365,0.00057832187,0.8850562,0.0027488042,0.000110747584,0.00005610574,0.00028618905,0.0005284472,0.017711438],"genre_scores_gemma":[0.90741706,0.00024558083,0.08690342,0.00023289474,0.000051851286,0.00009201143,0.00013163422,0.00006332048,0.004862209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996463,0.0001451255,0.000011431585,0.00006753064,0.00009958818,0.000030010613],"domain_scores_gemma":[0.9990336,0.00068502186,0.00007046058,0.00009929146,0.00007725167,0.0000342867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010386048,0.00028722463,0.00060058566,0.00035491175,0.00036147633,0.0010728713,0.0012975885,0.001419514,0.0059613404],"category_scores_gemma":[0.005341011,0.0003397765,0.0006235636,0.0004994172,0.0012365098,0.002978058,0.0009620709,0.001463197,0.0005321312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007610677,0.00003427118,0.00037561302,0.000109076675,0.0000414906,0.000093922245,0.00008892744,0.6554055,0.0040502213,0.31835312,0.0013451457,0.020026611],"study_design_scores_gemma":[0.0000062478616,0.000008321585,0.00009727383,0.0000052023324,0.0000032890464,0.000018327935,0.0000037062641,0.87858486,0.00018834864,0.12070361,0.00037551596,0.000005256693],"about_ca_topic_score_codex":0.001682631,"about_ca_topic_score_gemma":0.0011538062,"teacher_disagreement_score":0.0059613404,"about_ca_system_score_codex":0.0007979757,"about_ca_system_score_gemma":0.0005203359,"threshold_uncertainty_score":0.019942641},"labels":[],"label_agreement":null},{"id":"W4391992583","doi":"10.1109/cyberc58899.2023.00033","title":"On the Basin of Attraction and Capacity of Restricted Hopfield Network as an Auto-Associative Memory","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Hopfield network; Attraction; Content-addressable memory; Bidirectional associative memory; Associative property; Computer science; Structural basin; Content-addressable storage; Artificial neural network; Artificial intelligence; Mathematics; Geology; Paleontology","score_opus":0.03736260199448654,"score_gpt":0.2654837614350272,"score_spread":0.22812115944054068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391992583","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3851516,0.003304031,0.5930713,0.0006323987,0.00006281602,0.000051012157,0.0001378081,0.00052868325,0.017060425],"genre_scores_gemma":[0.98507315,0.0006319856,0.013087406,0.000035493787,0.000028396706,0.00003849942,0.000036680212,0.000025475554,0.0010428629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999739,0.000084358675,0.0000149675125,0.00006069971,0.00006143502,0.00003949587],"domain_scores_gemma":[0.9985935,0.0008815686,0.0001466084,0.000120549645,0.0001840622,0.000073627016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007328573,0.00036843435,0.00048727499,0.0010290302,0.00036913977,0.00067839737,0.00076291413,0.0005729164,0.001607433],"category_scores_gemma":[0.0036181835,0.00023675058,0.0005126748,0.000541335,0.0014420706,0.002718952,0.00077412854,0.0005505022,0.00017276319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014304076,0.00003755306,0.002523771,0.00017415572,0.000082202445,0.00023412293,0.00033670472,0.7941468,0.010478768,0.14915968,0.0009071753,0.041776095],"study_design_scores_gemma":[0.000004559903,0.000027425904,0.00054739433,0.000012975209,0.000010811592,0.00006770972,0.000026245234,0.94960326,0.001069769,0.048305776,0.0003073863,0.000016661072],"about_ca_topic_score_codex":0.0031800414,"about_ca_topic_score_gemma":0.001508991,"teacher_disagreement_score":0.0031800414,"about_ca_system_score_codex":0.0006511218,"about_ca_system_score_gemma":0.0005220628,"threshold_uncertainty_score":0.0063230395},"labels":[],"label_agreement":null},{"id":"W4392019740","doi":"10.48550/arxiv.2402.12519","title":"Dynamics Based Neural Encoding with Inter-Intra Region Connectivity","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Identification (biology); Dynamics (music); System dynamics; Neural system; Computer science; Artificial neural network; Artificial intelligence; System identification; Computer vision; Data mining; Psychology; Neuroscience; Biology","score_opus":0.052299484539967044,"score_gpt":0.18561463630384112,"score_spread":0.13331515176387407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392019740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14133313,0.0006533037,0.8515582,0.0005187154,0.000076484444,0.000065574204,0.0003897916,0.0010788129,0.004325936],"genre_scores_gemma":[0.9146809,0.0004656913,0.08210515,0.000117351046,0.000030972224,0.00005350408,0.0003848857,0.00010694863,0.0020546175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998535,0.000032922893,0.000008916721,0.00004421902,0.0000406118,0.000019704095],"domain_scores_gemma":[0.99945885,0.0002522639,0.000068921385,0.000108190776,0.00007881127,0.000033104814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032564686,0.00053136534,0.0002827699,0.00031200008,0.00012085332,0.0006082921,0.00070018007,0.00052296603,0.0016681174],"category_scores_gemma":[0.0027046634,0.00020658964,0.00043861382,0.0003651269,0.00044230954,0.0023139492,0.0007827806,0.0010570236,0.00028573172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027375395,0.0001285267,0.0027817409,0.00019724829,0.00007811144,0.00016673152,0.00017920123,0.6906881,0.057294074,0.032769926,0.0016594322,0.21378307],"study_design_scores_gemma":[0.0000058045284,0.000055361557,0.0008493056,0.000010795973,0.000015958063,0.000053495478,0.000014954829,0.9801564,0.008085152,0.010061444,0.0006812521,0.000009971797],"about_ca_topic_score_codex":0.0021519135,"about_ca_topic_score_gemma":0.0024017307,"teacher_disagreement_score":0.0021519135,"about_ca_system_score_codex":0.0005765634,"about_ca_system_score_gemma":0.0004414397,"threshold_uncertainty_score":0.0055804253},"labels":[],"label_agreement":null},{"id":"W4392128155","doi":"10.1016/j.asoc.2024.111403","title":"Fuzzy clustering-based neural network based on linear fitting residual-driven weighted fuzzy clustering and convolutional regularization strategy","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Computer science; Fuzzy clustering; Fuzzy logic; Cluster analysis; Artificial intelligence; Overfitting; Regularization (linguistics); Pattern recognition (psychology); Data mining; Mathematics; Algorithm; Artificial neural network","score_opus":0.01611995270003528,"score_gpt":0.24647697727157425,"score_spread":0.23035702457153898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392128155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009765381,0.00019524344,0.98829055,0.00008647935,0.000034381857,0.000030093417,0.00002868876,0.0002636557,0.001305443],"genre_scores_gemma":[0.51136535,0.0004020314,0.47880736,0.00017807426,0.00006992107,0.00021164394,0.00029590627,0.00013883867,0.0085308375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994985,0.00007393202,0.000033066273,0.00017056931,0.00017486089,0.00004900548],"domain_scores_gemma":[0.99950206,0.000102587765,0.0000441854,0.00003755467,0.0002891622,0.000024462144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010184495,0.00072675553,0.0013675028,0.0008066822,0.00089104904,0.0008652358,0.0024587603,0.0016729007,0.0017601404],"category_scores_gemma":[0.001715432,0.00048293077,0.0009929101,0.00094897655,0.0006406292,0.0012635546,0.0010077544,0.0009535127,0.00048481347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015149126,0.000086659995,0.0006606491,0.00014682813,0.00012890351,0.000070639704,0.000107045715,0.8083142,0.012938793,0.01880903,0.0026236936,0.15596208],"study_design_scores_gemma":[0.0000019325469,0.0000073682163,0.00005289783,0.000003345223,0.0000063727025,0.000010450906,0.000002499807,0.99836653,0.0006613159,0.0007710621,0.000110341854,0.00000598837],"about_ca_topic_score_codex":0.016735198,"about_ca_topic_score_gemma":0.01646365,"teacher_disagreement_score":0.016735198,"about_ca_system_score_codex":0.001317533,"about_ca_system_score_gemma":0.0016077284,"threshold_uncertainty_score":0.033275545},"labels":[],"label_agreement":null},{"id":"W4392131914","doi":"10.1007/978-981-99-9836-4_34","title":"A Low-Power Analog Bell-Shaped Classifier Based on Parallel-Connected Gaussian Function Circuits","year":2024,"lang":"en","type":"book-chapter","venue":"Frontiers of artificial intelligence, ethics and multidisciplinary applications.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Gaussian; Computer science; Electronic circuit; Classifier (UML); Electronic engineering; Electrical engineering; Physics; Artificial intelligence; Engineering; Quantum mechanics","score_opus":0.044491944216944634,"score_gpt":0.2935045115530999,"score_spread":0.2490125673361553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392131914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0120054465,0.0012224943,0.9585358,0.0005286237,0.0004103349,0.00015349379,0.00017866181,0.0031050262,0.023860022],"genre_scores_gemma":[0.5145185,0.0008897104,0.44580474,0.0018908887,0.00033464373,0.00014318113,0.00042197888,0.00020284668,0.035793524],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995869,0.00006366169,0.00001604476,0.0000970312,0.00019345772,0.000042847034],"domain_scores_gemma":[0.9996649,0.00010915442,0.000019390884,0.000045331246,0.00013872948,0.000022527951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041832126,0.00045701678,0.0005312015,0.000499632,0.000533586,0.0013559374,0.0023992837,0.0012543884,0.010131388],"category_scores_gemma":[0.0009994231,0.0002459989,0.00035307673,0.00066846085,0.00048274346,0.0015146148,0.0006689714,0.000985396,0.0034371712],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043875692,0.00027242323,0.00060603744,0.0003928467,0.00011692209,0.00030831233,0.00013460191,0.031123769,0.09358564,0.0634986,0.025681956,0.7838402],"study_design_scores_gemma":[0.000056128592,0.00048677393,0.0006206186,0.0000925675,0.00012999485,0.00076002517,0.000043155127,0.8923032,0.040717587,0.026332986,0.038404077,0.000052893578],"about_ca_topic_score_codex":0.0017610673,"about_ca_topic_score_gemma":0.004626759,"teacher_disagreement_score":0.010131388,"about_ca_system_score_codex":0.00078263815,"about_ca_system_score_gemma":0.0006544337,"threshold_uncertainty_score":0.03389287},"labels":[],"label_agreement":null},{"id":"W4392190236","doi":"10.18280/isi.290115","title":"Neuronal Network-Founded Machine Knowledge with Pythons in Data Mining for Vast Information Classifications","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Data science; Knowledge extraction; Data mining; Artificial intelligence","score_opus":0.040209189968901535,"score_gpt":0.27638345614688176,"score_spread":0.2361742661779802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392190236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033610333,0.0009444558,0.9916067,0.0005395187,0.000099423516,0.000091422706,0.0003027942,0.0018089196,0.0012457166],"genre_scores_gemma":[0.13964829,0.0013264597,0.8521606,0.00045823187,0.0002478693,0.00093056844,0.0014625837,0.00038175922,0.0033836735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981603,0.0006216669,0.00022580803,0.00042865283,0.0004302306,0.00013328635],"domain_scores_gemma":[0.99435,0.0038352953,0.00028320664,0.00068378117,0.0006998958,0.0001477967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004191604,0.0011556944,0.0020849025,0.0025708375,0.001036333,0.0027214668,0.0034290042,0.0017265701,0.0045268005],"category_scores_gemma":[0.014267964,0.0010865482,0.002229553,0.0031631645,0.0018565878,0.0027644392,0.003670885,0.0038907751,0.0017591304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016748614,0.00015851282,0.002214059,0.0005520563,0.0003803954,0.00023622485,0.00028613128,0.58427286,0.001005655,0.08584155,0.007969385,0.3169157],"study_design_scores_gemma":[0.000008665794,0.000012472151,0.00012660015,0.000034838555,0.000013138639,0.000017377903,0.000015684347,0.9385435,0.00029074866,0.059186526,0.0017423282,0.000008125742],"about_ca_topic_score_codex":0.0097794235,"about_ca_topic_score_gemma":0.011691985,"teacher_disagreement_score":0.0097794235,"about_ca_system_score_codex":0.0018477988,"about_ca_system_score_gemma":0.002870226,"threshold_uncertainty_score":0.022167623},"labels":[],"label_agreement":null},{"id":"W4392199949","doi":"10.1002/9781119865667.ch16","title":"On the Energy‐Based Limitations of the Information Capacity and Information Processing Rates in the Human Brain","year":2024,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Università di Bologna","keywords":"Energy (signal processing); Human brain; Computer science; Information processing; Data science; Psychology; Neuroscience; Statistics; Mathematics","score_opus":0.03614910173991099,"score_gpt":0.2480486777469286,"score_spread":0.21189957600701761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392199949","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21220572,0.04499814,0.42674807,0.021525672,0.0006877685,0.00007010687,0.00095775345,0.0007024363,0.2921043],"genre_scores_gemma":[0.9244684,0.018512523,0.044917837,0.0009704448,0.0005787089,0.00010394977,0.0001715667,0.00020275757,0.010073808],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995296,0.000110664405,0.000029500306,0.00013257356,0.00014547672,0.000052181524],"domain_scores_gemma":[0.9968148,0.0024721648,0.00019710802,0.00024344816,0.00019649354,0.0000759638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011590857,0.00049016887,0.000504056,0.0011432901,0.00048688546,0.002623766,0.001046785,0.0010864126,0.0074863746],"category_scores_gemma":[0.0060655917,0.00035455936,0.00043036815,0.0007949235,0.0035527048,0.005974386,0.0009800316,0.0015044105,0.0009631724],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091072296,0.00002716251,0.00096277567,0.0001886157,0.000038471564,0.0002109579,0.00034366903,0.018042235,0.0053837914,0.9325907,0.0023495574,0.03977086],"study_design_scores_gemma":[0.0000063771463,0.000031707183,0.0024156398,0.00007723404,0.000014097513,0.000288883,0.00012503224,0.025513543,0.0020522308,0.9615032,0.007941196,0.00003087696],"about_ca_topic_score_codex":0.00083898177,"about_ca_topic_score_gemma":0.00055613846,"teacher_disagreement_score":0.0074863746,"about_ca_system_score_codex":0.0007714862,"about_ca_system_score_gemma":0.000338799,"threshold_uncertainty_score":0.025044441},"labels":[],"label_agreement":null},{"id":"W4392201327","doi":"10.1101/2024.02.20.580731","title":"Recurrent neural networks as neuro-computational models of human speech recognition","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Recurrent neural network; Computer science; Speech recognition; Artificial intelligence; Connectionism; Artificial neural network","score_opus":0.028560245438955315,"score_gpt":0.2508283805557691,"score_spread":0.22226813511681381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392201327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28207767,0.0012400497,0.6998255,0.0019007112,0.00013303373,0.000040367166,0.00037899514,0.00089471054,0.013508921],"genre_scores_gemma":[0.965752,0.00039209606,0.028712206,0.00009201112,0.00004918129,0.000060669776,0.00012477844,0.000058353617,0.0047586747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989986,0.000046404894,0.000004137451,0.000020895272,0.000019956917,0.000008757765],"domain_scores_gemma":[0.9997161,0.00016676629,0.000041221218,0.000022324213,0.000035750345,0.000017809249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046301782,0.00031683056,0.00026553747,0.0002930804,0.00013830933,0.0006795394,0.00056805694,0.0005312663,0.0017786887],"category_scores_gemma":[0.0013409142,0.00022081574,0.00037647187,0.00021816239,0.0005674796,0.0008591618,0.00032725657,0.00052872073,0.00034701813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035774225,0.000015754993,0.0006626171,0.000029083212,0.000037981248,0.00007173706,0.000074528456,0.9252249,0.004020114,0.06293694,0.0006506653,0.006239874],"study_design_scores_gemma":[0.0000013750868,0.0000032262703,0.00009018857,0.0000016527972,0.0000021919136,0.0000048592224,0.0000023957298,0.9867791,0.00016913404,0.012823522,0.00012047968,0.0000019608444],"about_ca_topic_score_codex":0.0027050648,"about_ca_topic_score_gemma":0.0022137044,"teacher_disagreement_score":0.0027050648,"about_ca_system_score_codex":0.0005523362,"about_ca_system_score_gemma":0.0002376858,"threshold_uncertainty_score":0.0059503317},"labels":[],"label_agreement":null},{"id":"W4392251527","doi":"10.1109/tcyb.2024.3353753","title":"Fuzzy Adaptive Knowledge-Based Inference Neural Networks: Design and Analysis","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Shandong Province; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Natural Science Foundation of China","keywords":"Adaptive neuro fuzzy inference system; Computer science; Data mining; Cluster analysis; Artificial intelligence; Fuzzy logic; Curse of dimensionality; Artificial neural network; Neuro-fuzzy; Machine learning; Inference; Fuzzy clustering; Generalization; Fuzzy control system; Mathematics","score_opus":0.03037765377022903,"score_gpt":0.2728135057564867,"score_spread":0.2424358519862577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392251527","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008064676,0.0007690122,0.9875901,0.0001477395,0.000065806744,0.000075084674,0.000034262597,0.00021411246,0.0030392783],"genre_scores_gemma":[0.7575097,0.0014180087,0.23534705,0.00026127562,0.0000931566,0.0005634559,0.00020341083,0.00003442823,0.0045694914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952805,0.00009392364,0.00003339236,0.00013820807,0.0001599654,0.000046489185],"domain_scores_gemma":[0.9995584,0.00015212268,0.00005789988,0.000030614683,0.00018489007,0.000016015565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010593992,0.00070067646,0.0006994378,0.00044791834,0.0004192856,0.00091004214,0.0015493187,0.0013274457,0.0010852029],"category_scores_gemma":[0.0017402016,0.00034164893,0.000525222,0.00058759056,0.00056183076,0.00091075007,0.0007285505,0.0010777881,0.00021088446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008343937,0.000044123117,0.00091397756,0.00018785751,0.000065733846,0.000118414624,0.000084727166,0.8404298,0.0044161887,0.010846369,0.0011010737,0.1417083],"study_design_scores_gemma":[0.000003069502,0.000021793292,0.000086235574,0.0000071741883,0.000009566675,0.00001937916,0.000004392697,0.9980349,0.00054460636,0.0008057339,0.00045869814,0.0000044940157],"about_ca_topic_score_codex":0.0069953604,"about_ca_topic_score_gemma":0.0046809646,"teacher_disagreement_score":0.0069953604,"about_ca_system_score_codex":0.00080438715,"about_ca_system_score_gemma":0.00086636434,"threshold_uncertainty_score":0.01390928},"labels":[],"label_agreement":null},{"id":"W4392339646","doi":"","title":"Unveiling the Transport Dynamics of Neural Networks : a Least Action Principle for Deep Learning","year":2023,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Action (physics); Dynamics (music); Cognitive science; Artificial intelligence; Artificial neural network; Computer science; Psychology; Physics; Pedagogy","score_opus":0.0271001580829584,"score_gpt":0.26033737074932795,"score_spread":0.23323721266636954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392339646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004716571,0.000116416835,0.9936773,0.00036919178,0.000021063195,0.0000145900485,0.000020291678,0.00011519142,0.0009494388],"genre_scores_gemma":[0.58333784,0.0011966671,0.40459824,0.00061824074,0.00024883152,0.0003524182,0.00017454155,0.00054868823,0.008924562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994374,0.00020888624,0.000023133984,0.00012147494,0.00016198504,0.00004708103],"domain_scores_gemma":[0.99877566,0.00070511614,0.00016881482,0.00015341387,0.00011415852,0.00008282935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023927698,0.0012831202,0.00085219025,0.00065032917,0.0005096265,0.0011149882,0.0014834349,0.00186668,0.0018997171],"category_scores_gemma":[0.0048169955,0.00071297545,0.0013554494,0.0005001909,0.0032943427,0.003525629,0.0033191184,0.0037608447,0.00037617824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009522171,0.000042544823,0.0004779214,0.00016616863,0.00007776307,0.00013359322,0.00015670506,0.49563563,0.009480937,0.4584843,0.0015599785,0.03368931],"study_design_scores_gemma":[0.000007355868,0.000036117603,0.000054137516,0.000012651391,0.0000075750954,0.000020999112,0.0000070191722,0.90042204,0.0009760308,0.0976896,0.0007588103,0.000007728656],"about_ca_topic_score_codex":0.0018340687,"about_ca_topic_score_gemma":0.0013473718,"teacher_disagreement_score":0.0023927698,"about_ca_system_score_codex":0.0016009932,"about_ca_system_score_gemma":0.0011847002,"threshold_uncertainty_score":0.0126543045},"labels":[],"label_agreement":null},{"id":"W4392349361","doi":"10.18280/ts.410132","title":"Enhancing Image Classification Through a Hybrid Approach: Integrating Convolutional Neural Networks with Hidden Markov Mod","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mod; Convolutional neural network; Pattern recognition (psychology); Artificial intelligence; Computer science; Image (mathematics); Hidden Markov model; Contextual image classification; Machine learning","score_opus":0.02095736667449649,"score_gpt":0.24216430277258755,"score_spread":0.22120693609809106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392349361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061396863,0.0020488142,0.9276443,0.00052444066,0.00017653489,0.00008849262,0.0002205159,0.0041746055,0.0037253716],"genre_scores_gemma":[0.74573934,0.0010582043,0.24728143,0.00044600823,0.00013880586,0.000085117936,0.0005958615,0.00013499614,0.0045202156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994312,0.00008549956,0.000034552424,0.00014879611,0.00020499677,0.00009488411],"domain_scores_gemma":[0.9991448,0.00031389823,0.000107927626,0.00016548879,0.00022280104,0.00004498445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011777988,0.0011893009,0.0007862786,0.0014207346,0.0002919067,0.0010970321,0.0013371825,0.0009476005,0.0010740351],"category_scores_gemma":[0.00240154,0.00044502324,0.00087066105,0.001062613,0.0004497536,0.0020384667,0.001223817,0.001038727,0.00067427056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033184956,0.0002903866,0.005180373,0.00017724352,0.0003133586,0.00022453317,0.00010726992,0.25646418,0.027797168,0.0057267398,0.0034976855,0.69988924],"study_design_scores_gemma":[0.0000042424203,0.000048868602,0.0005747479,0.000010027955,0.00004312211,0.000042684278,0.0000113101205,0.99003637,0.0064032706,0.0018850149,0.0009285808,0.000011671179],"about_ca_topic_score_codex":0.012170147,"about_ca_topic_score_gemma":0.016163295,"teacher_disagreement_score":0.012170147,"about_ca_system_score_codex":0.0009709205,"about_ca_system_score_gemma":0.0009460643,"threshold_uncertainty_score":0.024198592},"labels":[],"label_agreement":null},{"id":"W4392349893","doi":"10.18280/ts.410124","title":"Enhancing Myoelectric Signal Classification Through Conditional Spectral Moments and Wavelet-Enhanced Time-Domain Descriptors","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wavelet; Pattern recognition (psychology); Time domain; Computer science; SIGNAL (programming language); Artificial intelligence; Domain (mathematical analysis); Speech recognition; Mathematics; Computer vision; Mathematical analysis","score_opus":0.017308528789338192,"score_gpt":0.24208120749165932,"score_spread":0.22477267870232112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392349893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14067696,0.0018562333,0.85192955,0.00032451673,0.00028921117,0.00008829455,0.0005966397,0.0013969882,0.0028416081],"genre_scores_gemma":[0.7867941,0.0014334084,0.20576543,0.00018491925,0.00021023514,0.00009536318,0.0021761246,0.000108188935,0.0032322651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995074,0.000056277215,0.000036496163,0.000092294315,0.00024863443,0.00005884851],"domain_scores_gemma":[0.99915457,0.00029986168,0.000115946386,0.00011141037,0.00028224307,0.000035942503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080802356,0.0006819989,0.0007579514,0.0012258932,0.00016178304,0.000770273,0.00052430766,0.00047423528,0.0010498366],"category_scores_gemma":[0.002800027,0.0001246719,0.0005587179,0.0012411642,0.00024346371,0.0011299746,0.0007024167,0.0008499961,0.0009266616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041778618,0.00026358693,0.0047516446,0.00018691541,0.0000880479,0.00016194025,0.00006780198,0.04369121,0.07487559,0.0035001584,0.0047052973,0.86729],"study_design_scores_gemma":[0.00002032381,0.00028259328,0.012347511,0.000038191884,0.00009276287,0.0003447708,0.00007477286,0.9373738,0.0402646,0.0033878044,0.0057167187,0.00005608128],"about_ca_topic_score_codex":0.0010453037,"about_ca_topic_score_gemma":0.0013016739,"teacher_disagreement_score":0.0012258932,"about_ca_system_score_codex":0.00023722621,"about_ca_system_score_gemma":0.00040800462,"threshold_uncertainty_score":0.0042732954},"labels":[],"label_agreement":null},{"id":"W4392400482","doi":"10.1504/ijcvr.2024.136996","title":"Quantitative analysis of transfer and incremental learning for image classification","year":2024,"lang":"en","type":"article","venue":"International Journal of Computational Vision and Robotics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Transfer of learning; Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Machine learning; Computer vision","score_opus":0.029477031150217034,"score_gpt":0.3580208673321261,"score_spread":0.3285438361819091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392400482","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04154547,0.0019179445,0.95099443,0.00053857354,0.000057978752,0.00010023792,0.0001912218,0.0005514574,0.0041025965],"genre_scores_gemma":[0.87404805,0.0010528576,0.12135642,0.00015934127,0.00019542496,0.00022221048,0.00045368343,0.0001558019,0.0023561437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980406,0.0005339642,0.000079443256,0.00026718475,0.00093796616,0.00014077405],"domain_scores_gemma":[0.9763889,0.016581204,0.0015072362,0.0018004872,0.0033549506,0.0003672216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005664805,0.0006818553,0.0006037758,0.0022379989,0.00042402258,0.0012632717,0.001898433,0.0009884132,0.0027765227],"category_scores_gemma":[0.034513433,0.00028202546,0.00054080045,0.0014994086,0.0019750004,0.0044563245,0.001593762,0.0015010171,0.0003145519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024790657,0.00012899612,0.0036734976,0.0006552456,0.00013161349,0.00020804329,0.0002552634,0.58193153,0.011792128,0.1570116,0.0030536903,0.24091054],"study_design_scores_gemma":[0.0000041422704,0.0001023098,0.0012088404,0.000018724215,0.000014223184,0.000083033905,0.000025305078,0.93446445,0.0028980935,0.06035738,0.0008092079,0.000014262031],"about_ca_topic_score_codex":0.0013393117,"about_ca_topic_score_gemma":0.00073941157,"teacher_disagreement_score":0.005664805,"about_ca_system_score_codex":0.0019552324,"about_ca_system_score_gemma":0.0008432789,"threshold_uncertainty_score":0.029958725},"labels":[],"label_agreement":null},{"id":"W4392538982","doi":"10.48550/arxiv.2403.03199","title":"Operator Learning Renormalization Group","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Science; Alliance de recherche numérique du Canada; National Science Foundation; Government of Canada; U.S. Department of Energy; Ministero dello Sviluppo Economico; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Operator (biology); Renormalization group; Group (periodic table); Mathematics; Mathematical physics; Physics; Quantum mechanics","score_opus":0.048342908185016756,"score_gpt":0.17984844662713084,"score_spread":0.1315055384421141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392538982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014860907,0.00029414016,0.9700178,0.0009014373,0.00018906363,0.00007205624,0.00004841148,0.00038765123,0.013228434],"genre_scores_gemma":[0.62076217,0.00077860226,0.36256462,0.0010905872,0.0005010882,0.00058220146,0.0001813452,0.00048223732,0.01305719],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993666,0.00028914565,0.000017453169,0.00009311149,0.00018947016,0.00004418299],"domain_scores_gemma":[0.9990169,0.00044657255,0.00006672368,0.00029121534,0.00010968139,0.00006893311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013922309,0.00073485536,0.0008933984,0.00060071325,0.0008940997,0.0010979343,0.0020084775,0.0015404754,0.004042349],"category_scores_gemma":[0.004552668,0.0003459288,0.0009900328,0.00036855432,0.0026685034,0.0026959751,0.0016075317,0.0019418668,0.00065930164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021433674,0.000041433494,0.00019787486,0.00005401586,0.000027926031,0.000050042338,0.000067696295,0.12877499,0.0017441125,0.8577798,0.0010821691,0.0101586105],"study_design_scores_gemma":[0.000009287276,0.00001892661,0.000045537916,0.0000062131835,0.0000032762666,0.000018808754,0.000005664516,0.639003,0.00042099904,0.3588882,0.001570077,0.000010100231],"about_ca_topic_score_codex":0.001084976,"about_ca_topic_score_gemma":0.00080477045,"teacher_disagreement_score":0.004042349,"about_ca_system_score_codex":0.0010580437,"about_ca_system_score_gemma":0.00083951466,"threshold_uncertainty_score":0.013523042},"labels":[],"label_agreement":null},{"id":"W4392595128","doi":"10.5194/egusphere-egu24-4238","title":"Letting neural networks talk: exploring two probabilistic neural network models for input variable selection","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Selection (genetic algorithm); Computer science; Variable (mathematics); Probabilistic logic; Probabilistic neural network; Artificial intelligence; Feature selection; Machine learning; Time delay neural network; Mathematics","score_opus":0.06098043961456535,"score_gpt":0.27469207211970814,"score_spread":0.2137116325051428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392595128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008304775,0.00066739233,0.98724836,0.0011436535,0.0000827016,0.000028213793,0.00007621968,0.00020257925,0.002246074],"genre_scores_gemma":[0.573628,0.002091909,0.41437253,0.0010452315,0.00035038745,0.00037579224,0.00036728452,0.0003004068,0.0074685337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998628,0.00065965526,0.000062445826,0.00031649097,0.00023455327,0.00009877844],"domain_scores_gemma":[0.99641955,0.0026854363,0.00025038395,0.00020889004,0.00034629882,0.000089433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003932405,0.001406416,0.001093093,0.0009136961,0.0006157541,0.0023291593,0.002639267,0.0020286283,0.003326403],"category_scores_gemma":[0.012165449,0.0010807429,0.0016343613,0.0011683666,0.0017304603,0.0045986082,0.0024076449,0.0036100482,0.000545226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014969877,0.00007404297,0.001947655,0.00017409935,0.00021452195,0.00019538388,0.00025019454,0.75152206,0.0013543421,0.1544328,0.0022624973,0.08742264],"study_design_scores_gemma":[0.000011109536,0.00001880024,0.00011302144,0.000020187534,0.00001552465,0.000021187569,0.000009063309,0.93938315,0.0003450363,0.05922321,0.0008262346,0.000013571246],"about_ca_topic_score_codex":0.0045683575,"about_ca_topic_score_gemma":0.004034424,"teacher_disagreement_score":0.0045683575,"about_ca_system_score_codex":0.001271424,"about_ca_system_score_gemma":0.001271823,"threshold_uncertainty_score":0.020796776},"labels":[],"label_agreement":null},{"id":"W4392624741","doi":"10.5194/egusphere-egu24-16034","title":"Developing perceptual models of hydrologic behavior across the North American continent","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Perception; Geography; Environmental science; Hydrology (agriculture); Geology; Psychology; Geotechnical engineering","score_opus":0.05684953532394016,"score_gpt":0.31737316288348943,"score_spread":0.2605236275595493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392624741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67854285,0.00031324133,0.2975535,0.0015460559,0.000033553,0.000083377054,0.00047625595,0.0005596099,0.020891497],"genre_scores_gemma":[0.95883495,0.0002145208,0.03893975,0.00006368531,0.000010371999,0.000085364336,0.00019129852,0.000031949898,0.0016281663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999151,0.000031335316,0.000003844843,0.000029159854,0.0000104989185,0.000010091754],"domain_scores_gemma":[0.9995542,0.00025263347,0.00006137483,0.00003627928,0.00005803763,0.00003744715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043732423,0.0002962671,0.00019602783,0.00033164865,0.00033854053,0.0013445581,0.00064809865,0.00036942158,0.0020144312],"category_scores_gemma":[0.0019485154,0.00033679287,0.00048567817,0.00033116527,0.00070345437,0.0013077472,0.0005596373,0.0005092458,0.00014190613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022934297,0.00003658249,0.0054671485,0.00002373169,0.000035433877,0.000056174333,0.0006297185,0.95044094,0.0007038012,0.031199973,0.00085210975,0.010531387],"study_design_scores_gemma":[0.000003574269,0.000007307184,0.0013208153,0.00000501774,0.0000057711713,0.0000055605824,0.00012596969,0.9858927,0.000050016326,0.012110874,0.00046854772,0.0000038193966],"about_ca_topic_score_codex":0.04646308,"about_ca_topic_score_gemma":0.036911845,"teacher_disagreement_score":0.04646308,"about_ca_system_score_codex":0.0015041091,"about_ca_system_score_gemma":0.0007869828,"threshold_uncertainty_score":0.09238523},"labels":[],"label_agreement":null},{"id":"W4392631689","doi":"10.1007/s42484-024-00149-0","title":"Parallel hybrid quantum-classical machine learning for kernelized time-series classification","year":2024,"lang":"en","type":"article","venue":"Quantum Machine Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agnostiq (Canada)","funders":"Office of Science; National Energy Research Scientific Computing Center; Advanced Scientific Computing Research; U.S. Department of Energy","keywords":"Series (stratigraphy); Computer science; Artificial intelligence; Quantum; Machine learning; Pattern recognition (psychology); Physics; Biology; Quantum mechanics","score_opus":0.03246793115205285,"score_gpt":0.2967693446659335,"score_spread":0.2643014135138807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392631689","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032170672,0.00041506387,0.96140116,0.00036770361,0.00019928349,0.000053707263,0.00013253941,0.0015768493,0.0036830201],"genre_scores_gemma":[0.71856844,0.00027396678,0.27382123,0.00020210736,0.00019162222,0.00015756449,0.00043220865,0.00021129887,0.006141536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994628,0.00014446468,0.000038143477,0.0001014,0.00018793598,0.00006537443],"domain_scores_gemma":[0.9990711,0.000272427,0.000052673353,0.0002728102,0.00026122257,0.00006973954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087273424,0.00045927262,0.00093304395,0.0004975655,0.0006375992,0.001199152,0.0017112553,0.00068268826,0.007063675],"category_scores_gemma":[0.0020943645,0.00025578,0.00068764307,0.0009222179,0.00062748283,0.0021252707,0.0012620768,0.001045015,0.0013078974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009721928,0.00043698595,0.0016942008,0.00029080335,0.0002957132,0.00016957687,0.00012540816,0.37937334,0.01745228,0.13577026,0.010179679,0.45323953],"study_design_scores_gemma":[0.000010624725,0.000019119563,0.00011309589,0.0000017324912,0.000006777035,0.0000140489465,0.000004785232,0.9880642,0.001173599,0.010030286,0.00055646675,0.00000525966],"about_ca_topic_score_codex":0.003075478,"about_ca_topic_score_gemma":0.0055818097,"teacher_disagreement_score":0.007063675,"about_ca_system_score_codex":0.0008028934,"about_ca_system_score_gemma":0.0013786264,"threshold_uncertainty_score":0.02363038},"labels":[],"label_agreement":null},{"id":"W4392781204","doi":"10.48550/arxiv.2403.07688","title":"Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Nvidia","keywords":"Maxwell's demon; Demon; Pruning; Saturation (graph theory); Work (physics); Computer science; Artificial intelligence; Physics; Mathematics; Biology; Quantum mechanics; Combinatorics; Botany","score_opus":0.05092135791348678,"score_gpt":0.17984012051637627,"score_spread":0.12891876260288948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392781204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21963511,0.0015793166,0.7677493,0.0009830826,0.00018771354,0.000095964526,0.00014173877,0.0020967128,0.0075310315],"genre_scores_gemma":[0.8541212,0.0004784968,0.14028272,0.00058569404,0.00006893492,0.00013950917,0.00016507397,0.00027633595,0.003881931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995933,0.00011956457,0.000030782223,0.0000693741,0.00012291313,0.00006420591],"domain_scores_gemma":[0.9982139,0.00092364504,0.00015613773,0.0003552579,0.00022761775,0.00012345634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011878953,0.00065604417,0.00072843244,0.0005280778,0.0004962608,0.00080450636,0.0019163331,0.000919629,0.002337363],"category_scores_gemma":[0.0064309747,0.00042912236,0.00037228048,0.0004743883,0.00093189295,0.0017776638,0.001799,0.0012808278,0.0004532555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006120558,0.00019298278,0.005063245,0.00029934972,0.00015077491,0.00057161704,0.00050484686,0.56279373,0.03459162,0.03762064,0.008985933,0.34861314],"study_design_scores_gemma":[0.00003907942,0.00013824066,0.00040123216,0.00004063303,0.00003132307,0.00015233572,0.000052097963,0.9663612,0.01199077,0.018191142,0.002586814,0.0000150492115],"about_ca_topic_score_codex":0.0013445985,"about_ca_topic_score_gemma":0.0036620814,"teacher_disagreement_score":0.002337363,"about_ca_system_score_codex":0.00060258666,"about_ca_system_score_gemma":0.0008452768,"threshold_uncertainty_score":0.007819295},"labels":[],"label_agreement":null},{"id":"W4392781618","doi":"10.48550/arxiv.2403.07059","title":"Better than classical? The subtle art of benchmarking quantum machine\\n learning models","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; University of Toronto; Innovation, Science and Economic Development Canada","keywords":"Benchmarking; Quantum; Computer science; Artificial intelligence; Cognitive science; Psychology; Physics; Economics; Quantum mechanics; Management","score_opus":0.08025898080473734,"score_gpt":0.1894861551547899,"score_spread":0.10922717435005255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392781618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40862834,0.008967553,0.46238118,0.03040389,0.0029368778,0.00024559625,0.0031959522,0.0048924163,0.07834815],"genre_scores_gemma":[0.9082746,0.001862664,0.079226926,0.002896877,0.00035523367,0.00028639563,0.00218198,0.0012529305,0.0036623694],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995282,0.002452602,0.00019792371,0.0006464513,0.0011294268,0.00029160874],"domain_scores_gemma":[0.9895734,0.0046661464,0.0003237743,0.0043014484,0.00081268215,0.0003225262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008485705,0.0007479877,0.001323628,0.00083959434,0.001139845,0.0033621823,0.0031592879,0.0021954337,0.0058104265],"category_scores_gemma":[0.034995425,0.00044177144,0.0008534839,0.0011770087,0.0027828284,0.010470287,0.0023839637,0.0037136232,0.0013704351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004980593,0.0004631057,0.0065783164,0.0010342499,0.00055404217,0.00009418627,0.00046821666,0.2852439,0.0052256566,0.5761293,0.035480265,0.0882307],"study_design_scores_gemma":[0.00008142013,0.00027072313,0.0016961572,0.00021641715,0.00006445582,0.000055347864,0.00024299702,0.54717976,0.0072827083,0.42011556,0.022708569,0.0000858714],"about_ca_topic_score_codex":0.0029698606,"about_ca_topic_score_gemma":0.004199168,"teacher_disagreement_score":0.008485705,"about_ca_system_score_codex":0.0016832413,"about_ca_system_score_gemma":0.0017113391,"threshold_uncertainty_score":0.04487723},"labels":[],"label_agreement":null},{"id":"W4393039780","doi":"10.7554/elife.91034.2","title":"A dynamic neural resource model bridges sensory and working memory","year":2024,"lang":"en","type":"preprint","venue":"eLife","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Wellcome Trust; McMaster University","keywords":"Sensory system; Resource (disambiguation); Computer science; Neuroscience; Psychology","score_opus":0.0305619531066555,"score_gpt":0.2705550303450802,"score_spread":0.23999307723842472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393039780","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51609707,0.0005229558,0.4650128,0.0029435472,0.0000918346,0.000044917688,0.00035183915,0.00036063616,0.014574368],"genre_scores_gemma":[0.985732,0.00016077541,0.01093595,0.00007832557,0.000022081702,0.000047303463,0.00005755571,0.00002046107,0.0029455542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998006,0.00005446723,0.000009794377,0.00006430128,0.000031559583,0.000039313785],"domain_scores_gemma":[0.9992174,0.00037114022,0.00012801323,0.00010217079,0.000101165046,0.00008015073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078761793,0.0003583584,0.0005743779,0.00041682916,0.00027754338,0.0011153027,0.0021934959,0.0011041026,0.003076724],"category_scores_gemma":[0.0030087195,0.00033924537,0.000505332,0.00039794063,0.0010768535,0.002303025,0.0008731825,0.00086377776,0.000384822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030079868,0.0001630748,0.002200049,0.000120449025,0.00013081804,0.00035380607,0.000242036,0.77806926,0.017078655,0.16892023,0.0018664437,0.03055452],"study_design_scores_gemma":[0.000012610808,0.000026721456,0.0004074728,0.000004735059,0.000010669146,0.000032178723,0.00001857599,0.94964755,0.0005144304,0.049078766,0.00023939477,0.0000069619655],"about_ca_topic_score_codex":0.0027357866,"about_ca_topic_score_gemma":0.0018342608,"teacher_disagreement_score":0.003076724,"about_ca_system_score_codex":0.0010721581,"about_ca_system_score_gemma":0.0005628121,"threshold_uncertainty_score":0.010292709},"labels":[],"label_agreement":null},{"id":"W4393147176","doi":"10.1609/aaai.v38i15.29576","title":"PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Architecture; Computer science; Computer architecture; Data science; World Wide Web; Information retrieval; Artificial intelligence; Geography","score_opus":0.1151290282026288,"score_gpt":0.3315411980751283,"score_spread":0.21641216987249948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393147176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03824724,0.00049460586,0.94139725,0.00039833653,0.00011659343,0.00020205877,0.0002863116,0.014265332,0.0045923125],"genre_scores_gemma":[0.5085294,0.00019722544,0.48070174,0.0006365052,0.000057837042,0.00029762762,0.0013464718,0.00067208894,0.007561202],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989844,0.0002498069,0.000064582724,0.0002698631,0.00024827692,0.0001830351],"domain_scores_gemma":[0.99880373,0.0003404035,0.00007090099,0.00049088383,0.0002085952,0.00008546936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001857357,0.0013079247,0.0011992833,0.0008563901,0.0008168194,0.0011303751,0.0027581877,0.0017020662,0.0043404116],"category_scores_gemma":[0.0049016457,0.0005053027,0.0009443207,0.0007712197,0.00070958654,0.0027180214,0.0025700135,0.0015679051,0.0013522041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043513608,0.00043791824,0.0035678588,0.00014368778,0.00015036782,0.0002412632,0.00017430383,0.33726627,0.005357477,0.010741802,0.016662776,0.6248211],"study_design_scores_gemma":[0.000044656943,0.000076554046,0.00020035796,0.00001026576,0.000019024885,0.00009576468,0.000039020226,0.98337585,0.0024794037,0.011301925,0.0023430912,0.0000141589135],"about_ca_topic_score_codex":0.006658058,"about_ca_topic_score_gemma":0.014223155,"teacher_disagreement_score":0.006658058,"about_ca_system_score_codex":0.001230908,"about_ca_system_score_gemma":0.001899803,"threshold_uncertainty_score":0.014520109},"labels":[],"label_agreement":null},{"id":"W4393148340","doi":"10.1609/aaai.v38i6.28349","title":"Neural Physical Simulation with Multi-Resolution Hash Grid Encoding","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"China Postdoctoral Science Foundation; Tsinghua University; National Natural Science Foundation of China","keywords":"Computer science; Hash function; Encoding (memory); Grid; Resolution (logic); Hash table; Parallel computing; Artificial intelligence; Programming language; Mathematics","score_opus":0.09871703021856838,"score_gpt":0.32721648795037256,"score_spread":0.2284994577318042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393148340","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051572777,0.00017904303,0.94095665,0.00026976148,0.000100136596,0.00003500476,0.00009915996,0.0006573707,0.0061300606],"genre_scores_gemma":[0.71927273,0.0001620099,0.2762034,0.00015307021,0.000032424792,0.0001320337,0.00019657078,0.0001574876,0.0036903664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999871,0.000030000901,0.000008025997,0.000020449746,0.000054300377,0.000016137312],"domain_scores_gemma":[0.99966216,0.00012351043,0.000027188884,0.00009071963,0.000070038346,0.000026392483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026653073,0.00024875868,0.00045090468,0.0002705826,0.00029353873,0.0006016197,0.0010366787,0.0006831826,0.002665954],"category_scores_gemma":[0.0012450401,0.00018604599,0.00036948937,0.000356782,0.00051359483,0.0011766017,0.001101664,0.0007479157,0.0003772797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048761493,0.00003121682,0.0005613398,0.000039735467,0.000015897318,0.000073123825,0.00005551319,0.93008524,0.00356406,0.03450604,0.00077156373,0.030247647],"study_design_scores_gemma":[0.0000027152382,0.000004005563,0.000017526825,7.687454e-7,6.94821e-7,0.0000041837143,0.0000022021177,0.9968869,0.0003024544,0.0025835694,0.00019360274,0.0000013783471],"about_ca_topic_score_codex":0.0022086871,"about_ca_topic_score_gemma":0.0021953585,"teacher_disagreement_score":0.002665954,"about_ca_system_score_codex":0.00043969162,"about_ca_system_score_gemma":0.00051518145,"threshold_uncertainty_score":0.008918524},"labels":[],"label_agreement":null},{"id":"W4393159569","doi":"10.1609/aaai.v38i10.28999","title":"Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Computer science; Artificial neural network; Cognitive science; Artificial intelligence; Deep neural networks; Psychology","score_opus":0.14119041759848408,"score_gpt":0.34680996097052397,"score_spread":0.2056195433720399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393159569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13753845,0.00025341584,0.8605497,0.0003336127,0.000017128757,0.000036341793,0.00020635438,0.00036037012,0.00070464285],"genre_scores_gemma":[0.8868356,0.000203966,0.11131266,0.00011016591,0.00002637522,0.00007971329,0.0004206422,0.00004550641,0.00096547656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995777,0.00011128156,0.000020482623,0.00015373767,0.00008660519,0.000050104838],"domain_scores_gemma":[0.9987232,0.00065573823,0.00024871196,0.00019363021,0.000121867764,0.000056767192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009654767,0.0007787837,0.0006619759,0.0007907064,0.00027125288,0.0010498241,0.0011102876,0.0008137837,0.00062398065],"category_scores_gemma":[0.0038474237,0.0005389939,0.0006347374,0.0006118553,0.0011723572,0.0025710012,0.0013238209,0.0021640407,0.00015260381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020468805,0.000108799446,0.0040837033,0.0001681061,0.000102587495,0.00021859218,0.00044435554,0.8171069,0.018207947,0.041045237,0.0013738647,0.116935216],"study_design_scores_gemma":[0.000004196541,0.0000127109815,0.00037802328,0.0000061551937,0.000005131833,0.000013471246,0.000025232608,0.9668684,0.0011923037,0.03134672,0.00014362451,0.000004100579],"about_ca_topic_score_codex":0.0036785752,"about_ca_topic_score_gemma":0.0047018565,"teacher_disagreement_score":0.0036785752,"about_ca_system_score_codex":0.0010854878,"about_ca_system_score_gemma":0.0006708695,"threshold_uncertainty_score":0.0078758},"labels":[],"label_agreement":null},{"id":"W4393159942","doi":"10.1609/aaai.v38i11.29095","title":"QLABGrad: A Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperparameter; Convergence (economics); Scheme (mathematics); Artificial intelligence; Computer science; Deep learning; Machine learning; Psychology; Mathematics; Economics","score_opus":0.0590356522462624,"score_gpt":0.29280082859496703,"score_spread":0.23376517634870464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393159942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003770632,0.00022595997,0.9922335,0.000186706,0.00006722564,0.00011775614,0.000044418095,0.0023229746,0.0010308124],"genre_scores_gemma":[0.20310673,0.00042674676,0.7882724,0.00081537553,0.00012713912,0.0007910394,0.00033642378,0.0017551415,0.004369041],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978903,0.0007582362,0.00018569827,0.00033651022,0.0006910667,0.00013828908],"domain_scores_gemma":[0.9963313,0.0013355467,0.00040402278,0.0008592946,0.00082066277,0.00024917795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006006829,0.0017002958,0.0013830062,0.001132001,0.0008881416,0.0015886705,0.0043219994,0.0028699017,0.004142941],"category_scores_gemma":[0.020167086,0.0010037902,0.000981537,0.0008718034,0.0022258817,0.0038856927,0.005234733,0.005730578,0.0025701027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006349937,0.00034140376,0.0024408395,0.0004400474,0.00015196235,0.00023257236,0.0005029081,0.49196833,0.016414484,0.07759841,0.015997764,0.39327633],"study_design_scores_gemma":[0.000063744184,0.000059951602,0.00010409472,0.000036375008,0.000010243264,0.000049256563,0.000011924272,0.9796951,0.003198523,0.014189308,0.0025586681,0.000022766197],"about_ca_topic_score_codex":0.0027671116,"about_ca_topic_score_gemma":0.0039401036,"teacher_disagreement_score":0.006006829,"about_ca_system_score_codex":0.0016660945,"about_ca_system_score_gemma":0.002857478,"threshold_uncertainty_score":0.031767488},"labels":[],"label_agreement":null},{"id":"W4393178464","doi":"10.1609/aaai.v38i21.30447","title":"Engineering the Neural Collapse Geometry of Supervised-Contrastive Loss (Student Abstract)","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; King Abdullah University of Science and Technology; Universities Space Research Association; National Science Foundation","keywords":"Geometry; Computer science; Geology; Artificial intelligence; Mathematics","score_opus":0.0395650906801416,"score_gpt":0.28374946331395356,"score_spread":0.24418437263381196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393178464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09758906,0.0004805057,0.892452,0.0018020897,0.00014075887,0.00012764243,0.00024204123,0.00080415973,0.006361761],"genre_scores_gemma":[0.86331767,0.0002539179,0.129642,0.0006343363,0.00019965418,0.00026319892,0.00048198298,0.00027985897,0.0049274373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823344,0.0007710803,0.00007819651,0.0003413632,0.00043088547,0.00014501372],"domain_scores_gemma":[0.99451137,0.0029896528,0.0004761305,0.00078153075,0.0009011489,0.00034016612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00575884,0.00096229365,0.0008223033,0.00079552556,0.00048375753,0.0014250774,0.0015412787,0.0015727676,0.0027915027],"category_scores_gemma":[0.01973888,0.00038696616,0.0005153219,0.00047910446,0.0024208953,0.003375629,0.003258346,0.0029437095,0.0006889221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072713674,0.00035327236,0.0056589353,0.00021214857,0.00009051771,0.0004756373,0.00020401223,0.5931793,0.012333605,0.15800385,0.019234344,0.2095273],"study_design_scores_gemma":[0.000016916534,0.00013230737,0.0004235035,0.000020362237,0.0000051981497,0.000093464005,0.000013562156,0.96465564,0.0025773356,0.031342734,0.00070786546,0.000010975451],"about_ca_topic_score_codex":0.001504137,"about_ca_topic_score_gemma":0.0014920705,"teacher_disagreement_score":0.00575884,"about_ca_system_score_codex":0.0015532863,"about_ca_system_score_gemma":0.0011036335,"threshold_uncertainty_score":0.030456007},"labels":[],"label_agreement":null},{"id":"W4393187894","doi":"10.1101/2024.03.20.585911","title":"Noise Reduction and Localization Accuracy in a Mobile Magnetoencephalography System","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Magnetoencephalography; Reduction (mathematics); Noise (video); Noise reduction; Computer science; Mathematics; Artificial intelligence; Psychology; Neuroscience; Electroencephalography","score_opus":0.009004985463512947,"score_gpt":0.21803889486244032,"score_spread":0.20903390939892738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393187894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9578265,0.000110952446,0.041140262,0.0000634632,0.000014704636,0.0000125427205,0.00002718348,0.00029682677,0.00050755526],"genre_scores_gemma":[0.99501204,0.00001777701,0.0047271065,0.000010704651,0.000003039044,0.0000049918067,0.00003150815,0.000008483806,0.00018431435],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997118,0.000085303305,0.000019353327,0.00006283666,0.00007045893,0.00005027268],"domain_scores_gemma":[0.99906677,0.0004519027,0.00012741337,0.00010463978,0.00021006912,0.000039233822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047077797,0.00038481868,0.00029234728,0.00027635437,0.00019859223,0.00039696816,0.00029621966,0.00038100395,0.00063255033],"category_scores_gemma":[0.0024419995,0.00013937934,0.00016061196,0.00014125624,0.0003115441,0.00037948132,0.00039048397,0.0001862334,0.0002249039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024034986,0.0002156835,0.01689757,0.00023802691,0.000171094,0.00048137864,0.00040244556,0.20855892,0.6505161,0.0009718683,0.0005634543,0.11858004],"study_design_scores_gemma":[0.000086718406,0.003943132,0.044761002,0.000044973047,0.00019316943,0.00055929547,0.000195776,0.47413802,0.47380555,0.0006835033,0.0015267755,0.000062139676],"about_ca_topic_score_codex":0.00090006704,"about_ca_topic_score_gemma":0.0006208035,"teacher_disagreement_score":0.00090006704,"about_ca_system_score_codex":0.00027910687,"about_ca_system_score_gemma":0.00022499911,"threshold_uncertainty_score":0.002489686},"labels":[],"label_agreement":null},{"id":"W4393380092","doi":"10.1109/ieeeconf59524.2023.10476813","title":"Signed-Digit Addition Based on CNFETs and Ternary Logic","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Ternary operation; Computer science; Numerical digit; Logic gate; Arithmetic; Programming language; Mathematics; Algorithm","score_opus":0.021834223160068823,"score_gpt":0.24599710087793888,"score_spread":0.22416287771787005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393380092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15606321,0.0042096754,0.7423628,0.0009775143,0.0015799694,0.00034712753,0.00039572117,0.0017189705,0.09234506],"genre_scores_gemma":[0.6812428,0.0015296558,0.30213708,0.00044841712,0.00013926432,0.00015125045,0.00014715923,0.000056783756,0.014147624],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979,0.00002451537,0.000019979345,0.000034668257,0.000104051156,0.000026754984],"domain_scores_gemma":[0.99983275,0.000033892204,0.000032985634,0.00002885106,0.000057349338,0.000014128696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016222979,0.00045497532,0.00031232505,0.0005445952,0.00038554426,0.00088760874,0.00097231835,0.0006863219,0.0019841997],"category_scores_gemma":[0.00040417816,0.00018946597,0.00035533035,0.00040588438,0.0004164441,0.0010964558,0.00035410555,0.00038336797,0.00062986155],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039267508,0.000115708484,0.000713402,0.00066357484,0.00007228371,0.0011393898,0.00022538082,0.021231329,0.49515083,0.23591024,0.0038487597,0.24053636],"study_design_scores_gemma":[0.00009019629,0.00088530005,0.00094884046,0.00016179442,0.00014221216,0.003268859,0.00006757871,0.19460551,0.64833015,0.025841922,0.12551899,0.00013862018],"about_ca_topic_score_codex":0.0005330246,"about_ca_topic_score_gemma":0.0012640962,"teacher_disagreement_score":0.0019841997,"about_ca_system_score_codex":0.00069730333,"about_ca_system_score_gemma":0.00044908636,"threshold_uncertainty_score":0.0066378117},"labels":[],"label_agreement":null},{"id":"W4393435355","doi":"10.32604/cmc.2024.048787","title":"Alternative Method of Constructing Granular Neural Networks","year":2024,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Interpretability; Granular computing; Granularity; Computer science; Artificial neural network; Data mining; Interval (graph theory); Process (computing); Artificial intelligence; Fuzzy logic; Machine learning; Algorithm; Rough set; Mathematics","score_opus":0.01412023348945132,"score_gpt":0.2652711569541157,"score_spread":0.2511509234646644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393435355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061354632,0.00010378753,0.9916094,0.000078350924,0.000040336756,0.00003823176,0.000044575114,0.00028831136,0.0016616024],"genre_scores_gemma":[0.32774732,0.00031781962,0.668325,0.0001554376,0.00006546712,0.00026609734,0.00026421584,0.00013384158,0.0027248678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940896,0.00014195956,0.00005489401,0.0001324628,0.00020285786,0.00005875378],"domain_scores_gemma":[0.9991054,0.00035766145,0.00009700426,0.00015623351,0.00024067564,0.000043015098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011573435,0.00067441154,0.0008518001,0.0013235555,0.0004678649,0.0013811673,0.0013423632,0.0009553718,0.0030309688],"category_scores_gemma":[0.0038191592,0.00045384563,0.0007897565,0.0013218656,0.00066915754,0.0019289466,0.0013265534,0.0012020802,0.00046791488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017757897,0.000054406337,0.001341395,0.0002499383,0.000094632676,0.00018379433,0.00016383901,0.6820403,0.007160284,0.07932126,0.0020434626,0.22716905],"study_design_scores_gemma":[0.000010273551,0.00001726125,0.00008967757,0.0000121917,0.000010519097,0.00002823197,0.0000116608035,0.98303694,0.0013205329,0.014304493,0.0011513736,0.0000068327113],"about_ca_topic_score_codex":0.0026315202,"about_ca_topic_score_gemma":0.0021972803,"teacher_disagreement_score":0.0030309688,"about_ca_system_score_codex":0.0008366992,"about_ca_system_score_gemma":0.000901436,"threshold_uncertainty_score":0.010139585},"labels":[],"label_agreement":null},{"id":"W4393459471","doi":"10.5281/zenodo.10231500","title":"Quarter Normal Map RTI","year":2017,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Cartography; Geography; Archaeology","score_opus":0.03238610134956776,"score_gpt":0.26536241170732666,"score_spread":0.2329763103577589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393459471","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062303483,0.00097444316,0.0030099507,0.00056926726,0.00078475126,0.00015687101,0.9339065,0.030150324,0.024217514],"genre_scores_gemma":[0.008036541,0.00021905768,0.003478787,0.0001214126,0.000056157245,0.000083662155,0.98055136,0.0012199155,0.006233051],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998735,0.00014147988,0.000066979665,0.00040404117,0.00038671418,0.00026569862],"domain_scores_gemma":[0.9983407,0.00014261589,0.00005430793,0.0009013782,0.00043252952,0.0001285549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007339253,0.0035957356,0.001491305,0.0026720732,0.0009993046,0.002601257,0.0029565447,0.0018188417,0.060457453],"category_scores_gemma":[0.004482104,0.0005374815,0.0018750196,0.004018337,0.00054743176,0.0031088444,0.0023765664,0.0024482522,0.1160197],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019365399,0.000067198685,0.00060808857,0.0002237977,0.000037732312,0.00003958051,0.000017856268,0.0010525638,0.00026543252,0.00047693565,0.9825362,0.014481016],"study_design_scores_gemma":[0.00037793134,0.0001526135,0.0046346406,0.0002079498,0.000059826976,0.0005070948,0.00026234123,0.011156023,0.0040395153,0.005270365,0.9732592,0.000072568706],"about_ca_topic_score_codex":0.021711413,"about_ca_topic_score_gemma":0.04681342,"teacher_disagreement_score":0.060457453,"about_ca_system_score_codex":0.0014101658,"about_ca_system_score_gemma":0.0015964737,"threshold_uncertainty_score":0.2022503},"labels":[],"label_agreement":null},{"id":"W4393500228","doi":"10.5281/zenodo.4473349","title":"SomaticSiMu Provided Datasets","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Geography","score_opus":0.03542594579845969,"score_gpt":0.26294851002204256,"score_spread":0.22752256422358286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393500228","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002620454,0.00024604783,0.0015922713,0.000246543,0.00011023688,0.00009055367,0.9882288,0.0031744028,0.0036906898],"genre_scores_gemma":[0.00281009,0.0000712829,0.0026092047,0.00013296116,0.0000099852205,0.0002939249,0.9925944,0.00036945968,0.0011087083],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99902177,0.00029880737,0.00007640802,0.00025071893,0.00024588575,0.000106351574],"domain_scores_gemma":[0.9984413,0.000605809,0.000075920885,0.00042311606,0.00032668476,0.00012718965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016883637,0.0019379015,0.0010490653,0.0020417133,0.0009996721,0.0015225026,0.003803168,0.0022780674,0.049830277],"category_scores_gemma":[0.006646138,0.0006206169,0.001679666,0.0034125128,0.0004713997,0.001010106,0.0014132404,0.0018540563,0.043566182],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022109644,0.00009372202,0.0017312627,0.00064192084,0.0001330139,0.00006715708,0.000032414027,0.01355991,0.0003918744,0.0018352129,0.9752969,0.0059954696],"study_design_scores_gemma":[0.0009804657,0.00011239244,0.0045814565,0.00025099976,0.00012467384,0.00023920024,0.00010252105,0.029944047,0.0027798873,0.012483079,0.9483169,0.00008436047],"about_ca_topic_score_codex":0.008555422,"about_ca_topic_score_gemma":0.02237655,"teacher_disagreement_score":0.049830277,"about_ca_system_score_codex":0.0015075555,"about_ca_system_score_gemma":0.0018536655,"threshold_uncertainty_score":0.16669887},"labels":[],"label_agreement":null},{"id":"W4393512195","doi":"10.5281/zenodo.3595600","title":"Navigable maps of structural brain networks across species","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Geography; Cartography; Computer science","score_opus":0.04050557670808171,"score_gpt":0.29800961749946275,"score_spread":0.25750404079138106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393512195","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020369187,0.0005361616,0.000742071,0.00018369735,0.000090988826,0.000029545694,0.99326754,0.001821222,0.0012919492],"genre_scores_gemma":[0.0045922166,0.00021864021,0.0015815836,0.0000680136,0.000019283461,0.00013717484,0.9923395,0.00019027374,0.00085330906],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992384,0.00013416131,0.00005784548,0.00033044102,0.00012367713,0.00011548686],"domain_scores_gemma":[0.99851316,0.00043958105,0.00013934198,0.0005061914,0.00026084593,0.00014081424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010040748,0.0027096753,0.0012784445,0.0039663184,0.0009824468,0.0020872115,0.0039628027,0.0024756088,0.039594214],"category_scores_gemma":[0.0065527414,0.00082816597,0.0026771342,0.0041949195,0.0006359069,0.0013128815,0.0030269974,0.002252366,0.045928143],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023498607,0.000084535386,0.0037856735,0.0020373117,0.00036768333,0.00012430626,0.00007992468,0.0032657336,0.00056472304,0.0013957209,0.97417575,0.013883716],"study_design_scores_gemma":[0.001103692,0.00016125942,0.027724966,0.001294443,0.00046910575,0.0008346178,0.00032298316,0.008046457,0.0022258267,0.019560475,0.93808645,0.00016978159],"about_ca_topic_score_codex":0.016803363,"about_ca_topic_score_gemma":0.047695234,"teacher_disagreement_score":0.039594214,"about_ca_system_score_codex":0.001137967,"about_ca_system_score_gemma":0.0014883995,"threshold_uncertainty_score":0.13245583},"labels":[],"label_agreement":null},{"id":"W4393525561","doi":"10.5281/zenodo.7562790","title":"FINTA/CINTA/GESTA/FIESTA Datasets","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Art; Geography","score_opus":0.0446547225153362,"score_gpt":0.27247387454373084,"score_spread":0.22781915202839464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393525561","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001308143,0.00028368158,0.0003806416,0.00025407964,0.00013692054,0.00007801169,0.99403906,0.0015135738,0.0020059333],"genre_scores_gemma":[0.000991357,0.000085990214,0.0010097994,0.000058630438,0.000017891885,0.00014564747,0.9961779,0.000103104445,0.0014095992],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985758,0.00023137098,0.00012150792,0.0003515732,0.00044040926,0.00027922486],"domain_scores_gemma":[0.9982021,0.00032945676,0.00011354156,0.00056148326,0.00057892,0.00021450265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001305317,0.004605157,0.0021361094,0.0042372793,0.0013923211,0.0023866007,0.0054441215,0.004301003,0.030712476],"category_scores_gemma":[0.0047699315,0.0006865198,0.0027473776,0.0055477386,0.00086769817,0.0011889877,0.0019523053,0.002577212,0.05554172],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010770183,0.00010306012,0.00042627665,0.00036423054,0.000043808363,0.000042265066,0.000010822647,0.0012128555,0.00019221398,0.00044034322,0.99219596,0.0048603807],"study_design_scores_gemma":[0.001013015,0.0001368891,0.008110291,0.00044191797,0.00015972013,0.00040167812,0.00022106132,0.014188454,0.002548745,0.006207302,0.9664274,0.00014350712],"about_ca_topic_score_codex":0.04331591,"about_ca_topic_score_gemma":0.07845893,"teacher_disagreement_score":0.04331591,"about_ca_system_score_codex":0.002143072,"about_ca_system_score_gemma":0.0030370366,"threshold_uncertainty_score":0.10274351},"labels":[],"label_agreement":null},{"id":"W4393577596","doi":"10.5281/zenodo.10573840","title":"Raw Data for Statistical Analyses Cortex Submission","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Raw data; Computer science; Information retrieval; Programming language","score_opus":0.13219103052696657,"score_gpt":0.3650636034744336,"score_spread":0.23287257294746705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393577596","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003792799,0.00004582095,0.00047141706,0.00007084202,0.000101171936,0.000102784856,0.9951361,0.0016691228,0.0020234464],"genre_scores_gemma":[0.0011105224,0.000041208492,0.0014504615,0.00009151026,0.000028926792,0.00063553924,0.99233294,0.0006093477,0.003699684],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99851424,0.00021941087,0.00015912246,0.00044413097,0.0004248716,0.00023825502],"domain_scores_gemma":[0.9952873,0.000988031,0.00022474298,0.0016656124,0.0014527481,0.00038164627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017857143,0.0032223817,0.0016305619,0.0029348012,0.0010851733,0.0025512632,0.0023960469,0.0016924146,0.18229067],"category_scores_gemma":[0.008712702,0.00065644825,0.0014754814,0.0031432034,0.00060786324,0.00084038754,0.0021297075,0.001520609,0.2062865],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101871585,0.00002706884,0.00029213808,0.00021704684,0.000016908582,0.000014269297,0.0000073616566,0.00018197396,0.00028180354,0.00020500881,0.9957973,0.002857238],"study_design_scores_gemma":[0.0006310547,0.00008168366,0.0039518313,0.00015081288,0.000066719265,0.00009284333,0.00007254309,0.0010810379,0.0023900191,0.0042128507,0.987212,0.000056597783],"about_ca_topic_score_codex":0.0097670285,"about_ca_topic_score_gemma":0.018213617,"teacher_disagreement_score":0.18229067,"about_ca_system_score_codex":0.0012379297,"about_ca_system_score_gemma":0.0037015858,"threshold_uncertainty_score":0.60982305},"labels":[],"label_agreement":null},{"id":"W4393579178","doi":"10.1145/3626202.3637585","title":"E4SA: An Ultra-Efficient Systolic Array Architecture for 4-Bit Convolutional Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Convolutional neural network; Parallel computing; Digital signal processing; Systolic array; Field-programmable gate array; Computer engineering; Quantization (signal processing); Overhead (engineering); Kernel (algebra); Computer hardware; Algorithm; Embedded system; Very-large-scale integration; Artificial intelligence","score_opus":0.015535730817702369,"score_gpt":0.2578875878322113,"score_spread":0.2423518570145089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393579178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.102353744,0.0011396104,0.8774369,0.0002666179,0.00019842763,0.00012250879,0.00034027218,0.0075058234,0.010636088],"genre_scores_gemma":[0.50085753,0.0005080888,0.4890423,0.00025785214,0.000058278834,0.00017441626,0.00087713584,0.00019298132,0.008031328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999874,0.00001691579,0.000014531403,0.000024786215,0.00004793733,0.00002184124],"domain_scores_gemma":[0.9998197,0.000033832668,0.000031137868,0.000034782417,0.000060573275,0.000020070978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023249855,0.00043873972,0.00023479486,0.0005762302,0.0002774471,0.0004295869,0.0012680666,0.00034326417,0.002803952],"category_scores_gemma":[0.00038179185,0.00021200783,0.00032860244,0.0005417094,0.0002614953,0.0007513136,0.0005210295,0.0005014653,0.00086405105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065206434,0.000232804,0.0032226588,0.00034289522,0.0001379189,0.0003430846,0.0002092565,0.13834174,0.23176342,0.025396813,0.013376848,0.58598053],"study_design_scores_gemma":[0.00006982771,0.00075723237,0.0014359663,0.00004298753,0.00005488209,0.0003092161,0.000050621362,0.85183465,0.102509156,0.0079652,0.034917884,0.000052333064],"about_ca_topic_score_codex":0.0018230947,"about_ca_topic_score_gemma":0.004028887,"teacher_disagreement_score":0.002803952,"about_ca_system_score_codex":0.0004973047,"about_ca_system_score_gemma":0.0009033655,"threshold_uncertainty_score":0.009380162},"labels":[],"label_agreement":null},{"id":"W4393668981","doi":"10.5281/zenodo.8157405","title":"Machine_Learning_to_Hardware_for_Instrumentation","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Instrumentation (computer programming); Computer science; Computer hardware; Embedded system; Operating system; Computer architecture","score_opus":0.034160890463387245,"score_gpt":0.2716441101109688,"score_spread":0.23748321964758154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393668981","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032347024,0.00021930102,0.00075922057,0.000413234,0.0002621342,0.00007823133,0.9895586,0.0018445365,0.0036299834],"genre_scores_gemma":[0.0035107036,0.000092952105,0.0015374788,0.00020304583,0.000021831416,0.00013394565,0.9917338,0.00013425066,0.0026320436],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989874,0.00013046917,0.00007582721,0.00030879743,0.00029839014,0.00019904935],"domain_scores_gemma":[0.99861753,0.00022426246,0.0000843438,0.00057156227,0.00034631055,0.00015591823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082861545,0.0031109273,0.0009941619,0.0016623038,0.0008204049,0.0015047903,0.0026842682,0.0025869936,0.02168911],"category_scores_gemma":[0.0035180098,0.00049076334,0.0014911984,0.001986218,0.0007879779,0.0012208371,0.0014105149,0.0024207768,0.033794504],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019925556,0.00012303838,0.00082209747,0.00039927976,0.00003378432,0.000050626582,0.00001655478,0.0013467881,0.00042999446,0.00065600727,0.9904986,0.0054240525],"study_design_scores_gemma":[0.00061727123,0.00014550613,0.0076923096,0.00023170839,0.00005334108,0.00023377594,0.00012605723,0.0054836096,0.004716652,0.004165987,0.97645926,0.00007458565],"about_ca_topic_score_codex":0.020294106,"about_ca_topic_score_gemma":0.044406295,"teacher_disagreement_score":0.02168911,"about_ca_system_score_codex":0.0013141412,"about_ca_system_score_gemma":0.0017684806,"threshold_uncertainty_score":0.07255733},"labels":[],"label_agreement":null},{"id":"W4393669999","doi":"10.5281/zenodo.6637469","title":"PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Artificial neural network; Geology; Mathematics; Biological system; Computer science; Artificial intelligence; Biology","score_opus":0.07026092760010828,"score_gpt":0.25552717408609416,"score_spread":0.18526624648598589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393669999","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04248011,0.001747291,0.02849484,0.0012751042,0.00078916113,0.0005095191,0.8877684,0.02388241,0.013053164],"genre_scores_gemma":[0.025188072,0.00023695976,0.022117844,0.00016777967,0.0000383446,0.00038170765,0.9479436,0.00037442724,0.0035511674],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993649,0.00011950294,0.000045937966,0.0001777572,0.00020188454,0.000089889785],"domain_scores_gemma":[0.9993395,0.00014452431,0.000051844396,0.00025335315,0.00015976756,0.000051009385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010563732,0.0035618169,0.001173588,0.0015373285,0.0006368282,0.0013505007,0.0043634777,0.0021114463,0.006780298],"category_scores_gemma":[0.0026466753,0.0005740832,0.0012705286,0.0017921918,0.00055726996,0.0010370476,0.0016695912,0.0018863102,0.009885417],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006467361,0.00052832253,0.0037613087,0.0010872363,0.0002547835,0.00019725428,0.000053688324,0.05299364,0.0021594085,0.0025656049,0.8737586,0.061993375],"study_design_scores_gemma":[0.0018372587,0.00040452296,0.011585846,0.0002866397,0.00015663824,0.0005853429,0.00020617219,0.45195895,0.01786496,0.017952392,0.49697217,0.00018909664],"about_ca_topic_score_codex":0.025324754,"about_ca_topic_score_gemma":0.062310416,"teacher_disagreement_score":0.025324754,"about_ca_system_score_codex":0.001486096,"about_ca_system_score_gemma":0.0013086126,"threshold_uncertainty_score":0.05035472},"labels":[],"label_agreement":null},{"id":"W4393672429","doi":"10.5281/zenodo.10573841","title":"Raw Data for Statistical Analyses Cortex Submission","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Raw data; Computer science; Statistics; Mathematics; Programming language","score_opus":0.13219103052696657,"score_gpt":0.3650636034744336,"score_spread":0.23287257294746705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393672429","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003792799,0.00004582095,0.00047141706,0.00007084202,0.000101171936,0.000102784856,0.9951361,0.0016691228,0.0020234464],"genre_scores_gemma":[0.0011105224,0.000041208492,0.0014504615,0.00009151026,0.000028926792,0.00063553924,0.99233294,0.0006093477,0.003699684],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99851424,0.00021941087,0.00015912246,0.00044413097,0.0004248716,0.00023825502],"domain_scores_gemma":[0.9952873,0.000988031,0.00022474298,0.0016656124,0.0014527481,0.00038164627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017857143,0.0032223817,0.0016305619,0.0029348012,0.0010851733,0.0025512632,0.0023960469,0.0016924146,0.18229067],"category_scores_gemma":[0.008712702,0.00065644825,0.0014754814,0.0031432034,0.00060786324,0.00084038754,0.0021297075,0.001520609,0.2062865],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101871585,0.00002706884,0.00029213808,0.00021704684,0.000016908582,0.000014269297,0.0000073616566,0.00018197396,0.00028180354,0.00020500881,0.9957973,0.002857238],"study_design_scores_gemma":[0.0006310547,0.00008168366,0.0039518313,0.00015081288,0.000066719265,0.00009284333,0.00007254309,0.0010810379,0.0023900191,0.0042128507,0.987212,0.000056597783],"about_ca_topic_score_codex":0.0097670285,"about_ca_topic_score_gemma":0.018213617,"teacher_disagreement_score":0.18229067,"about_ca_system_score_codex":0.0012379297,"about_ca_system_score_gemma":0.0037015858,"threshold_uncertainty_score":0.60982305},"labels":[],"label_agreement":null},{"id":"W4393723193","doi":"10.5281/zenodo.8157404","title":"Machine_Learning_to_Hardware_for_Instrumentation","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Instrumentation (computer programming); Computer science; Computer hardware; Embedded system; Computer architecture; Operating system","score_opus":0.034160890463387245,"score_gpt":0.2716441101109688,"score_spread":0.23748321964758154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393723193","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032347024,0.00021930102,0.00075922057,0.000413234,0.0002621342,0.00007823133,0.9895586,0.0018445365,0.0036299834],"genre_scores_gemma":[0.0035107036,0.000092952105,0.0015374788,0.00020304583,0.000021831416,0.00013394565,0.9917338,0.00013425066,0.0026320436],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989874,0.00013046917,0.00007582721,0.00030879743,0.00029839014,0.00019904935],"domain_scores_gemma":[0.99861753,0.00022426246,0.0000843438,0.00057156227,0.00034631055,0.00015591823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082861545,0.0031109273,0.0009941619,0.0016623038,0.0008204049,0.0015047903,0.0026842682,0.0025869936,0.02168911],"category_scores_gemma":[0.0035180098,0.00049076334,0.0014911984,0.001986218,0.0007879779,0.0012208371,0.0014105149,0.0024207768,0.033794504],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019925556,0.00012303838,0.00082209747,0.00039927976,0.00003378432,0.000050626582,0.00001655478,0.0013467881,0.00042999446,0.00065600727,0.9904986,0.0054240525],"study_design_scores_gemma":[0.00061727123,0.00014550613,0.0076923096,0.00023170839,0.00005334108,0.00023377594,0.00012605723,0.0054836096,0.004716652,0.004165987,0.97645926,0.00007458565],"about_ca_topic_score_codex":0.020294106,"about_ca_topic_score_gemma":0.044406295,"teacher_disagreement_score":0.02168911,"about_ca_system_score_codex":0.0013141412,"about_ca_system_score_gemma":0.0017684806,"threshold_uncertainty_score":0.07255733},"labels":[],"label_agreement":null},{"id":"W4393737020","doi":"10.5281/zenodo.7562789","title":"FINTA/CINTA/GESTA/FIESTA Datasets","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Art; Geography; Humanities","score_opus":0.0446547225153362,"score_gpt":0.27247387454373084,"score_spread":0.22781915202839464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393737020","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001308143,0.00028368158,0.0003806416,0.00025407964,0.00013692054,0.00007801169,0.99403906,0.0015135738,0.0020059333],"genre_scores_gemma":[0.000991357,0.000085990214,0.0010097994,0.000058630438,0.000017891885,0.00014564747,0.9961779,0.000103104445,0.0014095992],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985758,0.00023137098,0.00012150792,0.0003515732,0.00044040926,0.00027922486],"domain_scores_gemma":[0.9982021,0.00032945676,0.00011354156,0.00056148326,0.00057892,0.00021450265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001305317,0.004605157,0.0021361094,0.0042372793,0.0013923211,0.0023866007,0.0054441215,0.004301003,0.030712476],"category_scores_gemma":[0.0047699315,0.0006865198,0.0027473776,0.0055477386,0.00086769817,0.0011889877,0.0019523053,0.002577212,0.05554172],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010770183,0.00010306012,0.00042627665,0.00036423054,0.000043808363,0.000042265066,0.000010822647,0.0012128555,0.00019221398,0.00044034322,0.99219596,0.0048603807],"study_design_scores_gemma":[0.001013015,0.0001368891,0.008110291,0.00044191797,0.00015972013,0.00040167812,0.00022106132,0.014188454,0.002548745,0.006207302,0.9664274,0.00014350712],"about_ca_topic_score_codex":0.04331591,"about_ca_topic_score_gemma":0.07845893,"teacher_disagreement_score":0.04331591,"about_ca_system_score_codex":0.002143072,"about_ca_system_score_gemma":0.0030370366,"threshold_uncertainty_score":0.10274351},"labels":[],"label_agreement":null},{"id":"W4393761386","doi":"10.5281/zenodo.10231501","title":"Quarter Normal Map RTI","year":2017,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Cartography; Geography; Archaeology","score_opus":0.03238610134956776,"score_gpt":0.26536241170732666,"score_spread":0.2329763103577589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393761386","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062303483,0.00097444316,0.0030099507,0.00056926726,0.00078475126,0.00015687101,0.9339065,0.030150324,0.024217514],"genre_scores_gemma":[0.008036541,0.00021905768,0.003478787,0.0001214126,0.000056157245,0.000083662155,0.98055136,0.0012199155,0.006233051],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998735,0.00014147988,0.000066979665,0.00040404117,0.00038671418,0.00026569862],"domain_scores_gemma":[0.9983407,0.00014261589,0.00005430793,0.0009013782,0.00043252952,0.0001285549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007339253,0.0035957356,0.001491305,0.0026720732,0.0009993046,0.002601257,0.0029565447,0.0018188417,0.060457453],"category_scores_gemma":[0.004482104,0.0005374815,0.0018750196,0.004018337,0.00054743176,0.0031088444,0.0023765664,0.0024482522,0.1160197],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019365399,0.000067198685,0.00060808857,0.0002237977,0.000037732312,0.00003958051,0.000017856268,0.0010525638,0.00026543252,0.00047693565,0.9825362,0.014481016],"study_design_scores_gemma":[0.00037793134,0.0001526135,0.0046346406,0.0002079498,0.000059826976,0.0005070948,0.00026234123,0.011156023,0.0040395153,0.005270365,0.9732592,0.000072568706],"about_ca_topic_score_codex":0.021711413,"about_ca_topic_score_gemma":0.04681342,"teacher_disagreement_score":0.060457453,"about_ca_system_score_codex":0.0014101658,"about_ca_system_score_gemma":0.0015964737,"threshold_uncertainty_score":0.2022503},"labels":[],"label_agreement":null},{"id":"W4393774744","doi":"10.5281/zenodo.3949747","title":"Sample data of the CRCM5-LE for applications of the Latent Linear Adjustment autoencoder","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoencoder; Sample (material); Latent variable; Statistics; Artificial intelligence; Mathematics; Computer science; Artificial neural network; Physics; Thermodynamics","score_opus":0.07581017437242353,"score_gpt":0.2786174721793299,"score_spread":0.20280729780690637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393774744","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06446534,0.0013036582,0.07549944,0.0028565277,0.0027050734,0.0010118422,0.808688,0.025135174,0.018334964],"genre_scores_gemma":[0.07285123,0.00026651277,0.0756179,0.00053165556,0.000116042385,0.00091523345,0.8379083,0.0022668778,0.009526139],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99866176,0.00018507283,0.000076957236,0.00023825909,0.0006799867,0.00015796728],"domain_scores_gemma":[0.9965306,0.00048246264,0.00009285479,0.00096568203,0.0017435903,0.00018478285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024689692,0.0013119533,0.0006362139,0.0014971608,0.0007916975,0.0009459746,0.002410512,0.0015480188,0.026341334],"category_scores_gemma":[0.010124088,0.0005004918,0.0012371253,0.0020675824,0.0005205545,0.0012595484,0.0018696765,0.0025892707,0.017696708],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065690116,0.00036066,0.010206263,0.00041560392,0.00018845264,0.00023885937,0.00013154371,0.039009646,0.0046812734,0.0036866216,0.8505674,0.08985675],"study_design_scores_gemma":[0.0008525879,0.00040077328,0.05443027,0.00035699125,0.00011800429,0.00052633724,0.00037594358,0.23703793,0.025819734,0.01195401,0.6677718,0.0003555571],"about_ca_topic_score_codex":0.07550406,"about_ca_topic_score_gemma":0.1412492,"teacher_disagreement_score":0.07550406,"about_ca_system_score_codex":0.0016022979,"about_ca_system_score_gemma":0.002318736,"threshold_uncertainty_score":0.15012914},"labels":[],"label_agreement":null},{"id":"W4393796065","doi":"10.5281/zenodo.4473350","title":"SomaticSiMu Provided Datasets","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Information retrieval; Data mining","score_opus":0.03542594579845969,"score_gpt":0.26294851002204256,"score_spread":0.22752256422358286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393796065","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019961358,0.00023959887,0.0010763463,0.00021533229,0.00008540041,0.00007097607,0.99092215,0.002445059,0.0029488667],"genre_scores_gemma":[0.0024203584,0.00007046264,0.0019975947,0.00013273794,0.000009109578,0.00026034573,0.99379176,0.00034119393,0.0009764211],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99904436,0.00030531446,0.000074452604,0.000252534,0.0002201706,0.00010315262],"domain_scores_gemma":[0.9983323,0.0007129762,0.000084654814,0.0004296386,0.00030487057,0.00013550033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017496445,0.0020175579,0.0011075969,0.0020233325,0.0009890104,0.0015565397,0.003614869,0.0023983195,0.054745812],"category_scores_gemma":[0.0069729355,0.0006252072,0.0017145714,0.0034557153,0.0004705924,0.000964057,0.0014317879,0.001878775,0.04702541],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020518311,0.00008215589,0.001594443,0.00073181576,0.00013755703,0.00006697432,0.000030335133,0.010479129,0.000353844,0.001580095,0.979702,0.005036608],"study_design_scores_gemma":[0.0011002427,0.00011247966,0.0047473502,0.00030073887,0.00014624267,0.0002482768,0.00009979716,0.020597875,0.002364324,0.011342987,0.95885813,0.000081617836],"about_ca_topic_score_codex":0.007643085,"about_ca_topic_score_gemma":0.020018147,"teacher_disagreement_score":0.054745812,"about_ca_system_score_codex":0.0014935788,"about_ca_system_score_gemma":0.0018143989,"threshold_uncertainty_score":0.18314296},"labels":[],"label_agreement":null},{"id":"W4393970839","doi":"10.1002/9781119763222.ch7","title":"Computation of Fields Via Angular Spectral Representation","year":2024,"lang":"en","type":"other","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computation; Representation (politics); Computer science; Physics; Algorithm","score_opus":0.016102015525246906,"score_gpt":0.2893868071280853,"score_spread":0.27328479160283836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393970839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015923724,0.00018829666,0.9613962,0.00022831575,0.00008126569,0.000023570832,0.00009358191,0.0004097118,0.021655165],"genre_scores_gemma":[0.46997622,0.00075482664,0.5033141,0.00018643952,0.00012868247,0.000104624894,0.00039606914,0.00044067632,0.024698328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99987435,0.000028291857,0.0000073616807,0.000017367121,0.00005584085,0.000016853513],"domain_scores_gemma":[0.9997733,0.00009642261,0.000018083801,0.000029624289,0.00006509595,0.000017392582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003378327,0.0003885138,0.0003271433,0.0007834559,0.0002807466,0.001436521,0.00053425215,0.0004070346,0.007013117],"category_scores_gemma":[0.001505623,0.00019841318,0.00035336314,0.0006268086,0.0004581205,0.0017014317,0.0006882698,0.00063666067,0.0015619396],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006864924,0.000035500616,0.00076833705,0.000105713814,0.000025581498,0.00009118629,0.00015255256,0.21967518,0.009157362,0.6461652,0.0048708417,0.11888389],"study_design_scores_gemma":[0.00000584244,0.000010334382,0.0001572905,0.000014789673,0.000003150603,0.00003494566,0.000041091942,0.8891629,0.0018033145,0.10415923,0.0045984057,0.00000871532],"about_ca_topic_score_codex":0.0016845135,"about_ca_topic_score_gemma":0.0016769432,"teacher_disagreement_score":0.007013117,"about_ca_system_score_codex":0.00048002123,"about_ca_system_score_gemma":0.00044615564,"threshold_uncertainty_score":0.023461223},"labels":[],"label_agreement":null},{"id":"W4394039702","doi":"10.5281/zenodo.4778711","title":"Maximally Predictive Ensemble Dynamics from Data","year":2021,"lang":"en","type":"dataset","venue":"VU Research Portal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Dynamics (music); Computer science; Physics","score_opus":0.10975530224553652,"score_gpt":0.3848391323224565,"score_spread":0.27508383007691994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394039702","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049275793,0.0004917193,0.00530122,0.00037122724,0.00032775814,0.000114016264,0.97587866,0.008104359,0.0044833915],"genre_scores_gemma":[0.0066860043,0.00014013718,0.004499928,0.00006928988,0.000024906587,0.00023341022,0.9862592,0.00033848124,0.0017487446],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99924076,0.00018849368,0.00005329425,0.00018502936,0.00023120028,0.0001012963],"domain_scores_gemma":[0.9981651,0.00056208414,0.000085065956,0.00073881255,0.00034837515,0.00010055658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012488805,0.002802779,0.0010405142,0.0021558087,0.0006834529,0.0015783296,0.0024584467,0.0018223407,0.034007523],"category_scores_gemma":[0.0064769094,0.00055752683,0.0019274491,0.0022934997,0.00048364545,0.0010628742,0.0017039274,0.0024175087,0.041660022],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020923585,0.00012540432,0.0011618845,0.0006071859,0.000119368255,0.000064360946,0.000034858145,0.013283172,0.00047404473,0.001973144,0.9662793,0.015667947],"study_design_scores_gemma":[0.001199744,0.00027044912,0.009805524,0.000501977,0.00016852874,0.00040668744,0.00013420865,0.10722175,0.0055848802,0.023373095,0.8511725,0.00016058446],"about_ca_topic_score_codex":0.010899268,"about_ca_topic_score_gemma":0.019095335,"teacher_disagreement_score":0.034007523,"about_ca_system_score_codex":0.0010740806,"about_ca_system_score_gemma":0.0012200576,"threshold_uncertainty_score":0.11376649},"labels":[],"label_agreement":null},{"id":"W4394043160","doi":"10.5281/zenodo.5725648","title":"PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Artificial neural network; Computer science; Acoustics; Biological system; Geology; Physics; Artificial intelligence; Biology","score_opus":0.07026092760010828,"score_gpt":0.25552717408609416,"score_spread":0.18526624648598589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394043160","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04248011,0.001747291,0.02849484,0.0012751042,0.00078916113,0.0005095191,0.8877684,0.02388241,0.013053164],"genre_scores_gemma":[0.025188072,0.00023695976,0.022117844,0.00016777967,0.0000383446,0.00038170765,0.9479436,0.00037442724,0.0035511674],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993649,0.00011950294,0.000045937966,0.0001777572,0.00020188454,0.000089889785],"domain_scores_gemma":[0.9993395,0.00014452431,0.000051844396,0.00025335315,0.00015976756,0.000051009385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010563732,0.0035618169,0.001173588,0.0015373285,0.0006368282,0.0013505007,0.0043634777,0.0021114463,0.006780298],"category_scores_gemma":[0.0026466753,0.0005740832,0.0012705286,0.0017921918,0.00055726996,0.0010370476,0.0016695912,0.0018863102,0.009885417],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006467361,0.00052832253,0.0037613087,0.0010872363,0.0002547835,0.00019725428,0.000053688324,0.05299364,0.0021594085,0.0025656049,0.8737586,0.061993375],"study_design_scores_gemma":[0.0018372587,0.00040452296,0.011585846,0.0002866397,0.00015663824,0.0005853429,0.00020617219,0.45195895,0.01786496,0.017952392,0.49697217,0.00018909664],"about_ca_topic_score_codex":0.025324754,"about_ca_topic_score_gemma":0.062310416,"teacher_disagreement_score":0.025324754,"about_ca_system_score_codex":0.001486096,"about_ca_system_score_gemma":0.0013086126,"threshold_uncertainty_score":0.05035472},"labels":[],"label_agreement":null},{"id":"W4394256501","doi":"10.6084/m9.figshare.21405104.v1","title":"A reproducing kernel Hilbert space framework for functional classification","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reproducing kernel Hilbert space; Kernel (algebra); Hilbert space; Computer science; Space (punctuation); Mathematics; Representer theorem; Artificial intelligence; Pattern recognition (psychology); Kernel principal component analysis; Pure mathematics; Kernel method; Support vector machine","score_opus":0.09346509677783527,"score_gpt":0.30292358871969155,"score_spread":0.20945849194185628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394256501","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009743221,0.0009643255,0.9762788,0.00095607404,0.00012182215,0.00018451503,0.0075870156,0.0017941492,0.0023701093],"genre_scores_gemma":[0.29374242,0.0011856117,0.6569691,0.0004794865,0.00025357617,0.0014686933,0.039558552,0.00042199486,0.0059205783],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99812025,0.00083084585,0.0001388626,0.0003495181,0.00047103292,0.00008950214],"domain_scores_gemma":[0.9974119,0.000855342,0.00021300225,0.0008169682,0.0006097919,0.00009298956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040748413,0.0011748368,0.0010573006,0.002406162,0.00044331944,0.0016787626,0.0021778697,0.0013323119,0.0035423015],"category_scores_gemma":[0.009761902,0.00024106979,0.0010483924,0.0024775125,0.0007881176,0.0018875442,0.0016459847,0.0025344351,0.0028170366],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030244523,0.0005344255,0.0076634064,0.0006688293,0.00023774838,0.000268465,0.00019692877,0.2367266,0.0039858194,0.15490784,0.10803674,0.4864708],"study_design_scores_gemma":[0.00003519177,0.00007607586,0.0014848138,0.0000331316,0.000013366222,0.00014516046,0.00003091028,0.8871892,0.0013167436,0.091120504,0.01851895,0.000036087255],"about_ca_topic_score_codex":0.0038726085,"about_ca_topic_score_gemma":0.0043206923,"teacher_disagreement_score":0.0040748413,"about_ca_system_score_codex":0.0011890727,"about_ca_system_score_gemma":0.0012034688,"threshold_uncertainty_score":0.02155006},"labels":[],"label_agreement":null},{"id":"W4394270427","doi":"10.6084/m9.figshare.9642098.v1","title":"CODING DATA","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Coding (social sciences); Mathematics; Statistics","score_opus":0.19489541909377292,"score_gpt":0.3370179439368751,"score_spread":0.1421225248431022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394270427","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021617531,0.000027872278,0.000041659445,0.000027261145,0.0000145703,0.000011434256,0.9992849,0.00006801233,0.00030808093],"genre_scores_gemma":[0.001005517,0.000043168347,0.00020857746,0.000046777357,0.000007091251,0.00009463021,0.9972493,0.000037207163,0.0013076534],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988856,0.00014601409,0.00012814645,0.0003315157,0.0002244258,0.0002842782],"domain_scores_gemma":[0.994389,0.0014524407,0.0005394586,0.0014153279,0.0017680762,0.00043570105],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013083596,0.0017817798,0.0013744569,0.0042275246,0.0011560561,0.001823473,0.0027164577,0.002278425,0.10337065],"category_scores_gemma":[0.009798179,0.00065287034,0.0013150946,0.008822973,0.0006958267,0.0006760129,0.0012960914,0.0015022549,0.05641469],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010654843,0.000027651444,0.0028106933,0.00045170792,0.000046378827,0.000029952162,0.00001819102,0.0005442328,0.00010635879,0.0002503966,0.9922869,0.0033210036],"study_design_scores_gemma":[0.00042936808,0.00004385414,0.0493172,0.0008035369,0.00014129974,0.00013728783,0.00018898962,0.0011020114,0.00057934766,0.0011913562,0.945983,0.00008271934],"about_ca_topic_score_codex":0.32852155,"about_ca_topic_score_gemma":0.42570004,"teacher_disagreement_score":0.89662933,"about_ca_system_score_codex":0.0030103046,"about_ca_system_score_gemma":0.006232239,"threshold_uncertainty_score":0.6532187},"labels":[],"label_agreement":null},{"id":"W4394302759","doi":"10.6084/m9.figshare.9642098","title":"CODING DATA","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.19489541909377292,"score_gpt":0.3370179439368751,"score_spread":0.1421225248431022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394302759","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021617531,0.000027872278,0.000041659445,0.000027261145,0.0000145703,0.000011434256,0.9992849,0.00006801233,0.00030808093],"genre_scores_gemma":[0.001005517,0.000043168347,0.00020857746,0.000046777357,0.000007091251,0.00009463021,0.9972493,0.000037207163,0.0013076534],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988856,0.00014601409,0.00012814645,0.0003315157,0.0002244258,0.0002842782],"domain_scores_gemma":[0.994389,0.0014524407,0.0005394586,0.0014153279,0.0017680762,0.00043570105],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013083596,0.0017817798,0.0013744569,0.0042275246,0.0011560561,0.001823473,0.0027164577,0.002278425,0.10337065],"category_scores_gemma":[0.009798179,0.00065287034,0.0013150946,0.008822973,0.0006958267,0.0006760129,0.0012960914,0.0015022549,0.05641469],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010654843,0.000027651444,0.0028106933,0.00045170792,0.000046378827,0.000029952162,0.00001819102,0.0005442328,0.00010635879,0.0002503966,0.9922869,0.0033210036],"study_design_scores_gemma":[0.00042936808,0.00004385414,0.0493172,0.0008035369,0.00014129974,0.00013728783,0.00018898962,0.0011020114,0.00057934766,0.0011913562,0.945983,0.00008271934],"about_ca_topic_score_codex":0.32852155,"about_ca_topic_score_gemma":0.42570004,"teacher_disagreement_score":0.89662933,"about_ca_system_score_codex":0.0030103046,"about_ca_system_score_gemma":0.006232239,"threshold_uncertainty_score":0.6532187},"labels":[],"label_agreement":null},{"id":"W4394431507","doi":"10.6084/m9.figshare.1450804","title":"Cobre (for machine learning)","year":2015,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.0655782484007293,"score_gpt":0.2951327975933254,"score_spread":0.2295545491925961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394431507","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029321485,0.008304177,0.14029089,0.0030123058,0.0021231591,0.00084894296,0.5847505,0.20186976,0.055868186],"genre_scores_gemma":[0.0140118515,0.0025266735,0.22760402,0.0015745398,0.0005588045,0.0032653364,0.70557517,0.02020962,0.024674],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.996752,0.00071023137,0.00029737258,0.0010340848,0.0009371938,0.0002690665],"domain_scores_gemma":[0.9955029,0.0015204973,0.0002504181,0.0015305832,0.00092725817,0.00026834468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030274247,0.0030540335,0.0019296297,0.0034296205,0.0009462544,0.0030953619,0.0053508184,0.0023936192,0.13683969],"category_scores_gemma":[0.012398571,0.0009925223,0.0021648987,0.0033507277,0.0004679657,0.00300466,0.0034777762,0.0033015278,0.15510021],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102013626,0.000040378214,0.00032278398,0.0007946405,0.000103168204,0.00003865138,0.000019771074,0.0018314909,0.00044069273,0.004029336,0.9472947,0.044982437],"study_design_scores_gemma":[0.00029743605,0.00013525966,0.0017099181,0.00047194253,0.0000741354,0.00025985474,0.000029087796,0.032916356,0.0027951729,0.02416577,0.93704337,0.00010173884],"about_ca_topic_score_codex":0.003310778,"about_ca_topic_score_gemma":0.005511597,"teacher_disagreement_score":0.13683969,"about_ca_system_score_codex":0.001125816,"about_ca_system_score_gemma":0.0025261983,"threshold_uncertainty_score":0.45777434},"labels":[],"label_agreement":null},{"id":"W4394437715","doi":"10.6084/m9.figshare.18033302","title":"YASIN DATA for analysis mk.xlsx","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Physics","score_opus":0.15883121524300647,"score_gpt":0.3509933948569276,"score_spread":0.19216217961392112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394437715","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035539453,0.00062258,0.0057083145,0.0059712017,0.007430834,0.0061863386,0.9142485,0.0033791144,0.05289914],"genre_scores_gemma":[0.07686552,0.0033605816,0.06775377,0.010267637,0.0022367723,0.14737606,0.44889647,0.0071781925,0.236065],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955734,0.00072206004,0.0011024012,0.0007776018,0.0014707145,0.00035374818],"domain_scores_gemma":[0.96000344,0.012205893,0.0034673586,0.005249429,0.01795093,0.0011229811],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0056792013,0.0013888307,0.002807195,0.005311454,0.001176802,0.0030892733,0.0020164722,0.0015029666,0.6507173],"category_scores_gemma":[0.072106205,0.0007803682,0.0010150877,0.008610283,0.00088549353,0.0017538547,0.0024080467,0.0022504334,0.19167134],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039723286,0.000081043574,0.0014716587,0.0031189974,0.000034555545,0.00013623637,0.00044788216,0.00016831579,0.00022549664,0.0045451336,0.9478896,0.041483756],"study_design_scores_gemma":[0.00038939464,0.00010279959,0.008430913,0.007518351,0.00003758204,0.00011385597,0.001510812,0.000742151,0.00037141753,0.0054070526,0.9753323,0.000043363558],"about_ca_topic_score_codex":0.0036514855,"about_ca_topic_score_gemma":0.0030486984,"teacher_disagreement_score":0.34928268,"about_ca_system_score_codex":0.0017144253,"about_ca_system_score_gemma":0.007188416,"threshold_uncertainty_score":0.49820936},"labels":[],"label_agreement":null},{"id":"W4394554018","doi":"10.6084/m9.figshare.8959529","title":"Data for Tract-based Fractional Anisotropy predicts WAIS Intelligence Quotient indices and subtest performance","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quotient; Fractional anisotropy; Wechsler Adult Intelligence Scale; Psychology; Anisotropy; Mathematics; Physics; Medicine; Pure mathematics; Diffusion MRI; Optics; Cognition; Psychiatry; Magnetic resonance imaging","score_opus":0.11357855009483196,"score_gpt":0.3143540701793727,"score_spread":0.20077552008454075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394554018","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9319957,0.00024041352,0.0022293557,0.000122073,0.000018351659,0.00053928443,0.057299033,0.00019651605,0.007359313],"genre_scores_gemma":[0.9505169,0.00013607228,0.002787042,0.000040468716,0.000014295035,0.0017645827,0.038092274,0.00010030614,0.0065480815],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996061,0.00007264544,0.000053986543,0.00011079379,0.00010276031,0.000053860447],"domain_scores_gemma":[0.9978173,0.00034707956,0.00052864465,0.00047433,0.0006821759,0.0001505382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009225462,0.0007834832,0.0005927958,0.0022768984,0.00076352514,0.00067112746,0.00044599405,0.00036064044,0.009194063],"category_scores_gemma":[0.004428996,0.00025824143,0.00043925655,0.0013704429,0.00025976283,0.00036988023,0.00054419535,0.0005220822,0.0035060449],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009042925,0.00033365845,0.9632147,0.00008384441,0.00016842839,0.00029673538,0.00065564143,0.00041178928,0.0027163494,0.00040209584,0.0086956555,0.022116855],"study_design_scores_gemma":[0.000018034303,0.00009027236,0.9936139,0.000021904074,0.000034543853,0.00043214957,0.00023753656,0.0005115792,0.00057029194,0.00032485332,0.0041283634,0.000016574417],"about_ca_topic_score_codex":0.027978802,"about_ca_topic_score_gemma":0.030204795,"teacher_disagreement_score":0.027978802,"about_ca_system_score_codex":0.0006113581,"about_ca_system_score_gemma":0.0009314465,"threshold_uncertainty_score":0.055631936},"labels":[],"label_agreement":null},{"id":"W4394558804","doi":"10.6084/m9.figshare.23590435","title":"Additional file 4 of ExplaiNN: interpretable and transparent neural networks for genomics","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Children's Hospital; Canadian Institute for Advanced Research; University of British Columbia","funders":"","keywords":"Computer science; Artificial neural network; Genomics; Artificial intelligence; Computational biology; Biology; Genome; Genetics; Gene","score_opus":0.046649637656545256,"score_gpt":0.2588137362297308,"score_spread":0.21216409857318555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394558804","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008922025,0.000029726572,0.00014630647,0.000037517286,0.000017371085,0.000018878085,0.9989814,0.0003358385,0.000343736],"genre_scores_gemma":[0.0011393247,0.000048796956,0.0010333868,0.00010977043,0.0000149693005,0.00036066453,0.99561846,0.000316383,0.0013583599],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992459,0.00013907341,0.0000844633,0.000289829,0.00012794253,0.000112861635],"domain_scores_gemma":[0.9953685,0.0029544344,0.00022206355,0.00060008926,0.00063721347,0.0002178455],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014475728,0.0022140394,0.0017465965,0.0022905588,0.0010167432,0.0024875728,0.0033574877,0.0025212143,0.49817303],"category_scores_gemma":[0.011196915,0.0008051812,0.0015967564,0.003702657,0.0005762862,0.0017026666,0.0014159618,0.0021675806,0.17726298],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013307235,0.000055540993,0.0011661004,0.0013927382,0.00006063161,0.000028744736,0.000024435903,0.00070528116,0.00013541787,0.0006848257,0.9926271,0.0029860924],"study_design_scores_gemma":[0.0018547205,0.000117184994,0.0075621693,0.00094605493,0.00018284279,0.00021691801,0.00015498795,0.0023579532,0.001013965,0.013003022,0.97248334,0.00010688331],"about_ca_topic_score_codex":0.00884355,"about_ca_topic_score_gemma":0.022429507,"teacher_disagreement_score":0.49817303,"about_ca_system_score_codex":0.0012604755,"about_ca_system_score_gemma":0.0019976688,"threshold_uncertainty_score":0.7157953},"labels":[],"label_agreement":null},{"id":"W4394619363","doi":"10.1142/s0218348x24500683","title":"ANALYSIS OF THE EFFECT OF VARIOUS MENTAL TASKS ON THE EEG SIGNALS’ COMPLEXITY","year":2024,"lang":"en","type":"article","venue":"Fractals","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Shores Centre for Mental Health Sciences","funders":"","keywords":"Electroencephalography; Computer science; Psychology; Mental arithmetic; Cognitive psychology; Artificial intelligence; Mathematics; Pattern recognition (psychology); Neuroscience; Biology","score_opus":0.019673537008647476,"score_gpt":0.27795223875557445,"score_spread":0.25827870174692696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394619363","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.983429,0.00027451967,0.014661184,0.000043499444,0.000019744162,0.00004179349,0.00017521919,0.000046321304,0.0013087561],"genre_scores_gemma":[0.9952036,0.00012698825,0.0042705876,0.000013180815,0.000013671765,0.000029787418,0.00014453441,0.000015274985,0.00018245718],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997675,0.000056427303,0.000016646332,0.000054868262,0.000074364725,0.0000302218],"domain_scores_gemma":[0.9977501,0.0017268837,0.00019607581,0.00012204431,0.00012448072,0.00008023907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003495042,0.0003444732,0.00030562037,0.00057234033,0.00011225422,0.0003926842,0.000110236004,0.00018730869,0.0010538915],"category_scores_gemma":[0.0039318106,0.00009970244,0.00034077727,0.00034810064,0.00028952278,0.00041133154,0.00026690835,0.0002950263,0.000104789615],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002540084,0.00030319957,0.03754568,0.00054247974,0.00042893342,0.00035236462,0.0007494678,0.010142112,0.78982645,0.0012708333,0.0004167128,0.15588164],"study_design_scores_gemma":[0.00003209135,0.0012673301,0.8962795,0.000025858672,0.00015711106,0.00035988708,0.00020389254,0.037935387,0.06111228,0.0017806728,0.00078091456,0.00006503747],"about_ca_topic_score_codex":0.00039599568,"about_ca_topic_score_gemma":0.0003763827,"teacher_disagreement_score":0.0010538915,"about_ca_system_score_codex":0.000122001824,"about_ca_system_score_gemma":0.00009709197,"threshold_uncertainty_score":0.0035256743},"labels":[],"label_agreement":null},{"id":"W4394625702","doi":"10.1109/wacv57701.2024.00073","title":"Learning to Recognize Occluded and Small Objects with Partial Inputs","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision","score_opus":0.014838849892252027,"score_gpt":0.23381134857460167,"score_spread":0.21897249868234964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394625702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10528643,0.0006239427,0.8780248,0.0003944885,0.00016918882,0.00019096224,0.00071606704,0.01093628,0.0036578535],"genre_scores_gemma":[0.5829886,0.00025920276,0.40684697,0.0005805138,0.00011162289,0.00024443772,0.0032675602,0.00058677414,0.005114347],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990038,0.00013706335,0.000033719443,0.00051312486,0.00017210984,0.00014025524],"domain_scores_gemma":[0.998187,0.0005209923,0.00018471424,0.00074880966,0.00022881846,0.00012965754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010939024,0.0017139537,0.0012787502,0.00064147357,0.0005835874,0.0013332721,0.0026501818,0.001780488,0.0037169063],"category_scores_gemma":[0.0035008993,0.0005630213,0.0015249054,0.0005612719,0.0010421075,0.002718457,0.0022292624,0.0020925356,0.0023291558],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074329035,0.0005214352,0.0055390075,0.0004646637,0.00016499999,0.00029689493,0.00026471485,0.094732694,0.075263396,0.0041244924,0.014249809,0.8036346],"study_design_scores_gemma":[0.000020975487,0.00016373055,0.0011008421,0.000026833684,0.000032889737,0.00014403179,0.000073074574,0.9607241,0.026172657,0.009381122,0.0021407395,0.000018956098],"about_ca_topic_score_codex":0.0030870873,"about_ca_topic_score_gemma":0.0063653556,"teacher_disagreement_score":0.0037169063,"about_ca_system_score_codex":0.00073173206,"about_ca_system_score_gemma":0.0012031003,"threshold_uncertainty_score":0.012434304},"labels":[],"label_agreement":null},{"id":"W4394744983","doi":"10.1109/tiv.2024.3386915","title":"Extending Operational Design Domain for Perception Systems Through Robust Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Perception; Domain (mathematical analysis); Computer science; Human–computer interaction; Systems engineering; Cognitive science; Psychology; Engineering; Mathematics","score_opus":0.06804657316376415,"score_gpt":0.2956738788484827,"score_spread":0.2276273056847185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394744983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015703749,0.00005453265,0.9824055,0.000066825254,0.000012673327,0.000038561317,0.00002088098,0.00049587263,0.0012014862],"genre_scores_gemma":[0.71549946,0.00015345326,0.2822162,0.00013533449,0.00002585457,0.00012389984,0.00018174724,0.00018727616,0.0014768204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891293,0.00030918987,0.000067177316,0.00028102275,0.00032151304,0.00010818934],"domain_scores_gemma":[0.99787736,0.00072064175,0.0002963537,0.00072941283,0.00030187756,0.00007439109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019874177,0.0013510757,0.00060094974,0.00040993327,0.0003002609,0.0010558974,0.0010676002,0.00074190524,0.0017031173],"category_scores_gemma":[0.0047187656,0.00041082915,0.0009407162,0.0002529202,0.0012540169,0.0021757176,0.0028917636,0.0019805904,0.00059673784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013175594,0.00013026937,0.0011435528,0.00013786192,0.00003804648,0.00012366317,0.00015258497,0.8233338,0.025206001,0.016480027,0.00078847294,0.13233398],"study_design_scores_gemma":[0.000007934249,0.0000998677,0.0001647509,0.000008374341,0.0000069912207,0.000038815066,0.000026171027,0.98070127,0.008433303,0.009043461,0.0014581799,0.000010914077],"about_ca_topic_score_codex":0.0012731192,"about_ca_topic_score_gemma":0.0011458746,"teacher_disagreement_score":0.0019874177,"about_ca_system_score_codex":0.0006292324,"about_ca_system_score_gemma":0.001144154,"threshold_uncertainty_score":0.010510564},"labels":[],"label_agreement":null},{"id":"W4394780661","doi":"10.48550/arxiv.2404.07277","title":"On the coherent extension of some Fano-type learning bounds","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Colleges and Universities; Institut Périmètre de physique théorique; Government of Canada","keywords":"Quantum entanglement; Conditional entropy; Entropy (arrow of time); Information theory; Kullback–Leibler divergence; Bounded function; Mathematics; Theoretical computer science; Upper and lower bounds; Computer science; Quantum; Artificial intelligence; Principle of maximum entropy; Quantum mechanics; Statistics","score_opus":0.07776163154040454,"score_gpt":0.1981700676304622,"score_spread":0.12040843609005765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394780661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021768274,0.0025372354,0.93787706,0.0053202645,0.00018822648,0.00011367407,0.00046241202,0.00023073584,0.03150209],"genre_scores_gemma":[0.7569155,0.0056397077,0.21648915,0.0038204342,0.0016376416,0.0011855356,0.0012597607,0.000546405,0.012505768],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.990531,0.0039481223,0.00037958036,0.0018159924,0.0025731085,0.0007521887],"domain_scores_gemma":[0.90554476,0.079470254,0.0033555708,0.006082821,0.0038875148,0.0016590953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024532352,0.0021524255,0.0017965151,0.0054193405,0.0024648844,0.004904535,0.0031991058,0.0030387726,0.0064067193],"category_scores_gemma":[0.096602775,0.000957714,0.0018140449,0.0029579871,0.010738784,0.014655293,0.008239265,0.007771758,0.0009060256],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004453898,0.000030757474,0.00055700046,0.00008579425,0.000023735282,0.000041213592,0.000116172865,0.021220304,0.0004636074,0.9663981,0.0011195438,0.009899265],"study_design_scores_gemma":[0.00001501875,0.000039352224,0.00031448965,0.00009270693,0.000013288464,0.000044776858,0.00001733883,0.13758291,0.0006912283,0.85947603,0.0016873569,0.000025564354],"about_ca_topic_score_codex":0.0022124208,"about_ca_topic_score_gemma":0.0015843397,"teacher_disagreement_score":0.024532352,"about_ca_system_score_codex":0.0048226817,"about_ca_system_score_gemma":0.0025156275,"threshold_uncertainty_score":0.12974101},"labels":[],"label_agreement":null},{"id":"W4394828294","doi":"10.1109/tcsi.2024.3383839","title":"Resilient Synchronization for Insecure Markovian Jump Neural Networks to Mitigate Dual Cyber Attacks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Science and Technology Major Project; Australian Research Council; Natural Science Foundation of Shanghai","keywords":"Jump; Synchronization (alternating current); Dual (grammatical number); Computer science; Artificial neural network; Computer security; Computer network; Distributed computing; Artificial intelligence; Physics","score_opus":0.013272409331174947,"score_gpt":0.23753012219353262,"score_spread":0.22425771286235768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394828294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051870342,0.0004309799,0.94178456,0.0001982565,0.00011599212,0.000043436816,0.000030559633,0.0004918588,0.005033985],"genre_scores_gemma":[0.98836154,0.00016687866,0.009948088,0.000053282103,0.000025343743,0.00003359518,0.000018995603,0.000014303478,0.0013779004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962246,0.000049014925,0.000024233714,0.00013249867,0.00010734818,0.00006446708],"domain_scores_gemma":[0.9996463,0.000114322014,0.000100566926,0.000038372884,0.00007776177,0.000022618851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054768415,0.00074247393,0.00043469225,0.0003156255,0.00039727046,0.0005987087,0.00080608134,0.00060361595,0.0014177111],"category_scores_gemma":[0.0013934604,0.00019914136,0.00042724353,0.0001719051,0.0006268561,0.000657575,0.000926173,0.0008508036,0.00015178302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022920524,0.000049733422,0.0008120942,0.00017871833,0.000079960104,0.00020359107,0.0001523395,0.8797778,0.03019966,0.03387783,0.0007800098,0.05365906],"study_design_scores_gemma":[0.000009457961,0.00006144611,0.00010923887,0.0000057751163,0.00001278607,0.0000181099,0.0000057922152,0.99518085,0.0018232142,0.0024379266,0.00032985143,0.0000056553517],"about_ca_topic_score_codex":0.0025569375,"about_ca_topic_score_gemma":0.001955867,"teacher_disagreement_score":0.0025569375,"about_ca_system_score_codex":0.000519145,"about_ca_system_score_gemma":0.0005706675,"threshold_uncertainty_score":0.0050840974},"labels":[],"label_agreement":null},{"id":"W4394867770","doi":"10.48550/arxiv.2404.09101","title":"Mixtures of Neural Operators Reduce Active Complexity in Operator Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission; Rice University; U.S. Department of Energy","keywords":"Curse of dimensionality; Mathematics; Operator (biology); Lipschitz continuity; Constraint (computer-aided design); Scaling; Artificial neural network; Rank (graph theory); Discrete mathematics; Linear operators; Pure mathematics; Applied mathematics; Combinatorics; Computer science; Mathematical analysis; Artificial intelligence","score_opus":0.10200285803968687,"score_gpt":0.22999783280849395,"score_spread":0.12799497476880708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394867770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07763652,0.00020959758,0.9177904,0.0003623658,0.000028173292,0.000048254497,0.00004910853,0.00054928893,0.0033263373],"genre_scores_gemma":[0.71727514,0.00024912794,0.2764833,0.00025480465,0.000106169864,0.00014027553,0.00013839411,0.00027302158,0.0050797025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832076,0.0005029923,0.000113816895,0.00041114938,0.0005016667,0.00014964772],"domain_scores_gemma":[0.98724467,0.00868544,0.0009628132,0.0017534425,0.0008276958,0.00052598055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002817488,0.0008652432,0.0008280953,0.00066407316,0.00050456624,0.0021221288,0.0017544307,0.0016445161,0.0034680413],"category_scores_gemma":[0.019840129,0.0006790028,0.0010369128,0.00042692496,0.0020655557,0.008238583,0.0044625485,0.0027729415,0.00064031145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061860116,0.0001847156,0.002818827,0.0003082514,0.0000992369,0.00022272095,0.00059427525,0.3785935,0.025239421,0.46341524,0.0015898345,0.12631537],"study_design_scores_gemma":[0.00001512127,0.00013278658,0.00032308538,0.000012311551,0.000016796244,0.00007361269,0.000038982373,0.9034644,0.004337369,0.09074889,0.0008219791,0.000014722059],"about_ca_topic_score_codex":0.00080415065,"about_ca_topic_score_gemma":0.0008913293,"teacher_disagreement_score":0.0034680413,"about_ca_system_score_codex":0.0012749528,"about_ca_system_score_gemma":0.0007831693,"threshold_uncertainty_score":0.014900446},"labels":[],"label_agreement":null},{"id":"W4394911410","doi":"10.1109/ic-etite58242.2024.10493483","title":"Convolutional Neural Network Hardware Optimization Using Bayesian Method","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Convolutional neural network; Bayesian optimization; Artificial intelligence; Bayesian network; Bayesian probability; Artificial neural network; Pattern recognition (psychology); Machine learning","score_opus":0.027607270416049455,"score_gpt":0.3028084299017787,"score_spread":0.27520115948572926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394911410","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021944854,0.00067463337,0.9671631,0.00035729754,0.000051861596,0.00007103785,0.00013602397,0.0012410028,0.008360189],"genre_scores_gemma":[0.5408714,0.0008324667,0.44856095,0.00028540278,0.00004581327,0.00040300866,0.00067047146,0.00052409346,0.007806341],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996068,0.0000877508,0.000019512687,0.00006775278,0.00015733564,0.000060905168],"domain_scores_gemma":[0.99952376,0.00020968172,0.000050860177,0.00003439259,0.00016572121,0.000015570273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068012933,0.0010287557,0.00065669755,0.0006032717,0.000357149,0.00074504316,0.000956823,0.00078338647,0.0031373594],"category_scores_gemma":[0.002384229,0.0005875186,0.00067556306,0.0005221078,0.00037280557,0.0009435415,0.00061296084,0.0010730497,0.00073959905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034473418,0.000019465968,0.0004582756,0.00006360983,0.00003021217,0.000024939915,0.000013532598,0.9438079,0.0026893786,0.0037749193,0.0013977711,0.047685634],"study_design_scores_gemma":[0.0000030164572,0.000008043625,0.00008247746,0.0000054836387,0.000005588675,0.0000058384885,0.0000026794457,0.99765885,0.0007774517,0.00097207143,0.00047571238,0.0000028798338],"about_ca_topic_score_codex":0.0117669655,"about_ca_topic_score_gemma":0.014679239,"teacher_disagreement_score":0.0117669655,"about_ca_system_score_codex":0.0015859078,"about_ca_system_score_gemma":0.0021341394,"threshold_uncertainty_score":0.023396969},"labels":[],"label_agreement":null},{"id":"W4394948009","doi":"10.1117/12.3012777","title":"Physics-informed neural networks for modeling hysteretic behavior in magnetorheological dampers","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Magnetorheological fluid; Damper; Artificial neural network; Hysteresis; Computer science; Physics; Control engineering; Control theory (sociology); Structural engineering; Mechanical engineering; Materials science; Artificial intelligence; Condensed matter physics; Engineering; Control (management)","score_opus":0.042095302973256074,"score_gpt":0.29736178176550493,"score_spread":0.25526647879224884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394948009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043199338,0.00083442795,0.9519381,0.00034019048,0.00005367427,0.000041507446,0.000060932274,0.000258105,0.0032737665],"genre_scores_gemma":[0.92573196,0.0008054011,0.069107726,0.00012814009,0.000043808657,0.0001633653,0.000111035486,0.000044696866,0.0038638948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998286,0.000055937857,0.000010259226,0.000036850095,0.000051796163,0.000016485537],"domain_scores_gemma":[0.9996082,0.0002500826,0.000050711515,0.000021496578,0.000057606554,0.000011861965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060232973,0.0007076049,0.0005240479,0.00033934144,0.0002920723,0.0006562793,0.00087911275,0.0011123403,0.0009778291],"category_scores_gemma":[0.0018941053,0.00042919908,0.00045808076,0.0003139608,0.0006735759,0.0010665277,0.0006218985,0.0013907672,0.00016495532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008952557,0.000008332256,0.00010582511,0.000016856695,0.0000061176206,0.000015410147,0.000012438192,0.99345464,0.0007292461,0.002028694,0.000055384997,0.003557984],"study_design_scores_gemma":[4.7132093e-7,0.000003445347,0.000020252712,0.0000016915618,8.0866965e-7,0.0000017169749,0.0000010039354,0.9991493,0.00011771753,0.0006436922,0.000058675134,0.000001115882],"about_ca_topic_score_codex":0.004126623,"about_ca_topic_score_gemma":0.004136959,"teacher_disagreement_score":0.004126623,"about_ca_system_score_codex":0.0006104393,"about_ca_system_score_gemma":0.00059558614,"threshold_uncertainty_score":0.008205175},"labels":[],"label_agreement":null},{"id":"W4395012209","doi":"10.31234/osf.io/qds8r","title":"Generating random partial correlation matrices with an application to redundant variables and bridge centrality","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Centrality; Partial correlation; Bridge (graph theory); Mathematics; Correlation; Random variable; Computer science; Combinatorics; Statistics; Geometry","score_opus":0.016910478211423607,"score_gpt":0.26729321541307194,"score_spread":0.25038273720164833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395012209","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020584015,0.00006737482,0.977807,0.00010556411,0.000021626734,0.00017512437,0.00022109607,0.0005547339,0.00046347195],"genre_scores_gemma":[0.15462576,0.000086563356,0.84242374,0.00009921992,0.00002548555,0.0009870298,0.00070298306,0.00031405065,0.0007351822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954218,0.0031958313,0.00015253243,0.00061290164,0.00046376445,0.0001532542],"domain_scores_gemma":[0.96287805,0.030365184,0.0013806842,0.003152439,0.0018545368,0.0003690458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010222067,0.0009962261,0.0011993736,0.0023395335,0.0012540055,0.0012501302,0.0022767943,0.0014984893,0.0046524066],"category_scores_gemma":[0.054804422,0.0010654913,0.0017717168,0.0025588437,0.0016881452,0.00176713,0.002679697,0.0022795517,0.0006167855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017894432,0.00013557615,0.006993107,0.00025856603,0.0001821477,0.0003550011,0.00079911057,0.7190358,0.002154379,0.17203909,0.0029509969,0.09491731],"study_design_scores_gemma":[0.000032926564,0.00003266444,0.0004850059,0.000025310497,0.000013773066,0.000050784063,0.000029911236,0.93754804,0.00067057926,0.060020246,0.0010682628,0.000022512846],"about_ca_topic_score_codex":0.0092820525,"about_ca_topic_score_gemma":0.010320094,"teacher_disagreement_score":0.010222067,"about_ca_system_score_codex":0.0013499986,"about_ca_system_score_gemma":0.0021517272,"threshold_uncertainty_score":0.0540601},"labels":[],"label_agreement":null},{"id":"W4395463679","doi":"10.18280/isi.290226","title":"Unifying Variable Importance Scores from Different Machine Learning Models Using Simulated Annealing","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Variable (mathematics); Machine learning; Computer science; Psychology; Cognitive psychology; Mathematics","score_opus":0.03358331488182129,"score_gpt":0.24719896369419428,"score_spread":0.213615648812373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395463679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029272567,0.00010470101,0.9692076,0.000072920964,0.000035731053,0.000105687555,0.0000126833465,0.00028140377,0.00090660265],"genre_scores_gemma":[0.3995744,0.0001312599,0.59839886,0.000066598506,0.000030811625,0.00032793003,0.00010858815,0.00016313563,0.0011983428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977729,0.0011572252,0.00013558748,0.0002587332,0.0005181666,0.00015743132],"domain_scores_gemma":[0.99583,0.0026570624,0.00030441195,0.00043040529,0.00069519196,0.0000830322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005020957,0.0011056684,0.0013899719,0.001418162,0.0005762733,0.0011812245,0.0009795472,0.0009083712,0.0013269471],"category_scores_gemma":[0.011979629,0.0008574471,0.0014827162,0.001020282,0.00060508965,0.0015315806,0.0009305692,0.0017065412,0.00031836628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012006946,0.0001284248,0.0029682568,0.0001159603,0.0001484403,0.000050036648,0.00015693977,0.8624823,0.0043804804,0.0077087074,0.0004895522,0.12125083],"study_design_scores_gemma":[0.0000102799195,0.000049159255,0.0003431516,0.0000075431603,0.000015234159,0.000009249267,0.000012914583,0.99542445,0.0016751696,0.0021242115,0.00032015413,0.000008463954],"about_ca_topic_score_codex":0.0035804708,"about_ca_topic_score_gemma":0.003750044,"teacher_disagreement_score":0.005020957,"about_ca_system_score_codex":0.0009972194,"about_ca_system_score_gemma":0.0013421644,"threshold_uncertainty_score":0.02655369},"labels":[],"label_agreement":null},{"id":"W4395959889","doi":"10.1088/978-0-7503-6244-3ch11","title":"Knowledge distillation and its applications in DINO and SAM","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Distillation; Computer science; Process engineering; Chromatography; Chemistry; Engineering","score_opus":0.023182116798061658,"score_gpt":0.26245505434079,"score_spread":0.23927293754272833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395959889","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006907949,0.014819451,0.7675467,0.0017261647,0.0017791816,0.00008685058,0.001180959,0.0025169246,0.20343578],"genre_scores_gemma":[0.1285221,0.0082965605,0.677053,0.0010452288,0.00075692835,0.0001946539,0.0026103833,0.0011069151,0.18041423],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997441,0.000051875428,0.000017734494,0.00006105771,0.000098497105,0.000026729578],"domain_scores_gemma":[0.9997497,0.000115557865,0.000007805941,0.00006261227,0.000048133476,0.000016169628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068659533,0.0007038279,0.0007807639,0.00086955546,0.00087876344,0.0015539305,0.0012427582,0.00064544345,0.017627388],"category_scores_gemma":[0.0014661122,0.0003618323,0.0006575076,0.0021219573,0.0012328776,0.0028494948,0.0019217646,0.0019473984,0.004529586],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091441405,0.00005410083,0.00014536323,0.0002605977,0.000022898514,0.00006174409,0.00009026419,0.009942105,0.002127692,0.4595031,0.031830203,0.49587047],"study_design_scores_gemma":[0.000010544029,0.00003707359,0.0002362298,0.000103952654,0.000021231908,0.00016468881,0.00006056015,0.099474184,0.008125993,0.70847124,0.18325724,0.000036998852],"about_ca_topic_score_codex":0.0017970556,"about_ca_topic_score_gemma":0.004527892,"teacher_disagreement_score":0.017627388,"about_ca_system_score_codex":0.000807557,"about_ca_system_score_gemma":0.0007071954,"threshold_uncertainty_score":0.058969498},"labels":[],"label_agreement":null},{"id":"W4395959910","doi":"10.1088/978-0-7503-6244-3ch2","title":"Image classifications by convolutional neural networks","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.018076613468355353,"score_gpt":0.23638675269801093,"score_spread":0.2183101392296556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395959910","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065214597,0.0279968,0.8473575,0.0013252497,0.001519565,0.00008889973,0.0008582501,0.0043019536,0.11003027],"genre_scores_gemma":[0.0973618,0.03142423,0.58679557,0.00081664,0.0009936049,0.00015699852,0.0031583474,0.0012110386,0.2780817],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998418,0.00001539772,0.0000098634155,0.00004652206,0.00006970792,0.000016656986],"domain_scores_gemma":[0.99978036,0.000082968196,0.000018225886,0.00004510696,0.00006440861,0.000008976748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023776219,0.0009848193,0.0005440481,0.00094698404,0.00024086806,0.0015409742,0.00094967574,0.00074965606,0.01584418],"category_scores_gemma":[0.0008065492,0.00050387153,0.00064809335,0.0014391554,0.00050274364,0.0020825337,0.00070104044,0.001225397,0.011548342],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039533916,0.000027553651,0.00020289131,0.00045574375,0.000044250562,0.00004074539,0.000044878918,0.015645776,0.012402746,0.05681156,0.06537198,0.84891236],"study_design_scores_gemma":[0.000010553033,0.000048846687,0.0012312809,0.0003597642,0.000073357915,0.00038916187,0.00007757286,0.45298332,0.03480681,0.19729337,0.3126719,0.00005403397],"about_ca_topic_score_codex":0.0022679844,"about_ca_topic_score_gemma":0.0037098762,"teacher_disagreement_score":0.01584418,"about_ca_system_score_codex":0.00073619804,"about_ca_system_score_gemma":0.0003578806,"threshold_uncertainty_score":0.053004026},"labels":[],"label_agreement":null},{"id":"W4396625970","doi":"10.3390/environments11050094","title":"Assessing the Interpretability–Performance Trade-Off of Artificial Neural Networks Using Sentinel Fish Health Data","year":2024,"lang":"en","type":"article","venue":"Environments","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Environment and Protected Areas; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Environment and Parks","keywords":"Interpretability; Artificial neural network; Machine learning; Computer science; Artificial intelligence; Proxy (statistics); Ecology; Data mining; Biology","score_opus":0.06129989081271294,"score_gpt":0.3264110105046933,"score_spread":0.26511111969198037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396625970","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93135613,0.0012170114,0.062154476,0.0013986797,0.00012144019,0.0001500877,0.0004590809,0.00042536482,0.0027178566],"genre_scores_gemma":[0.9858482,0.0001995481,0.0128856,0.00011743475,0.000050370585,0.00004960153,0.0004391078,0.000031031617,0.00037907605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.992365,0.004657981,0.000826002,0.0008254409,0.0010285134,0.0002971729],"domain_scores_gemma":[0.89333683,0.094040185,0.004811411,0.0026970026,0.004603738,0.0005108293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028333655,0.001968911,0.0008585897,0.0019479351,0.0005088981,0.0020706602,0.00092400424,0.0021669413,0.00070417643],"category_scores_gemma":[0.0854934,0.0004272287,0.0009346865,0.0009873953,0.0008965706,0.002793881,0.0013511814,0.0013912275,0.00017772954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002158531,0.00038644453,0.09522378,0.00021986286,0.00076617044,0.00019172938,0.00022981892,0.82174397,0.0023367347,0.0010098242,0.0007946648,0.07493851],"study_design_scores_gemma":[0.00003368652,0.00043307428,0.01350432,0.000033471835,0.00007689134,0.000043785843,0.0000733698,0.98283,0.0014576718,0.0013546543,0.00013182178,0.000027278355],"about_ca_topic_score_codex":0.007512401,"about_ca_topic_score_gemma":0.0055154325,"teacher_disagreement_score":0.028333655,"about_ca_system_score_codex":0.0016376855,"about_ca_system_score_gemma":0.00058264757,"threshold_uncertainty_score":0.14984447},"labels":[],"label_agreement":null},{"id":"W4396766218","doi":"10.1371/journal.pone.0301513","title":"Dynamic multilayer growth: Parallel vs. sequential approaches","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Initialization; Computer science; Context (archaeology); Benchmark (surveying); Perceptron; Layer (electronics); Population; Artificial intelligence; Constructive; Artificial neural network; Algorithm; Pattern recognition (psychology); Parallel computing","score_opus":0.09221602388305478,"score_gpt":0.24691999918632493,"score_spread":0.15470397530327015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396766218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019373098,0.0008052716,0.97206086,0.00040213903,0.00008653737,0.00009448826,0.000046333724,0.0007231079,0.0064082416],"genre_scores_gemma":[0.3862413,0.0011240666,0.60358196,0.00023104803,0.00013627297,0.00032125952,0.00015261093,0.00049177505,0.0077197016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990934,0.00020791002,0.00006230987,0.00020844727,0.00031537784,0.000112613925],"domain_scores_gemma":[0.9969195,0.0014821925,0.00028785368,0.00066313427,0.0004927922,0.00015451724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022728064,0.0011584262,0.00075557875,0.0009956686,0.0006056605,0.0015840448,0.0020355782,0.0013022281,0.0037798295],"category_scores_gemma":[0.006291821,0.0007289234,0.0009836237,0.0008074147,0.0012933938,0.003979765,0.0027714744,0.0015749331,0.0010658741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025660955,0.0001406535,0.001768897,0.00038640207,0.0000970231,0.00021507744,0.00041196708,0.4179608,0.022124857,0.0884817,0.0036296593,0.46452639],"study_design_scores_gemma":[0.000025994494,0.00010829129,0.00024677854,0.000037316837,0.000043882428,0.0001785086,0.0000880245,0.9392886,0.010340962,0.043824334,0.0057967333,0.000020537427],"about_ca_topic_score_codex":0.0018845506,"about_ca_topic_score_gemma":0.0033674957,"teacher_disagreement_score":0.0037798295,"about_ca_system_score_codex":0.0010357794,"about_ca_system_score_gemma":0.0009573528,"threshold_uncertainty_score":0.012644768},"labels":[],"label_agreement":null},{"id":"W4396782969","doi":"10.1109/tfuzz.2024.3398719","title":"SCINN: Semantic Concept-Based Inference Neural Networks With Explainable and Deep Fuzzy Structure","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Interpretability; Computer science; Artificial intelligence; Generalization; Adaptive neuro fuzzy inference system; Fuzzy logic; Neuro-fuzzy; Machine learning; Inference; Fuzzy set; Data mining; Artificial neural network; Fuzzy rule; Fuzzy control system; Mathematics","score_opus":0.010560274029907606,"score_gpt":0.2293291385618836,"score_spread":0.218768864531976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396782969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017491398,0.0007382604,0.97724986,0.000334271,0.00012281467,0.00006241861,0.00019303962,0.0007102012,0.0030976767],"genre_scores_gemma":[0.6277809,0.001104457,0.36208144,0.0007392161,0.00013439907,0.0003010902,0.0010740524,0.00009540561,0.0066890744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974173,0.00004705658,0.000016558979,0.00008165694,0.00008665865,0.000026363088],"domain_scores_gemma":[0.9996451,0.00015298776,0.0000347926,0.000041101783,0.00010820475,0.00001774952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084344525,0.00069015595,0.00061109243,0.0005216065,0.00029894355,0.00065521314,0.0016781754,0.001011048,0.0021219826],"category_scores_gemma":[0.0017409455,0.00035451356,0.00075441535,0.000581103,0.00053083274,0.0016043946,0.0009002284,0.0013510115,0.00039427952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018977547,0.00012784959,0.0017195621,0.0002350312,0.00019611372,0.00021433391,0.00013778324,0.58479697,0.0072048265,0.033780098,0.005287378,0.36611035],"study_design_scores_gemma":[0.000006149858,0.00002174873,0.000119690034,0.0000077779605,0.000012632477,0.000019035746,0.0000051996385,0.9907176,0.0007479094,0.0075388644,0.0007977148,0.000005707805],"about_ca_topic_score_codex":0.0059138434,"about_ca_topic_score_gemma":0.00767371,"teacher_disagreement_score":0.0059138434,"about_ca_system_score_codex":0.000810524,"about_ca_system_score_gemma":0.0007766303,"threshold_uncertainty_score":0.011758864},"labels":[],"label_agreement":null},{"id":"W4396840581","doi":"10.1017/9781009282284.009","title":"Tree-Based Classification and Regression","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Tree (set theory); Regression; Forestry; Statistics; Geography; Mathematics; Combinatorics","score_opus":0.032103412687953095,"score_gpt":0.2149476433231133,"score_spread":0.1828442306351602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396840581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018748853,0.08724042,0.7598097,0.0049126633,0.0034001758,0.00023382185,0.0018370565,0.0031908464,0.13750046],"genre_scores_gemma":[0.046419926,0.10872148,0.6205496,0.0029696622,0.0053575225,0.0006317697,0.0049821055,0.0018510778,0.20851682],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997879,0.00042229885,0.00010179737,0.00047288803,0.0010132602,0.00011072532],"domain_scores_gemma":[0.99807715,0.00114989,0.00012440236,0.000210141,0.0003798482,0.00005843937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022338256,0.0015101222,0.0014652209,0.0031763117,0.0006799841,0.003991548,0.002172122,0.002475251,0.035776198],"category_scores_gemma":[0.006887609,0.0006012188,0.0011854722,0.006506451,0.0014026847,0.0049693678,0.0014531056,0.0029499372,0.026099756],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036925783,0.00003585201,0.00054776884,0.0009422502,0.00007982429,0.00010855037,0.00019149172,0.011275124,0.0005843115,0.27155966,0.14927237,0.56536585],"study_design_scores_gemma":[0.000013260397,0.000046152603,0.00087170734,0.00053938106,0.000035094043,0.0004237619,0.00010573811,0.04558457,0.0010008271,0.24685054,0.7044631,0.000065877684],"about_ca_topic_score_codex":0.0023174225,"about_ca_topic_score_gemma":0.0027915146,"teacher_disagreement_score":0.035776198,"about_ca_system_score_codex":0.0011874179,"about_ca_system_score_gemma":0.0011144999,"threshold_uncertainty_score":0.119683325},"labels":[],"label_agreement":null},{"id":"W4396867270","doi":"10.1007/s42484-024-00166-z","title":"Exploring the features of quanvolutional neural networks for improved image classification","year":2024,"lang":"en","type":"article","venue":"Quantum Machine Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Image (mathematics); Artificial neural network; Pattern recognition (psychology); Contextual image classification; Computer vision","score_opus":0.07627676104902367,"score_gpt":0.31126012160259353,"score_spread":0.23498336055356986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396867270","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5654052,0.0010071638,0.4184209,0.001328887,0.00011065671,0.0000557011,0.00006137203,0.0003396383,0.013270565],"genre_scores_gemma":[0.9666725,0.0001577326,0.031043392,0.00008984086,0.000019694993,0.000026036048,0.000029878844,0.000037907957,0.0019231129],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990773,0.00003423792,0.0000044512967,0.000016708727,0.000022655362,0.000014301851],"domain_scores_gemma":[0.9995179,0.00028459792,0.00003915224,0.000059602928,0.00007398869,0.00002470078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062343886,0.00023516025,0.00041285795,0.00027721465,0.00031365684,0.00063492224,0.0006155807,0.0006178937,0.0022949358],"category_scores_gemma":[0.0025139758,0.00020973227,0.00030264514,0.0002930385,0.0005331049,0.0012392665,0.0005791142,0.0006859998,0.0001560386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029963173,0.00017287218,0.0046688966,0.00017396455,0.00009388265,0.0002126121,0.0002621696,0.6414422,0.033315506,0.16754991,0.0023377053,0.1494706],"study_design_scores_gemma":[0.000005238306,0.000021283435,0.00029679787,0.0000028181303,0.000005352418,0.000018591078,0.000009340152,0.98184884,0.0006653157,0.016908018,0.00021530117,0.0000032079167],"about_ca_topic_score_codex":0.0012200257,"about_ca_topic_score_gemma":0.0018033354,"teacher_disagreement_score":0.0022949358,"about_ca_system_score_codex":0.00041368703,"about_ca_system_score_gemma":0.00023453597,"threshold_uncertainty_score":0.0076773167},"labels":[],"label_agreement":null},{"id":"W4396885222","doi":"10.1007/978-3-031-54908-3_9","title":"Neural Networks","year":2024,"lang":"en","type":"book-chapter","venue":"Compact textbooks in mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"","keywords":"Computer science","score_opus":0.03443062072672448,"score_gpt":0.2697600465653592,"score_spread":0.2353294258386347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396885222","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034770495,0.03577275,0.40472668,0.0030350585,0.0040416685,0.000067987,0.0011589524,0.0025981555,0.54512167],"genre_scores_gemma":[0.050108414,0.017535659,0.085411236,0.0010265654,0.001530471,0.00015305096,0.0020549085,0.000770482,0.8414093],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998185,0.000020147549,0.000008667576,0.000044802655,0.00009715945,0.000010754052],"domain_scores_gemma":[0.99981624,0.000049358066,0.00001068698,0.00005244947,0.00006010638,0.000011235783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022161873,0.00077836297,0.00057391217,0.00065958034,0.00030127482,0.0013881187,0.00084065384,0.0010107909,0.05771265],"category_scores_gemma":[0.0009807196,0.00033672273,0.00029079337,0.0009568066,0.0004959695,0.0016697096,0.0008841559,0.0013362544,0.031987708],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026813992,0.000036255533,0.00017008492,0.0003137646,0.000039664243,0.00005303458,0.000041615687,0.015675884,0.0030541432,0.1535815,0.185767,0.6412401],"study_design_scores_gemma":[0.000010323491,0.00004071021,0.0006126653,0.00021938454,0.00003504229,0.00025767216,0.000034651937,0.08479051,0.004077374,0.23694165,0.6729491,0.000030900363],"about_ca_topic_score_codex":0.001009022,"about_ca_topic_score_gemma":0.0019369123,"teacher_disagreement_score":0.05771265,"about_ca_system_score_codex":0.00048443265,"about_ca_system_score_gemma":0.00042178854,"threshold_uncertainty_score":0.19306803},"labels":[],"label_agreement":null},{"id":"W4396925359","doi":"10.3390/stats7020027","title":"Multivariate and Matrix-Variate Logistic Models in the Real and Complex Domains","year":2024,"lang":"en","type":"article","venue":"Stats","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Random variate; Multivariate statistics; Logistic regression; Multivariate analysis; Mathematics; Econometrics; Statistics; Computer science; Random variable","score_opus":0.08053115015664743,"score_gpt":0.3442012222432785,"score_spread":0.26367007208663107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396925359","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02195916,0.0020484373,0.9563244,0.0019028693,0.00018107788,0.00005107585,0.0004908304,0.00031014826,0.016732026],"genre_scores_gemma":[0.72117585,0.00863728,0.19760579,0.0009613925,0.0009977613,0.0004095189,0.0010380787,0.00030645597,0.06886791],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99845576,0.00074010756,0.000069081565,0.00024469898,0.00033225023,0.00015818945],"domain_scores_gemma":[0.9945839,0.0032619496,0.0006846096,0.00052379275,0.0007346572,0.00021114475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038080798,0.000996837,0.0009172768,0.001664242,0.00049692893,0.002535501,0.002098877,0.0017273697,0.008500634],"category_scores_gemma":[0.0153623335,0.00060031604,0.0014930399,0.0026362927,0.0021231247,0.004289697,0.0025677762,0.0033810376,0.0024050544],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006902694,0.000033873286,0.0013623046,0.000093512426,0.000040425653,0.0002515772,0.00016238567,0.13275789,0.000463123,0.83657503,0.004494527,0.02369625],"study_design_scores_gemma":[0.000017199369,0.000027759737,0.00091450673,0.0000532855,0.000018608678,0.0003110902,0.00007104683,0.6159702,0.0002517071,0.37405097,0.008259555,0.000054060456],"about_ca_topic_score_codex":0.004488278,"about_ca_topic_score_gemma":0.0040591853,"teacher_disagreement_score":0.008500634,"about_ca_system_score_codex":0.0012941025,"about_ca_system_score_gemma":0.00081036147,"threshold_uncertainty_score":0.028437436},"labels":[],"label_agreement":null},{"id":"W4396939176","doi":"10.3765/plsa.v9i1.5715","title":"Information-theoretic applications to Hupa verbal morphology","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Linguistic Society of America","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Morphology (biology); Computer science; Biology","score_opus":0.007946462237943406,"score_gpt":0.250100587754512,"score_spread":0.2421541255165686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396939176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12607887,0.0026662862,0.8280249,0.004600471,0.00016360436,0.000090964415,0.0005099214,0.0003973812,0.037467577],"genre_scores_gemma":[0.840931,0.0013083033,0.1531756,0.00043121565,0.0003589282,0.00013308853,0.00032610222,0.0001595887,0.0031762228],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859506,0.00063500076,0.00011750377,0.00025464044,0.00033391578,0.00006383758],"domain_scores_gemma":[0.98405254,0.012964716,0.00093499763,0.0010094171,0.0008582914,0.00018009785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027269062,0.0005404972,0.0005978565,0.0027469676,0.0011338136,0.0028322577,0.00093608926,0.0007658865,0.0039053743],"category_scores_gemma":[0.022204366,0.00041890866,0.00094851194,0.0022161407,0.0038309747,0.00401839,0.0023189124,0.001561712,0.00036737093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008004963,0.000054900167,0.003065972,0.00016559643,0.00007482368,0.00027905664,0.0010624528,0.071669795,0.0026720352,0.8520591,0.001006565,0.06780966],"study_design_scores_gemma":[0.000006762734,0.000033249267,0.0012662387,0.000033193064,0.000015458336,0.000101461184,0.00015536675,0.12141494,0.00079746894,0.8734208,0.0027277495,0.000027466365],"about_ca_topic_score_codex":0.0032495619,"about_ca_topic_score_gemma":0.0021424305,"teacher_disagreement_score":0.0039053743,"about_ca_system_score_codex":0.0024740573,"about_ca_system_score_gemma":0.0010581975,"threshold_uncertainty_score":0.017950594},"labels":[],"label_agreement":null},{"id":"W4396986677","doi":"10.48550/arxiv.2405.08921","title":"Neural Active Learning Meets the Partial Monitoring Framework","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs","keywords":"Computer science; Artificial intelligence","score_opus":0.072784804146181,"score_gpt":0.21553661461575538,"score_spread":0.14275181046957436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396986677","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021325028,0.00073340815,0.97219527,0.001416749,0.00006959325,0.00007916321,0.00021713208,0.0008023655,0.0031613044],"genre_scores_gemma":[0.86064386,0.00042749825,0.13343368,0.0006137199,0.0002781482,0.00029305968,0.0003745935,0.00019956117,0.0037359018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970611,0.0012933228,0.00016099199,0.00075738796,0.0004939833,0.00023327555],"domain_scores_gemma":[0.9841325,0.011237976,0.0011785968,0.0021383127,0.0008141733,0.0004983529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005988116,0.0016118643,0.002038588,0.0007065305,0.0006899034,0.0028591473,0.003880707,0.0036912533,0.003242317],"category_scores_gemma":[0.021699019,0.0008185265,0.00082102825,0.0008565554,0.0024710076,0.0067214672,0.0043311296,0.005534981,0.0006774973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006353158,0.00038005924,0.0035459136,0.0005712089,0.00018310954,0.00031202566,0.00038397658,0.6043175,0.0048985574,0.13939455,0.0067343256,0.23864353],"study_design_scores_gemma":[0.000014811533,0.000045011984,0.00013006055,0.000013934318,0.000007784589,0.000035283847,0.000010114602,0.9460322,0.0008738517,0.052225377,0.00060321623,0.000008226699],"about_ca_topic_score_codex":0.002011164,"about_ca_topic_score_gemma":0.0019750737,"teacher_disagreement_score":0.005988116,"about_ca_system_score_codex":0.0013834508,"about_ca_system_score_gemma":0.0015101833,"threshold_uncertainty_score":0.031668603},"labels":[],"label_agreement":null},{"id":"W4398459186","doi":"10.7910/dvn/itvlik/74virf","title":"prepare-commit-msg.sample","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Commit; Sample (material); Computer science; Chemistry; Database; Chromatography","score_opus":0.02129709123044644,"score_gpt":0.2483001193539491,"score_spread":0.22700302812350268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398459186","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002365134,0.000069529044,0.00013483313,0.00013198674,0.00010269437,0.000036657162,0.99531686,0.0023440758,0.0016269218],"genre_scores_gemma":[0.0008124631,0.000042897926,0.00056831096,0.00013426451,0.000030538537,0.00013561174,0.9961844,0.00055481895,0.0015366155],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983773,0.00025585742,0.00015728472,0.0005610652,0.00033543943,0.00031298987],"domain_scores_gemma":[0.9950825,0.0010755163,0.00023780804,0.0019831962,0.0011236551,0.0004973692],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016646786,0.0036638023,0.0015591059,0.0037164327,0.0013742626,0.0036303352,0.0038950855,0.0034662045,0.2254312],"category_scores_gemma":[0.012025375,0.0010529261,0.0015704586,0.004852826,0.0007304039,0.0025721253,0.002782458,0.0017783442,0.2833904],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005696259,0.000018300654,0.00023830445,0.00019539862,0.000010310634,0.0000072202374,0.0000064710557,0.000102138576,0.000049092134,0.00018189392,0.9976533,0.0014807013],"study_design_scores_gemma":[0.0009317582,0.00006775977,0.0019955381,0.00023077561,0.000030492478,0.00007354643,0.000093025315,0.0011695903,0.0012109734,0.0031243302,0.99103093,0.00004137267],"about_ca_topic_score_codex":0.012538802,"about_ca_topic_score_gemma":0.025748635,"teacher_disagreement_score":0.7745688,"about_ca_system_score_codex":0.0019076465,"about_ca_system_score_gemma":0.0029056338,"threshold_uncertainty_score":0.7541425},"labels":[],"label_agreement":null},{"id":"W4398534730","doi":"10.7910/dvn/itvlik/q09tez","title":"applypatch-msg.sample","year":2020,"lang":"ne","type":"dataset","venue":"Harvard Dataverse","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Sample (material); Chemistry; Chromatography","score_opus":0.025089256091312833,"score_gpt":0.24619546002196324,"score_spread":0.22110620393065042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398534730","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00079326145,0.00019139153,0.0002632048,0.00015554059,0.00015412451,0.000054886284,0.9929624,0.0033927648,0.00203241],"genre_scores_gemma":[0.0013805607,0.000071339404,0.0006518426,0.00009080022,0.000030114676,0.00013139582,0.99601185,0.00032069304,0.0013114207],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984895,0.00022265385,0.0001225334,0.0005223285,0.0003451452,0.0002978131],"domain_scores_gemma":[0.99751365,0.00046553838,0.00012575356,0.0010411909,0.00059840817,0.00025547913],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015262724,0.00504193,0.0018437138,0.00432546,0.0013824906,0.0033235666,0.0047712764,0.0036188576,0.09097945],"category_scores_gemma":[0.008246887,0.0009570883,0.002226655,0.0047202283,0.00085799186,0.002039474,0.002777798,0.0020514445,0.15565701],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071875605,0.00004118429,0.0004145301,0.00028526015,0.000027854674,0.000016924354,0.000009558213,0.00025543914,0.00009856842,0.00022981685,0.99549025,0.0030587008],"study_design_scores_gemma":[0.0010281389,0.000120949684,0.0035346863,0.0003235059,0.000066626984,0.00017328728,0.00011734444,0.0029773049,0.0019696292,0.0033122944,0.9863129,0.00006320316],"about_ca_topic_score_codex":0.016044317,"about_ca_topic_score_gemma":0.03488319,"teacher_disagreement_score":0.90902054,"about_ca_system_score_codex":0.0017561495,"about_ca_system_score_gemma":0.0026143703,"threshold_uncertainty_score":0.30435658},"labels":[],"label_agreement":null},{"id":"W4398796082","doi":"10.48550/arxiv.2405.13738","title":"Interpolation with deep neural networks with non-polynomial activations: necessary and sufficient numbers of neurons","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Layer (electronics); Polynomial; Artificial neural network; Computer science; Mathematics; Artificial intelligence; Materials science; Composite material; Mathematical analysis","score_opus":0.023025617790885636,"score_gpt":0.17731846903956855,"score_spread":0.15429285124868292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398796082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05369938,0.0007062357,0.93859583,0.0013176918,0.00008111311,0.000055598284,0.00029497643,0.0005619819,0.0046872357],"genre_scores_gemma":[0.74791515,0.0009931825,0.24435861,0.00038158346,0.00017519131,0.00029121118,0.0005267348,0.00039652106,0.004961851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99868923,0.0003673301,0.000110424044,0.00026454742,0.00033311325,0.00023531847],"domain_scores_gemma":[0.9917019,0.006253684,0.0005024758,0.0006403405,0.0005599229,0.00034176794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028061513,0.0011494078,0.0013723315,0.000599679,0.00059262913,0.0011566128,0.0020534913,0.0017811778,0.00347831],"category_scores_gemma":[0.021813361,0.0010994185,0.0009376947,0.00061630656,0.002253917,0.0048209424,0.0032704882,0.004528206,0.0007013351],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009925005,0.00016359166,0.001859412,0.0006872339,0.00007049099,0.0004936777,0.00020028553,0.69910103,0.016522042,0.22368766,0.0032076233,0.053014543],"study_design_scores_gemma":[0.00003027228,0.000055208333,0.00012592932,0.00004661381,0.000010562159,0.00004488975,0.000021729264,0.9264114,0.0031908744,0.069421165,0.0006296532,0.000011655197],"about_ca_topic_score_codex":0.0017725064,"about_ca_topic_score_gemma":0.0031820477,"teacher_disagreement_score":0.00347831,"about_ca_system_score_codex":0.001092008,"about_ca_system_score_gemma":0.001839179,"threshold_uncertainty_score":0.014840543},"labels":[],"label_agreement":null},{"id":"W4399115775","doi":"10.48550/arxiv.2405.16563","title":"Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Generalization; Transformer; Path (computing); Mathematics; Computer science; Electrical engineering; Engineering; Mathematical analysis; Computer network; Voltage","score_opus":0.06907321891883815,"score_gpt":0.21318471425489388,"score_spread":0.14411149533605572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399115775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014958959,0.00084234343,0.9775724,0.0008006232,0.000055757886,0.00004297404,0.00025264424,0.00093435065,0.0045400304],"genre_scores_gemma":[0.7060913,0.0027474537,0.2748065,0.0012199387,0.00033060656,0.0005001398,0.0013390473,0.0019090924,0.011055953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980076,0.0005612895,0.000116656294,0.0004984936,0.00056789286,0.0002481198],"domain_scores_gemma":[0.9774405,0.017248338,0.0009921896,0.0024412435,0.0013340851,0.0005437832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054490035,0.0025235969,0.0015184155,0.0015228478,0.00090074446,0.002400301,0.0029576835,0.0021603566,0.00886956],"category_scores_gemma":[0.043697257,0.0010701999,0.001868769,0.001117857,0.003223785,0.010350651,0.005406982,0.0078109545,0.0018899387],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025009798,0.000114512586,0.002491277,0.00042465274,0.00008630421,0.00019944855,0.00034533866,0.5211628,0.0054120403,0.39059737,0.0066939136,0.07222233],"study_design_scores_gemma":[0.0000067080164,0.000030109324,0.00023324699,0.000043107735,0.000014097106,0.000046977355,0.000015490867,0.867288,0.0013905576,0.13010614,0.0008117556,0.000013875573],"about_ca_topic_score_codex":0.00392195,"about_ca_topic_score_gemma":0.0061285174,"teacher_disagreement_score":0.00886956,"about_ca_system_score_codex":0.00395549,"about_ca_system_score_gemma":0.0023340064,"threshold_uncertainty_score":0.02967161},"labels":[],"label_agreement":null},{"id":"W4399135167","doi":"10.1002/sim.10116","title":"Structured learning in time‐dependent Cox models","year":2024,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Ministère de la Santé et des Services sociaux; Réseau Québécois de Recherche sur les Médicaments; Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Computer science; Proportional hazards model; Econometrics; Statistics; Mathematics","score_opus":0.017194394397149235,"score_gpt":0.3004267088436853,"score_spread":0.283232314446536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399135167","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009459822,0.00021319481,0.9891362,0.00035086364,0.000021606354,0.000049250244,0.00016691552,0.00021776615,0.000384322],"genre_scores_gemma":[0.43344057,0.0009942556,0.55595833,0.00062671764,0.00029249967,0.00095585635,0.0020513507,0.00023977472,0.005440681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973628,0.0019294693,0.000084242405,0.00030484446,0.00020970903,0.00010882747],"domain_scores_gemma":[0.97706485,0.020334594,0.00086596777,0.0007438523,0.0007045555,0.00028617628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008072981,0.0007647083,0.0011683718,0.0008754489,0.0005240561,0.00096076104,0.0017862031,0.0012450437,0.002881851],"category_scores_gemma":[0.026357288,0.00074843975,0.0011859908,0.00089170266,0.0009552495,0.0012836398,0.0015928971,0.0024792228,0.0005667161],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017342456,0.000074639574,0.004217313,0.00015794823,0.00011584163,0.00018905623,0.00015784056,0.84870625,0.00043821067,0.070354775,0.0035212226,0.07189344],"study_design_scores_gemma":[0.000021085767,0.000014410888,0.00017066911,0.000010472442,0.000007992988,0.000012956799,0.000007359652,0.9611133,0.00009983669,0.038121033,0.00041513157,0.0000057991233],"about_ca_topic_score_codex":0.005233412,"about_ca_topic_score_gemma":0.007947012,"teacher_disagreement_score":0.008072981,"about_ca_system_score_codex":0.0009985337,"about_ca_system_score_gemma":0.001698225,"threshold_uncertainty_score":0.04269457},"labels":[],"label_agreement":null},{"id":"W4399164941","doi":"10.36227/techrxiv.171710237.79956036/v1","title":"Big Data-driven Control of Nonlinear Processes through Dynamic Latent Variables using an Autoencoder","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Autoencoder; Latent variable; Nonlinear system; Control (management); Big data; Computer science; Artificial intelligence; Data mining; Artificial neural network; Physics","score_opus":0.0913336656164138,"score_gpt":0.3259276649410979,"score_spread":0.2345939993246841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399164941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008207862,0.00016813003,0.99069583,0.00013329984,0.00003382174,0.000013586704,0.000033417313,0.00015899114,0.0005549802],"genre_scores_gemma":[0.8542861,0.00049201376,0.14185445,0.00018283972,0.0000859003,0.00015437492,0.00021557884,0.000079228856,0.0026495252],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995919,0.00010616171,0.00002399514,0.00013251761,0.00010885757,0.000036529505],"domain_scores_gemma":[0.99923325,0.0004446545,0.0000969682,0.000077360906,0.000120772376,0.00002695022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009139503,0.00071506447,0.0006747041,0.00023910165,0.0002910899,0.00085330213,0.000936083,0.00058481237,0.00071969273],"category_scores_gemma":[0.002054202,0.00046179534,0.0006419292,0.0003224983,0.0009965312,0.0011032606,0.0011602312,0.0016262811,0.00013023756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065828084,0.00004636494,0.00058909494,0.00010351728,0.00008433797,0.000054469197,0.000082653256,0.9218846,0.0063387235,0.024874892,0.00053355034,0.045341972],"study_design_scores_gemma":[0.0000022908648,0.000007990201,0.00005773804,0.0000032866328,0.0000033979866,0.0000029864693,0.0000017445939,0.9962132,0.00055012334,0.0029813726,0.00017297799,0.0000029059643],"about_ca_topic_score_codex":0.0043301964,"about_ca_topic_score_gemma":0.0045252945,"teacher_disagreement_score":0.0043301964,"about_ca_system_score_codex":0.00060288596,"about_ca_system_score_gemma":0.0008922351,"threshold_uncertainty_score":0.008609951},"labels":[],"label_agreement":null},{"id":"W4399266878","doi":"10.1007/978-3-031-62110-9_3","title":"Decoding the Alignment Problem: Revisiting the 1958 NYT Report on Rosenblatt’s Perceptron Through the Lens of Information Theory","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Decoding methods; Multilayer perceptron; Computer science; Through-the-lens metering; Perceptron; Psychology; Artificial intelligence; Lens (geology); Artificial neural network; Algorithm; Physics; Optics","score_opus":0.04354096666828808,"score_gpt":0.2996474996249506,"score_spread":0.2561065329566625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399266878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007356078,0.022310508,0.86029005,0.04318971,0.004903996,0.000040540832,0.0005198057,0.0006324808,0.060756825],"genre_scores_gemma":[0.2600762,0.035285927,0.59431607,0.01656688,0.014083741,0.00020933711,0.0012706266,0.0023989442,0.07579229],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968154,0.0015154522,0.00020246324,0.00054125633,0.0007592463,0.00016619203],"domain_scores_gemma":[0.99194825,0.0063121216,0.0002438218,0.00059863215,0.0007676479,0.00012948725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057644835,0.0011444488,0.0017642363,0.0017984768,0.0015199172,0.0056193834,0.002800405,0.0037251967,0.008370932],"category_scores_gemma":[0.025747446,0.0010134971,0.0010207668,0.0046186806,0.008867259,0.019521018,0.0034030264,0.0073202276,0.0044432194],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044489698,0.00002530857,0.000195086,0.0001601613,0.00002411458,0.00006349741,0.00039829777,0.0069917873,0.00031979763,0.8695017,0.023321122,0.098954625],"study_design_scores_gemma":[0.000007624072,0.000008484407,0.000071035,0.000078891615,0.0000067741967,0.000052241336,0.000055574666,0.01590966,0.00045836665,0.95667404,0.026654823,0.000022478727],"about_ca_topic_score_codex":0.011062579,"about_ca_topic_score_gemma":0.011006333,"teacher_disagreement_score":0.011062579,"about_ca_system_score_codex":0.002784307,"about_ca_system_score_gemma":0.0039050202,"threshold_uncertainty_score":0.030485868},"labels":[],"label_agreement":null},{"id":"W4399426455","doi":"10.1109/access.2024.3411073","title":"Bounded Low Latency via Inverse Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); University of Toronto","funders":"","keywords":"Bounded function; Computer science; Reinforcement learning; Latency (audio); Inverse; Artificial intelligence; Theoretical computer science; Mathematics; Telecommunications","score_opus":0.024770775269869257,"score_gpt":0.29550002445895535,"score_spread":0.2707292491890861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399426455","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03208014,0.00021693991,0.9635028,0.0002899612,0.000046163364,0.000051805546,0.000033758577,0.00060449046,0.0031739466],"genre_scores_gemma":[0.94988936,0.00013724399,0.047851816,0.00010288819,0.00003255909,0.000121439385,0.000044852044,0.000060763337,0.001759033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994351,0.00013876468,0.000025264324,0.00011680656,0.00016259214,0.00012142695],"domain_scores_gemma":[0.99795294,0.0013131929,0.0002678258,0.00014157282,0.00023457315,0.000089888796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010283568,0.0010063123,0.00087122037,0.00034959157,0.00046753456,0.00094462396,0.0012354644,0.0007350357,0.001625271],"category_scores_gemma":[0.004407692,0.00038264596,0.00038683356,0.00035910445,0.0010231896,0.0013286872,0.0011825108,0.0015150215,0.00027781818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052742504,0.00005135301,0.00046154766,0.000037730522,0.000013114544,0.000045108623,0.000035042827,0.9719715,0.0015853473,0.005727504,0.00049119454,0.019527737],"study_design_scores_gemma":[0.0000033313731,0.000009637911,0.000021176074,0.0000014144475,0.0000014320449,0.0000032527998,0.0000022212841,0.9982311,0.000149635,0.0015099008,0.00006553311,0.0000012722995],"about_ca_topic_score_codex":0.007364389,"about_ca_topic_score_gemma":0.0059241853,"teacher_disagreement_score":0.007364389,"about_ca_system_score_codex":0.0011018687,"about_ca_system_score_gemma":0.0017066005,"threshold_uncertainty_score":0.014643073},"labels":[],"label_agreement":null},{"id":"W4399581870","doi":"10.32614/cran.package.stargazer","title":"stargazer: Well-Formatted Regression and Summary Statistics Tables","year":2012,"lang":"en","type":"dataset","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":337,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Masarykova Univerzita; Universität Stuttgart; Universität Ulm; Universiteit Antwerpen; University of Exeter; Lunds Universitet; University of Illinois at Urbana-Champaign; Toulouse School of Economics; Universiteit van Amsterdam; Universität Mannheim; McGill University; University of Wisconsin-Madison; Northwestern University; Harvard University","keywords":"Statistics; Regression; Regression analysis; Summary statistics; Geography; Mathematics","score_opus":0.013537637700553178,"score_gpt":0.2557678203220695,"score_spread":0.24223018262151633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399581870","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015496039,0.00005720012,0.00040653785,0.00006459439,0.000035997167,0.000034467623,0.99770975,0.0011243626,0.00041221885],"genre_scores_gemma":[0.0010665139,0.0000823182,0.0019962895,0.00007756945,0.000031004485,0.0003105507,0.99529946,0.00030970603,0.0008265635],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971366,0.00054076087,0.00072642806,0.00063646457,0.00071802246,0.00024172172],"domain_scores_gemma":[0.98095614,0.009566931,0.0019323998,0.0030730334,0.0039499835,0.0005215527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031568578,0.0020833167,0.0017261788,0.007410628,0.00052128365,0.002881107,0.0028777407,0.0015052137,0.1356221],"category_scores_gemma":[0.032896683,0.0008728398,0.0014535742,0.011126482,0.0004050182,0.001626734,0.0013837031,0.0020747238,0.07807333],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006973393,0.000034367065,0.0008375468,0.00063788245,0.000052560124,0.000025232986,0.00000824039,0.0006347603,0.00009427651,0.0006611916,0.99261934,0.004324903],"study_design_scores_gemma":[0.0010117524,0.00006675942,0.0066864784,0.0004344042,0.000108820735,0.000120983845,0.000062342624,0.0028359366,0.0010594175,0.007939795,0.979576,0.00009732494],"about_ca_topic_score_codex":0.007897979,"about_ca_topic_score_gemma":0.016323252,"teacher_disagreement_score":0.1356221,"about_ca_system_score_codex":0.0015035622,"about_ca_system_score_gemma":0.0035371182,"threshold_uncertainty_score":0.45370114},"labels":[],"label_agreement":null},{"id":"W4399706319","doi":"10.1016/j.jval.2024.03.2419","title":"MSR104 Use of Stochastic Search Variable Selection for Dimension Reduction in Large NMAs","year":2024,"lang":"en","type":"article","venue":"Value in Health","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"EVERSANA (Canada)","funders":"","keywords":"Reduction (mathematics); Selection (genetic algorithm); Dimensionality reduction; Dimension (graph theory); Variable (mathematics); Feature selection; Mathematics; Computer science; Mathematical optimization; Artificial intelligence; Combinatorics","score_opus":0.07256519078660467,"score_gpt":0.3319942968801131,"score_spread":0.25942910609350844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399706319","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03590559,0.00046486032,0.9566233,0.00044322564,0.00012868855,0.000121337674,0.00029216916,0.0018110867,0.004209726],"genre_scores_gemma":[0.4139576,0.00021440936,0.57959014,0.00030638304,0.00015906588,0.00035383788,0.000930421,0.0004081302,0.0040800725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860877,0.0008409043,0.0000741363,0.00014184293,0.00026341467,0.000070937465],"domain_scores_gemma":[0.9967495,0.0021870038,0.00014465018,0.00031975438,0.0005330116,0.00006606463],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0025135274,0.000628716,0.0010186917,0.0010002018,0.00053719344,0.00095931033,0.0010333385,0.0009778515,0.004959543],"category_scores_gemma":[0.008359521,0.00047019648,0.001161858,0.00078145135,0.00042578654,0.0008707888,0.0010492632,0.0012176329,0.0009215666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038969592,0.00023135114,0.0022539818,0.00019906339,0.00029709731,0.00013128533,0.00007399259,0.67761904,0.0058322027,0.031552933,0.0076429187,0.2737764],"study_design_scores_gemma":[0.000018891229,0.000029643676,0.00017046838,0.0000065481954,0.0000088169145,0.000011941907,0.0000040986706,0.9957255,0.00070721086,0.0027064919,0.0006058918,0.0000045349802],"about_ca_topic_score_codex":0.0028794268,"about_ca_topic_score_gemma":0.0057634125,"teacher_disagreement_score":0.9974865,"about_ca_system_score_codex":0.0004612406,"about_ca_system_score_gemma":0.0012814746,"threshold_uncertainty_score":0.01659137},"labels":[],"label_agreement":null},{"id":"W4399732880","doi":"10.2139/ssrn.4857358","title":"Relative Monte Carlo for Reinforcement Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Monte Carlo method; Reinforcement learning; Computer science; Statistical physics; Artificial intelligence; Econometrics; Economics; Mathematics; Physics; Statistics","score_opus":0.01361410814969361,"score_gpt":0.26733146106514566,"score_spread":0.25371735291545205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399732880","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051097237,0.00058098574,0.99062854,0.0003889692,0.00008041449,0.000036901005,0.000051469342,0.00020967484,0.0029131684],"genre_scores_gemma":[0.60406846,0.001073545,0.3767154,0.00058880943,0.00038066352,0.00093124766,0.0003954109,0.0005089985,0.015337504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692035,0.0019768765,0.00009153001,0.00034294257,0.00047790143,0.00019047606],"domain_scores_gemma":[0.9788124,0.018652752,0.0005465951,0.000794505,0.00076608546,0.00042776717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006672742,0.0013161453,0.0031533136,0.0013286833,0.00097596634,0.0022421854,0.003450825,0.0033151836,0.009902784],"category_scores_gemma":[0.029758291,0.0015035785,0.0012072973,0.001562414,0.0036381534,0.0035606052,0.0029669856,0.005255021,0.0011418354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008768115,0.0000452344,0.00019874627,0.00007359212,0.00004671135,0.000023168648,0.000032618984,0.84214705,0.000117962,0.14538212,0.0009028445,0.010942264],"study_design_scores_gemma":[0.00001280615,0.000007598863,0.000019408062,0.000008146328,0.0000050018634,0.0000039091656,0.0000017445462,0.93856776,0.0000432154,0.061104346,0.0002220438,0.0000040087366],"about_ca_topic_score_codex":0.008252722,"about_ca_topic_score_gemma":0.006132213,"teacher_disagreement_score":0.009902784,"about_ca_system_score_codex":0.0026989612,"about_ca_system_score_gemma":0.0023398073,"threshold_uncertainty_score":0.035289228},"labels":[],"label_agreement":null},{"id":"W4399799627","doi":"10.1007/978-3-031-56740-7_6","title":"The z-Transform","year":2024,"lang":"en","type":"book-chapter","venue":"Digital Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.014627191003276366,"score_gpt":0.2281033272103076,"score_spread":0.21347613620703124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399799627","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003521955,0.026115349,0.2144323,0.002823494,0.004629728,0.000036015936,0.00033834862,0.0007567672,0.74734604],"genre_scores_gemma":[0.098552056,0.026332596,0.08027225,0.0022760427,0.003453106,0.000113589726,0.0006284131,0.0008749636,0.7874971],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99980813,0.000032529322,0.0000073579295,0.000047360998,0.000087528155,0.000017100114],"domain_scores_gemma":[0.999892,0.00003188728,0.000008979411,0.000028863114,0.000030078796,0.000008266645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023417934,0.0008116459,0.00045257073,0.0010511937,0.00047310826,0.0018537809,0.0005005357,0.0008165167,0.02383632],"category_scores_gemma":[0.0007072183,0.00021874747,0.00031083447,0.0010950847,0.0017534472,0.0018912733,0.0010560856,0.0017115893,0.016328383],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001662037,0.0000062387817,0.000026948903,0.000073794974,0.0000054318352,0.000030228208,0.000050470248,0.00042639705,0.0023908878,0.84582573,0.02726271,0.12388444],"study_design_scores_gemma":[0.0000070525284,0.000021037778,0.00015890776,0.000047248905,0.000007263809,0.0002747304,0.000050151786,0.0020778223,0.0032877945,0.42291352,0.57114136,0.0000130595145],"about_ca_topic_score_codex":0.0003666465,"about_ca_topic_score_gemma":0.00036364258,"teacher_disagreement_score":0.02383632,"about_ca_system_score_codex":0.0005636982,"about_ca_system_score_gemma":0.00040708075,"threshold_uncertainty_score":0.079740405},"labels":[],"label_agreement":null},{"id":"W4399799646","doi":"10.1007/978-3-031-56740-7_9","title":"Multirate Digital Signal Processing","year":2024,"lang":"en","type":"book-chapter","venue":"Digital Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; SIGNAL (programming language); Signal processing; Digital signal processing; Computer hardware; Programming language","score_opus":0.022485651568062686,"score_gpt":0.24128989831137082,"score_spread":0.21880424674330815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399799646","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030021372,0.021223292,0.50856715,0.0005687086,0.0029898826,0.00008261568,0.00026262496,0.0022340606,0.46106958],"genre_scores_gemma":[0.031684898,0.017198812,0.10568077,0.00064576924,0.0015211389,0.00006693751,0.00040850262,0.0004929802,0.84230024],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99978894,0.000015928572,0.000008439986,0.000034939996,0.00013773591,0.0000140007905],"domain_scores_gemma":[0.999808,0.000047967274,0.000011142535,0.000054207343,0.000063608764,0.0000150508395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022155915,0.0008801059,0.0005184346,0.0010178476,0.00036985415,0.0015170418,0.0008113215,0.0007982487,0.061136205],"category_scores_gemma":[0.0005349837,0.0003623221,0.0002946617,0.0009288701,0.00042008917,0.001336975,0.001057692,0.0011677481,0.03284491],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006503061,0.000049520073,0.000074080475,0.00047338018,0.000028895303,0.00013096654,0.00008837401,0.0036079432,0.037894573,0.08113793,0.0639911,0.81245816],"study_design_scores_gemma":[0.0000101509495,0.00009312952,0.000453117,0.00027193953,0.000033798508,0.0009012643,0.00003719063,0.023802867,0.021605233,0.03354291,0.9192157,0.000032766544],"about_ca_topic_score_codex":0.00023276449,"about_ca_topic_score_gemma":0.00056237023,"teacher_disagreement_score":0.061136205,"about_ca_system_score_codex":0.00030906737,"about_ca_system_score_gemma":0.00022434832,"threshold_uncertainty_score":0.20452094},"labels":[],"label_agreement":null},{"id":"W4399800491","doi":"10.1109/ojcoms.2024.3416902","title":"Partially Cooperative RL for Hybrid Action CRNs With Imperfect CSI","year":2024,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de recherche du Québec","keywords":"Imperfect; Computer science; Action (physics); Physics","score_opus":0.08269689077946012,"score_gpt":0.36205781034978163,"score_spread":0.2793609195703215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399800491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05325464,0.00057849416,0.939103,0.00065731636,0.00007603766,0.000055367662,0.00005423181,0.0004383689,0.0057826107],"genre_scores_gemma":[0.9640825,0.00013403672,0.033117965,0.00018139645,0.00002233129,0.00008145141,0.00004656212,0.000039123777,0.0022946324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923134,0.00028782754,0.00003048003,0.00015933716,0.00014578435,0.00014523996],"domain_scores_gemma":[0.99618125,0.0028732675,0.00033117106,0.00015812228,0.00030570535,0.00015049863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021646041,0.0011879337,0.0011000135,0.0003826921,0.0003746113,0.0009413334,0.0012799882,0.0010771326,0.0015961636],"category_scores_gemma":[0.0059324303,0.00043040302,0.0004696169,0.00032507614,0.0015416978,0.0009782088,0.001218693,0.0014973761,0.00023418896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005394311,0.000026711772,0.00026309374,0.000029302024,0.000018088469,0.000034740744,0.000026302061,0.9873425,0.00040334498,0.0055761463,0.0002613133,0.0059645097],"study_design_scores_gemma":[0.000005419752,0.00001053555,0.000020798989,0.0000023856064,0.000002200022,0.0000033027848,0.0000025057775,0.998486,0.00006330326,0.0013561785,0.000045883913,0.0000015409348],"about_ca_topic_score_codex":0.007310909,"about_ca_topic_score_gemma":0.00513396,"teacher_disagreement_score":0.007310909,"about_ca_system_score_codex":0.0013745805,"about_ca_system_score_gemma":0.0015645102,"threshold_uncertainty_score":0.014536679},"labels":[],"label_agreement":null},{"id":"W4399850109","doi":"10.1109/appeec57400.2023.10561942","title":"An Image-Based Disaggregation Study of Time Series Energy Data Using Gramian Angular Field","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Memorial University of Newfoundland","funders":"","keywords":"Gramian matrix; Series (stratigraphy); Time series; Field (mathematics); Computer science; Artificial intelligence; Mathematics; Geology; Physics; Machine learning","score_opus":0.03803683499954205,"score_gpt":0.3083581722740928,"score_spread":0.27032133727455077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399850109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36259946,0.0012824886,0.6300019,0.00045903528,0.00020125133,0.0000766836,0.00088391715,0.0016199918,0.0028752445],"genre_scores_gemma":[0.83917725,0.00058529247,0.15765108,0.00007030066,0.00009706211,0.000030353121,0.0010785462,0.00005546135,0.0012547063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999833,0.000034768418,0.000008438861,0.000049697373,0.00004687409,0.000027198172],"domain_scores_gemma":[0.9996302,0.00016679108,0.00005186962,0.0000528374,0.00007693847,0.000021325224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005738121,0.0005714657,0.00048528326,0.0015697171,0.00016021168,0.0005448163,0.0003477714,0.00038914615,0.00074883684],"category_scores_gemma":[0.0018624052,0.00012227937,0.0005337847,0.0015164107,0.00024832407,0.0008902598,0.0002913395,0.000571096,0.00028272948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004093822,0.00017922789,0.016722407,0.00021434428,0.00011340311,0.00030476527,0.0002601682,0.22611248,0.028818296,0.0061203702,0.0045124902,0.7162327],"study_design_scores_gemma":[0.0000049922096,0.000043810676,0.0076310905,0.000009639874,0.000010063913,0.00007984778,0.00007717325,0.9846468,0.0047751665,0.001453298,0.0012548569,0.000013290536],"about_ca_topic_score_codex":0.0052768416,"about_ca_topic_score_gemma":0.005202752,"teacher_disagreement_score":0.0052768416,"about_ca_system_score_codex":0.00032898362,"about_ca_system_score_gemma":0.00035322132,"threshold_uncertainty_score":0.010492206},"labels":[],"label_agreement":null},{"id":"W4399886668","doi":"10.3723/wpem7746","title":"Prediction of Submarine Turbidity Currents Using Stacked LSTMs with Tree-Structured Parzen Estimator Optimisation","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Estimator; Submarine; Computer science; Turbidity; Tree (set theory); Artificial intelligence; Geology; Statistics; Marine engineering; Mathematics; Engineering","score_opus":0.06202838212985404,"score_gpt":0.2758222302040307,"score_spread":0.21379384807417665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399886668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23746352,0.00043892747,0.75822705,0.0002516463,0.00008337585,0.000039767427,0.00023299547,0.0014882735,0.0017743722],"genre_scores_gemma":[0.9278594,0.00014174472,0.070302725,0.000080755235,0.00002095917,0.0000442606,0.00026899445,0.000040674884,0.001240595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998629,0.00003193182,0.0000104581995,0.000038333128,0.000029564004,0.000026812671],"domain_scores_gemma":[0.999482,0.00030089568,0.00006557369,0.000023839915,0.00010484943,0.000022969998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000575507,0.00068834395,0.0005581622,0.00032461344,0.00019219579,0.00054640486,0.00061309023,0.00064924883,0.0007569628],"category_scores_gemma":[0.0017449877,0.00041573448,0.0005848335,0.00037275904,0.00027306922,0.0007285814,0.00044069637,0.0011047976,0.00022551845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004258385,0.000033282235,0.0007771417,0.000020329544,0.00003838957,0.000026055252,0.000025225741,0.9666772,0.0020583959,0.0006149506,0.00038424574,0.02930224],"study_design_scores_gemma":[5.9675915e-7,0.0000037120417,0.000057743215,6.962438e-7,0.0000012997082,8.35338e-7,8.650808e-7,0.9995926,0.00017403334,0.00015200042,0.000014917458,7.752057e-7],"about_ca_topic_score_codex":0.014485359,"about_ca_topic_score_gemma":0.015872369,"teacher_disagreement_score":0.014485359,"about_ca_system_score_codex":0.00065210235,"about_ca_system_score_gemma":0.0008664642,"threshold_uncertainty_score":0.028802097},"labels":[],"label_agreement":null},{"id":"W4399951357","doi":"10.1109/iotm.001.2300285","title":"Efficient Transformer-Based Hyper-Parameter Optimization for Resource-Constrained IoT Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Magazine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Transformer; Internet of Things; Mathematical optimization; Distributed computing; Electrical engineering; Embedded system; Engineering; Mathematics; Voltage","score_opus":0.01411798135094935,"score_gpt":0.23946696044993923,"score_spread":0.22534897909898988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399951357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051176112,0.0005457936,0.9412881,0.00029585077,0.000058338865,0.00006209666,0.000065513,0.0020411902,0.004466973],"genre_scores_gemma":[0.8905382,0.00020160795,0.106570065,0.00019009653,0.000028399369,0.000079288315,0.00016610093,0.00027724533,0.0019489705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997211,0.0000761385,0.000015508238,0.00006784905,0.00006050605,0.00005887423],"domain_scores_gemma":[0.99943036,0.0003240576,0.000060603532,0.00007178867,0.00007445232,0.000038704264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007595927,0.0010054422,0.0008015671,0.0003574581,0.00028005446,0.00087831856,0.0012057527,0.0007573741,0.0020775679],"category_scores_gemma":[0.0026228677,0.000492505,0.00045480335,0.0003156743,0.0007458979,0.0016816924,0.001397406,0.0015073488,0.00045649882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008235476,0.00005850072,0.000851794,0.000058388134,0.000036661222,0.000113565926,0.000049698658,0.92179507,0.0038898722,0.004425814,0.0016047461,0.06703348],"study_design_scores_gemma":[0.0000035813766,0.000008829721,0.000032712323,0.0000019617287,0.0000028518173,0.000007806608,0.0000052884457,0.99777967,0.00043615437,0.0015620927,0.00015725667,0.0000017672565],"about_ca_topic_score_codex":0.004389324,"about_ca_topic_score_gemma":0.006899315,"teacher_disagreement_score":0.004389324,"about_ca_system_score_codex":0.0007833559,"about_ca_system_score_gemma":0.0010875843,"threshold_uncertainty_score":0.0087275505},"labels":[],"label_agreement":null},{"id":"W4399985308","doi":"","title":"An efficient workflow for predicting various logging variables using simple machine-learning programs","year":2023,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"iNano Medical (Canada)","funders":"","keywords":"Workflow; Computer science; Logging; Simple (philosophy); Machine learning; Artificial intelligence; Data mining; Software engineering; Database; Forestry","score_opus":0.020415256154212955,"score_gpt":0.2513089527198482,"score_spread":0.23089369656563524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399985308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044269175,0.00009531435,0.861552,0.00011736719,0.000080931284,0.00024215646,0.003980758,0.12789606,0.0016085433],"genre_scores_gemma":[0.07300988,0.0002009968,0.8990387,0.00022197115,0.00007315965,0.0007589404,0.011086689,0.0055793086,0.010030403],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941504,0.00005888293,0.000068176145,0.00021098212,0.00019332135,0.00005356236],"domain_scores_gemma":[0.9982987,0.00073440786,0.00009328925,0.00030357216,0.00045867456,0.000111386726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008763783,0.003335008,0.0014767844,0.0018716787,0.0010808768,0.0023338743,0.002374032,0.0010262097,0.024552088],"category_scores_gemma":[0.0034338308,0.0011941424,0.0015532515,0.0014284076,0.00040282108,0.0012885923,0.0013361876,0.0020734041,0.017680725],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010032498,0.00058870343,0.007929074,0.00079591916,0.00031962,0.0010097206,0.0002746018,0.061783705,0.052023996,0.008573287,0.09763641,0.7680617],"study_design_scores_gemma":[0.00028609188,0.00015914514,0.005014769,0.00009809446,0.00016006983,0.00047760506,0.00015081317,0.7886594,0.11226587,0.034770377,0.057796143,0.00016164083],"about_ca_topic_score_codex":0.009091148,"about_ca_topic_score_gemma":0.0109351,"teacher_disagreement_score":0.024552088,"about_ca_system_score_codex":0.00081807,"about_ca_system_score_gemma":0.0025046268,"threshold_uncertainty_score":0.0821349},"labels":[],"label_agreement":null},{"id":"W4399986752","doi":"10.48550/arxiv.2406.14723","title":"Online Training of Hopfield Networks using Predictive Coding","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Predictive coding; Computer science; Coding (social sciences); Hopfield network; Artificial intelligence; Training (meteorology); Machine learning; Artificial neural network; Mathematics; Statistics; Geography","score_opus":0.1436465073991262,"score_gpt":0.2202220965371843,"score_spread":0.07657558913805809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399986752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13626683,0.0010825298,0.8482246,0.0004818267,0.00016445127,0.00015010573,0.00014161275,0.0026507874,0.010837229],"genre_scores_gemma":[0.9036643,0.00020011891,0.09281038,0.00018115396,0.000036644164,0.00013215239,0.00021110562,0.000078004945,0.0026862768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996847,0.00005883459,0.000024479497,0.00008284436,0.00008571574,0.000063535575],"domain_scores_gemma":[0.9981957,0.0011631096,0.00012506847,0.00012172641,0.0003233228,0.00007114744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009981095,0.0010810224,0.00083571277,0.00060763274,0.00050966075,0.00089132367,0.0016410968,0.0013647804,0.0032490876],"category_scores_gemma":[0.0055699833,0.0006981698,0.00047686827,0.00045241372,0.00078640704,0.0011791291,0.00092915964,0.0014737422,0.0004249332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007060131,0.00004386784,0.0006733719,0.00004798067,0.00002555094,0.000051367708,0.000052680123,0.9290287,0.0013332597,0.0026612305,0.0006445041,0.0653669],"study_design_scores_gemma":[0.0000037271739,0.000007997748,0.0000269357,0.0000030096796,0.0000018250905,0.0000023919358,0.0000017426647,0.999014,0.00021768507,0.0006611497,0.00005791506,0.0000014848765],"about_ca_topic_score_codex":0.023049025,"about_ca_topic_score_gemma":0.022691714,"teacher_disagreement_score":0.023049025,"about_ca_system_score_codex":0.0014082663,"about_ca_system_score_gemma":0.0014706125,"threshold_uncertainty_score":0.045829713},"labels":[],"label_agreement":null},{"id":"W4400010849","doi":"10.18280/jesa.570326","title":"Efficient MRI Image Real-Time Processing Using FPGA-Based IIR Filters","year":2024,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Field-programmable gate array; Infinite impulse response; Computer science; Image processing; Computer vision; Artificial intelligence; Real-time MRI; Image (mathematics); Filter (signal processing); Computer hardware; Digital filter; Magnetic resonance imaging; Medicine; Radiology","score_opus":0.027670515341743963,"score_gpt":0.28657510046601264,"score_spread":0.25890458512426867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400010849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06219694,0.0006588872,0.92083097,0.0002168544,0.00017395617,0.00017732204,0.0001628105,0.0059121437,0.009670155],"genre_scores_gemma":[0.5322034,0.00061366,0.45732403,0.00024552218,0.000068671536,0.00018559446,0.0004721974,0.00018796639,0.008699052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973375,0.000052020343,0.00001911799,0.000045611043,0.000121190846,0.000028390903],"domain_scores_gemma":[0.99979895,0.000055273034,0.00003127203,0.000034888737,0.0000709378,0.000008757783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031387727,0.000577026,0.0002617435,0.00047281405,0.00016845202,0.00058520405,0.00044797684,0.00031505703,0.0036308374],"category_scores_gemma":[0.0006397549,0.00013730227,0.00018482633,0.000332879,0.000148479,0.0005004725,0.0001747155,0.00028778106,0.0012744046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009911614,0.00016330085,0.0024749553,0.0005242886,0.00011259095,0.0005327411,0.00018722,0.052023973,0.25498223,0.010287977,0.0088060275,0.66891354],"study_design_scores_gemma":[0.00017419415,0.0014296,0.0044412157,0.00009270564,0.00009180839,0.0014960463,0.00008402272,0.62548757,0.32548735,0.0029466331,0.038193397,0.0000754682],"about_ca_topic_score_codex":0.00068053586,"about_ca_topic_score_gemma":0.0010336215,"teacher_disagreement_score":0.0036308374,"about_ca_system_score_codex":0.0002234477,"about_ca_system_score_gemma":0.000354183,"threshold_uncertainty_score":0.012146413},"labels":[],"label_agreement":null},{"id":"W4400032749","doi":"10.1109/tac.2024.3419186","title":"A Stochastic Operator Framework for Optimization and Learning With Sub-Weibull Errors","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Weibull distribution; Operator (biology); Computer science; Stochastic optimization; Mathematical optimization; Stochastic process; Mathematics; Statistics","score_opus":0.008424791981342147,"score_gpt":0.2380686838757317,"score_spread":0.22964389189438955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400032749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012341142,0.00046900802,0.99639326,0.0003314033,0.000048977927,0.000015678595,0.000028735032,0.00003235099,0.0014464428],"genre_scores_gemma":[0.3283192,0.005214834,0.64882743,0.00095393223,0.0012852167,0.00064773916,0.0003125031,0.00023482704,0.014204321],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99775666,0.0010824251,0.000105648935,0.00031823877,0.00060160045,0.00013541925],"domain_scores_gemma":[0.99369746,0.004515337,0.00054518366,0.00031683978,0.00064064463,0.00028460202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053437096,0.0020370597,0.0013944487,0.00162346,0.0007037803,0.0020260948,0.0023327922,0.0021498166,0.00311313],"category_scores_gemma":[0.011793471,0.0006198718,0.0017468377,0.0015481764,0.0033575324,0.0036988014,0.0028490885,0.0044410727,0.000687588],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019011053,0.000023779165,0.00025723953,0.000100128724,0.0000416097,0.00007007071,0.000088916735,0.23401532,0.0006458071,0.7556947,0.001059617,0.00798372],"study_design_scores_gemma":[0.000008799785,0.00004135714,0.00008972956,0.000023278417,0.000009714369,0.000038201237,0.000015555439,0.6982724,0.0001867519,0.29859608,0.002702317,0.00001578826],"about_ca_topic_score_codex":0.0031115944,"about_ca_topic_score_gemma":0.0015996532,"teacher_disagreement_score":0.0053437096,"about_ca_system_score_codex":0.0016224579,"about_ca_system_score_gemma":0.0020090032,"threshold_uncertainty_score":0.028260529},"labels":[],"label_agreement":null},{"id":"W4400096933","doi":"10.1002/cjce.25377","title":"Autoassociative neural network for missing data imputation: A case study via the styrene production process","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Imputation (statistics); Artificial neural network; Computer science; Missing data; Artificial intelligence; Process (computing); Data mining; Machine learning","score_opus":0.02803142672968885,"score_gpt":0.2764767870959734,"score_spread":0.24844536036628456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400096933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80568224,0.00051941536,0.19002375,0.0010388907,0.00005548351,0.0001396567,0.000643574,0.0003346652,0.0015623687],"genre_scores_gemma":[0.9646046,0.00016568913,0.03400153,0.000039788985,0.000013847284,0.00007589875,0.00026046703,0.000015586747,0.0008225704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857223,0.00076310843,0.000094659634,0.00019457917,0.0002940127,0.00008134677],"domain_scores_gemma":[0.98992574,0.007706498,0.00051373435,0.0007198322,0.0010340728,0.000100161546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005526266,0.00055398914,0.00094607775,0.0008382921,0.00076127716,0.00100522,0.0016255962,0.002133314,0.0012050254],"category_scores_gemma":[0.010224244,0.000383826,0.000864191,0.0015049251,0.0006775506,0.0012950351,0.0007650797,0.0011875355,0.00016694303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005217532,0.0005133082,0.031881724,0.00023976456,0.00015853198,0.0023738404,0.00032698302,0.90523636,0.0012639738,0.0032036975,0.00091997354,0.05336008],"study_design_scores_gemma":[0.000021739614,0.00009869606,0.0033236383,0.000020476851,0.000025452058,0.00010479563,0.00011482173,0.9922875,0.0018811208,0.001689826,0.000415213,0.00001668122],"about_ca_topic_score_codex":0.01438625,"about_ca_topic_score_gemma":0.011398001,"teacher_disagreement_score":0.01438625,"about_ca_system_score_codex":0.0011115518,"about_ca_system_score_gemma":0.0011300881,"threshold_uncertainty_score":0.029226005},"labels":[],"label_agreement":null},{"id":"W4400116019","doi":"10.1523/jneurosci.1619-23.2024","title":"Hierarchical Gradients of Encoded Spatial and Sensory Information in the Neocortex Are Attenuated by Dorsal Hippocampal Lesions","year":2024,"lang":"en","type":"article","venue":"Journal of Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Foothills Medical Centre; University of Lethbridge","funders":"National Institute of Neurological Disorders and Stroke; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Compute Canada; National Institutes of Health; University of Lethbridge","keywords":"Neocortex; Dorsum; Hippocampal formation; Sensory system; Neuroscience; Anatomy; Biology; Communication; Psychology","score_opus":0.01971817408699902,"score_gpt":0.2619391652280755,"score_spread":0.24222099114107645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400116019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99769396,0.00019425164,0.0011786126,0.000053270243,0.000010415509,0.0000055692653,0.00019483235,0.0001355146,0.0005335846],"genre_scores_gemma":[0.99618584,0.00017788354,0.0011593784,0.000026754296,0.0000040761347,0.0000142497975,0.00017226323,0.000053324515,0.0022063365],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981993,0.0000090906005,0.00002199037,0.00004557302,0.00004952913,0.000053757987],"domain_scores_gemma":[0.9994937,0.000032960743,0.00018666658,0.00006270659,0.000038215258,0.00018567464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010598275,0.0004650038,0.00035302102,0.000461528,0.00011019502,0.0003790751,0.0003700998,0.00023877971,0.0012634234],"category_scores_gemma":[0.00023252139,0.0003506,0.00024394724,0.00010787404,0.0005354862,0.00032350773,0.00033222145,0.0010209093,0.0003090382],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009761785,0.000013948701,0.000374271,0.000021362557,0.000005809756,0.00006285641,0.000021918828,0.000045212455,0.9982565,0.00010054211,0.000017904697,0.0009820397],"study_design_scores_gemma":[0.0000344696,0.00022449285,0.038527884,0.000013795502,0.000034391913,0.00062082073,0.00011899733,0.001560677,0.9574938,0.00030775706,0.0010533038,0.000009533618],"about_ca_topic_score_codex":0.0010153092,"about_ca_topic_score_gemma":0.0017534179,"teacher_disagreement_score":0.0012634234,"about_ca_system_score_codex":0.00035593385,"about_ca_system_score_gemma":0.00034567344,"threshold_uncertainty_score":0.0042265058},"labels":[],"label_agreement":null},{"id":"W4400195204","doi":"10.1101/2024.06.29.601346","title":"A Markovian neural barcode representing mesoscale cortical spatiotemporal dynamics","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Barcode; Dynamics (music); Mesoscale meteorology; Computer science; Markov process; Artificial intelligence; Geography; Physics; Mathematics; Meteorology; Acoustics","score_opus":0.013010302369625634,"score_gpt":0.23133762166334507,"score_spread":0.21832731929371943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400195204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051246386,0.00012223191,0.9448685,0.0002882825,0.00007696133,0.000032754077,0.00035377307,0.0006577979,0.0023533332],"genre_scores_gemma":[0.7869737,0.00025356014,0.20572111,0.00023798562,0.00007690953,0.00015488015,0.0005346936,0.00024370411,0.005803499],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997894,0.000044643373,0.000010838165,0.000045417037,0.000070093505,0.000039637212],"domain_scores_gemma":[0.9987445,0.00058836606,0.00026061444,0.00016188384,0.00016597034,0.00007862969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049903983,0.00031473787,0.000356994,0.0006983302,0.0003041255,0.00067083974,0.00097075955,0.00111014,0.0028484573],"category_scores_gemma":[0.0022384864,0.00027406428,0.0005110007,0.00043498425,0.001212183,0.0010641286,0.00068163086,0.0011087481,0.0005128918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018199383,0.000048397305,0.0015703948,0.00009001919,0.000027847182,0.00027847433,0.00013681567,0.6432586,0.04717499,0.2805687,0.001869535,0.024794262],"study_design_scores_gemma":[0.000003000989,0.000012959792,0.00013531189,0.000005608254,0.00000151617,0.000030422203,0.0000039351744,0.9793698,0.0024660993,0.017221816,0.0007387183,0.000010790776],"about_ca_topic_score_codex":0.0035479867,"about_ca_topic_score_gemma":0.0030414232,"teacher_disagreement_score":0.0035479867,"about_ca_system_score_codex":0.0008947167,"about_ca_system_score_gemma":0.0008275581,"threshold_uncertainty_score":0.009528995},"labels":[],"label_agreement":null},{"id":"W4400230261","doi":"10.1109/iscas58744.2024.10558045","title":"Optimized Transformer Models: ℓ′ BERT with CNN-like Pruning and Quantization","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transformer; Quantization (signal processing); Pruning; Artificial intelligence; Algorithm; Electrical engineering; Engineering; Voltage","score_opus":0.016896739619012616,"score_gpt":0.22707848566655206,"score_spread":0.21018174604753945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400230261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040147435,0.00056601514,0.9496385,0.0002625258,0.00009632719,0.00006143324,0.00017902414,0.0010821937,0.00796658],"genre_scores_gemma":[0.78225935,0.00053817156,0.20576838,0.00028768447,0.000049165403,0.00012198765,0.0004668779,0.0003060861,0.010202326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987257,0.000021866364,0.000008387868,0.000028027127,0.000046307785,0.000022809721],"domain_scores_gemma":[0.9996444,0.00016076726,0.000034822446,0.00005229762,0.000087120534,0.000020570222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043586755,0.00063380675,0.00056705944,0.00033813217,0.00020371987,0.0007648869,0.0012066104,0.0006377067,0.003111769],"category_scores_gemma":[0.001886277,0.0002838073,0.00049732724,0.000371605,0.00036174915,0.0012569616,0.0006137716,0.0009334018,0.00065246195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014681359,0.000047536607,0.0006842423,0.00006848184,0.00003164245,0.00008255037,0.000038429414,0.82690793,0.0047531715,0.020339739,0.0034392527,0.14346024],"study_design_scores_gemma":[0.0000032806786,0.0000121738985,0.000026860405,0.0000035588137,0.00000363244,0.00001104428,0.0000026415603,0.996725,0.00076371094,0.0020909263,0.00035508777,0.0000020975187],"about_ca_topic_score_codex":0.00690307,"about_ca_topic_score_gemma":0.010988461,"teacher_disagreement_score":0.00690307,"about_ca_system_score_codex":0.0008891137,"about_ca_system_score_gemma":0.0008912299,"threshold_uncertainty_score":0.013725817},"labels":[],"label_agreement":null},{"id":"W4400230701","doi":"10.1109/iscas58744.2024.10558100","title":"BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; University of Toronto","funders":"","keywords":"Computer science; Quantization (signal processing); Artificial intelligence; Computer vision","score_opus":0.017250101380114666,"score_gpt":0.29069228421776594,"score_spread":0.27344218283765126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400230701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047904424,0.0009832729,0.9343209,0.000663688,0.00026968462,0.0001556375,0.0006401873,0.01125248,0.003809679],"genre_scores_gemma":[0.3915298,0.00045058143,0.59694576,0.00070546614,0.000118376185,0.00046847406,0.0015553402,0.0023491578,0.005877116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989341,0.00023273779,0.00011182846,0.00029159832,0.0003320672,0.00009763102],"domain_scores_gemma":[0.99739313,0.0009484626,0.00021346209,0.0008929942,0.00046178882,0.00009011004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001515484,0.001169087,0.000938053,0.00082480314,0.0006745424,0.001429757,0.002784,0.0013374698,0.007967797],"category_scores_gemma":[0.012645102,0.0005554736,0.00050134986,0.0010179402,0.0012836918,0.003496914,0.0024043657,0.0025690445,0.0028646772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011100209,0.00028562557,0.0029704839,0.0003543333,0.00010473742,0.00018283191,0.00035023157,0.20535201,0.044189222,0.035740025,0.02736136,0.6819992],"study_design_scores_gemma":[0.00010153808,0.00014641312,0.0003670271,0.00006686986,0.000020740837,0.00011191377,0.000056471566,0.93440264,0.026054963,0.032725878,0.005905152,0.000040366183],"about_ca_topic_score_codex":0.0028131052,"about_ca_topic_score_gemma":0.0057483087,"teacher_disagreement_score":0.007967797,"about_ca_system_score_codex":0.001051944,"about_ca_system_score_gemma":0.0013181668,"threshold_uncertainty_score":0.026654959},"labels":[],"label_agreement":null},{"id":"W4400282035","doi":"10.32473/flairs.37.1.135283","title":"Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Encoder; Transformer; Computer science; Artificial intelligence; Sequence (biology); Sequence learning; Speech recognition; Pattern recognition (psychology); Engineering; Electrical engineering; Biology; Voltage","score_opus":0.374587593761272,"score_gpt":0.47993995937539824,"score_spread":0.10535236561412625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400282035","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8498836,0.0033510274,0.12672688,0.00594222,0.00032324807,0.0004517726,0.0011095601,0.0019341579,0.010277454],"genre_scores_gemma":[0.958795,0.00045191566,0.0367514,0.0004626612,0.0001046876,0.00018089845,0.0013012807,0.00016393139,0.0017882205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9908693,0.005388341,0.0007289827,0.0011199443,0.0012635263,0.00063002546],"domain_scores_gemma":[0.8531854,0.12869762,0.0030497801,0.0070077907,0.0066379,0.0014214101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030144023,0.0022174467,0.0012160214,0.0016503251,0.0015927189,0.0030754064,0.0017931798,0.0033114261,0.003292997],"category_scores_gemma":[0.12434994,0.0007531285,0.0010874124,0.0013083733,0.0017863502,0.008320585,0.0029339236,0.004920341,0.0014599451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0041301353,0.0014179946,0.06139661,0.00039239312,0.0004878714,0.00019109731,0.00032140134,0.6283706,0.0038518817,0.0070821443,0.006173405,0.2861845],"study_design_scores_gemma":[0.00009374776,0.00060923153,0.0029558402,0.00006035316,0.000089366134,0.000053114934,0.00017551055,0.9844876,0.0038842803,0.0070929783,0.0004647359,0.000033281012],"about_ca_topic_score_codex":0.011860864,"about_ca_topic_score_gemma":0.01664337,"teacher_disagreement_score":0.030144023,"about_ca_system_score_codex":0.0037848442,"about_ca_system_score_gemma":0.0030636934,"threshold_uncertainty_score":0.1594187},"labels":[],"label_agreement":null},{"id":"W4400302744","doi":"10.1007/978-981-97-0562-7_49","title":"Iterative Multiplication Unit for Inference and Train Neural Networks","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Multiplication (music); Inference; Artificial neural network; Unit (ring theory); Computer science; Arithmetic; Artificial intelligence; Mathematics; Mathematics education; Combinatorics","score_opus":0.013784408237843594,"score_gpt":0.24343709052495813,"score_spread":0.22965268228711452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400302744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016347716,0.00038551586,0.99382836,0.000054448046,0.00011259175,0.000029292121,0.00007833239,0.0017328695,0.0021438864],"genre_scores_gemma":[0.048113804,0.00027288366,0.93653077,0.00010677282,0.00010874165,0.00021212574,0.00039641059,0.00051483815,0.013743638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947244,0.00013344362,0.000044852888,0.0001343965,0.00016811823,0.000046874084],"domain_scores_gemma":[0.9991002,0.00039102827,0.00003302975,0.00020746994,0.0002458598,0.000022525319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077434874,0.0010943891,0.0007301617,0.0004596241,0.0004469774,0.0009352499,0.0019051108,0.0011583351,0.016403394],"category_scores_gemma":[0.0036102575,0.0005465684,0.00066335965,0.0011112812,0.00046067088,0.0013269774,0.0011719608,0.0020817658,0.006618474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016431307,0.000059703474,0.00023673182,0.0002291848,0.00010768806,0.00008774244,0.00010179271,0.061245874,0.011982658,0.026391266,0.018265469,0.88112754],"study_design_scores_gemma":[0.000022431179,0.00007979727,0.00024187745,0.000031005784,0.000036712285,0.00010573567,0.000021491362,0.9398338,0.021741958,0.020817347,0.017048273,0.00001951101],"about_ca_topic_score_codex":0.0036740866,"about_ca_topic_score_gemma":0.0056989184,"teacher_disagreement_score":0.016403394,"about_ca_system_score_codex":0.000523476,"about_ca_system_score_gemma":0.0007299995,"threshold_uncertainty_score":0.054874837},"labels":[],"label_agreement":null},{"id":"W4400312122","doi":"10.5206/mase/17138","title":"Medical diagnosis using artificial neural networks","year":2024,"lang":"en","type":"article","venue":"Mathematics in Applied Sciences and Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Probabilistic neural network; Artificial intelligence; Computer science; Machine learning; Learning vector quantization; Nervous system network models; Probabilistic logic; Recurrent neural network; Time delay neural network; Deep learning; Feedforward neural network","score_opus":0.03137003804821565,"score_gpt":0.2675595587684515,"score_spread":0.23618952072023586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400312122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016567318,0.0059888,0.9616796,0.001816373,0.00037406822,0.00015995915,0.00084354426,0.0024778505,0.010092476],"genre_scores_gemma":[0.57122326,0.00785617,0.41072014,0.00074900384,0.00042933255,0.00026610377,0.0022358564,0.0001613113,0.0063588144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991856,0.0002102689,0.000085038184,0.00015421586,0.00032559197,0.00003927761],"domain_scores_gemma":[0.99894124,0.0005070889,0.00014059263,0.0000819087,0.0003015419,0.00002765074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009901215,0.00089321705,0.0008455825,0.0024765076,0.00038537855,0.0016620363,0.00095355493,0.001199757,0.0037578335],"category_scores_gemma":[0.004913885,0.000353858,0.0007883316,0.0015620028,0.00047049733,0.0012742874,0.001091108,0.0011589566,0.0012852871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017268606,0.00010648745,0.0048011444,0.0006130768,0.00020101038,0.00037957545,0.00006774089,0.49350187,0.0043084053,0.011847836,0.0123678055,0.47163236],"study_design_scores_gemma":[0.000009510311,0.000031437783,0.0007234416,0.000095359006,0.000027035136,0.0001353222,0.000026488522,0.97848195,0.0016839419,0.013999159,0.0047657294,0.000020575839],"about_ca_topic_score_codex":0.0055724955,"about_ca_topic_score_gemma":0.004967833,"teacher_disagreement_score":0.0055724955,"about_ca_system_score_codex":0.0009361597,"about_ca_system_score_gemma":0.0008800187,"threshold_uncertainty_score":0.012571216},"labels":[],"label_agreement":null},{"id":"W4400375663","doi":"10.1145/3654777.3676345","title":"Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decomposition","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Decomposition; Computer science; Task (project management); Human–computer interaction; Systems engineering; Chemistry; Engineering","score_opus":0.022117657312948666,"score_gpt":0.30875662083274596,"score_spread":0.2866389635197973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400375663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085936,0.00012643295,0.8961338,0.00047570173,0.00006599311,0.0014468816,0.00017412391,0.012449738,0.0031913056],"genre_scores_gemma":[0.31989896,0.00007367605,0.6747382,0.00029153968,0.000028829183,0.0018862158,0.0003211224,0.0009319567,0.0018295643],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9588317,0.028672047,0.0025462927,0.0053983424,0.003316047,0.0012356085],"domain_scores_gemma":[0.7267587,0.2092261,0.010199042,0.035828426,0.014783966,0.0032037727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.042263787,0.0018493015,0.0009795276,0.0016866712,0.0011326105,0.0042590676,0.0043472997,0.0016053122,0.007947289],"category_scores_gemma":[0.21260764,0.0014801302,0.00096747116,0.00093632564,0.0029100233,0.006339501,0.00790648,0.0026708106,0.002751144],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003567825,0.00232508,0.018169299,0.0022855147,0.00020863251,0.00055290945,0.043431144,0.020911718,0.09891166,0.014773585,0.0070217974,0.78784084],"study_design_scores_gemma":[0.0020551346,0.003841244,0.025417691,0.000989541,0.00038601801,0.0013434631,0.010905837,0.6248748,0.1723441,0.09718813,0.05989642,0.00075762754],"about_ca_topic_score_codex":0.0023733918,"about_ca_topic_score_gemma":0.0027285914,"teacher_disagreement_score":0.042263787,"about_ca_system_score_codex":0.0015059521,"about_ca_system_score_gemma":0.0046327077,"threshold_uncertainty_score":0.22351497},"labels":[],"label_agreement":null},{"id":"W4400381755","doi":"10.1007/978-981-97-4399-5_32","title":"3W-SLP: A Conceptual Model of Three-Way Single Layer Perceptrons","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Computer science; Perceptron; Layer (electronics); Artificial intelligence; Artificial neural network","score_opus":0.04712098352897267,"score_gpt":0.2567260298617731,"score_spread":0.20960504633280044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400381755","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00311124,0.00017584568,0.9914904,0.00019913426,0.00007702332,0.00002244668,0.000390076,0.0014228701,0.0031108868],"genre_scores_gemma":[0.21326926,0.00074347696,0.7679911,0.00043542316,0.00010158366,0.00028402245,0.0014991623,0.00075327174,0.014922696],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997688,0.00005004746,0.000018874845,0.000063669264,0.000070313,0.000028374523],"domain_scores_gemma":[0.99965155,0.00012097212,0.0000233892,0.00007525082,0.00009422828,0.000034626515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005026756,0.0006024813,0.0006114793,0.00046255506,0.0003025142,0.0016678324,0.0024687517,0.0013152764,0.008601829],"category_scores_gemma":[0.0015251217,0.00043160745,0.0011362204,0.000846721,0.0008458134,0.0030197748,0.0011193452,0.0021022884,0.0032267973],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021042528,0.00008589564,0.00037260356,0.00037123394,0.000085716434,0.00021985876,0.00018329287,0.3302395,0.010466961,0.38279822,0.013655633,0.26131073],"study_design_scores_gemma":[0.000009985998,0.000023768813,0.000061836676,0.000017970118,0.000011085469,0.000038730006,0.000010783547,0.8158568,0.0020371457,0.17576483,0.0061546224,0.000012503694],"about_ca_topic_score_codex":0.0029227843,"about_ca_topic_score_gemma":0.0037122897,"teacher_disagreement_score":0.008601829,"about_ca_system_score_codex":0.00060858123,"about_ca_system_score_gemma":0.00092804345,"threshold_uncertainty_score":0.02877593},"labels":[],"label_agreement":null},{"id":"W4400404142","doi":"10.55092/aias20240003","title":"Bridging domain gaps in CNNs: a comprehensive approach with adaptation and randomization strategies","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence and Autonomous Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bridging (networking); Domain adaptation; Computer science; Adaptation (eye); Randomization; Psychology; Artificial intelligence; Medicine; Randomized controlled trial; Neuroscience; Computer network","score_opus":0.04160335173629109,"score_gpt":0.261799411560041,"score_spread":0.2201960598237499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400404142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01888705,0.0005777544,0.9765846,0.00034174483,0.000059680446,0.00007561906,0.00008438763,0.0010626332,0.0023263982],"genre_scores_gemma":[0.6873259,0.0008527306,0.30362946,0.0006642919,0.00016442128,0.00037173103,0.00051963306,0.00051050953,0.0059612878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991229,0.00025240672,0.00005562265,0.0002692306,0.000198561,0.00010129092],"domain_scores_gemma":[0.9986695,0.0004815898,0.00016748456,0.00047165836,0.00012710765,0.000082617174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018411813,0.0014148159,0.0012405047,0.00085760484,0.00054313097,0.0011737411,0.0024641717,0.0019539339,0.0019538454],"category_scores_gemma":[0.0048700324,0.00072495185,0.00129863,0.0007344549,0.0016111739,0.0029480387,0.0037963868,0.002467482,0.00067214976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015564298,0.00008466391,0.0011510575,0.00011229439,0.00010540595,0.00019475886,0.00016119397,0.7812307,0.010761239,0.043115135,0.0028362444,0.16009176],"study_design_scores_gemma":[0.000008205498,0.00003911668,0.00012404255,0.000015621647,0.0000149567,0.000062346466,0.000018311497,0.9717448,0.0025262411,0.02333795,0.00209601,0.000012436859],"about_ca_topic_score_codex":0.0027075436,"about_ca_topic_score_gemma":0.0021819647,"teacher_disagreement_score":0.0027075436,"about_ca_system_score_codex":0.0013482184,"about_ca_system_score_gemma":0.0012929419,"threshold_uncertainty_score":0.009782016},"labels":[],"label_agreement":null},{"id":"W4400446725","doi":"10.1109/tfuzz.2024.3421544","title":"Self-Organizing Hybrid Fuzzy Polynomial Neural Network Classifier Driven Through Dynamically Adaptive Structure and Compound Regularization Technique","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Natural Science Foundation of China","keywords":"Artificial neural network; Computer science; Regularization (linguistics); Fuzzy logic; Artificial intelligence; Classifier (UML); Polynomial; Backpropagation; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.012258301392967595,"score_gpt":0.22403962919132459,"score_spread":0.21178132779835698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400446725","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035123557,0.000330309,0.9620571,0.00013938446,0.00004774023,0.000047479698,0.00004683855,0.00038996688,0.0018176535],"genre_scores_gemma":[0.77568823,0.00027324122,0.2186534,0.00013895423,0.000056392804,0.000113741706,0.0001904639,0.00005044274,0.0048351916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995504,0.00007480576,0.000021617325,0.00012195708,0.00017382397,0.000057421676],"domain_scores_gemma":[0.9994236,0.00016133007,0.00006432732,0.00005698362,0.00026954032,0.00002425347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008343041,0.000535669,0.0007048845,0.0005353178,0.00039549675,0.000615853,0.0011786777,0.0008465044,0.001068543],"category_scores_gemma":[0.0016712935,0.00023227742,0.0007099853,0.00048128166,0.00039090187,0.0011289374,0.000573892,0.0008753534,0.0003191385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018063647,0.0000780631,0.0016633659,0.0000908896,0.00007665477,0.00010334365,0.000070340626,0.7155281,0.01545355,0.012028523,0.0021713518,0.25255522],"study_design_scores_gemma":[0.0000016963519,0.000010601513,0.00006193767,0.0000014169848,0.0000033552267,0.000008493019,0.0000018213453,0.9984074,0.0007741061,0.00056882104,0.00015809205,0.0000022407653],"about_ca_topic_score_codex":0.0069492455,"about_ca_topic_score_gemma":0.007184949,"teacher_disagreement_score":0.0069492455,"about_ca_system_score_codex":0.00091677567,"about_ca_system_score_gemma":0.0010008424,"threshold_uncertainty_score":0.013817608},"labels":[],"label_agreement":null},{"id":"W4400449735","doi":"10.31219/osf.io/w7jcm","title":"Diffusion Models: Tutorial and Survey","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Noise (video); Stochastic differential equation; Diffusion; Computer science; Gaussian noise; Diffusion process; Applied mathematics; Schedule; Probabilistic logic; Statistical physics; Algorithm; Mathematical optimization; Mathematics; Physics; Artificial intelligence","score_opus":0.04961711034299878,"score_gpt":0.27271399999774654,"score_spread":0.22309688965474775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400449735","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017541075,0.7174934,0.2345093,0.0034287518,0.0022862498,0.00007523827,0.000978058,0.00067620096,0.03879867],"genre_scores_gemma":[0.03277099,0.83319104,0.09583199,0.0018838323,0.0060297754,0.00024385648,0.0022270451,0.0005435631,0.027277961],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992493,0.00015492016,0.00007815189,0.0002015238,0.0002614591,0.000054656874],"domain_scores_gemma":[0.9989423,0.00063279027,0.000059232567,0.00010053033,0.00021458764,0.0000505761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013266823,0.0019471148,0.0018299451,0.002669863,0.0005421765,0.0022746453,0.0018515923,0.0025296852,0.011581352],"category_scores_gemma":[0.0031036143,0.0012441422,0.0016449859,0.005782656,0.0009801633,0.0044915113,0.0013589438,0.0033663516,0.0072939685],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004870363,0.00016901584,0.0012443095,0.005054271,0.00020842842,0.00037201238,0.00033481643,0.028284622,0.0016684749,0.3984636,0.11168287,0.4524689],"study_design_scores_gemma":[0.000012892951,0.000054357548,0.0007517122,0.0008506674,0.000087959306,0.00096884003,0.00007854863,0.03739615,0.00055190525,0.2687926,0.6903803,0.00007410329],"about_ca_topic_score_codex":0.0048904107,"about_ca_topic_score_gemma":0.0026182388,"teacher_disagreement_score":0.011581352,"about_ca_system_score_codex":0.0014034356,"about_ca_system_score_gemma":0.0016394304,"threshold_uncertainty_score":0.038743496},"labels":[],"label_agreement":null},{"id":"W4400491142","doi":"10.1109/cscwd61410.2024.10580009","title":"End-edge collaborative DNN inference acceleration via E-CARGO and RBC","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University","funders":"National Science Foundation","keywords":"Acceleration; Computer science; Enhanced Data Rates for GSM Evolution; Inference; Artificial intelligence; Physics","score_opus":0.016755389005335564,"score_gpt":0.2817840344936914,"score_spread":0.2650286454883558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400491142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03114427,0.00020090588,0.9624066,0.00018861682,0.00007989952,0.00007251735,0.00006502082,0.0019179067,0.0039243153],"genre_scores_gemma":[0.74482125,0.00015297726,0.24832854,0.00023789538,0.00005033304,0.000111654466,0.0003306872,0.0001811567,0.0057855514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950576,0.00008149251,0.00002453204,0.00016054274,0.00013036383,0.00009731607],"domain_scores_gemma":[0.9994338,0.0001367409,0.000046878034,0.00011045686,0.00018391479,0.00008819971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009000483,0.00093939103,0.0008071324,0.00042440978,0.0005987869,0.00086522184,0.0021997404,0.00093052845,0.0039822077],"category_scores_gemma":[0.0020057429,0.00035240568,0.0005003361,0.0003971652,0.00044364372,0.001736121,0.002086551,0.0013498848,0.0011689472],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042713757,0.00026630887,0.002144597,0.00007714318,0.000050133727,0.0002863863,0.0001945693,0.68265873,0.009389157,0.011682911,0.0056265197,0.28719637],"study_design_scores_gemma":[0.000008364862,0.000019349669,0.00008487838,0.000002216681,0.000005307789,0.0000142085655,0.000016097882,0.99643624,0.0014358507,0.001440938,0.00053372444,0.000002949936],"about_ca_topic_score_codex":0.011418878,"about_ca_topic_score_gemma":0.014310431,"teacher_disagreement_score":0.011418878,"about_ca_system_score_codex":0.0009575434,"about_ca_system_score_gemma":0.001389444,"threshold_uncertainty_score":0.02270484},"labels":[],"label_agreement":null},{"id":"W4400680585","doi":"10.1109/ispass61541.2024.00044","title":"Distributed Training of Neural Radiance Fields: A Performance Characterization","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Radiance; Training (meteorology); Computer science; Characterization (materials science); Artificial neural network; Artificial intelligence; Remote sensing; Geology; Optics; Meteorology","score_opus":0.023285578268625658,"score_gpt":0.2355153192751852,"score_spread":0.21222974100655956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400680585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5029477,0.0022521198,0.46866748,0.0013515586,0.00015468315,0.00019457896,0.0006022052,0.007917794,0.01591182],"genre_scores_gemma":[0.9110631,0.00049760565,0.08447665,0.00013175678,0.000047139187,0.00012449274,0.0008028234,0.00033475418,0.0025217668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9993536,0.00008512681,0.00003622664,0.00021428714,0.00019422141,0.00011662629],"domain_scores_gemma":[0.9974981,0.0012235729,0.00014882717,0.00067610847,0.0003385768,0.00011473906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010956508,0.00079724105,0.0006341792,0.00043153175,0.0003767449,0.0007439435,0.0014643791,0.00094459835,0.0029562213],"category_scores_gemma":[0.005786394,0.00027818978,0.0003110645,0.0007501595,0.0005352316,0.0019744732,0.0011977718,0.0009843619,0.00068840035],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015279558,0.00037768268,0.006117879,0.0003049808,0.00009865152,0.00020610393,0.00019277453,0.62234,0.03078801,0.006493666,0.006286636,0.32526562],"study_design_scores_gemma":[0.00004299664,0.00013901076,0.0010788756,0.000010384516,0.000011789205,0.0000729781,0.000039266328,0.9834796,0.012283345,0.0018513076,0.0009829272,0.0000076002716],"about_ca_topic_score_codex":0.005955262,"about_ca_topic_score_gemma":0.006193639,"teacher_disagreement_score":0.005955262,"about_ca_system_score_codex":0.0011257844,"about_ca_system_score_gemma":0.001180123,"threshold_uncertainty_score":0.011841178},"labels":[],"label_agreement":null},{"id":"W4400681592","doi":"10.36227/techrxiv.172114799.94469004/v1","title":"Restricted Hopfield Network as an Auto-associative Memory","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Hopfield network; Bidirectional associative memory; Content-addressable memory; Associative property; Computer science; Artificial neural network; Artificial intelligence; Cognitive science; Psychology; Mathematics; Pure mathematics","score_opus":0.020755353698376777,"score_gpt":0.28685108760309835,"score_spread":0.2660957339047216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400681592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17513432,0.002765614,0.79974216,0.00048211435,0.00016191092,0.00010548735,0.00045991573,0.0022629704,0.01888558],"genre_scores_gemma":[0.8788978,0.0012314055,0.110116154,0.00016424072,0.000048587168,0.00008999872,0.0003014535,0.00004272104,0.009107697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99979,0.00003653553,0.000016835691,0.00006070269,0.00006491316,0.000031000676],"domain_scores_gemma":[0.9995907,0.00014295237,0.000040719322,0.00008935729,0.00011490796,0.000021376834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057822897,0.00038009338,0.0003992721,0.000481045,0.00022805438,0.0007881314,0.0010451971,0.0005150808,0.0030467673],"category_scores_gemma":[0.0011917907,0.00021133636,0.0003671367,0.0006129544,0.000559787,0.0016047201,0.00045067418,0.00047817486,0.0004974192],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003496495,0.00013655775,0.002641999,0.00035463434,0.00021445916,0.00026945907,0.00016653957,0.5740512,0.030087452,0.053158455,0.0037259758,0.33484367],"study_design_scores_gemma":[0.000020676081,0.00007907982,0.0006266176,0.000018787045,0.000038837854,0.0001302111,0.000020142146,0.96057826,0.0071259052,0.028590243,0.0027506866,0.00002044054],"about_ca_topic_score_codex":0.0052350583,"about_ca_topic_score_gemma":0.0052923122,"teacher_disagreement_score":0.0052350583,"about_ca_system_score_codex":0.00057473604,"about_ca_system_score_gemma":0.00066762586,"threshold_uncertainty_score":0.010409176},"labels":[],"label_agreement":null},{"id":"W4400681763","doi":"10.1109/tcomm.2024.3429170","title":"Federated Unfolding Learning for CSI Feedback in Distributed Edge Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Distributed learning; Distributed computing; Electronic engineering; Artificial intelligence; Engineering","score_opus":0.035331092696284025,"score_gpt":0.29249002089749704,"score_spread":0.257158928201213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400681763","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04470197,0.00018150118,0.9525073,0.00016273772,0.000030991225,0.000030149851,0.000052871612,0.0010245374,0.0013078701],"genre_scores_gemma":[0.9263497,0.000097579876,0.071009666,0.00017128089,0.00002442924,0.00006386173,0.00015756713,0.00005175726,0.0020740707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995426,0.00010015921,0.00002081145,0.00014593411,0.00009180972,0.000098651806],"domain_scores_gemma":[0.99910897,0.00041336502,0.00006787065,0.00016374126,0.00019360361,0.000052374697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008300032,0.00076205895,0.0010313635,0.00024249351,0.0005304918,0.00058312394,0.0016321603,0.0009821993,0.0013536736],"category_scores_gemma":[0.0028946388,0.00043812115,0.00035356786,0.00040269937,0.0007209583,0.0015932999,0.0013573653,0.0015296027,0.00026085286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015413514,0.00008398439,0.0007961708,0.000033500542,0.000024812436,0.00008434004,0.000074869,0.913367,0.0025687818,0.0034064192,0.00093350175,0.07847258],"study_design_scores_gemma":[0.0000027294059,0.000010675656,0.000038027614,0.0000010376485,0.0000018007235,0.0000057339676,0.0000042226634,0.99832255,0.00047405995,0.001068814,0.00006835812,0.0000019918205],"about_ca_topic_score_codex":0.006748281,"about_ca_topic_score_gemma":0.00950046,"teacher_disagreement_score":0.006748281,"about_ca_system_score_codex":0.00072319875,"about_ca_system_score_gemma":0.0012173831,"threshold_uncertainty_score":0.013418019},"labels":[],"label_agreement":null},{"id":"W4400682234","doi":"10.1145/3672198.3673801","title":"SqueezeNIC: Low-Latency In-NIC Compression for Distributed Deep Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Latency (audio); Compression (physics); Telecommunications","score_opus":0.011324100538454519,"score_gpt":0.2669269734314043,"score_spread":0.2556028728929498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400682234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09661274,0.002866264,0.854588,0.0006379411,0.0006299013,0.00029428542,0.0008258967,0.030145973,0.013398986],"genre_scores_gemma":[0.6835897,0.0010788565,0.29902196,0.0006797576,0.00017504649,0.00035573452,0.0027120367,0.00089969684,0.011487156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999673,0.000033983553,0.000018439801,0.00006248649,0.0001527519,0.0000592443],"domain_scores_gemma":[0.99940765,0.00017227566,0.000041783936,0.00018545566,0.00014570139,0.000047128506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004371787,0.00096161815,0.0005418182,0.00074825826,0.000477886,0.0010148861,0.0021589468,0.00050946063,0.0069626286],"category_scores_gemma":[0.0019341625,0.00027707306,0.00023962333,0.00093287596,0.0004610065,0.0018270784,0.0012833391,0.0012709444,0.001327246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014026097,0.00053894223,0.0036736657,0.00049347075,0.00016836936,0.00045643505,0.0002326477,0.10612471,0.050108258,0.020412674,0.06066852,0.7557196],"study_design_scores_gemma":[0.00016276847,0.00035441745,0.0011587254,0.000058880036,0.000047721176,0.0003170594,0.00010262096,0.88144296,0.0870937,0.005696936,0.023517514,0.000046598467],"about_ca_topic_score_codex":0.0028363143,"about_ca_topic_score_gemma":0.0062246714,"teacher_disagreement_score":0.0069626286,"about_ca_system_score_codex":0.00086076616,"about_ca_system_score_gemma":0.0009869429,"threshold_uncertainty_score":0.023292303},"labels":[],"label_agreement":null},{"id":"W4400727838","doi":"10.1109/iwcmc61514.2024.10592314","title":"A Deployment Method to Improve the Generalizability of Recurrent Neural Network","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Generalizability theory; Software deployment; Computer science; Artificial neural network; Artificial intelligence; Software engineering; Statistics; Mathematics","score_opus":0.024206174781400525,"score_gpt":0.3174914561133054,"score_spread":0.2932852813319049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400727838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016107997,0.00023039943,0.979151,0.00030097502,0.00010210379,0.000049468286,0.00003803569,0.002090225,0.0019297438],"genre_scores_gemma":[0.52523977,0.00049379974,0.46731487,0.00038041125,0.00019500007,0.00022534902,0.00026115152,0.0008578334,0.005031809],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922156,0.00019814189,0.00006596991,0.00023583647,0.00019641123,0.00008207872],"domain_scores_gemma":[0.9978156,0.00064165937,0.00016535725,0.0007859826,0.0005106501,0.0000807661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016770357,0.0012800107,0.00068516674,0.0007074448,0.0003537381,0.0007579427,0.0014475252,0.0009933879,0.0031068765],"category_scores_gemma":[0.008799149,0.00045941235,0.0005493401,0.00046272244,0.00065814017,0.001974259,0.0019494253,0.0016421949,0.0014139189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032115608,0.00018517837,0.0024698952,0.00025942118,0.00009149988,0.0005566374,0.00048792953,0.195877,0.14170325,0.0413774,0.007034241,0.6096364],"study_design_scores_gemma":[0.000027465821,0.00022385517,0.00070825225,0.000032855205,0.000043647746,0.00024093737,0.000049801958,0.9571881,0.023499291,0.013128234,0.004831102,0.000026337057],"about_ca_topic_score_codex":0.002085994,"about_ca_topic_score_gemma":0.0024004446,"teacher_disagreement_score":0.0031068765,"about_ca_system_score_codex":0.0004829634,"about_ca_system_score_gemma":0.0005366816,"threshold_uncertainty_score":0.0103935},"labels":[],"label_agreement":null},{"id":"W4400831779","doi":"10.1016/b978-0-323-91723-0.00010-6","title":"Data Acquisition and Recording","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Computer science","score_opus":0.035736313230367085,"score_gpt":0.268952162842352,"score_spread":0.23321584961198494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400831779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052648406,0.002666948,0.6645901,0.00050785526,0.0012086208,0.0019733827,0.020982401,0.03179796,0.27100784],"genre_scores_gemma":[0.018505415,0.003935191,0.34647247,0.0010290956,0.000335538,0.0028098167,0.021104274,0.006998183,0.59880996],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99931264,0.00004133793,0.000054002976,0.00023348906,0.00032760878,0.00003094303],"domain_scores_gemma":[0.9990312,0.00021731087,0.000022701697,0.00034993098,0.0003342611,0.000044579112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007305573,0.0015590955,0.0013961776,0.0020454787,0.0010080189,0.001910126,0.0018890271,0.000979332,0.26715803],"category_scores_gemma":[0.0019174288,0.00092852634,0.00045481356,0.0021850325,0.0006803332,0.0014656096,0.0018894621,0.0013791715,0.16581565],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017661773,0.00006964051,0.00031760978,0.0008409268,0.000026026582,0.00019501087,0.00016466665,0.00052551605,0.091968,0.006687665,0.14753243,0.7514958],"study_design_scores_gemma":[0.000038144433,0.00016955158,0.0024043655,0.00032806926,0.000064256215,0.00095677865,0.00012050224,0.0038126572,0.059509,0.005905807,0.9266302,0.000060692677],"about_ca_topic_score_codex":0.0014197755,"about_ca_topic_score_gemma":0.0030445317,"teacher_disagreement_score":0.26715803,"about_ca_system_score_codex":0.00039542397,"about_ca_system_score_gemma":0.0010092667,"threshold_uncertainty_score":0.89373267},"labels":[],"label_agreement":null},{"id":"W4400832971","doi":"10.1016/b978-0-323-91723-0.00015-5","title":"Neural Networks, Convolutional Neural Networks and Deep Learning","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Convolutional neural network; Computer science; Artificial neural network; Deep learning; Artificial intelligence","score_opus":0.012126551458786933,"score_gpt":0.22462332082617215,"score_spread":0.2124967693673852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400832971","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031183837,0.2878961,0.11091424,0.004926188,0.005460281,0.00005175284,0.0014174262,0.0019433254,0.5842723],"genre_scores_gemma":[0.02145818,0.16261792,0.05404826,0.0010319122,0.0030518002,0.00009578271,0.0018532749,0.0007239368,0.755119],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99981326,0.000014556957,0.000008833098,0.00004293463,0.00010482288,0.000015707434],"domain_scores_gemma":[0.999796,0.00010295317,0.000016638602,0.000025753983,0.000042695516,0.000015970283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002337145,0.0011538046,0.000870705,0.0011581976,0.00032663535,0.0023718388,0.00081302994,0.0013132972,0.059622884],"category_scores_gemma":[0.00084229716,0.00044221952,0.0003191202,0.0030971693,0.0006661793,0.0025397758,0.00092069205,0.0017404949,0.026294483],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027724785,0.000042917447,0.00017334736,0.0009141558,0.000022384293,0.000049296967,0.00004038255,0.0047514983,0.0019008195,0.08590148,0.22272322,0.68345284],"study_design_scores_gemma":[0.000007597609,0.000021984955,0.00065134646,0.00046865045,0.000018394116,0.0002531613,0.000050146424,0.012430365,0.0010906502,0.13427077,0.8507156,0.000021351105],"about_ca_topic_score_codex":0.0023366115,"about_ca_topic_score_gemma":0.0046305708,"teacher_disagreement_score":0.059622884,"about_ca_system_score_codex":0.00082623755,"about_ca_system_score_gemma":0.0007331179,"threshold_uncertainty_score":0.19945842},"labels":[],"label_agreement":null},{"id":"W4400839344","doi":"10.21203/rs.3.rs-4602415/v1","title":"DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Discriminator; Dual (grammatical number); Computer science; Current (fluid); Artificial neural network; Artificial intelligence; Deep learning; Electrical engineering; Telecommunications; Engineering; Art","score_opus":0.11770546266593193,"score_gpt":0.4696569831381085,"score_spread":0.3519515204721766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400839344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026825678,0.00043991345,0.9588036,0.00028093535,0.0003222095,0.00004744846,0.00035640266,0.004436044,0.008487767],"genre_scores_gemma":[0.5880839,0.00028343638,0.3891965,0.00042064884,0.00009063094,0.00009595138,0.0011237583,0.0005497781,0.020155404],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998771,0.000008517948,0.000005181817,0.000054994147,0.000033994966,0.000020329235],"domain_scores_gemma":[0.99982625,0.000023140776,0.000016124144,0.00005406903,0.000050557603,0.000029974963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027849045,0.0006788122,0.00041537816,0.00034144506,0.0003233517,0.00094905205,0.0020585486,0.00096732593,0.0054829577],"category_scores_gemma":[0.0006450994,0.00041341461,0.00032072363,0.00043039827,0.00046039122,0.001285254,0.0014242899,0.0012744131,0.0014682973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042035908,0.00028510677,0.0018667815,0.00025909801,0.00013319668,0.00024144442,0.000113262366,0.2425131,0.08715067,0.057897013,0.028031351,0.58108866],"study_design_scores_gemma":[0.000010501129,0.000029132107,0.00023569984,0.000011852765,0.0000102225085,0.000046447738,0.000008316058,0.9677376,0.016552681,0.009713408,0.0056325933,0.000011413411],"about_ca_topic_score_codex":0.005045311,"about_ca_topic_score_gemma":0.0077989525,"teacher_disagreement_score":0.0054829577,"about_ca_system_score_codex":0.0008159159,"about_ca_system_score_gemma":0.00055120845,"threshold_uncertainty_score":0.018342376},"labels":[],"label_agreement":null},{"id":"W4400908105","doi":"10.1101/2024.07.19.604252","title":"Spiking neural network models of sound localisation via a massively collaborative process","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Ottawa","funders":"","keywords":"Sound (geography); Massively parallel; Computer science; Process (computing); Artificial neural network; Artificial intelligence; Acoustics; Parallel computing; Physics","score_opus":0.01944136607257393,"score_gpt":0.23976910841459995,"score_spread":0.220327742342026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400908105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2434239,0.00047329182,0.73740274,0.002700094,0.00017321436,0.000080459264,0.00027620918,0.00047059898,0.014999488],"genre_scores_gemma":[0.9701858,0.00016662746,0.021393484,0.00014023318,0.00004910935,0.00009677497,0.000075898795,0.000065430155,0.007826512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957305,0.00014786034,0.000018080922,0.00011619989,0.000084796644,0.000059965758],"domain_scores_gemma":[0.9985147,0.00074644666,0.0002242212,0.00015650221,0.0001939801,0.00016403482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015425268,0.0005530345,0.0009108173,0.00059600495,0.0006481907,0.0016026442,0.0025589361,0.0020733676,0.0039023405],"category_scores_gemma":[0.0049268333,0.00062966225,0.0011580184,0.0005945363,0.002091532,0.0024576904,0.0021400137,0.0016411305,0.00043570885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006173061,0.000032495762,0.00054293807,0.000021055808,0.000028885355,0.000098608645,0.000078427525,0.9570623,0.0010953007,0.037919205,0.0005715863,0.0024874688],"study_design_scores_gemma":[0.0000054671564,0.0000043477953,0.000052066804,0.000001406017,0.0000019678068,0.0000045171437,0.0000033781193,0.9924043,0.00004669812,0.0073912917,0.00008224965,0.0000023228768],"about_ca_topic_score_codex":0.008286624,"about_ca_topic_score_gemma":0.0045501636,"teacher_disagreement_score":0.008286624,"about_ca_system_score_codex":0.0015079115,"about_ca_system_score_gemma":0.0007973121,"threshold_uncertainty_score":0.01647681},"labels":[],"label_agreement":null},{"id":"W4400915117","doi":"10.1103/physrevresearch.6.033098","title":"Waddington landscape for prototype learning in generalized Hopfield networks","year":2024,"lang":"en","type":"article","venue":"Physical Review Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Université de Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Chan Zuckerberg Initiative; Simons Foundation; Northwestern University; Silicon Valley Community Foundation; National Science Foundation","keywords":"Hopfield network; Computer science; Cognitive science; Artificial intelligence; Artificial neural network; Psychology","score_opus":0.08804667514139429,"score_gpt":0.4502189883582383,"score_spread":0.362172313216844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400915117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66914237,0.0013723833,0.28780887,0.0025466378,0.00006345179,0.00013718232,0.00025407816,0.00035967815,0.038315434],"genre_scores_gemma":[0.98651135,0.00022434471,0.010642526,0.00010611199,0.00001365995,0.00008345928,0.00007152321,0.000030377165,0.0023165755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983025,0.000056838515,0.000007875973,0.000032787964,0.000040210823,0.000032079573],"domain_scores_gemma":[0.99911016,0.0004442473,0.00013011986,0.00008552983,0.00010538162,0.00012452969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072290975,0.00034069445,0.0005282414,0.00097612565,0.0007982639,0.0014538461,0.0009317514,0.0014846241,0.0045811865],"category_scores_gemma":[0.0047362545,0.00035633583,0.00058538956,0.00030264084,0.0018262358,0.0022120746,0.00091242726,0.0011195265,0.00028923558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013295341,0.000060232873,0.0026761335,0.00016573355,0.000047616417,0.00046922048,0.0003932253,0.46531618,0.011479069,0.5035978,0.0027684963,0.012893364],"study_design_scores_gemma":[0.000024194153,0.00003607002,0.00094684976,0.000028270999,0.000006211522,0.00008543129,0.00007782748,0.7975728,0.00055563083,0.19987343,0.0007698626,0.000023474659],"about_ca_topic_score_codex":0.0024068903,"about_ca_topic_score_gemma":0.0017710541,"teacher_disagreement_score":0.0045811865,"about_ca_system_score_codex":0.0012324227,"about_ca_system_score_gemma":0.0004878499,"threshold_uncertainty_score":0.015325606},"labels":[],"label_agreement":null},{"id":"W4400927378","doi":"10.1007/s12559-024-10324-x","title":"Editorial: What AI and Neuroscience Can Learn from Each Other—Open Problems in Models and Theories","year":2024,"lang":"en","type":"editorial","venue":"Cognitive Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Cognitive science; Computational neuroscience; Computer science; Neuroscience; Artificial intelligence; Psychology; Data science","score_opus":0.025616979606926257,"score_gpt":0.30905042158942764,"score_spread":0.28343344198250137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400927378","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002127038,0.0036037692,0.00013733249,0.035911653,0.95915437,0.000013759487,0.00003741558,0.000038665214,0.001081812],"genre_scores_gemma":[0.00031281397,0.0017578471,0.00009268147,0.014451942,0.97778636,0.000021717115,0.00002156947,0.000035270466,0.005519753],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9918921,0.00166257,0.00081462093,0.00095923117,0.004108532,0.00056299666],"domain_scores_gemma":[0.96062064,0.019745298,0.0022267587,0.0010673276,0.01144501,0.004895045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011149154,0.004646501,0.005618286,0.0053840033,0.0053273444,0.011560432,0.00456321,0.023858914,0.021485193],"category_scores_gemma":[0.039742872,0.0016093495,0.0038938257,0.0023522459,0.004247512,0.006394937,0.0027392427,0.02413643,0.0125711085],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026513893,0.0000075377884,0.000010544577,0.00008902492,0.000017857616,0.000054150583,0.000006041634,0.000025973757,0.000026683136,0.00033032356,0.9969664,0.0024388295],"study_design_scores_gemma":[0.00014596099,0.000030058029,0.00023718936,0.00060269784,0.000097325705,0.0002502788,0.000046810233,0.0004948658,0.000121707504,0.0052974448,0.99263954,0.000036259808],"about_ca_topic_score_codex":0.0021432657,"about_ca_topic_score_gemma":0.0065105646,"teacher_disagreement_score":0.023858914,"about_ca_system_score_codex":0.0048082992,"about_ca_system_score_gemma":0.0047787065,"threshold_uncertainty_score":0.071875095},"labels":[],"label_agreement":null},{"id":"W4401011496","doi":"10.1016/b978-0-443-24010-2.00007-x","title":"Artificial neural networks","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence","score_opus":0.020050062205784702,"score_gpt":0.24207909493938704,"score_spread":0.22202903273360233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401011496","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004208532,0.06865802,0.39484978,0.0028559684,0.0046951873,0.00014574477,0.0017674954,0.0046229884,0.5181963],"genre_scores_gemma":[0.029804423,0.027967004,0.10893474,0.00083058194,0.0009842333,0.0002480191,0.00242727,0.00097326824,0.82783055],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99974185,0.000036943802,0.000015990818,0.00005383972,0.0001356276,0.00001579242],"domain_scores_gemma":[0.99970895,0.0001095803,0.000016190015,0.000070752314,0.000080823185,0.0000136745075],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033157563,0.0010375812,0.0010740996,0.00090551464,0.00037185912,0.0016400269,0.0009633386,0.0014164392,0.0539814],"category_scores_gemma":[0.0012978914,0.0004986616,0.00041037428,0.0016985016,0.0004413846,0.0016369497,0.0012357098,0.0014265521,0.03623211],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022631906,0.00004189191,0.00017747823,0.0004432099,0.00003911166,0.00006609998,0.000046573277,0.0144905625,0.0016444167,0.031263478,0.14806119,0.8037034],"study_design_scores_gemma":[0.000015051969,0.00005234674,0.00084295106,0.0005843876,0.0000459603,0.00032117404,0.00005479125,0.08277037,0.003537576,0.10303771,0.80869585,0.000041852672],"about_ca_topic_score_codex":0.001600207,"about_ca_topic_score_gemma":0.0024921636,"teacher_disagreement_score":0.9460186,"about_ca_system_score_codex":0.0004215637,"about_ca_system_score_gemma":0.00047248811,"threshold_uncertainty_score":0.18058574},"labels":[],"label_agreement":null},{"id":"W4401042441","doi":"10.18653/v1/2024.naacl-long.350","title":"ContraSim – Analyzing Neural Representations Based on Contrastive Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Azrieli Foundation; Open Philanthropy Project","keywords":"Computer science; Artificial intelligence; Artificial neural network; Natural language processing","score_opus":0.014674030816039123,"score_gpt":0.2851852182575468,"score_spread":0.27051118744150765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401042441","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06439802,0.003418058,0.92357266,0.0012012359,0.0004193641,0.000056074798,0.00022845803,0.0009523125,0.005753826],"genre_scores_gemma":[0.84099054,0.0014570066,0.14918609,0.00033742172,0.0003273677,0.000106191575,0.00073502795,0.000379306,0.0064811017],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996985,0.00010012315,0.000018814899,0.00008887864,0.000060988237,0.000032704138],"domain_scores_gemma":[0.998904,0.000620164,0.000115057795,0.00016508719,0.00014457792,0.000051196133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097414915,0.0006867877,0.00068393897,0.00095829676,0.00038681808,0.0017603263,0.001532949,0.00089911564,0.0038840687],"category_scores_gemma":[0.00548255,0.0003755494,0.0007212455,0.000782351,0.0010081528,0.0034016767,0.0015062863,0.0017237951,0.0008273062],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005899954,0.00019553342,0.003109606,0.0004885476,0.00030576318,0.00030108218,0.0004633215,0.12625907,0.032349605,0.1711818,0.015404228,0.64935136],"study_design_scores_gemma":[0.000017105815,0.00007967748,0.00079087995,0.000024729416,0.000029001727,0.000117013464,0.00004541003,0.87853783,0.0053772116,0.11276009,0.0021956165,0.000025439456],"about_ca_topic_score_codex":0.0018944236,"about_ca_topic_score_gemma":0.0017466688,"teacher_disagreement_score":0.0038840687,"about_ca_system_score_codex":0.00046473768,"about_ca_system_score_gemma":0.0003875655,"threshold_uncertainty_score":0.012993515},"labels":[],"label_agreement":null},{"id":"W4401043372","doi":"10.18653/v1/2024.naacl-short.19","title":"Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University; Canadian Institute for Advanced Research; Nvidia","keywords":"Singleton; Coreference; Computer science; End-to-end principle; Resolution (logic); Artificial intelligence","score_opus":0.03356020446073785,"score_gpt":0.27700173795383176,"score_spread":0.24344153349309391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401043372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12287433,0.0057251127,0.8355109,0.0010589773,0.0011327927,0.00020647008,0.00102984,0.021064583,0.011396997],"genre_scores_gemma":[0.59663904,0.0012805341,0.37773785,0.0009877811,0.0003727134,0.00024340115,0.0042608157,0.002385444,0.016092522],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968881,0.00093617477,0.00020210008,0.0012819272,0.00038174077,0.00030988755],"domain_scores_gemma":[0.9946243,0.003040099,0.00013865337,0.0009243685,0.0010975132,0.0001751349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003781473,0.0023074073,0.0020722058,0.0020048993,0.0014298994,0.0022520777,0.0037552156,0.003951813,0.008018545],"category_scores_gemma":[0.016917411,0.0008829008,0.001091398,0.0017435611,0.0007022012,0.0064089173,0.0038809062,0.0034840037,0.006970261],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011792611,0.00048145515,0.0017043302,0.00021865142,0.00023185376,0.00017346904,0.00021244145,0.031793434,0.02654343,0.0015844014,0.018799413,0.9170779],"study_design_scores_gemma":[0.00008332914,0.00019668415,0.0016976147,0.000059275237,0.00020389557,0.0002587138,0.00018720988,0.9384195,0.043906506,0.009791745,0.0051232735,0.0000722403],"about_ca_topic_score_codex":0.0060431184,"about_ca_topic_score_gemma":0.015971033,"teacher_disagreement_score":0.008018545,"about_ca_system_score_codex":0.0008948237,"about_ca_system_score_gemma":0.0017757178,"threshold_uncertainty_score":0.026824713},"labels":[],"label_agreement":null},{"id":"W4401043454","doi":"10.18653/v1/2024.naacl-long.332","title":"Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Economic and Social Research Council; Social Sciences and Humanities Research Council of Canada","keywords":"Debiasing; Computer science; Projection (relational algebra); Vector projection; Artificial intelligence; Theoretical computer science; Algorithm; Cognitive science; Psychology","score_opus":0.019752271568207483,"score_gpt":0.3154674531806135,"score_spread":0.295715181612406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401043454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007234621,0.0009615136,0.9819381,0.0013318114,0.00009183392,0.00003805802,0.00015433649,0.00021693876,0.008032793],"genre_scores_gemma":[0.6014194,0.0031640322,0.3758248,0.0014087481,0.0011256288,0.0006748442,0.0009366729,0.00045533633,0.014990501],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977379,0.00085124077,0.00016407188,0.00047990849,0.00050639204,0.00026044983],"domain_scores_gemma":[0.9931947,0.0036585026,0.00048150908,0.0014857906,0.0008937148,0.00028582255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040348475,0.0010507992,0.0012581048,0.0024011584,0.001343852,0.0035835141,0.0021531128,0.0020227905,0.0087322695],"category_scores_gemma":[0.016207334,0.0006688464,0.0014257391,0.0024626756,0.0058664116,0.011016869,0.0046258564,0.004260686,0.0014412922],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022816248,0.00001264188,0.00011785162,0.00004836141,0.000010441578,0.000025527313,0.00009567546,0.0032988633,0.00020030132,0.98194444,0.0015936894,0.012629414],"study_design_scores_gemma":[0.000011554905,0.000017739407,0.00005427834,0.000022497841,0.000005520115,0.00003623014,0.000026442252,0.03701202,0.00015492541,0.961103,0.001546695,0.0000091596185],"about_ca_topic_score_codex":0.0018512107,"about_ca_topic_score_gemma":0.0014833912,"teacher_disagreement_score":0.0087322695,"about_ca_system_score_codex":0.001255863,"about_ca_system_score_gemma":0.0017191203,"threshold_uncertainty_score":0.029212296},"labels":[],"label_agreement":null},{"id":"W4401069860","doi":"10.1109/access.2024.3434617","title":"Predictive Analytics: An Optimization Perspective","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Concordia University","funders":"","keywords":"Computer science; Predictive analytics; Perspective (graphical); Machine learning; Analytics; Data analysis; Artificial intelligence; Data science; Artificial neural network; Data mining; Mathematical optimization; Mathematics","score_opus":0.03430678945445772,"score_gpt":0.3439158340500761,"score_spread":0.3096090445956184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401069860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016090698,0.048797768,0.9075935,0.018569008,0.0008823103,0.000042317562,0.00029630214,0.00018188785,0.022027723],"genre_scores_gemma":[0.3853558,0.2235947,0.34560916,0.008732702,0.019872423,0.000557268,0.00090013916,0.00042860935,0.0149491755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996311,0.0014916,0.0001860546,0.00051748217,0.0013447516,0.00014901995],"domain_scores_gemma":[0.9939201,0.004926019,0.00024487588,0.00023801801,0.0005545456,0.00011647916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004138879,0.0021863657,0.0022159216,0.0024197958,0.0005620602,0.0048410613,0.002230355,0.002435801,0.0025993378],"category_scores_gemma":[0.00885386,0.0008197465,0.0013768334,0.0032413232,0.004373543,0.006439408,0.00175068,0.0060907863,0.0010532174],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027906244,0.000047909096,0.0002743329,0.00041063028,0.000092106384,0.00008009869,0.00005600841,0.087705575,0.00027619043,0.8609858,0.007188232,0.042855173],"study_design_scores_gemma":[0.00001116607,0.000043166347,0.00018065778,0.00019326876,0.00002984493,0.00005353369,0.000035230034,0.16461736,0.00029930568,0.81139946,0.023108097,0.00002894524],"about_ca_topic_score_codex":0.0028605463,"about_ca_topic_score_gemma":0.0012169911,"teacher_disagreement_score":0.0048410613,"about_ca_system_score_codex":0.0024530173,"about_ca_system_score_gemma":0.0016648617,"threshold_uncertainty_score":0.021888733},"labels":[],"label_agreement":null},{"id":"W4401113779","doi":"10.1109/ims40175.2024.10600417","title":"Coupled Electromagnetic-Thermal Analysis for Temperature-Dependent Materials with Physics-Informed Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Thermal; Computer science; Thermal physics; Thermal analysis; Physics; Engineering physics; Artificial intelligence; Quantum mechanics; Thermodynamics","score_opus":0.008769536634015501,"score_gpt":0.2399221548064144,"score_spread":0.2311526181723989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401113779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059410222,0.00024770317,0.9353211,0.0002386035,0.000030215613,0.00003336144,0.000053290132,0.0001587179,0.0045068045],"genre_scores_gemma":[0.8675011,0.00028374142,0.12562153,0.00016741047,0.00003665238,0.00022006196,0.000103953775,0.000100931924,0.0059646526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983704,0.000054931617,0.000005230149,0.000025907331,0.00005971323,0.00001711645],"domain_scores_gemma":[0.99960023,0.00026729135,0.00004782075,0.000024790726,0.00004489586,0.000014991234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005620878,0.0005321007,0.00038401777,0.00035248112,0.00028592272,0.00047891293,0.0008441982,0.00082445855,0.0012056781],"category_scores_gemma":[0.0014915244,0.00043948687,0.00045296867,0.00029674766,0.00075933186,0.00085747114,0.0007881036,0.00069710106,0.00012449406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000125169145,0.000011006768,0.00010614972,0.0000137061925,0.000012077391,0.000016332982,0.000010851802,0.9917862,0.0016496523,0.0039743255,0.000055285076,0.0023519776],"study_design_scores_gemma":[6.6300146e-7,0.0000016072869,0.000016540991,4.902458e-7,6.5862656e-7,0.0000013562777,6.2744783e-7,0.9991473,0.00013071038,0.00066515827,0.00003397168,9.444689e-7],"about_ca_topic_score_codex":0.003090034,"about_ca_topic_score_gemma":0.003927682,"teacher_disagreement_score":0.003090034,"about_ca_system_score_codex":0.0008324732,"about_ca_system_score_gemma":0.00069000036,"threshold_uncertainty_score":0.006144047},"labels":[],"label_agreement":null},{"id":"W4401328656","doi":"10.1109/mm.2024.3423785","title":"Parallelization Strategies for DLRM Embedding Bag Operator on AMD CPUs","year":2024,"lang":"en","type":"article","venue":"IEEE Micro","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Parallel computing; Embedding; Operator (biology); Artificial intelligence","score_opus":0.023815185270078528,"score_gpt":0.30903484501187933,"score_spread":0.2852196597418008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401328656","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4900887,0.0016773118,0.4552265,0.0010626763,0.0006973427,0.0002697249,0.0007060544,0.02558082,0.024690868],"genre_scores_gemma":[0.76037323,0.0003013877,0.22728963,0.00024563223,0.00006478113,0.00018862718,0.0010903685,0.00057671935,0.009869592],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991585,0.0001092151,0.00007089554,0.0001814744,0.00026328105,0.00021665647],"domain_scores_gemma":[0.9989317,0.00025190756,0.000050560662,0.0003045066,0.0003626286,0.000098708275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064592774,0.00084196177,0.00037913545,0.0007184398,0.0005588921,0.0011184467,0.0016334639,0.00039990232,0.009811035],"category_scores_gemma":[0.0025911531,0.00037718692,0.0004158371,0.0009856443,0.00038704195,0.0019008568,0.0010893164,0.0010500196,0.0022588617],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029205098,0.0006616331,0.011732208,0.00041092787,0.00017530947,0.0005112036,0.0007290663,0.10937351,0.085026585,0.02212231,0.044016246,0.7223205],"study_design_scores_gemma":[0.00022107123,0.0003786291,0.0021008186,0.000029882178,0.00004935392,0.00012769949,0.00020757267,0.9117191,0.06134755,0.0073918044,0.016386148,0.0000402793],"about_ca_topic_score_codex":0.007285487,"about_ca_topic_score_gemma":0.013384277,"teacher_disagreement_score":0.009811035,"about_ca_system_score_codex":0.0012922013,"about_ca_system_score_gemma":0.0017253048,"threshold_uncertainty_score":0.032821238},"labels":[],"label_agreement":null},{"id":"W4401503623","doi":"10.1016/j.ins.2024.121317","title":"An analytic formulation of convolutional neural network learning for pattern recognition","year":2024,"lang":"en","type":"article","venue":"Information Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Ministry of Education, Science and Technology; National Research Foundation of Korea; National Natural Science Foundation of China","keywords":"MNIST database; Computer science; Interpretability; Convolutional neural network; Artificial intelligence; Machine learning; Overfitting; Benchmark (surveying); Artificial neural network; Regularization (linguistics); Deep learning; Perceptron; Pattern recognition (psychology)","score_opus":0.044810043358564096,"score_gpt":0.3101929272162299,"score_spread":0.2653828838576658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401503623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004059533,0.0012229383,0.9832606,0.0011112946,0.00013485718,0.00002004307,0.00011634445,0.00007962662,0.009994691],"genre_scores_gemma":[0.54594475,0.006056459,0.3907609,0.0013532751,0.0011063782,0.0002488781,0.0004645267,0.00030888186,0.05375593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997887,0.00006237166,0.000011979733,0.0000346542,0.000079852674,0.000022349395],"domain_scores_gemma":[0.99953675,0.0002151905,0.000050275386,0.000037517955,0.00013567507,0.000024551573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071311544,0.00064264325,0.0004627103,0.00069875695,0.00031729185,0.0011373309,0.001077401,0.00091467856,0.0039413143],"category_scores_gemma":[0.0028638316,0.0003145678,0.00054077903,0.000695364,0.0009538393,0.0019427079,0.000782426,0.0012732064,0.00071290135],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007923099,0.0000118517,0.00013178543,0.00008576791,0.000014190147,0.000045851288,0.000046786045,0.07595325,0.0016638942,0.90179557,0.0028312835,0.017411841],"study_design_scores_gemma":[0.0000020071302,0.000007167233,0.00007727195,0.000014871587,0.0000042595457,0.000033626136,0.000010459138,0.63193375,0.00023241698,0.3645451,0.0031336537,0.0000053924846],"about_ca_topic_score_codex":0.0034059517,"about_ca_topic_score_gemma":0.0034356772,"teacher_disagreement_score":0.0039413143,"about_ca_system_score_codex":0.0011687112,"about_ca_system_score_gemma":0.0009239193,"threshold_uncertainty_score":0.013185024},"labels":[],"label_agreement":null},{"id":"W4401560582","doi":"10.31219/osf.io/wj56u","title":"Evaluating Kolmogorov–Arnold Networks for Scientific Discovery: A Simple Yet Effective Approach","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Normalization (sociology); Computer science; Metric (unit); Simple (philosophy); Focus (optics); Ranking (information retrieval); Artificial neural network; Function (biology); Kolmogorov complexity; Machine learning; Theoretical computer science; Artificial intelligence; Black box; Algorithm","score_opus":0.05908456250423836,"score_gpt":0.3497246361123384,"score_spread":0.2906400736081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401560582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09460452,0.00213807,0.8923176,0.0014874694,0.00016815394,0.000191705,0.0005277424,0.00139816,0.007166527],"genre_scores_gemma":[0.789417,0.00092849025,0.20510149,0.0002823858,0.00017387,0.00028547941,0.00089752465,0.00022249705,0.0026912594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957135,0.0016130186,0.0003359624,0.0005124706,0.0016399403,0.00018524795],"domain_scores_gemma":[0.9811453,0.013222963,0.0013378492,0.0014938215,0.0022068874,0.0005932182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064689703,0.0017099931,0.0014550055,0.0036633415,0.00078578875,0.003196914,0.0013287808,0.00224511,0.002811829],"category_scores_gemma":[0.03748218,0.0004579945,0.0007623814,0.0017315042,0.0016783568,0.0054244855,0.0031590206,0.0019086257,0.0006608832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056085875,0.00016067707,0.008248462,0.00053189456,0.0003212458,0.00019006341,0.00014869528,0.7413055,0.004456857,0.06768284,0.0032992165,0.17309366],"study_design_scores_gemma":[0.00001720197,0.00011154761,0.0006517871,0.000031003525,0.000025298017,0.00005389843,0.00003315789,0.95272183,0.0018315158,0.043850794,0.00064715877,0.000024782981],"about_ca_topic_score_codex":0.0016582111,"about_ca_topic_score_gemma":0.0020490598,"teacher_disagreement_score":0.0064689703,"about_ca_system_score_codex":0.0017222647,"about_ca_system_score_gemma":0.001800003,"threshold_uncertainty_score":0.034211576},"labels":[],"label_agreement":null},{"id":"W4401568285","doi":"10.1109/pn62551.2024.10621770","title":"Dynamical Measurements on the Matrix Product State","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Canada Research Chairs","keywords":"Product (mathematics); Matrix (chemical analysis); State (computer science); Computer science; Mathematics; Algorithm; Materials science","score_opus":0.04290144863839599,"score_gpt":0.2935032186819772,"score_spread":0.2506017700435812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401568285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35275817,0.00048576115,0.56942415,0.0016489525,0.0007219366,0.00016132856,0.0016978014,0.0016290207,0.07147283],"genre_scores_gemma":[0.9628599,0.00026363935,0.032276165,0.0001649281,0.000081860366,0.00009657427,0.00041794975,0.00038011378,0.00345889],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992937,0.00020369903,0.000026816224,0.00015732012,0.00024285831,0.000075625176],"domain_scores_gemma":[0.9984509,0.0006923751,0.00013040123,0.0003932953,0.00020318048,0.00012978858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011278308,0.00043757024,0.00044950694,0.0006686721,0.00069812,0.0015523842,0.0010290374,0.0008147454,0.010199229],"category_scores_gemma":[0.007096035,0.0003673872,0.0004709494,0.0004829602,0.002099637,0.0056200842,0.0013335465,0.0019802877,0.001030218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018841306,0.00011111654,0.0014683641,0.00013632863,0.000052457064,0.00010908527,0.0004114731,0.061189964,0.033903796,0.880822,0.0031773276,0.018429654],"study_design_scores_gemma":[0.000031710377,0.00012821177,0.0013849434,0.00005787412,0.000015824078,0.00010092992,0.00016230287,0.5091632,0.019182649,0.462707,0.0069716442,0.00009374897],"about_ca_topic_score_codex":0.0009579709,"about_ca_topic_score_gemma":0.000603941,"teacher_disagreement_score":0.010199229,"about_ca_system_score_codex":0.0007548954,"about_ca_system_score_gemma":0.0005126775,"threshold_uncertainty_score":0.034119785},"labels":[],"label_agreement":null},{"id":"W4401568833","doi":"10.23919/date58400.2024.10546827","title":"CASCO: Cascaded Co-Optimization for Holistic Neural Network Acceleration","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Artificial neural network; Acceleration; Computer science; Artificial intelligence; Physics","score_opus":0.055525694108044886,"score_gpt":0.32541200844911683,"score_spread":0.26988631434107196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401568833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0391356,0.0008084538,0.94782364,0.00017987489,0.000111547204,0.00009281073,0.00009409444,0.004164428,0.0075894897],"genre_scores_gemma":[0.5920237,0.00025449044,0.4017205,0.00035422904,0.000048559537,0.00030253295,0.00032671128,0.0006147438,0.004354568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960107,0.00007027559,0.000015421372,0.00009266935,0.00014147485,0.00007920173],"domain_scores_gemma":[0.99964774,0.00012276588,0.000035933088,0.00008833325,0.000066865476,0.000038331716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005186529,0.0016375802,0.00070838054,0.00047674534,0.00032905198,0.0005443178,0.0013859351,0.0006187535,0.005015833],"category_scores_gemma":[0.0012471289,0.0005113069,0.0008536852,0.0004992247,0.00052544783,0.00084945926,0.0012917195,0.0012549578,0.0007985504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020698473,0.00012783275,0.0014537898,0.00019264691,0.000118820295,0.000112193804,0.000053112668,0.78056365,0.019425001,0.009293976,0.007010559,0.18144147],"study_design_scores_gemma":[0.00001753735,0.00007829866,0.000118797325,0.0000058926353,0.000016310776,0.000026165337,0.000006870214,0.99360424,0.0021910446,0.0024031934,0.0015256576,0.0000061279907],"about_ca_topic_score_codex":0.0021880798,"about_ca_topic_score_gemma":0.007105361,"teacher_disagreement_score":0.005015833,"about_ca_system_score_codex":0.00056826003,"about_ca_system_score_gemma":0.0015703024,"threshold_uncertainty_score":0.016779602},"labels":[],"label_agreement":null},{"id":"W4401649637","doi":"10.1016/j.eswa.2024.125120","title":"Logic-oriented fuzzy neural networks: A survey","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fuzzy logic; Artificial neural network; Artificial intelligence; Neuro-fuzzy; Machine learning; Fuzzy control system","score_opus":0.02237162754913257,"score_gpt":0.2724644143570859,"score_spread":0.2500927868079533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401649637","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005248364,0.8571014,0.10085531,0.001859635,0.0011990515,0.00008832154,0.0003000612,0.00024310891,0.033104807],"genre_scores_gemma":[0.05417846,0.88818324,0.046170995,0.0008046591,0.0015896267,0.000105768675,0.000643723,0.00005779903,0.008265666],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970156,0.000049318063,0.0000398002,0.00006402556,0.0001234477,0.000021825814],"domain_scores_gemma":[0.9995059,0.00025872522,0.000039431907,0.000021594069,0.0001576607,0.000016724996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005701347,0.00096706615,0.0007322758,0.0020160098,0.00036543986,0.0012197695,0.0012240292,0.0012246538,0.004233543],"category_scores_gemma":[0.0015756185,0.00039275177,0.0006208488,0.0027672458,0.00039242298,0.0019000914,0.0005478943,0.0009205359,0.0013900403],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007880338,0.000104930135,0.001249951,0.006543063,0.000107770844,0.0002774636,0.0001333885,0.015269598,0.0016840304,0.0371112,0.019154016,0.91828585],"study_design_scores_gemma":[0.000022970278,0.00030882677,0.0028961985,0.006887858,0.0002467381,0.0022899106,0.00029387616,0.09608543,0.0041124746,0.10175654,0.78495044,0.00014868517],"about_ca_topic_score_codex":0.0025136769,"about_ca_topic_score_gemma":0.002115636,"teacher_disagreement_score":0.004233543,"about_ca_system_score_codex":0.0006701762,"about_ca_system_score_gemma":0.0009814252,"threshold_uncertainty_score":0.0141626},"labels":[],"label_agreement":null},{"id":"W4401692272","doi":"10.1109/isit57864.2024.10619241","title":"Conditional Mutual Information Constrained Deep Learning: Framework and Preliminary Results","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo","funders":"","keywords":"Mutual information; Computer science; Artificial intelligence; Machine learning","score_opus":0.007955137614758929,"score_gpt":0.24076736511704058,"score_spread":0.23281222750228164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401692272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010991019,0.0046976027,0.9763699,0.00093964505,0.00008616169,0.000082494254,0.00031979082,0.00038494501,0.006128424],"genre_scores_gemma":[0.637918,0.007608286,0.34198684,0.0010753938,0.0005707692,0.000560024,0.0016469889,0.00037966584,0.008254086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982615,0.0006769829,0.00008379645,0.00032697036,0.0005094365,0.00014133676],"domain_scores_gemma":[0.9945634,0.0036751495,0.00026950132,0.00043080322,0.00085449335,0.00020667838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047548953,0.0015814089,0.0012533063,0.0010992985,0.00045241287,0.0017057973,0.0019929325,0.0013937929,0.0033323492],"category_scores_gemma":[0.014888211,0.00072869286,0.00087155,0.0013654532,0.0019991659,0.0031818454,0.003341987,0.0040660305,0.00059306255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025613073,0.00021537929,0.0023063463,0.00056449365,0.00018341394,0.00026836948,0.00021923531,0.55273384,0.0037200241,0.30604675,0.0077924617,0.12569349],"study_design_scores_gemma":[0.000006339871,0.000042101048,0.00026910246,0.000040277864,0.000013450581,0.000049799986,0.000012405658,0.946936,0.0011481771,0.049583144,0.0018808197,0.000018333463],"about_ca_topic_score_codex":0.0062343953,"about_ca_topic_score_gemma":0.004982693,"teacher_disagreement_score":0.0062343953,"about_ca_system_score_codex":0.002859914,"about_ca_system_score_gemma":0.0019519699,"threshold_uncertainty_score":0.025146604},"labels":[],"label_agreement":null},{"id":"W4401693510","doi":"10.1109/isit57864.2024.10619345","title":"Improved Bounds on the Interactive Capacity via Error Pattern Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Error analysis; Algorithm; Mathematics; Applied mathematics","score_opus":0.024102647221694584,"score_gpt":0.27056249859041276,"score_spread":0.24645985136871817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401693510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03232106,0.0021519572,0.92591405,0.0022323676,0.0002829122,0.00021559393,0.00085179403,0.0018129818,0.0342173],"genre_scores_gemma":[0.7883319,0.0028254546,0.19067597,0.001184147,0.0008624109,0.0014467774,0.0012520242,0.0014990799,0.011922139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9795873,0.0043072826,0.0008239088,0.0030700436,0.00826719,0.00394434],"domain_scores_gemma":[0.8653523,0.096233584,0.0046773064,0.023773741,0.0076860534,0.0022770006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0098737795,0.0037729233,0.0030121384,0.0044684606,0.0025714429,0.005281737,0.0089695705,0.00429534,0.013177715],"category_scores_gemma":[0.080046125,0.001973965,0.0034415775,0.003679699,0.008067924,0.019578312,0.013811743,0.013842919,0.0027800007],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070661935,0.00027515547,0.0016805598,0.0005385495,0.00017172306,0.00037583173,0.0005287703,0.5144059,0.012566519,0.42433065,0.006951864,0.037467834],"study_design_scores_gemma":[0.000041273768,0.000090170324,0.00036884253,0.000115617346,0.00006665484,0.00013354617,0.000044577388,0.7149242,0.010178073,0.2708388,0.0030972706,0.00010101178],"about_ca_topic_score_codex":0.0032640018,"about_ca_topic_score_gemma":0.0020218513,"teacher_disagreement_score":0.013177715,"about_ca_system_score_codex":0.0058636647,"about_ca_system_score_gemma":0.005007639,"threshold_uncertainty_score":0.05221814},"labels":[],"label_agreement":null},{"id":"W4401693689","doi":"10.1109/isit57864.2024.10619246","title":"Common Function Reconstruction with Information Swapping Terminals","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Function (biology)","score_opus":0.008688835017761003,"score_gpt":0.2187655366362127,"score_spread":0.2100767016184517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401693689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039689947,0.00038338648,0.9494969,0.0009666167,0.00007906114,0.000095989235,0.00019197875,0.00023014018,0.00886595],"genre_scores_gemma":[0.86622065,0.0004810755,0.1294468,0.00043169045,0.00009539036,0.00021011784,0.00026298367,0.000056309196,0.002795009],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963844,0.0014579587,0.00016423353,0.0006996556,0.00068950374,0.00060422404],"domain_scores_gemma":[0.98581475,0.0104350615,0.0006257354,0.0022153335,0.00055415276,0.0003550065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004480074,0.001487161,0.0014394295,0.0008220898,0.0012412316,0.002348584,0.00237574,0.003344634,0.003683085],"category_scores_gemma":[0.017412664,0.00073622784,0.001198424,0.0012806803,0.0037850353,0.006104125,0.0074188123,0.0046399822,0.00084150414],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077174976,0.000110834604,0.0005157491,0.00024046078,0.00010532109,0.0009827435,0.000537861,0.25553784,0.010957839,0.70506155,0.002595892,0.022582138],"study_design_scores_gemma":[0.00011016531,0.00017351464,0.00016413315,0.00006285554,0.00004783841,0.0005736606,0.00013439152,0.63833576,0.013483057,0.3443916,0.0024530715,0.000070009904],"about_ca_topic_score_codex":0.000747454,"about_ca_topic_score_gemma":0.00063661917,"teacher_disagreement_score":0.004480074,"about_ca_system_score_codex":0.0017385506,"about_ca_system_score_gemma":0.0011759375,"threshold_uncertainty_score":0.023693204},"labels":[],"label_agreement":null},{"id":"W4401694196","doi":"10.1109/isit57864.2024.10619231","title":"Rate of Convergence of an Over-Parametrized Convolutional Neural Network Image Classifier Learned by Gradient Descent","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Gradient descent; Convolutional neural network; Computer science; Artificial intelligence; Classifier (UML); Rate of convergence; Pattern recognition (psychology); Artificial neural network; Stochastic gradient descent; Convergence (economics); Contextual image classification; Image (mathematics); Telecommunications","score_opus":0.022385250685256434,"score_gpt":0.2788589898650074,"score_spread":0.25647373917975097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401694196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03203805,0.0013869811,0.96253556,0.0007358808,0.00013234522,0.00011461893,0.00013699081,0.00104326,0.001876283],"genre_scores_gemma":[0.6291636,0.0016491404,0.35496393,0.0006438398,0.00025538975,0.00069107703,0.0009363595,0.0013454905,0.010351116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99657637,0.0012631402,0.00021953517,0.0008110808,0.0008064861,0.00032334088],"domain_scores_gemma":[0.97524863,0.016477624,0.0013033911,0.0025032437,0.0038071491,0.00066000724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0097982995,0.0022011497,0.0024263116,0.0011260723,0.0008062879,0.0031423832,0.0031351412,0.0034903996,0.0033947034],"category_scores_gemma":[0.048827562,0.0010645097,0.0012181167,0.0007901016,0.0020187537,0.004046555,0.004175858,0.0050954362,0.0014603566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011317316,0.00018117722,0.005665467,0.00048955606,0.0003393311,0.00041041741,0.0002225899,0.7279803,0.014890564,0.06542796,0.0053151785,0.17794576],"study_design_scores_gemma":[0.000011424225,0.000058291695,0.00031967956,0.000031048756,0.00001746003,0.00009509358,0.00000896066,0.9889839,0.0029918216,0.007092019,0.0003733176,0.000016880449],"about_ca_topic_score_codex":0.003206593,"about_ca_topic_score_gemma":0.0022219613,"teacher_disagreement_score":0.0097982995,"about_ca_system_score_codex":0.002844731,"about_ca_system_score_gemma":0.002405735,"threshold_uncertainty_score":0.051819026},"labels":[],"label_agreement":null},{"id":"W4401706939","doi":"10.2139/ssrn.4926395","title":"Automated Strength-Interval Curve Generation Using Actors","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Interval (graph theory); Mathematics; Econometrics; Statistics; Combinatorics","score_opus":0.0279476496493188,"score_gpt":0.29853696550143555,"score_spread":0.27058931585211676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401706939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011642042,0.000107824686,0.96985203,0.00013109924,0.00009794217,0.0001576214,0.00040110608,0.012410369,0.0051999763],"genre_scores_gemma":[0.36041236,0.00016137707,0.6281607,0.00010267968,0.00007379677,0.00026312534,0.0016967073,0.0027304145,0.0063988324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992894,0.00011617589,0.000040947358,0.00022185195,0.00024314394,0.000088441906],"domain_scores_gemma":[0.9981664,0.0007504142,0.000117891665,0.00038104903,0.00044250017,0.0001417467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008523844,0.0014220373,0.0012096778,0.0018711427,0.0006081637,0.0016707424,0.0020823642,0.0016725376,0.021193812],"category_scores_gemma":[0.0058472245,0.00066609454,0.0012566491,0.0011573524,0.0005448514,0.0015719741,0.002511534,0.0016515723,0.005810446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000764763,0.00014979258,0.001918782,0.00029554748,0.000068217436,0.00042698195,0.00023947543,0.16724327,0.017585754,0.023441125,0.02035084,0.7675155],"study_design_scores_gemma":[0.000043241645,0.000052583062,0.00019291254,0.00002374679,0.000013802748,0.00011204462,0.000047765596,0.9672965,0.0075705214,0.019833418,0.0047960025,0.000017545239],"about_ca_topic_score_codex":0.001929921,"about_ca_topic_score_gemma":0.002071696,"teacher_disagreement_score":0.021193812,"about_ca_system_score_codex":0.0007163648,"about_ca_system_score_gemma":0.00087532256,"threshold_uncertainty_score":0.07090032},"labels":[],"label_agreement":null},{"id":"W4401719975","doi":"10.1109/icce62051.2024.10634689","title":"Classification of Electroencephalography Signals with Auditory Stimuli using Complex Network","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Electroencephalography; Computer science; Speech recognition; EEG-fMRI; Neuroscience; Psychology","score_opus":0.052890160043646,"score_gpt":0.2953731200492714,"score_spread":0.24248296000562541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401719975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4015413,0.00056585623,0.59458405,0.00015847846,0.00006896301,0.000091581496,0.0003151038,0.00047115618,0.002203584],"genre_scores_gemma":[0.93011266,0.00039507676,0.06816558,0.000019661235,0.000036275083,0.00004423023,0.00035094414,0.000026256052,0.00084935647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983346,0.00003425193,0.000010469408,0.000050325718,0.000051674553,0.000019685667],"domain_scores_gemma":[0.99975103,0.00012358115,0.00004448391,0.000018897026,0.000048937432,0.000013048382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022426769,0.0005603232,0.00025821905,0.0011765991,0.0001597909,0.0005111578,0.0002831125,0.00029474348,0.00072110805],"category_scores_gemma":[0.0010542449,0.000093238355,0.00033352798,0.0010647288,0.00023959707,0.0006738218,0.00024246181,0.00027344856,0.00015583058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047027596,0.00024632041,0.017926116,0.00021133176,0.00017671715,0.000310358,0.0001773528,0.14117628,0.09519017,0.0037467275,0.0014640883,0.7389043],"study_design_scores_gemma":[0.0000081695125,0.00008977662,0.017906573,0.000009053758,0.000029017292,0.00011431383,0.000056736324,0.9698971,0.008784663,0.0024127397,0.0006764204,0.000015488822],"about_ca_topic_score_codex":0.0017765996,"about_ca_topic_score_gemma":0.0021736338,"teacher_disagreement_score":0.0017765996,"about_ca_system_score_codex":0.00026321047,"about_ca_system_score_gemma":0.00015962933,"threshold_uncertainty_score":0.0035324693},"labels":[],"label_agreement":null},{"id":"W4401752634","doi":"10.1109/icdcs60910.2024.00017","title":"When the Edge Meets Transformers: Distributed Inference with Transformer Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Transformer; Inference; Computer science; Electrical engineering; Artificial intelligence; Engineering; Voltage","score_opus":0.02416374468594464,"score_gpt":0.24960273741175898,"score_spread":0.22543899272581433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401752634","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030418418,0.00017283366,0.96478313,0.00036500974,0.00006986988,0.000045742065,0.00016166184,0.0018212897,0.0021620162],"genre_scores_gemma":[0.7302419,0.00022271869,0.2647784,0.0003406993,0.00007869867,0.0000754,0.00059512246,0.00049156474,0.0031754682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990484,0.00023541163,0.000050428018,0.00031904696,0.0002130173,0.00013363617],"domain_scores_gemma":[0.9971529,0.0013585987,0.00016754518,0.0008180119,0.00034181532,0.00016106549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015598885,0.000852349,0.00094623683,0.00052952266,0.00072523725,0.0021774056,0.0021778576,0.001035995,0.0029613418],"category_scores_gemma":[0.009077764,0.0007827083,0.0009410357,0.0006018064,0.0011863547,0.0054073976,0.0023920531,0.002259168,0.00071200245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006745549,0.00018420281,0.006288608,0.00016902419,0.0001336389,0.0004774405,0.00031012116,0.7482686,0.008675547,0.08769157,0.0073575424,0.1397691],"study_design_scores_gemma":[0.0000140029,0.000017352559,0.0000934261,0.0000049679425,0.000009793852,0.000026006384,0.000028512404,0.9639647,0.0016505257,0.033495154,0.0006893186,0.0000062312374],"about_ca_topic_score_codex":0.008781101,"about_ca_topic_score_gemma":0.01478952,"teacher_disagreement_score":0.008781101,"about_ca_system_score_codex":0.0009783328,"about_ca_system_score_gemma":0.0020008532,"threshold_uncertainty_score":0.017459989},"labels":[],"label_agreement":null},{"id":"W4401752666","doi":"10.1109/spcom60851.2024.10631618","title":"Spectral and Pitch Components of CQT Spectrum for Emotion Recognition","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministry of Electronics and Information technology","keywords":"Computer science; Speech recognition; Pattern recognition (psychology); Spectrum (functional analysis); Artificial intelligence; Physics","score_opus":0.03451748201890028,"score_gpt":0.2605222238690428,"score_spread":0.2260047418501425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401752666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31319147,0.0059561734,0.63517654,0.0009402822,0.001744908,0.00040834627,0.00775721,0.003413013,0.03141208],"genre_scores_gemma":[0.80320984,0.0032371525,0.17409503,0.0003350568,0.00060503214,0.00034784983,0.007673488,0.00036960474,0.010126953],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997223,0.000026216909,0.000017681348,0.00006763335,0.00012846825,0.00003776409],"domain_scores_gemma":[0.99963975,0.00007963447,0.000036317833,0.000046718666,0.00017611575,0.000021418839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030748587,0.0006942072,0.00035351646,0.0011229935,0.00020257744,0.0005916325,0.00030079094,0.00048061713,0.0062982887],"category_scores_gemma":[0.0015454568,0.00010975217,0.0004829637,0.0009746348,0.00022504611,0.0007359357,0.00040692944,0.0005279409,0.0032698167],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007233664,0.000120917444,0.006791198,0.00047093522,0.00007497718,0.00032056822,0.00015431103,0.0056415224,0.28580377,0.0018846735,0.013099206,0.68491447],"study_design_scores_gemma":[0.00012343167,0.000891909,0.17627881,0.0002886,0.00043060488,0.0031244745,0.00074765744,0.45568755,0.2582649,0.0072850604,0.09661882,0.00025815677],"about_ca_topic_score_codex":0.0015407328,"about_ca_topic_score_gemma":0.001794634,"teacher_disagreement_score":0.0062982887,"about_ca_system_score_codex":0.00020035551,"about_ca_system_score_gemma":0.00024487448,"threshold_uncertainty_score":0.021069825},"labels":[],"label_agreement":null},{"id":"W4401753977","doi":"10.1109/nano61778.2024.10628960","title":"Enhancement of a Stochastic-Computing Morphological Neural Network Through Approximate Adders","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Adder; Computer science; Stochastic computing; Artificial neural network; Stochastic neural network; Parallel computing; Theoretical computer science; Artificial intelligence; Time delay neural network; Telecommunications; Latency (audio)","score_opus":0.027759308274676636,"score_gpt":0.27989007995857396,"score_spread":0.2521307716838973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401753977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06440271,0.00032256753,0.9277127,0.0002173385,0.00009952636,0.00005057324,0.00006601758,0.0006234788,0.006505125],"genre_scores_gemma":[0.6444866,0.00027003145,0.3523166,0.00011214143,0.00003221331,0.000043211374,0.000058310576,0.000024824478,0.0026561564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998598,0.000019337353,0.000010930587,0.000020991602,0.000075607866,0.0000132842115],"domain_scores_gemma":[0.99979013,0.000058331967,0.000038539416,0.0000270651,0.000075620046,0.000010391556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027355758,0.00030133314,0.00023995027,0.0002742488,0.00023717193,0.00050392246,0.0007507431,0.00033526286,0.0013179654],"category_scores_gemma":[0.0006572282,0.00015878725,0.00024858132,0.00031376237,0.00022146093,0.0007077051,0.00029459147,0.00028110415,0.00023786674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002549281,0.00011915015,0.0009915993,0.0002839411,0.0000891592,0.00036099285,0.0000786957,0.37043524,0.2262917,0.05345747,0.0020357843,0.34560135],"study_design_scores_gemma":[0.0000081312255,0.00013460667,0.0002822334,0.000011761879,0.000028623606,0.00019351575,0.000007562471,0.95125395,0.041309234,0.0031763741,0.0035811977,0.000012864591],"about_ca_topic_score_codex":0.000656758,"about_ca_topic_score_gemma":0.0019506987,"teacher_disagreement_score":0.0013179654,"about_ca_system_score_codex":0.00038072056,"about_ca_system_score_gemma":0.0004883915,"threshold_uncertainty_score":0.004409075},"labels":[],"label_agreement":null},{"id":"W4401809337","doi":"10.1109/cvprw50498.2020.10645327","title":"Retraction Notice: FoNet: A Memory-efficient Fourier-based Orthogonal Network for Object Recognition","year":2020,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Notice; Computer science; Cognitive neuroscience of visual object recognition; Object (grammar); Artificial intelligence; Computer vision","score_opus":0.04616984897163188,"score_gpt":0.2601526964405105,"score_spread":0.2139828474688786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401809337","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00095899665,0.0041905553,0.00821179,0.13739227,0.8376553,0.000086967055,0.0019256516,0.001424739,0.008153669],"genre_scores_gemma":[0.02480561,0.010465696,0.021863183,0.08944217,0.45234174,0.0005014896,0.0067865876,0.0022413142,0.39155224],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99820125,0.00021784134,0.00020354835,0.00033622282,0.00085536245,0.00018571292],"domain_scores_gemma":[0.9921009,0.0022134122,0.000427264,0.00050287024,0.0039935224,0.0007619482],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0029127211,0.001503129,0.0008731409,0.0012780945,0.0011750287,0.0017176184,0.0026453075,0.0045738555,0.027968708],"category_scores_gemma":[0.025598457,0.00049709855,0.000727368,0.000910308,0.0017371931,0.0026530544,0.0014180102,0.010236072,0.02119019],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005110758,0.000009259467,0.000066577944,0.000067322544,0.000007805006,0.00010338114,0.00001530071,0.00009356977,0.00027737347,0.0017247617,0.9807638,0.016819797],"study_design_scores_gemma":[0.000042161,0.000046227673,0.0004310264,0.000074690586,0.000014344763,0.0002987871,0.00001596911,0.0010313382,0.0010083047,0.0015941256,0.9954184,0.00002464237],"about_ca_topic_score_codex":0.0057212096,"about_ca_topic_score_gemma":0.007516843,"teacher_disagreement_score":0.9954261,"about_ca_system_score_codex":0.0021971096,"about_ca_system_score_gemma":0.0027431496,"threshold_uncertainty_score":0.09356463},"labels":[{"model":"gemma","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4401875795","doi":"10.1190/gem2024-078.1","title":"An analytical study on the effect of inverse prediction based on two types of convolutional neural networks NestU-Net and ResU-Net++","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Net (polyhedron); Inverse; Computer science; Artificial neural network; Artificial intelligence; Mathematics","score_opus":0.01773050152836605,"score_gpt":0.286184655337143,"score_spread":0.26845415380877696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401875795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4756826,0.0013170353,0.51209736,0.0007981983,0.000121530626,0.00007411651,0.00013918201,0.001294107,0.008475937],"genre_scores_gemma":[0.9464073,0.00030268423,0.05099578,0.00008714189,0.000018272327,0.000040244213,0.000111980364,0.00007349008,0.0019632054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963665,0.00011208997,0.000014568216,0.000064931184,0.00009683885,0.00007488159],"domain_scores_gemma":[0.99647313,0.0024770405,0.00019335064,0.00021387765,0.00059178617,0.0000506873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020073443,0.00081616116,0.00033428686,0.0005364768,0.00031445565,0.0005016264,0.00093316,0.00066339097,0.0012840598],"category_scores_gemma":[0.008282914,0.0003062831,0.00040492922,0.00038897927,0.0007268653,0.001369238,0.00056872686,0.00078470074,0.00016466976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039235374,0.00010323888,0.006898798,0.000121533354,0.00009984379,0.000116549796,0.000059069185,0.8552107,0.010845458,0.006867218,0.0009547695,0.11833048],"study_design_scores_gemma":[0.000002632816,0.000036710637,0.0006889812,0.0000039028273,0.000010532459,0.000014723763,0.000006583907,0.99547404,0.0032051988,0.000447631,0.00010501622,0.000003998875],"about_ca_topic_score_codex":0.013569481,"about_ca_topic_score_gemma":0.01394302,"teacher_disagreement_score":0.013569481,"about_ca_system_score_codex":0.001108038,"about_ca_system_score_gemma":0.0008221611,"threshold_uncertainty_score":0.026980996},"labels":[],"label_agreement":null},{"id":"W4401921511","doi":"10.1142/s0219477525500099","title":"Fisher-Based Inaccuracy Information Measure","year":2024,"lang":"en","type":"article","venue":"Fluctuation and Noise Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Measure (data warehouse); Fisher information; Statistics; Statistical physics; Mathematics; Econometrics; Computer science; Physics; Data mining","score_opus":0.00943489920071296,"score_gpt":0.22123381895977534,"score_spread":0.21179891975906237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401921511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028173886,0.0013226445,0.96058685,0.00063542725,0.00014969442,0.00009459269,0.0006140734,0.00042228383,0.008000481],"genre_scores_gemma":[0.8377129,0.001445557,0.15399285,0.0004659864,0.00042987958,0.00029608796,0.0011070893,0.0002391657,0.0043105227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99249744,0.0012032553,0.00073346106,0.0011107367,0.003946583,0.0005085144],"domain_scores_gemma":[0.9767601,0.013627216,0.0028033229,0.0025722405,0.0037305863,0.0005065677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006299491,0.0014921629,0.0017815315,0.005855979,0.0012195389,0.0031601079,0.0021899024,0.002060096,0.0042777355],"category_scores_gemma":[0.04010203,0.0005864467,0.0011356794,0.0034909986,0.003193181,0.008223947,0.0033105467,0.0021319534,0.00074448466],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004174251,0.00015304143,0.018206047,0.0006290822,0.00038068602,0.00062791584,0.0006251952,0.30514082,0.0123976,0.49274662,0.0064411005,0.1622344],"study_design_scores_gemma":[0.00003164137,0.00018594769,0.007161874,0.00018168318,0.00012177652,0.0008293661,0.00017729077,0.55441755,0.008616887,0.4204489,0.00754676,0.0002803662],"about_ca_topic_score_codex":0.0023056418,"about_ca_topic_score_gemma":0.0010345407,"teacher_disagreement_score":0.006299491,"about_ca_system_score_codex":0.0026136655,"about_ca_system_score_gemma":0.0014709402,"threshold_uncertainty_score":0.0333153},"labels":[],"label_agreement":null},{"id":"W4401931949","doi":"10.1016/j.scib.2024.08.029","title":"Connectome-constrained neural decoding reveals a representational hierarchy from perception to cognition to action","year":2024,"lang":"en","type":"article","venue":"Science Bulletin","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Institut Universitaire de Gériatrie de Montréal; Montreal Neurological Institute and Hospital; McGill University","funders":"National Natural Science Foundation of China","keywords":"Cognition; Embodied cognition; Perception; Action (physics); Hierarchy; Neural decoding; Computer science; Sensory system; Decoding methods; Connectome; Working memory; Psychology; Cognitive psychology; Cognitive science; Neuroscience; Artificial intelligence; Functional connectivity","score_opus":0.03712553056474756,"score_gpt":0.3315914583568917,"score_spread":0.29446592779214414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401931949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45733377,0.00032050413,0.528862,0.0008514829,0.00009355523,0.00005806495,0.0021335108,0.0016118768,0.008735193],"genre_scores_gemma":[0.98219323,0.00010612213,0.015398917,0.000060446564,0.000012880706,0.000022686967,0.00059515156,0.00012730551,0.0014832121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999008,0.000015851338,0.0000044008716,0.000039533843,0.000019437697,0.000019894605],"domain_scores_gemma":[0.9998288,0.00006002282,0.000023216613,0.00003127028,0.000034729368,0.000021901908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022562707,0.00041624365,0.0002860408,0.000409352,0.00017703357,0.00078530365,0.0003889522,0.00047467372,0.002610206],"category_scores_gemma":[0.0016845295,0.00027177294,0.0003613948,0.00051662547,0.000353872,0.0010235021,0.00051005784,0.0009317851,0.00038086233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034549693,0.00015617258,0.0152757615,0.0002714394,0.0003523075,0.00057892496,0.00046481917,0.44575903,0.29630366,0.049029548,0.006857165,0.18460575],"study_design_scores_gemma":[0.000010432294,0.0000474235,0.026644334,0.000021017617,0.000042208387,0.00013358484,0.000052250693,0.90807855,0.013312623,0.050306264,0.0013217636,0.00002952871],"about_ca_topic_score_codex":0.0065411367,"about_ca_topic_score_gemma":0.009726882,"teacher_disagreement_score":0.0065411367,"about_ca_system_score_codex":0.0005005801,"about_ca_system_score_gemma":0.00059758907,"threshold_uncertainty_score":0.013006091},"labels":[],"label_agreement":null},{"id":"W4401935122","doi":"10.1109/tfuzz.2024.3450000","title":"FSCNN: Fuzzy Channel Filter-Based Separable Convolution Neural Networks for Medical Imaging Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea; National Natural Science Foundation of China","keywords":"Convolution (computer science); Separable space; Artificial intelligence; Computer science; Medical imaging; Pattern recognition (psychology); Artificial neural network; Channel (broadcasting); Fuzzy logic; Filtering theory; Filter (signal processing); Computer vision; Mathematics; Telecommunications","score_opus":0.024596949565174452,"score_gpt":0.2627372034576854,"score_spread":0.23814025389251098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401935122","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018428436,0.0008523651,0.9766663,0.00025602407,0.000110557885,0.000053935542,0.00021166015,0.0013703412,0.0020504156],"genre_scores_gemma":[0.5300607,0.0011975641,0.45836347,0.00049646717,0.00014521909,0.00021143927,0.0010819905,0.0001682235,0.008274844],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997588,0.000037514157,0.000014254365,0.000057436057,0.00009495097,0.000037000136],"domain_scores_gemma":[0.99971646,0.0000940586,0.000025982537,0.000036536432,0.00010865456,0.000018211567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006721766,0.00066926813,0.0005471642,0.00055731885,0.00022319854,0.0004543915,0.0010837937,0.00085386174,0.0019055723],"category_scores_gemma":[0.0015344577,0.00023603225,0.00058819127,0.0005212367,0.00045515576,0.00085061835,0.00059755455,0.0009573705,0.0005664126],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028570346,0.00010961114,0.0015539466,0.00014000398,0.00014457223,0.00013662639,0.00006327027,0.3949542,0.020393435,0.012524631,0.008965379,0.56072867],"study_design_scores_gemma":[0.0000052575997,0.000020814172,0.0001910106,0.000006918014,0.000009856045,0.000034945915,0.0000029865962,0.99318767,0.0028003468,0.0027052236,0.0010281304,0.000006869135],"about_ca_topic_score_codex":0.01170216,"about_ca_topic_score_gemma":0.013786208,"teacher_disagreement_score":0.01170216,"about_ca_system_score_codex":0.0008264828,"about_ca_system_score_gemma":0.0011687713,"threshold_uncertainty_score":0.023268044},"labels":[],"label_agreement":null},{"id":"W4401954518","doi":"10.18280/mmep.110825","title":"The Degree of Best Downsampled Convolutional Neural Network Approximation in Terms of Orlicz Modulus of Smoothness","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Degree (music); Smoothness; Mathematics; Overfitting; Convolution (computer science); Artificial neural network; Inverse; Applied mathematics; Mathematical analysis; Computer science; Artificial intelligence; Physics; Geometry","score_opus":0.044790493061636606,"score_gpt":0.22726384685407794,"score_spread":0.18247335379244134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401954518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64684516,0.001816129,0.3335325,0.0029784779,0.00029635985,0.00004460458,0.00051027554,0.000582576,0.013394006],"genre_scores_gemma":[0.9718015,0.00065892184,0.024760505,0.00016188204,0.00010369039,0.000026652633,0.00014321589,0.00016249191,0.0021812348],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99910945,0.00025820438,0.000059158185,0.00019846667,0.00024428123,0.00013039593],"domain_scores_gemma":[0.99212,0.004514535,0.000896171,0.0010020332,0.00078151264,0.00068573485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027767613,0.0008230167,0.0010239864,0.0013043216,0.00063496846,0.0024856937,0.0011883621,0.0016721572,0.0031618627],"category_scores_gemma":[0.019262407,0.0005788195,0.00059731,0.00059202005,0.0023468148,0.0044434494,0.0023705864,0.0022837278,0.00043380575],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018737522,0.00016605471,0.01214979,0.0006879765,0.00019997184,0.00051179144,0.00057047274,0.32393998,0.067134276,0.5154803,0.0041998588,0.07308579],"study_design_scores_gemma":[0.000038411694,0.00021411004,0.0049277614,0.00011834004,0.000060743594,0.0003209431,0.00015144586,0.8290719,0.01910883,0.14346318,0.002437271,0.000087131084],"about_ca_topic_score_codex":0.0010702524,"about_ca_topic_score_gemma":0.0009513724,"teacher_disagreement_score":0.0031618627,"about_ca_system_score_codex":0.0011904796,"about_ca_system_score_gemma":0.00082248624,"threshold_uncertainty_score":0.014685094},"labels":[],"label_agreement":null},{"id":"W4402029539","doi":"10.1007/978-3-031-70368-3_16","title":"Simultaneous Linear Connectivity of Neural Networks Modulo Permutation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Google (Canada); Mila - Quebec Artificial Intelligence Institute; Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Modulo; Permutation (music); Artificial neural network; Algorithm; Theoretical computer science; Artificial intelligence; Discrete mathematics; Mathematics","score_opus":0.013001490242660235,"score_gpt":0.25164923161420083,"score_spread":0.2386477413715406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402029539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09499207,0.0014960837,0.78934795,0.00065229594,0.0003164418,0.00009685828,0.00027307175,0.001147044,0.111678146],"genre_scores_gemma":[0.8738705,0.0013797658,0.08565393,0.00023465896,0.00032571994,0.00019979049,0.00031491555,0.00028801235,0.037732646],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997795,0.000061982115,0.000010759603,0.00006593144,0.000045856992,0.000035932484],"domain_scores_gemma":[0.99951684,0.0002491357,0.00005553458,0.00010093465,0.000044071454,0.00003350427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021698841,0.0004085674,0.00038125136,0.00045533458,0.0003817309,0.0007387615,0.0008740845,0.0005094711,0.010716094],"category_scores_gemma":[0.0013314532,0.0003330034,0.00043604584,0.0007221579,0.0007342492,0.0018227092,0.0012720888,0.0009785934,0.001314699],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119864235,0.00004090835,0.00018697217,0.0002301649,0.000044015593,0.00026280817,0.0001584482,0.043609355,0.014023275,0.80702084,0.00698338,0.12731996],"study_design_scores_gemma":[0.000030882624,0.000085637126,0.00031918188,0.000027225324,0.000024125362,0.00023037283,0.000044851797,0.18856715,0.0051559084,0.79755783,0.007941249,0.000015564328],"about_ca_topic_score_codex":0.0005085148,"about_ca_topic_score_gemma":0.00092571165,"teacher_disagreement_score":0.010716094,"about_ca_system_score_codex":0.00036629557,"about_ca_system_score_gemma":0.0002878247,"threshold_uncertainty_score":0.035848856},"labels":[],"label_agreement":null},{"id":"W4402187256","doi":"10.1109/tvt.2024.3454574","title":"Hierarchical Transformers for Motion Forecasting Based on Inverse Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Reinforcement learning; Transformer; Artificial intelligence; Computer science; Engineering; Machine learning; Electrical engineering; Voltage","score_opus":0.02042884831216716,"score_gpt":0.24657343336356205,"score_spread":0.2261445850513949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402187256","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01880661,0.000121371275,0.97916836,0.00011549398,0.000024335146,0.00003404307,0.00009100255,0.00056987524,0.0010688223],"genre_scores_gemma":[0.91134655,0.00011788818,0.08671251,0.000097385695,0.000024202964,0.000094274656,0.0002441228,0.00007430939,0.0012887114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971837,0.00006773069,0.000015078998,0.00008307184,0.00007180169,0.0000439682],"domain_scores_gemma":[0.99915755,0.00048488387,0.0000946586,0.00007649985,0.00012717678,0.000059226008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070010405,0.00067433255,0.0007356492,0.0006067184,0.00027398329,0.00053818844,0.00133313,0.00067191594,0.0020766745],"category_scores_gemma":[0.0032411024,0.00037909855,0.00058699807,0.00046139912,0.0007063726,0.0011377243,0.000882022,0.0011329273,0.0003217462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000476814,0.000027223386,0.0008178385,0.000024534778,0.000012357279,0.000037560214,0.000042462216,0.9551741,0.0011353652,0.0055316193,0.00056840584,0.036580857],"study_design_scores_gemma":[0.0000017813334,0.0000065551258,0.00003263969,0.0000010858522,0.000001097633,0.000003416494,0.0000018759376,0.99832433,0.00012645613,0.0014417283,0.00005767749,0.0000013786229],"about_ca_topic_score_codex":0.01343547,"about_ca_topic_score_gemma":0.01233656,"teacher_disagreement_score":0.01343547,"about_ca_system_score_codex":0.0010460941,"about_ca_system_score_gemma":0.001046642,"threshold_uncertainty_score":0.026714563},"labels":[],"label_agreement":null},{"id":"W4402189021","doi":"10.32920/26866540.v1","title":"DENL: Diverse Ensemble and Noisy Logits for Improved Robustness of Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Robustness (evolution); Artificial neural network; Computer science; Artificial intelligence; Deep neural networks; Machine learning; Biology","score_opus":0.028636098377503468,"score_gpt":0.2685019897753963,"score_spread":0.23986589139789283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402189021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081696965,0.0008952779,0.9106814,0.00063494395,0.00013490325,0.000056617093,0.00030041634,0.0032199265,0.0023795066],"genre_scores_gemma":[0.8473868,0.00031406022,0.14659812,0.00043613464,0.00010021171,0.00014097456,0.00089307554,0.00034394124,0.003786729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986971,0.00044831753,0.00006823094,0.00030840255,0.00033055476,0.00014740384],"domain_scores_gemma":[0.9971283,0.0013507834,0.00025868343,0.0005998651,0.00052945025,0.00013298179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034839027,0.001433352,0.0014755685,0.0009824862,0.0007250962,0.0010428369,0.002509658,0.0014302472,0.0016983772],"category_scores_gemma":[0.00923293,0.00072264526,0.00096930494,0.0005606095,0.001020705,0.0024017252,0.0034822908,0.0029358924,0.0005753244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001302535,0.0000681308,0.0013366047,0.000036544763,0.00006862954,0.00006734495,0.000058568814,0.9283382,0.0022007432,0.003648555,0.0014084474,0.062638044],"study_design_scores_gemma":[0.0000034908676,0.000021267984,0.000090361995,0.0000039654615,0.0000045738348,0.000008809825,0.0000040648183,0.9971011,0.00086161395,0.0017012628,0.00019500332,0.0000043167056],"about_ca_topic_score_codex":0.004210223,"about_ca_topic_score_gemma":0.0053436034,"teacher_disagreement_score":0.004210223,"about_ca_system_score_codex":0.0013786631,"about_ca_system_score_gemma":0.00086353254,"threshold_uncertainty_score":0.018424869},"labels":[],"label_agreement":null},{"id":"W4402191225","doi":"10.32920/26866540","title":"DENL: Diverse Ensemble and Noisy Logits for Improved Robustness of Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Robustness (evolution); Computer science; Artificial neural network; Deep neural networks; Artificial intelligence; Machine learning; Chemistry","score_opus":0.028636098377503468,"score_gpt":0.2685019897753963,"score_spread":0.23986589139789283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402191225","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081696965,0.0008952779,0.9106814,0.00063494395,0.00013490325,0.000056617093,0.00030041634,0.0032199265,0.0023795066],"genre_scores_gemma":[0.8473868,0.00031406022,0.14659812,0.00043613464,0.00010021171,0.00014097456,0.00089307554,0.00034394124,0.003786729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986971,0.00044831753,0.00006823094,0.00030840255,0.00033055476,0.00014740384],"domain_scores_gemma":[0.9971283,0.0013507834,0.00025868343,0.0005998651,0.00052945025,0.00013298179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034839027,0.001433352,0.0014755685,0.0009824862,0.0007250962,0.0010428369,0.002509658,0.0014302472,0.0016983772],"category_scores_gemma":[0.00923293,0.00072264526,0.00096930494,0.0005606095,0.001020705,0.0024017252,0.0034822908,0.0029358924,0.0005753244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001302535,0.0000681308,0.0013366047,0.000036544763,0.00006862954,0.00006734495,0.000058568814,0.9283382,0.0022007432,0.003648555,0.0014084474,0.062638044],"study_design_scores_gemma":[0.0000034908676,0.000021267984,0.000090361995,0.0000039654615,0.0000045738348,0.000008809825,0.0000040648183,0.9971011,0.00086161395,0.0017012628,0.00019500332,0.0000043167056],"about_ca_topic_score_codex":0.004210223,"about_ca_topic_score_gemma":0.0053436034,"teacher_disagreement_score":0.004210223,"about_ca_system_score_codex":0.0013786631,"about_ca_system_score_gemma":0.00086353254,"threshold_uncertainty_score":0.018424869},"labels":[],"label_agreement":null},{"id":"W4402216363","doi":"10.1109/tfuzz.2024.3454335","title":"Detecting Characteristic Points for the Analysis of Bioimpedance Signal Through a Synergy of Fuzzy Rule-Based Models and Granular Neural Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Recruitment Program of Global Experts; Education Department of Shaanxi Province; National Natural Science Foundation of China","keywords":"Artificial neural network; Signal processing; Fuzzy logic; Computer science; SIGNAL (programming language); Artificial intelligence; Fuzzy set; Pattern recognition (psychology); Data mining; Digital signal processing","score_opus":0.02735054722139579,"score_gpt":0.2574809060619461,"score_spread":0.23013035884055033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402216363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012102373,0.00015266395,0.98705184,0.000028581679,0.000012933897,0.000021328684,0.00002422875,0.00016191158,0.00044406738],"genre_scores_gemma":[0.56694865,0.00035804856,0.4317519,0.0000548316,0.000029588628,0.00009235382,0.0001122919,0.000040649345,0.00061162654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941516,0.00009283626,0.000055497483,0.00014673072,0.0002467292,0.00004302753],"domain_scores_gemma":[0.9988084,0.00046333447,0.00026196553,0.00021960278,0.00020782772,0.00003896311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001040887,0.0008430588,0.00081497134,0.0017198053,0.00035462022,0.0014716458,0.0010812198,0.0009381089,0.0006146283],"category_scores_gemma":[0.0032587626,0.00036597133,0.0005511459,0.0010818157,0.0006845294,0.0014758995,0.0008918509,0.00092701917,0.00023749121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002342483,0.000106159394,0.0043352703,0.00032480806,0.00013190725,0.00042771565,0.00033406986,0.56184816,0.03977264,0.018071206,0.00073473586,0.37367913],"study_design_scores_gemma":[0.0000047956205,0.000030425848,0.0008267638,0.00002105024,0.000019037778,0.00008418772,0.00003850353,0.98679197,0.0040373816,0.0076903845,0.00043517334,0.000020409754],"about_ca_topic_score_codex":0.0026004952,"about_ca_topic_score_gemma":0.002717229,"teacher_disagreement_score":0.0026004952,"about_ca_system_score_codex":0.0005285907,"about_ca_system_score_gemma":0.00058072264,"threshold_uncertainty_score":0.0055048466},"labels":[],"label_agreement":null},{"id":"W4402259427","doi":"10.1109/tnnls.2024.3449443","title":"Explainable Fault Diagnosis Using Invertible Neural Networks—A Left Manifold-Based Solution","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Science and Technology Major Project; State Key Laboratory of Industrial Control Technology; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Invertible matrix; Manifold (fluid mechanics); Fault (geology); Artificial neural network; Computer science; Mathematics; Artificial intelligence; Pure mathematics; Geology; Engineering; Mechanical engineering; Seismology","score_opus":0.02270094770682286,"score_gpt":0.24571890557666937,"score_spread":0.22301795786984652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402259427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006385724,0.000119275086,0.99213713,0.0002035655,0.000016656853,0.000011480865,0.000011890511,0.00009601642,0.0010181246],"genre_scores_gemma":[0.69430226,0.00042081185,0.3008517,0.00018259588,0.000098066484,0.00007242971,0.00008374371,0.00006218783,0.003926185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998011,0.00005077754,0.000010867209,0.000059986538,0.00005168801,0.000025639327],"domain_scores_gemma":[0.99961346,0.00019445189,0.00006086482,0.000056473247,0.00005833896,0.000016314472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047496977,0.000501035,0.00043865482,0.0004874225,0.0003018291,0.000556786,0.00070185994,0.00089553633,0.0015409521],"category_scores_gemma":[0.001473578,0.00018627247,0.0005278345,0.0002671222,0.00076349964,0.0008530892,0.0011709409,0.0010341333,0.00014448646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007558802,0.000036113976,0.001032497,0.00013276756,0.00005579617,0.0002391445,0.00023013628,0.71208715,0.008311076,0.117484845,0.0011606689,0.15915422],"study_design_scores_gemma":[0.0000025862778,0.000018336752,0.00008545713,0.0000054201582,0.0000042394836,0.000029291574,0.000009924556,0.9801308,0.0010017918,0.018024616,0.0006825258,0.0000050480107],"about_ca_topic_score_codex":0.0020771122,"about_ca_topic_score_gemma":0.00172792,"teacher_disagreement_score":0.0020771122,"about_ca_system_score_codex":0.000642151,"about_ca_system_score_gemma":0.000594627,"threshold_uncertainty_score":0.005155027},"labels":[],"label_agreement":null},{"id":"W4402260379","doi":"10.62973/09-012","title":"OWS-6 Symbology-Encoding Harmonization ER","year":2009,"lang":"en","type":"report","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense; Natural Resources Canada; U.S. Geological Survey; U.S. Department of Homeland Security","keywords":"Harmonization; Encoding (memory); Computer science; Computer graphics (images); Artificial intelligence; Art; Aesthetics","score_opus":0.046955719558149964,"score_gpt":0.30005255787721274,"score_spread":0.25309683831906277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402260379","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007403536,0.0004471781,0.6868778,0.0030407936,0.0038179017,0.0010795256,0.022553716,0.11563943,0.15914018],"genre_scores_gemma":[0.09530811,0.0010716725,0.5385549,0.0030390648,0.0008574676,0.0017451224,0.10542218,0.046568286,0.20743315],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963903,0.00070483395,0.0004826913,0.0005022655,0.001426033,0.0004937993],"domain_scores_gemma":[0.99653804,0.00036846157,0.00016299037,0.0015224812,0.0012335533,0.00017445904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031185432,0.001091319,0.000971627,0.0018829,0.0013378419,0.0044201617,0.0027843306,0.0018208555,0.08997646],"category_scores_gemma":[0.00781356,0.00068019255,0.0010914234,0.0020892452,0.0013364549,0.0051089185,0.0045532775,0.0025318796,0.06298371],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001179114,0.00020730024,0.00078258617,0.00040741824,0.00005797769,0.00032362193,0.0005509044,0.0033693444,0.012173195,0.1779196,0.58368325,0.21934572],"study_design_scores_gemma":[0.000104017396,0.0000784916,0.0003084454,0.00009477199,0.00002121108,0.00020743087,0.00015995954,0.007901637,0.019493362,0.024874449,0.9466993,0.000057035115],"about_ca_topic_score_codex":0.0040457863,"about_ca_topic_score_gemma":0.003068887,"teacher_disagreement_score":0.08997646,"about_ca_system_score_codex":0.0016346237,"about_ca_system_score_gemma":0.0036478043,"threshold_uncertainty_score":0.30100125},"labels":[],"label_agreement":null},{"id":"W4402261780","doi":"10.23919/acc60939.2024.10644247","title":"Compositionally Verifiable Vector Neural Lyapunov Functions for Stability Analysis of Interconnected Nonlinear Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nonlinear system; Verifiable secret sharing; Control theory (sociology); Lyapunov function; Stability (learning theory); Computer science; Artificial neural network; Mathematics; Artificial intelligence; Physics; Control (management); Machine learning","score_opus":0.024448936502784898,"score_gpt":0.2657287602124139,"score_spread":0.24127982370962897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402261780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03789311,0.000067913,0.9595354,0.00009881642,0.000015892485,0.00005769361,0.000054640142,0.0004791462,0.0017973281],"genre_scores_gemma":[0.87078285,0.00008402939,0.12811707,0.00004580461,0.00000929588,0.000099011384,0.0001157949,0.00005348614,0.00069258735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99919075,0.00025030784,0.000058321242,0.00009681569,0.00033850555,0.00006524393],"domain_scores_gemma":[0.9977221,0.001185305,0.00030811114,0.0003144297,0.00043102633,0.000039016242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018888185,0.0007306667,0.0004489686,0.0004415523,0.00047466758,0.0007805311,0.0007440446,0.0005350436,0.0026787822],"category_scores_gemma":[0.007154143,0.0003213477,0.0005304754,0.00026134576,0.0012576554,0.0015426523,0.0014217421,0.0011109874,0.00034119535],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013428018,0.00006506638,0.0011807994,0.00022323278,0.000057219328,0.00019471617,0.00018051926,0.7961137,0.024423419,0.12787348,0.0004383329,0.04911529],"study_design_scores_gemma":[0.0000062904187,0.000025541418,0.000064957676,0.000011835369,0.0000043719056,0.00001104816,0.000015588399,0.9735405,0.003982849,0.0221298,0.00020231736,0.0000048745346],"about_ca_topic_score_codex":0.0021014682,"about_ca_topic_score_gemma":0.0037791312,"teacher_disagreement_score":0.0026787822,"about_ca_system_score_codex":0.0007786642,"about_ca_system_score_gemma":0.0016851969,"threshold_uncertainty_score":0.009989083},"labels":[],"label_agreement":null},{"id":"W4402306980","doi":"10.18280/ts.410442","title":"Performance Analysis of Hybrid – BCI Signals Using CNN for Motor Movement Classification","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Brain–computer interface; Movement (music); Computer science; Speech recognition; Motor imagery; Artificial intelligence; Pattern recognition (psychology); Electroencephalography; Psychology; Neuroscience; Acoustics; Physics","score_opus":0.049389881820638415,"score_gpt":0.2873874282148783,"score_spread":0.23799754639423987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402306980","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92001665,0.0027784517,0.06420114,0.000288138,0.00024747138,0.000056705227,0.0010093198,0.0013018795,0.010100214],"genre_scores_gemma":[0.9874254,0.00026081465,0.007951747,0.000044853376,0.000025772237,0.000021425229,0.00072844117,0.000030823274,0.0035107564],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997676,0.000028114824,0.000015561673,0.00005006215,0.000081335034,0.000057350466],"domain_scores_gemma":[0.99955803,0.00015480332,0.000032769156,0.000034491386,0.00019975672,0.000020203093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004323386,0.00062997197,0.00038075866,0.00045953758,0.0001875308,0.00045584203,0.0004033279,0.00048240027,0.0025916141],"category_scores_gemma":[0.001268478,0.00012489513,0.00028036462,0.00038132898,0.00012375138,0.0002800913,0.0002688958,0.0002894055,0.00052552647],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028568422,0.00035333974,0.020331616,0.00044097664,0.00050444994,0.00028838462,0.00007617079,0.17924267,0.094861224,0.0008365258,0.005586541,0.6946214],"study_design_scores_gemma":[0.0000138276855,0.00024404968,0.019028699,0.000017713022,0.000081659826,0.000119457756,0.000020532381,0.95714444,0.022417517,0.00013580792,0.00076190825,0.000014446487],"about_ca_topic_score_codex":0.019784637,"about_ca_topic_score_gemma":0.020424712,"teacher_disagreement_score":0.019784637,"about_ca_system_score_codex":0.00051302556,"about_ca_system_score_gemma":0.00039973445,"threshold_uncertainty_score":0.039338946},"labels":[],"label_agreement":null},{"id":"W4402401721","doi":"10.1109/tnano.2024.3457533","title":"A Review of Ising Machines Implemented in Conventional and Emerging Technologies","year":2024,"lang":"en","type":"review","venue":"IEEE Transactions on Nanotechnology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ising model; Computer science; Statistical physics; Physics","score_opus":0.03323785182795081,"score_gpt":0.34837671216179555,"score_spread":0.31513886033384475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402401721","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022953383,0.99446625,0.000888278,0.00023954258,0.00031943686,0.000008669606,0.000025458728,0.000016937624,0.0038059354],"genre_scores_gemma":[0.0014292083,0.99567103,0.0009672883,0.00022115577,0.00029202222,0.000014302924,0.000041381154,0.0000049635582,0.0013586415],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997104,0.000044182096,0.000041175565,0.00005597564,0.000121374695,0.00002684929],"domain_scores_gemma":[0.9996432,0.0001815375,0.000041291074,0.00001655583,0.00009852757,0.000018867997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043908806,0.0010638336,0.0010111,0.0027512475,0.0004133772,0.0009822283,0.0010586496,0.0011839935,0.0038808251],"category_scores_gemma":[0.00089346693,0.00053300883,0.0005367369,0.0035349256,0.00054207956,0.0021410985,0.00059043546,0.001493231,0.0023702031],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056445097,0.000106435386,0.00023808281,0.026723873,0.00007831589,0.00024646564,0.00012584416,0.0017339941,0.0029738024,0.035038255,0.041257937,0.89142054],"study_design_scores_gemma":[0.000005772585,0.000085902044,0.00040399554,0.0030637365,0.00004803618,0.00065106846,0.00003612128,0.0003277791,0.00075007847,0.00476695,0.989838,0.000022464625],"about_ca_topic_score_codex":0.0011775723,"about_ca_topic_score_gemma":0.001418302,"teacher_disagreement_score":0.0038808251,"about_ca_system_score_codex":0.00070608826,"about_ca_system_score_gemma":0.0011558743,"threshold_uncertainty_score":0.0129826665},"labels":[],"label_agreement":null},{"id":"W4402452664","doi":"10.11159/mvml24.113","title":"Classification of complex network using bidirectional LSTM","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Secretaría de Investigación y Posgrado, Instituto Politécnico Nacional; Instituto Politécnico Nacional","keywords":"Computer science; Artificial intelligence; Complex network; World Wide Web","score_opus":0.020709228132000882,"score_gpt":0.2407346902942144,"score_spread":0.22002546216221353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402452664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1775511,0.0006078907,0.81091654,0.0006068264,0.00022425508,0.00009161621,0.00085170683,0.003319822,0.0058301487],"genre_scores_gemma":[0.8976876,0.00028425304,0.09608919,0.00012820183,0.000074900796,0.00007891616,0.0010763637,0.00013534176,0.0044452082],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998424,0.000023793853,0.000008468924,0.000060109163,0.000033453172,0.00003168739],"domain_scores_gemma":[0.99967706,0.00009820341,0.0000605826,0.000041727133,0.00009207163,0.000030314635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035023704,0.00074540253,0.00041501856,0.0010389552,0.00029988153,0.00075113494,0.000567591,0.0006636382,0.0032079206],"category_scores_gemma":[0.0012542133,0.00021877376,0.0005529789,0.0008021291,0.000323148,0.0011410508,0.0006839771,0.0008662319,0.0008241357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003665699,0.00018655443,0.007234833,0.00018766131,0.000101255144,0.0004024689,0.00018733185,0.39204106,0.040726162,0.01368463,0.0101979105,0.5346836],"study_design_scores_gemma":[0.0000015617517,0.000009984497,0.00035302897,0.0000036799986,0.0000035555595,0.000013937116,0.000009987582,0.9950427,0.0013765584,0.0028618278,0.0003198623,0.0000032771845],"about_ca_topic_score_codex":0.0040114666,"about_ca_topic_score_gemma":0.0048953164,"teacher_disagreement_score":0.0040114666,"about_ca_system_score_codex":0.00063204166,"about_ca_system_score_gemma":0.00038417158,"threshold_uncertainty_score":0.010731578},"labels":[],"label_agreement":null},{"id":"W4402471162","doi":"10.31219/osf.io/d97b3","title":"Backpropagation and Optimization in Deep Learning: Tutorial and Survey","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backpropagation; Gradient descent; Stochastic gradient descent; Artificial neural network; Convergence (economics); Computer science; Parameterized complexity; Mathematical optimization; Minification; Stochastic optimization; Method of steepest descent; Deep learning; Momentum (technical analysis); Artificial intelligence; Algorithm; Mathematics","score_opus":0.01936290719349731,"score_gpt":0.25646134455794783,"score_spread":0.23709843736445052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402471162","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017214627,0.59735954,0.37480476,0.0024186838,0.0027390884,0.00007770888,0.0004294786,0.00079859485,0.019650683],"genre_scores_gemma":[0.027224094,0.7562646,0.18178876,0.0022071379,0.006761346,0.00031341717,0.0014119175,0.00084794627,0.023180867],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989807,0.00018143935,0.000104246814,0.0002215194,0.00045594535,0.000056133038],"domain_scores_gemma":[0.9990891,0.0005880408,0.000050710325,0.00007898109,0.00015665498,0.000036543603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013492543,0.002148848,0.001795786,0.0019211656,0.00038110605,0.001935135,0.0013844359,0.0022786593,0.007796581],"category_scores_gemma":[0.0032371846,0.0012092217,0.0009344015,0.006259002,0.0012875176,0.0042818254,0.0015959988,0.0039801374,0.0059180693],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007060145,0.00015634096,0.00065027585,0.004322795,0.00015347476,0.00014295179,0.00014601363,0.025214542,0.0016948597,0.11589168,0.07231988,0.7792365],"study_design_scores_gemma":[0.000029424033,0.00014141934,0.0011226141,0.0015148058,0.00008810267,0.0007064321,0.000062671555,0.05846198,0.0031839036,0.22559707,0.7089912,0.00010028658],"about_ca_topic_score_codex":0.0016067522,"about_ca_topic_score_gemma":0.0013466887,"teacher_disagreement_score":0.007796581,"about_ca_system_score_codex":0.0009853528,"about_ca_system_score_gemma":0.0013364339,"threshold_uncertainty_score":0.026082158},"labels":[],"label_agreement":null},{"id":"W4402474358","doi":"10.1109/tcsii.2024.3459091","title":"Closed-Form Expressions for the Input Impedance of Some 2-D Fractal Circuit Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; McMaster University","funders":"","keywords":"Fractal; Electrical impedance; Mathematics; Topology (electrical circuits); Computer science; Mathematical analysis; Electrical engineering; Engineering; Combinatorics","score_opus":0.024400538819936993,"score_gpt":0.2576076978664309,"score_spread":0.23320715904649392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039839003,0.00075158564,0.9333262,0.0002367334,0.000051170926,0.000040140625,0.00017319719,0.00028810903,0.025293836],"genre_scores_gemma":[0.8353217,0.0016642093,0.14596537,0.00024158247,0.00007228836,0.00020485072,0.00022684272,0.00013801364,0.016165087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9999057,0.000019842842,0.000005931002,0.00001726011,0.000039022394,0.000012236659],"domain_scores_gemma":[0.99974376,0.00013138285,0.00003116961,0.000019456213,0.00006335513,0.000010725768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028638626,0.00048977404,0.00029372127,0.00064485386,0.0002470267,0.00073839125,0.00049245945,0.0007915274,0.0032493656],"category_scores_gemma":[0.0013416448,0.00020372492,0.00033572968,0.00046036998,0.00044595663,0.0010859971,0.00035744996,0.00046974825,0.0005276738],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041709784,0.000040235118,0.000637742,0.00022137802,0.000024707988,0.00045773495,0.0003855566,0.39978945,0.030684248,0.52101207,0.0025259631,0.04417915],"study_design_scores_gemma":[0.0000057308534,0.00001954603,0.00028077103,0.000025584504,0.0000069555144,0.00020598812,0.00003673797,0.91616935,0.0020087436,0.07834883,0.0028757905,0.0000158692],"about_ca_topic_score_codex":0.00049902627,"about_ca_topic_score_gemma":0.0008973466,"teacher_disagreement_score":0.0032493656,"about_ca_system_score_codex":0.000719,"about_ca_system_score_gemma":0.00018265234,"threshold_uncertainty_score":0.010870278},"labels":[],"label_agreement":null},{"id":"W4402511305","doi":"10.1101/2024.09.09.611671","title":"Burst firing optimizes invariant coding of natural communication signals by electrosensory neural populations","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Coding (social sciences); Invariant (physics); Neural coding; Computer science; Artificial intelligence; Mathematics; Statistics","score_opus":0.0220284571922487,"score_gpt":0.24429944946888657,"score_spread":0.22227099227663788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402511305","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8281059,0.00008659665,0.1698586,0.00007821752,0.000013708141,0.00001388626,0.000051775693,0.00013018641,0.0016609885],"genre_scores_gemma":[0.9941906,0.000021061718,0.0055054864,0.000010914615,0.0000040045966,0.000008033325,0.000017506578,0.000019390976,0.00022302674],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99992,0.000018417342,0.000006571385,0.000022206488,0.000015622145,0.000017206597],"domain_scores_gemma":[0.99967587,0.00013397042,0.00007612475,0.00003558109,0.000036164925,0.000042359785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000269774,0.00016935744,0.00020942173,0.00017645367,0.000110809546,0.00036820682,0.00029553895,0.00017833643,0.0006743184],"category_scores_gemma":[0.0010443735,0.00012233885,0.0001716843,0.00013121685,0.00036705413,0.00035303988,0.00041140823,0.00026478234,0.00007507619],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000351586,0.00009855636,0.005454567,0.00008565486,0.000067812936,0.0001598904,0.00019448521,0.116949774,0.8110615,0.020194603,0.00039152053,0.044990167],"study_design_scores_gemma":[0.000020703888,0.00015846793,0.009912537,0.000008191136,0.000025778843,0.00012054534,0.000059162296,0.92780626,0.04601185,0.0154605815,0.0003980674,0.000017914032],"about_ca_topic_score_codex":0.00035542488,"about_ca_topic_score_gemma":0.0003705104,"teacher_disagreement_score":0.0006743184,"about_ca_system_score_codex":0.0003520175,"about_ca_system_score_gemma":0.00014220875,"threshold_uncertainty_score":0.0025539994},"labels":[],"label_agreement":null},{"id":"W4402659322","doi":"","title":"Fast analytical method to integrate multivariate Gaussians over hidden variables","year":2024,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Multivariate statistics; Computer science; Mixture model; Multivariate analysis; Econometrics; Statistics; Mathematics; Artificial intelligence","score_opus":0.018194059560990825,"score_gpt":0.2818958697680698,"score_spread":0.26370181020707895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402659322","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011901698,0.00011120788,0.9974898,0.000057486614,0.00010316218,0.000011852234,0.000020386886,0.00024341352,0.0007726052],"genre_scores_gemma":[0.10242826,0.0007981753,0.8734052,0.00025791733,0.00034381726,0.00021277215,0.00017609546,0.0009650859,0.021412661],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99958485,0.00014409251,0.000020296022,0.00004692417,0.0001655972,0.00003825397],"domain_scores_gemma":[0.9982901,0.001014061,0.000063748674,0.0001513912,0.0003904257,0.00009013294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015840551,0.0010799807,0.0008408453,0.0011366572,0.00055828964,0.0011493849,0.0015619433,0.0012429352,0.007652892],"category_scores_gemma":[0.0054914667,0.0005411178,0.0011146803,0.0011288552,0.0008830917,0.0016439486,0.0015514407,0.002291907,0.0024948427],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001502517,0.00009951205,0.000457525,0.00033405222,0.00016478144,0.00035448902,0.0003407568,0.197334,0.012382364,0.5369431,0.011407847,0.24003136],"study_design_scores_gemma":[0.000011901962,0.000011956171,0.00007564781,0.000014356562,0.000014774338,0.000059259382,0.000017482227,0.9313871,0.0012722492,0.06307708,0.0040425127,0.000015659762],"about_ca_topic_score_codex":0.0033242144,"about_ca_topic_score_gemma":0.0041778632,"teacher_disagreement_score":0.007652892,"about_ca_system_score_codex":0.0008274028,"about_ca_system_score_gemma":0.001241435,"threshold_uncertainty_score":0.025601447},"labels":[],"label_agreement":null},{"id":"W4402701808","doi":"10.48550/arxiv.2408.13885","title":"Neural Spacetimes for DAG Representation Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Representation (politics); Computer science; Artificial intelligence; Artificial neural network; Cognitive science; Psychology; Political science; Law","score_opus":0.09503553977245331,"score_gpt":0.22505556170213795,"score_spread":0.13002002192968465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402701808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015974248,0.00043207948,0.97859234,0.00061959116,0.00008303484,0.000038931346,0.00071109616,0.0015258571,0.0020227795],"genre_scores_gemma":[0.56645256,0.0010397924,0.42040226,0.00059250643,0.00015677228,0.00029942393,0.003392505,0.00047040815,0.0071937414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994287,0.0001483412,0.000040171763,0.0001980836,0.0001286156,0.000056051416],"domain_scores_gemma":[0.9990484,0.00030357018,0.00013940058,0.00025425342,0.00016077564,0.0000936643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094418356,0.0011480165,0.0009918333,0.0012503559,0.0004949426,0.0010343401,0.0024595754,0.0012946695,0.004171181],"category_scores_gemma":[0.00523786,0.0006154897,0.0011301603,0.0012091238,0.0013754363,0.004528378,0.0026561462,0.0023709869,0.001055463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012048151,0.0000506889,0.0011543409,0.00016961443,0.000062585634,0.00009354349,0.00010369044,0.61370564,0.0025523594,0.25417757,0.0061469544,0.12166254],"study_design_scores_gemma":[0.000007455815,0.000023731462,0.000086059285,0.00001164478,0.0000057372386,0.000018779936,0.000009819832,0.85679734,0.00064528803,0.1401906,0.0021943275,0.000009241821],"about_ca_topic_score_codex":0.004101084,"about_ca_topic_score_gemma":0.0061215186,"teacher_disagreement_score":0.004171181,"about_ca_system_score_codex":0.0021622335,"about_ca_system_score_gemma":0.0010702406,"threshold_uncertainty_score":0.01568818},"labels":[],"label_agreement":null},{"id":"W4402728835","doi":"10.1007/978-3-031-55917-4_2","title":"Statistical Profiling of Hybrid CNN-SVM Effectiveness","year":2024,"lang":"en","type":"book-chapter","venue":"Studies in classification, data analysis, and knowledge organization","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Profiling (computer programming); Support vector machine; Computer science; Artificial intelligence; Pattern recognition (psychology); Machine learning; Operating system","score_opus":0.09016797561016919,"score_gpt":0.361576834046765,"score_spread":0.2714088584365958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402728835","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86415267,0.0063847564,0.09403178,0.0008151798,0.00016484124,0.00006860408,0.005163204,0.0019727664,0.027246289],"genre_scores_gemma":[0.9826382,0.00046151894,0.010943151,0.000055924345,0.00006084394,0.000030622832,0.0023577297,0.00015194711,0.0033002007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986217,0.0002235212,0.000087052984,0.00026736275,0.00065493013,0.00014546259],"domain_scores_gemma":[0.9917949,0.005249567,0.00049668725,0.0005758235,0.0017144139,0.00016856167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020510168,0.00045784633,0.0004796967,0.0016353568,0.00020872481,0.0015372518,0.0007337365,0.00047387168,0.00252093],"category_scores_gemma":[0.0097644925,0.00017772971,0.00028801546,0.0015448116,0.00032164194,0.0018437849,0.00048418864,0.00042805987,0.0012075837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010797998,0.00017649568,0.14179155,0.0004223942,0.00042136078,0.00018878822,0.00018714728,0.14466706,0.03152042,0.0079748295,0.015557838,0.6560123],"study_design_scores_gemma":[0.000017945476,0.00045689996,0.1524404,0.00008135592,0.00014015587,0.0006096878,0.00020190298,0.7885416,0.04177048,0.0078039556,0.007866881,0.00006872247],"about_ca_topic_score_codex":0.0026507315,"about_ca_topic_score_gemma":0.0040220446,"teacher_disagreement_score":0.0026507315,"about_ca_system_score_codex":0.00084716774,"about_ca_system_score_gemma":0.0004429969,"threshold_uncertainty_score":0.010846913},"labels":[],"label_agreement":null},{"id":"W4402741181","doi":"10.31219/osf.io/vqxh8","title":"PAC Learnability and Information Bottleneck in Deep Learning: Tutorial and Survey","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Learnability; Bottleneck; Information bottleneck method; Generalization; Computer science; VC dimension; Deep learning; Artificial intelligence; Dimension (graph theory); Machine learning; Theoretical computer science; Mathematics","score_opus":0.01282128306459303,"score_gpt":0.25095807698429695,"score_spread":0.2381367939197039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402741181","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004810052,0.4712386,0.48881164,0.007031114,0.0012548955,0.000095649724,0.00038832685,0.0004613536,0.02590835],"genre_scores_gemma":[0.17824598,0.6785628,0.11198445,0.0058784494,0.011403897,0.00057341706,0.0009896285,0.00057271175,0.011788712],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99738187,0.00069650146,0.00022200082,0.00048133524,0.0010189833,0.00019934558],"domain_scores_gemma":[0.99219257,0.006316302,0.00030048005,0.0003871603,0.0006340957,0.00016947261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051686526,0.0020984684,0.0024927207,0.0025505885,0.00060766,0.0030149121,0.0017779879,0.0025716936,0.0037382534],"category_scores_gemma":[0.012942963,0.0013886569,0.0013043676,0.0051503624,0.003532578,0.010232184,0.0029730205,0.0071366094,0.0011111129],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009948367,0.00018123344,0.0015043698,0.0041245376,0.0001896217,0.00024101249,0.00025775912,0.03240007,0.0010206118,0.59913516,0.028724357,0.3321218],"study_design_scores_gemma":[0.000023874827,0.0001823004,0.0009145295,0.0013562947,0.000104333485,0.0006562016,0.000090887486,0.07624296,0.0019679395,0.81203425,0.10632852,0.00009797299],"about_ca_topic_score_codex":0.0017693343,"about_ca_topic_score_gemma":0.00095271214,"teacher_disagreement_score":0.0051686526,"about_ca_system_score_codex":0.0027937628,"about_ca_system_score_gemma":0.0021465458,"threshold_uncertainty_score":0.02733475},"labels":[],"label_agreement":null},{"id":"W4402769082","doi":"10.36227/techrxiv.172114799.94469004/v2","title":"A Comparative Study of Restricted Hopfield Network and Dense Associative Memory","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Hopfield network; Content-addressable memory; Bidirectional associative memory; Associative property; Computer science; Artificial neural network; Artificial intelligence; Cognitive science; Psychology; Mathematics","score_opus":0.042370160542317424,"score_gpt":0.3107925898763368,"score_spread":0.2684224293340194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402769082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.756747,0.0068734568,0.22163613,0.0005715361,0.00009735521,0.00013844833,0.00063893496,0.0011068919,0.012190287],"genre_scores_gemma":[0.9717467,0.0009439275,0.025741085,0.000060568356,0.000025932162,0.000051839725,0.00029476927,0.000024949803,0.0011102023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947935,0.000107313776,0.000048214304,0.00012620824,0.0001638543,0.00007510215],"domain_scores_gemma":[0.9969247,0.0018607134,0.00023091705,0.0004578609,0.00042259588,0.00010321363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015747509,0.00050720415,0.0006128729,0.0010844934,0.00024122497,0.0009136051,0.0008793921,0.0006019165,0.0017432849],"category_scores_gemma":[0.007813485,0.00022336013,0.00035967902,0.0009412572,0.0007326416,0.003116965,0.000781604,0.00041405568,0.00019360504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012937232,0.0002210323,0.009412589,0.0008786903,0.0003122467,0.00040017406,0.00029694627,0.6819124,0.014972946,0.028671877,0.0018341348,0.2597931],"study_design_scores_gemma":[0.00003063349,0.00029133487,0.002022371,0.00003390217,0.00006149532,0.00018675595,0.00009786129,0.967282,0.0061308546,0.022763481,0.0010740721,0.000025219892],"about_ca_topic_score_codex":0.0060545225,"about_ca_topic_score_gemma":0.0051725996,"teacher_disagreement_score":0.0060545225,"about_ca_system_score_codex":0.00083897775,"about_ca_system_score_gemma":0.00087318465,"threshold_uncertainty_score":0.0120385885},"labels":[],"label_agreement":null},{"id":"W4402769448","doi":"10.1109/cvpr52733.2024.01984","title":"Gaussian Shadow Casting for Neural Characters","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Shadow (psychology); Computer science; Gaussian; Artificial intelligence; Artificial neural network; Casting; Computer vision; Pattern recognition (psychology); Computer graphics (images); Metallurgy; Materials science; Physics; Psychology","score_opus":0.02638546873663814,"score_gpt":0.2792128011333502,"score_spread":0.252827332396712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402769448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020994805,0.00016487311,0.9718688,0.0001237829,0.000043975575,0.000028616163,0.00012139243,0.0018615361,0.004792237],"genre_scores_gemma":[0.6459433,0.00040628234,0.33753335,0.0002156037,0.00005720431,0.00014671827,0.0005263276,0.0007373354,0.014433852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988484,0.000022164706,0.0000048517013,0.000025485204,0.000043571763,0.000018935005],"domain_scores_gemma":[0.9997607,0.000083718776,0.000023176783,0.000048240756,0.00005590958,0.000028335515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022386607,0.000565075,0.00038684258,0.00031579167,0.00027737647,0.0006911439,0.00082646863,0.0005739677,0.004823455],"category_scores_gemma":[0.0011491653,0.000317059,0.0005781485,0.00026366633,0.00052961276,0.0007579954,0.0008173925,0.000925862,0.0011164072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009400403,0.00003695599,0.00055412715,0.00008092495,0.000022739694,0.000096908625,0.000107076536,0.8281174,0.008668516,0.025464235,0.0028796922,0.1338774],"study_design_scores_gemma":[0.0000032865667,0.000008442086,0.00003466951,0.0000050498297,0.0000021546905,0.000017469072,0.0000070477704,0.9932893,0.0017870264,0.0038379913,0.00100409,0.0000034338332],"about_ca_topic_score_codex":0.0063137095,"about_ca_topic_score_gemma":0.008970439,"teacher_disagreement_score":0.0063137095,"about_ca_system_score_codex":0.0009435967,"about_ca_system_score_gemma":0.000673974,"threshold_uncertainty_score":0.01613605},"labels":[],"label_agreement":null},{"id":"W4402771893","doi":"10.1007/s42484-024-00195-8","title":"Integrating quantum CI and generative AI for Taiwanese/English co-learning","year":2024,"lang":"en","type":"article","venue":"Quantum Machine Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Generative grammar; Linguistics; Artificial intelligence; Psychology; Natural language processing; Computer science; Philosophy","score_opus":0.01962914619275681,"score_gpt":0.31758490104823706,"score_spread":0.29795575485548026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402771893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2169589,0.0013391881,0.64898175,0.0032624758,0.00033393092,0.00019643073,0.00038056073,0.0020426917,0.1265041],"genre_scores_gemma":[0.94178236,0.00019840566,0.05307395,0.00015376085,0.000036111203,0.000046353685,0.0001534323,0.00008200375,0.0044736145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996978,0.00012183171,0.000017280845,0.000067372916,0.00005330739,0.000042475116],"domain_scores_gemma":[0.9987244,0.00066693843,0.000056925506,0.00025437705,0.00020127643,0.000095970914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073603,0.00028711208,0.00033424544,0.00067071285,0.0007224862,0.0012641967,0.00070112094,0.0003560686,0.0068521095],"category_scores_gemma":[0.0038535234,0.00016584733,0.0003813735,0.000952023,0.001081265,0.0020192098,0.0014578431,0.0010287086,0.00065561663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014132037,0.0001930778,0.010163702,0.0003894046,0.00015141783,0.0005472473,0.0020148747,0.040147837,0.015299031,0.5139642,0.007502764,0.40948522],"study_design_scores_gemma":[0.000030113499,0.00007890824,0.006069844,0.00008824805,0.00008420986,0.00032593464,0.0010824361,0.54571587,0.008176952,0.42080262,0.017484376,0.000060453753],"about_ca_topic_score_codex":0.013582859,"about_ca_topic_score_gemma":0.0203173,"teacher_disagreement_score":0.013582859,"about_ca_system_score_codex":0.0012767281,"about_ca_system_score_gemma":0.00168351,"threshold_uncertainty_score":0.02700758},"labels":[],"label_agreement":null},{"id":"W4402774473","doi":"10.1109/cvpr52733.2024.01896","title":"Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Toronto","funders":"","keywords":"Radiance; Bayes' theorem; Computer science; Artificial intelligence; Bayesian probability; Remote sensing; Geology","score_opus":0.03129972282208907,"score_gpt":0.2915943015245558,"score_spread":0.2602945787024667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402774473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018794121,0.00017906925,0.9966736,0.00008254159,0.000020193302,0.000024406369,0.000059227026,0.0002451951,0.00083646923],"genre_scores_gemma":[0.25433883,0.0009800707,0.73704666,0.00034218052,0.00019311476,0.0003436892,0.00068428373,0.00074290344,0.0053282925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982607,0.00053715496,0.00009608256,0.00029349854,0.0007174867,0.000095166244],"domain_scores_gemma":[0.9958139,0.002696757,0.00039294138,0.00031264062,0.0006601493,0.00012359521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043640807,0.001441181,0.001156539,0.0020145464,0.0007148474,0.0023215108,0.0021058545,0.0015942081,0.0035072276],"category_scores_gemma":[0.017529305,0.0008784508,0.0010642766,0.0009090789,0.0019150448,0.0038172381,0.0034057107,0.002912987,0.0007486752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015501636,0.00004459062,0.0014284876,0.00021356398,0.000109587156,0.00007350531,0.00016188798,0.594639,0.0038908455,0.17371361,0.0036903857,0.22187959],"study_design_scores_gemma":[0.000007852246,0.000028142511,0.00020881144,0.00007591431,0.000012247065,0.00004781157,0.000019485049,0.9019529,0.002730745,0.09286636,0.002027464,0.000022239894],"about_ca_topic_score_codex":0.0043719155,"about_ca_topic_score_gemma":0.0041308226,"teacher_disagreement_score":0.0043719155,"about_ca_system_score_codex":0.002191149,"about_ca_system_score_gemma":0.001645148,"threshold_uncertainty_score":0.023079753},"labels":[],"label_agreement":null},{"id":"W4402847819","doi":"10.2139/ssrn.4967333","title":"Implicit Prediction as a Consequence of Statistical Learning","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Statistical learning; Psychology; Computer science; Econometrics; Cognitive psychology; Artificial intelligence; Mathematics","score_opus":0.008983775217000807,"score_gpt":0.27347509322980695,"score_spread":0.2644913180128061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402847819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16925928,0.0007149321,0.80023956,0.003523294,0.0005123633,0.000042217576,0.00035536682,0.000847219,0.024505874],"genre_scores_gemma":[0.94834805,0.0004775348,0.037877534,0.00044822742,0.0004140803,0.00005091451,0.0003788695,0.00020497777,0.011799832],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883467,0.0003215262,0.00005930271,0.00033426034,0.00032219625,0.0001280627],"domain_scores_gemma":[0.98206824,0.011590221,0.0012795696,0.003493123,0.0011439892,0.0004247726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016358731,0.00042561023,0.0008651979,0.0005394953,0.0005706093,0.0019572005,0.0019136883,0.0015763515,0.0069022956],"category_scores_gemma":[0.019588154,0.0006258049,0.0005744395,0.000858247,0.0017392765,0.005468398,0.0018939376,0.0030907907,0.00092126324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036760772,0.00022404527,0.0042959033,0.00021873132,0.00010876266,0.00048289335,0.00025603618,0.0750895,0.0072028516,0.8258501,0.0058032153,0.080100276],"study_design_scores_gemma":[0.000021776761,0.00003116784,0.0011509354,0.000011649215,0.000014623433,0.00008539333,0.000012253786,0.38696787,0.0011983246,0.60989124,0.00060011883,0.000014617438],"about_ca_topic_score_codex":0.0012486302,"about_ca_topic_score_gemma":0.001609794,"teacher_disagreement_score":0.0069022956,"about_ca_system_score_codex":0.0005840239,"about_ca_system_score_gemma":0.0008802485,"threshold_uncertainty_score":0.023090541},"labels":[],"label_agreement":null},{"id":"W4402875181","doi":"10.3103/s1060992x24700176","title":"uSF: Learning Neural Semantic Field with Uncertainty","year":2024,"lang":"en","type":"article","venue":"Optical Memory and Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Field (mathematics); Machine learning; Mathematics","score_opus":0.009714979194430845,"score_gpt":0.2353598474910027,"score_spread":0.22564486829657185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402875181","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013390604,0.00061628915,0.98166037,0.00047303343,0.000070986855,0.00005237948,0.00048562273,0.0020862764,0.0011644698],"genre_scores_gemma":[0.5549879,0.000993101,0.43304348,0.0007799933,0.00029782476,0.0003700817,0.0030885835,0.00058777793,0.0058512334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994373,0.00015208207,0.000030153586,0.00016655371,0.00015232418,0.00006152446],"domain_scores_gemma":[0.99884725,0.00063657283,0.00011241151,0.00017843822,0.00016701844,0.00005827134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001704161,0.0011660835,0.0014873687,0.0018497321,0.0006019604,0.0010432249,0.002519192,0.002186264,0.0035403986],"category_scores_gemma":[0.005187537,0.000667021,0.001328326,0.0014607927,0.0011351223,0.003885019,0.002016008,0.0023696867,0.00060381007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001667617,0.00007587349,0.0009992295,0.0001252561,0.00010418467,0.00009852639,0.000084773055,0.6987342,0.0017608902,0.03917542,0.007920413,0.2507545],"study_design_scores_gemma":[0.0000061196192,0.000010772826,0.00006141899,0.000010945647,0.0000041640424,0.00001638167,0.000005145006,0.9734914,0.000409598,0.025363082,0.0006143944,0.0000066134417],"about_ca_topic_score_codex":0.012655669,"about_ca_topic_score_gemma":0.0095398845,"teacher_disagreement_score":0.012655669,"about_ca_system_score_codex":0.0016679696,"about_ca_system_score_gemma":0.0012246432,"threshold_uncertainty_score":0.025164008},"labels":[],"label_agreement":null},{"id":"W4402903985","doi":"10.1109/cvprw63382.2024.00765","title":"ATOM: Attention Mixer for Efficient Dataset Distillation","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Bank of Canada; University of Toronto","funders":"","keywords":"Distillation; Atom (system on chip); Computer science; Atom optics; Atomic physics; Physics; Chemistry; Parallel computing; Chromatography","score_opus":0.021919318662890836,"score_gpt":0.293850966176401,"score_spread":0.2719316475135102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402903985","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034814548,0.00079291785,0.9226461,0.000557556,0.00027896615,0.00034281667,0.0013422326,0.035520885,0.0037040075],"genre_scores_gemma":[0.328638,0.00041754267,0.64703786,0.0014402297,0.000261932,0.0007949639,0.00923394,0.0017950368,0.010380429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989924,0.00018073565,0.00005752902,0.00034645837,0.0002657245,0.00015720625],"domain_scores_gemma":[0.99863094,0.00040030878,0.000090843154,0.0005602006,0.00019976319,0.00011792764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016412367,0.0020046828,0.0016640606,0.0016541848,0.0008257688,0.0012899049,0.003989863,0.0015551352,0.008619311],"category_scores_gemma":[0.0044090454,0.0007363917,0.0014162867,0.001805705,0.00089220493,0.0042522405,0.005206265,0.0032954954,0.0033479452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011473913,0.0005805855,0.0025961369,0.000371622,0.0002902111,0.000279748,0.00022906589,0.077645525,0.047375232,0.02153475,0.03963941,0.80831033],"study_design_scores_gemma":[0.0001675619,0.00028660876,0.0007428937,0.000026704414,0.000077636905,0.00015863158,0.00006244354,0.9315516,0.031783212,0.022302533,0.012784535,0.000055706005],"about_ca_topic_score_codex":0.004799645,"about_ca_topic_score_gemma":0.010279313,"teacher_disagreement_score":0.008619311,"about_ca_system_score_codex":0.0012363219,"about_ca_system_score_gemma":0.0020298644,"threshold_uncertainty_score":0.028834462},"labels":[],"label_agreement":null},{"id":"W4402904833","doi":"10.1167/jov.24.10.618","title":"Further evidence that the speed of working memory consolidation is a structural limit","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Consolidation (business); Limit (mathematics); Memory consolidation; Psychology; Cognitive psychology; Computer science; Mathematics; Economics; Neuroscience; Mathematical analysis; Accounting","score_opus":0.07334142011308961,"score_gpt":0.32933044463526767,"score_spread":0.2559890245221781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402904833","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79535216,0.009285554,0.16574657,0.0021183179,0.000285284,0.00024546694,0.0008365139,0.0005720258,0.025558056],"genre_scores_gemma":[0.9646386,0.0014455528,0.03018396,0.00055828213,0.00013216177,0.0003037844,0.0005549184,0.0001568749,0.002025854],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99768686,0.00019034585,0.00023378331,0.0010543003,0.0007443672,0.00009027275],"domain_scores_gemma":[0.9742475,0.01142451,0.0048052818,0.0073473104,0.0016680732,0.0005073201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038866173,0.00054140517,0.0006668202,0.0007243462,0.00039604702,0.0016530622,0.0014144732,0.0009175311,0.0060778996],"category_scores_gemma":[0.02013469,0.00070421165,0.000551991,0.0005751163,0.0024885125,0.0038849192,0.0019508473,0.0025740566,0.0010843019],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018924244,0.0006518005,0.03608668,0.0017075315,0.00049935904,0.0002504442,0.0021710084,0.0015989715,0.7845334,0.03332572,0.00093959714,0.13634312],"study_design_scores_gemma":[0.00023326403,0.0055981097,0.48513427,0.0004986527,0.0005403195,0.003500571,0.0005246409,0.010182241,0.3586346,0.10429157,0.030637128,0.00022464704],"about_ca_topic_score_codex":0.00053617393,"about_ca_topic_score_gemma":0.0005125761,"teacher_disagreement_score":0.0060778996,"about_ca_system_score_codex":0.0006429756,"about_ca_system_score_gemma":0.0005277593,"threshold_uncertainty_score":0.020554602},"labels":[],"label_agreement":null},{"id":"W4402915488","doi":"10.1109/icip51287.2024.10648201","title":"Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Graph; Laplace operator; Theoretical computer science; Mathematics","score_opus":0.006988654261156777,"score_gpt":0.2360386747248113,"score_spread":0.22905002046365452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402915488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006891784,0.00005516377,0.9915599,0.000081199636,0.000025599182,0.000016234786,0.000025475463,0.00046834236,0.000876276],"genre_scores_gemma":[0.21446314,0.00018422205,0.776785,0.00029647004,0.000052109408,0.00008394979,0.00022828364,0.0003111279,0.0075955996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997985,0.00003366491,0.000008068143,0.00007066633,0.00006295933,0.000026219459],"domain_scores_gemma":[0.9997669,0.00008071402,0.00002582968,0.00005210101,0.000052305328,0.000022099703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050371693,0.00085698,0.00058570877,0.00045737677,0.0002586855,0.0006473554,0.0008858505,0.0012222455,0.0025708338],"category_scores_gemma":[0.0015287655,0.0004266294,0.0006839396,0.0002992589,0.00086760434,0.00094167393,0.0010582951,0.0017291683,0.0010245729],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019721473,0.000107990585,0.0006254914,0.00015655371,0.00009121687,0.00019838146,0.00018843476,0.5551982,0.11458879,0.03511497,0.003989819,0.289543],"study_design_scores_gemma":[0.000011125396,0.000034839966,0.00010120079,0.0000069734438,0.000010285443,0.000040969226,0.00001626237,0.97821516,0.010622191,0.0090881325,0.0018423235,0.000010556382],"about_ca_topic_score_codex":0.0021590157,"about_ca_topic_score_gemma":0.0053497804,"teacher_disagreement_score":0.0025708338,"about_ca_system_score_codex":0.00038549613,"about_ca_system_score_gemma":0.0006062727,"threshold_uncertainty_score":0.008600295},"labels":[],"label_agreement":null},{"id":"W4402951965","doi":"10.54254/2753-8818/42/20240663","title":"An overview of the development and applications of information entropy","year":2024,"lang":"en","type":"article","venue":"Theoretical and Natural Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalsa Corporation","funders":"","keywords":"Computer science; Data science","score_opus":0.008311037155415224,"score_gpt":0.2693062714784326,"score_spread":0.2609952343230174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402951965","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038404642,0.68402797,0.20071758,0.015323657,0.0029295362,0.00015612939,0.00052933017,0.00023947479,0.092235856],"genre_scores_gemma":[0.12714891,0.7439616,0.10336916,0.0040081004,0.0111552775,0.00040052287,0.000617532,0.00016599524,0.009172953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99596596,0.0015891456,0.00039514434,0.0005485214,0.0013397647,0.0001615624],"domain_scores_gemma":[0.99359024,0.004702121,0.0002893196,0.00036223646,0.0009301787,0.00012588143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005135948,0.0012013762,0.0012092101,0.008813283,0.0010955735,0.0045009535,0.0011511309,0.0021090826,0.0033571112],"category_scores_gemma":[0.0085281115,0.0006569511,0.001264805,0.00724543,0.0046667615,0.0077286055,0.002346568,0.003992494,0.0010090859],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002768816,0.00003760171,0.0010752995,0.0019873434,0.00007262505,0.00018679825,0.00039948217,0.0044660633,0.0006214693,0.8270284,0.010332591,0.15376465],"study_design_scores_gemma":[0.000005721222,0.000055861085,0.001352082,0.0017919412,0.000039271632,0.0004978787,0.00016527621,0.0070912535,0.0009488295,0.7159776,0.27198747,0.000086903456],"about_ca_topic_score_codex":0.001971981,"about_ca_topic_score_gemma":0.0010284602,"teacher_disagreement_score":0.008813283,"about_ca_system_score_codex":0.0038452973,"about_ca_system_score_gemma":0.002836774,"threshold_uncertainty_score":0.027899742},"labels":[],"label_agreement":null},{"id":"W4402973064","doi":"10.1101/2024.09.27.615384","title":"Activation mapping in multi-center rat sensory-evoked functional MRI datasets using a unified pipeline","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Douglas Mental Health University Institute","funders":"Biotechnology and Biological Sciences Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Pipeline (software); Sensory system; Center (category theory); Computer science; Neuroscience; Artificial intelligence; Chemistry; Psychology","score_opus":0.05104491617280692,"score_gpt":0.26051780236607924,"score_spread":0.2094728861932723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402973064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.147605,0.0005003158,0.75614965,0.00039777992,0.00015608217,0.0005519948,0.022007488,0.07133936,0.0012922802],"genre_scores_gemma":[0.28390434,0.00031375274,0.6626472,0.0002075459,0.000050275947,0.0024888495,0.04266606,0.006194504,0.0015273781],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992418,0.00016646304,0.0000731856,0.0003065022,0.00013706711,0.000074972755],"domain_scores_gemma":[0.9989384,0.0003359418,0.00012095536,0.00026167004,0.00028501614,0.000058015812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003209862,0.0012734763,0.0008856045,0.0022241825,0.00062650966,0.0013114186,0.0012023029,0.00069017074,0.003037913],"category_scores_gemma":[0.005766706,0.0004841517,0.0016121424,0.0013895372,0.00043920026,0.0007320215,0.0014738413,0.0011898272,0.001546759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031157823,0.000778693,0.025759207,0.0019595916,0.0025236513,0.00089454016,0.001237166,0.11538841,0.3569573,0.008441409,0.06783346,0.41511077],"study_design_scores_gemma":[0.00034795122,0.000635737,0.067260936,0.00016095761,0.0007075995,0.0006699254,0.00031731316,0.7649546,0.11444141,0.01683123,0.03337232,0.00029999777],"about_ca_topic_score_codex":0.004537148,"about_ca_topic_score_gemma":0.00861769,"teacher_disagreement_score":0.004537148,"about_ca_system_score_codex":0.0005977531,"about_ca_system_score_gemma":0.0018166596,"threshold_uncertainty_score":0.016975582},"labels":[],"label_agreement":null},{"id":"W4402980041","doi":"10.1109/otcon60325.2024.10685851","title":"Implicit Bias Analysis in The Training of Compact Neural Networks For Inverse Problems","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Artificial neural network; Training (meteorology); Inverse; Artificial intelligence; Inverse problem; Mathematics; Mathematical analysis; Physics","score_opus":0.08984463416283504,"score_gpt":0.3077156043412511,"score_spread":0.21787097017841606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402980041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014516545,0.00032352906,0.9832917,0.00025420694,0.000054797976,0.000024711919,0.000020801228,0.00052287325,0.0009909077],"genre_scores_gemma":[0.32816604,0.00046492257,0.6679507,0.00037332752,0.00012112799,0.00019643264,0.00012493713,0.00036772963,0.0022347132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883026,0.00041643946,0.00008124048,0.00017851367,0.0004101347,0.00008350457],"domain_scores_gemma":[0.99546057,0.0027695582,0.00031969952,0.0006231342,0.0007235912,0.00010343946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003998601,0.0009677541,0.00096679624,0.00065453316,0.00048580833,0.0011450988,0.0015759659,0.0011049265,0.0016579207],"category_scores_gemma":[0.021521563,0.0006137201,0.0005370675,0.00050214573,0.0012116175,0.00267744,0.002294604,0.0020074265,0.00049743295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033638012,0.00009225512,0.002534205,0.00032220472,0.00011761298,0.00013154793,0.00033767027,0.6034871,0.016279226,0.048148535,0.0021999106,0.32601324],"study_design_scores_gemma":[0.00001402296,0.000049661216,0.00018622876,0.000027260308,0.0000114965005,0.00003863682,0.000013366096,0.9810022,0.0043524974,0.013228652,0.0010647594,0.000011161861],"about_ca_topic_score_codex":0.0018620101,"about_ca_topic_score_gemma":0.0032610293,"teacher_disagreement_score":0.003998601,"about_ca_system_score_codex":0.0009009908,"about_ca_system_score_gemma":0.0010181905,"threshold_uncertainty_score":0.021146894},"labels":[],"label_agreement":null},{"id":"W4402982608","doi":"10.1109/otcon60325.2024.10688269","title":"Robustness Analysis of Neuro-Inspired Architectures against Common Perturbations","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence","score_opus":0.015986845795505907,"score_gpt":0.2605226252233555,"score_spread":0.2445357794278496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402982608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1738195,0.00070907664,0.8170274,0.00046320775,0.00006621273,0.000073158684,0.00007853558,0.00077047903,0.006992444],"genre_scores_gemma":[0.974819,0.00019308907,0.02338242,0.00005379119,0.000020439003,0.0000465993,0.00005283571,0.000069463094,0.0013624119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994579,0.00016161679,0.000029168516,0.00010243308,0.00016629795,0.000082501865],"domain_scores_gemma":[0.9973683,0.0014090338,0.00042795032,0.00038442595,0.00030421562,0.000106160565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020404856,0.0008908329,0.00054184085,0.0009237262,0.00041692768,0.00086775323,0.0009046225,0.0008650594,0.001541515],"category_scores_gemma":[0.006760783,0.00035603007,0.0008834823,0.00024365066,0.0012352561,0.00088037807,0.0012183703,0.0011050943,0.0002240982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006310044,0.000016353099,0.0008326594,0.00004069663,0.00007064589,0.000056282413,0.00003640984,0.9734623,0.0048845643,0.008329616,0.00020653033,0.012000802],"study_design_scores_gemma":[0.0000016937604,0.00003684754,0.0002669935,0.0000065144004,0.000008516052,0.000017609509,0.000006237908,0.99484974,0.0014714594,0.0031984162,0.00013117144,0.000004847726],"about_ca_topic_score_codex":0.0018750089,"about_ca_topic_score_gemma":0.0010304516,"teacher_disagreement_score":0.0020404856,"about_ca_system_score_codex":0.0009810948,"about_ca_system_score_gemma":0.0005613153,"threshold_uncertainty_score":0.010791302},"labels":[],"label_agreement":null},{"id":"W4402986877","doi":"10.21428/e2759450.4481de80","title":"Perceptual Learning","year":2024,"lang":"en","type":"book-chapter","venue":"MIT Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Perceptual learning; Psychology; Perception; Computer science; Neuroscience","score_opus":0.03751582116250683,"score_gpt":0.24540329289426924,"score_spread":0.20788747173176242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402986877","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005334093,0.009373376,0.20584606,0.0022182874,0.0012000747,0.00007514826,0.00084611005,0.002207993,0.7728989],"genre_scores_gemma":[0.14083843,0.0095206285,0.09765308,0.0010933515,0.0005394213,0.00014847984,0.0020448966,0.00095966726,0.747202],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998356,0.00001791953,0.000005973829,0.000061868064,0.000066955254,0.000011571462],"domain_scores_gemma":[0.9997813,0.00005864633,0.000008556798,0.00006750679,0.00006219729,0.000021775659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023110904,0.00067496195,0.00034778254,0.00044111564,0.0004497731,0.0019272271,0.00082101964,0.00068595435,0.08811668],"category_scores_gemma":[0.0012807656,0.00036277782,0.00029800477,0.0005570589,0.000781196,0.0021849037,0.00088981586,0.0011150626,0.026551936],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005370895,0.000045902823,0.00021399028,0.00019959328,0.000018357636,0.0000440515,0.00011917033,0.0020812368,0.005148183,0.14197831,0.11392198,0.73617566],"study_design_scores_gemma":[0.00001971519,0.000060256487,0.001807406,0.00017384024,0.000027324622,0.0003179571,0.0001784927,0.018868558,0.007980344,0.3801655,0.590364,0.00003664625],"about_ca_topic_score_codex":0.0020272685,"about_ca_topic_score_gemma":0.0026686685,"teacher_disagreement_score":0.08811668,"about_ca_system_score_codex":0.0005099161,"about_ca_system_score_gemma":0.00049522123,"threshold_uncertainty_score":0.29477966},"labels":[],"label_agreement":null},{"id":"W4403012217","doi":"10.48550/arxiv.2408.11336","title":"FATE: Focal-modulated Attention Encoder for Multivariate Time-series Forecasting","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Encoder; Computer science; Environmental science; Psychology","score_opus":0.08465611907014035,"score_gpt":0.2055650713498737,"score_spread":0.12090895227973335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403012217","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12846804,0.0016577919,0.8535451,0.001015589,0.00025127426,0.0001021998,0.0021734189,0.006819971,0.005966668],"genre_scores_gemma":[0.8878753,0.0005726889,0.10323833,0.00041923235,0.00010536319,0.00009152333,0.0021382573,0.00014509716,0.005414297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998851,0.000027433056,0.000007515489,0.000034317392,0.000026020003,0.000019663437],"domain_scores_gemma":[0.9996276,0.00019146291,0.000024388739,0.000058622663,0.00007587921,0.000021945834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045954876,0.00065964035,0.00034199646,0.0003451818,0.00017477965,0.0004092915,0.00084087776,0.0004710318,0.002472229],"category_scores_gemma":[0.0020996141,0.00017398951,0.0003016752,0.0003954072,0.00024588234,0.0011604936,0.00075759104,0.00094775576,0.00047779418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047604006,0.00021602822,0.004243028,0.0002091988,0.000106726395,0.00020429447,0.00015690457,0.30983233,0.022281898,0.022787185,0.019013993,0.6204724],"study_design_scores_gemma":[0.000009390706,0.00002839834,0.00027302958,0.0000065589393,0.000011111854,0.000015913838,0.0000071877066,0.99006224,0.0029748876,0.005818796,0.00078740687,0.0000051819516],"about_ca_topic_score_codex":0.008875825,"about_ca_topic_score_gemma":0.018025056,"teacher_disagreement_score":0.008875825,"about_ca_system_score_codex":0.0006596036,"about_ca_system_score_gemma":0.00074185757,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4403038160","doi":"10.7554/elife.100664.1.sa2","title":"eLife Assessment: Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Centre for Biological Sciences; Department of Atomic Energy, Government of India; Tata Institute of Fundamental Research","keywords":"Feed forward; Feedforward neural network; Computer science; Computation; Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Machine learning; Algorithm; Engineering","score_opus":0.03744109347287813,"score_gpt":0.34268542735376695,"score_spread":0.30524433388088884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403038160","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08044277,0.0005755082,0.8975448,0.001120257,0.00022480566,0.0001596449,0.0008624423,0.007812536,0.011257206],"genre_scores_gemma":[0.55466133,0.00054764404,0.42924297,0.00023541783,0.00011738751,0.00031418272,0.0014100453,0.0012714146,0.012199633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990858,0.00020292323,0.000047576304,0.00014639783,0.00043347006,0.000083819905],"domain_scores_gemma":[0.9969426,0.0013875914,0.00024829173,0.00027421344,0.0009629713,0.00018437473],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0027662513,0.0003934287,0.0007176457,0.0013435014,0.0005250018,0.001238421,0.0019254732,0.0011573789,0.008302981],"category_scores_gemma":[0.015934242,0.0004733963,0.0004418685,0.00064453733,0.0007208789,0.0017105615,0.0013530279,0.0008509532,0.002078832],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039499515,0.000074492695,0.01332051,0.0005201021,0.00017883466,0.00042706978,0.0003745248,0.4544529,0.019155644,0.048283346,0.021239603,0.44157806],"study_design_scores_gemma":[0.000029042427,0.000034416713,0.0010490366,0.00003979158,0.000020299705,0.000071572744,0.0000421362,0.95822626,0.007934517,0.023662759,0.008872489,0.000017710528],"about_ca_topic_score_codex":0.0030140453,"about_ca_topic_score_gemma":0.0065869354,"teacher_disagreement_score":0.99723375,"about_ca_system_score_codex":0.0009078511,"about_ca_system_score_gemma":0.0019213184,"threshold_uncertainty_score":0.027776241},"labels":[],"label_agreement":null},{"id":"W4403044638","doi":"10.5755/j02.eie.38220","title":"Logical Resonance in Izhikevich Neuron","year":2024,"lang":"en","type":"article","venue":"Elektronika ir Elektrotechnika","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Erciyes Üniversitesi","keywords":"Resonance (particle physics); Computer science; Physics; Atomic physics","score_opus":0.014839490530246425,"score_gpt":0.2583880736984891,"score_spread":0.24354858316824268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403044638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39233866,0.0019129507,0.52725667,0.0010899206,0.00035055648,0.00008118685,0.00020662407,0.00056523207,0.076198325],"genre_scores_gemma":[0.970974,0.00039430076,0.019375756,0.0001376898,0.000023529185,0.000065073065,0.000048201375,0.000027439493,0.008953966],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998908,0.000018273937,0.0000057826055,0.000024992283,0.000032790515,0.000027336322],"domain_scores_gemma":[0.99994075,0.000011977009,0.000008985186,0.000009730667,0.000016363621,0.000012294429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012815096,0.00024169806,0.0004853066,0.00026631178,0.00051171007,0.0007602296,0.00081204384,0.0008087249,0.0019755305],"category_scores_gemma":[0.0002404947,0.00013867673,0.00044464308,0.00015008979,0.001066703,0.0009035535,0.00059475197,0.00053595187,0.00038325746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022017858,0.0000728736,0.0017988969,0.00021433741,0.00007968049,0.0008596019,0.00056928926,0.330323,0.08508495,0.5596053,0.0017385513,0.019433318],"study_design_scores_gemma":[0.000038625785,0.00016115917,0.0010977092,0.000025325367,0.00003068979,0.00036549152,0.00010873691,0.880676,0.0068207406,0.10363543,0.006997759,0.00004229052],"about_ca_topic_score_codex":0.0021729697,"about_ca_topic_score_gemma":0.0015848759,"teacher_disagreement_score":0.0021729697,"about_ca_system_score_codex":0.00058139575,"about_ca_system_score_gemma":0.0006151367,"threshold_uncertainty_score":0.006608844},"labels":[],"label_agreement":null},{"id":"W4403179600","doi":"10.31219/osf.io/34d9u","title":"Maximum Mean Discrepancy and Generative Moment Matching Networks: Tutorial and Survey","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reproducing kernel Hilbert space; Kernel (algebra); Moment (physics); Matching (statistics); Mathematics; Kernel embedding of distributions; Generative grammar; Function (biology); Variable kernel density estimation; Algorithm; Statistics; Applied mathematics; Kernel method; Artificial intelligence; Computer science; Hilbert space; Combinatorics; Pure mathematics; Support vector machine","score_opus":0.02371588229274364,"score_gpt":0.2703653398854374,"score_spread":0.24664945759269374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403179600","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063823485,0.20154014,0.76519823,0.0025151868,0.0007811089,0.00009279892,0.00049763406,0.00046665902,0.022525959],"genre_scores_gemma":[0.24224985,0.30056754,0.4170906,0.0028661413,0.005553522,0.00094760396,0.0023277968,0.0007972612,0.027599687],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990957,0.00030600408,0.00006645262,0.00025053142,0.00023362611,0.000047662565],"domain_scores_gemma":[0.99851614,0.0010779252,0.00009514594,0.00011030577,0.00015746237,0.000043066048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00212402,0.001438206,0.0014180526,0.0015593985,0.00032338683,0.0018816871,0.001719956,0.0022357586,0.003788156],"category_scores_gemma":[0.005733429,0.00082204636,0.00096359605,0.0029685067,0.0011819038,0.0032910628,0.0015950752,0.0024639994,0.0015956857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119086966,0.00015275474,0.0020439061,0.0015804825,0.00016012775,0.00019357339,0.00016832002,0.107804194,0.001941904,0.2376465,0.022057958,0.6261312],"study_design_scores_gemma":[0.000024058854,0.00020898122,0.0019731421,0.0006653556,0.00009873777,0.0009729134,0.00009058821,0.49685237,0.0024379024,0.38596416,0.11059533,0.00011648075],"about_ca_topic_score_codex":0.0017099223,"about_ca_topic_score_gemma":0.0014000288,"teacher_disagreement_score":0.003788156,"about_ca_system_score_codex":0.0012967186,"about_ca_system_score_gemma":0.00081097404,"threshold_uncertainty_score":0.012672603},"labels":[],"label_agreement":null},{"id":"W4403208981","doi":"10.2139/ssrn.4980474","title":"Using Triple Point Alone to Predict Saturation Line","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Triple point; Saturation (graph theory); Point (geometry); Mathematics; Statistics; Computer science; Geometry; Physics; Combinatorics; Thermodynamics","score_opus":0.022893271379957662,"score_gpt":0.2890869613519072,"score_spread":0.26619368997194953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403208981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76822305,0.0008042469,0.20126301,0.00017543406,0.0002959514,0.00009385179,0.0022583753,0.005833576,0.021052405],"genre_scores_gemma":[0.99227834,0.000081420505,0.0054050474,0.000017370363,0.0000140969505,0.000012218325,0.0003528884,0.00009149406,0.0017471417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998907,0.000011336636,0.000004322207,0.0000307932,0.000040046743,0.000022804044],"domain_scores_gemma":[0.99932027,0.00019986449,0.000075156895,0.00007155801,0.00025809932,0.00007502585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023069626,0.00060341397,0.00051788497,0.0016069233,0.00038107517,0.00091013376,0.0004592492,0.00094309874,0.0054236855],"category_scores_gemma":[0.0012639199,0.00023082693,0.00035539255,0.0009748353,0.00020690622,0.0009877327,0.0004062103,0.0008196204,0.0034110732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033450136,0.0005727847,0.13425757,0.00054763915,0.00021983303,0.0012348304,0.0002954134,0.26419777,0.2480366,0.003010617,0.013329987,0.330952],"study_design_scores_gemma":[0.000024770965,0.00032480757,0.018542077,0.000031110372,0.000047054484,0.0002456237,0.00008994412,0.925421,0.050465804,0.0027802847,0.0019884554,0.00003910154],"about_ca_topic_score_codex":0.0023901113,"about_ca_topic_score_gemma":0.002692271,"teacher_disagreement_score":0.0054236855,"about_ca_system_score_codex":0.00037723026,"about_ca_system_score_gemma":0.00022993513,"threshold_uncertainty_score":0.018144071},"labels":[],"label_agreement":null},{"id":"W4403211036","doi":"10.1109/iri62200.2024.00022","title":"Squeeze and Excitation Block Based Neural Architecture Search with Randomization for CNN Construction","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Block (permutation group theory); Architecture; Randomization; Artificial intelligence; Computer architecture; Speech recognition; Mathematics","score_opus":0.00884911561578896,"score_gpt":0.23610611481223262,"score_spread":0.22725699919644365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403211036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02663734,0.00016895011,0.96934265,0.00010336364,0.000023116218,0.000059087117,0.00005247154,0.0018223649,0.0017905743],"genre_scores_gemma":[0.3893904,0.0001295354,0.6044893,0.00018345458,0.00002457387,0.00030475156,0.00030839726,0.00045165193,0.0047179493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996731,0.000105285355,0.000020405982,0.00007181844,0.00009002004,0.00003940884],"domain_scores_gemma":[0.99946934,0.00024173677,0.00006273479,0.00011920865,0.000082923325,0.000023968872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006576283,0.000630833,0.0006005372,0.0004687184,0.0002763852,0.00041929624,0.0008810385,0.000739129,0.004037439],"category_scores_gemma":[0.001957397,0.00040071382,0.00055314833,0.00042458906,0.0005558853,0.0009036801,0.00085914985,0.00090992864,0.0009180024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031193555,0.00012076578,0.0019239817,0.00014550459,0.00008073241,0.00014603927,0.00011115327,0.54555595,0.05742798,0.028114766,0.0038360753,0.36222517],"study_design_scores_gemma":[0.000019190082,0.0000820169,0.00015870776,0.0000059825284,0.000010743286,0.000037880403,0.000008451607,0.9870003,0.0071613276,0.004048872,0.001458908,0.0000076221354],"about_ca_topic_score_codex":0.0017983133,"about_ca_topic_score_gemma":0.004333656,"teacher_disagreement_score":0.004037439,"about_ca_system_score_codex":0.00056294916,"about_ca_system_score_gemma":0.0011212032,"threshold_uncertainty_score":0.013506591},"labels":[],"label_agreement":null},{"id":"W4403278303","doi":"10.1109/fpl64840.2024.00034","title":"A Software-Programmable Neural Processing Unit for Graph Neural Network Inference on FPGAs","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Field-programmable gate array; Artificial neural network; Inference; Software; Graph; Computer architecture; Artificial intelligence; Parallel computing; Embedded system; Theoretical computer science; Programming language","score_opus":0.041482157338701135,"score_gpt":0.3073724189518651,"score_spread":0.265890261613164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403278303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15629253,0.0011906167,0.70301795,0.00062407035,0.00059038785,0.00041807911,0.0015108531,0.085618295,0.05073719],"genre_scores_gemma":[0.7046406,0.00036959662,0.27796605,0.000651943,0.000079224556,0.00038277244,0.0019047653,0.0010411901,0.012963852],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973613,0.000034439414,0.000017566244,0.000074637996,0.000082797116,0.000054426695],"domain_scores_gemma":[0.9997471,0.00006915975,0.000026713655,0.000057200778,0.00007547644,0.000024442814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020826579,0.0007722026,0.00030034332,0.000587328,0.00020297186,0.0006290609,0.0016136026,0.00026165042,0.014082356],"category_scores_gemma":[0.00084919634,0.00030156193,0.00028719177,0.00054417516,0.00021709355,0.00078801654,0.0004199298,0.0006270542,0.002615165],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013018063,0.00041865098,0.0057463264,0.0008144437,0.00025437644,0.0008424823,0.0002790996,0.104976125,0.078511186,0.030116256,0.09838431,0.6783551],"study_design_scores_gemma":[0.00023259223,0.0005889762,0.0019726886,0.0000741726,0.000100942285,0.00036691237,0.000087413355,0.8616327,0.07712123,0.006076428,0.051683217,0.00006268097],"about_ca_topic_score_codex":0.0038638315,"about_ca_topic_score_gemma":0.0057745962,"teacher_disagreement_score":0.014082356,"about_ca_system_score_codex":0.0009143719,"about_ca_system_score_gemma":0.00090507214,"threshold_uncertainty_score":0.04711014},"labels":[],"label_agreement":null},{"id":"W4403305478","doi":"10.1017/9781009302180.008","title":"Iterative Sorting Algorithms","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Sorting algorithm; Computer science; Algorithm; Sorting","score_opus":0.024609334462989595,"score_gpt":0.21715321287285563,"score_spread":0.19254387840986603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403305478","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010791121,0.005107922,0.833113,0.001149449,0.0009051008,0.00020782453,0.00060858123,0.0035322104,0.15429683],"genre_scores_gemma":[0.018692462,0.006533599,0.77706563,0.0015921503,0.00040526045,0.00055971474,0.0021050386,0.0021950374,0.19085106],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99859315,0.00023642746,0.00009600023,0.00021920817,0.000746929,0.000108261615],"domain_scores_gemma":[0.99915826,0.0003522795,0.00003395223,0.00016742597,0.00025381302,0.00003428561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011335415,0.0017425781,0.00094178825,0.0014408214,0.000825805,0.003483105,0.0018217275,0.0015352279,0.06258054],"category_scores_gemma":[0.00433699,0.00072749937,0.001229304,0.0026132814,0.0012013876,0.003956189,0.0020483471,0.0036539272,0.04392626],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041076735,0.00005225188,0.00022105002,0.0005338708,0.00003838677,0.000062980755,0.000252482,0.00976936,0.0017868642,0.36708817,0.1586935,0.46146005],"study_design_scores_gemma":[0.000021709435,0.000040859017,0.00018077633,0.00029453624,0.000014432998,0.00031804302,0.00007335962,0.022605289,0.0024321522,0.2763481,0.69763994,0.00003079207],"about_ca_topic_score_codex":0.001114982,"about_ca_topic_score_gemma":0.0016450775,"teacher_disagreement_score":0.06258054,"about_ca_system_score_codex":0.0015206685,"about_ca_system_score_gemma":0.0015820144,"threshold_uncertainty_score":0.20935279},"labels":[],"label_agreement":null},{"id":"W4403305533","doi":"10.1017/9781009302180.027","title":"Greedy Algorithms","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Algorithm; Greedy algorithm","score_opus":0.023858047411239144,"score_gpt":0.20421969633756581,"score_spread":0.18036164892632667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403305533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013007629,0.009812088,0.75606775,0.0032968908,0.001214576,0.00018004386,0.0009514842,0.00362501,0.22355142],"genre_scores_gemma":[0.034240536,0.01361923,0.67301375,0.0037564472,0.00086833234,0.0007828634,0.0028525495,0.002895994,0.2679703],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991037,0.00020171728,0.00005342713,0.00018299447,0.00038583303,0.00007220367],"domain_scores_gemma":[0.9993849,0.0003039587,0.000026170921,0.0001336947,0.00011972823,0.00003145483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009656082,0.0015953197,0.0008902552,0.00090695196,0.00055068167,0.0032155518,0.0015948792,0.0016735331,0.06527152],"category_scores_gemma":[0.0036733989,0.00058488874,0.00086756464,0.0015992403,0.0010608126,0.00359051,0.0018940154,0.002904403,0.049945425],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039715866,0.00004600079,0.00020789403,0.0005333695,0.00004623036,0.000057893587,0.00013232476,0.0119551765,0.0010744213,0.38694614,0.23096856,0.36799222],"study_design_scores_gemma":[0.000027828784,0.000042198797,0.00021000339,0.00028957744,0.000016612794,0.00031950176,0.00006064729,0.026441997,0.0011152107,0.37774563,0.5937018,0.000028994953],"about_ca_topic_score_codex":0.00073752063,"about_ca_topic_score_gemma":0.0012051499,"teacher_disagreement_score":0.06527152,"about_ca_system_score_codex":0.0011352289,"about_ca_system_score_gemma":0.0011072609,"threshold_uncertainty_score":0.218355},"labels":[],"label_agreement":null},{"id":"W4403306664","doi":"10.1017/9781009302180.004","title":"Iterative Algorithms: Measures of Progress and Loop Invariants","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Loop (graph theory); Algorithm; Computer science; Mathematics; Combinatorics","score_opus":0.03570930877731271,"score_gpt":0.21965086994018027,"score_spread":0.18394156116286756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403306664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002949282,0.022621699,0.8498974,0.0023986802,0.0020248468,0.00011129258,0.00061881926,0.0023424681,0.11703541],"genre_scores_gemma":[0.097655304,0.025919642,0.6937403,0.0018160954,0.0025072969,0.0010161441,0.0018946937,0.003435806,0.17201474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978933,0.00055400823,0.00014501213,0.000426595,0.00088717847,0.00009393185],"domain_scores_gemma":[0.99625874,0.0022296966,0.0002640745,0.00044095836,0.0007167304,0.00008973654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020132514,0.0017775614,0.0010139646,0.0021745327,0.00043547945,0.0051490334,0.0015500122,0.0017629485,0.022825092],"category_scores_gemma":[0.017313987,0.00071356003,0.0007784104,0.0032574928,0.0026561336,0.00865985,0.0018955565,0.0036788757,0.01188792],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006645344,0.000049403607,0.000540112,0.00059076655,0.000041963016,0.000051253097,0.0004319388,0.014116813,0.001163056,0.53974986,0.11454789,0.32865041],"study_design_scores_gemma":[0.000022872338,0.00013012775,0.0011985089,0.0005546563,0.000039379338,0.00032274387,0.00022290063,0.05758645,0.0024082602,0.6591109,0.27831277,0.00009040942],"about_ca_topic_score_codex":0.00076183525,"about_ca_topic_score_gemma":0.00070534274,"teacher_disagreement_score":0.022825092,"about_ca_system_score_codex":0.0015134298,"about_ca_system_score_gemma":0.0010466784,"threshold_uncertainty_score":0.07635754},"labels":[],"label_agreement":null},{"id":"W4403331479","doi":"10.1109/lcsys.2024.3478272","title":"Computation and Formal Verification of Neural Network Contraction Metrics","year":2024,"lang":"en","type":"article","venue":"IEEE Control Systems Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Computation; Artificial neural network; Formal verification; Contraction (grammar); Programming language; Artificial intelligence; Theoretical computer science; Medicine; Internal medicine","score_opus":0.013217673623527331,"score_gpt":0.23479302739458635,"score_spread":0.22157535377105902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403331479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017874517,0.000025392032,0.9788238,0.0001496159,0.000023117811,0.000045356937,0.000055510296,0.0002263195,0.0027763091],"genre_scores_gemma":[0.6632409,0.00008967129,0.3335875,0.00009915487,0.000025919237,0.0002238523,0.00020731446,0.00024526048,0.002280383],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977648,0.00064352085,0.00014329249,0.0003734753,0.0009106371,0.00016427092],"domain_scores_gemma":[0.99431,0.0031599011,0.00047431252,0.0008914717,0.0010659592,0.00009829732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00333945,0.00071894214,0.00057272805,0.0006238897,0.0006212516,0.0018320481,0.0014832873,0.00088504516,0.003302639],"category_scores_gemma":[0.017675964,0.0004439872,0.00087314256,0.00036779672,0.002861236,0.0023666439,0.0027670602,0.0020759779,0.00034691594],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029859997,0.00002389417,0.0005360715,0.00007330027,0.00001743104,0.00015879115,0.00011510655,0.48704454,0.0048762755,0.48946285,0.0005041045,0.017157773],"study_design_scores_gemma":[0.0000060740176,0.000014147292,0.00003708533,0.000008968149,0.00000195776,0.000018846233,0.000012800028,0.9221219,0.0025901392,0.0746825,0.0004998772,0.000005749975],"about_ca_topic_score_codex":0.0022187836,"about_ca_topic_score_gemma":0.0018200756,"teacher_disagreement_score":0.00333945,"about_ca_system_score_codex":0.001613549,"about_ca_system_score_gemma":0.0024502408,"threshold_uncertainty_score":0.017660916},"labels":[],"label_agreement":null},{"id":"W4403370751","doi":"10.48550/arxiv.2408.02654","title":"On Using Quasirandom Sequences in Machine Learning for Model Weight Initialization","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Initialization; Computer science; Artificial intelligence; Mathematics; Programming language","score_opus":0.1229236932282941,"score_gpt":0.23161727841566837,"score_spread":0.10869358518737426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403370751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08557446,0.0015098554,0.9066662,0.0009293366,0.0001124587,0.00015634949,0.00015745287,0.0020721438,0.002821681],"genre_scores_gemma":[0.56667733,0.0009963367,0.42831504,0.000493548,0.000077324745,0.00038722294,0.0007371384,0.00061714865,0.0016988162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975205,0.0016142917,0.000119229735,0.0003460093,0.0003105381,0.00008942857],"domain_scores_gemma":[0.9888475,0.0077598416,0.0007182792,0.0017431269,0.0008078302,0.00012345477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053300913,0.0013918523,0.0007380353,0.00088467717,0.0006108758,0.0010034116,0.0011984593,0.0010187203,0.001240861],"category_scores_gemma":[0.023060892,0.0006681914,0.000591081,0.000806459,0.0015061421,0.003436588,0.0013399866,0.0023913851,0.00073098595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002898764,0.00012483621,0.003768755,0.00015805718,0.00009245941,0.00008803694,0.000120078534,0.86801946,0.004297425,0.020794334,0.0020682728,0.1001784],"study_design_scores_gemma":[0.000031451185,0.00012036228,0.00040283476,0.000027773656,0.0000126936075,0.000035209883,0.000013111703,0.98502064,0.0036916751,0.00982932,0.0007981474,0.000016844835],"about_ca_topic_score_codex":0.0034204312,"about_ca_topic_score_gemma":0.006202062,"teacher_disagreement_score":0.0053300913,"about_ca_system_score_codex":0.0010280213,"about_ca_system_score_gemma":0.0012967027,"threshold_uncertainty_score":0.028188527},"labels":[],"label_agreement":null},{"id":"W4403417080","doi":"10.31219/osf.io/4n2cb","title":"Neural Network Compression and Knowledge Distillation: Tutorial and Survey","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Computer science; Distillation; Compression (physics); Data compression; Quantization (signal processing); Artificial intelligence; Machine learning; Algorithm; Chemistry","score_opus":0.035300544765060324,"score_gpt":0.2970556952759879,"score_spread":0.26175515051092757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403417080","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039272374,0.694916,0.26832286,0.0022090445,0.0017061182,0.00011381397,0.0006459269,0.00061692944,0.027542064],"genre_scores_gemma":[0.042514294,0.8033203,0.12816322,0.0013470155,0.0048048287,0.00030440837,0.0019401157,0.00032138874,0.01728444],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993598,0.000103317136,0.0000787562,0.00013159643,0.0002886874,0.000037841986],"domain_scores_gemma":[0.99923694,0.0004475935,0.000046158675,0.000080873055,0.00016332646,0.00002501966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008562853,0.0012085963,0.0010414118,0.0022455093,0.00034723952,0.0014892913,0.0010574552,0.0012226874,0.005840152],"category_scores_gemma":[0.0025190758,0.0006380387,0.0006735993,0.004571835,0.0008862232,0.0034264503,0.001160293,0.0020422835,0.002837764],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068806155,0.000099504236,0.00041534618,0.0036257776,0.000096280426,0.00013005974,0.000117427706,0.011672765,0.0028254786,0.059975438,0.03255942,0.88841367],"study_design_scores_gemma":[0.000033477,0.00026778766,0.0024629114,0.0023349596,0.00014954625,0.0023448274,0.00015047085,0.11079558,0.012452709,0.19916561,0.66967654,0.00016558598],"about_ca_topic_score_codex":0.0014268794,"about_ca_topic_score_gemma":0.0011418973,"teacher_disagreement_score":0.005840152,"about_ca_system_score_codex":0.000770625,"about_ca_system_score_gemma":0.0008711295,"threshold_uncertainty_score":0.01953727},"labels":[],"label_agreement":null},{"id":"W4403486982","doi":"10.3233/faia240850","title":"NeurCAM: Interpretable Neural Clustering via Additive Models","year":2024,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Artificial intelligence; Computer science; Artificial neural network; Pattern recognition (psychology)","score_opus":0.0339232935883639,"score_gpt":0.2582907061184919,"score_spread":0.224367412530128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403486982","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034799122,0.0003002332,0.99394387,0.00020279795,0.000035709527,0.000026085045,0.00017345251,0.00064339006,0.0011944781],"genre_scores_gemma":[0.20661037,0.00093168806,0.78210306,0.00050070626,0.00014883363,0.00028815167,0.0016793421,0.00052723044,0.007210614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988404,0.00044911713,0.000050253777,0.00027760194,0.00032862715,0.00005408226],"domain_scores_gemma":[0.998516,0.00079340016,0.0001403371,0.00024413658,0.00025936333,0.000046754525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001601302,0.0010871835,0.00095719856,0.0012851182,0.00055301626,0.0018487234,0.0026677994,0.0014666317,0.003813367],"category_scores_gemma":[0.005663828,0.000536531,0.0013820423,0.0016719326,0.0010288488,0.0024024553,0.0019627821,0.002537793,0.0013594131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016050799,0.00007962,0.0011333181,0.00029082265,0.00021898255,0.0001905905,0.00035495157,0.5632631,0.003997319,0.11824964,0.0120951645,0.29996604],"study_design_scores_gemma":[0.0000052619116,0.000010865473,0.0001157986,0.000015848675,0.000011446036,0.00002303911,0.00001853069,0.9476299,0.00062996574,0.049665544,0.0018635279,0.0000102442245],"about_ca_topic_score_codex":0.0046907943,"about_ca_topic_score_gemma":0.008319747,"teacher_disagreement_score":0.0046907943,"about_ca_system_score_codex":0.0011953834,"about_ca_system_score_gemma":0.0010839894,"threshold_uncertainty_score":0.012756944},"labels":[],"label_agreement":null},{"id":"W4403498726","doi":"10.22541/au.172914739.97186665/v1","title":"on “AutoRepar: A method to obtain identifiable and observable reparameterizations of dynamic models with mechanistic insights”: Existence of an identifiable reparameterization","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Observable; Statistical physics; Econometrics; Mathematical economics; Computer science; Applied mathematics; Theoretical physics; Mathematics; Physics; Quantum mechanics","score_opus":0.02272030645448421,"score_gpt":0.3013411536496978,"score_spread":0.27862084719521363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403498726","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027295323,0.000050118153,0.99607444,0.00007864191,0.00001294786,0.000027266755,0.000028906361,0.00017293147,0.00082528684],"genre_scores_gemma":[0.16335246,0.00042676713,0.8281272,0.00034474902,0.00015327615,0.00055286475,0.00036842714,0.0007555205,0.0059187133],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99817514,0.000809556,0.00010241552,0.00041685012,0.00039001272,0.00010595959],"domain_scores_gemma":[0.99324256,0.0038123415,0.0006354657,0.0017215274,0.00039518907,0.000192897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057084183,0.0015898395,0.0015668418,0.0018437558,0.0012516893,0.0016260333,0.002309694,0.002199278,0.007820024],"category_scores_gemma":[0.015786938,0.0011286819,0.0028001827,0.0008620795,0.0045092255,0.005328517,0.006410251,0.004695276,0.0017952695],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016152144,0.00009283661,0.00069503865,0.0003012172,0.00013936957,0.00034893022,0.00057026686,0.10130497,0.0085411025,0.7984006,0.0019310479,0.08751323],"study_design_scores_gemma":[0.00002951115,0.00012632938,0.00018088928,0.000047828562,0.000031216212,0.0001481604,0.0000785579,0.509083,0.0044995593,0.4808251,0.0048793205,0.00007048677],"about_ca_topic_score_codex":0.0005644915,"about_ca_topic_score_gemma":0.0010217665,"teacher_disagreement_score":0.007820024,"about_ca_system_score_codex":0.0008873204,"about_ca_system_score_gemma":0.0010870653,"threshold_uncertainty_score":0.030189335},"labels":[],"label_agreement":null},{"id":"W4403499756","doi":"10.3997/2214-4609.202481003","title":"Clifford Fourier Neural Operators for Learning the Multi-Components Elastic Wavefields","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Fourier transform; Artificial neural network; Artificial intelligence; Mathematics; Mathematical analysis","score_opus":0.03558345156234323,"score_gpt":0.2853966092656984,"score_spread":0.24981315770335516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403499756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021170389,0.00031330544,0.97439164,0.00023709153,0.00010397974,0.000036028872,0.000045568657,0.00016466496,0.0035373906],"genre_scores_gemma":[0.48006374,0.000977645,0.5009612,0.00039027224,0.00017557018,0.00024108427,0.00027833987,0.00019677849,0.016715337],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977213,0.00007983284,0.000014909996,0.000030998202,0.00008054504,0.000021627895],"domain_scores_gemma":[0.9991886,0.00039826566,0.00005924497,0.00012091494,0.00018111228,0.000051815958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015431312,0.0004637922,0.00037387654,0.000806736,0.00037421868,0.0007234904,0.001307845,0.001138129,0.0029884172],"category_scores_gemma":[0.004655552,0.00026744406,0.00038752987,0.0008667405,0.0009704451,0.0019111633,0.0011994981,0.0016708625,0.0005369815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016381724,0.00016134178,0.00090306293,0.00019476426,0.00005304832,0.00008948366,0.0001452027,0.36469242,0.012385358,0.401254,0.0043598507,0.21559767],"study_design_scores_gemma":[0.000004128781,0.000008878427,0.00006795078,0.0000073343526,0.0000023796356,0.000013461999,0.000008602395,0.96519077,0.0008169306,0.03339677,0.00047736109,0.0000054406028],"about_ca_topic_score_codex":0.0018495533,"about_ca_topic_score_gemma":0.0027269141,"teacher_disagreement_score":0.0029884172,"about_ca_system_score_codex":0.00052541256,"about_ca_system_score_gemma":0.00074525696,"threshold_uncertainty_score":0.009997308},"labels":[],"label_agreement":null},{"id":"W4403511241","doi":"10.1109/tnsm.2024.3483013","title":"A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Algorithm; Data mining","score_opus":0.04574966949544952,"score_gpt":0.2960385690265718,"score_spread":0.25028889953112227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403511241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028171546,0.000087159926,0.9962943,0.00006619644,0.000023168497,0.000026841602,0.000047587746,0.00040985917,0.0002277219],"genre_scores_gemma":[0.093188345,0.0003273904,0.90219796,0.0001301233,0.00008130213,0.00013036204,0.0008162307,0.00021689088,0.002911382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987668,0.00020934975,0.000090946705,0.00028825097,0.0005285521,0.000116194446],"domain_scores_gemma":[0.99790657,0.00053149526,0.00021497051,0.00045048643,0.00073827896,0.0001582216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018332177,0.0011556332,0.0012905261,0.0012474117,0.0006950867,0.0010103019,0.001704651,0.0011465045,0.0025382447],"category_scores_gemma":[0.0053120237,0.00052395696,0.0012725798,0.0016569983,0.0010304658,0.0024304537,0.0016364473,0.0029866814,0.0016349776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042531776,0.00029831438,0.001269318,0.00030690926,0.000095742,0.00022776803,0.00025708656,0.34169614,0.038226258,0.025950281,0.011408711,0.5798381],"study_design_scores_gemma":[0.000010811564,0.000045739485,0.00015188214,0.000007110081,0.0000069383054,0.00006231251,0.000021232328,0.9871321,0.006578843,0.004039095,0.001924478,0.000019489493],"about_ca_topic_score_codex":0.007005119,"about_ca_topic_score_gemma":0.006063864,"teacher_disagreement_score":0.007005119,"about_ca_system_score_codex":0.0006985588,"about_ca_system_score_gemma":0.0025439325,"threshold_uncertainty_score":0.013928711},"labels":[],"label_agreement":null},{"id":"W4403528304","doi":"10.1088/2632-2153/ad88d5","title":"Robust quantum dots charge autotuning using neural network uncertainty","year":2024,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute; University of Waterloo; National Research Council Canada; Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Innovation, Science and Economic Development Canada; Institut Périmètre de physique théorique; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Ministero dello Sviluppo Economico","keywords":"Charge (physics); Artificial neural network; Quantum dot; Computer science; Quantum; Artificial intelligence; Neuroscience; Physics; Biology; Optoelectronics; Quantum mechanics","score_opus":0.022674843618693777,"score_gpt":0.2638208348481396,"score_spread":0.24114599122944586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403528304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23391834,0.0003996952,0.7601412,0.0002301071,0.00004320394,0.000067674715,0.00009518706,0.0019301316,0.0031745348],"genre_scores_gemma":[0.9545049,0.00004509372,0.044799007,0.000041374242,0.000007908637,0.00003207063,0.000039924915,0.00006670199,0.00046297442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999556,0.00009026505,0.00002211454,0.00013271079,0.00015656058,0.00004225586],"domain_scores_gemma":[0.9988991,0.00057979516,0.00016930634,0.000192945,0.00012823081,0.000030558505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010423379,0.00047614836,0.00048962136,0.00046463578,0.00028783924,0.0006969639,0.0007052224,0.0004174592,0.00093342457],"category_scores_gemma":[0.0034095417,0.00026455623,0.00031002826,0.00026894244,0.00048268127,0.00097483944,0.0009623904,0.00066546287,0.000172086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026379852,0.00010192283,0.0019245425,0.0001394731,0.00006737118,0.000078614125,0.00009487341,0.74758035,0.09017651,0.005733723,0.00050647015,0.15333232],"study_design_scores_gemma":[0.000004735676,0.000020027988,0.00023938986,0.000004141334,0.0000036305398,0.000010101784,0.0000041179755,0.98574936,0.01226931,0.0014820613,0.0002053228,0.000007732921],"about_ca_topic_score_codex":0.0012720211,"about_ca_topic_score_gemma":0.0016901426,"teacher_disagreement_score":0.0012720211,"about_ca_system_score_codex":0.00055204256,"about_ca_system_score_gemma":0.0005217146,"threshold_uncertainty_score":0.005512476},"labels":[],"label_agreement":null},{"id":"W4403534694","doi":"10.1109/codit62066.2024.10708148","title":"Least Squares Solution for Training of Two-Layer Quadratic Neural Networks with Applications to System Identification","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Identification (biology); Artificial neural network; Computer science; Layer (electronics); Least-squares function approximation; Training (meteorology); Quadratic equation; Training set; Artificial intelligence; Machine learning; Mathematics; Statistics; Biology; Chemistry","score_opus":0.0429141255186952,"score_gpt":0.29725494433427174,"score_spread":0.2543408188155765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403534694","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018926306,0.00016700082,0.9968491,0.00009847666,0.000017725248,0.000012299425,0.0000072194966,0.00011393082,0.00084153947],"genre_scores_gemma":[0.34350553,0.00071484357,0.6469266,0.0001646015,0.00007784142,0.0002508337,0.000111062465,0.00023337234,0.00801543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956745,0.00018094663,0.000024009067,0.000073413954,0.00012253612,0.000031692634],"domain_scores_gemma":[0.9988142,0.0007978352,0.00006685695,0.00004204769,0.00025906943,0.000019856523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010719611,0.0008179371,0.00055898505,0.00032768017,0.00039591416,0.00071258756,0.00065773545,0.0012214222,0.0025956552],"category_scores_gemma":[0.0045573143,0.0004940279,0.00039858013,0.00063165417,0.00071070186,0.0007648632,0.0008148394,0.0014727882,0.0005629366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030166477,0.000025526817,0.00013706388,0.00013291326,0.000017531964,0.000038244678,0.00009095901,0.9320509,0.0028724659,0.012059217,0.0009098838,0.051635124],"study_design_scores_gemma":[0.0000016253135,0.000007326194,0.00001748567,0.0000037095385,0.000001110474,0.0000057238,0.0000037231232,0.99778557,0.0003635738,0.0014952464,0.00031250974,0.0000023388136],"about_ca_topic_score_codex":0.0043723322,"about_ca_topic_score_gemma":0.004155932,"teacher_disagreement_score":0.0043723322,"about_ca_system_score_codex":0.0006313632,"about_ca_system_score_gemma":0.0010151366,"threshold_uncertainty_score":0.008693755},"labels":[],"label_agreement":null},{"id":"W4403577866","doi":"10.1145/3627673.3679876","title":"Attentional Neural Integral Equation for Temporal Knowledge Graph Forecasting","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; National Science and Technology Major Project; China Postdoctoral Science Foundation; National Natural Science Foundation of China; Idaho State University","keywords":"Computer science; Graph; Artificial neural network; Artificial intelligence; Knowledge graph; Theoretical computer science","score_opus":0.1006774045307974,"score_gpt":0.31165645654231094,"score_spread":0.21097905201151354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403577866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02636373,0.00077869935,0.9685435,0.000563776,0.00012028534,0.00003105485,0.0003109111,0.0005349817,0.002752907],"genre_scores_gemma":[0.8490732,0.0011749809,0.14127658,0.00037738035,0.00012955055,0.00012864257,0.0009883457,0.00011285158,0.006738461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978536,0.000027012573,0.000014728953,0.00006537826,0.00007063785,0.000036760124],"domain_scores_gemma":[0.9992556,0.00046638935,0.000068333975,0.00003636091,0.00014015673,0.000033007047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006639994,0.0005374216,0.0007187075,0.0007635353,0.00028536716,0.0007175044,0.0013811495,0.00109829,0.0024230753],"category_scores_gemma":[0.003726967,0.00032941953,0.0006705585,0.0011367395,0.00048578493,0.0014989397,0.00068657566,0.0016314649,0.00028528637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035780904,0.00003400865,0.0012219704,0.000058102516,0.000030755862,0.0000976117,0.000046259927,0.9200852,0.0014615896,0.022657134,0.0020636667,0.05220785],"study_design_scores_gemma":[7.395371e-7,0.0000011095501,0.000030160107,0.0000012430448,0.0000016592141,0.0000028968436,0.0000011605649,0.9981664,0.00006526007,0.0016415793,0.00008670696,9.870283e-7],"about_ca_topic_score_codex":0.037685808,"about_ca_topic_score_gemma":0.029099183,"teacher_disagreement_score":0.037685808,"about_ca_system_score_codex":0.001351088,"about_ca_system_score_gemma":0.0015119705,"threshold_uncertainty_score":0.07493293},"labels":[],"label_agreement":null},{"id":"W4403588989","doi":"10.36227/techrxiv.172954544.45684627/v1","title":"Stochastic-Based Morphological Neural Network VLSI Design with 25K Synapses","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Agencia Estatal de Investigación; Ministerio de Ciencia e Innovación; China Scholarship Council; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; European Commission; University of Alberta","keywords":"Very-large-scale integration; Artificial neural network; Computer science; Neuroscience; Computer architecture; Artificial intelligence; Biology; Embedded system","score_opus":0.04009238015866208,"score_gpt":0.26100354032632406,"score_spread":0.220911160167662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403588989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.098268814,0.00054161786,0.88270193,0.0003448036,0.00011567617,0.0001254967,0.00020513372,0.0013167146,0.016379843],"genre_scores_gemma":[0.6847041,0.00031518828,0.3096367,0.00010428667,0.000025830763,0.00012798836,0.00015206911,0.000048109654,0.0048856884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998623,0.00001534093,0.00001058882,0.000028676677,0.00006651307,0.00001652433],"domain_scores_gemma":[0.9998838,0.0000182535,0.00002408667,0.000018150942,0.00004717538,0.0000085772435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001589657,0.00022476414,0.00023310007,0.00020911406,0.000206768,0.0005011203,0.00095116714,0.0003746727,0.0022329595],"category_scores_gemma":[0.00031753787,0.00016383734,0.0002832364,0.00027763206,0.00019681235,0.00035081865,0.00024362626,0.0002285764,0.00062707334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018201517,0.00013765726,0.00120875,0.00045364487,0.00014325371,0.00038270597,0.000095894815,0.4225423,0.26177132,0.04564277,0.003486931,0.26395276],"study_design_scores_gemma":[0.000023659402,0.00024789368,0.00085033407,0.000017795588,0.000038241356,0.00034197926,0.000013406309,0.94298077,0.04011369,0.005270334,0.010081859,0.000020095087],"about_ca_topic_score_codex":0.0010597698,"about_ca_topic_score_gemma":0.002426937,"teacher_disagreement_score":0.0022329595,"about_ca_system_score_codex":0.0005639537,"about_ca_system_score_gemma":0.0007491698,"threshold_uncertainty_score":0.0074700117},"labels":[],"label_agreement":null},{"id":"W4403605386","doi":"10.1088/1742-5468/ad6427","title":"Long sequence Hopfield memory*","year":2024,"lang":"en","type":"article","venue":"Journal of Statistical Mechanics Theory and Experiment","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sequence (biology); Hopfield network; Computer science; Artificial intelligence; Artificial neural network; Biology; Genetics","score_opus":0.020039673521377067,"score_gpt":0.3094714071806634,"score_spread":0.2894317336592863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403605386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4664239,0.0017386533,0.4977033,0.0011321354,0.00026261795,0.00006741789,0.00036517088,0.0010890729,0.031217724],"genre_scores_gemma":[0.9878685,0.00019385769,0.008180579,0.000068535526,0.00003726667,0.000018062381,0.00005616885,0.000016733211,0.0035602998],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998826,0.000014580057,0.000009092436,0.00003409509,0.000034415498,0.000025248746],"domain_scores_gemma":[0.99937075,0.00022381713,0.00010564419,0.000113014794,0.00011294691,0.0000738319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031456,0.00023682373,0.0003615723,0.0003690689,0.00032289393,0.0006240425,0.00084177003,0.0005272072,0.0051848604],"category_scores_gemma":[0.0013609254,0.00013510017,0.0002586088,0.0003370643,0.0008540697,0.0015924067,0.00046527627,0.00040355022,0.00034731187],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002793931,0.00013889612,0.0021803633,0.00029909262,0.000121168494,0.00065242156,0.00017561312,0.42542458,0.042172596,0.4591378,0.0038137394,0.06560428],"study_design_scores_gemma":[0.000024060222,0.00005634245,0.0006333003,0.000018661487,0.000015699916,0.00012031419,0.000014947099,0.78968203,0.0053907945,0.20238292,0.0016403821,0.000020548008],"about_ca_topic_score_codex":0.0025812306,"about_ca_topic_score_gemma":0.0017206038,"teacher_disagreement_score":0.0051848604,"about_ca_system_score_codex":0.00059945957,"about_ca_system_score_gemma":0.00041355772,"threshold_uncertainty_score":0.01734507},"labels":[],"label_agreement":null},{"id":"W4403605673","doi":"10.1088/1742-5468/ad642b","title":"Dynamics of finite width Kernel and prediction fluctuations in mean field neural networks<sup>*</sup>","year":2024,"lang":"en","type":"article","venue":"Journal of Statistical Mechanics Theory and Experiment","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Statistical physics; Dynamics (music); Artificial neural network; Mean field theory; Kernel (algebra); Physics; Field (mathematics); Mathematics; Computer science; Quantum mechanics; Artificial intelligence; Combinatorics; Pure mathematics","score_opus":0.00921355551361523,"score_gpt":0.26273330701720066,"score_spread":0.2535197515035854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403605673","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6582766,0.0011630368,0.31219545,0.0056733508,0.00029302202,0.00004111598,0.00037881415,0.0014114594,0.020567043],"genre_scores_gemma":[0.98887235,0.00019594183,0.0068509756,0.00023932819,0.00006840173,0.000027888509,0.00010608473,0.00021427844,0.0034247492],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996946,0.00009046779,0.0000109378525,0.000050470597,0.00009999156,0.0000535204],"domain_scores_gemma":[0.99690515,0.0018478095,0.00035213146,0.00024712205,0.0003497302,0.00029805428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010141921,0.00038041963,0.00048881903,0.00062559557,0.00054154504,0.001131052,0.0011574666,0.0008497597,0.0040763724],"category_scores_gemma":[0.011288426,0.00031060932,0.00044250058,0.00032327586,0.0016863043,0.0026375204,0.00093407754,0.0016908483,0.0003902343],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002154697,0.00009831046,0.0033108206,0.00013970029,0.00006288258,0.00042269035,0.00029952946,0.4656787,0.013238999,0.49460164,0.00754478,0.014386573],"study_design_scores_gemma":[0.0000045545235,0.000012722852,0.00060480594,0.000009957685,0.0000044854,0.000021184813,0.000013320089,0.95332,0.0008170009,0.04490998,0.00027085622,0.00001107436],"about_ca_topic_score_codex":0.004614007,"about_ca_topic_score_gemma":0.00256367,"teacher_disagreement_score":0.004614007,"about_ca_system_score_codex":0.0014079228,"about_ca_system_score_gemma":0.00062075147,"threshold_uncertainty_score":0.013636768},"labels":[],"label_agreement":null},{"id":"W4403621105","doi":"10.48550/arxiv.2409.06890","title":"Learning Representations for Independence Testing","year":2024,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Independence (probability theory); Parametric statistics; Artificial intelligence; Computer science; Machine learning; Econometrics; Mathematics; Statistics","score_opus":0.10295108457041083,"score_gpt":0.33716105855472084,"score_spread":0.23420997398431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403621105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077459505,0.0002351481,0.98832816,0.00067071675,0.00004440119,0.000091644724,0.0002709389,0.00042313137,0.0021898595],"genre_scores_gemma":[0.45510113,0.0006395516,0.53437644,0.001417721,0.00055728445,0.0013667294,0.0026129228,0.0006202076,0.0033079712],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98220074,0.011003356,0.00088021095,0.0026038063,0.0026297877,0.0006820584],"domain_scores_gemma":[0.9054989,0.07630687,0.004398964,0.008249447,0.004119373,0.0014264915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018601995,0.0023765166,0.0025908474,0.004261795,0.0011027781,0.003884567,0.003941099,0.0033514253,0.008182158],"category_scores_gemma":[0.1229704,0.0010681562,0.0023375633,0.003048252,0.006455554,0.008329128,0.0067528565,0.0077479924,0.0015036422],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033630015,0.00024207517,0.0056015193,0.00035853533,0.00023912282,0.00022759855,0.00031498287,0.15927349,0.0011654638,0.63622624,0.0075776665,0.188437],"study_design_scores_gemma":[0.000050666513,0.00009025867,0.00045903499,0.000069275826,0.000022299508,0.00007561963,0.000034386703,0.47509992,0.0007706775,0.5217101,0.0015857683,0.000031982257],"about_ca_topic_score_codex":0.0016191332,"about_ca_topic_score_gemma":0.001262176,"teacher_disagreement_score":0.018601995,"about_ca_system_score_codex":0.0023713885,"about_ca_system_score_gemma":0.0030135836,"threshold_uncertainty_score":0.09837794},"labels":[],"label_agreement":null},{"id":"W4403666564","doi":"10.48550/arxiv.2409.09111","title":"Transformers from Diffusion: A Unified Framework for Neural Message Passing","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Shanghai Jiao Tong University; National Natural Science Foundation of China; Institute for Catastrophic Loss Reduction","keywords":"Diffusion; Message passing; Computer science; Energy (signal processing); Distributed computing; Physics; Thermodynamics","score_opus":0.06988674151167362,"score_gpt":0.21287287189657889,"score_spread":0.14298613038490526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403666564","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033265038,0.0001110987,0.994919,0.00027006268,0.000032917127,0.000019722052,0.000051055245,0.00014443947,0.0011252398],"genre_scores_gemma":[0.5238746,0.0014173677,0.46022955,0.00049561117,0.00026692537,0.00039147038,0.00036947033,0.00031057902,0.012644537],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943334,0.00020316851,0.000044408574,0.000118079246,0.00015273494,0.000048188966],"domain_scores_gemma":[0.9988224,0.00065106084,0.00011287213,0.0001701301,0.00017725925,0.00006623935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016091167,0.0007779251,0.0007536411,0.0010587869,0.00043301022,0.00159028,0.002011005,0.0013405037,0.0030185105],"category_scores_gemma":[0.0053813946,0.0004451573,0.0010832811,0.00094459206,0.0016331613,0.004708091,0.0019093562,0.0025167873,0.00061281765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041567106,0.000020205149,0.00026280605,0.00007344629,0.00002853195,0.00006902135,0.00012768514,0.23303185,0.0019329835,0.73130673,0.001180663,0.031924468],"study_design_scores_gemma":[0.000007258459,0.000018851208,0.000032135285,0.00000730167,0.0000070430606,0.000024129697,0.000009388468,0.7716447,0.0006010703,0.22644624,0.0011946559,0.000007280081],"about_ca_topic_score_codex":0.002804392,"about_ca_topic_score_gemma":0.0024977599,"teacher_disagreement_score":0.0030185105,"about_ca_system_score_codex":0.0015330433,"about_ca_system_score_gemma":0.0012526487,"threshold_uncertainty_score":0.011123061},"labels":[],"label_agreement":null},{"id":"W4403680373","doi":"10.3390/e26110899","title":"Interval-Valued Random Matrices","year":2024,"lang":"en","type":"article","venue":"Entropy","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"North Carolina Agricultural and Technical State University","keywords":"Frequentist inference; Computer science; Matrix norm; Estimator; Statistical inference; Inference; Bayesian probability; Interval (graph theory); Random matrix; Algorithm; Bayesian inference; Data mining; Mathematics; Artificial intelligence; Statistics","score_opus":0.011267087041849037,"score_gpt":0.26181700707641853,"score_spread":0.2505499200345695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403680373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030913288,0.0003061466,0.9943764,0.00020413006,0.00005211135,0.000018918172,0.00014164495,0.000079061974,0.001730301],"genre_scores_gemma":[0.41512823,0.0022554179,0.5761577,0.0004816376,0.0005125173,0.00031581178,0.0007271637,0.00012609077,0.00429541],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962424,0.0016584486,0.0001940643,0.000836034,0.00092409656,0.00014503895],"domain_scores_gemma":[0.98637086,0.009242724,0.0017745295,0.0012356195,0.0011037018,0.00027249948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041135103,0.0007791474,0.0010052957,0.0024126307,0.0005049194,0.0032281117,0.0019102796,0.0011698026,0.0041625146],"category_scores_gemma":[0.021977339,0.00045429016,0.0008581533,0.002235429,0.002734464,0.004628053,0.0018085119,0.002280234,0.0008798212],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028574423,0.000020290376,0.0010505108,0.00012279567,0.000048746555,0.00012945574,0.00013254907,0.0685242,0.0011562499,0.9008857,0.0010115805,0.026889319],"study_design_scores_gemma":[0.0000093384415,0.00003955467,0.00055073487,0.000063106156,0.000017021763,0.00017651595,0.000049933788,0.38126725,0.00067572366,0.6107308,0.0063818498,0.000038131686],"about_ca_topic_score_codex":0.0010143052,"about_ca_topic_score_gemma":0.0007562311,"teacher_disagreement_score":0.0041625146,"about_ca_system_score_codex":0.001039408,"about_ca_system_score_gemma":0.0010252598,"threshold_uncertainty_score":0.021754563},"labels":[],"label_agreement":null},{"id":"W4403681732","doi":"10.1016/j.dsp.2024.104832","title":"Physical Knowledge and Data-Driven Technologies for Integrated Sensing, Communication and Computing","year":2024,"lang":"en","type":"editorial","venue":"Digital Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"National Natural Science Foundation of China","keywords":"Computer science; Data science; Distributed computing; Human–computer interaction; Theoretical computer science","score_opus":0.023204717664575405,"score_gpt":0.3125603284317976,"score_spread":0.2893556107672222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403681732","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000035165427,0.012883118,0.00081190956,0.02748578,0.95614445,0.000011983354,0.000027771142,0.000037981867,0.002561789],"genre_scores_gemma":[0.00083671213,0.00910058,0.0004139774,0.011686034,0.96051514,0.000022650767,0.000018286106,0.000040323714,0.01736631],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942941,0.0009806379,0.00048443023,0.00052122073,0.0034799716,0.0002395742],"domain_scores_gemma":[0.9809404,0.010721008,0.0007148657,0.00062365615,0.0052534225,0.0017467458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008046795,0.0029507326,0.0026500078,0.0037134716,0.0030107442,0.008885718,0.0033231925,0.01610366,0.011982395],"category_scores_gemma":[0.018513696,0.0012020452,0.001959344,0.0017087278,0.005071084,0.0062138024,0.0023883793,0.02029679,0.006953469],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003122282,0.000016047685,0.000016081007,0.00034417628,0.000031262207,0.000119567456,0.000021969503,0.00009134426,0.00016652017,0.0046150587,0.98072344,0.013823216],"study_design_scores_gemma":[0.00003083724,0.000022319204,0.00009163414,0.00019732665,0.000035187557,0.00015719846,0.000025536607,0.00037955842,0.00017631521,0.008013637,0.99085253,0.00001786434],"about_ca_topic_score_codex":0.0012838776,"about_ca_topic_score_gemma":0.0041512535,"teacher_disagreement_score":0.01610366,"about_ca_system_score_codex":0.0033391914,"about_ca_system_score_gemma":0.0029466362,"threshold_uncertainty_score":0.042555988},"labels":[],"label_agreement":null},{"id":"W4403711778","doi":"10.20944/preprints202410.1853.v2","title":"Data Augmentation Strategies for Improved PM&lt;sub&gt;2.5&lt;/sub&gt; Forecasting Using Transformer Architectures","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transformer; Computer science; Electrical engineering; Engineering; Voltage","score_opus":0.22496572936782733,"score_gpt":0.37311280334219604,"score_spread":0.14814707397436871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403711778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25531888,0.0012410461,0.72946775,0.00089548546,0.00034908383,0.00011570237,0.0005177618,0.0061594103,0.005934827],"genre_scores_gemma":[0.9112249,0.0002675818,0.08481181,0.0002814838,0.000059769325,0.0000845445,0.0008296,0.00012329104,0.0023170023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999785,0.000050045408,0.00001654022,0.000060542297,0.00004594137,0.000042002506],"domain_scores_gemma":[0.9993986,0.00023318046,0.00004328685,0.000073571355,0.00021135081,0.000040079667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009825138,0.0009380807,0.00065587915,0.00043292975,0.0003548984,0.00067290926,0.0013082225,0.00056998775,0.0018947645],"category_scores_gemma":[0.0023032213,0.00041572534,0.0008157678,0.00040034135,0.00044056764,0.0010915826,0.00079023826,0.0016250019,0.0007617768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027354225,0.0001871199,0.0037210737,0.00005255087,0.000067782494,0.00009466539,0.00012556398,0.8403103,0.007236873,0.002044062,0.0028466783,0.14303982],"study_design_scores_gemma":[0.000002638792,0.000013004344,0.00010440025,0.0000018604505,0.0000034066277,0.000002979321,0.0000057845273,0.99853706,0.0007788845,0.00039018353,0.0001573648,0.000002384746],"about_ca_topic_score_codex":0.020793278,"about_ca_topic_score_gemma":0.021741154,"teacher_disagreement_score":0.020793278,"about_ca_system_score_codex":0.0007062548,"about_ca_system_score_gemma":0.0011757341,"threshold_uncertainty_score":0.041344523},"labels":[],"label_agreement":null},{"id":"W4403712048","doi":"10.20944/preprints202410.1853.v1","title":"Data Augmentation Strategies for Improved PM&lt;sub&gt;2.5&lt;/sub&gt; Forecasting Using Transformer Architectures","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transformer; Environmental science; Computer science; Electrical engineering; Engineering; Voltage","score_opus":0.22496572936782733,"score_gpt":0.37311280334219604,"score_spread":0.14814707397436871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403712048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25531888,0.0012410461,0.72946775,0.00089548546,0.00034908383,0.00011570237,0.0005177618,0.0061594103,0.005934827],"genre_scores_gemma":[0.9112249,0.0002675818,0.08481181,0.0002814838,0.000059769325,0.0000845445,0.0008296,0.00012329104,0.0023170023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999785,0.000050045408,0.00001654022,0.000060542297,0.00004594137,0.000042002506],"domain_scores_gemma":[0.9993986,0.00023318046,0.00004328685,0.000073571355,0.00021135081,0.000040079667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009825138,0.0009380807,0.00065587915,0.00043292975,0.0003548984,0.00067290926,0.0013082225,0.00056998775,0.0018947645],"category_scores_gemma":[0.0023032213,0.00041572534,0.0008157678,0.00040034135,0.00044056764,0.0010915826,0.00079023826,0.0016250019,0.0007617768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027354225,0.0001871199,0.0037210737,0.00005255087,0.000067782494,0.00009466539,0.00012556398,0.8403103,0.007236873,0.002044062,0.0028466783,0.14303982],"study_design_scores_gemma":[0.000002638792,0.000013004344,0.00010440025,0.0000018604505,0.0000034066277,0.000002979321,0.0000057845273,0.99853706,0.0007788845,0.00039018353,0.0001573648,0.000002384746],"about_ca_topic_score_codex":0.020793278,"about_ca_topic_score_gemma":0.021741154,"teacher_disagreement_score":0.020793278,"about_ca_system_score_codex":0.0007062548,"about_ca_system_score_gemma":0.0011757341,"threshold_uncertainty_score":0.041344523},"labels":[],"label_agreement":null},{"id":"W4403713454","doi":"10.48550/arxiv.2409.09323","title":"Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Fourier transform; Artificial neural network; Fourier analysis; Fourier series; Computer science; Mathematics; Artificial intelligence; Mathematical analysis","score_opus":0.04741009380113815,"score_gpt":0.1979860626677815,"score_spread":0.15057596886664334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403713454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018877177,0.00031891686,0.97759277,0.00021705288,0.000059093694,0.000030613577,0.000098172575,0.00061961357,0.0021865638],"genre_scores_gemma":[0.6763913,0.00067422143,0.31076342,0.00027288918,0.0001127916,0.00022767826,0.0005929051,0.00021402552,0.010750766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996693,0.00009955439,0.000021334907,0.000075222735,0.00009806255,0.000036526348],"domain_scores_gemma":[0.99923706,0.0003421311,0.00010069875,0.00013943235,0.0001411863,0.000039599567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061874674,0.00069678493,0.00061169144,0.00057854515,0.00030038916,0.0010404494,0.0013291909,0.0011013157,0.0024510708],"category_scores_gemma":[0.0030794013,0.00043093078,0.0006476436,0.0006105141,0.0007039412,0.002150322,0.0014598068,0.0015492331,0.000861066],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013709946,0.00006159141,0.0007929402,0.00009478641,0.000055754594,0.00009222698,0.00009804909,0.72446096,0.006393704,0.04663118,0.0027065806,0.2184752],"study_design_scores_gemma":[0.0000022750326,0.00000954213,0.00003140097,0.000003378164,0.0000023587502,0.000010438605,0.00000371104,0.993767,0.00047826342,0.005396155,0.00029160726,0.0000038040298],"about_ca_topic_score_codex":0.0031178216,"about_ca_topic_score_gemma":0.003928891,"teacher_disagreement_score":0.0031178216,"about_ca_system_score_codex":0.0007007589,"about_ca_system_score_gemma":0.0007140682,"threshold_uncertainty_score":0.008199632},"labels":[],"label_agreement":null},{"id":"W4403800865","doi":"10.1101/2024.10.25.620272","title":"Brain-like learning with exponentiated gradients","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Psychology; Computer science; Mathematics; Artificial intelligence","score_opus":0.011637183480582257,"score_gpt":0.21123340639958926,"score_spread":0.199596222919007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403800865","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067961134,0.00018732445,0.9292941,0.00030163673,0.000047647776,0.000038285114,0.000036151745,0.0005075571,0.0016262099],"genre_scores_gemma":[0.72582805,0.00015253035,0.270431,0.0002270696,0.000036331756,0.000090211935,0.00010350487,0.00014718894,0.0029841173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976486,0.00009976236,0.00001716723,0.0000531131,0.00004160483,0.00002354916],"domain_scores_gemma":[0.99892044,0.0006246268,0.00010983261,0.00013234172,0.00016535148,0.000047391026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010371731,0.00048620836,0.0005707312,0.0003884288,0.00015214339,0.0006350009,0.0007883361,0.0008779764,0.0013206267],"category_scores_gemma":[0.0040706904,0.00029771027,0.00037203837,0.00031720925,0.0008854513,0.0010877273,0.0008589043,0.0009287432,0.00032543647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011171831,0.000036336514,0.0010710991,0.00006185955,0.0000349181,0.00006863442,0.000060515507,0.92451847,0.0038690185,0.028886296,0.00095271866,0.040328488],"study_design_scores_gemma":[0.0000073636215,0.000014762307,0.00004707375,0.0000031048326,0.0000014193095,0.0000095726,0.0000015810456,0.99232554,0.00051959854,0.0069066496,0.00016115526,0.0000022366212],"about_ca_topic_score_codex":0.0013488233,"about_ca_topic_score_gemma":0.0010452594,"teacher_disagreement_score":0.0013488233,"about_ca_system_score_codex":0.0005413146,"about_ca_system_score_gemma":0.00049927895,"threshold_uncertainty_score":0.005485177},"labels":[],"label_agreement":null},{"id":"W4403851978","doi":"10.48550/arxiv.2410.01643","title":"Stable Offline Value Function Learning with Bisimulation-based Representations","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Sandia National Laboratories; University of Wisconsin-Madison; Institute for Catastrophic Loss Reduction; National Science Foundation","keywords":"Bisimulation; Value (mathematics); Function (biology); Computer science; Mathematics; Theoretical computer science; Artificial intelligence; Machine learning","score_opus":0.05318057816770258,"score_gpt":0.19938301841675915,"score_spread":0.14620244024905657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403851978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03142791,0.000130123,0.9661451,0.00025411206,0.000024456825,0.00007531401,0.00003923194,0.00065022823,0.0012534667],"genre_scores_gemma":[0.7910077,0.00014988055,0.2053302,0.0002597126,0.000035358033,0.00038712975,0.00020551113,0.00029643057,0.0023280832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997973,0.0009185838,0.00013310232,0.00041875854,0.0003500861,0.00020638465],"domain_scores_gemma":[0.99057966,0.006146359,0.00093639793,0.001103377,0.00092121377,0.00031296545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038424854,0.0014687064,0.0016537957,0.0007923107,0.00061697955,0.0018251863,0.0021934123,0.0018677189,0.002303423],"category_scores_gemma":[0.020924337,0.0008862021,0.00082963466,0.00056552223,0.0023565835,0.004290079,0.003168636,0.003724811,0.00068845093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002735006,0.00019296647,0.001371047,0.000087345376,0.00006888021,0.000059249258,0.00018785144,0.8550515,0.0023329305,0.058494963,0.0009206113,0.08095912],"study_design_scores_gemma":[0.0000094587185,0.00003135604,0.000024718689,0.0000066853886,0.0000029928262,0.0000051101,0.000005127894,0.98484796,0.0006096525,0.014337188,0.00011503697,0.000004719557],"about_ca_topic_score_codex":0.0035169986,"about_ca_topic_score_gemma":0.0027833818,"teacher_disagreement_score":0.0038424854,"about_ca_system_score_codex":0.0023703137,"about_ca_system_score_gemma":0.0025196834,"threshold_uncertainty_score":0.02032125},"labels":[],"label_agreement":null},{"id":"W4403851989","doi":"10.48550/arxiv.2410.01686","title":"Positional Attention: Expressivity and Learnability of Algorithmic Computation","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alexander S. Onassis Public Benefit Foundation; DeepMind","keywords":"Generalization; Computer science; Expressivity; Artificial intelligence; Artificial neural network; Distribution (mathematics); Cognitive science; Psychology; Mathematics","score_opus":0.04893848728949905,"score_gpt":0.19983943773253954,"score_spread":0.15090095044304047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403851989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21909107,0.00042738992,0.769324,0.0017499404,0.00003443997,0.000069799666,0.00025791262,0.0013689493,0.0076765283],"genre_scores_gemma":[0.9354227,0.00042558962,0.060245637,0.000289583,0.00006987708,0.00012092667,0.0003249741,0.00031077053,0.0027898622],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976966,0.00075237104,0.00015454923,0.0005886108,0.0005417275,0.00026619626],"domain_scores_gemma":[0.97723,0.015512593,0.0014818916,0.0040837997,0.0011868617,0.00050476263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003114896,0.00074093055,0.00087238336,0.0010019118,0.00071291294,0.0028583345,0.0022487084,0.0015616273,0.0039289286],"category_scores_gemma":[0.03614186,0.00083495857,0.0010880723,0.0010648698,0.0035516366,0.010189645,0.002922085,0.0029966575,0.00053615245],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006316974,0.00022223506,0.009518033,0.00037430492,0.00012909007,0.00026910956,0.0008465671,0.2787488,0.014964603,0.5503176,0.0024811875,0.14149672],"study_design_scores_gemma":[0.000023694294,0.000076362005,0.0008233501,0.000020731417,0.00002939764,0.00007780078,0.00004672098,0.5811466,0.005260621,0.4117499,0.00073149026,0.000013283578],"about_ca_topic_score_codex":0.002745548,"about_ca_topic_score_gemma":0.0024967028,"teacher_disagreement_score":0.0039289286,"about_ca_system_score_codex":0.0026658855,"about_ca_system_score_gemma":0.0016955999,"threshold_uncertainty_score":0.019342482},"labels":[],"label_agreement":null},{"id":"W4403864655","doi":"10.1109/ihmsc62065.2024.00031","title":"Enhancing Data Preprocessing using Positional Self-Attention Autoencoders","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Preprocessor; Artificial intelligence; Data pre-processing; Pattern recognition (psychology); Data mining","score_opus":0.04328345059102858,"score_gpt":0.3194445845874818,"score_spread":0.27616113399645326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403864655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14069533,0.0006697367,0.85245514,0.0003309214,0.00015115798,0.00005212155,0.00010702647,0.0029473603,0.0025912155],"genre_scores_gemma":[0.8031163,0.00042748876,0.19256623,0.00033274796,0.00012468277,0.00006390257,0.00036590395,0.0001980626,0.002804631],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996779,0.00006626839,0.000024037425,0.00008668651,0.00010245679,0.00004264498],"domain_scores_gemma":[0.9986964,0.0006784916,0.0001109827,0.00017990576,0.0003094644,0.000024853302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010590925,0.0006535299,0.00041118925,0.00048424472,0.00022916931,0.00057334005,0.000659702,0.00052554725,0.0014019356],"category_scores_gemma":[0.00426712,0.0002789023,0.00050592126,0.00045923982,0.0003109874,0.0011052523,0.00063857884,0.00065315637,0.0005434448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031638384,0.00033102743,0.008515648,0.000266334,0.0002240863,0.0002506392,0.0002819152,0.20706579,0.08979137,0.004686305,0.002919132,0.68535143],"study_design_scores_gemma":[0.000013843249,0.00012624686,0.0072571496,0.000019011446,0.000057500336,0.000077253644,0.000050089922,0.9541383,0.034525536,0.0016433614,0.0020711308,0.000020657977],"about_ca_topic_score_codex":0.0034830768,"about_ca_topic_score_gemma":0.005790927,"teacher_disagreement_score":0.0034830768,"about_ca_system_score_codex":0.0002793406,"about_ca_system_score_gemma":0.00070048275,"threshold_uncertainty_score":0.0069256425},"labels":[],"label_agreement":null},{"id":"W4403865717","doi":"10.36227/techrxiv.172114799.94469004/v3","title":"Implementing Restricted Hopfield Network as Robust Auto-associative Memory Using Subspace Rotation Algorithm","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Bidirectional associative memory; Subspace topology; Content-addressable memory; Computer science; Associative property; Algorithm; Hopfield network; Rotation (mathematics); Artificial intelligence; Artificial neural network; Mathematics","score_opus":0.03544505904308897,"score_gpt":0.3021798569693481,"score_spread":0.26673479792625915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403865717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09539928,0.0007767957,0.8951347,0.000169652,0.00008275751,0.00008668704,0.000110911744,0.002477618,0.005761675],"genre_scores_gemma":[0.7851669,0.00033972604,0.20976536,0.000111300295,0.00003130185,0.00012741622,0.00019889769,0.00006092439,0.0041981908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997917,0.00003756376,0.000018146657,0.00005477653,0.00006143615,0.000036481797],"domain_scores_gemma":[0.99969304,0.00007895237,0.000030299889,0.000075833756,0.00010309554,0.000018897406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005423803,0.00043820057,0.00052824744,0.00040217378,0.00023938518,0.0004993546,0.0012029856,0.0004863991,0.0024090584],"category_scores_gemma":[0.001039466,0.00021543528,0.00037700718,0.00042554937,0.00041995224,0.0011437725,0.00064281997,0.0003740733,0.00044610014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003174,0.00013636696,0.0012703666,0.00016513627,0.00013208519,0.00014914009,0.000102176215,0.5214592,0.03536945,0.012848895,0.0024641478,0.42558566],"study_design_scores_gemma":[0.000016990713,0.00005688921,0.00014495947,0.0000047013964,0.000013905038,0.000040244846,0.00000765275,0.98785466,0.00735371,0.0037363423,0.00076056697,0.000009260191],"about_ca_topic_score_codex":0.0055138404,"about_ca_topic_score_gemma":0.0051162723,"teacher_disagreement_score":0.0055138404,"about_ca_system_score_codex":0.00040528044,"about_ca_system_score_gemma":0.0008586919,"threshold_uncertainty_score":0.0109635},"labels":[],"label_agreement":null},{"id":"W4403881903","doi":"10.1177/00222437241296453","title":"Addressing Endogeneity Using a Two-Stage Copula Generated Regressor Approach","year":2024,"lang":"en","type":"article","venue":"Journal of Marketing Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Endogeneity; Copula (linguistics); Econometrics; Economics; Statistics; Mathematics","score_opus":0.37622124411294894,"score_gpt":0.46887370731414274,"score_spread":0.0926524632011938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403881903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021671355,0.0000784868,0.9967775,0.00007589694,0.00002577874,0.000057153484,0.000041535735,0.00016819277,0.0006082631],"genre_scores_gemma":[0.20827314,0.00058271945,0.78424144,0.00038475497,0.00013705902,0.00040540018,0.00052146486,0.00034118525,0.00511284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99641126,0.0019545506,0.0001856072,0.00058156735,0.0006969919,0.00017001538],"domain_scores_gemma":[0.985225,0.009384116,0.0014454154,0.0020973047,0.0016434569,0.00020464312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006607729,0.0018716762,0.0016147253,0.0018065039,0.00057148153,0.001675901,0.0025156168,0.0015722803,0.004811033],"category_scores_gemma":[0.024205288,0.00087818224,0.0020810578,0.0017301522,0.00094874285,0.0016783287,0.0020910853,0.002593217,0.0008111271],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013446636,0.00032765928,0.013095272,0.00059476745,0.00094832515,0.00073747674,0.00046800263,0.42243305,0.004928029,0.2774907,0.006172124,0.27267015],"study_design_scores_gemma":[0.000024407755,0.00006161258,0.0015376706,0.000034985107,0.00008450042,0.000097115255,0.00003573383,0.9643334,0.002510084,0.026772313,0.0044662263,0.000041895095],"about_ca_topic_score_codex":0.006102633,"about_ca_topic_score_gemma":0.005613288,"teacher_disagreement_score":0.006607729,"about_ca_system_score_codex":0.0009639229,"about_ca_system_score_gemma":0.0028145942,"threshold_uncertainty_score":0.03494543},"labels":[],"label_agreement":null},{"id":"W4403944598","doi":"10.18280/ijsse.140516","title":"Medical Image Authentication Using Statistical Correlations","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Image (mathematics); Computer science; Authentication (law); Computer security; Artificial intelligence","score_opus":0.007069827646268185,"score_gpt":0.26826990720833843,"score_spread":0.26120007956207025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403944598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04812905,0.0005524358,0.9476479,0.00028620745,0.00010589322,0.000070212365,0.00013903598,0.0006666584,0.002402574],"genre_scores_gemma":[0.8785016,0.00070413615,0.11744502,0.00013897532,0.00019685295,0.000089915695,0.00033734788,0.00008138117,0.0025047893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981615,0.0006649571,0.00012358121,0.0002762959,0.0006368849,0.00013674301],"domain_scores_gemma":[0.99465805,0.0022344966,0.0009746453,0.00089414086,0.0010962967,0.00014233691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002078622,0.0005871217,0.0007730334,0.0020881174,0.00046784684,0.0012506399,0.00054677395,0.00082947855,0.0021303876],"category_scores_gemma":[0.009444129,0.00045022092,0.0007545139,0.001958847,0.0007393587,0.0021441255,0.0013858237,0.0008880617,0.0012146755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014856608,0.0003901345,0.016521316,0.00033433523,0.00047023944,0.0005950123,0.00019005823,0.22987954,0.05652908,0.052701347,0.006397119,0.63450617],"study_design_scores_gemma":[0.000020696221,0.00016859874,0.0032535533,0.000026315667,0.00006937197,0.0002864968,0.000027093853,0.9722358,0.011506387,0.011178978,0.0011966774,0.000030002408],"about_ca_topic_score_codex":0.0007180708,"about_ca_topic_score_gemma":0.001075403,"teacher_disagreement_score":0.0021303876,"about_ca_system_score_codex":0.00041128037,"about_ca_system_score_gemma":0.00098592,"threshold_uncertainty_score":0.010992944},"labels":[],"label_agreement":null},{"id":"W4403947484","doi":"10.18280/mmep.111022","title":"A Tree-Structured Deep Learning Model for Improving Classification with Self-Adaption and Self-Learning","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Machine learning; Tree (set theory); Mathematics","score_opus":0.016473492555384054,"score_gpt":0.20344944637538595,"score_spread":0.1869759538200019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403947484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01616736,0.00040007723,0.9809676,0.00023939766,0.00012599799,0.000033240798,0.00009001812,0.0007781905,0.0011981017],"genre_scores_gemma":[0.4645025,0.0005214384,0.5254873,0.00068726955,0.00015325728,0.00018793168,0.00067692157,0.00026916518,0.0075141387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997429,0.00005164593,0.000018118953,0.00007110315,0.00008209265,0.000034113906],"domain_scores_gemma":[0.9992964,0.00020265114,0.00005310908,0.00010390541,0.00029255103,0.000051300816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077359204,0.00051409786,0.0008558807,0.00052441435,0.00032661663,0.00061963336,0.00152545,0.0013193922,0.0018256181],"category_scores_gemma":[0.0019381312,0.0003555852,0.0007369193,0.0007796836,0.00040431513,0.0012914422,0.001073311,0.0017689157,0.00082536566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013971319,0.00022790102,0.0009775585,0.0001112867,0.000093422874,0.000085119056,0.00009065,0.54760087,0.019178174,0.023623511,0.010582894,0.39728886],"study_design_scores_gemma":[0.000002209047,0.00001271854,0.000029382612,0.000002753932,0.0000042054003,0.0000063845464,0.0000011227,0.99681395,0.0006420836,0.0022210763,0.00026196957,0.0000020924133],"about_ca_topic_score_codex":0.0037688138,"about_ca_topic_score_gemma":0.0055387565,"teacher_disagreement_score":0.0037688138,"about_ca_system_score_codex":0.00060807914,"about_ca_system_score_gemma":0.0008899719,"threshold_uncertainty_score":0.0074937344},"labels":[],"label_agreement":null},{"id":"W4403976095","doi":"10.55016/ojs/cdm.v19i3.62620","title":"On characterization and recognition of proper tagged probe interval graphs","year":2024,"lang":"en","type":"article","venue":"Contributions to Discrete Mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University Grants Commission","keywords":"Characterization (materials science); Combinatorics; Interval (graph theory); Mathematics; Discrete mathematics; Physics","score_opus":0.016638996920622304,"score_gpt":0.27113760204364906,"score_spread":0.25449860512302674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403976095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093173556,0.00029483865,0.90204597,0.00024035337,0.00002978719,0.00006356928,0.000286066,0.00052766345,0.0033383132],"genre_scores_gemma":[0.6497551,0.00054790283,0.34401312,0.00023972767,0.00009547064,0.00014173317,0.0019078365,0.00025221985,0.0030468716],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991732,0.00015003183,0.000053001397,0.00033587002,0.00019047041,0.00009742342],"domain_scores_gemma":[0.9955369,0.0027078893,0.0005565329,0.0005812286,0.00043955605,0.00017786367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005074225,0.0004732569,0.00047973555,0.0011475978,0.00049634196,0.0012708827,0.00087683223,0.0007347063,0.002306874],"category_scores_gemma":[0.0066224188,0.00027351975,0.0005600036,0.0016666062,0.0012359415,0.0032341257,0.00089616067,0.0012393101,0.0004983855],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053395325,0.00020103993,0.006993374,0.00039118578,0.000048506005,0.0004991742,0.00093864836,0.13919319,0.056466356,0.41407573,0.008880171,0.37177864],"study_design_scores_gemma":[0.000023876175,0.00016693576,0.0032674072,0.000039134393,0.00002753748,0.0006038576,0.00037272583,0.60373247,0.014867194,0.3672159,0.009623619,0.000059386693],"about_ca_topic_score_codex":0.0011941083,"about_ca_topic_score_gemma":0.0010588579,"teacher_disagreement_score":0.002306874,"about_ca_system_score_codex":0.0006782522,"about_ca_system_score_gemma":0.0004067349,"threshold_uncertainty_score":0.007717252},"labels":[],"label_agreement":null},{"id":"W4403979554","doi":"10.5206/mt.v4i3.21612","title":"An Investigation of the Funk Transform","year":2024,"lang":"en","type":"article","venue":"Maple Transactions","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Funk; Art; Computer science; Art history","score_opus":0.014088127187397093,"score_gpt":0.23865762280414013,"score_spread":0.22456949561674303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403979554","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17624038,0.0050745965,0.15053847,0.017082471,0.001389448,0.000052271924,0.00034371525,0.00028752824,0.64899117],"genre_scores_gemma":[0.9333231,0.002239207,0.0133800125,0.0014907867,0.0010356404,0.00003722624,0.00019544015,0.00027154264,0.048027106],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992399,0.00024145804,0.000025732836,0.0001026874,0.00024727904,0.00014288162],"domain_scores_gemma":[0.99850404,0.0007356319,0.00015842749,0.0001514791,0.00028258652,0.00016784438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092006114,0.0004201334,0.00048624494,0.0014246425,0.0015788617,0.0031095967,0.0008405761,0.001496637,0.022157857],"category_scores_gemma":[0.007332834,0.00020410231,0.0007121141,0.0009987972,0.0038151878,0.0057384893,0.0026535415,0.0021775693,0.0019182586],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000639367,0.000006312399,0.00008552808,0.0000116892825,0.0000019809404,0.00004706699,0.000089002424,0.0002883002,0.00013783334,0.9959847,0.0013619234,0.001979363],"study_design_scores_gemma":[0.0000060475045,0.000011162765,0.00016451896,0.000019254412,0.0000028139928,0.0001215492,0.00013446587,0.005996222,0.00017879717,0.9849629,0.008395284,0.000007046515],"about_ca_topic_score_codex":0.0016118721,"about_ca_topic_score_gemma":0.00077844603,"teacher_disagreement_score":0.022157857,"about_ca_system_score_codex":0.0010952611,"about_ca_system_score_gemma":0.0010238728,"threshold_uncertainty_score":0.07412541},"labels":[],"label_agreement":null},{"id":"W4404033260","doi":"10.1088/2632-2153/ad8ea8","title":"Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II","year":2024,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique","funders":"Basic Energy Sciences; Office of Science; Canada Research Chairs; U.S. Department of Energy","keywords":"Field-programmable gate array; Enhanced Data Rates for GSM Evolution; Computer science; Data acquisition; Computer architecture; Artificial intelligence; Embedded system; Operating system","score_opus":0.02373263902774665,"score_gpt":0.28496313777567295,"score_spread":0.2612304987479263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404033260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84521335,0.0007214068,0.12239017,0.0017148228,0.00021565455,0.00033004602,0.00057550566,0.0076983925,0.02114072],"genre_scores_gemma":[0.9491638,0.00013888041,0.046269,0.00017506222,0.000021567826,0.000040287854,0.00018326988,0.00012986203,0.0038783385],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999549,0.00012567412,0.00002398599,0.000062976214,0.00017042021,0.00006798827],"domain_scores_gemma":[0.9986613,0.0006273634,0.000066493325,0.00021108131,0.00034246623,0.000091188434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083045405,0.00038829574,0.00023297587,0.00036847597,0.00032988875,0.00084016816,0.0009654817,0.0005141287,0.003776969],"category_scores_gemma":[0.0017959012,0.00014499464,0.00013678269,0.0006355555,0.00050975574,0.00077878777,0.00036530974,0.00056333776,0.0007919008],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005233627,0.0014335816,0.0415026,0.0010830646,0.0001686208,0.00935734,0.0016589889,0.20902291,0.14211431,0.021165522,0.039147582,0.5281118],"study_design_scores_gemma":[0.00039822375,0.0017550745,0.012036448,0.000105630344,0.00006118875,0.0014606984,0.00061005127,0.62022436,0.312119,0.0037906535,0.04734471,0.00009382359],"about_ca_topic_score_codex":0.003946089,"about_ca_topic_score_gemma":0.0040154103,"teacher_disagreement_score":0.003946089,"about_ca_system_score_codex":0.0007258294,"about_ca_system_score_gemma":0.00045975926,"threshold_uncertainty_score":0.012635231},"labels":[],"label_agreement":null},{"id":"W4404131245","doi":"10.1137/1.9781611978162.ch3","title":"Chapter 3: Identifying Nonlinear Dynamics","year":2024,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Nonlinear system; Dynamics (music); Computer science; Statistical physics; Physics; Acoustics","score_opus":0.06894547054863967,"score_gpt":0.2616224852718014,"score_spread":0.1926770147231617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404131245","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004425745,0.02026054,0.82247496,0.002896698,0.0021527612,0.00014326688,0.00089470827,0.0011299537,0.14562137],"genre_scores_gemma":[0.08193921,0.040457454,0.43785086,0.0019382448,0.0026210812,0.00045238037,0.002593527,0.0015736291,0.43057355],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997124,0.000054499884,0.0000118038615,0.00006987914,0.00013847898,0.000012883868],"domain_scores_gemma":[0.9997272,0.0001530082,0.000012088687,0.00003471577,0.00006513355,0.000007802811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048750942,0.0010141834,0.0005855994,0.0006538701,0.000436035,0.0012147427,0.00070199743,0.0008340366,0.024832137],"category_scores_gemma":[0.0017374998,0.0004667533,0.00066648854,0.00060307246,0.00063138123,0.0013871348,0.0010717977,0.0016779214,0.008445039],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065995315,0.00006625621,0.00070098066,0.0013285527,0.0000672933,0.00030014326,0.0006288097,0.06653465,0.007559895,0.4092479,0.1356923,0.37780723],"study_design_scores_gemma":[0.000009083038,0.00004423278,0.0006644469,0.0006934278,0.000026993841,0.00038882048,0.00015997051,0.08283746,0.007686266,0.2897036,0.6177421,0.000043576965],"about_ca_topic_score_codex":0.0016966922,"about_ca_topic_score_gemma":0.001576128,"teacher_disagreement_score":0.024832137,"about_ca_system_score_codex":0.0007065769,"about_ca_system_score_gemma":0.0006371205,"threshold_uncertainty_score":0.08307177},"labels":[],"label_agreement":null},{"id":"W4404131338","doi":"10.1137/1.9781611978162.ch5","title":"Chapter 5: Learning Dynamics with Neural Networks","year":2024,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Dynamics (music); Computer science; Artificial neural network; Artificial intelligence; Psychology; Pedagogy","score_opus":0.03332561382580486,"score_gpt":0.22120709468357608,"score_spread":0.18788148085777123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404131338","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031739054,0.072695754,0.7565628,0.0072129336,0.0035299317,0.0001085327,0.00075465126,0.0010997178,0.15486181],"genre_scores_gemma":[0.14902079,0.16527313,0.42297748,0.005640976,0.010000205,0.00078367966,0.0029417756,0.0014930979,0.24186881],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997366,0.00006647256,0.000014855675,0.0000634446,0.000100594516,0.000017925893],"domain_scores_gemma":[0.9997012,0.0001966328,0.000011921839,0.000030860443,0.000046198973,0.000013100999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003818614,0.00093966536,0.00060426776,0.0006160091,0.00042425527,0.001665315,0.0008374709,0.0013436474,0.022078073],"category_scores_gemma":[0.0018024585,0.00036948957,0.00064153224,0.0008939928,0.00072685943,0.0020289565,0.0010838872,0.0024146743,0.005581918],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034570596,0.00007313886,0.00044753915,0.00082552613,0.00008655587,0.00013436601,0.00025289337,0.070392706,0.0015696482,0.60333616,0.080143146,0.24270381],"study_design_scores_gemma":[0.000009285175,0.000032096737,0.0003088246,0.00054926577,0.000023062614,0.00018323322,0.00005225195,0.08475603,0.0014201773,0.5282601,0.3843775,0.000028067587],"about_ca_topic_score_codex":0.0012938615,"about_ca_topic_score_gemma":0.0013389451,"teacher_disagreement_score":0.022078073,"about_ca_system_score_codex":0.00078359817,"about_ca_system_score_gemma":0.0005531135,"threshold_uncertainty_score":0.0738585},"labels":[],"label_agreement":null},{"id":"W4404131455","doi":"10.1137/1.9781611978162.ch6","title":"Chapter 6: Autoencoder Neural Networks","year":2024,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Autoencoder; Artificial neural network; Computer science; Artificial intelligence","score_opus":0.05467811406183863,"score_gpt":0.24217265685610864,"score_spread":0.18749454279427002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404131455","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034691554,0.07603921,0.7634673,0.0028814035,0.003712429,0.000121867975,0.0011514392,0.0026437058,0.14651346],"genre_scores_gemma":[0.07187071,0.10933371,0.44235846,0.002271221,0.004097464,0.00038950675,0.005485905,0.0015188664,0.3626741],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997433,0.00003865131,0.000015367745,0.000058882048,0.0001279091,0.00001592227],"domain_scores_gemma":[0.9998394,0.00006744307,0.000006854858,0.000021642101,0.000056817255,0.000007695461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029831546,0.00083854893,0.00048554584,0.00061531,0.00023957879,0.0012383715,0.00077918416,0.0010624268,0.020814449],"category_scores_gemma":[0.0010857654,0.00034229,0.0004267577,0.0010348881,0.0003372313,0.001226476,0.0006988019,0.0017294614,0.011098481],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049089416,0.00008615075,0.00028568643,0.0009772219,0.00010349128,0.00009870292,0.00010210041,0.06727773,0.0041690744,0.06881898,0.11797674,0.740055],"study_design_scores_gemma":[0.000014571167,0.00006190193,0.00060245406,0.0006735889,0.00004319374,0.00020134874,0.000045657194,0.20322609,0.006512874,0.098222554,0.69035923,0.00003660544],"about_ca_topic_score_codex":0.001425983,"about_ca_topic_score_gemma":0.0022330307,"teacher_disagreement_score":0.020814449,"about_ca_system_score_codex":0.00045878912,"about_ca_system_score_gemma":0.00041719503,"threshold_uncertainty_score":0.06963128},"labels":[],"label_agreement":null},{"id":"W4404145829","doi":"10.1007/978-3-031-73125-9_21","title":"Using Mixed Exponentials for Unsupervised Discretization","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Exponential function; Discretization; Applied mathematics; Mathematics; Computer science; Mathematical analysis","score_opus":0.04210775949803296,"score_gpt":0.269354246962044,"score_spread":0.22724648746401105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404145829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009793376,0.00010618546,0.99800175,0.00004697235,0.000026034972,0.000009144924,0.00003225848,0.00019138592,0.000606916],"genre_scores_gemma":[0.096162856,0.00044430184,0.8922953,0.00020485077,0.00011305039,0.00018485489,0.00052984216,0.0006876466,0.00937733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914944,0.00043363826,0.000055343426,0.00012733425,0.0001908872,0.000043230313],"domain_scores_gemma":[0.9940671,0.0045242966,0.00016606101,0.0006607369,0.00047289013,0.00010896926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025307862,0.000767235,0.0010474016,0.0008534156,0.00049930584,0.0014214125,0.0019310456,0.0014559313,0.008133482],"category_scores_gemma":[0.011350557,0.0008817658,0.0012202971,0.0011047665,0.0011520562,0.0029830176,0.0025085898,0.0031751902,0.0020713313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021490359,0.00011177935,0.00067924935,0.00033558774,0.00013271127,0.00008890087,0.0001576949,0.50129133,0.005078625,0.2500969,0.007008779,0.23480356],"study_design_scores_gemma":[0.000005407094,0.0000051713764,0.000025542826,0.000008986629,0.0000033238516,0.000009744517,0.0000051896463,0.96517223,0.00038901702,0.03341672,0.00095458364,0.000004065584],"about_ca_topic_score_codex":0.0023693263,"about_ca_topic_score_gemma":0.004240214,"teacher_disagreement_score":0.008133482,"about_ca_system_score_codex":0.0008676556,"about_ca_system_score_gemma":0.00071484986,"threshold_uncertainty_score":0.027209163},"labels":[],"label_agreement":null},{"id":"W4404259735","doi":"10.1101/2024.11.12.623145","title":"High-level visual cortex representations are organized along visual rather than abstract principles","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Israel Science Foundation; Canadian Institute for Advanced Research","keywords":"Visual cortex; Linguistics; Computer science; Psychology; Cognitive science; Cognitive psychology; Communication; Neuroscience; Philosophy","score_opus":0.028143915675403856,"score_gpt":0.26843971058444616,"score_spread":0.2402957949090423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404259735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88867897,0.0004402324,0.10358445,0.00043623947,0.000035671663,0.00005388922,0.00056874513,0.00033864606,0.005863091],"genre_scores_gemma":[0.9927457,0.00009245634,0.0059462828,0.000037279257,0.000007403038,0.00001670714,0.0002019284,0.000018648529,0.000933487],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998611,0.000031396736,0.000006866267,0.0000399813,0.000036749305,0.000023856852],"domain_scores_gemma":[0.9996362,0.00016670415,0.00008466795,0.000042246513,0.000032838987,0.0000372832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035247928,0.00029550967,0.0002149277,0.0003148276,0.00010129526,0.0008751078,0.00023061554,0.00030246345,0.0018227701],"category_scores_gemma":[0.0015555775,0.00018306593,0.000271888,0.00021822666,0.0005638957,0.0008734164,0.00038329142,0.0005225333,0.00026859064],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070014264,0.00014858428,0.021329246,0.00042476816,0.00018927384,0.0003498477,0.00032491647,0.044303373,0.8298894,0.015223172,0.0015386527,0.08557861],"study_design_scores_gemma":[0.00009257216,0.0004678723,0.39151922,0.00010044436,0.00011505486,0.0004238574,0.00031855176,0.43046945,0.120632924,0.0535,0.0022967015,0.000063403386],"about_ca_topic_score_codex":0.0013534713,"about_ca_topic_score_gemma":0.0014555432,"teacher_disagreement_score":0.0018227701,"about_ca_system_score_codex":0.00036378633,"about_ca_system_score_gemma":0.00022121973,"threshold_uncertainty_score":0.0060977936},"labels":[],"label_agreement":null},{"id":"W4404306338","doi":"10.48550/arxiv.2410.18222","title":"Kubo-Martin-Schwinger states of Path-structured Flow in Directed Brain Synaptic Networks","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Information flow; Flow (mathematics); Neuroscience; Psychology; Computer science; Physics; Mechanics; Philosophy","score_opus":0.025761038808713187,"score_gpt":0.17820566476518257,"score_spread":0.15244462595646938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404306338","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77209455,0.00048742426,0.21169095,0.0006611582,0.00004462339,0.000043477925,0.00026105263,0.00021038321,0.01450634],"genre_scores_gemma":[0.98961276,0.00020697177,0.008443841,0.00005813212,0.00003577405,0.00004686329,0.00008237685,0.000029224282,0.0014840283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998056,0.00006490538,0.000010266644,0.0000313335,0.000045003344,0.00004289886],"domain_scores_gemma":[0.9992181,0.0003126521,0.00015439144,0.00007175947,0.00009963537,0.0001435346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005884837,0.0002485856,0.00037309606,0.0011980382,0.00055133394,0.0012005115,0.0005188106,0.0005561636,0.002084227],"category_scores_gemma":[0.0023556803,0.00025732812,0.0003780554,0.00046344745,0.0020254836,0.0021697134,0.0009078722,0.0006982983,0.00024160821],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032186672,0.000019743964,0.000587737,0.0000279047,0.0000085515385,0.00006319719,0.00012758576,0.03593818,0.002385374,0.9579175,0.00040564043,0.0024864594],"study_design_scores_gemma":[0.000011012899,0.000017479302,0.0007240909,0.000008276899,0.0000031479028,0.000050764862,0.000048833874,0.24627699,0.00061093224,0.7518721,0.0003547616,0.000021587764],"about_ca_topic_score_codex":0.0014124708,"about_ca_topic_score_gemma":0.0011488721,"teacher_disagreement_score":0.002084227,"about_ca_system_score_codex":0.000820424,"about_ca_system_score_gemma":0.0005513684,"threshold_uncertainty_score":0.006972432},"labels":[],"label_agreement":null},{"id":"W4404313495","doi":"10.48550/arxiv.2410.19912","title":"Simmering: Sufficient is better than optimal for training neural networks","year":2024,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Universities Space Research Association","keywords":"Training (meteorology); Artificial neural network; Computer science; Deep neural networks; Training set; Artificial intelligence; Machine learning; Geography","score_opus":0.07564053337504896,"score_gpt":0.29518079702115907,"score_spread":0.21954026364611012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404313495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012125469,0.0005905124,0.97490567,0.0015258619,0.0002562845,0.000060807743,0.00032715296,0.0024679075,0.0077403625],"genre_scores_gemma":[0.40965495,0.00069058343,0.57334626,0.0022157133,0.00067491905,0.0005272101,0.0012478991,0.0028196753,0.008822804],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99485576,0.0025456976,0.00042759549,0.0009304313,0.0008789593,0.00036160837],"domain_scores_gemma":[0.9898816,0.0061698547,0.0003733227,0.0023856198,0.0009172258,0.00027230298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007638019,0.0014478182,0.0016322084,0.0018260594,0.001148062,0.0021769528,0.0022835613,0.0026180085,0.011399241],"category_scores_gemma":[0.033348657,0.0009687689,0.0014162267,0.0013974067,0.002786829,0.0066646747,0.0035398398,0.0044849343,0.0024683422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008857244,0.00026567891,0.0030309397,0.0006412637,0.00025477863,0.00020879744,0.00041015164,0.15265247,0.007065352,0.55517,0.031215401,0.24819952],"study_design_scores_gemma":[0.00006722222,0.00016181433,0.0002919662,0.00011615158,0.00003186948,0.00009462149,0.000042896078,0.5136301,0.005700791,0.47077414,0.009057842,0.0000306991],"about_ca_topic_score_codex":0.0013693379,"about_ca_topic_score_gemma":0.0033185554,"teacher_disagreement_score":0.011399241,"about_ca_system_score_codex":0.0010484578,"about_ca_system_score_gemma":0.0023205876,"threshold_uncertainty_score":0.040394187},"labels":[],"label_agreement":null},{"id":"W4404341549","doi":"10.48550/arxiv.2410.22065","title":"Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Delaware Research Foundation; National Science Foundation","keywords":"Monte Carlo method; Statistical physics; Hybrid Monte Carlo; Artificial neural network; Computer science; Physics; Artificial intelligence; Markov chain Monte Carlo; Mathematics; Statistics","score_opus":0.05227459089419588,"score_gpt":0.18676910221436518,"score_spread":0.13449451132016932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404341549","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14875473,0.00066507515,0.84181905,0.0012847491,0.00007529769,0.000114021546,0.00009285556,0.0008410537,0.006353195],"genre_scores_gemma":[0.8474557,0.0002307552,0.1476487,0.00042577987,0.000052182888,0.00031521826,0.00014781026,0.00034517882,0.0033785184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980356,0.0011562827,0.000061125036,0.00020155832,0.00038294308,0.00016244392],"domain_scores_gemma":[0.98050046,0.015677363,0.0008053409,0.0015595013,0.0009992074,0.000458135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006724598,0.0005949677,0.0016765904,0.0008634713,0.0012520648,0.0013824155,0.0024792466,0.002036426,0.0032766853],"category_scores_gemma":[0.029762132,0.0008168591,0.00049020204,0.000810894,0.0028817207,0.0028454673,0.001865338,0.0020973154,0.00042158877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001625581,0.00006424379,0.0011265432,0.00009815918,0.0000484565,0.0000947357,0.00010946888,0.8637598,0.00086248916,0.11702594,0.0011413697,0.0155062135],"study_design_scores_gemma":[0.000009290199,0.000007858103,0.00004161586,0.000004350402,0.0000019563054,0.0000067369806,0.000004884306,0.98207825,0.00019677982,0.017571459,0.00007282992,0.0000040080304],"about_ca_topic_score_codex":0.008304441,"about_ca_topic_score_gemma":0.008619304,"teacher_disagreement_score":0.008304441,"about_ca_system_score_codex":0.0023490118,"about_ca_system_score_gemma":0.0019295454,"threshold_uncertainty_score":0.03556347},"labels":[],"label_agreement":null},{"id":"W4404354508","doi":"","title":"LIMITED-MEMORY STOCHASTIC PARTITIONED QUASI-NEWTON TRAINING","year":2022,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Training (meteorology); Parallel computing; Mathematical optimization; Mathematics","score_opus":0.019290107116000845,"score_gpt":0.23374063772950238,"score_spread":0.21445053061350153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404354508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014413733,0.0003185004,0.97752064,0.00030693362,0.00018807282,0.00005451898,0.00015485003,0.0013833416,0.0056593893],"genre_scores_gemma":[0.45212355,0.00025843162,0.5266445,0.00041860712,0.00016697268,0.0003360704,0.000727894,0.00065103266,0.018672965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996885,0.00009501368,0.00001455303,0.00005712786,0.00010114955,0.000043692977],"domain_scores_gemma":[0.9989165,0.00055463845,0.000056873127,0.00018845174,0.0002030009,0.0000804106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068291445,0.0007556493,0.0011673348,0.00026987153,0.00041582287,0.0006478263,0.0016811467,0.001553003,0.011688349],"category_scores_gemma":[0.003340652,0.0006943912,0.00041066098,0.00042051345,0.0006565884,0.0010254581,0.0015090699,0.001363084,0.002197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003984151,0.00009493619,0.00041638213,0.00014229515,0.00005996751,0.0000770893,0.000057454014,0.85603964,0.0023744884,0.013985368,0.0066279722,0.11972599],"study_design_scores_gemma":[0.000016712369,0.000014767122,0.000042675187,0.000004984961,0.000002432156,0.000004926678,0.0000020271561,0.9977182,0.00035026085,0.0013502182,0.00049061305,0.0000021940116],"about_ca_topic_score_codex":0.0071058017,"about_ca_topic_score_gemma":0.014678164,"teacher_disagreement_score":0.011688349,"about_ca_system_score_codex":0.00051607523,"about_ca_system_score_gemma":0.0015179015,"threshold_uncertainty_score":0.03910142},"labels":[],"label_agreement":null},{"id":"W4404423528","doi":"10.18280/ts.410501","title":"Effects of Different Pre-Training Algorithms on Video Classification Results","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Training (meteorology); Computer science; Algorithm; Artificial intelligence; Pattern recognition (psychology); Machine learning; Geography; Meteorology","score_opus":0.027759076669687834,"score_gpt":0.269155738625589,"score_spread":0.24139666195590118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404423528","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8488604,0.009595135,0.11012067,0.0011504568,0.003545721,0.0011814358,0.001676178,0.014319656,0.009550329],"genre_scores_gemma":[0.8823691,0.001509302,0.10453538,0.00060397387,0.00022303728,0.000597105,0.0056362846,0.0007375285,0.0037882486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960497,0.0008012857,0.00065857044,0.0011688183,0.00071112084,0.00061052386],"domain_scores_gemma":[0.9911794,0.0047833305,0.0003906858,0.0008314608,0.0025067008,0.0003085161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051980703,0.003300491,0.001682177,0.0017541978,0.0011429938,0.0022624796,0.0018766643,0.0024715073,0.0025206567],"category_scores_gemma":[0.026136106,0.00048391684,0.0010740878,0.001047192,0.00077967247,0.002291619,0.0010261231,0.0027662828,0.0015402796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048607443,0.0022965972,0.019378012,0.0010288581,0.00041525014,0.00056100806,0.00046048674,0.07183306,0.023154626,0.00077818905,0.010991995,0.86424124],"study_design_scores_gemma":[0.0002855925,0.0025838413,0.027893899,0.0005265105,0.00066491443,0.0005228032,0.0012225,0.82168573,0.13478288,0.0014104424,0.008236839,0.0001840189],"about_ca_topic_score_codex":0.01257475,"about_ca_topic_score_gemma":0.008492793,"teacher_disagreement_score":0.01257475,"about_ca_system_score_codex":0.0012176469,"about_ca_system_score_gemma":0.0015126913,"threshold_uncertainty_score":0.027490377},"labels":[],"label_agreement":null},{"id":"W4404430582","doi":"10.1101/2024.11.13.623324","title":"Feature similarity: a sensitive method to capture the functional interaction of brain regions and networks to support flexible behavior","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Institute of Psychology, Chinese Academy of Sciences; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Similarity (geometry); Feature (linguistics); Functional connectivity; Computer science; Artificial intelligence; Pattern recognition (psychology); Data mining; Psychology; Neuroscience","score_opus":0.024011995454396282,"score_gpt":0.27470590925696325,"score_spread":0.25069391380256695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404430582","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13104938,0.00064688525,0.85993654,0.00024567705,0.000084820116,0.00015968013,0.0018972661,0.002894815,0.0030849238],"genre_scores_gemma":[0.7156018,0.0003508817,0.2798329,0.00017987788,0.00014007674,0.00034499337,0.0017180305,0.0005313115,0.0013001164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990238,0.00021983917,0.00007447884,0.00027591106,0.00030128652,0.00010471684],"domain_scores_gemma":[0.9969319,0.0013646566,0.00071678456,0.0005101986,0.00030526918,0.0001711257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017797125,0.0009852669,0.00077157153,0.0045086155,0.00064835226,0.0012179943,0.00084329495,0.0009997835,0.0025679132],"category_scores_gemma":[0.008166079,0.00037389935,0.0010074966,0.0026778039,0.0011149432,0.0021828015,0.0016160565,0.0011778303,0.0005691146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012269217,0.00036904626,0.061053943,0.0010785246,0.0013398712,0.00078315823,0.0014986781,0.04388225,0.3155294,0.038104095,0.01415589,0.5209783],"study_design_scores_gemma":[0.000117128984,0.00067468896,0.16962989,0.00013040987,0.0004836446,0.0032616665,0.00057245616,0.5800408,0.11283963,0.11070259,0.021136528,0.0004106187],"about_ca_topic_score_codex":0.0023368185,"about_ca_topic_score_gemma":0.002603647,"teacher_disagreement_score":0.0045086155,"about_ca_system_score_codex":0.0006392977,"about_ca_system_score_gemma":0.00070046674,"threshold_uncertainty_score":0.00941211},"labels":[],"label_agreement":null},{"id":"W4404480495","doi":"10.1109/les.2024.3500020","title":"FPGA Implementation of an Image Classifier Using Pipelined FFT Architecture","year":2024,"lang":"en","type":"article","venue":"IEEE Embedded Systems Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Mitacs","keywords":"Computer science; Field-programmable gate array; Fast Fourier transform; Architecture; Computer architecture; Embedded system; Image processing; Classifier (UML); Computer hardware; Image (mathematics); Parallel computing; Artificial intelligence; Algorithm","score_opus":0.023047357234774638,"score_gpt":0.31591048329469085,"score_spread":0.2928631260599162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404480495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19151847,0.0011472818,0.71885824,0.00045422892,0.0005367769,0.00034881488,0.0014408753,0.031683497,0.054011796],"genre_scores_gemma":[0.74351877,0.00032774874,0.23921463,0.00022934466,0.000054905508,0.00017580339,0.0013938555,0.00017462735,0.0149102975],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998996,0.000010196309,0.000009036212,0.00002441779,0.00003482722,0.000021897862],"domain_scores_gemma":[0.9999105,0.000020399557,0.000008335235,0.00001618911,0.00003820273,0.000006468117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000090998554,0.000530958,0.0002331254,0.00043395985,0.00019814755,0.00038370106,0.0008179873,0.00028004564,0.010678705],"category_scores_gemma":[0.00021290279,0.00015136287,0.00017394539,0.00032257088,0.00010490108,0.0004217011,0.00012973508,0.00024368617,0.0021797472],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009343361,0.00025753462,0.0031127909,0.0007004066,0.00013750579,0.0011142166,0.00019788528,0.102478586,0.18554729,0.0117324395,0.03752224,0.65626484],"study_design_scores_gemma":[0.00026005475,0.0008978082,0.0028227048,0.00006675676,0.00009116261,0.00087679556,0.00006889083,0.7938594,0.15554473,0.0018900388,0.043571334,0.00005029904],"about_ca_topic_score_codex":0.00478319,"about_ca_topic_score_gemma":0.0049182638,"teacher_disagreement_score":0.010678705,"about_ca_system_score_codex":0.0004918496,"about_ca_system_score_gemma":0.00054305646,"threshold_uncertainty_score":0.035723865},"labels":[],"label_agreement":null},{"id":"W4404525026","doi":"","title":"Information Integration theory and Functional Measurement","year":2021,"lang":"en","type":"other","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Computer science; Information theory; Information integration; Data science; Data mining; Mathematics; Statistics","score_opus":0.015496593681885818,"score_gpt":0.20565204061931644,"score_spread":0.1901554469374306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404525026","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011561299,0.033301033,0.8749169,0.009103519,0.00092694454,0.000033711396,0.00040469284,0.00019391133,0.06955795],"genre_scores_gemma":[0.75481063,0.029916763,0.16984546,0.0017159226,0.003736036,0.00028171457,0.0006253003,0.00022610209,0.038842138],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99821955,0.00092320866,0.0000963445,0.00027273525,0.0004103282,0.00007774992],"domain_scores_gemma":[0.9951191,0.0035548902,0.00031923316,0.00038326066,0.00054492377,0.00007861939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025035883,0.00094145455,0.0012405732,0.0025320158,0.00064279366,0.0032521326,0.0010213363,0.0019671463,0.007414797],"category_scores_gemma":[0.008281559,0.00033920846,0.00082242984,0.0038821765,0.0038644061,0.005669381,0.0015179001,0.0020013019,0.0011114135],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005108941,0.000004018492,0.000059887945,0.000050128336,0.000010392493,0.000015390355,0.000024314706,0.0010325032,0.00006729409,0.9870522,0.0011196632,0.010559077],"study_design_scores_gemma":[0.0000021627204,0.0000037037084,0.00009150263,0.000014942709,0.000004907103,0.000033311866,0.000010171488,0.0062891725,0.00006365486,0.99082804,0.002653084,0.0000053536623],"about_ca_topic_score_codex":0.0017612551,"about_ca_topic_score_gemma":0.000833513,"teacher_disagreement_score":0.007414797,"about_ca_system_score_codex":0.0016574292,"about_ca_system_score_gemma":0.00077296124,"threshold_uncertainty_score":0.02480495},"labels":[],"label_agreement":null},{"id":"W4404603191","doi":"10.1214/24-aos2434","title":"On the existence of powerful p-values and e-values for composite hypotheses","year":2024,"lang":"en","type":"article","venue":"The Annals of Statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Composite number; Mathematics; Statistics; Psychology; Algorithm","score_opus":0.10202062081467692,"score_gpt":0.3544260630360375,"score_spread":0.25240544222136063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404603191","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017613264,0.0033375223,0.9710036,0.0026481468,0.00019053923,0.000121271885,0.00036353362,0.00022700104,0.0044950712],"genre_scores_gemma":[0.45178244,0.0048956955,0.53324455,0.0025695974,0.0024942996,0.0013764547,0.001051267,0.0003874679,0.002198176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94140005,0.042403184,0.0027039705,0.0058484357,0.006579527,0.0010648426],"domain_scores_gemma":[0.19333658,0.78270006,0.006092994,0.012195533,0.004258754,0.0014161211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10077868,0.0035200063,0.0068252156,0.008527487,0.0021160287,0.007208798,0.0064974665,0.0068021338,0.0068715997],"category_scores_gemma":[0.46253482,0.0025455928,0.0036216544,0.007063946,0.019829184,0.016393634,0.009984122,0.013013802,0.0012321367],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007833875,0.00020635368,0.0057048206,0.0014447656,0.00079421763,0.0014680675,0.0010733157,0.029986877,0.0018837727,0.85207903,0.004421421,0.100153945],"study_design_scores_gemma":[0.00013582614,0.00014111675,0.0010700568,0.00017516414,0.00008470368,0.00052230136,0.000107515494,0.036574796,0.000786356,0.95884436,0.0014997266,0.000058102843],"about_ca_topic_score_codex":0.00041098116,"about_ca_topic_score_gemma":0.00031093287,"teacher_disagreement_score":0.10077868,"about_ca_system_score_codex":0.001519519,"about_ca_system_score_gemma":0.0021850448,"threshold_uncertainty_score":0.53297496},"labels":[],"label_agreement":null},{"id":"W4404667245","doi":"10.5220/0012940500003886","title":"Deep Learning and Multi-Objective Evolutionary Fuzzy Classifiers: A Comparative Analysis for Brain Tumor Classification in MRI Images","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Fuzzy logic; Pattern recognition (psychology); Machine learning","score_opus":0.038586244484361966,"score_gpt":0.3217854134463332,"score_spread":0.2831991689619712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404667245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52067316,0.015453672,0.45200112,0.0016109308,0.00021305827,0.00015122516,0.0005545896,0.0008602686,0.008482009],"genre_scores_gemma":[0.91887736,0.0020251365,0.076740846,0.0001574068,0.000054314307,0.00005710544,0.0005165282,0.000041919997,0.0015293632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989054,0.0002931964,0.00008489604,0.00015520112,0.00045108574,0.00011014795],"domain_scores_gemma":[0.99661773,0.0022391935,0.00018236235,0.00019492439,0.0006751563,0.000090678004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003940354,0.0008369101,0.00093759963,0.0022095132,0.00027873935,0.001114812,0.0009039522,0.0013569336,0.00072532747],"category_scores_gemma":[0.0071124495,0.00019076861,0.0007534972,0.0011173722,0.00038500564,0.0011796324,0.00061277096,0.0008900727,0.0001897822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061139005,0.0002591789,0.0129103055,0.00029760465,0.0002952504,0.00015864847,0.00011789956,0.56570673,0.002941289,0.004197114,0.0017507786,0.41075385],"study_design_scores_gemma":[0.0000075370845,0.00014950374,0.0020351897,0.000040050098,0.00003490614,0.000052103383,0.000040044906,0.9941637,0.0014991692,0.0014636221,0.0005029728,0.00001131662],"about_ca_topic_score_codex":0.0059945704,"about_ca_topic_score_gemma":0.004328548,"teacher_disagreement_score":0.0059945704,"about_ca_system_score_codex":0.0011407901,"about_ca_system_score_gemma":0.00086110743,"threshold_uncertainty_score":0.020838857},"labels":[],"label_agreement":null},{"id":"W4404688880","doi":"10.1109/oceans55160.2024.10753861","title":"Two-Pass Continuous Active Sonar Processing","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"Defence Science and Technology Organisation; Defence Science and Technology Laboratory","keywords":"Marine mammals and sonar; Sonar; Computer science; Artificial intelligence","score_opus":0.013149525835773728,"score_gpt":0.2756831627714895,"score_spread":0.2625336369357158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404688880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11755253,0.00044639158,0.8620671,0.00023823188,0.00022593877,0.00025582887,0.00043419295,0.0026860964,0.016093712],"genre_scores_gemma":[0.5239211,0.00050977303,0.42363313,0.00034867544,0.00017004253,0.00046639866,0.0012496042,0.00030719393,0.04939411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971145,0.000016884871,0.000014742619,0.000059648934,0.00015323651,0.00004395762],"domain_scores_gemma":[0.99964523,0.00008512178,0.000021030493,0.00004756138,0.00017999978,0.000021137907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028210157,0.0008027962,0.00049695995,0.00035689495,0.00029400704,0.0011914269,0.0006782231,0.0008619049,0.007623861],"category_scores_gemma":[0.00081767404,0.00024480632,0.00035285432,0.000373169,0.00025507255,0.0005407636,0.0006349149,0.00073109637,0.002289504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083057274,0.00032860538,0.0014106882,0.00026345853,0.00008472786,0.00028882103,0.00021096622,0.031881604,0.28658718,0.0041378275,0.004127992,0.6698476],"study_design_scores_gemma":[0.000100075566,0.00143177,0.010465315,0.000040607883,0.000104735256,0.0007647443,0.00014365777,0.67895,0.26890862,0.002954716,0.036052484,0.00008318588],"about_ca_topic_score_codex":0.002176224,"about_ca_topic_score_gemma":0.0035118687,"teacher_disagreement_score":0.007623861,"about_ca_system_score_codex":0.0002748671,"about_ca_system_score_gemma":0.00086751924,"threshold_uncertainty_score":0.02550435},"labels":[],"label_agreement":null},{"id":"W4404727858","doi":"10.54254/2755-2721/2024.17892","title":"Using LLM Model to Process Sensor-detected Images","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Process (computing); Computer science; Operating system","score_opus":0.018830862268492156,"score_gpt":0.2591383070586208,"score_spread":0.24030744479012864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404727858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041498896,0.0002430848,0.95051235,0.00042916034,0.00010509618,0.00010386563,0.00016824876,0.003047802,0.0038915384],"genre_scores_gemma":[0.7097362,0.00030951464,0.27898183,0.0003480406,0.000056347773,0.00025013438,0.00050620374,0.00024397866,0.009567741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967325,0.00005359894,0.000022595057,0.00010068931,0.00011154861,0.000038252572],"domain_scores_gemma":[0.999559,0.00017677728,0.000046698588,0.00004995866,0.00014749603,0.000020064577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053656293,0.0004684363,0.00041831427,0.00048450488,0.00032734952,0.0009372637,0.0010089701,0.0007797003,0.0026683055],"category_scores_gemma":[0.0015950296,0.00020462881,0.0007207196,0.0003860003,0.0003357529,0.0013194084,0.0005220208,0.00084006565,0.0011571727],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061680033,0.00030980379,0.0047507845,0.00039905857,0.00011696612,0.0007093498,0.0005437099,0.4919673,0.09461392,0.021317504,0.004898899,0.3797559],"study_design_scores_gemma":[0.000007128046,0.000049847873,0.0002825575,0.000005098929,0.000010021364,0.000053053172,0.000025075678,0.988567,0.0077520795,0.0022047204,0.0010330325,0.000010397122],"about_ca_topic_score_codex":0.0055342643,"about_ca_topic_score_gemma":0.004048314,"teacher_disagreement_score":0.0055342643,"about_ca_system_score_codex":0.0006718734,"about_ca_system_score_gemma":0.000808315,"threshold_uncertainty_score":0.01100409},"labels":[],"label_agreement":null},{"id":"W4404762458","doi":"10.28924/2291-8639-22-2024-217","title":"Control and Function Projective Synchronization of 3D Chaotic System","year":2024,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Projective test; Chaotic; Synchronization (alternating current); Function (biology); Synchronization of chaos; Control (management); Chaotic systems; Control theory (sociology); Pure mathematics; Computer science; Topology (electrical circuits); Artificial intelligence; Combinatorics","score_opus":0.005262137593280938,"score_gpt":0.24474011669791473,"score_spread":0.2394779791046338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404762458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21620029,0.00035890966,0.7718942,0.0002464684,0.00008010599,0.000048025082,0.000041946987,0.00021758977,0.010912466],"genre_scores_gemma":[0.9909787,0.00010428459,0.0077556064,0.000019742543,0.000012976922,0.000027749706,0.000010169688,0.000006242264,0.0010844984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999884,0.000030809326,0.0000056144186,0.000029900977,0.00003454629,0.000015135744],"domain_scores_gemma":[0.9998901,0.000033040833,0.000035345216,0.000008283937,0.000024264651,0.000008901465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022996911,0.00034816036,0.0002053616,0.0002374851,0.00024145715,0.00038031867,0.00024584203,0.0003043619,0.0011208587],"category_scores_gemma":[0.0004030133,0.00010341623,0.0002520668,0.00014193267,0.0004942903,0.00030372592,0.0005658058,0.00020023939,0.00008013965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010117543,0.000029337076,0.0014069213,0.00014291568,0.000044354227,0.00052470784,0.00033947767,0.8295389,0.061173398,0.074191906,0.00058893184,0.03191799],"study_design_scores_gemma":[0.000007869239,0.00007857244,0.0003974268,0.000004135399,0.000006340562,0.000047233494,0.000024873665,0.99178934,0.0032178573,0.0038182838,0.00060010055,0.000007940107],"about_ca_topic_score_codex":0.0019388231,"about_ca_topic_score_gemma":0.00074692356,"teacher_disagreement_score":0.0019388231,"about_ca_system_score_codex":0.00029980892,"about_ca_system_score_gemma":0.00025557622,"threshold_uncertainty_score":0.0038550496},"labels":[],"label_agreement":null},{"id":"W4404781872","doi":"10.18653/v1/2024.findings-emnlp.422","title":"Intermediate Layer Distillation with the Reused Teacher Classifier: A Study on the Importance of the Classifier of Attention-based Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Classifier (UML); Distillation; Computer science; Artificial intelligence; Machine learning; Chemistry; Chromatography","score_opus":0.04510357437240407,"score_gpt":0.27000634110827,"score_spread":0.2249027667358659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404781872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1438077,0.0018548066,0.8434969,0.0005352097,0.00014571857,0.00009818094,0.00012174418,0.005171249,0.0047684824],"genre_scores_gemma":[0.7690546,0.00076941465,0.22086038,0.0003003138,0.00011663485,0.000108794455,0.00055884285,0.0005685443,0.0076625575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926156,0.0002432515,0.000040928728,0.00019503634,0.0001525141,0.000106695516],"domain_scores_gemma":[0.99634725,0.0022353223,0.00015504612,0.00063965714,0.0004553071,0.00016753696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014048483,0.0012069144,0.00081564306,0.0005732447,0.00043785042,0.0009671762,0.0018488053,0.0008201677,0.0026084662],"category_scores_gemma":[0.008536497,0.00043729815,0.00056807644,0.0007056574,0.000709668,0.0033333122,0.001994425,0.0028497644,0.0009983102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000698536,0.0002932621,0.00327418,0.0002961742,0.00010663166,0.00018120657,0.00030599834,0.14086436,0.030366484,0.008493177,0.0042067715,0.81091326],"study_design_scores_gemma":[0.000023520513,0.00015801977,0.0005316265,0.000018176806,0.000032154927,0.000074779484,0.000038030754,0.976851,0.018681059,0.0018713344,0.0017059658,0.000014396573],"about_ca_topic_score_codex":0.007356272,"about_ca_topic_score_gemma":0.009752259,"teacher_disagreement_score":0.007356272,"about_ca_system_score_codex":0.0008070713,"about_ca_system_score_gemma":0.0014270025,"threshold_uncertainty_score":0.01462692},"labels":[],"label_agreement":null},{"id":"W4404782786","doi":"10.18653/v1/2024.emnlp-main.338","title":"CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; McGill University; Canadian Institute for Advanced Research; Nvidia","keywords":"Sequence (biology); Computer science; State (computer science); Eviction; Algorithm; Chemistry; Political science","score_opus":0.04809721246414919,"score_gpt":0.30238976006060003,"score_spread":0.2542925475964508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404782786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02277731,0.00048570856,0.95624745,0.00020859764,0.000087524015,0.00006679637,0.0004276586,0.018744871,0.00095405936],"genre_scores_gemma":[0.39196536,0.0003349356,0.59569925,0.00039411496,0.00006995068,0.0002453186,0.002381655,0.0020346155,0.0068749394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970275,0.000082278864,0.000019446825,0.00011662654,0.00004839961,0.00003049409],"domain_scores_gemma":[0.9990701,0.0004921094,0.00006170566,0.00024326405,0.00009074549,0.00004205076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064590405,0.0009985009,0.0006142371,0.0004015685,0.00037159174,0.0005969023,0.0017274439,0.0007893423,0.0036008798],"category_scores_gemma":[0.0027107708,0.00043583306,0.0007048515,0.00039024095,0.0004364563,0.001774399,0.0010131125,0.0017835303,0.001274503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059956557,0.00018956578,0.0019056551,0.00027687024,0.0001547142,0.00032265205,0.00048564508,0.3018058,0.035640217,0.016096482,0.017899914,0.62462294],"study_design_scores_gemma":[0.00001953408,0.000042646094,0.00013359627,0.00000770866,0.00001094069,0.00003538761,0.00002157447,0.9803774,0.00987861,0.007495206,0.0019661256,0.000011298937],"about_ca_topic_score_codex":0.006607113,"about_ca_topic_score_gemma":0.016210733,"teacher_disagreement_score":0.006607113,"about_ca_system_score_codex":0.0007624933,"about_ca_system_score_gemma":0.0013642027,"threshold_uncertainty_score":0.013137341},"labels":[],"label_agreement":null},{"id":"W4404787947","doi":"10.1109/tcsi.2024.3502519","title":"Attention Mechanism Combined With Deep Recurrent Network for Nonlinear Circuit Macromodeling","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Beijing Municipal Natural Science Foundation; National Natural Science Foundation of China","keywords":"Nonlinear system; Equivalent circuit; Mechanism (biology); Computer science; Network analysis; Electronic engineering; Control theory (sociology); Voltage; Engineering; Electrical engineering; Physics; Artificial intelligence","score_opus":0.020304706884360584,"score_gpt":0.2296555581952543,"score_spread":0.2093508513108937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404787947","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013716593,0.00025574755,0.9836648,0.00008771719,0.00003119312,0.000016195778,0.000034799865,0.00080226467,0.001390628],"genre_scores_gemma":[0.829816,0.0004071043,0.16419986,0.00012177058,0.00005162881,0.00010142614,0.00015351009,0.00017301016,0.0049756244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998853,0.000025477004,0.0000062509266,0.000030365285,0.000036209705,0.00001634068],"domain_scores_gemma":[0.9998572,0.000058851925,0.000023195538,0.000019663883,0.00003170478,0.000009330039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034989178,0.0006352948,0.0004460706,0.00031443406,0.00024791976,0.00034411458,0.00111927,0.0006973376,0.0015924551],"category_scores_gemma":[0.0006787682,0.00032766518,0.0006596976,0.0002688294,0.0003091204,0.00087545975,0.00046368764,0.00073717244,0.00031434206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038689715,0.000021387592,0.0003034728,0.000049891143,0.000038143873,0.00008977742,0.000042456977,0.9326745,0.013864957,0.0067283325,0.0005593613,0.045589067],"study_design_scores_gemma":[9.3724043e-7,0.000005740352,0.000022935801,8.460788e-7,0.0000030848207,0.0000055081846,7.8449e-7,0.99865377,0.00057428767,0.00059280806,0.00013798253,0.0000013464123],"about_ca_topic_score_codex":0.0068037705,"about_ca_topic_score_gemma":0.008421523,"teacher_disagreement_score":0.0068037705,"about_ca_system_score_codex":0.0006006417,"about_ca_system_score_gemma":0.00051579985,"threshold_uncertainty_score":0.013528347},"labels":[],"label_agreement":null},{"id":"W4404788786","doi":"10.1016/j.engappai.2024.109669","title":"Adaptive neural boundary control for multi-agent manipulators system with uncertainties through cooperative disturbance observers network","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; Key Technologies Research and Development Program; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Computer science; Disturbance (geology); Artificial neural network; Control theory (sociology); Control (management); Boundary (topology); Adaptive control; Artificial intelligence","score_opus":0.04407918740945853,"score_gpt":0.2722849772131153,"score_spread":0.22820578980365674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404788786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09842002,0.0006822464,0.89299107,0.0002943818,0.0002184068,0.000051641553,0.000023038026,0.00029848886,0.0070208004],"genre_scores_gemma":[0.98877406,0.000121549834,0.0090058,0.000034175675,0.000020768326,0.000056927933,0.00001785877,0.000008072919,0.0019607537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971634,0.00004751172,0.000020377787,0.00008365066,0.0000864604,0.0000457271],"domain_scores_gemma":[0.99959177,0.00011910063,0.000101382335,0.000028169643,0.00013777259,0.000021856013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061318954,0.0006895836,0.00080177217,0.00031866843,0.00068967865,0.0009113598,0.0010359752,0.0009925308,0.0009956491],"category_scores_gemma":[0.0009833205,0.00034584387,0.00038953868,0.00030486227,0.00076872355,0.0008883101,0.0011954501,0.0007049358,0.00013836895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026137836,0.000082095095,0.0006928271,0.00015210293,0.000049102215,0.00022873523,0.00024120165,0.935596,0.0126103535,0.0059447065,0.00075639685,0.0433851],"study_design_scores_gemma":[0.000014965307,0.00005795291,0.00014621363,0.0000056660797,0.000006932454,0.0000087525605,0.000010292275,0.9982399,0.00068014854,0.000595068,0.00022995876,0.000004223853],"about_ca_topic_score_codex":0.008319578,"about_ca_topic_score_gemma":0.0059120576,"teacher_disagreement_score":0.008319578,"about_ca_system_score_codex":0.00049781555,"about_ca_system_score_gemma":0.00058745197,"threshold_uncertainty_score":0.016542315},"labels":[],"label_agreement":null},{"id":"W4404795413","doi":"10.32388/2aw04s","title":"Review of: \"On Optimal Linear Prediction\"","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Mathematics; Econometrics","score_opus":0.039985536710840645,"score_gpt":0.3299510552820078,"score_spread":0.28996551857116715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404795413","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013116491,0.22619136,0.03977833,0.3190182,0.332922,0.00028914775,0.0015148425,0.00078499183,0.07818949],"genre_scores_gemma":[0.03359227,0.3468362,0.024193307,0.092706576,0.30376783,0.0004125301,0.0029134438,0.0012245754,0.19435339],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99139833,0.0025841235,0.00052658486,0.0007711961,0.004283216,0.00043649567],"domain_scores_gemma":[0.9224434,0.02510824,0.001672795,0.003941332,0.045283522,0.0015507243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012717132,0.0008657702,0.00165481,0.0036433688,0.0014900997,0.0032375536,0.0024201067,0.003324268,0.028452786],"category_scores_gemma":[0.07223968,0.00048079423,0.00052090664,0.0041830405,0.0023773103,0.0053053163,0.0021300765,0.0038250051,0.01834601],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003683698,0.00001520275,0.00007789433,0.00052613457,0.000016757094,0.000016106858,0.000013380729,0.00032815602,0.00006960376,0.010082694,0.89185786,0.096959375],"study_design_scores_gemma":[0.00001967619,0.000031109033,0.00042693457,0.0007751808,0.000035963698,0.00005964228,0.000029219716,0.0014534633,0.00034502803,0.012544774,0.9842518,0.000027128812],"about_ca_topic_score_codex":0.005130374,"about_ca_topic_score_gemma":0.008437528,"teacher_disagreement_score":0.028452786,"about_ca_system_score_codex":0.002840679,"about_ca_system_score_gemma":0.008024946,"threshold_uncertainty_score":0.09518403},"labels":[],"label_agreement":null},{"id":"W4404939495","doi":"10.31224/4184","title":"The dissolved oxygen ramp is immoral: facing global water challenges with mathematical analysis","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Reuse; Feature (linguistics); SIGNAL (programming language); Computer science; Process (computing); Resource (disambiguation); Calibration; Water quality; Code (set theory); Identification (biology); Environmental science; Process engineering; Data mining; Artificial intelligence; Engineering; Mathematics; Waste management; Ecology; Statistics","score_opus":0.025512242751001436,"score_gpt":0.2725394497131147,"score_spread":0.24702720696211328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404939495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05372101,0.0015144544,0.93269527,0.0059137815,0.00013911186,0.000028431814,0.00018253586,0.0002850358,0.005520447],"genre_scores_gemma":[0.7495256,0.0022203375,0.24390447,0.0007626753,0.00024475396,0.00011295543,0.0002729537,0.00023171159,0.002724566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989506,0.00037938796,0.00006355483,0.00020628244,0.00033650495,0.00006353064],"domain_scores_gemma":[0.994255,0.0038294161,0.00065685436,0.00060171296,0.00051797787,0.00013909022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028242199,0.00075307506,0.00080026273,0.0008973133,0.00050772005,0.0021009573,0.0008352431,0.0008840801,0.0011515181],"category_scores_gemma":[0.011784205,0.00043203533,0.001021224,0.00051773887,0.0030985668,0.003144163,0.0027796822,0.0026169664,0.0002638723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101781516,0.000053133972,0.005153648,0.00037932367,0.00016477956,0.00037238884,0.0002832323,0.58554447,0.00741797,0.32899353,0.002396254,0.06913939],"study_design_scores_gemma":[0.0000055814344,0.000020226717,0.00049411505,0.000043018026,0.000012476053,0.000034588276,0.000041777763,0.7324987,0.001849093,0.26203156,0.00294905,0.000019855825],"about_ca_topic_score_codex":0.0022991071,"about_ca_topic_score_gemma":0.0016177593,"teacher_disagreement_score":0.0028242199,"about_ca_system_score_codex":0.0012673211,"about_ca_system_score_gemma":0.001124879,"threshold_uncertainty_score":0.0149360895},"labels":[],"label_agreement":null},{"id":"W4404987056","doi":"10.48550/arxiv.2411.15919","title":"Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regression; Regression analysis; Artificial neural network; Symbolic data analysis; Artificial intelligence; Symbolic regression; Computer science; The Symbolic; Statistical physics; Psychology; Mathematics; Machine learning; Theoretical computer science; Statistics; Physics","score_opus":0.028907479238052015,"score_gpt":0.19101097194693567,"score_spread":0.16210349270888366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404987056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018965071,0.00030454886,0.9778946,0.00025704448,0.00003640257,0.000017688635,0.00005222305,0.00069035904,0.0017820165],"genre_scores_gemma":[0.45757052,0.0009520628,0.53623277,0.00026050894,0.000088884255,0.00015490488,0.0004989221,0.00040195807,0.0038394101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994299,0.00021147315,0.000040610586,0.000103827166,0.00017683572,0.000037317903],"domain_scores_gemma":[0.99845445,0.0008734703,0.00016732622,0.00025697274,0.000213386,0.000034291766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014140025,0.001106184,0.00086529145,0.0007850414,0.00045423539,0.0010183296,0.0014440207,0.0008611259,0.002269324],"category_scores_gemma":[0.0080513675,0.00043175704,0.00077531155,0.0008756074,0.0012358662,0.0024933538,0.0021648596,0.001796469,0.00061090256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004859949,0.00006357802,0.001156007,0.00017036233,0.000071034316,0.00008783948,0.00013576758,0.78881997,0.007392482,0.062120147,0.0013884003,0.13854578],"study_design_scores_gemma":[0.0000011511695,0.000006083069,0.00004034316,0.0000051518427,0.0000023255614,0.000005312258,0.0000042332745,0.98803586,0.001152989,0.010422206,0.0003202622,0.0000041758153],"about_ca_topic_score_codex":0.0025029227,"about_ca_topic_score_gemma":0.0027430137,"teacher_disagreement_score":0.0025029227,"about_ca_system_score_codex":0.00071121985,"about_ca_system_score_gemma":0.001017471,"threshold_uncertainty_score":0.007591665},"labels":[],"label_agreement":null},{"id":"W4404989004","doi":"10.1017/9781009562942.003","title":"Fundamentals of SOC","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science","score_opus":0.02502267739206396,"score_gpt":0.2099460642133272,"score_spread":0.18492338682126325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404989004","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019393489,0.030301042,0.07290401,0.0039806063,0.0029768005,0.000071139366,0.0009780186,0.0010861909,0.8857628],"genre_scores_gemma":[0.16173322,0.067536816,0.05491418,0.0027081205,0.005299063,0.0003248138,0.0031628197,0.0010111202,0.7033099],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99972814,0.000033243272,0.000017370943,0.000065592714,0.00011181834,0.000043825417],"domain_scores_gemma":[0.9997627,0.00004059019,0.000009470685,0.000043543467,0.00010770536,0.000035994537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034250022,0.0008901839,0.00040588825,0.0012785656,0.0006817349,0.0024551512,0.0008716326,0.00066998997,0.07761035],"category_scores_gemma":[0.00067618623,0.00030161598,0.00050907134,0.0010464948,0.0013289858,0.0023172507,0.0010689354,0.0012992419,0.03133624],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021533871,0.000020387935,0.00010557496,0.0005173622,0.000011037366,0.00008526319,0.00014320096,0.0025678165,0.0008460495,0.7072682,0.14470033,0.14371322],"study_design_scores_gemma":[0.000003631073,0.000022783464,0.0001996065,0.00018074937,0.000004471018,0.00018413531,0.000050316707,0.002094603,0.00041919493,0.22889683,0.7679308,0.000012840338],"about_ca_topic_score_codex":0.0018265139,"about_ca_topic_score_gemma":0.0018159023,"teacher_disagreement_score":0.07761035,"about_ca_system_score_codex":0.0014114267,"about_ca_system_score_gemma":0.0009488731,"threshold_uncertainty_score":0.25963247},"labels":[],"label_agreement":null},{"id":"W4404989278","doi":"10.1017/9781108779791.013","title":"Overview of Synchronization Approaches","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Synchronization (alternating current); Computer science; Computer network","score_opus":0.07073550164570894,"score_gpt":0.21934466574324396,"score_spread":0.14860916409753502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404989278","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021095087,0.16812606,0.54031146,0.0026492358,0.0031914876,0.0002253326,0.0007516465,0.0024518173,0.28018346],"genre_scores_gemma":[0.061938506,0.29910138,0.36909658,0.002545785,0.00405712,0.0007091027,0.002420474,0.0020410581,0.25808993],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99911255,0.00012196679,0.00008455744,0.0002308897,0.00037836755,0.000071584356],"domain_scores_gemma":[0.99947006,0.00020942745,0.00004128278,0.0000902709,0.00015776319,0.000031334424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008455395,0.001166229,0.0007895138,0.0019774465,0.0010164798,0.0031607507,0.0022772688,0.0019947279,0.037580736],"category_scores_gemma":[0.0019476291,0.0008031176,0.0007105986,0.0034379733,0.0009999541,0.0049731582,0.0020317324,0.0020154496,0.022254592],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007432413,0.000046432455,0.0002031588,0.0019013528,0.000029582796,0.00019833703,0.00044138625,0.00592127,0.0043176333,0.37593764,0.04147865,0.56945014],"study_design_scores_gemma":[0.000010024388,0.00005510207,0.000168062,0.00055795134,0.00002333963,0.0005965806,0.000107974265,0.00787229,0.0023595188,0.07677887,0.91143984,0.00003041676],"about_ca_topic_score_codex":0.0014944047,"about_ca_topic_score_gemma":0.0012288673,"teacher_disagreement_score":0.037580736,"about_ca_system_score_codex":0.0013866264,"about_ca_system_score_gemma":0.0013826282,"threshold_uncertainty_score":0.12572008},"labels":[],"label_agreement":null},{"id":"W4404989481","doi":"10.1017/9781108779791.004","title":"Decision Circuits","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.023361209288186188,"score_gpt":0.20840768481045768,"score_spread":0.1850464755222715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404989481","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009620716,0.016273042,0.5170306,0.0015587804,0.0018354909,0.0002515072,0.0012616931,0.0030469852,0.44912118],"genre_scores_gemma":[0.19286823,0.015711758,0.22665945,0.0016212388,0.0006901507,0.000330916,0.0016114252,0.00042943866,0.5600774],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99979466,0.000020043733,0.000010592144,0.000051953706,0.00009901737,0.000023701747],"domain_scores_gemma":[0.99982446,0.0000657612,0.000009766591,0.000018232584,0.000071247974,0.000010560182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001668299,0.00064969575,0.00022818489,0.0004966093,0.00041093706,0.0015688302,0.0009280532,0.00070905895,0.037927017],"category_scores_gemma":[0.0005492475,0.00025045357,0.00022506135,0.0005081054,0.0003723325,0.0011863144,0.00051621767,0.0007905158,0.012847742],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010926428,0.000045137032,0.00013844675,0.00048042872,0.00002112665,0.00012076346,0.0001273885,0.003252649,0.030007917,0.39553204,0.045432843,0.52473205],"study_design_scores_gemma":[0.000044033768,0.0001202542,0.00022794786,0.00017778725,0.000033290995,0.0005984084,0.000054865435,0.014952385,0.042310774,0.14067672,0.80076945,0.00003409783],"about_ca_topic_score_codex":0.0002743283,"about_ca_topic_score_gemma":0.0005114197,"teacher_disagreement_score":0.037927017,"about_ca_system_score_codex":0.0006502424,"about_ca_system_score_gemma":0.0002643777,"threshold_uncertainty_score":0.12687844},"labels":[],"label_agreement":null},{"id":"W4405144851","doi":"","title":"Error Reduction in Neural Network Approximations of Boundary-Value Problems by a Multi-level Approach","year":2024,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Reduction (mathematics); Artificial neural network; Boundary value problem; Computer science; Value (mathematics); Approximation error; Mathematics; Algorithm; Mathematical optimization; Applied mathematics; Artificial intelligence; Machine learning; Mathematical analysis; Geometry","score_opus":0.027578755810535614,"score_gpt":0.2454466035214996,"score_spread":0.21786784771096399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405144851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024760948,0.00031322063,0.9714272,0.00030861289,0.00010801825,0.000039113296,0.000027521794,0.00018332884,0.0028319021],"genre_scores_gemma":[0.53389883,0.0003764855,0.45716873,0.00026746295,0.00016265956,0.0003065766,0.00015069282,0.00036936387,0.007299177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992142,0.0003982318,0.000038751365,0.000071086484,0.00021196857,0.00006571379],"domain_scores_gemma":[0.99652857,0.002553447,0.00017947229,0.00019783888,0.00041140587,0.00012923291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024122095,0.0006861701,0.0015039258,0.0009433833,0.00071523315,0.0015672371,0.0018228368,0.0026914058,0.0025225196],"category_scores_gemma":[0.007897235,0.0008242417,0.0009951954,0.0006513787,0.0013528224,0.0015853083,0.0023624226,0.0026623087,0.00042545426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010330194,0.000070761125,0.000307416,0.000099477715,0.00003337658,0.000051306553,0.000060930015,0.959076,0.0020456791,0.016512832,0.0005676024,0.021071406],"study_design_scores_gemma":[0.0000016747435,0.0000032770474,0.000009735672,0.000001977486,8.2667066e-7,0.0000011193508,0.0000012376468,0.9990716,0.0000852373,0.0007753543,0.00004707004,8.8070755e-7],"about_ca_topic_score_codex":0.0068142507,"about_ca_topic_score_gemma":0.0058639706,"teacher_disagreement_score":0.0068142507,"about_ca_system_score_codex":0.0010715394,"about_ca_system_score_gemma":0.0011025912,"threshold_uncertainty_score":0.013549149},"labels":[],"label_agreement":null},{"id":"W4405235590","doi":"10.7554/elife.100664.3.sa2","title":"eLife Assessment: Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Centre for Biological Sciences; Department of Atomic Energy, Government of India; Tata Institute of Fundamental Research","keywords":"Feed forward; Feedforward neural network; Computation; Artificial neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Algorithm; Engineering","score_opus":0.03744109347287813,"score_gpt":0.34268542735376695,"score_spread":0.30524433388088884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405235590","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08044277,0.0005755082,0.8975448,0.001120257,0.00022480566,0.0001596449,0.0008624423,0.007812536,0.011257206],"genre_scores_gemma":[0.55466133,0.00054764404,0.42924297,0.00023541783,0.00011738751,0.00031418272,0.0014100453,0.0012714146,0.012199633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990858,0.00020292323,0.000047576304,0.00014639783,0.00043347006,0.000083819905],"domain_scores_gemma":[0.9969426,0.0013875914,0.00024829173,0.00027421344,0.0009629713,0.00018437473],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0027662513,0.0003934287,0.0007176457,0.0013435014,0.0005250018,0.001238421,0.0019254732,0.0011573789,0.008302981],"category_scores_gemma":[0.015934242,0.0004733963,0.0004418685,0.00064453733,0.0007208789,0.0017105615,0.0013530279,0.0008509532,0.002078832],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039499515,0.000074492695,0.01332051,0.0005201021,0.00017883466,0.00042706978,0.0003745248,0.4544529,0.019155644,0.048283346,0.021239603,0.44157806],"study_design_scores_gemma":[0.000029042427,0.000034416713,0.0010490366,0.00003979158,0.000020299705,0.000071572744,0.0000421362,0.95822626,0.007934517,0.023662759,0.008872489,0.000017710528],"about_ca_topic_score_codex":0.0030140453,"about_ca_topic_score_gemma":0.0065869354,"teacher_disagreement_score":0.99723375,"about_ca_system_score_codex":0.0009078511,"about_ca_system_score_gemma":0.0019213184,"threshold_uncertainty_score":0.027776241},"labels":[],"label_agreement":null},{"id":"W4405305984","doi":"10.1109/indin58382.2024.10774467","title":"Distributed Predictive Maintenance Through Edge Computing","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kelowna General Hospital; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Distributed computing; Edge computing; Enhanced Data Rates for GSM Evolution; Artificial intelligence","score_opus":0.01583319792862425,"score_gpt":0.2643125198897267,"score_spread":0.24847932196110245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405305984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05221149,0.00054835883,0.9330751,0.0005326673,0.00011029237,0.00005389614,0.00009578457,0.0011377634,0.012234658],"genre_scores_gemma":[0.9178794,0.00027882963,0.0779544,0.000107758395,0.00005145714,0.000046933375,0.00009407651,0.000041039606,0.003546189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997942,0.000037536924,0.0000073762835,0.000057945254,0.00006422776,0.00003869248],"domain_scores_gemma":[0.99972004,0.0000936465,0.000026743035,0.000071762515,0.00006456948,0.00002319417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031701787,0.00030976825,0.00035010185,0.0002796729,0.0003999315,0.00086564064,0.0012608622,0.0004193004,0.0018419403],"category_scores_gemma":[0.00078957656,0.00012580713,0.00020548618,0.00032339024,0.00042673637,0.0012301842,0.00080301735,0.0006387612,0.0002372416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002849797,0.00011340964,0.0016357971,0.000056914083,0.000038681814,0.0002797949,0.00010601653,0.70932674,0.0066176974,0.09754821,0.0057646404,0.17822716],"study_design_scores_gemma":[0.000009574921,0.000023476676,0.00017153929,0.000005097341,0.000006731128,0.00003114829,0.000014210199,0.98010373,0.0009858712,0.01648757,0.0021564572,0.000004577095],"about_ca_topic_score_codex":0.004331498,"about_ca_topic_score_gemma":0.0045191958,"teacher_disagreement_score":0.004331498,"about_ca_system_score_codex":0.0005909991,"about_ca_system_score_gemma":0.0005620396,"threshold_uncertainty_score":0.008612573},"labels":[],"label_agreement":null},{"id":"W4405319588","doi":"10.1037/rev0000520","title":"A spiking neural model of decision making and the speed–accuracy trade-off.","year":2024,"lang":"en","type":"article","venue":"Psychological Review","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Air Force Office of Scientific Research; Ontario Innovation Trust","keywords":"Computer science; Artificial intelligence; Machine learning; Spiking neural network; Artificial neural network; Pattern recognition (psychology)","score_opus":0.08442437514903849,"score_gpt":0.3979545028208868,"score_spread":0.3135301276718483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405319588","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124608256,0.0016140937,0.8300985,0.005542895,0.00028705643,0.00010669758,0.0007284597,0.00031532548,0.036698714],"genre_scores_gemma":[0.93824357,0.00082024164,0.044273898,0.00043174258,0.00009524961,0.0002102224,0.000197646,0.000054853594,0.015672453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996964,0.000077573444,0.000020577001,0.00008306827,0.0000669227,0.000055432894],"domain_scores_gemma":[0.9992588,0.00041669022,0.00011404667,0.000052280044,0.00009428751,0.0000638663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009983567,0.00056365196,0.0005922977,0.0005640158,0.00047221433,0.0013628535,0.0019209902,0.0021711974,0.0030487624],"category_scores_gemma":[0.0036082868,0.00038971697,0.0012189884,0.0007756071,0.0012190929,0.0019949642,0.00075408584,0.0014181181,0.00051197695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007119074,0.000049958082,0.0018767482,0.000055029675,0.00006157233,0.00019491359,0.0001700621,0.70798,0.0037737559,0.27585927,0.0013189375,0.008588582],"study_design_scores_gemma":[0.000010278149,0.000012827596,0.00025283522,0.000005302649,0.000009812296,0.000042247368,0.0000101467385,0.923411,0.00012648726,0.075566314,0.0005457328,0.0000070218407],"about_ca_topic_score_codex":0.006489886,"about_ca_topic_score_gemma":0.004568277,"teacher_disagreement_score":0.006489886,"about_ca_system_score_codex":0.0015686083,"about_ca_system_score_gemma":0.001037437,"threshold_uncertainty_score":0.012904167},"labels":[],"label_agreement":null},{"id":"W4405431858","doi":"10.1088/2632-2153/ad9fd0","title":"Closed-form interpretation of neural network classifiers with symbolic gradients","year":2024,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Regional Municipality of Waterloo; Perimeter Institute; University of Waterloo","funders":"Mitacs","keywords":"Interpretation (philosophy); Artificial neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Programming language","score_opus":0.005275268148137342,"score_gpt":0.2337410787661297,"score_spread":0.22846581061799237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405431858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008174428,0.00024627493,0.98282397,0.000623005,0.0001079466,0.000032030846,0.00007352605,0.00012210938,0.007796639],"genre_scores_gemma":[0.64073896,0.00062761223,0.34600767,0.00062477647,0.00034936325,0.00033208914,0.00031564748,0.0003057476,0.010698094],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99758255,0.001001281,0.00016089717,0.00037915265,0.00070179283,0.00017428535],"domain_scores_gemma":[0.99732435,0.0013540937,0.00026766965,0.00024024797,0.00073751475,0.000076182165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033901457,0.0008144492,0.00091088755,0.0014074282,0.0005820475,0.003202127,0.0016138355,0.0013639033,0.004823112],"category_scores_gemma":[0.012859189,0.00036715492,0.00091710285,0.00082924025,0.0031694586,0.004354546,0.001801978,0.002525803,0.00080303114],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015842323,0.000015440619,0.00014895527,0.00004835916,0.0000113437645,0.000055077886,0.00012108719,0.047748357,0.0006858245,0.9375805,0.0010772609,0.0124919135],"study_design_scores_gemma":[0.0000053951003,0.000010087568,0.00004101541,0.000022552833,0.0000032746163,0.000018658158,0.000017942468,0.3692208,0.00035238048,0.62906355,0.0012376803,0.000006709702],"about_ca_topic_score_codex":0.0016193058,"about_ca_topic_score_gemma":0.0010879154,"teacher_disagreement_score":0.004823112,"about_ca_system_score_codex":0.0019841753,"about_ca_system_score_gemma":0.0010030379,"threshold_uncertainty_score":0.017929018},"labels":[],"label_agreement":null},{"id":"W4405434058","doi":"10.48550/arxiv.2412.10252","title":"What if we had built a prediction model with a survival super learner\\n instead of a Cox model 10 years ago?","year":2024,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut de Valorisation des Données; Université de Montréal","keywords":"Proportional hazards model; Computer science; Statistics; Mathematics","score_opus":0.08458905336813033,"score_gpt":0.20511416654651118,"score_spread":0.12052511317838085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405434058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6190379,0.009588678,0.3081744,0.044867665,0.0051767556,0.00030646916,0.0035307808,0.0018837071,0.007433652],"genre_scores_gemma":[0.88311726,0.0026048443,0.094394214,0.008470238,0.0020561179,0.00024388389,0.0041375,0.00055620767,0.004419708],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99099934,0.005070553,0.0004945186,0.0017130191,0.0012011005,0.00052149745],"domain_scores_gemma":[0.95657825,0.029263942,0.0023822128,0.0039846343,0.0065838103,0.0012070475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04645936,0.0011066541,0.0018868048,0.0010074544,0.00067722,0.0029052815,0.0019239545,0.0018459406,0.0032085488],"category_scores_gemma":[0.0966394,0.0005555917,0.002409423,0.0007722166,0.0010450891,0.007289567,0.0014613346,0.0042859525,0.002483173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031528994,0.00059135276,0.55273515,0.0007812165,0.0028877722,0.00071333075,0.0010811057,0.06715946,0.0020048274,0.010760886,0.01799587,0.3401362],"study_design_scores_gemma":[0.0004482803,0.007395099,0.15623865,0.00218805,0.0026787731,0.0018089248,0.001965338,0.7256438,0.012821976,0.041849587,0.04639056,0.0005709905],"about_ca_topic_score_codex":0.0066515394,"about_ca_topic_score_gemma":0.0075377356,"teacher_disagreement_score":0.04645936,"about_ca_system_score_codex":0.0016413695,"about_ca_system_score_gemma":0.0025977201,"threshold_uncertainty_score":0.24570352},"labels":[],"label_agreement":null},{"id":"W4405470007","doi":"10.48550/arxiv.2412.11521","title":"On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Homogeneous space; Artificial neural network; Computer science; Kernel (algebra); Artificial intelligence; Deep neural networks; Cognitive science; Data science; Mathematics; Psychology; Pure mathematics; Geometry","score_opus":0.07335424489021844,"score_gpt":0.20806074836304356,"score_spread":0.13470650347282512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405470007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061124034,0.0013100208,0.924514,0.005054474,0.00011264661,0.000032927444,0.00020412456,0.0003585635,0.0072892504],"genre_scores_gemma":[0.9190432,0.0024001803,0.07220686,0.0009409025,0.0004338669,0.00010675451,0.00026708347,0.00016918221,0.0044320184],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992306,0.00024659125,0.000044566714,0.0001935491,0.00019696544,0.00008765339],"domain_scores_gemma":[0.993079,0.004490744,0.000539949,0.0011759293,0.0005296794,0.00018466692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029860071,0.0007537661,0.00069046026,0.0012834625,0.00064856827,0.0016148415,0.0014066938,0.0016677404,0.002148109],"category_scores_gemma":[0.013589518,0.00045397022,0.0009667368,0.0007690533,0.0045843166,0.00730123,0.0028128764,0.0033780618,0.00039401947],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009078878,0.000063563595,0.0022118408,0.00015144,0.000075236545,0.00013048075,0.00025298548,0.15533519,0.0035640849,0.7826469,0.0028260483,0.052651454],"study_design_scores_gemma":[0.00000853586,0.00003940168,0.0007272962,0.000035084675,0.000010142704,0.000050126615,0.0000260904,0.4689215,0.0010087229,0.52839047,0.0007634377,0.00001901565],"about_ca_topic_score_codex":0.0023121652,"about_ca_topic_score_gemma":0.0011943983,"teacher_disagreement_score":0.0029860071,"about_ca_system_score_codex":0.0016211662,"about_ca_system_score_gemma":0.00059305865,"threshold_uncertainty_score":0.015791714},"labels":[],"label_agreement":null},{"id":"W4405621646","doi":"10.1007/978-3-031-75204-9_2","title":"Interpretable Ensemble Model For Associative Classification","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Associative property; Computer science; Artificial intelligence; Mathematics","score_opus":0.034736805113055096,"score_gpt":0.28571171093732795,"score_spread":0.25097490582427284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405621646","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013678202,0.0017257443,0.9798099,0.00037368227,0.00024568246,0.00003502937,0.00051157374,0.0011589531,0.0024612853],"genre_scores_gemma":[0.55610675,0.002429361,0.4065957,0.0005029731,0.00069466047,0.0003930278,0.0047340053,0.00041430062,0.028129231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995389,0.00014995053,0.00003462268,0.00013056697,0.0000953792,0.00005056472],"domain_scores_gemma":[0.9987669,0.0006343458,0.000049399834,0.00023192896,0.00028401765,0.000033482807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001229701,0.0009043671,0.0014253876,0.0006866728,0.0004165606,0.0011033813,0.0021062463,0.0014459898,0.005702235],"category_scores_gemma":[0.002872909,0.00041814783,0.0011063031,0.0011540266,0.0003958002,0.0025456415,0.0011773317,0.0029645592,0.0019960238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018793566,0.00022741614,0.0008741383,0.00016932454,0.0002614554,0.00012090814,0.00012965317,0.36941972,0.00367005,0.040286284,0.016660856,0.5679923],"study_design_scores_gemma":[0.0000024082588,0.000015242046,0.000091620765,0.000008910283,0.000013234894,0.000019315938,0.000006469597,0.98190755,0.0004052669,0.016701087,0.00082388375,0.000005042661],"about_ca_topic_score_codex":0.0036799458,"about_ca_topic_score_gemma":0.004792724,"teacher_disagreement_score":0.005702235,"about_ca_system_score_codex":0.0005440792,"about_ca_system_score_gemma":0.0005562861,"threshold_uncertainty_score":0.01907587},"labels":[],"label_agreement":null},{"id":"W4405633082","doi":"10.1109/tifs.2024.3520863","title":"zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Zero-knowledge proof; Mathematical proof; Zero (linguistics); Artificial intelligence; Computer security; Cryptography; Mathematics","score_opus":0.01283229624321787,"score_gpt":0.23863970107056853,"score_spread":0.22580740482735068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405633082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0096978145,0.00073800376,0.9716581,0.0013311263,0.00028101832,0.00020454134,0.0004159296,0.007924775,0.007748709],"genre_scores_gemma":[0.4634336,0.0010248313,0.51969004,0.0017748867,0.00043484068,0.0006723152,0.0010180247,0.0024154487,0.009535976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98491675,0.0045289337,0.0010294302,0.0021006165,0.0058231046,0.0016010477],"domain_scores_gemma":[0.9563152,0.027809104,0.0021212134,0.010456414,0.002640469,0.0006574635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007634283,0.0014516169,0.0014373693,0.0017187119,0.0018706092,0.0066528977,0.0048165726,0.002274828,0.01638325],"category_scores_gemma":[0.05258766,0.0016941024,0.002715692,0.0012845399,0.004851937,0.012144549,0.010038622,0.0062005483,0.004164195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006093458,0.00019978888,0.001184692,0.0009616801,0.0001484923,0.00059455866,0.0006408765,0.051824432,0.013771479,0.690728,0.019424574,0.21991205],"study_design_scores_gemma":[0.00021147636,0.00015693692,0.00016350637,0.00025187098,0.000091541275,0.00034591267,0.0000796163,0.23592655,0.02546115,0.71409,0.023124473,0.00009693004],"about_ca_topic_score_codex":0.0014016205,"about_ca_topic_score_gemma":0.002177385,"teacher_disagreement_score":0.01638325,"about_ca_system_score_codex":0.0033529194,"about_ca_system_score_gemma":0.005619266,"threshold_uncertainty_score":0.054807484},"labels":[],"label_agreement":null},{"id":"W4405644932","doi":"10.3233/fun-2006-712-310","title":"Reinforcement Learning with Approximation Spaces","year":2006,"lang":"en","type":"article","venue":"Fundamenta Informaticae","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Reinforcement learning; Computer science; Reinforcement; Artificial intelligence; Mathematics; Psychology; Social psychology","score_opus":0.008276264063803971,"score_gpt":0.20870670398314867,"score_spread":0.2004304399193447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405644932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068501313,0.00053330825,0.98917216,0.0002925394,0.00005644238,0.000042237123,0.000020126896,0.00011176977,0.0029212178],"genre_scores_gemma":[0.707848,0.0011442071,0.28478092,0.00023586246,0.00020347706,0.00039276516,0.00011514756,0.00006528118,0.0052142735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976164,0.0013201297,0.00012094572,0.0002651652,0.00053673453,0.00014070979],"domain_scores_gemma":[0.99460816,0.00400992,0.0003714943,0.00040385826,0.0004175708,0.00018894838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033650962,0.001109556,0.0015348317,0.000683462,0.00044773752,0.0017133754,0.0015841617,0.0013239168,0.00260134],"category_scores_gemma":[0.010851099,0.00040228132,0.0009909661,0.0006734062,0.0020840068,0.0019701621,0.0018670148,0.0022829508,0.00037393445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009449006,0.00006826509,0.00058696436,0.00015475892,0.00009829104,0.000068603425,0.0001532294,0.7401604,0.00060324284,0.2140011,0.00069189013,0.043318734],"study_design_scores_gemma":[0.000024878262,0.000065070024,0.000058713533,0.000015451935,0.000010165229,0.000016279146,0.000012799309,0.9232623,0.0002109039,0.075013705,0.0013010165,0.000008798825],"about_ca_topic_score_codex":0.002351146,"about_ca_topic_score_gemma":0.0014511931,"teacher_disagreement_score":0.0033650962,"about_ca_system_score_codex":0.0016117478,"about_ca_system_score_gemma":0.0010957646,"threshold_uncertainty_score":0.017796576},"labels":[],"label_agreement":null},{"id":"W4405655508","doi":"10.32388/bojdxm","title":"Influence Functions for Scalable Data Attribution in Diffusion Models","year":2024,"lang":"en","type":"preprint","venue":"Qeios","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; University of Toronto","funders":"Engineering and Physical Sciences Research Council","keywords":"Attribution; Diffusion; Scalability; Computer science; Econometrics; Data science; Data mining; Psychology; Mathematics; Database; Physics; Thermodynamics; Social psychology","score_opus":0.07695452809674291,"score_gpt":0.31016974952806353,"score_spread":0.23321522143132062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405655508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002617013,0.00012585668,0.99609286,0.00014876682,0.000020167452,0.000031753163,0.00003934342,0.00048517378,0.00043908425],"genre_scores_gemma":[0.33430183,0.00071290426,0.65737873,0.00033997043,0.00020196807,0.00050453463,0.00068933633,0.0010199634,0.0048507713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99642295,0.0016408497,0.0002468763,0.0006186921,0.0008553868,0.00021526693],"domain_scores_gemma":[0.9864848,0.009079862,0.0008772733,0.0017648102,0.0013826228,0.00041067888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071444833,0.0016634032,0.0015545838,0.0018434013,0.0009973058,0.0022315152,0.0028820701,0.0023427878,0.00351738],"category_scores_gemma":[0.04077072,0.0010396404,0.0019135561,0.0014655266,0.0021394722,0.004321437,0.0040887375,0.0042421776,0.0014173607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001468572,0.00007507416,0.001794526,0.00023392658,0.000110524175,0.00014527838,0.00031075545,0.71615785,0.0027804594,0.12737277,0.002892318,0.1479797],"study_design_scores_gemma":[0.000005524555,0.000009658953,0.00007709603,0.00001129078,0.000006227071,0.000017402737,0.000007981273,0.96980494,0.00063783664,0.028691417,0.00072253513,0.000008087146],"about_ca_topic_score_codex":0.0066878507,"about_ca_topic_score_gemma":0.006638197,"teacher_disagreement_score":0.0071444833,"about_ca_system_score_codex":0.0024461893,"about_ca_system_score_gemma":0.0021542164,"threshold_uncertainty_score":0.0377841},"labels":[],"label_agreement":null},{"id":"W4405785295","doi":"10.1109/iros58592.2024.10802293","title":"MFC-EQ: Mean-Field Control with Envelope Q-learning for Moving Decentralized Agents in Formation","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Envelope (radar); Q-learning; Field (mathematics); Control (management); Computer science; Control theory (sociology); Physics; Mathematics; Artificial intelligence; Reinforcement learning; Telecommunications; Pure mathematics","score_opus":0.02435700498624562,"score_gpt":0.2707431987775771,"score_spread":0.24638619379133145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405785295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022114024,0.00020808465,0.9753347,0.00027456513,0.000045500325,0.000048089187,0.000035530233,0.0002416254,0.0016978491],"genre_scores_gemma":[0.9163295,0.0001523626,0.08128578,0.00020843021,0.000044204764,0.0001380067,0.000083637475,0.000045863046,0.0017121269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938893,0.00021062439,0.000024277366,0.00013595872,0.000117604024,0.00012252641],"domain_scores_gemma":[0.99735475,0.0016959683,0.00030436704,0.000183625,0.00029936436,0.00016193479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022732713,0.0008819103,0.001419541,0.00045032526,0.00052463234,0.0008217256,0.0017394976,0.0014654744,0.0020590648],"category_scores_gemma":[0.0055910703,0.00044564749,0.0006001647,0.0004956774,0.0015198453,0.0012943484,0.0017976605,0.0015931998,0.0002246795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027303768,0.000020743382,0.00023418908,0.000020359908,0.000011516323,0.000021918531,0.000020105099,0.98572063,0.00028637404,0.00541338,0.0002924286,0.007930997],"study_design_scores_gemma":[0.000007890976,0.00001912351,0.000029288212,0.0000019634815,0.000001611809,0.000003543872,0.000002347341,0.9970542,0.00006171678,0.0026877017,0.00012874063,0.0000018455089],"about_ca_topic_score_codex":0.007435274,"about_ca_topic_score_gemma":0.004709928,"teacher_disagreement_score":0.007435274,"about_ca_system_score_codex":0.0010650966,"about_ca_system_score_gemma":0.001645159,"threshold_uncertainty_score":0.014783978},"labels":[],"label_agreement":null},{"id":"W4405786857","doi":"10.1109/iros58592.2024.10802068","title":"Predicting Long-Term Human Behaviors in Discrete Representations via Physics-Guided Diffusion","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Term (time); Diffusion; Statistical physics; Computer science; Physics; Quantum mechanics","score_opus":0.028596825896915746,"score_gpt":0.33859642827911535,"score_spread":0.3099996023821996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405786857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112317815,0.00092946,0.88170755,0.0007434779,0.00008757891,0.00005052829,0.0011580561,0.0017055618,0.0012999319],"genre_scores_gemma":[0.8989406,0.0005955956,0.09472356,0.00019245826,0.0000538405,0.00008656799,0.0021875086,0.00012923576,0.0030907078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982136,0.000031150572,0.000007775421,0.00008079119,0.000033931017,0.000025025487],"domain_scores_gemma":[0.99941504,0.00029437646,0.00007963442,0.00007085912,0.00008557339,0.000054564272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046507077,0.00074502756,0.0005888975,0.0008107998,0.00029322435,0.00051210116,0.0012263206,0.0009912334,0.0011868287],"category_scores_gemma":[0.002437339,0.00041675303,0.0006893681,0.0006979307,0.0006147933,0.0011785132,0.0006286693,0.0015953463,0.00046297172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013215955,0.00007717272,0.00493654,0.00007206018,0.00004734655,0.0001121708,0.00010129091,0.90556127,0.0036941466,0.0050299666,0.00298298,0.07725277],"study_design_scores_gemma":[0.0000025570566,0.000009166836,0.00032976543,0.000004886588,0.000002456559,0.000013657986,0.0000071764234,0.9961062,0.00032478175,0.002993668,0.00020164656,0.0000040394807],"about_ca_topic_score_codex":0.021210551,"about_ca_topic_score_gemma":0.025986357,"teacher_disagreement_score":0.021210551,"about_ca_system_score_codex":0.00090362626,"about_ca_system_score_gemma":0.0008086944,"threshold_uncertainty_score":0.04217416},"labels":[],"label_agreement":null},{"id":"W4405935254","doi":"10.1109/tmtt.2024.3521389","title":"Physics-Informed Deep Operator Network for 3-D Time-Domain Electromagnetic Modeling","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Operator (biology); Physics; Time domain; Computer science","score_opus":0.008993035237946702,"score_gpt":0.2469685292903939,"score_spread":0.2379754940524472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405935254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004460957,0.000080214864,0.99260956,0.000112854505,0.000018952376,0.0000130353965,0.00004801575,0.00035112628,0.002305234],"genre_scores_gemma":[0.4691969,0.0004969459,0.5202663,0.00035377577,0.00006291326,0.00022750595,0.00042928185,0.00040397048,0.008562435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998636,0.000033148048,0.000004600999,0.000018661809,0.00006822951,0.00001175877],"domain_scores_gemma":[0.9997403,0.00013167982,0.000026846788,0.000036317608,0.000046249275,0.000018686502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003667236,0.00057243067,0.000288393,0.00028645014,0.0002250459,0.0005114468,0.0010655036,0.00075230503,0.0023694567],"category_scores_gemma":[0.0010649181,0.00032718419,0.0005015633,0.00029235455,0.0005342779,0.0009637107,0.00089119474,0.0012209453,0.00046758956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019075398,0.000018327371,0.0002248119,0.000043654694,0.000017443543,0.000053753065,0.00003974237,0.9547457,0.004661766,0.019549035,0.001116335,0.019510357],"study_design_scores_gemma":[9.583399e-7,0.0000034071238,0.000012438909,0.0000014869639,8.614237e-7,0.0000082084,0.0000019341114,0.9965551,0.00035426285,0.0025354016,0.00052460266,0.0000013221434],"about_ca_topic_score_codex":0.0026629546,"about_ca_topic_score_gemma":0.0042845425,"teacher_disagreement_score":0.0026629546,"about_ca_system_score_codex":0.0006142184,"about_ca_system_score_gemma":0.0007957921,"threshold_uncertainty_score":0.007926643},"labels":[],"label_agreement":null},{"id":"W4405942492","doi":"10.1016/j.artint.2024.104273","title":"A semantic framework for neurosymbolic computation","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Computation; Natural language processing; Programming language","score_opus":0.07593160393922248,"score_gpt":0.35936569587295397,"score_spread":0.2834340919337315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405942492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042215586,0.0007706513,0.96281195,0.0025632326,0.00019119952,0.00006847402,0.00023557943,0.00040219474,0.028735042],"genre_scores_gemma":[0.3418174,0.0014656495,0.6430892,0.0014431392,0.00050138996,0.00046558035,0.0006549735,0.00037254844,0.010190087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978509,0.000861267,0.00021789181,0.0003821211,0.0005129664,0.0001748854],"domain_scores_gemma":[0.9981781,0.00082520867,0.00013590154,0.0003791296,0.00035689445,0.00012476736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028417637,0.0007428267,0.0005877886,0.0021918423,0.0018429934,0.004090499,0.0018115529,0.001787929,0.006070475],"category_scores_gemma":[0.004868625,0.00049135287,0.0017642934,0.0017521898,0.009656167,0.010272087,0.003630506,0.0030784474,0.0010229206],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026080206,0.0000022387376,0.000012722659,0.000013553253,0.0000018267008,0.000010443151,0.00006478246,0.00058863417,0.00007735549,0.9972813,0.00021911862,0.0017253392],"study_design_scores_gemma":[0.0000044971757,0.000005217004,0.000015630063,0.00001909007,0.000004534609,0.000032326876,0.00004497757,0.0052024345,0.00023714345,0.98495513,0.009473706,0.00000538809],"about_ca_topic_score_codex":0.0026257532,"about_ca_topic_score_gemma":0.0027206182,"teacher_disagreement_score":0.006070475,"about_ca_system_score_codex":0.002879335,"about_ca_system_score_gemma":0.001985715,"threshold_uncertainty_score":0.02089113},"labels":[],"label_agreement":null},{"id":"W4405969792","doi":"10.31234/osf.io/cnh5w","title":"Temporally dissociable neural representations of pitch height and chroma","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Deafness and Other Communication Disorders; National Institutes of Health; York University; Leon Levy Foundation","keywords":"Mathematics; Psychology","score_opus":0.016393785803094335,"score_gpt":0.2888737920651812,"score_spread":0.2724800062620869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405969792","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9622453,0.00028341924,0.034500632,0.00009029779,0.000021798263,0.00002746725,0.0002502528,0.00007423134,0.002506564],"genre_scores_gemma":[0.9931218,0.00013712543,0.005843355,0.000021662197,0.000009850674,0.00001223808,0.00018929427,0.000019383897,0.0006453534],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99991107,0.000016241016,0.000005144801,0.000032861837,0.000019035318,0.000015680682],"domain_scores_gemma":[0.9995672,0.00027713738,0.00006758283,0.000036944886,0.000026656733,0.000024411373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024254923,0.0002390679,0.00015264383,0.00022951371,0.00007439856,0.00056652626,0.00013765962,0.00023755622,0.0012705517],"category_scores_gemma":[0.0020939838,0.00021713493,0.00024065938,0.0001597353,0.0003023582,0.0004144135,0.0003726123,0.00028450356,0.00016871934],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053330604,0.000058462272,0.030912425,0.00011667063,0.00009089993,0.0001438389,0.00039623017,0.0023399016,0.8784652,0.0015965109,0.00014918347,0.08519737],"study_design_scores_gemma":[0.000034067554,0.00030598082,0.8428465,0.000032306878,0.00014788267,0.00048099036,0.00028959324,0.052887574,0.09587158,0.0060386267,0.0010239949,0.000040845014],"about_ca_topic_score_codex":0.0007338536,"about_ca_topic_score_gemma":0.0015426097,"teacher_disagreement_score":0.0012705517,"about_ca_system_score_codex":0.00013099007,"about_ca_system_score_gemma":0.00017397724,"threshold_uncertainty_score":0.004250467},"labels":[],"label_agreement":null},{"id":"W4405974764","doi":"10.1109/sec62691.2024.00055","title":"Hierarchical Inference at the Edge: A Batch Processing Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Inference; Enhanced Data Rates for GSM Evolution; Artificial intelligence","score_opus":0.02737667419048538,"score_gpt":0.2874015279704264,"score_spread":0.260024853779941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405974764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065859086,0.00017177721,0.9908037,0.00029391528,0.000053546366,0.000058641785,0.00012256208,0.0010128845,0.00089694385],"genre_scores_gemma":[0.38000262,0.00039709613,0.6089157,0.0007390948,0.00037107145,0.00028783618,0.0010341159,0.0005353776,0.007717219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987141,0.0003441868,0.000059180024,0.00036541175,0.00029552449,0.00022165658],"domain_scores_gemma":[0.9960743,0.0024863554,0.00019705178,0.0005694921,0.00046877007,0.00020397495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028656814,0.0013844518,0.0018083008,0.0006301212,0.00085366576,0.001430013,0.004125712,0.0015622758,0.0047209137],"category_scores_gemma":[0.007465942,0.00123136,0.0009896703,0.001112495,0.0013919213,0.002744847,0.0019398318,0.0035354614,0.0011411698],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079679413,0.00020556433,0.0015774057,0.00016555154,0.00013755378,0.00023379261,0.0001746751,0.8121452,0.0072750654,0.020805938,0.008802347,0.14768001],"study_design_scores_gemma":[0.000012382981,0.00001724302,0.00007065202,0.0000026765495,0.000009581108,0.0000072093303,0.0000072331864,0.99432015,0.00079034123,0.004416676,0.00034025204,0.0000054596976],"about_ca_topic_score_codex":0.021969084,"about_ca_topic_score_gemma":0.031043882,"teacher_disagreement_score":0.021969084,"about_ca_system_score_codex":0.0015449978,"about_ca_system_score_gemma":0.0028649117,"threshold_uncertainty_score":0.043682396},"labels":[],"label_agreement":null},{"id":"W4406038664","doi":"10.1007/978-3-031-68915-4_7","title":"Linear Least Squares Estimation","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Estimation; Least-squares function approximation; Mathematics; Statistics; Computer science; Engineering","score_opus":0.020771448496354705,"score_gpt":0.2541420711890939,"score_spread":0.23337062269273917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406038664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00051546394,0.0038792605,0.947547,0.00036757183,0.0004971831,0.000022456297,0.00025696025,0.002137986,0.044776153],"genre_scores_gemma":[0.025877262,0.0085400585,0.48865947,0.0007347903,0.000828845,0.00013457134,0.0019827546,0.0017177205,0.47152457],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999691,0.00005317963,0.000010455953,0.000078835445,0.00015287196,0.000013621887],"domain_scores_gemma":[0.99967945,0.00013041197,0.000017276934,0.00006691334,0.00009787341,0.0000081361395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003288522,0.0011232354,0.00089539593,0.00057176704,0.00034272383,0.0011034076,0.00087122206,0.0011351861,0.038239464],"category_scores_gemma":[0.0011868639,0.0005548841,0.00047820428,0.0011337099,0.00047200892,0.0011808522,0.00097243825,0.0014901884,0.048737418],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036618338,0.000036288326,0.0001380418,0.00031358763,0.000043837576,0.000059996397,0.00004474476,0.02604336,0.008065106,0.033102777,0.11368553,0.8184302],"study_design_scores_gemma":[0.000015036188,0.0000859152,0.0007859987,0.00022305852,0.00007438766,0.0005387216,0.00007132218,0.35437062,0.023839192,0.07886273,0.5410508,0.000082309096],"about_ca_topic_score_codex":0.001129009,"about_ca_topic_score_gemma":0.002115158,"teacher_disagreement_score":0.038239464,"about_ca_system_score_codex":0.00027237006,"about_ca_system_score_gemma":0.00042177652,"threshold_uncertainty_score":0.12792373},"labels":[],"label_agreement":null},{"id":"W4406138065","doi":"10.1109/jstsp.2024.3522437","title":"Editorial Introduction to the Special Issue on Learning-Based Signal Processing for Integrated Sensing and Communications","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Signal processing; Telecommunications; Radar","score_opus":0.016978317916384756,"score_gpt":0.2869723455077209,"score_spread":0.2699940275913361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406138065","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000053999087,0.0040875836,0.00049033115,0.012400386,0.9812368,0.000018336066,0.0000825939,0.00008299865,0.0015469899],"genre_scores_gemma":[0.00040520882,0.002959193,0.00014617291,0.0039059967,0.98720855,0.000011057984,0.000038423077,0.000052220752,0.0052731587],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99738306,0.00024248575,0.0002656595,0.0006117727,0.0012924011,0.00020465201],"domain_scores_gemma":[0.9865043,0.0038909626,0.00074402377,0.0003929096,0.0059744953,0.0024932816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033005003,0.003958258,0.003268369,0.0029491659,0.0018075239,0.0067586605,0.0029983532,0.007901129,0.045438986],"category_scores_gemma":[0.010398604,0.0008438239,0.002193382,0.0010956107,0.0014958291,0.0043604956,0.0012719356,0.0117637655,0.024050396],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064398424,0.000021262873,0.000050085484,0.00030047333,0.000021246417,0.00009443135,0.000009923931,0.0000716763,0.00021265299,0.00045761606,0.98794997,0.010746269],"study_design_scores_gemma":[0.000047888607,0.000086895285,0.00034776633,0.000270267,0.00005007295,0.00042781516,0.000027505228,0.0005249818,0.00033276138,0.001418054,0.996442,0.000023857745],"about_ca_topic_score_codex":0.0005370547,"about_ca_topic_score_gemma":0.0011845772,"teacher_disagreement_score":0.045438986,"about_ca_system_score_codex":0.0013309859,"about_ca_system_score_gemma":0.001479318,"threshold_uncertainty_score":0.15200853},"labels":[],"label_agreement":null},{"id":"W4406242209","doi":"10.1142/s0219493725500029","title":"Large deviations of one-hidden-layer neural networks","year":2024,"lang":"en","type":"article","venue":"Stochastics and Dynamics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Zoo","funders":"Villum Fonden","keywords":"Mathematics; Layer (electronics); Artificial neural network; Statistical physics; Artificial intelligence; Computer science; Nanotechnology; Physics; Materials science","score_opus":0.013618967753652381,"score_gpt":0.25233209417704744,"score_spread":0.23871312642339507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406242209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13274415,0.0028612495,0.8452456,0.0026685982,0.00023519265,0.000045039662,0.00015782847,0.00033977738,0.015702624],"genre_scores_gemma":[0.9473206,0.001076544,0.038533792,0.00034633966,0.00020471899,0.000111579255,0.00017699624,0.00022749884,0.012002074],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99913484,0.00035874516,0.000034504807,0.00013539837,0.00023538193,0.00010115023],"domain_scores_gemma":[0.9936441,0.0046084495,0.0006438386,0.00023921904,0.0005036798,0.00036079015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026430541,0.000924165,0.0010178054,0.0007378834,0.00056668743,0.001559668,0.0022316554,0.0018450781,0.0019362734],"category_scores_gemma":[0.014438811,0.000599645,0.00082399126,0.00045061036,0.0025864276,0.0023156505,0.0017648747,0.0022719577,0.00032236887],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006423463,0.00004353866,0.0010371587,0.00010112641,0.00007512611,0.00017833666,0.00009917527,0.60035884,0.001784252,0.39014006,0.0013241945,0.004793946],"study_design_scores_gemma":[0.000005328686,0.000009304806,0.00010660674,0.0000075101657,0.0000032882783,0.000011032168,0.0000045649917,0.93557984,0.00014278952,0.06393905,0.00018458824,0.0000061627184],"about_ca_topic_score_codex":0.0037016142,"about_ca_topic_score_gemma":0.003256516,"teacher_disagreement_score":0.0037016142,"about_ca_system_score_codex":0.0016167932,"about_ca_system_score_gemma":0.0011710614,"threshold_uncertainty_score":0.013977945},"labels":[],"label_agreement":null},{"id":"W4406256348","doi":"10.1088/2634-4386/ada8d4","title":"Maximizing information in neuron populations for neuromorphic spike encoding","year":2025,"lang":"en","type":"article","venue":"Neuromorphic Computing and Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Neuromorphic engineering; Spike (software development); Encoding (memory); Computer science; Artificial intelligence; Neuroscience; Biology; Artificial neural network; Software engineering","score_opus":0.031261405762644155,"score_gpt":0.24092333094432417,"score_spread":0.20966192518168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406256348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10891197,0.00041847222,0.8877687,0.0002768743,0.000021595624,0.00005068764,0.000030751202,0.0002476868,0.0022732185],"genre_scores_gemma":[0.881193,0.00011993116,0.1173089,0.00009683567,0.000027159595,0.00008087899,0.00003448698,0.000037676342,0.0011012459],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995777,0.00015961386,0.000029309127,0.000068712994,0.00011345843,0.000051227635],"domain_scores_gemma":[0.9990778,0.000535839,0.00013677955,0.000077078985,0.00012929973,0.00004314772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087296125,0.00053449895,0.00057047635,0.0004686713,0.00023069163,0.00059337326,0.00074213085,0.0006230174,0.0008525647],"category_scores_gemma":[0.0028532762,0.00025339785,0.00030170337,0.00045983665,0.0005086896,0.0009108898,0.0008639445,0.0005166014,0.0001929179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018056316,0.00014936698,0.0010563289,0.00011173123,0.00007921367,0.00007462123,0.00011409543,0.72198313,0.058303777,0.01779382,0.0008937975,0.19925961],"study_design_scores_gemma":[0.000008234884,0.000060350027,0.00020689775,0.000008653144,0.000012527178,0.00003998869,0.000012249644,0.98265153,0.011044938,0.0055168183,0.00043081652,0.0000070619913],"about_ca_topic_score_codex":0.00045902753,"about_ca_topic_score_gemma":0.0005707548,"teacher_disagreement_score":0.00087296125,"about_ca_system_score_codex":0.00068410125,"about_ca_system_score_gemma":0.00038797103,"threshold_uncertainty_score":0.004963517},"labels":[],"label_agreement":null},{"id":"W4406261200","doi":"10.1109/qce60285.2024.00195","title":"Fermionic Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.013356307993621969,"score_gpt":0.2504201593100623,"score_spread":0.2370638513164403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406261200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01131253,0.0010857279,0.97269714,0.0014764153,0.00013596257,0.000066643435,0.00056675775,0.0009944622,0.011664347],"genre_scores_gemma":[0.60880876,0.0014907252,0.37424448,0.0017723772,0.00040247335,0.00048648077,0.002055038,0.00034188366,0.010397753],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99874616,0.0005772908,0.00006361901,0.00020600708,0.00029972024,0.0001072729],"domain_scores_gemma":[0.9982406,0.00078644487,0.00010875324,0.00055568584,0.00022390859,0.000084504834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019700252,0.00068373134,0.00084990537,0.001323635,0.00067843613,0.0017705138,0.0018477215,0.0013089186,0.006767085],"category_scores_gemma":[0.0077376575,0.0002748498,0.00079952565,0.0010753189,0.0016211316,0.003083714,0.002436786,0.0018094039,0.0014199081],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037468923,0.00005627327,0.0008388616,0.00014067371,0.000052883915,0.00006970611,0.00006961882,0.11439943,0.0011259281,0.8036382,0.0069648875,0.072606064],"study_design_scores_gemma":[0.0000069154776,0.000018544852,0.00010270456,0.00002410992,0.000004806805,0.0000294598,0.000008882834,0.56188923,0.00045218327,0.43198648,0.0054671965,0.000009478208],"about_ca_topic_score_codex":0.0014270983,"about_ca_topic_score_gemma":0.0018672327,"teacher_disagreement_score":0.006767085,"about_ca_system_score_codex":0.001469089,"about_ca_system_score_gemma":0.0011340372,"threshold_uncertainty_score":0.022638202},"labels":[],"label_agreement":null},{"id":"W4406261511","doi":"10.1109/bibm62325.2024.10822786","title":"Global-Local Brain Network based on Functional Connectivity for Individualized Prediction","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"National Natural Science Foundation of China","keywords":"Computer science; Functional connectivity; Neuroscience; Psychology","score_opus":0.021230738051411995,"score_gpt":0.26862627788177246,"score_spread":0.24739553983036047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406261511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20111963,0.0033257366,0.7793393,0.00083206844,0.00015975194,0.00018566789,0.0054830383,0.0039603612,0.0055944645],"genre_scores_gemma":[0.87357503,0.00087592035,0.11679476,0.0001442163,0.00019588797,0.00022710467,0.005312135,0.00025218926,0.0026228211],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995183,0.00012415051,0.00001779713,0.00021116657,0.00008050939,0.0000481502],"domain_scores_gemma":[0.9992599,0.0003299059,0.00014087802,0.000103060214,0.00012293072,0.00004326516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008231056,0.0012415957,0.000704961,0.0027694046,0.0004190283,0.0005739781,0.0006932245,0.0007955904,0.0020030444],"category_scores_gemma":[0.0036273645,0.00020123774,0.00064446364,0.0017957772,0.00037556695,0.0011422347,0.00071955216,0.00079023826,0.0007168439],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006170812,0.00030753485,0.07269668,0.00051925844,0.00073833036,0.00069911446,0.0003993956,0.3039227,0.01760906,0.011675858,0.031003188,0.5598117],"study_design_scores_gemma":[0.000025012514,0.00007291761,0.022190426,0.00006062824,0.0001259764,0.00040835235,0.00007641947,0.9459596,0.00449484,0.02233054,0.0042133178,0.000041967487],"about_ca_topic_score_codex":0.009599908,"about_ca_topic_score_gemma":0.019641524,"teacher_disagreement_score":0.009599908,"about_ca_system_score_codex":0.0006159102,"about_ca_system_score_gemma":0.00055842457,"threshold_uncertainty_score":0.01908803},"labels":[],"label_agreement":null},{"id":"W4406267102","doi":"10.1109/vtc2024-fall63153.2024.10757965","title":"Distributed Critic-Based Neuro-Fuzzy Learning in Swarm Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Neuro-fuzzy; Swarm behaviour; Artificial intelligence; Fuzzy logic; Fuzzy control system","score_opus":0.014159144585797758,"score_gpt":0.25483693354166354,"score_spread":0.24067778895586578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406267102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07056645,0.00031733364,0.9243017,0.00027675825,0.000064841006,0.000045949393,0.000016043381,0.00022401367,0.004186919],"genre_scores_gemma":[0.9719794,0.00008186868,0.026283553,0.000036573143,0.000014998441,0.000052861822,0.000011119746,0.000012104225,0.0015274909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997987,0.00006271956,0.000009201668,0.000037194648,0.000065955086,0.00002631528],"domain_scores_gemma":[0.99940455,0.00031175456,0.0000831767,0.00002939541,0.00014227923,0.000028911098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007845355,0.00044459265,0.0004982461,0.0002645749,0.00032503987,0.0005500871,0.0008662042,0.0006610372,0.0006031463],"category_scores_gemma":[0.0015714773,0.0002488068,0.00027507945,0.00019698922,0.0007274349,0.00034419826,0.00053302967,0.00070386817,0.00010310596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022375816,0.000012166089,0.00022348978,0.000015393729,0.000010804688,0.000026075517,0.000026895264,0.98829716,0.0009846312,0.0025467388,0.00010779652,0.007726518],"study_design_scores_gemma":[0.000003002858,0.000012624541,0.000028539436,0.0000011547675,0.0000014078435,0.000003143237,0.0000025812126,0.9991309,0.00017189454,0.0005683043,0.00007503116,0.0000015062169],"about_ca_topic_score_codex":0.00663037,"about_ca_topic_score_gemma":0.0041666343,"teacher_disagreement_score":0.00663037,"about_ca_system_score_codex":0.000750452,"about_ca_system_score_gemma":0.0007840654,"threshold_uncertainty_score":0.013183534},"labels":[],"label_agreement":null},{"id":"W4406276158","doi":"10.1109/mmm.2024.3486588","title":"Neuro-Impedance Function for Electromagnetic Modeling","year":2025,"lang":"en","type":"article","venue":"IEEE Microwave Magazine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Electrical impedance; Function (biology); Computational electromagnetics; Electronic engineering; Computer science; Acoustics; Electromagnetic field; Engineering; Electrical engineering; Physics","score_opus":0.014112568199203148,"score_gpt":0.24687167106175656,"score_spread":0.2327591028625534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406276158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023950099,0.004041776,0.9796578,0.00072900247,0.00017062997,0.000016609029,0.000092411836,0.00021889377,0.012677906],"genre_scores_gemma":[0.50571674,0.026147692,0.41718718,0.00086671935,0.0007833167,0.00045759053,0.0006086675,0.00032415087,0.047908016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997899,0.00006684578,0.000011597182,0.000031951357,0.00008685479,0.000012892167],"domain_scores_gemma":[0.99983644,0.00006794714,0.0000196287,0.000020138998,0.000049879396,0.0000059457907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038372923,0.0006985049,0.00049272337,0.0005949597,0.00029448056,0.00093152677,0.00078283617,0.0011633082,0.002896022],"category_scores_gemma":[0.0009963333,0.00022875541,0.0007674551,0.00081974186,0.00055212545,0.0012840262,0.00064087834,0.0014122119,0.0013360311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004346184,0.00003567309,0.00071406347,0.00037661998,0.00007090247,0.00022980399,0.00014387787,0.4886375,0.008664077,0.3444379,0.008736654,0.14790943],"study_design_scores_gemma":[0.0000034140294,0.000019599056,0.00022670039,0.000058629666,0.000013473344,0.00008744205,0.000025228044,0.89874536,0.001293742,0.07475976,0.024752216,0.0000144871365],"about_ca_topic_score_codex":0.0016584686,"about_ca_topic_score_gemma":0.0010532421,"teacher_disagreement_score":0.002896022,"about_ca_system_score_codex":0.0006384873,"about_ca_system_score_gemma":0.00052584766,"threshold_uncertainty_score":0.009688139},"labels":[],"label_agreement":null},{"id":"W4406337119","doi":"10.1016/j.acags.2025.100219","title":"Automatic variogram inference using pre-trained Convolutional Neural Networks","year":2025,"lang":"en","type":"article","venue":"Applied Computing and Geosciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cenovus Energy (Canada); University of Alberta","funders":"University of Alberta","keywords":"Variogram; Convolutional neural network; Inference; Artificial intelligence; Computer science; Pattern recognition (psychology); Machine learning; Kriging","score_opus":0.01289214953972531,"score_gpt":0.28077695451410634,"score_spread":0.26788480497438105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406337119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009434855,0.00007208768,0.9858644,0.00006960103,0.00001826088,0.000022810758,0.00024847884,0.0036479607,0.0006214922],"genre_scores_gemma":[0.28759402,0.00021712373,0.7056052,0.00016995297,0.000056373676,0.00014307983,0.0021895347,0.0008335554,0.0031910993],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937433,0.00010300077,0.000032155738,0.00022927446,0.00018820738,0.00007307269],"domain_scores_gemma":[0.9985428,0.00057975115,0.00019753182,0.00032922238,0.00030186895,0.000048860857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012186407,0.0014635768,0.00072807155,0.0014416607,0.0004748586,0.0011671904,0.0015275403,0.00061328744,0.0030801306],"category_scores_gemma":[0.0043808864,0.00089650747,0.0011618419,0.001090151,0.0005552711,0.0013373411,0.0014147406,0.0017569246,0.0012143019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031828607,0.0001320289,0.0073653543,0.00019936284,0.00023821727,0.00015529236,0.00010413903,0.3805198,0.0463952,0.009886774,0.005438488,0.54924697],"study_design_scores_gemma":[0.000006902567,0.0000124029675,0.001232872,0.000009987396,0.000011658668,0.000028231218,0.000008413391,0.9811281,0.010790041,0.005534847,0.0012201905,0.000016314747],"about_ca_topic_score_codex":0.01798589,"about_ca_topic_score_gemma":0.02585716,"teacher_disagreement_score":0.01798589,"about_ca_system_score_codex":0.0013167496,"about_ca_system_score_gemma":0.0020404682,"threshold_uncertainty_score":0.03576237},"labels":[],"label_agreement":null},{"id":"W4406356004","doi":"10.31234/osf.io/m8vdj","title":"Efficient neural encoding as revealed by bilingualism","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University; Centre for Research on Brain, Language and Music","keywords":"Computer science; Neuroscience of multilingualism; Encoding (memory); Artificial neural network; Cognition; Natural language processing; Artificial intelligence; Psychology; Neuroscience","score_opus":0.017756727988414697,"score_gpt":0.29388028253183224,"score_spread":0.2761235545434175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406356004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9731863,0.000115376984,0.019471131,0.00023037323,0.000010412975,0.0000048205043,0.000107312255,0.0001615283,0.006712683],"genre_scores_gemma":[0.9981579,0.00003608688,0.001470223,0.000008279371,0.0000017087599,0.0000023491755,0.000029375155,0.00001368835,0.0002803811],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998783,0.0000388969,0.000006391336,0.000033489865,0.00002068882,0.000022107373],"domain_scores_gemma":[0.9996389,0.00013986153,0.00006547568,0.00007955448,0.00003778868,0.000038440543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029017276,0.00016739413,0.00020478516,0.00018325506,0.00014981168,0.0006352448,0.00016887885,0.00027847211,0.00120124],"category_scores_gemma":[0.001780153,0.00019679828,0.00016611749,0.00013868572,0.0005724732,0.000666065,0.0005343296,0.00036260634,0.00013153905],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010262141,0.00010882539,0.06508592,0.0003120205,0.00017787033,0.0023533506,0.0030637737,0.15063941,0.5613436,0.12558602,0.0013057331,0.088997245],"study_design_scores_gemma":[0.00006926727,0.00024480285,0.10017782,0.000036055706,0.00008212508,0.0014337881,0.0005258893,0.62229747,0.0583209,0.21350545,0.0032188965,0.000087529974],"about_ca_topic_score_codex":0.0026336247,"about_ca_topic_score_gemma":0.0038386271,"teacher_disagreement_score":0.0026336247,"about_ca_system_score_codex":0.00039939236,"about_ca_system_score_gemma":0.00041687462,"threshold_uncertainty_score":0.0052366257},"labels":[],"label_agreement":null},{"id":"W4406356703","doi":"10.1109/ictc62082.2024.10826926","title":"Attention Network Analysis for Image Feature Matching","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Ministry of Trade, Industry and Energy","keywords":"Computer science; Feature (linguistics); Image (mathematics); Matching (statistics); Feature extraction; Artificial intelligence; Computer vision; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.011184367635264194,"score_gpt":0.2711581400682279,"score_spread":0.2599737724329637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406356703","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04993888,0.0010774775,0.94196564,0.0005469866,0.000062951134,0.000059956674,0.00013776778,0.00042068647,0.0057895966],"genre_scores_gemma":[0.9029398,0.0008834652,0.088797815,0.00019258578,0.00010879737,0.00011598044,0.00027364286,0.00012490786,0.0065629613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996891,0.00008252387,0.00001339531,0.00006383917,0.0000928579,0.00005839838],"domain_scores_gemma":[0.99925214,0.00038055496,0.00008762234,0.000053501306,0.0001894098,0.000036731475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009050455,0.0004360454,0.00050887535,0.0015065518,0.0003477585,0.0006450136,0.00096766197,0.0007885315,0.0030883262],"category_scores_gemma":[0.004158834,0.00022077828,0.0005427949,0.000936186,0.0004921347,0.0014358603,0.000720215,0.0007592197,0.00030434565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016846169,0.00006423488,0.0018941903,0.00012800145,0.000088684814,0.00015052626,0.000085166,0.7933555,0.012230518,0.07360229,0.0030008582,0.11523155],"study_design_scores_gemma":[0.0000014163619,0.0000047162607,0.00030434734,0.0000022013242,0.000004707763,0.000011123744,0.000003582813,0.9899568,0.0005152519,0.008993179,0.00020036158,0.0000022773158],"about_ca_topic_score_codex":0.008726068,"about_ca_topic_score_gemma":0.004587634,"teacher_disagreement_score":0.008726068,"about_ca_system_score_codex":0.0016838361,"about_ca_system_score_gemma":0.000655676,"threshold_uncertainty_score":0.017350554},"labels":[],"label_agreement":null},{"id":"W4406369621","doi":"10.1121/10.0035084","title":"The neural representation of emotional cues investigated using the speech frequency following response: A potential tool to evaluate speech prosody","year":2024,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Prosody; Representation (politics); Speech recognition; Computer science; Emotional prosody; Psychology; Natural language processing","score_opus":0.02855567721006579,"score_gpt":0.3202755303554427,"score_spread":0.2917198531453769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406369621","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95542604,0.0008186742,0.038925603,0.000103514154,0.000050242885,0.00012718416,0.0005363971,0.00018482718,0.003827572],"genre_scores_gemma":[0.98201305,0.00042887693,0.016496586,0.00006613616,0.00003573976,0.00008952353,0.0001645971,0.00003022363,0.000675319],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998234,0.00006124779,0.000011747917,0.000044215794,0.000042562035,0.000016846403],"domain_scores_gemma":[0.99922085,0.00043360394,0.00013364089,0.0000419132,0.00012836071,0.0000415203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008079589,0.0003293288,0.00019353251,0.0006168387,0.000093410286,0.00037807683,0.00016915571,0.00038118302,0.0018178506],"category_scores_gemma":[0.0030405773,0.00007513219,0.00014997834,0.00022303093,0.00025198725,0.00038172436,0.00021179671,0.00016106729,0.00031868668],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011077301,0.00013616137,0.040709186,0.0006515364,0.000084876214,0.0004904724,0.00076768576,0.0015387277,0.728349,0.00052382884,0.0005497404,0.22509097],"study_design_scores_gemma":[0.00004811421,0.0018862903,0.8180041,0.00017470532,0.00017802777,0.0031895784,0.00107267,0.021674994,0.14820975,0.0017892024,0.0036732187,0.00009935431],"about_ca_topic_score_codex":0.00047447172,"about_ca_topic_score_gemma":0.00070549664,"teacher_disagreement_score":0.0018178506,"about_ca_system_score_codex":0.00010889358,"about_ca_system_score_gemma":0.00012746434,"threshold_uncertainty_score":0.0060813427},"labels":[],"label_agreement":null},{"id":"W4406443348","doi":"10.1016/s0197-2510(10)70307-7","title":"10.1016/s0197-2510(10)70307-7","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.006626835858418587,"score_gpt":0.1769412232297791,"score_spread":0.1703143873713605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406443348","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004892397,0.0005828818,0.0010508309,0.00045561817,0.00041185386,0.00012359812,0.0012159091,0.0012992283,0.994371],"genre_scores_gemma":[0.00065064285,0.00023473625,0.00046943777,0.00020923479,0.00007207669,0.000065836066,0.00054180174,0.00020518019,0.99755114],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990808,0.00006649221,0.00007916027,0.00032262466,0.00024868833,0.0002022354],"domain_scores_gemma":[0.99742776,0.00077963725,0.0001940778,0.0003170931,0.0004859551,0.0007954753],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013185455,0.0033711528,0.002309062,0.0032635306,0.0024045098,0.00460571,0.004151902,0.006640587,0.98906124],"category_scores_gemma":[0.0020087021,0.0011151546,0.0016898416,0.0035665345,0.0023064448,0.0063466993,0.003481834,0.0033133626,0.9929911],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004430473,0.00023941764,0.0008695043,0.0007204949,0.00004648091,0.00029346428,0.00009526418,0.00066075515,0.00231815,0.0072320383,0.3581442,0.6289372],"study_design_scores_gemma":[0.0000713193,0.0001499967,0.0006933506,0.0004259125,0.000017826678,0.0003263768,0.00012304973,0.00050559116,0.00055438903,0.0010080291,0.99608827,0.000035866695],"about_ca_topic_score_codex":0.004407311,"about_ca_topic_score_gemma":0.003378399,"teacher_disagreement_score":0.010938764,"about_ca_system_score_codex":0.0014693786,"about_ca_system_score_gemma":0.0014962608,"threshold_uncertainty_score":0.015602708},"labels":[],"label_agreement":null},{"id":"W4406446414","doi":"10.1016/s0967-0653(97)89114-4","title":"10.1016/s0967-0653(97)89114-4","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Inverse; Work (physics); Computer science; Mathematics; Engineering; Geometry; Mechanical engineering","score_opus":0.008864495760440991,"score_gpt":0.18392392940707233,"score_spread":0.17505943364663135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406446414","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006504551,0.0006738309,0.0012114096,0.0005063636,0.00047300453,0.00016828638,0.0012625706,0.0014385934,0.9936155],"genre_scores_gemma":[0.00066336023,0.0002663576,0.00049198465,0.0002473346,0.00006796423,0.00007223421,0.00051691866,0.00022527933,0.9974485],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99905306,0.00006722808,0.000084293475,0.0003700614,0.00022068985,0.00020461225],"domain_scores_gemma":[0.9972192,0.0007869892,0.00019462674,0.00038766846,0.00061885704,0.0007926448],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014166388,0.0037199531,0.0025234038,0.0033545103,0.0026960724,0.004977981,0.0044815782,0.0063122613,0.98927224],"category_scores_gemma":[0.002002243,0.0012487818,0.0018619674,0.003650122,0.0026389055,0.0072779004,0.0037762206,0.0035970584,0.9934661],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005153147,0.0002542034,0.00094956707,0.00080446195,0.000059678176,0.00030635277,0.00012305268,0.00065867684,0.002602916,0.0063701184,0.3737343,0.61362135],"study_design_scores_gemma":[0.00007957447,0.00016376917,0.0007395938,0.000490488,0.000024738652,0.00041058485,0.00015234629,0.0004525131,0.00053897366,0.00087651255,0.996031,0.00003987325],"about_ca_topic_score_codex":0.0046256105,"about_ca_topic_score_gemma":0.0036187426,"teacher_disagreement_score":0.010727763,"about_ca_system_score_codex":0.001447222,"about_ca_system_score_gemma":0.0012351037,"threshold_uncertainty_score":0.015301764},"labels":[],"label_agreement":null},{"id":"W4406448033","doi":"10.48550/arxiv.2501.07433","title":"Equivalence between exponential concentration in quantum machine learning kernels and barren plateaus in variational algorithms","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Stewart Blusson Quantum Matter Institute, University of British Columbia; Natural Sciences and Engineering Research Council of Canada","keywords":"Equivalence (formal languages); Exponential function; Algorithm; Quantum; Computer science; Mathematics; Artificial intelligence; Discrete mathematics; Applied mathematics; Theoretical computer science; Pure mathematics; Physics; Quantum mechanics; Mathematical analysis","score_opus":0.04978411328209734,"score_gpt":0.2949332914206484,"score_spread":0.24514917813855105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406448033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077512346,0.000501514,0.89296293,0.0022737402,0.00010971265,0.00008753163,0.00012874811,0.00035356046,0.026069917],"genre_scores_gemma":[0.9007403,0.00044540974,0.091362745,0.0008314298,0.00014484503,0.00028190267,0.00012523666,0.00025901725,0.0058091194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985864,0.0005050062,0.000071808405,0.00019006377,0.00046917153,0.00017757299],"domain_scores_gemma":[0.9935448,0.0045042103,0.0003102718,0.00071196276,0.0006487139,0.00028006005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031381932,0.00058009004,0.00083096465,0.0009006415,0.0010955579,0.0025385588,0.0017927216,0.002183294,0.0068289083],"category_scores_gemma":[0.01861565,0.00057979184,0.00083203503,0.0007237431,0.004756748,0.0056115054,0.003492998,0.0044049094,0.000675388],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021197999,0.00002053041,0.00012595484,0.000029342198,0.0000068314416,0.00003566815,0.00007903641,0.015475721,0.0010328168,0.97948486,0.00048142503,0.0032066412],"study_design_scores_gemma":[0.000014927215,0.000020933112,0.00008196419,0.0000141177425,0.0000027730796,0.000030322732,0.000017453214,0.17850594,0.0005850191,0.82011276,0.0006037562,0.000010064212],"about_ca_topic_score_codex":0.0009489287,"about_ca_topic_score_gemma":0.00060205074,"teacher_disagreement_score":0.0068289083,"about_ca_system_score_codex":0.0016181244,"about_ca_system_score_gemma":0.0010593674,"threshold_uncertainty_score":0.02284497},"labels":[],"label_agreement":null},{"id":"W4406483676","doi":"10.1016/0967-0653(95)90019-u","title":"10.1016/0967-0653(95)90019-u","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Predictability; Transient (computer programming); Econometrics; Mathematics; Control theory (sociology); Statistics; Computer science; Artificial intelligence","score_opus":0.007418705712377727,"score_gpt":0.17998831064352064,"score_spread":0.1725696049311429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406483676","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063775055,0.0004961906,0.0009831741,0.00046698222,0.00037921566,0.00016272467,0.0009747552,0.0011469978,0.99475217],"genre_scores_gemma":[0.00092079083,0.00028794655,0.00059188594,0.00031829343,0.00006900562,0.000090597336,0.0005104622,0.00023977796,0.99697113],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991806,0.00006193919,0.000072516676,0.0003198414,0.00017923451,0.00018583026],"domain_scores_gemma":[0.99765587,0.0006872758,0.00012778111,0.00032864086,0.0004790394,0.00072137866],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012246812,0.0030481333,0.0021008602,0.002957374,0.0028743818,0.0041979323,0.0036529326,0.0061468715,0.98915464],"category_scores_gemma":[0.0020260147,0.0011478859,0.0015108645,0.0028877174,0.0027760682,0.006328178,0.0032442096,0.0030751943,0.99317884],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005217086,0.00029156628,0.0011357572,0.00067593675,0.00004652773,0.0003225586,0.00012419245,0.0006010708,0.002499038,0.0054615163,0.42749608,0.560824],"study_design_scores_gemma":[0.00008225961,0.00017518032,0.0011212957,0.00044453243,0.000023028655,0.0004572352,0.00019192493,0.00039540007,0.00048330612,0.0010646478,0.99552286,0.000038379574],"about_ca_topic_score_codex":0.005886362,"about_ca_topic_score_gemma":0.0046483343,"teacher_disagreement_score":0.010845363,"about_ca_system_score_codex":0.0011842044,"about_ca_system_score_gemma":0.0010667396,"threshold_uncertainty_score":0.015469611},"labels":[],"label_agreement":null},{"id":"W4406516408","doi":"10.1016/s0967-0653(97)81980-1","title":"10.1016/s0967-0653(97)81980-1","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Linear regression; Artificial neural network; Regression; Computer science; Artificial intelligence; Regression analysis; Statistics; Econometrics; Machine learning; Mathematics","score_opus":0.008807378132429563,"score_gpt":0.18386442543979395,"score_spread":0.17505704730736438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406516408","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00060183206,0.0006214496,0.0011269704,0.00051680376,0.00045766457,0.0001482858,0.0011526295,0.0012799943,0.9940943],"genre_scores_gemma":[0.00063478394,0.0002507347,0.00047176555,0.00022675982,0.00006316301,0.00006369867,0.0004977171,0.00020538432,0.997586],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991623,0.00006386556,0.000072033276,0.00031066735,0.00021124604,0.0001797751],"domain_scores_gemma":[0.9974808,0.0006876764,0.00016776402,0.0003502054,0.0005842298,0.0007293824],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012291395,0.003104873,0.002053854,0.0028336882,0.0021730836,0.0043722307,0.0036418354,0.0053591086,0.988171],"category_scores_gemma":[0.0019619705,0.0009932299,0.0014490692,0.003040226,0.0021120473,0.006099834,0.0030819601,0.003118281,0.9923873],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041922287,0.00020008981,0.00077658746,0.0006404852,0.000042368763,0.0002526956,0.000098814155,0.00051675126,0.0021794643,0.005463022,0.37040582,0.61900467],"study_design_scores_gemma":[0.000057293782,0.00012642272,0.0006701207,0.00038576667,0.000017497898,0.00031189402,0.00012075806,0.00037948726,0.00041509618,0.0007636352,0.99672323,0.000028875029],"about_ca_topic_score_codex":0.004128338,"about_ca_topic_score_gemma":0.0033024475,"teacher_disagreement_score":0.011829019,"about_ca_system_score_codex":0.0012257397,"about_ca_system_score_gemma":0.0011264929,"threshold_uncertainty_score":0.016872585},"labels":[],"label_agreement":null},{"id":"W4406543512","doi":"10.1016/0967-0653(95)91282-9","title":"10.1016/0967-0653(95)91282-9","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interpolation (computer graphics); Missing data; Chaotic; Linear interpolation; Mathematics; Computer science; Applied mathematics; Statistics; Econometrics; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.007458734092192774,"score_gpt":0.17992941393822043,"score_spread":0.17247067984602765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406543512","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006685158,0.00063554797,0.00092102913,0.0004967118,0.00042071444,0.0001691995,0.0012436095,0.0012791944,0.9941654],"genre_scores_gemma":[0.0008900232,0.00036048846,0.0005377784,0.00037844694,0.000083353225,0.00008987767,0.00062617013,0.00024894715,0.99678504],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99908066,0.0000674277,0.00009267739,0.00034657557,0.00020697222,0.00020562613],"domain_scores_gemma":[0.9971468,0.00089244335,0.00016446173,0.00037456013,0.000621118,0.0008006386],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014096475,0.0036565147,0.002426817,0.0031514931,0.0028821744,0.004895021,0.0040173274,0.0064477846,0.9896013],"category_scores_gemma":[0.0020789464,0.0013151835,0.0017043282,0.0032172059,0.002993895,0.006929743,0.0037716273,0.003640742,0.9936466],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057212746,0.00029468595,0.001165177,0.00084084505,0.000060881455,0.00036060062,0.0001245426,0.00071350834,0.0028111406,0.005195111,0.41788223,0.56997913],"study_design_scores_gemma":[0.00011165102,0.00020879395,0.0012137272,0.00058597064,0.000029324767,0.00049311615,0.00021878895,0.0004628448,0.0006314651,0.0011477001,0.9948478,0.0000489454],"about_ca_topic_score_codex":0.005520163,"about_ca_topic_score_gemma":0.004512643,"teacher_disagreement_score":0.010398686,"about_ca_system_score_codex":0.0013819049,"about_ca_system_score_gemma":0.0010631938,"threshold_uncertainty_score":0.014832437},"labels":[],"label_agreement":null},{"id":"W4406729762","doi":"10.1109/telepresence63209.2024.10841535","title":"Mean-Field Representation for EEG Classifications","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates","keywords":"Electroencephalography; Computer science; Representation (politics); Field (mathematics); Artificial intelligence; Natural language processing; Pattern recognition (psychology); Mathematics; Psychology; Neuroscience","score_opus":0.05321702648005261,"score_gpt":0.33743106103841664,"score_spread":0.284214034558364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406729762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015315296,0.00014888262,0.98264104,0.00013625779,0.00004284423,0.000035746518,0.00024425564,0.00053818244,0.00089748466],"genre_scores_gemma":[0.644713,0.0003723401,0.34926134,0.00020717896,0.00015633604,0.00026191326,0.0012496267,0.00013712146,0.0036411884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996124,0.00010608595,0.00002846255,0.00010694094,0.000100769634,0.00004528972],"domain_scores_gemma":[0.9992581,0.00035775808,0.000077379875,0.00010115016,0.00018045884,0.0000250992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010822249,0.0005079042,0.000541512,0.0011654999,0.00025332707,0.00083339075,0.00067790743,0.00080755673,0.0038111326],"category_scores_gemma":[0.0040689316,0.0001512127,0.0008228481,0.0010620251,0.0003289996,0.00095813884,0.0005305773,0.0009464817,0.00075990625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002777244,0.00010567651,0.0021732436,0.00012290565,0.00007441655,0.00011318054,0.00012636092,0.30756876,0.014574552,0.037542064,0.00670981,0.6306113],"study_design_scores_gemma":[0.0000057858742,0.000035162735,0.0007128152,0.0000085390875,0.000007967499,0.000057260444,0.000009980767,0.98234504,0.001573619,0.014166691,0.0010663695,0.00001062129],"about_ca_topic_score_codex":0.0024296332,"about_ca_topic_score_gemma":0.0017396532,"teacher_disagreement_score":0.0038111326,"about_ca_system_score_codex":0.00048295077,"about_ca_system_score_gemma":0.00053243176,"threshold_uncertainty_score":0.012749493},"labels":[],"label_agreement":null},{"id":"W4406780284","doi":"10.1561/2200000112","title":"Generalization Bounds: Perspectives from Information Theory and PAC-Bayes","year":2025,"lang":"en","type":"article","venue":"Foundations and Trends® in Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Generalization; Bayes' theorem; Mathematics; Mathematical economics; Computer science; Bayesian probability; Artificial intelligence","score_opus":0.005312522459407974,"score_gpt":0.26074809854910774,"score_spread":0.25543557608969975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406780284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006877173,0.04382552,0.8726361,0.019925596,0.00091938226,0.00006400453,0.00037924486,0.00020368726,0.055169206],"genre_scores_gemma":[0.6190841,0.06588743,0.28045475,0.010535927,0.012705887,0.0007927238,0.00054084585,0.0005518235,0.009446451],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9889298,0.0045073023,0.0004975997,0.0014173404,0.003973002,0.0006748833],"domain_scores_gemma":[0.95462894,0.036499042,0.0020512294,0.0035717795,0.002606027,0.00064298953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015177448,0.0021585687,0.002657718,0.003495176,0.0021089853,0.0066082175,0.0037836633,0.004384388,0.003821066],"category_scores_gemma":[0.050082505,0.0011798077,0.0025706235,0.00418715,0.013226513,0.017828053,0.005807698,0.014806267,0.0008093956],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010945633,0.000009670547,0.00010302225,0.00008888732,0.000020563088,0.000033666758,0.0000937852,0.005514347,0.000069330104,0.9855554,0.0015635235,0.006936883],"study_design_scores_gemma":[0.0000031075451,0.0000081754,0.000062037616,0.000058498008,0.000008056331,0.00003719626,0.000019385241,0.0115348,0.00008064015,0.9854747,0.0027037098,0.000009614601],"about_ca_topic_score_codex":0.0030102045,"about_ca_topic_score_gemma":0.0014141148,"teacher_disagreement_score":0.015177448,"about_ca_system_score_codex":0.006482094,"about_ca_system_score_gemma":0.002641653,"threshold_uncertainty_score":0.08026695},"labels":[],"label_agreement":null},{"id":"W4406840519","doi":"10.48550/arxiv.2501.14055","title":"Learning to See: Applying Inverse Recurrent Inference Machines to See through Refractive Scattering","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Oak Ridge National Laboratory; Argonne National Laboratory; Natural Sciences and Engineering Research Council of Canada; Office of Science; University of Illinois at Urbana-Champaign; National Science Foundation; Government of Canada; U.S. Department of Energy; Ministero dello Sviluppo Economico; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Inference; Computer science; Inverse; Inverse scattering problem; Inverse problem; Artificial intelligence; Scattering; Optics; Mathematics; Physics; Mathematical analysis; Geometry","score_opus":0.06103693243219521,"score_gpt":0.3358854867718641,"score_spread":0.27484855433966887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406840519","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18256372,0.0008121549,0.8086794,0.0010655745,0.00017088633,0.00004758856,0.00021016429,0.0022479568,0.004202555],"genre_scores_gemma":[0.90736634,0.00017208509,0.08778687,0.0002391303,0.00010914832,0.000053576434,0.00038423363,0.00010958252,0.0037791277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997775,0.00006676805,0.000009799159,0.000072181974,0.000034630943,0.000039111535],"domain_scores_gemma":[0.9987902,0.00082817016,0.00011045681,0.00008777365,0.00013683618,0.00004654609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079259614,0.0007569019,0.00081775704,0.00030751433,0.0002779692,0.0007441874,0.0012503702,0.00093962334,0.0013268358],"category_scores_gemma":[0.0029628007,0.00045658217,0.00059401913,0.0002904368,0.000565492,0.00087902095,0.0006917897,0.0012379987,0.00030657463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007499244,0.000068420486,0.0014339465,0.00003118592,0.000059403155,0.000081578924,0.000057565194,0.9379628,0.0018351505,0.0042875865,0.0010984782,0.053008992],"study_design_scores_gemma":[0.000001584378,0.000004740967,0.000036742193,8.172216e-7,0.0000015401034,0.0000016742092,0.0000015899511,0.9985014,0.00016143934,0.0012471611,0.000040062216,0.0000011591446],"about_ca_topic_score_codex":0.008308173,"about_ca_topic_score_gemma":0.008972038,"teacher_disagreement_score":0.008308173,"about_ca_system_score_codex":0.0007257423,"about_ca_system_score_gemma":0.0006102793,"threshold_uncertainty_score":0.016519606},"labels":[],"label_agreement":null},{"id":"W4406894927","doi":"10.1109/icecs61496.2024.10849010","title":"Attention-Based Dynamic Channel-Wise Pruning of Neural Networks with Knowledge Distillation Fine-Tuning","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial neural network; Pruning; Distillation; Artificial intelligence; Channel (broadcasting); Machine learning; Telecommunications; Chemistry; Chromatography; Biology","score_opus":0.011167563853278035,"score_gpt":0.2506292269282378,"score_spread":0.23946166307495978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406894927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06020063,0.0011496473,0.9278623,0.00042662286,0.00015684807,0.00010881232,0.00018084307,0.00406379,0.0058505526],"genre_scores_gemma":[0.7393832,0.00046072985,0.25135735,0.0007288772,0.00014618844,0.00026498738,0.00059461524,0.00056949724,0.0064944853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945515,0.00010210343,0.000032848762,0.00014164293,0.00015216776,0.00011607886],"domain_scores_gemma":[0.9988445,0.0006250378,0.000088297114,0.00018075077,0.00020614902,0.00005519257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009456726,0.0016227531,0.001311551,0.0008359295,0.00059476384,0.0010509058,0.0025242202,0.0012319103,0.0029246772],"category_scores_gemma":[0.0040136813,0.0005842898,0.00068399595,0.00074968394,0.0009407475,0.0020821611,0.0017212017,0.0024400789,0.0006871484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022827568,0.00019956489,0.0011978097,0.00013774908,0.00011431884,0.00021945302,0.00011548849,0.64099926,0.014292269,0.010999652,0.007096463,0.32439968],"study_design_scores_gemma":[0.000014496711,0.000028301554,0.000103813654,0.0000108224485,0.000018819399,0.000032082222,0.000009727592,0.9898028,0.004226377,0.0050849975,0.0006617233,0.000006200678],"about_ca_topic_score_codex":0.006106469,"about_ca_topic_score_gemma":0.014671249,"teacher_disagreement_score":0.006106469,"about_ca_system_score_codex":0.0009885484,"about_ca_system_score_gemma":0.0019090516,"threshold_uncertainty_score":0.012141824},"labels":[],"label_agreement":null},{"id":"W4406975238","doi":"10.48550/arxiv.2501.17284","title":"Nonlinear dynamics of localization in neural receptive fields","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Receptive field; Nonlinear system; Dynamics (music); Artificial neural network; Biological system; Computer science; Statistical physics; Physics; Artificial intelligence; Biology; Acoustics","score_opus":0.02818301662498407,"score_gpt":0.2825982889290483,"score_spread":0.25441527230406424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406975238","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5096153,0.00085027,0.46288967,0.002847172,0.000076203534,0.000044077955,0.00023111996,0.0004305574,0.023015719],"genre_scores_gemma":[0.98892957,0.00027195417,0.008087735,0.00007125749,0.000024702564,0.00002798137,0.000033972767,0.0000614094,0.0024913982],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997917,0.000057122423,0.0000080769805,0.000039749957,0.00006045276,0.00004288787],"domain_scores_gemma":[0.99912816,0.0004648258,0.00013545998,0.000069590846,0.00011144452,0.00009052455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047099494,0.00024161537,0.00035063978,0.0004884739,0.00039335407,0.00083192135,0.00062349107,0.0006677614,0.0017014593],"category_scores_gemma":[0.003397008,0.00032444604,0.00036074547,0.00023909706,0.0017384074,0.0019982336,0.0010061389,0.0008946817,0.00027895923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067739806,0.000035304558,0.0020112575,0.0000903894,0.000030068817,0.00030314116,0.0003900064,0.33492747,0.026644815,0.6230233,0.001670608,0.010806008],"study_design_scores_gemma":[0.000010225442,0.0000116380315,0.0008134948,0.000010885363,0.000003502608,0.00006522727,0.000041655578,0.8609953,0.0011610405,0.13631551,0.00055760646,0.000013980254],"about_ca_topic_score_codex":0.0026903576,"about_ca_topic_score_gemma":0.0015945663,"teacher_disagreement_score":0.0026903576,"about_ca_system_score_codex":0.001345445,"about_ca_system_score_gemma":0.0004445301,"threshold_uncertainty_score":0.0097619295},"labels":[],"label_agreement":null},{"id":"W4407309011","doi":"10.48550/arxiv.2502.04551","title":"Stability of Jordan Recurrent Neural Network Estimator","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Estimator; Artificial neural network; Stability (learning theory); Computer science; Artificial intelligence; Mathematics; Econometrics; Statistics; Machine learning","score_opus":0.057136536165478695,"score_gpt":0.2995047359159294,"score_spread":0.2423681997504507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407309011","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17266223,0.0011377061,0.8067112,0.00076617993,0.00006655877,0.000049387807,0.00014821254,0.0005094391,0.017949088],"genre_scores_gemma":[0.97088706,0.00028056995,0.023402425,0.00007749042,0.00002487361,0.00006861389,0.0001090351,0.00007793026,0.005071977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994266,0.00020682505,0.000027857757,0.00013516707,0.00014655662,0.000056979334],"domain_scores_gemma":[0.99673456,0.0016950779,0.0004454886,0.00017998612,0.0008690808,0.000075818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001427918,0.0005811487,0.00065114256,0.0004922478,0.0005828867,0.0008541348,0.00071345223,0.0007596336,0.0019251214],"category_scores_gemma":[0.0077217994,0.000275112,0.00048226464,0.00024692828,0.0011797121,0.0009363997,0.0010728693,0.00092562736,0.0003774522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025178032,0.00003946756,0.003433666,0.00017287175,0.000077274024,0.0002740028,0.00035292478,0.8437528,0.013724071,0.10622204,0.0017538911,0.029945282],"study_design_scores_gemma":[0.0000032139862,0.000012487199,0.0001902721,0.0000060731813,0.00000289917,0.000018375484,0.0000105136105,0.99132067,0.00087105355,0.007414268,0.00014505995,0.000005184047],"about_ca_topic_score_codex":0.003809212,"about_ca_topic_score_gemma":0.0019877506,"teacher_disagreement_score":0.003809212,"about_ca_system_score_codex":0.00082231115,"about_ca_system_score_gemma":0.0005892279,"threshold_uncertainty_score":0.0075740814},"labels":[],"label_agreement":null},{"id":"W4407309348","doi":"10.48550/arxiv.2502.04681","title":"CALF-SBM: A Covariate-Assisted Latent Factor Stochastic Block Model","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Covariate; Latent variable; Econometrics; Factor (programming language); Block (permutation group theory); Factor analysis; Mathematics; Statistics; Computer science; Combinatorics","score_opus":0.10402155059593032,"score_gpt":0.2997774708556004,"score_spread":0.1957559202596701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407309348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063547282,0.00014433372,0.99163765,0.00022966965,0.000039599447,0.00003634487,0.00045272813,0.00021580467,0.00088906556],"genre_scores_gemma":[0.5146938,0.0011187415,0.46702564,0.00049639307,0.0002690398,0.00074758584,0.003208106,0.00037024842,0.012070405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988329,0.0005585637,0.000035742207,0.00028769084,0.0001698848,0.00011529191],"domain_scores_gemma":[0.9976044,0.0013795578,0.00028684718,0.0002957642,0.00029579495,0.00013764628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025075425,0.00073523505,0.001322419,0.0010864395,0.00058333797,0.0012666526,0.0041661947,0.0017009936,0.0046431525],"category_scores_gemma":[0.0057806084,0.00078494457,0.0015348515,0.001708121,0.0011309114,0.002114202,0.0016034684,0.0022483114,0.0014046999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012216654,0.00006659776,0.0034312159,0.000098365155,0.00011994939,0.0001350818,0.00021922891,0.7361719,0.0018209021,0.21380736,0.004616252,0.039390985],"study_design_scores_gemma":[0.000013248875,0.000017825385,0.00026547452,0.000009301421,0.0000149165235,0.000029529172,0.0000096365275,0.9684775,0.00015928676,0.029064568,0.0019253896,0.0000133501135],"about_ca_topic_score_codex":0.013345451,"about_ca_topic_score_gemma":0.016961457,"teacher_disagreement_score":0.013345451,"about_ca_system_score_codex":0.0012817889,"about_ca_system_score_gemma":0.0020211826,"threshold_uncertainty_score":0.02653557},"labels":[],"label_agreement":null},{"id":"W4407376740","doi":"10.1109/iciba62489.2024.10868589","title":"Time–Frequency Analysis of Phase Encoding in Spiking Neural Networks Using Polar Coordinates","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Encoding (memory); Computer science; Polar; Phase (matter); Artificial neural network; Polar coordinate system; Time–frequency analysis; Spiking neural network; Artificial intelligence; Mathematics; Computer vision; Physics","score_opus":0.023993616025106548,"score_gpt":0.3056510932321746,"score_spread":0.2816574772070681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407376740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018008329,0.00035034775,0.9790125,0.00008148009,0.000041824325,0.000012924385,0.000046485544,0.00011106175,0.0023351652],"genre_scores_gemma":[0.584205,0.0025270886,0.40876135,0.00008396552,0.00009123005,0.00005715519,0.00024887247,0.00017118856,0.0038541567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998956,0.000028586377,0.0000061217493,0.000021284657,0.000038810307,0.000009594899],"domain_scores_gemma":[0.99972457,0.000116523915,0.000044389668,0.000029184175,0.000072816096,0.000012541977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002449478,0.000355856,0.00018277936,0.000424429,0.00013457802,0.0005522094,0.00030769967,0.00023754446,0.001510871],"category_scores_gemma":[0.0012645725,0.00015300873,0.00027927748,0.0005165809,0.00038996595,0.00069586217,0.00037581712,0.00046066343,0.0003010698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022889442,0.000037160655,0.002186042,0.00025870238,0.00006832857,0.0003291737,0.0002264858,0.4915313,0.1250204,0.18895957,0.0014479769,0.18970597],"study_design_scores_gemma":[0.0000036715885,0.000023096749,0.00047184952,0.000011631554,0.000008448983,0.00010708803,0.0000268156,0.9692432,0.009260077,0.018422054,0.002410653,0.0000113086735],"about_ca_topic_score_codex":0.0012980236,"about_ca_topic_score_gemma":0.00084058696,"teacher_disagreement_score":0.001510871,"about_ca_system_score_codex":0.0002593738,"about_ca_system_score_gemma":0.0002745099,"threshold_uncertainty_score":0.0050543547},"labels":[],"label_agreement":null},{"id":"W4407554672","doi":"10.1101/2025.02.12.637846","title":"A common algorithm for confidence judgements across visual, auditory and audio-visual decisions","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Audio visual; Computer science; Speech recognition; Cognitive psychology; Psychology; Multimedia","score_opus":0.020585626154977443,"score_gpt":0.3020307268877577,"score_spread":0.28144510073278023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407554672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10931072,0.0002360016,0.88557523,0.0005951991,0.0000899574,0.00020699271,0.00013019505,0.0006147303,0.0032410696],"genre_scores_gemma":[0.7567414,0.00010617513,0.24106297,0.000209047,0.000041226693,0.00028987037,0.00016396852,0.0001755549,0.0012096986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9936592,0.0023803916,0.00052008306,0.0017241354,0.001357651,0.0003585157],"domain_scores_gemma":[0.9526207,0.034331363,0.004855363,0.003808878,0.003380989,0.001002847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010937839,0.0007747584,0.0013025122,0.0016088217,0.00071269594,0.0053475383,0.002781519,0.0025082475,0.0045417934],"category_scores_gemma":[0.102467924,0.0009136799,0.0016437443,0.00087563664,0.0027218072,0.005374365,0.003415492,0.0035560457,0.0007778017],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015524112,0.000478801,0.028790595,0.00088909833,0.0007189372,0.00032402365,0.0018708499,0.37476984,0.0366242,0.1784028,0.0036969795,0.37188146],"study_design_scores_gemma":[0.000064435015,0.00014387676,0.005116606,0.00012188057,0.000051451014,0.00018040149,0.00010372596,0.8744138,0.0059512313,0.112922,0.000841105,0.00008954101],"about_ca_topic_score_codex":0.002403786,"about_ca_topic_score_gemma":0.0015519661,"teacher_disagreement_score":0.010937839,"about_ca_system_score_codex":0.0017537377,"about_ca_system_score_gemma":0.0017347133,"threshold_uncertainty_score":0.057845533},"labels":[],"label_agreement":null},{"id":"W4407585816","doi":"10.3390/bs15020211","title":"Estimating the Minimum Sample Size for Neural Network Model Fitting—A Monte Carlo Simulation Study","year":2025,"lang":"en","type":"article","venue":"Behavioral Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Monte Carlo method; Sample size determination; Artificial neural network; Computer science; Sample (material); Statistics; Mathematics; Artificial intelligence; Physics","score_opus":0.08374965081522025,"score_gpt":0.3862219202035477,"score_spread":0.30247226938832744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407585816","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25320134,0.0009730597,0.7391591,0.0016216973,0.00010624792,0.00045491665,0.00025583428,0.00030227177,0.003925548],"genre_scores_gemma":[0.83668774,0.00037660083,0.16088079,0.00021805672,0.000044153032,0.00076168997,0.00031570328,0.00009204722,0.0006231411],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99435186,0.004401402,0.00019777194,0.00042476883,0.00042631725,0.00019780023],"domain_scores_gemma":[0.8036714,0.18378873,0.0030080648,0.0041704695,0.0047043255,0.0006569757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022448933,0.000674861,0.001250435,0.0012676338,0.00091224426,0.0010923013,0.0018936655,0.0017313714,0.0018921574],"category_scores_gemma":[0.1358705,0.0005465981,0.0011596942,0.0010515547,0.0014384594,0.002472014,0.0010491202,0.002526137,0.00014152884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003359129,0.00018471784,0.01253215,0.00020660857,0.00014673702,0.0002055042,0.0002482883,0.92600286,0.00077885273,0.045184754,0.0011094025,0.013064224],"study_design_scores_gemma":[0.000034312572,0.00005463247,0.0005949675,0.000039069728,0.000023770097,0.000031449443,0.000037492668,0.98976374,0.00037576645,0.008806919,0.00022436895,0.000013538755],"about_ca_topic_score_codex":0.008803525,"about_ca_topic_score_gemma":0.007047606,"teacher_disagreement_score":0.022448933,"about_ca_system_score_codex":0.0015295794,"about_ca_system_score_gemma":0.0016411485,"threshold_uncertainty_score":0.11872268},"labels":[],"label_agreement":null},{"id":"W4407593337","doi":"10.2139/ssrn.5138999","title":"Sliding Sigmoid Filter-Driven Neural Network Training: A Comprehensive Study of Derivations and Results","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Sigmoid function; Training (meteorology); Filter (signal processing); Artificial neural network; Computer science; Control theory (sociology); Artificial intelligence; Mathematics; Computer vision; Physics; Control (management)","score_opus":0.04504945151720993,"score_gpt":0.29049299310708937,"score_spread":0.24544354158987944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407593337","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071721124,0.0044913874,0.9745589,0.00039019057,0.00013630884,0.000037498165,0.000062822946,0.00014714904,0.013003693],"genre_scores_gemma":[0.54253864,0.019876521,0.39615998,0.0006354813,0.00062576897,0.00027851487,0.00036134757,0.0006672778,0.038856465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994893,0.00016660034,0.000037776463,0.00008141062,0.00020178262,0.00002307822],"domain_scores_gemma":[0.9973803,0.0018030251,0.000096975025,0.00020754147,0.00047441563,0.000037614012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021001478,0.00096258207,0.0008184096,0.00067997805,0.00040094578,0.0013113325,0.0010621774,0.0014894656,0.004439125],"category_scores_gemma":[0.009818381,0.0005442003,0.00064612535,0.0010548579,0.0008804443,0.0021731157,0.0010717497,0.002008511,0.0010027034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006067785,0.000098547185,0.0007413926,0.0006482348,0.000108823064,0.0002932645,0.00022676185,0.4754038,0.005312203,0.3276954,0.0036058787,0.18580508],"study_design_scores_gemma":[0.0000036157721,0.000027661434,0.00018990124,0.000064674576,0.000016790345,0.00011898719,0.000013425193,0.9324694,0.0012739602,0.063589215,0.0022210518,0.0000112945645],"about_ca_topic_score_codex":0.0025141002,"about_ca_topic_score_gemma":0.0027433098,"teacher_disagreement_score":0.004439125,"about_ca_system_score_codex":0.00085183745,"about_ca_system_score_gemma":0.0011821098,"threshold_uncertainty_score":0.014850318},"labels":[],"label_agreement":null},{"id":"W4407639540","doi":"10.1109/tit.2025.3541181","title":"Analysis of the Rate of Convergence of an Over-Parametrized Deep Neural Network Estimate Learned by Gradient Descent","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gradient descent; Convergence (economics); Artificial neural network; Computer science; Rate of convergence; Deep neural networks; Stochastic gradient descent; Artificial intelligence; Telecommunications","score_opus":0.00809871770870112,"score_gpt":0.2539479325295855,"score_spread":0.24584921482088434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407639540","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08330642,0.0029722564,0.90632373,0.0015575544,0.00015083834,0.00014314044,0.0003427855,0.0012552948,0.0039478783],"genre_scores_gemma":[0.7805066,0.0025636854,0.20230483,0.0006176732,0.0001851716,0.00068685843,0.0016584694,0.002110016,0.009366662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972783,0.0013340259,0.00017143873,0.0004090271,0.0005826807,0.00022461666],"domain_scores_gemma":[0.94099075,0.047794692,0.0029023178,0.0029922393,0.00455335,0.0007666176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0119440295,0.002565296,0.0018571323,0.0012604059,0.0007382576,0.0016643141,0.0027542415,0.0031921372,0.0027856152],"category_scores_gemma":[0.08341106,0.0011073343,0.0011344986,0.0009648744,0.0023588291,0.0034353323,0.0034156085,0.004518321,0.00074480893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006058013,0.000069454625,0.004120342,0.0004926623,0.00020841706,0.0004085697,0.00023755578,0.92623985,0.004931089,0.030014854,0.0024234452,0.03024792],"study_design_scores_gemma":[0.000009833007,0.000057675017,0.00052812404,0.0000526385,0.00001888729,0.00009284006,0.00002166461,0.9929132,0.00226169,0.003649342,0.00037512512,0.000018959747],"about_ca_topic_score_codex":0.007223125,"about_ca_topic_score_gemma":0.0043633,"teacher_disagreement_score":0.0119440295,"about_ca_system_score_codex":0.0023944175,"about_ca_system_score_gemma":0.002008594,"threshold_uncertainty_score":0.0631668},"labels":[],"label_agreement":null},{"id":"W4407682342","doi":"10.54097/j77b0p65","title":"An Investigation of Cross-dataset Model Generalization of Convolutional Neural Network","year":2025,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Generalization; Convolutional neural network; Computer science; Artificial intelligence; Artificial neural network; Machine learning; Mathematics","score_opus":0.009793717266692106,"score_gpt":0.2604311749367791,"score_spread":0.250637457670087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407682342","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8600605,0.0050056553,0.11765867,0.0019439663,0.000721649,0.00047081514,0.0016686664,0.003446027,0.009024025],"genre_scores_gemma":[0.97486603,0.0005732337,0.018704336,0.00036822152,0.00006210634,0.00014705314,0.0027225718,0.0001742912,0.0023822284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99633706,0.0014540708,0.00029007407,0.0010172128,0.0005977463,0.00030378372],"domain_scores_gemma":[0.9886044,0.0044829673,0.0006419754,0.00431741,0.0017511117,0.00020208245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013780471,0.0019870137,0.00089882483,0.0009256661,0.0006805028,0.0011917652,0.001764642,0.0012978528,0.0013519793],"category_scores_gemma":[0.02717344,0.00039029677,0.0013122982,0.0009072017,0.0009654767,0.003514301,0.0018054337,0.0028172696,0.00060340017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010242591,0.0008953992,0.04847923,0.00060486444,0.0015912736,0.00055575557,0.00067722093,0.62527823,0.012950363,0.005457264,0.01418918,0.28829706],"study_design_scores_gemma":[0.000035394147,0.0010074065,0.014579821,0.0001016426,0.00021719914,0.00026338705,0.00028641536,0.960329,0.014637814,0.005094488,0.0033851578,0.000062257204],"about_ca_topic_score_codex":0.01297325,"about_ca_topic_score_gemma":0.013877925,"teacher_disagreement_score":0.013780471,"about_ca_system_score_codex":0.0016139714,"about_ca_system_score_gemma":0.0013123339,"threshold_uncertainty_score":0.07287902},"labels":[],"label_agreement":null},{"id":"W4407692407","doi":"10.1109/mecom61498.2024.10881273","title":"Air-ODE Neural Network with Distributed RISs Aided Communication Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China; National Research Foundation; Ministry of Education","keywords":"Ode; Computer science; Artificial neural network; Distributed computing; Artificial intelligence; Mathematics","score_opus":0.012810011545896456,"score_gpt":0.2367122226510314,"score_spread":0.22390221110513495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407692407","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045986656,0.00042321096,0.942726,0.00031119908,0.00016077193,0.000044989752,0.000076147066,0.0010560469,0.00921489],"genre_scores_gemma":[0.7970018,0.00031653576,0.18886474,0.00020866642,0.00005879973,0.000110946254,0.00014910781,0.00005439974,0.013234987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998424,0.000023620309,0.000010732425,0.000045876408,0.00005810981,0.0000191531],"domain_scores_gemma":[0.9998165,0.000049997845,0.000022618777,0.00002877626,0.00007242959,0.0000096771555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026481994,0.0003493548,0.00026904888,0.00020977948,0.00021705752,0.00049413176,0.00078957813,0.0005131028,0.0030054117],"category_scores_gemma":[0.0008239775,0.00015457725,0.0003022873,0.00022689707,0.00035187192,0.0007184035,0.0006626955,0.0006622854,0.000490228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017478513,0.00012368758,0.0012841258,0.00013433423,0.000052297502,0.00016373055,0.000113744405,0.73938364,0.034362372,0.025437327,0.0028545165,0.19591552],"study_design_scores_gemma":[0.0000054539764,0.000027164966,0.00006653866,0.0000033225256,0.000005196826,0.0000142640465,0.000004017895,0.9941776,0.0033771791,0.0009778441,0.0013377366,0.000003763823],"about_ca_topic_score_codex":0.003483522,"about_ca_topic_score_gemma":0.006121209,"teacher_disagreement_score":0.003483522,"about_ca_system_score_codex":0.00053572794,"about_ca_system_score_gemma":0.00061321113,"threshold_uncertainty_score":0.0100541115},"labels":[],"label_agreement":null},{"id":"W4407715518","doi":"10.1007/s10230-025-01030-7","title":"Exploring Spatiotemporal Trends in Piezometer Network Data Using Self-Organizing Maps","year":2025,"lang":"en","type":"article","venue":"Mine Water and the Environment","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Piezometer; Geology; Hydrogeology; Groundwater; Geotechnical engineering; Aquifer","score_opus":0.072487821097132,"score_gpt":0.2409141279492098,"score_spread":0.1684263068520778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407715518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85398,0.00046122275,0.14139687,0.00041524126,0.000065321605,0.000036223566,0.0017484395,0.0005931635,0.001303484],"genre_scores_gemma":[0.98497957,0.00012429818,0.0136811575,0.000014751636,0.000018968534,0.000016490825,0.0007994682,0.000016374903,0.00034886715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998307,0.000035271703,0.000014193736,0.000050753577,0.00004504819,0.000023930823],"domain_scores_gemma":[0.99931765,0.00037552183,0.0001088112,0.000054509546,0.0001171059,0.000026434565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000438856,0.00026120353,0.0002483274,0.001962643,0.0002232805,0.0006024592,0.0004024645,0.00030688551,0.00044984877],"category_scores_gemma":[0.0023529967,0.00019474572,0.0003544622,0.0019989556,0.0001988975,0.000852113,0.00036544522,0.00035014888,0.00011495902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005713889,0.00037765154,0.1970475,0.000263395,0.00045391847,0.00052427675,0.000510364,0.5131638,0.014987284,0.0043044863,0.0042336597,0.26356226],"study_design_scores_gemma":[0.000004704315,0.000025024932,0.024413226,0.000008481951,0.000021815771,0.000043886754,0.00013839923,0.9702361,0.0015188793,0.0030603025,0.00051886425,0.0000103250795],"about_ca_topic_score_codex":0.00855256,"about_ca_topic_score_gemma":0.013485552,"teacher_disagreement_score":0.00855256,"about_ca_system_score_codex":0.00033448995,"about_ca_system_score_gemma":0.00035502782,"threshold_uncertainty_score":0.017005563},"labels":[],"label_agreement":null},{"id":"W4407805455","doi":"10.1016/j.cognition.2025.106088","title":"Implicit prediction as a consequence of statistical learning","year":2025,"lang":"en","type":"article","venue":"Cognition","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Statistical learning; Cognitive psychology; Cognitive science; Artificial intelligence","score_opus":0.010974223792294631,"score_gpt":0.2800616317361242,"score_spread":0.26908740794382957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407805455","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9706908,0.00007543427,0.02444835,0.00018849657,0.000035448305,0.000027893677,0.000053250005,0.00012652321,0.0043539586],"genre_scores_gemma":[0.9945392,0.00003016315,0.004801202,0.00003891949,0.000009072803,0.000014904781,0.00004452386,0.000024054947,0.00049790344],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992223,0.00016612039,0.000052752814,0.00021937033,0.00024279882,0.00009664752],"domain_scores_gemma":[0.9939468,0.0032597731,0.0009834867,0.0011595289,0.00030918425,0.00034134721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000984224,0.00023004037,0.00028743476,0.00015714396,0.00014428337,0.0007907448,0.0005865468,0.0004642597,0.0020285153],"category_scores_gemma":[0.008666849,0.00026162347,0.00020797587,0.00016152363,0.0007867923,0.0014467025,0.00092699454,0.0010041179,0.00015348817],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016732351,0.0004886741,0.048932448,0.00037588718,0.00015524671,0.00069018704,0.0008269569,0.0149995005,0.78992033,0.022908492,0.00066989,0.11835912],"study_design_scores_gemma":[0.0001492752,0.0019881565,0.4337914,0.00006431394,0.00018524897,0.0012659166,0.0003863192,0.22394252,0.222566,0.112387516,0.003129341,0.00014393259],"about_ca_topic_score_codex":0.0005876068,"about_ca_topic_score_gemma":0.0008571898,"teacher_disagreement_score":0.0020285153,"about_ca_system_score_codex":0.00032709623,"about_ca_system_score_gemma":0.00046080793,"threshold_uncertainty_score":0.006786108},"labels":[],"label_agreement":null},{"id":"W4407827554","doi":"10.1109/tcyb.2025.3539275","title":"An Arbitrarily Predefined-Time Convergent RNN for Dynamic LMVE With Its Applications in UR3 Robotic Arm Control and Multiagent Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Robotic arm; Computer science; Control (management); Multi-agent system; Control theory (sociology); Artificial intelligence; Control engineering; Engineering","score_opus":0.007598501457113577,"score_gpt":0.23808953489802365,"score_spread":0.23049103344091007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407827554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0129327085,0.00063567166,0.98204213,0.0001334888,0.00005383815,0.000029311157,0.000029824432,0.00034550607,0.0037974603],"genre_scores_gemma":[0.8401883,0.00059802295,0.15330221,0.00018188139,0.000032573265,0.00017435937,0.00011784161,0.00010138122,0.0053035025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975175,0.000060308,0.000018707186,0.00007231958,0.00007055193,0.000026330565],"domain_scores_gemma":[0.99965835,0.00014398206,0.0000453572,0.000029148028,0.00010797734,0.000015168162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062931667,0.0007023605,0.00044105275,0.00020971426,0.00030468852,0.00039521785,0.000828561,0.0007707179,0.0016897089],"category_scores_gemma":[0.0014101034,0.00021460456,0.00043715225,0.0002553497,0.0005121097,0.000474341,0.00063214864,0.0009804085,0.0003573973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008196129,0.0000164521,0.00045107093,0.00014636143,0.000021859209,0.00009974075,0.00009289025,0.8846239,0.011933746,0.0125274975,0.0010826673,0.08892186],"study_design_scores_gemma":[0.0000024700116,0.000020210147,0.000044096363,0.000005995037,0.0000032802604,0.000016807631,0.0000037112802,0.99707043,0.001293283,0.0008875047,0.00064746686,0.0000046538876],"about_ca_topic_score_codex":0.0057626883,"about_ca_topic_score_gemma":0.004580995,"teacher_disagreement_score":0.0057626883,"about_ca_system_score_codex":0.0004860937,"about_ca_system_score_gemma":0.0007559868,"threshold_uncertainty_score":0.011458278},"labels":[],"label_agreement":null},{"id":"W4407829771","doi":"10.1109/mdat.2025.3544537","title":"Understanding Error Propagation in Deep-Learning Neural Networks’ Accelerators and Applications","year":2025,"lang":"en","type":"article","venue":"IEEE Design and Test","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Deep learning; Artificial neural network; Artificial intelligence; Backpropagation; Deep neural networks; Machine learning","score_opus":0.062119503069993316,"score_gpt":0.26950321838210184,"score_spread":0.20738371531210853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407829771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062961556,0.015584262,0.87320554,0.020919556,0.0067691486,0.00006926899,0.00050435105,0.003461394,0.01652503],"genre_scores_gemma":[0.70454144,0.012884303,0.22849755,0.004536161,0.0034336864,0.00007518157,0.00071495713,0.0013175356,0.043999184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994211,0.000082095976,0.000031662137,0.00007104963,0.00034860868,0.00004551519],"domain_scores_gemma":[0.996651,0.0018627649,0.00016128684,0.00019304556,0.0010700884,0.0000617169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016056807,0.0006845555,0.0003768649,0.00079008995,0.00031440228,0.0012570207,0.0008426644,0.001049655,0.002650448],"category_scores_gemma":[0.009664254,0.0005287502,0.0003113232,0.0005821566,0.0006617602,0.002411394,0.0005126802,0.0024782585,0.00055600994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046533524,0.00010220654,0.0068106516,0.00064326584,0.00015441913,0.0004989058,0.00038147077,0.22977002,0.032278996,0.11458742,0.07218299,0.54212433],"study_design_scores_gemma":[0.000033903652,0.00012707053,0.0023966413,0.00012586048,0.0000452133,0.0003145279,0.000059927374,0.81265527,0.036398422,0.09503151,0.05276332,0.000048288013],"about_ca_topic_score_codex":0.002840776,"about_ca_topic_score_gemma":0.0037906126,"teacher_disagreement_score":0.002840776,"about_ca_system_score_codex":0.0009350312,"about_ca_system_score_gemma":0.0006688687,"threshold_uncertainty_score":0.008866608},"labels":[],"label_agreement":null},{"id":"W4407832213","doi":"10.1109/icdm59182.2024.00015","title":"Continuous Exact Explanations of Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.013053888821944226,"score_gpt":0.25043033920111607,"score_spread":0.23737645037917185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407832213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026769321,0.00070507365,0.96327,0.0019028132,0.00009597194,0.00006335861,0.0008982606,0.0015424227,0.004752832],"genre_scores_gemma":[0.64633805,0.00093546114,0.3452473,0.00062747975,0.00012683183,0.00022748167,0.0014114269,0.0004278036,0.004658182],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99834096,0.0006904211,0.00011479287,0.00029439654,0.00043869935,0.00012066568],"domain_scores_gemma":[0.9938491,0.0040392145,0.00048737347,0.001070818,0.0004800642,0.00007336321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014960897,0.0011657125,0.0004472161,0.0010451345,0.0005436235,0.0015470771,0.0015599309,0.0017951602,0.006396186],"category_scores_gemma":[0.013727942,0.00055117765,0.0010993032,0.0006701027,0.0018329815,0.0035663673,0.0015261743,0.0024375685,0.000608614],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020427068,0.000047413145,0.004188687,0.00054518954,0.00010447174,0.00090261694,0.0012460392,0.28441522,0.0045576324,0.59571195,0.0079987645,0.10007788],"study_design_scores_gemma":[0.000022950428,0.000027922213,0.0006189083,0.00011338431,0.000030404899,0.00014824503,0.00011464505,0.5641035,0.0025545105,0.42244297,0.009795064,0.000027509774],"about_ca_topic_score_codex":0.0028486077,"about_ca_topic_score_gemma":0.0042645717,"teacher_disagreement_score":0.006396186,"about_ca_system_score_codex":0.0013579011,"about_ca_system_score_gemma":0.0008189837,"threshold_uncertainty_score":0.021397352},"labels":[],"label_agreement":null},{"id":"W4407871789","doi":"10.1007/978-981-96-1078-5_2","title":"The Discrete Fourier Transform","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Discrete Fourier transform (general); Fourier transform; Computer science; Mathematics; Fractional Fourier transform; Fourier analysis; Mathematical analysis","score_opus":0.013874714720187644,"score_gpt":0.23487793734777995,"score_spread":0.2210032226275923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407871789","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014390011,0.12527774,0.2093821,0.004597254,0.010846851,0.000050243463,0.0003447347,0.0009061898,0.6471558],"genre_scores_gemma":[0.075886235,0.10724114,0.12474636,0.0033224297,0.010884253,0.00023306384,0.0007650576,0.0010453681,0.67587614],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99962676,0.00008679301,0.000016892782,0.00007293782,0.00017101227,0.000025552163],"domain_scores_gemma":[0.9996414,0.00017836955,0.00001973092,0.000074264506,0.0000671368,0.00001911349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005780411,0.0010617469,0.0007210596,0.0014792222,0.0005117657,0.0026509494,0.00062376185,0.0014237163,0.022036117],"category_scores_gemma":[0.0016614825,0.00043224607,0.00039931908,0.0015378008,0.0026954685,0.003340201,0.0011499927,0.0033427672,0.0145192295],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025132162,0.000018308674,0.00006829259,0.00019892585,0.000013512502,0.00006265016,0.00013612284,0.0010073868,0.0016938007,0.71247643,0.087261796,0.19703755],"study_design_scores_gemma":[0.0000078379735,0.00001565834,0.0001946937,0.00014509998,0.000007223779,0.00024464185,0.000048239315,0.002895952,0.001026757,0.28308633,0.71230644,0.000021095711],"about_ca_topic_score_codex":0.00060727954,"about_ca_topic_score_gemma":0.0005816926,"teacher_disagreement_score":0.022036117,"about_ca_system_score_codex":0.0009198665,"about_ca_system_score_gemma":0.0005564404,"threshold_uncertainty_score":0.07371813},"labels":[],"label_agreement":null},{"id":"W4407893628","doi":"10.1016/j.cogsys.2025.101338","title":"Neurons as autonomous agents: A biologically inspired framework for cognitive architectures in artificial intelligence","year":2025,"lang":"en","type":"article","venue":"Cognitive Systems Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Autonomous agent; Artificial intelligence; Computer science; Cognition; Intelligent agent; Cognitive science; Human–computer interaction; Neuroscience; Psychology","score_opus":0.1635600864353549,"score_gpt":0.44504370553738937,"score_spread":0.28148361910203445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407893628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009739502,0.052834455,0.86663693,0.009366142,0.0009116896,0.000110044246,0.00015463961,0.0004259402,0.0598207],"genre_scores_gemma":[0.44174057,0.07148645,0.4542428,0.0026890053,0.0013571235,0.0008949374,0.000258405,0.00020949876,0.027121175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996574,0.00012682693,0.000024374933,0.00007082583,0.00008764935,0.000032856413],"domain_scores_gemma":[0.99965966,0.00015709583,0.000051405183,0.000039517752,0.000057026817,0.000035249894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068039866,0.00085966947,0.0006414531,0.0009751154,0.00062216824,0.0029882835,0.0017907874,0.0024885365,0.0022453386],"category_scores_gemma":[0.0010248823,0.00041097723,0.00071204896,0.00083693437,0.004096173,0.0034464153,0.0014796983,0.0025662526,0.00071847433],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012056236,0.000011994354,0.00020958931,0.00029810646,0.000037510912,0.00010146238,0.00022231955,0.022708274,0.0014961878,0.9473168,0.0016442236,0.025941605],"study_design_scores_gemma":[0.000011682625,0.00003693808,0.00017142968,0.00017551394,0.000027967972,0.00012443263,0.00010033134,0.061190818,0.00071173575,0.88037425,0.057049446,0.000025427464],"about_ca_topic_score_codex":0.0022333995,"about_ca_topic_score_gemma":0.002125842,"teacher_disagreement_score":0.0029882835,"about_ca_system_score_codex":0.0012199745,"about_ca_system_score_gemma":0.0010966172,"threshold_uncertainty_score":0.008851588},"labels":[],"label_agreement":null},{"id":"W4407900548","doi":"10.1109/tnnls.2025.3540014","title":"Conditional Mutual Information Constrained Deep Learning for Classification","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mutual information; Artificial intelligence; Computer science; Machine learning; Pattern recognition (psychology)","score_opus":0.012702583376656019,"score_gpt":0.24138996967108278,"score_spread":0.22868738629442675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407900548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064164596,0.0012599376,0.98897237,0.00037526613,0.000055693545,0.000042064516,0.00017383805,0.0007287246,0.0019756407],"genre_scores_gemma":[0.60630673,0.0019112475,0.3796209,0.0007387084,0.00034778778,0.00051058887,0.0016357007,0.00037633855,0.008551925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985642,0.0004438754,0.00007841168,0.00032278954,0.0004606502,0.00013009158],"domain_scores_gemma":[0.99805814,0.0010802797,0.0002485243,0.0002383068,0.00030338584,0.00007129425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024738337,0.0014728458,0.0013321699,0.0011918037,0.00042612784,0.0012985349,0.0023628012,0.0015451559,0.002607748],"category_scores_gemma":[0.006404443,0.00050640927,0.0007598244,0.0016283853,0.0013988687,0.0021607154,0.0021799754,0.0032797218,0.00071073405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014241501,0.000108896376,0.00090521335,0.00016835715,0.000104800645,0.0000825558,0.00006969022,0.7535081,0.0028686721,0.07272031,0.007741667,0.16157937],"study_design_scores_gemma":[0.000002522873,0.000013151913,0.000064547276,0.0000074894538,0.0000037842337,0.000009836603,0.000002291929,0.9856376,0.00058041996,0.0132027455,0.000469964,0.0000056710423],"about_ca_topic_score_codex":0.004273026,"about_ca_topic_score_gemma":0.0039653443,"teacher_disagreement_score":0.004273026,"about_ca_system_score_codex":0.0022811443,"about_ca_system_score_gemma":0.0019499098,"threshold_uncertainty_score":0.016550958},"labels":[],"label_agreement":null},{"id":"W4407933327","doi":"10.1016/j.brs.2024.12.741","title":"Large-Scale Neural Dynamics as a Predictor of Stimulus Effects Variability","year":2025,"lang":"en","type":"article","venue":"Brain stimulation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Addiction and Mental Health","funders":"","keywords":"Stimulus (psychology); Psychology; Cognitive psychology","score_opus":0.005416410521068931,"score_gpt":0.2607589337298136,"score_spread":0.2553425232087447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407933327","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96089,0.00017765937,0.03593113,0.000052338968,0.00006933198,0.00028007058,0.0006021022,0.0003265927,0.0016707311],"genre_scores_gemma":[0.99158597,0.00006638819,0.0070178206,0.000014767323,0.000014489611,0.00020764489,0.000371464,0.000033107102,0.0006883016],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975663,0.00004121141,0.000021061258,0.000075778284,0.000064681924,0.0000405805],"domain_scores_gemma":[0.9993832,0.0003895738,0.00006662204,0.00004842998,0.0000620615,0.000050046812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040577838,0.0006117775,0.00037322874,0.00035167154,0.00013151923,0.00028084754,0.0001826452,0.0003683641,0.0028197756],"category_scores_gemma":[0.0030626783,0.00012673184,0.000304377,0.0003278808,0.0002463695,0.00018482175,0.00029518022,0.00042711836,0.00023787057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009048492,0.0015976935,0.09698147,0.00064036227,0.0004311751,0.000571552,0.00042303914,0.038764056,0.48555762,0.0007580953,0.0013728341,0.3638536],"study_design_scores_gemma":[0.0001254733,0.0053586024,0.7197244,0.000051421906,0.00020767504,0.00079666934,0.00018196064,0.21579857,0.05490157,0.0008456232,0.001924693,0.000083316714],"about_ca_topic_score_codex":0.0018118144,"about_ca_topic_score_gemma":0.0025742461,"teacher_disagreement_score":0.0028197756,"about_ca_system_score_codex":0.00018149249,"about_ca_system_score_gemma":0.00019770254,"threshold_uncertainty_score":0.009433091},"labels":[],"label_agreement":null},{"id":"W4407937729","doi":"10.1109/thms.2025.3538098","title":"Time Series Signal Analysis With Information Granulation Based on Permutation Entropy: An Application to Electroencephalography Signals","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Electroencephalography; SIGNAL (programming language); Pattern recognition (psychology); Series (stratigraphy); Computer science; Entropy (arrow of time); Signal processing; Permutation (music); Artificial intelligence; Speech recognition; Data mining; Mathematics; Psychology; Neuroscience; Acoustics; Physics; Biology; Telecommunications","score_opus":0.0065784996221192,"score_gpt":0.2515574771403906,"score_spread":0.2449789775182714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407937729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0329767,0.00060258235,0.96483654,0.00017446853,0.00006427992,0.000054735276,0.00010973348,0.0004245936,0.0007564747],"genre_scores_gemma":[0.5460291,0.0008240527,0.45161906,0.0000946523,0.0002152697,0.000099424135,0.00029678986,0.00009915144,0.00072250323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993436,0.00013004306,0.000092005896,0.00013382119,0.0002569025,0.000043709402],"domain_scores_gemma":[0.9983309,0.00091174216,0.00024214237,0.00023756492,0.00021612138,0.0000614608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008929775,0.0005169676,0.0006647346,0.00214568,0.00027897756,0.0010386984,0.0003653451,0.0004165207,0.00073609914],"category_scores_gemma":[0.0036824725,0.00019306951,0.00074953894,0.0022460362,0.00068191125,0.0015838327,0.00077248446,0.0007192523,0.00015797144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004888211,0.00012500252,0.004720424,0.0004259087,0.00019856298,0.00047545825,0.00036212683,0.1268765,0.07691124,0.027003607,0.0022491284,0.7601633],"study_design_scores_gemma":[0.000019165687,0.00014471015,0.0057928762,0.000023589377,0.00006583612,0.00027976892,0.000060657778,0.9516055,0.017925985,0.020931536,0.0030958902,0.00005443762],"about_ca_topic_score_codex":0.0008102314,"about_ca_topic_score_gemma":0.00051764265,"teacher_disagreement_score":0.00214568,"about_ca_system_score_codex":0.00036374593,"about_ca_system_score_gemma":0.00028557618,"threshold_uncertainty_score":0.004722595},"labels":[],"label_agreement":null},{"id":"W4407939460","doi":"10.1007/s10994-025-06752-x","title":"A contrastive neural disentanglement approach for query performance prediction","year":2025,"lang":"en","type":"article","venue":"Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Artificial neural network; Natural language processing","score_opus":0.009212424396369148,"score_gpt":0.238969669819153,"score_spread":0.22975724542278383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407939460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12770028,0.0012729478,0.861486,0.0007299286,0.00017486906,0.00013032199,0.00049979135,0.0010221527,0.006983682],"genre_scores_gemma":[0.901279,0.00027789842,0.093606494,0.0001719262,0.00018777869,0.00007961164,0.00044038173,0.000100493824,0.003856365],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987669,0.0003581817,0.00006364139,0.00030184514,0.00038498375,0.00012441546],"domain_scores_gemma":[0.99617565,0.002353674,0.0003037151,0.0004626443,0.00058200763,0.00012225212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002414919,0.0007365352,0.0009495061,0.0016367098,0.0004020722,0.0014292403,0.0018952353,0.0008662877,0.0026971702],"category_scores_gemma":[0.008778914,0.00032214524,0.0004306669,0.0014898862,0.00055220095,0.0027552682,0.0014165848,0.0020538373,0.0005458133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00246515,0.0011070127,0.018369082,0.00022072012,0.0002481618,0.0002856175,0.00026773335,0.11192227,0.052272197,0.052451003,0.0057267994,0.7546642],"study_design_scores_gemma":[0.000017911052,0.00011967579,0.0028660924,0.000009366476,0.000032734577,0.00004969507,0.00002078107,0.9807998,0.003934805,0.011140531,0.0009903222,0.000018197483],"about_ca_topic_score_codex":0.0048671863,"about_ca_topic_score_gemma":0.0069613294,"teacher_disagreement_score":0.0048671863,"about_ca_system_score_codex":0.0009321145,"about_ca_system_score_gemma":0.00075182953,"threshold_uncertainty_score":0.012771428},"labels":[],"label_agreement":null},{"id":"W4407948409","doi":"10.1109/tai.2025.3544591","title":"Enhancement of Robot Dynamics Learning by Integrating Analytical Models into Deep Neural Networks: A Data Fusion Perspective","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Perspective (graphical); Deep learning; Artificial intelligence; Artificial neural network; Computer science; Sensor fusion; Fusion; Dynamics (music); Machine learning; Psychology","score_opus":0.04404852999620597,"score_gpt":0.3245595524312559,"score_spread":0.28051102243504994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407948409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01788354,0.00069548475,0.97865456,0.00036137752,0.00005180852,0.000021480027,0.00007941015,0.00063345267,0.0016188005],"genre_scores_gemma":[0.75415623,0.0015318843,0.24066833,0.00019513936,0.00008717291,0.00009847021,0.00040444385,0.00008406682,0.002774208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979955,0.000033688968,0.000016623339,0.00004893682,0.00007997548,0.00002125223],"domain_scores_gemma":[0.9995912,0.0001424947,0.00006210166,0.000057161807,0.00013008509,0.000017019196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079534145,0.0008342278,0.0005164544,0.0005695883,0.00016108558,0.00061172846,0.0006841789,0.00066385075,0.0009706375],"category_scores_gemma":[0.0017215795,0.0003953725,0.0005359077,0.00047231046,0.00033294878,0.0011976355,0.0007806754,0.001022981,0.00029730183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006265764,0.00007551398,0.0011727873,0.00015312938,0.00006838599,0.00007819248,0.000071345006,0.76439035,0.015712945,0.006225087,0.0008310955,0.21115856],"study_design_scores_gemma":[0.000001629941,0.00001793497,0.00019346636,0.0000070885662,0.0000059019017,0.000011516977,0.000006013114,0.9947606,0.0022563527,0.0021228578,0.00061163935,0.0000049911882],"about_ca_topic_score_codex":0.0030434697,"about_ca_topic_score_gemma":0.0036266511,"teacher_disagreement_score":0.0030434697,"about_ca_system_score_codex":0.00049942173,"about_ca_system_score_gemma":0.0007270669,"threshold_uncertainty_score":0.0060515404},"labels":[],"label_agreement":null},{"id":"W4407949500","doi":"10.1109/cdc56724.2024.10885787","title":"Decentralized Stochastic Control in Borel Spaces: Centralized MDP Reductions, Near Optimality of Finite Window Local Information, and Q-Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Window (computing); Control (management); Computer science; Optimal control; Mathematical optimization; Q-learning; Mathematics; Artificial intelligence; Reinforcement learning","score_opus":0.006106602439118621,"score_gpt":0.23091518898046734,"score_spread":0.22480858654134872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407949500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032138463,0.0002607515,0.9620034,0.0010529554,0.00003648839,0.000061797975,0.000082249506,0.00011652346,0.0042474144],"genre_scores_gemma":[0.88225734,0.00042477818,0.112102255,0.0003114437,0.00013182,0.0003323909,0.00018395887,0.00009155101,0.0041644624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99753594,0.0009869438,0.00008963454,0.00057225535,0.00057097105,0.00024424703],"domain_scores_gemma":[0.98978233,0.0076439264,0.0009002942,0.000754644,0.00050347444,0.00041542188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041544917,0.00096580974,0.0018305262,0.00057052856,0.0006591904,0.00159048,0.0019498903,0.0019206731,0.002286973],"category_scores_gemma":[0.014998241,0.00047740949,0.0012817913,0.00068773545,0.0032502196,0.003509592,0.0028544231,0.0037040063,0.00022410697],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000853849,0.00012444358,0.0004545654,0.000102418206,0.000053650125,0.000101161015,0.00011820539,0.5434139,0.001558595,0.44047385,0.0008825765,0.012631274],"study_design_scores_gemma":[0.000019169896,0.000038770926,0.00009507792,0.000009591692,0.0000053687345,0.00001306791,0.000015461637,0.8453851,0.00041785763,0.15369499,0.00029705846,0.00000854945],"about_ca_topic_score_codex":0.0029782944,"about_ca_topic_score_gemma":0.0018850976,"teacher_disagreement_score":0.0041544917,"about_ca_system_score_codex":0.002623918,"about_ca_system_score_gemma":0.002726761,"threshold_uncertainty_score":0.021971285},"labels":[],"label_agreement":null},{"id":"W4407990957","doi":"10.1037/rev0000526","title":"Differences in learning across the lifespan emerge via resource-rational computations.","year":2025,"lang":"en","type":"article","venue":"Psychological Review","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Mental Health; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Deutsche Forschungsgemeinschaft; Max Planck School of Cognition","keywords":"Resource (disambiguation); Cognitive psychology; Psychology; Computer science; Cognitive science","score_opus":0.057254611894581586,"score_gpt":0.38815538248877207,"score_spread":0.3309007705941905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407990957","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8421447,0.10714789,0.008830575,0.0038296243,0.00023013039,0.000048284437,0.001817859,0.00012648405,0.035824515],"genre_scores_gemma":[0.951746,0.040114053,0.0035063867,0.0005524783,0.00005759839,0.000044728575,0.00052066764,0.000019961246,0.0034381242],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998766,0.000025504884,0.00000955538,0.000054367418,0.000020495117,0.000013468293],"domain_scores_gemma":[0.9991191,0.0003451819,0.00030419652,0.000080223144,0.00008914714,0.000062252424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085816317,0.00015310769,0.00021025406,0.00038101137,0.00011973711,0.0005238331,0.00029375992,0.00043042353,0.0027968679],"category_scores_gemma":[0.0034535532,0.00010351647,0.00020736225,0.0003763985,0.00076876424,0.0008087657,0.0002928737,0.0005162863,0.00043203493],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037148056,0.00011418005,0.1528241,0.001328683,0.00039361528,0.0005054477,0.0013820973,0.001635531,0.007793442,0.046566427,0.009314395,0.77777064],"study_design_scores_gemma":[0.000025559828,0.00029156284,0.9293217,0.00033362207,0.00014252757,0.0013633566,0.0003228658,0.0006564363,0.0017180076,0.035213124,0.030585263,0.00002597125],"about_ca_topic_score_codex":0.0021585329,"about_ca_topic_score_gemma":0.003513381,"teacher_disagreement_score":0.0027968679,"about_ca_system_score_codex":0.00043778407,"about_ca_system_score_gemma":0.00019610635,"threshold_uncertainty_score":0.009356439},"labels":[],"label_agreement":null},{"id":"W4408074826","doi":"10.1007/978-3-031-82150-9","title":"Intelligent Systems and Pattern Recognition","year":2025,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ajman University; National Technical University of Athens; Technical University of Sofia; Université Cadi Ayyad; Macau University of Science and Technology; Università degli Studi di Perugia; National Institute of Development Administration; University of Technology - Iraq; Zayed University; Universidad Simón Bolívar; Universidad de Las Palmas de Gran Canaria; National and Kapodistrian University of Athens; National University of Sciences and Technology; Qatar University; Università degli Studi di Salerno; Lakehead University; Indian Institute of Technology Roorkee; Université du Québec à Montréal; Lviv Polytechnic National University; United Arab Emirates University; Queen's University; Ain Shams University; Université de Sfax; Amrita Vishwa Vidyapeetham University; Galgotias University; Universidad de Oviedo; Asia University; Southern Federal University; Netaji Subhas University of Technology; University of Gabès; Université de Technologie de Compiègne; Chaoyang University of Technology; Bahria University; Universitatea 'Dunărea de Jos' Galați; University of Lethbridge; McGill University; Université Mohammed V de Rabat; Arkansas Tech University; Ball State University; Università degli Studi di Milano-Bicocca; Middle Tennessee State University; Taibah University; University of Dayton","keywords":"Computer science; Thesaurus; Information retrieval; Artificial intelligence; Natural language processing","score_opus":0.04969903879415186,"score_gpt":0.2953616755603279,"score_spread":0.24566263676617603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408074826","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015922254,0.09168571,0.2641994,0.0026247501,0.0069446065,0.00014203305,0.0007316162,0.0027648457,0.6293148],"genre_scores_gemma":[0.013895622,0.03308497,0.057483595,0.0011793382,0.0022183647,0.00018229133,0.00083532033,0.0005656431,0.89055485],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996519,0.00004292942,0.000019426056,0.000065245775,0.00020187724,0.000018581519],"domain_scores_gemma":[0.9996897,0.00012145208,0.00001769663,0.00007682888,0.00007491121,0.000019412828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002943829,0.001521581,0.0015818757,0.0016958524,0.00046765065,0.0029757894,0.0010725709,0.0009810377,0.07294886],"category_scores_gemma":[0.0010642426,0.00050166296,0.0005018086,0.0023617977,0.0010633888,0.0027402577,0.0012932424,0.0014742616,0.05450353],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022811393,0.000030615833,0.0001136239,0.00063356676,0.00004344686,0.000074084135,0.00009474837,0.0028691837,0.0019339647,0.12334317,0.24664447,0.6241963],"study_design_scores_gemma":[0.0000074899554,0.000035379184,0.00037304024,0.00019437908,0.000023385104,0.00028478785,0.000042666732,0.0072216876,0.0009174242,0.11466206,0.8762215,0.000016313918],"about_ca_topic_score_codex":0.0007802961,"about_ca_topic_score_gemma":0.0014258695,"teacher_disagreement_score":0.07294886,"about_ca_system_score_codex":0.00056305545,"about_ca_system_score_gemma":0.0005541827,"threshold_uncertainty_score":0.24403822},"labels":[],"label_agreement":null},{"id":"W4408092492","doi":"10.1109/tit.2025.3546976","title":"Sharp Signal Detection Under Ferromagnetic Ising Models","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ising model; Ferromagnetism; Detection theory; Statistical physics; Computer science; SIGNAL (programming language); Physics; Condensed matter physics; Telecommunications; Detector","score_opus":0.011037320475722137,"score_gpt":0.22531731304482303,"score_spread":0.21427999256910088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408092492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4502356,0.0010949561,0.5388571,0.0021551617,0.00007605525,0.00012277007,0.00017302246,0.0006134916,0.006671781],"genre_scores_gemma":[0.9820749,0.00023207974,0.016640835,0.00015383748,0.00006018217,0.000038001297,0.0000521763,0.00003174864,0.0007162702],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99635845,0.001437831,0.00017199051,0.0006366008,0.0009216042,0.00047347712],"domain_scores_gemma":[0.9323579,0.054993283,0.0060703824,0.0028359476,0.00209686,0.0016455774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007028458,0.0013682598,0.0018629559,0.0024639573,0.0010080842,0.0027837444,0.0035418,0.0036288141,0.0013670503],"category_scores_gemma":[0.082261756,0.001093244,0.0008168069,0.00093794754,0.0066434317,0.0078058597,0.0042601177,0.003975397,0.0002455909],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012131355,0.00020042782,0.005446861,0.0005278335,0.00015032511,0.0013028085,0.0010071674,0.3263736,0.0248393,0.62045264,0.0011847361,0.017301166],"study_design_scores_gemma":[0.00007577378,0.00016187673,0.0005956927,0.00004167484,0.00002547051,0.00027222934,0.00008520368,0.76238567,0.0060988483,0.22991192,0.00027299914,0.000072692616],"about_ca_topic_score_codex":0.002134811,"about_ca_topic_score_gemma":0.0010819089,"teacher_disagreement_score":0.007028458,"about_ca_system_score_codex":0.0030144893,"about_ca_system_score_gemma":0.0010285935,"threshold_uncertainty_score":0.03717047},"labels":[],"label_agreement":null},{"id":"W4408099797","doi":"10.2139/ssrn.5161889","title":"A Perceptual Meta-model based on the Ontology of Mental Models","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Regional Municipality of Niagara","funders":"","keywords":"Mental model; Ontology; Computer science; Perception; Psychology; Cognitive science; Epistemology; Philosophy; Neuroscience","score_opus":0.02762282312978754,"score_gpt":0.2669536089843114,"score_spread":0.2393307858545239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408099797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018432489,0.00027186994,0.9577178,0.001338626,0.00018959644,0.00008481574,0.00076695264,0.0007553506,0.020442486],"genre_scores_gemma":[0.5058745,0.00050054357,0.4858562,0.00039437594,0.00008951625,0.0002513552,0.0013726479,0.00029014776,0.00537075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990804,0.0002499353,0.00008299698,0.00029024208,0.00021029288,0.00008607403],"domain_scores_gemma":[0.9987142,0.00038460712,0.00010839449,0.00037077154,0.0002568639,0.00016514683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009345596,0.0005962941,0.0005937076,0.0012464214,0.000950908,0.004957376,0.0017851152,0.0011457431,0.008623673],"category_scores_gemma":[0.0042086137,0.000558117,0.00215249,0.0010678372,0.0017180003,0.008401922,0.0023576184,0.0020366102,0.0013828218],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008449512,0.000058894468,0.0009887053,0.00015776827,0.00008706296,0.00016693825,0.0010916025,0.0063133016,0.0037413104,0.9430484,0.002375714,0.04188579],"study_design_scores_gemma":[0.000026954698,0.000048275404,0.00072655146,0.000091868445,0.0000900742,0.00020222078,0.0004311571,0.112957574,0.001777033,0.8716815,0.011920882,0.000045884903],"about_ca_topic_score_codex":0.0038074448,"about_ca_topic_score_gemma":0.0035509043,"teacher_disagreement_score":0.008623673,"about_ca_system_score_codex":0.0011658275,"about_ca_system_score_gemma":0.0015106142,"threshold_uncertainty_score":0.028849006},"labels":[],"label_agreement":null},{"id":"W4408112773","doi":"10.1145/3721136","title":"Mathematics-Inspired Models: A Green and Interpretable Learning Paradigm for Multimedia Computing","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Multimedia; Artificial intelligence; Human–computer interaction; Theoretical computer science; Data science; Cognitive science","score_opus":0.031049249823865213,"score_gpt":0.29549821535349374,"score_spread":0.26444896552962854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408112773","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004814951,0.0011175635,0.98315454,0.0033811007,0.00011246322,0.00002988491,0.00010873501,0.00014635928,0.007134315],"genre_scores_gemma":[0.50738245,0.0048496234,0.47308862,0.0021498208,0.0009975143,0.0003781574,0.00035212093,0.0002702492,0.010531399],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923456,0.0003432797,0.000032925494,0.00012525714,0.00021809139,0.00004595178],"domain_scores_gemma":[0.99789643,0.0012560756,0.00019560866,0.0003475594,0.00019903136,0.00010538196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015161855,0.0008254117,0.0008554685,0.0010954546,0.0006022238,0.002625105,0.002090359,0.0016391682,0.0028544758],"category_scores_gemma":[0.005476064,0.00038328374,0.001200699,0.00085399725,0.0034398164,0.00495871,0.002379073,0.0039717755,0.0005677506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001679428,0.000020111818,0.00026143063,0.00008911896,0.000026350277,0.000052823205,0.0001473758,0.057838194,0.00083592156,0.9212892,0.0018093761,0.017613396],"study_design_scores_gemma":[0.000005570692,0.000016406735,0.00006034328,0.000027734635,0.000006417756,0.0000353184,0.000024822122,0.2675582,0.0003140144,0.7271311,0.004809839,0.00001025706],"about_ca_topic_score_codex":0.0014577666,"about_ca_topic_score_gemma":0.0018896484,"teacher_disagreement_score":0.0028544758,"about_ca_system_score_codex":0.0016778127,"about_ca_system_score_gemma":0.00092081353,"threshold_uncertainty_score":0.012173414},"labels":[],"label_agreement":null},{"id":"W4408182352","doi":"10.1109/lwc.2025.3548929","title":"Enhancing Localization and Synchronization Through Neural Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Synchronization (alternating current); Artificial neural network; Computer network; Artificial intelligence","score_opus":0.013515198261181211,"score_gpt":0.2590314020781683,"score_spread":0.2455162038169871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408182352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027103897,0.00023885538,0.9704762,0.00013550268,0.00004155663,0.000008495409,0.0000094678735,0.00018029097,0.0018056947],"genre_scores_gemma":[0.8241066,0.00045306972,0.17258176,0.00013671666,0.000090030015,0.000042511514,0.00003864528,0.000056347937,0.0024943503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997025,0.00008201241,0.00001862051,0.000072041774,0.00009291593,0.000031892432],"domain_scores_gemma":[0.9994948,0.00028203026,0.00008555459,0.00004750776,0.00007771855,0.000012438359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005618936,0.0004994603,0.0003509026,0.00024340847,0.00019581617,0.00044844297,0.0004997158,0.0005761341,0.00064051745],"category_scores_gemma":[0.002296242,0.00018811229,0.00026340026,0.00025609139,0.00045780314,0.0009554897,0.0007796595,0.00077510625,0.00017215149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001464942,0.000044865734,0.0008130464,0.0001371261,0.000043444405,0.00012877886,0.00017301977,0.7027114,0.05288967,0.022293635,0.0006595116,0.219959],"study_design_scores_gemma":[0.000003911656,0.000034023986,0.00014923017,0.000006721089,0.0000073845586,0.000027142954,0.0000083942405,0.9908845,0.0052629183,0.0029349679,0.00067438727,0.000006369294],"about_ca_topic_score_codex":0.0009642168,"about_ca_topic_score_gemma":0.0011199894,"teacher_disagreement_score":0.0009642168,"about_ca_system_score_codex":0.00033611286,"about_ca_system_score_gemma":0.0003323019,"threshold_uncertainty_score":0.0029715896},"labels":[],"label_agreement":null},{"id":"W4408218699","doi":"10.21468/scipostphyscore.8.1.027","title":"Bayesian RG flow in neural network field theories","year":2025,"lang":"en","type":"article","venue":"SciPost Physics Core","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute","funders":"University of Illinois at Urbana-Champaign; Institut Périmètre de physique théorique; Gordon and Betty Moore Foundation; National Science Foundation","keywords":"Artificial neural network; Field (mathematics); Bayesian network; Bayesian probability; Computer science; Flow (mathematics); Artificial intelligence; Econometrics; Mathematics; Pure mathematics; Geometry","score_opus":0.011204275298481411,"score_gpt":0.2623864785078966,"score_spread":0.2511822032094152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408218699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22492754,0.0034078453,0.6244787,0.011501018,0.00061094493,0.00013803373,0.0006408195,0.00097197713,0.13332316],"genre_scores_gemma":[0.89232564,0.0015111134,0.074672416,0.0012415414,0.0007398894,0.00028251042,0.000509789,0.00035967713,0.028357355],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99926454,0.00029927815,0.000024683337,0.0001081122,0.00021305749,0.00009031924],"domain_scores_gemma":[0.9987207,0.0005924643,0.00016340968,0.00013151643,0.00021832854,0.00017351694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022149854,0.00057526084,0.0007159038,0.0019600883,0.0010424535,0.0018458817,0.0010965968,0.0020943885,0.0051074666],"category_scores_gemma":[0.0058091707,0.00038248475,0.0009034291,0.00057316816,0.0026127049,0.0042144554,0.0014634667,0.0018775387,0.0005576246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053334766,0.0000062810996,0.000070754424,0.000010723434,0.0000027426802,0.0000145496115,0.000036436817,0.0058574355,0.00011842968,0.99180824,0.00058385957,0.0014851859],"study_design_scores_gemma":[0.0000045835154,0.0000050925105,0.00008790527,0.000007645902,0.0000011410316,0.0000098534565,0.000008836777,0.0572739,0.000030898733,0.94194365,0.00062102015,0.000005572892],"about_ca_topic_score_codex":0.0038511665,"about_ca_topic_score_gemma":0.0020023114,"teacher_disagreement_score":0.0051074666,"about_ca_system_score_codex":0.0030409822,"about_ca_system_score_gemma":0.0013206336,"threshold_uncertainty_score":0.02206397},"labels":[],"label_agreement":null},{"id":"W4408222968","doi":"10.1109/cisp-bmei64163.2024.10906289","title":"Neural Network-based Control System Enhanced with a Kalman Filter","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Ottawa","funders":"","keywords":"Kalman filter; Computer science; Artificial neural network; Control (management); Moving horizon estimation; Extended Kalman filter; Artificial intelligence","score_opus":0.008132226706022122,"score_gpt":0.2156155368763021,"score_spread":0.20748331017028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408222968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013479561,0.0003228265,0.9769146,0.000116123476,0.00014496579,0.000058485904,0.000057164434,0.002084027,0.0068222615],"genre_scores_gemma":[0.83669,0.00037628447,0.15232025,0.00011214163,0.00007061025,0.00019183755,0.0001382019,0.00006519945,0.010035496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997086,0.00004674515,0.0000228643,0.000081421254,0.00011483408,0.000025493011],"domain_scores_gemma":[0.99976295,0.0000564144,0.000030122535,0.000026011267,0.000116917334,0.00000752334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046207427,0.00041115354,0.00050439604,0.0002504825,0.0003966298,0.0005641569,0.00065587444,0.0005990261,0.002150809],"category_scores_gemma":[0.00087749626,0.00018433898,0.0003768049,0.00024076842,0.00020750011,0.0006807457,0.0003835385,0.00058827223,0.0006309809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002717538,0.00011874427,0.0011738968,0.00021635408,0.00012415557,0.00019325744,0.00012999684,0.62157273,0.03560466,0.01051738,0.0032342486,0.32684276],"study_design_scores_gemma":[0.000015509768,0.00007355633,0.00030372632,0.000009404262,0.000023192324,0.000034355297,0.0000040451,0.99052835,0.0053451397,0.0007632709,0.002887163,0.000012293668],"about_ca_topic_score_codex":0.011734167,"about_ca_topic_score_gemma":0.011730777,"teacher_disagreement_score":0.011734167,"about_ca_system_score_codex":0.0005120936,"about_ca_system_score_gemma":0.00069867435,"threshold_uncertainty_score":0.023331702},"labels":[],"label_agreement":null},{"id":"W4408231657","doi":"10.1523/jneurosci.1932-24.2025","title":"Visual Statistical Learning Alters Low-Dimensional Cortical Architecture","year":2025,"lang":"en","type":"article","venue":"Journal of Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Architecture; Neuroscience; Psychology; Visual cortex; Statistical learning; Computer science; Artificial intelligence; Cognitive science; Art; Visual arts","score_opus":0.011134268572981528,"score_gpt":0.29655135066780897,"score_spread":0.2854170820948274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408231657","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9896778,0.000040341045,0.009361898,0.00007890443,0.0000048107963,0.0000047439917,0.000038247057,0.00007522725,0.0007178919],"genre_scores_gemma":[0.99820864,0.000022069886,0.0014453592,0.0000109833245,0.0000024808692,0.000004059833,0.00003723752,0.000010801253,0.0002583335],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99985695,0.000021804322,0.000006295078,0.000046026602,0.000040418785,0.000028451543],"domain_scores_gemma":[0.999582,0.00012167224,0.000127594,0.00008817443,0.000027291651,0.00005320447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019720852,0.00017061735,0.00013495493,0.00020877857,0.00011140402,0.00036823194,0.00026825513,0.00019644582,0.0007962624],"category_scores_gemma":[0.0014856672,0.00014841318,0.00013559886,0.000100832,0.0004984119,0.0004604007,0.00047325913,0.00040790852,0.00005990146],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003199954,0.00007875603,0.020363098,0.00004851972,0.00005738416,0.00022701528,0.0004943577,0.009998279,0.91549444,0.0040386897,0.00034498834,0.04853446],"study_design_scores_gemma":[0.000042012685,0.0006709416,0.60802346,0.000019307201,0.000059652873,0.0011222605,0.00057004584,0.12755106,0.22935142,0.030643284,0.0018867409,0.000059787624],"about_ca_topic_score_codex":0.0009268823,"about_ca_topic_score_gemma":0.0013233445,"teacher_disagreement_score":0.0009268823,"about_ca_system_score_codex":0.0002855751,"about_ca_system_score_gemma":0.00022083512,"threshold_uncertainty_score":0.0026637316},"labels":[],"label_agreement":null},{"id":"W4408241909","doi":"10.1109/tcyb.2025.3544257","title":"Big Data-Driven Control of Nonlinear Processes Through Dynamic Latent Variables Using an Autoencoder","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Australian Research Council","keywords":"Autoencoder; Latent variable; Big data; Computer science; Nonlinear system; Control (management); Artificial intelligence; Econometrics; Data mining; Mathematics; Artificial neural network; Physics","score_opus":0.05127373973871882,"score_gpt":0.2989443295002338,"score_spread":0.247670589761515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408241909","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008065584,0.0001678536,0.9908145,0.00012970994,0.000036501224,0.000015385061,0.00003404307,0.00018288945,0.0005535343],"genre_scores_gemma":[0.8523661,0.00045971846,0.14404191,0.00018932146,0.00008365256,0.00016509714,0.00022215338,0.00007479897,0.0023971854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995771,0.00010726052,0.00002605966,0.00013633649,0.00011482687,0.000038324186],"domain_scores_gemma":[0.99923027,0.00044240797,0.00009661337,0.000074326046,0.0001298364,0.000026597096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009552181,0.0007623121,0.00072460616,0.00025475613,0.0003081914,0.0008829428,0.00095955486,0.00061185,0.0007160256],"category_scores_gemma":[0.0021111278,0.00047552894,0.0006718167,0.00034029045,0.0009878131,0.0010864818,0.0011546953,0.0017393187,0.00013517865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006933719,0.000051116484,0.00059702975,0.0001024792,0.00008683273,0.00005877224,0.00008487337,0.9230878,0.0061945696,0.02076042,0.0005513025,0.048355438],"study_design_scores_gemma":[0.0000022139468,0.000008315181,0.00005544779,0.000003180188,0.0000033541755,0.00000307038,0.0000017800492,0.99696356,0.00052097294,0.002286818,0.00014841385,0.0000028280892],"about_ca_topic_score_codex":0.004800048,"about_ca_topic_score_gemma":0.0052153515,"teacher_disagreement_score":0.004800048,"about_ca_system_score_codex":0.00061729684,"about_ca_system_score_gemma":0.0010000551,"threshold_uncertainty_score":0.009544253},"labels":[],"label_agreement":null},{"id":"W4408295007","doi":"10.22214/ijraset.2025.67226","title":"Smart Learning Using Generative AI","year":2025,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Generative grammar; Computer science; Artificial intelligence; Psychology; Medicine","score_opus":0.056565270381461634,"score_gpt":0.41385272537357426,"score_spread":0.3572874549921126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408295007","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029974036,0.002497665,0.7241391,0.003980507,0.000695134,0.00025112057,0.00009805339,0.0027516084,0.2356128],"genre_scores_gemma":[0.58535117,0.0041489163,0.2979817,0.0021158908,0.00032171566,0.00067971135,0.00027886388,0.0005747253,0.10854731],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994677,0.00021512146,0.000021123877,0.00008624227,0.00014931727,0.000060631864],"domain_scores_gemma":[0.99921787,0.0004852624,0.000035050693,0.00013898704,0.00005249758,0.00007034251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077224564,0.00059088867,0.00032513472,0.0006043547,0.0007667928,0.0029817657,0.0012300642,0.0013524501,0.011079639],"category_scores_gemma":[0.0020767397,0.00022337078,0.0007605662,0.00040349827,0.0020537348,0.0036196196,0.0038145536,0.0013831182,0.0025235203],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072556344,0.0001885565,0.001352612,0.0007451277,0.00004960102,0.0005225731,0.0049653533,0.014557569,0.016435921,0.6769346,0.012149076,0.2720264],"study_design_scores_gemma":[0.000044273456,0.00020934541,0.0011430188,0.00055642944,0.000044373635,0.0013036026,0.0017641005,0.063953154,0.018599743,0.43090466,0.4813646,0.000112859954],"about_ca_topic_score_codex":0.00032997588,"about_ca_topic_score_gemma":0.0004579546,"teacher_disagreement_score":0.011079639,"about_ca_system_score_codex":0.0007650743,"about_ca_system_score_gemma":0.00046409958,"threshold_uncertainty_score":0.03706509},"labels":[],"label_agreement":null},{"id":"W4408353987","doi":"10.1109/icassp49660.2025.10890777","title":"Universal Training of Neural Networks to Achieve Bayes Optimal Classification Accuracy","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Arm","keywords":"Computer science; Training (meteorology); Artificial intelligence; Artificial neural network; Machine learning; Bayes' theorem; Naive Bayes classifier; Training set; Pattern recognition (psychology); Bayesian probability; Support vector machine","score_opus":0.030305674890309168,"score_gpt":0.28533491668618804,"score_spread":0.2550292417958789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408353987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043284416,0.0009850659,0.9514426,0.0006544393,0.00007954331,0.00006719921,0.00009760881,0.000872656,0.0025163863],"genre_scores_gemma":[0.81319296,0.00062350754,0.18220007,0.00062572764,0.00016731412,0.00026306647,0.0004166854,0.00025759824,0.0022530796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952259,0.0018989941,0.0003863869,0.0009359433,0.0010875096,0.00046526335],"domain_scores_gemma":[0.98709863,0.008527709,0.000889528,0.0017816359,0.001323579,0.00037896988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011644287,0.0018956519,0.0023898783,0.0014781922,0.00082403485,0.002087415,0.002364092,0.0024462743,0.0015055062],"category_scores_gemma":[0.041188616,0.00088161364,0.0010082566,0.00096036243,0.0024598164,0.004486395,0.004237424,0.0041472046,0.0006676751],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002608661,0.00018657578,0.0050109345,0.00027959343,0.00020279473,0.00016859575,0.00035539368,0.7241816,0.0060135373,0.06274221,0.0038679242,0.19672996],"study_design_scores_gemma":[0.000008005656,0.00004382717,0.00023772958,0.00003405824,0.000009422053,0.00004256464,0.0000147194605,0.9657998,0.0015355542,0.03197706,0.00028901338,0.000008159538],"about_ca_topic_score_codex":0.0022174013,"about_ca_topic_score_gemma":0.002815534,"teacher_disagreement_score":0.011644287,"about_ca_system_score_codex":0.002358665,"about_ca_system_score_gemma":0.0023222067,"threshold_uncertainty_score":0.06158161},"labels":[],"label_agreement":null},{"id":"W4408354194","doi":"10.1109/icassp49660.2025.10890471","title":"Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Artificial neural network; Theoretical computer science; Artificial intelligence; Speech recognition","score_opus":0.007790981948046948,"score_gpt":0.25787458669017516,"score_spread":0.2500836047421282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408354194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017271588,0.00031636553,0.97911316,0.00020354727,0.00005834219,0.00003074353,0.00009109319,0.0006568306,0.0022584368],"genre_scores_gemma":[0.6518245,0.0006589732,0.33487114,0.00028259872,0.000107312495,0.00023281989,0.0005954507,0.00022603499,0.011201235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996855,0.0000903435,0.000020499683,0.000072998824,0.000095223666,0.00003547433],"domain_scores_gemma":[0.99928516,0.00032107875,0.000089584464,0.00012981924,0.00013756467,0.00003681953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006355667,0.00068656064,0.00061020604,0.0005523217,0.0002934394,0.0009978395,0.0014010072,0.0010627821,0.0025860858],"category_scores_gemma":[0.002923458,0.0004249553,0.0006533748,0.0005844535,0.0006810098,0.002111152,0.0014140239,0.0015651502,0.0009067199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013431293,0.000062084204,0.0007218482,0.00009251877,0.00005421095,0.000090600646,0.00009617549,0.71591777,0.0064950855,0.04394068,0.0027075969,0.2296872],"study_design_scores_gemma":[0.0000023810494,0.000009537723,0.000028787423,0.000003324793,0.000002352775,0.000010312894,0.0000035386565,0.9944728,0.0004910437,0.0046840594,0.0002880306,0.0000037729023],"about_ca_topic_score_codex":0.0031590865,"about_ca_topic_score_gemma":0.0042114104,"teacher_disagreement_score":0.0031590865,"about_ca_system_score_codex":0.0006946774,"about_ca_system_score_gemma":0.0007162279,"threshold_uncertainty_score":0.008651376},"labels":[],"label_agreement":null},{"id":"W4408355458","doi":"10.1109/icassp49660.2025.10889610","title":"Explicit Spatial Hint and Implicit Logits Relation: Distilling Heterogeneous Knowledge From Vision Transformer to CNN","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Research and Development; National Natural Science Foundation of China","keywords":"Computer science; Relation (database); Transformer; Artificial intelligence; Spatial relation; Computer vision; Data mining; Engineering; Electrical engineering; Voltage","score_opus":0.011387343925040967,"score_gpt":0.2775094681271086,"score_spread":0.2661221242020676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408355458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05596551,0.0004261027,0.93846464,0.0002831896,0.00005675699,0.000070055656,0.00017195578,0.002433824,0.0021279934],"genre_scores_gemma":[0.83582914,0.00031867903,0.15849017,0.00033715885,0.000050947718,0.000094940086,0.0005044149,0.00018567672,0.0041887914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963164,0.000053985346,0.000024899839,0.00013649797,0.00009584087,0.000057160087],"domain_scores_gemma":[0.99948007,0.00014940146,0.00007009748,0.00015719226,0.00009464618,0.000048728878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000813396,0.0012257812,0.00085159845,0.00067732914,0.00032448937,0.00094882015,0.0018931015,0.0008698169,0.002449318],"category_scores_gemma":[0.002781462,0.0004028785,0.0006819954,0.0007620317,0.00082015514,0.003212698,0.0026482604,0.0017994284,0.0005782945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039538427,0.00021386129,0.002465943,0.00022542437,0.00010878286,0.00025397964,0.00019371124,0.298703,0.037287686,0.030759344,0.00455879,0.62483406],"study_design_scores_gemma":[0.00001556279,0.00006641685,0.00023621018,0.000011856857,0.000023069359,0.000052209394,0.000020719133,0.9761762,0.0101813385,0.012282467,0.00092192064,0.000012044804],"about_ca_topic_score_codex":0.004041915,"about_ca_topic_score_gemma":0.0048258705,"teacher_disagreement_score":0.004041915,"about_ca_system_score_codex":0.00076861476,"about_ca_system_score_gemma":0.0010775246,"threshold_uncertainty_score":0.008193731},"labels":[],"label_agreement":null},{"id":"W4408400976","doi":"10.1109/ictbig64922.2024.10911872","title":"Enhancing the Efficiency of Convolutional Neural Networks through Quantization","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Convolutional neural network; Quantization (signal processing); Computer science; Artificial intelligence; Computer vision","score_opus":0.016460429718471777,"score_gpt":0.26409828609705255,"score_spread":0.24763785637858077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408400976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10945072,0.002355745,0.8754986,0.0011734999,0.00017112827,0.00007886474,0.00017765952,0.002743872,0.008349915],"genre_scores_gemma":[0.8101382,0.0010037973,0.18442897,0.00030891885,0.0000668794,0.00008381456,0.00025031378,0.00022210646,0.0034969272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955875,0.000087566514,0.000037976537,0.000080719,0.00018101359,0.00005390044],"domain_scores_gemma":[0.9985771,0.0006962729,0.00014148773,0.00028680582,0.00026107495,0.000037159687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000998449,0.00075822626,0.000611892,0.00046269022,0.00029576896,0.001039122,0.0015081133,0.0006679129,0.0025934402],"category_scores_gemma":[0.0063275243,0.00030000138,0.0003005258,0.00060812983,0.00067867804,0.0027657028,0.0010961369,0.0011808341,0.0007377836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026672546,0.0001831808,0.0021518709,0.00031657552,0.00007675465,0.00015161332,0.00015027702,0.58299726,0.034735795,0.0464612,0.0055159307,0.32699284],"study_design_scores_gemma":[0.0000131486795,0.00004045901,0.00016861541,0.000021763955,0.000011824149,0.000028938726,0.000015919237,0.97853106,0.009133019,0.010625409,0.0014027071,0.000007155805],"about_ca_topic_score_codex":0.0040387604,"about_ca_topic_score_gemma":0.0059241825,"teacher_disagreement_score":0.0040387604,"about_ca_system_score_codex":0.0009540352,"about_ca_system_score_gemma":0.0008909934,"threshold_uncertainty_score":0.008675873},"labels":[],"label_agreement":null},{"id":"W4408428670","doi":"10.5194/egusphere-egu25-13722","title":"Using convolutional neural networks to classify unexploded ordnance from multicomponent electromagnetic induction data &amp;#160;","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Unexploded ordnance; Convolutional neural network; Electromagnetic induction; Artificial intelligence; Pattern recognition (psychology); Computer science; Artificial neural network; Machine learning; Engineering; Remote sensing; Geology; Electrical engineering","score_opus":0.1645093363069872,"score_gpt":0.3459184344335812,"score_spread":0.18140909812659403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408428670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44531783,0.0009174077,0.5355862,0.00084557565,0.0003133058,0.000111439906,0.00086836383,0.004827095,0.01121277],"genre_scores_gemma":[0.78321993,0.00039884652,0.20191595,0.00041977124,0.00007643946,0.000073643445,0.002277166,0.00017009678,0.011448143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983513,0.000014726034,0.000006572576,0.000049117454,0.00005153477,0.000042911743],"domain_scores_gemma":[0.99965096,0.00011077563,0.00004631823,0.000040636467,0.00012832844,0.000022919263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039641684,0.00087923044,0.00026355425,0.0006854737,0.00022435901,0.0007235487,0.00088580494,0.0006968055,0.001242125],"category_scores_gemma":[0.0010209933,0.00030007976,0.00039416185,0.0004943671,0.00034944012,0.0008057327,0.000517214,0.000702856,0.00056703214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035159857,0.00029843755,0.00838482,0.00011082517,0.00013631754,0.00037941025,0.00011214502,0.40579844,0.09062219,0.0030127387,0.0066559343,0.48413724],"study_design_scores_gemma":[0.0000035804903,0.000027144919,0.00095479196,0.000006374288,0.000009823881,0.000019155326,0.000013289491,0.9865479,0.010916725,0.00072894024,0.00076517474,0.0000069769117],"about_ca_topic_score_codex":0.010842274,"about_ca_topic_score_gemma":0.014888253,"teacher_disagreement_score":0.010842274,"about_ca_system_score_codex":0.0008214947,"about_ca_system_score_gemma":0.00048473934,"threshold_uncertainty_score":0.021558285},"labels":[],"label_agreement":null},{"id":"W4408482809","doi":"10.1016/j.spa.2025.104631","title":"Asymptotics for irregularly observed long memory processes","year":2025,"lang":"en","type":"article","venue":"Stochastic Processes and their Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Mathematics; Long memory; Statistical physics; Econometrics","score_opus":0.019128884484681214,"score_gpt":0.2503635088769889,"score_spread":0.23123462439230766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408482809","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27204973,0.0060101235,0.6909422,0.004919909,0.00067048956,0.00006903677,0.0003966152,0.001002211,0.023939716],"genre_scores_gemma":[0.95815384,0.0034305423,0.020302428,0.0005352053,0.0011016798,0.0001542541,0.0004390466,0.00037060032,0.015512426],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988193,0.00040517695,0.000082252656,0.00019248814,0.00034881834,0.00015189673],"domain_scores_gemma":[0.970659,0.020677146,0.003142277,0.0016538483,0.0026974473,0.001170306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006411213,0.0012307644,0.0018732767,0.003282005,0.00095702265,0.003213602,0.0033350394,0.0027789462,0.0060968944],"category_scores_gemma":[0.05373632,0.0010581525,0.0012499153,0.0018814935,0.0053116935,0.0071781185,0.0037075507,0.004269011,0.00059364055],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008054301,0.00006150902,0.001991267,0.0002240434,0.000072862254,0.0003090348,0.00031550677,0.076966725,0.0020428437,0.90703547,0.0018110629,0.009089114],"study_design_scores_gemma":[0.000019119605,0.000019260633,0.0010001657,0.00004562034,0.000020715419,0.00012614392,0.00005640704,0.5304367,0.00027456338,0.46737227,0.0005959131,0.00003313195],"about_ca_topic_score_codex":0.0032334866,"about_ca_topic_score_gemma":0.0022262237,"teacher_disagreement_score":0.006411213,"about_ca_system_score_codex":0.0022343528,"about_ca_system_score_gemma":0.0012193188,"threshold_uncertainty_score":0.033906102},"labels":[],"label_agreement":null},{"id":"W4408483837","doi":"10.5194/egusphere-egu25-21430","title":"Optimizing the revisiting frequency of remotely sensed thermal observations for continuous estimation of ecosystem evapotranspiration and productivity using Bayesian inference","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Evapotranspiration; Inference; Productivity; Bayesian probability; Environmental science; Estimation; Bayesian inference; Ecosystem; Remote sensing; Computer science; Artificial intelligence; Geography; Ecology; Engineering; Economics","score_opus":0.06224423002692685,"score_gpt":0.30181684136688114,"score_spread":0.23957261133995428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408483837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14808467,0.0005130725,0.8485861,0.00021987504,0.000039853443,0.00006291822,0.0003011564,0.00070248044,0.0014898153],"genre_scores_gemma":[0.80172175,0.00024092387,0.1963155,0.000087097374,0.00005693977,0.00013717507,0.00075421546,0.000105739484,0.0005806464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944705,0.00018772739,0.00004513508,0.00016337172,0.00009546707,0.00006129052],"domain_scores_gemma":[0.9964594,0.0024339375,0.00037536718,0.00025637468,0.00039446147,0.00008042268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023671587,0.000738822,0.00072706037,0.00068906235,0.00037356262,0.00064747786,0.001120308,0.0006563841,0.0011239713],"category_scores_gemma":[0.009543034,0.0007137232,0.00069054833,0.0006582368,0.00034035812,0.0012039532,0.0006778955,0.0011496793,0.00043802554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026180188,0.00016841188,0.011892576,0.00012491389,0.0001430916,0.000046469577,0.0001163088,0.82033277,0.006872538,0.0017052868,0.0009161842,0.15741973],"study_design_scores_gemma":[0.00002063359,0.0000136430435,0.0020250273,0.000013537569,0.000014187901,0.000009339093,0.000009942834,0.99586135,0.00096534233,0.00088638195,0.0001724663,0.000008144551],"about_ca_topic_score_codex":0.023164732,"about_ca_topic_score_gemma":0.025665233,"teacher_disagreement_score":0.023164732,"about_ca_system_score_codex":0.00065049034,"about_ca_system_score_gemma":0.001574218,"threshold_uncertainty_score":0.046059787},"labels":[],"label_agreement":null},{"id":"W4408591874","doi":"10.1016/j.jspi.2025.106291","title":"Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent","year":2025,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Mathematics; Gradient descent; Rate of convergence; Classifier (UML); Convolutional neural network; Stochastic gradient descent; Artificial intelligence; Pattern recognition (psychology); Convergence (economics); Artificial neural network; Applied mathematics; Computer science","score_opus":0.02429904016378327,"score_gpt":0.31943546584970856,"score_spread":0.2951364256859253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408591874","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5133089,0.0026052846,0.47604626,0.0015028317,0.00018601818,0.00014072032,0.000530814,0.0018595742,0.003819554],"genre_scores_gemma":[0.9352433,0.0004197321,0.059450973,0.00016310415,0.000055385062,0.000110232206,0.0008484427,0.00062268524,0.0030860668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984622,0.00063044165,0.000089687135,0.00030076446,0.0003661469,0.00015072689],"domain_scores_gemma":[0.9565279,0.036085013,0.0014853578,0.0019673551,0.0033970552,0.00053737377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007635107,0.00092423277,0.0010990283,0.00086891855,0.0006160021,0.0012773648,0.0016067948,0.0019540566,0.002275105],"category_scores_gemma":[0.058170807,0.00058957335,0.00060139125,0.00068615447,0.0012372396,0.0022912605,0.0013635143,0.0022896228,0.00041416567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015082546,0.00015333736,0.011698011,0.00030871958,0.00022684736,0.00025373927,0.00018097216,0.889227,0.0103725605,0.015573336,0.002876887,0.06762027],"study_design_scores_gemma":[0.000008806641,0.000042761065,0.0008256884,0.000010124141,0.000011372689,0.000038368333,0.000012040029,0.996161,0.0016957488,0.0010832035,0.00010343685,0.0000074459263],"about_ca_topic_score_codex":0.008562531,"about_ca_topic_score_gemma":0.00539995,"teacher_disagreement_score":0.008562531,"about_ca_system_score_codex":0.002051132,"about_ca_system_score_gemma":0.0018310577,"threshold_uncertainty_score":0.04037875},"labels":[],"label_agreement":null},{"id":"W4408607526","doi":"10.1101/2025.03.17.643652","title":"Overcoming sensory-memory interference in working memory circuits","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Sensory memory; Sensory system; Computer science; Interference (communication); Memory refresh; Electronic circuit; Interference theory; Working memory; Cognitive psychology; Neuroscience; Psychology; Computer hardware; Semiconductor memory; Electrical engineering; Telecommunications; Computer memory; Engineering; Cognition","score_opus":0.026452407133078927,"score_gpt":0.23872630077813495,"score_spread":0.21227389364505603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408607526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8752463,0.00011342243,0.12295558,0.000109848006,0.000013824759,0.000007465412,0.0000147158735,0.00025070185,0.0012881987],"genre_scores_gemma":[0.9930375,0.000019854308,0.0066507882,0.00001358491,0.0000025002337,0.0000046274267,0.000008913731,0.000011258786,0.00025099382],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998956,0.000025633144,0.0000073750684,0.000022747596,0.0000227852,0.000025832513],"domain_scores_gemma":[0.9996019,0.0001364861,0.000091333706,0.00008518037,0.000046242385,0.000038943672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037450704,0.0002968733,0.00023492295,0.00014548212,0.00014796016,0.00048747478,0.0005778816,0.00034903173,0.00074763596],"category_scores_gemma":[0.0011598262,0.00017010272,0.00027888393,0.00008864351,0.0006113732,0.00083579955,0.00059129257,0.00040282682,0.00008168484],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026674123,0.0001021079,0.003819358,0.000093244584,0.00011982814,0.0002519768,0.00015499639,0.16336337,0.7701103,0.020724231,0.00025163722,0.04074219],"study_design_scores_gemma":[0.000025347237,0.00020270338,0.0021848103,0.00000775534,0.00002988515,0.00010611881,0.000026162274,0.8675245,0.1161637,0.013213749,0.0005028002,0.000012439179],"about_ca_topic_score_codex":0.00044368726,"about_ca_topic_score_gemma":0.0005038653,"teacher_disagreement_score":0.00074763596,"about_ca_system_score_codex":0.00032403183,"about_ca_system_score_gemma":0.0001973146,"threshold_uncertainty_score":0.00250113},"labels":[],"label_agreement":null},{"id":"W4408802802","doi":"10.1101/2025.03.20.644276","title":"MEEGNet: An open source python library for the application of convolutional neural networks to MEG","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; Institut Universitaire de Gériatrie de Montréal; Université de Montréal","funders":"","keywords":"Python (programming language); Convolutional neural network; Computer science; Open source; Artificial intelligence; Programming language; Software","score_opus":0.01852737366774025,"score_gpt":0.25069387688265743,"score_spread":0.2321665032149172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408802802","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008343861,0.0004157396,0.51276106,0.0005165858,0.00027189724,0.0003455736,0.023807295,0.44542673,0.008111184],"genre_scores_gemma":[0.16041148,0.0015134718,0.6297182,0.0021452997,0.00020668247,0.0038366043,0.054016653,0.11747927,0.030672446],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996908,0.000044594548,0.000028840333,0.00007918528,0.00010820051,0.00004842687],"domain_scores_gemma":[0.9994764,0.00020407152,0.00005894621,0.00008877749,0.00011185919,0.00005990224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008927062,0.0017304107,0.00067130534,0.00088107167,0.0004104536,0.00095683296,0.0031263668,0.00074231863,0.04937381],"category_scores_gemma":[0.0030510807,0.00087283313,0.0011952439,0.00061462045,0.0006317523,0.0014338633,0.0022589616,0.0020041114,0.017825639],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010428412,0.00036694837,0.0059825857,0.002972551,0.00066461065,0.0012042195,0.0004734384,0.08558126,0.031019863,0.032653898,0.55194306,0.28609487],"study_design_scores_gemma":[0.00039560397,0.00016389965,0.0048975307,0.0003384295,0.00009889652,0.0006753276,0.000065932014,0.6706675,0.04702024,0.05011822,0.22534122,0.00021721647],"about_ca_topic_score_codex":0.0039177886,"about_ca_topic_score_gemma":0.006149479,"teacher_disagreement_score":0.04937381,"about_ca_system_score_codex":0.00067033933,"about_ca_system_score_gemma":0.0019946091,"threshold_uncertainty_score":0.16517186},"labels":[],"label_agreement":null},{"id":"W4408861654","doi":"10.1109/dikw64278.2024.00018","title":"Spike Frequency Adaptation for A Novel Logistic Spiking Neuron Model","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Spike (software development); Computer science; Biological neuron model; Adaptation (eye); Spiking neural network; Artificial intelligence; Neuroscience; Artificial neural network; Psychology","score_opus":0.11845818873062289,"score_gpt":0.31194206031552785,"score_spread":0.19348387158490496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408861654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19773938,0.0006484572,0.7763132,0.00079132745,0.00008479084,0.000086774606,0.00021288032,0.0002540594,0.023869138],"genre_scores_gemma":[0.9778807,0.0003369734,0.015228237,0.00006974296,0.000020236093,0.00010709948,0.0000578389,0.000022192511,0.006276947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999095,0.000016492724,0.0000051518923,0.000025964144,0.00002979772,0.0000130946955],"domain_scores_gemma":[0.9998963,0.000028749837,0.000028361042,0.000009143403,0.000027524822,0.000009959821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019794493,0.00030061233,0.00030924333,0.00017901756,0.00023450065,0.0003706545,0.0007104737,0.00064133905,0.0012809132],"category_scores_gemma":[0.0005549029,0.000131684,0.00047076607,0.00021265283,0.00035098608,0.0003891419,0.0004892507,0.00052028784,0.0002128985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106915344,0.000043119297,0.0016934834,0.00012742862,0.000038529335,0.00042781842,0.00023028944,0.87376946,0.053039413,0.053586915,0.0010338328,0.015902797],"study_design_scores_gemma":[0.0000036539925,0.000009197331,0.000104142564,0.0000023129576,0.0000033114052,0.00003294809,0.0000047545964,0.99714357,0.00045731966,0.0019571057,0.00027779612,0.0000038628136],"about_ca_topic_score_codex":0.0030327372,"about_ca_topic_score_gemma":0.0016843821,"teacher_disagreement_score":0.0030327372,"about_ca_system_score_codex":0.00050909363,"about_ca_system_score_gemma":0.00042998593,"threshold_uncertainty_score":0.006030202},"labels":[],"label_agreement":null},{"id":"W4409013456","doi":"10.1007/978-3-031-85628-0_1","title":"High-Precision Method to Reduce Overfitting in ANNs Using Highly Uniform Chaotic Sequences","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Overfitting; Chaotic; Computer science; Pattern recognition (psychology); Artificial intelligence; Biological system; Algorithm; Artificial neural network; Biology","score_opus":0.05721733729900588,"score_gpt":0.35056691635209053,"score_spread":0.2933495790530847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409013456","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012378236,0.00029589137,0.9845159,0.00006120452,0.000094970055,0.000025635985,0.00003965512,0.0009107414,0.0016777301],"genre_scores_gemma":[0.22674814,0.00025191117,0.7640123,0.0001353675,0.000057926918,0.00009691954,0.00020384463,0.00029371967,0.008199977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996824,0.00007076346,0.000029900935,0.00007486595,0.000116668336,0.000025396352],"domain_scores_gemma":[0.9989396,0.00049953855,0.00006342709,0.00018609752,0.00028944327,0.000021826698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007802991,0.0006868185,0.0005319077,0.00047326207,0.00046327876,0.0007123744,0.000785401,0.00089988793,0.003545287],"category_scores_gemma":[0.0026287895,0.0004037957,0.0003919306,0.0005690801,0.00029404458,0.00069438916,0.0006743248,0.0012579376,0.00081759423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019622224,0.00008842945,0.0009796929,0.00023506732,0.0001239038,0.00016325583,0.00015502574,0.33024895,0.030415399,0.010104925,0.00484544,0.62244374],"study_design_scores_gemma":[0.000009784759,0.000036588102,0.00032501583,0.000015610478,0.000016421478,0.00006191062,0.000009611221,0.9866231,0.009254094,0.0019264643,0.0017129958,0.000008368789],"about_ca_topic_score_codex":0.0018729497,"about_ca_topic_score_gemma":0.0035731632,"teacher_disagreement_score":0.003545287,"about_ca_system_score_codex":0.00034680366,"about_ca_system_score_gemma":0.000498634,"threshold_uncertainty_score":0.011860132},"labels":[],"label_agreement":null},{"id":"W4409014815","doi":"10.1109/tvt.2025.3556459","title":"DeCo-MeSC: Deep Compression-Based Memory-Constrained Split Computing Framework for Cooperative Inference of Neural Network","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Ministry of Science and ICT, South Korea","keywords":"Inference; Artificial neural network; Computer science; Data compression; Compression (physics); Deep neural networks; Artificial intelligence","score_opus":0.012004639673184419,"score_gpt":0.2852749304212395,"score_spread":0.2732702907480551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409014815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015295081,0.00037890536,0.9814184,0.00013113354,0.000041266772,0.00006137313,0.000083828665,0.0009493051,0.0016407245],"genre_scores_gemma":[0.52867556,0.00037397668,0.46453783,0.00034339615,0.00007807916,0.00026483912,0.0005042812,0.00017342769,0.0050484994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967885,0.000053716627,0.0000166649,0.000071741764,0.00012667879,0.00005230571],"domain_scores_gemma":[0.99963295,0.000099083634,0.000027570635,0.000103419916,0.00009916984,0.000037842983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075561047,0.000795576,0.00073489913,0.00047753943,0.00053842575,0.0006529083,0.002683395,0.00096171803,0.0021263538],"category_scores_gemma":[0.0013542507,0.00027935204,0.00041684383,0.0005429824,0.00080820837,0.00207882,0.0016466375,0.0013488111,0.00039244862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004399424,0.00023615596,0.001037494,0.00014189581,0.000111039415,0.00022546282,0.00019025969,0.47584167,0.031093065,0.051388897,0.007967433,0.43132663],"study_design_scores_gemma":[0.000010681375,0.00003235357,0.000058516693,0.000003391743,0.000005949641,0.00002221007,0.000009515599,0.99059474,0.0041577355,0.004276115,0.00082232896,0.000006470814],"about_ca_topic_score_codex":0.0072810287,"about_ca_topic_score_gemma":0.016238276,"teacher_disagreement_score":0.0072810287,"about_ca_system_score_codex":0.00095929933,"about_ca_system_score_gemma":0.0017909288,"threshold_uncertainty_score":0.014477313},"labels":[],"label_agreement":null},{"id":"W4409112215","doi":"10.2139/ssrn.5203127","title":"Relaxing the Applicability Conditions for Incremental Nonlinear Dynamics Inversion","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Inversion (geology); Nonlinear system; Geology; Dynamics (music); Computer science; Statistical physics; Geomorphology; Physics","score_opus":0.010740823272851096,"score_gpt":0.2739632885548711,"score_spread":0.26322246528202004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409112215","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023652146,0.00031993963,0.949088,0.001280907,0.00029235412,0.00012606438,0.00025466672,0.00020880639,0.024777148],"genre_scores_gemma":[0.76796126,0.0007887479,0.20945163,0.001013313,0.0007814863,0.0004962758,0.0006099731,0.0005114458,0.018385923],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990957,0.00031722526,0.00006394564,0.00018923012,0.00021385276,0.00012003217],"domain_scores_gemma":[0.99423605,0.0040986934,0.00025755996,0.0006054366,0.00063158287,0.00017067355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024664525,0.00092553836,0.0007231137,0.0007150164,0.0006166938,0.0015247217,0.0014452663,0.0015395544,0.013750473],"category_scores_gemma":[0.017215407,0.00044648428,0.00072057295,0.00043617556,0.0019321312,0.0039634653,0.004999089,0.003948001,0.0018978781],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031059317,0.00010419594,0.0010601135,0.0006155099,0.00006735696,0.0007618833,0.0004683698,0.14166284,0.021551222,0.738443,0.006855648,0.08809919],"study_design_scores_gemma":[0.00003204035,0.0000714331,0.00022390486,0.0000615348,0.000014895314,0.00016237173,0.00009856894,0.7255512,0.005804209,0.26311737,0.004840062,0.000022447473],"about_ca_topic_score_codex":0.0013522769,"about_ca_topic_score_gemma":0.0014397133,"teacher_disagreement_score":0.013750473,"about_ca_system_score_codex":0.00039738129,"about_ca_system_score_gemma":0.0010817411,"threshold_uncertainty_score":0.045999885},"labels":[],"label_agreement":null},{"id":"W4409133367","doi":"10.1109/hpec62836.2024.10938473","title":"HENNC: Hardware Engine for Artificial Neural Network-Based Chaotic Oscillators","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Institut National de la Recherche Scientifique; Polytechnique Montréal","funders":"","keywords":"Artificial neural network; Computer science; Chaotic; Physical neural network; Computer hardware; Artificial intelligence; Time delay neural network; Types of artificial neural networks","score_opus":0.024610176851350786,"score_gpt":0.26018087356124586,"score_spread":0.23557069670989508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409133367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011646795,0.00045614806,0.92557126,0.00012882995,0.00012552174,0.000244033,0.0009830127,0.04957825,0.011266219],"genre_scores_gemma":[0.1800392,0.00054718845,0.7981552,0.00020967048,0.000050803446,0.00062272843,0.004032759,0.004956746,0.011385707],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996087,0.000053795186,0.000036116588,0.00007377184,0.0001860688,0.00004159992],"domain_scores_gemma":[0.999602,0.0001468808,0.000031456475,0.000089498,0.000113781825,0.000016308311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006440086,0.00088246336,0.00048384396,0.0006501513,0.0003601743,0.00081182184,0.0020860278,0.0005742862,0.009597053],"category_scores_gemma":[0.0016200953,0.00046550564,0.00079928426,0.00031871372,0.00048598475,0.0010158955,0.0007993147,0.0008553731,0.002860194],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000666933,0.00022728353,0.0031066996,0.0018506254,0.00022491362,0.0007216391,0.00041940526,0.3620889,0.08432822,0.10838675,0.050713327,0.38726538],"study_design_scores_gemma":[0.00009589404,0.00013967558,0.00049821055,0.000103159386,0.00005486989,0.00026641323,0.000031777585,0.84713256,0.063537404,0.015873626,0.07221459,0.000051784165],"about_ca_topic_score_codex":0.0035507104,"about_ca_topic_score_gemma":0.0058432505,"teacher_disagreement_score":0.009597053,"about_ca_system_score_codex":0.0009237551,"about_ca_system_score_gemma":0.0012073254,"threshold_uncertainty_score":0.032105386},"labels":[],"label_agreement":null},{"id":"W4409136717","doi":"10.1088/2632-2153/adc8fc","title":"Wilsonian renormalization of neural network Gaussian processes<sup>*</sup>","year":2025,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute","funders":"Institut Périmètre de physique théorique; Israel Science Foundation; Gordon and Betty Moore Foundation; National Science Foundation","keywords":"Renormalization; Statistical physics; Artificial neural network; Gaussian; Physics; Mathematical physics; Computer science; Artificial intelligence; Quantum mechanics","score_opus":0.004768710325753749,"score_gpt":0.23745112930346415,"score_spread":0.23268241897771041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409136717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24182022,0.0006643126,0.71844304,0.002878046,0.0003529349,0.000059642913,0.00019128462,0.0011809777,0.03440952],"genre_scores_gemma":[0.93937254,0.00043494624,0.049553204,0.00051738805,0.00025495075,0.0000989348,0.00014225309,0.00051651365,0.009109339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956554,0.00019270416,0.000015153829,0.00005656454,0.000107901746,0.0000620372],"domain_scores_gemma":[0.9976311,0.0012783034,0.00029174198,0.00032385878,0.00027485957,0.00020012465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016160812,0.0004659924,0.00053329975,0.00076030643,0.0005564812,0.0011209955,0.0009778991,0.0009851439,0.00409644],"category_scores_gemma":[0.009018328,0.00028337364,0.00064273516,0.00039689857,0.002349993,0.0019519746,0.001120839,0.0015102165,0.0005130615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055452227,0.000030562558,0.000752533,0.00005881701,0.00002215346,0.00014537429,0.000107480955,0.07592673,0.0029023304,0.90740883,0.002766452,0.009823262],"study_design_scores_gemma":[0.0000061369597,0.0000123069785,0.00033258632,0.000014514447,0.00000450657,0.000036413185,0.000013089296,0.6475957,0.00076608005,0.35031968,0.0008868642,0.000012010313],"about_ca_topic_score_codex":0.0017680075,"about_ca_topic_score_gemma":0.0016485889,"teacher_disagreement_score":0.00409644,"about_ca_system_score_codex":0.0008974864,"about_ca_system_score_gemma":0.0007187065,"threshold_uncertainty_score":0.013704002},"labels":[],"label_agreement":null},{"id":"W4409152107","doi":"10.1117/12.3047029","title":"A diffusion model-based self-explainable generative classifier for retinal image analysis","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Generative grammar; Classifier (UML); Pattern recognition (psychology); Generative model; Computer vision","score_opus":0.011735838466892554,"score_gpt":0.27132638878593657,"score_spread":0.259590550319044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409152107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056772493,0.0015035947,0.9375189,0.0008167761,0.00011636028,0.000059476373,0.00027635763,0.001409797,0.0015263135],"genre_scores_gemma":[0.8596359,0.00071291154,0.13304856,0.00060128997,0.00017672607,0.00010808829,0.00081395387,0.00019613406,0.0047064866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996377,0.00007732324,0.000020413725,0.00011790633,0.00008550891,0.00006109933],"domain_scores_gemma":[0.99882835,0.00071123824,0.00012398874,0.000098593446,0.0001793009,0.000058585403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009132156,0.000679873,0.0010019041,0.0010790619,0.0003734085,0.0009226609,0.0013386135,0.0013466098,0.0010324853],"category_scores_gemma":[0.0026299737,0.00042003713,0.0015472649,0.0005849715,0.0006093546,0.0008273011,0.0007698947,0.0016982438,0.00045802485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000187102,0.00012250314,0.0055844095,0.00010053192,0.00017482287,0.00026918264,0.00016681843,0.7035302,0.012242442,0.01248419,0.00463049,0.26050726],"study_design_scores_gemma":[0.0000021676808,0.000006807637,0.00014053889,0.000003105764,0.0000060884504,0.000022788283,0.0000017972812,0.9979577,0.00040939733,0.0013073822,0.00013884607,0.000003465563],"about_ca_topic_score_codex":0.009706253,"about_ca_topic_score_gemma":0.008231258,"teacher_disagreement_score":0.009706253,"about_ca_system_score_codex":0.0013011383,"about_ca_system_score_gemma":0.00066451856,"threshold_uncertainty_score":0.019299507},"labels":[],"label_agreement":null},{"id":"W4409156079","doi":"10.1109/ciss64860.2025.10944754","title":"Minimizing Inconsistency in Pairwise Comparison Matrices Using Genetic Algorithm","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Pairwise comparison; Computer science; Algorithm; Genetic algorithm; Artificial intelligence; Machine learning","score_opus":0.02418742897281829,"score_gpt":0.2995795232826228,"score_spread":0.2753920943098045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409156079","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010220854,0.00010381274,0.98873645,0.00006807751,0.000025078014,0.00006635687,0.000017132279,0.00031019133,0.00045207827],"genre_scores_gemma":[0.16001065,0.00008546377,0.8386482,0.000094535295,0.000032688127,0.00021279675,0.00012541676,0.00017596153,0.0006143834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99215007,0.0029973893,0.00039172947,0.0014393987,0.0026826204,0.000338704],"domain_scores_gemma":[0.98334205,0.009893848,0.0018883675,0.0015001118,0.0030769024,0.00029877288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007702678,0.0016373121,0.0017996142,0.003604103,0.0016164451,0.0016216672,0.0033373383,0.001648013,0.0014766374],"category_scores_gemma":[0.032047916,0.0009110282,0.001350533,0.0030066965,0.001742476,0.0027032811,0.0027080162,0.0024235558,0.00038887767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022667546,0.00018241844,0.004008964,0.00026779048,0.00039567202,0.00032160457,0.00063649745,0.6129257,0.013544443,0.031299297,0.0019460159,0.3342449],"study_design_scores_gemma":[0.000049875147,0.00012196215,0.00059425033,0.000028167824,0.000077716984,0.00017069103,0.00009094549,0.95963204,0.005857654,0.03170503,0.0016327087,0.00003897267],"about_ca_topic_score_codex":0.004838246,"about_ca_topic_score_gemma":0.0041419007,"teacher_disagreement_score":0.007702678,"about_ca_system_score_codex":0.0014941486,"about_ca_system_score_gemma":0.0027723943,"threshold_uncertainty_score":0.04073614},"labels":[],"label_agreement":null},{"id":"W4409216326","doi":"10.1016/j.aej.2025.03.115","title":"DSPCI-MTL: Dynamic split point computing in multi-task learning implementation with collaborative intelligence","year":2025,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Task (project management); Computer science; Point (geometry); Artificial intelligence; Human–computer interaction; Engineering; Mathematics; Systems engineering","score_opus":0.006384424211204054,"score_gpt":0.27864922981208873,"score_spread":0.2722648056008847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409216326","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023692766,0.00028081704,0.96940774,0.00022095145,0.00009000764,0.00012191885,0.000048817397,0.0028434452,0.003293665],"genre_scores_gemma":[0.62493855,0.00016514739,0.37030107,0.00037284993,0.00006332295,0.0003303647,0.00026794872,0.00017561263,0.0033851385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994715,0.000077381635,0.000029204233,0.00012586084,0.00019845567,0.000097647586],"domain_scores_gemma":[0.9996,0.00009786451,0.00003865793,0.00009406101,0.000109144836,0.000060314836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008169868,0.00074638915,0.0006178301,0.00041441506,0.0004984869,0.00085187564,0.002657229,0.00095735,0.0032896805],"category_scores_gemma":[0.0016309726,0.00031052053,0.0004946591,0.00049931294,0.0005935056,0.001499348,0.0015922269,0.0016483042,0.0009639702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041635684,0.0005860906,0.0017367743,0.00016586369,0.00009597331,0.00026264694,0.0001981924,0.3839501,0.020259688,0.009706707,0.00811259,0.57450897],"study_design_scores_gemma":[0.000017895849,0.00006169463,0.00010450793,0.000003137812,0.000004230957,0.00001819272,0.000009805378,0.9954204,0.0024780591,0.0012082142,0.00066838344,0.0000054369116],"about_ca_topic_score_codex":0.0059081954,"about_ca_topic_score_gemma":0.007290607,"teacher_disagreement_score":0.0059081954,"about_ca_system_score_codex":0.0009891094,"about_ca_system_score_gemma":0.0014295882,"threshold_uncertainty_score":0.011747599},"labels":[],"label_agreement":null},{"id":"W4409217496","doi":"10.2139/ssrn.5200864","title":"Ordinary Least Squares as an Attention Mechanism","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Mechanism (biology); Ordinary least squares; Econometrics; Psychology; Economics; Mathematics; Epistemology; Philosophy","score_opus":0.01082456392695959,"score_gpt":0.27292091738777063,"score_spread":0.26209635346081106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409217496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012233341,0.0003869955,0.9824426,0.00060489215,0.00019137579,0.000026519938,0.00004793401,0.00062548683,0.0034409],"genre_scores_gemma":[0.5929076,0.0006439609,0.37376606,0.0010460629,0.00063230225,0.00011702658,0.00017159684,0.0003871999,0.03032818],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99903524,0.00039592947,0.000045061526,0.00024215749,0.00018879074,0.00009292443],"domain_scores_gemma":[0.99758816,0.0014558301,0.00012213149,0.0003458005,0.00041897726,0.00006908535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020338055,0.0005856859,0.0007336655,0.00056447374,0.00039861337,0.0011014674,0.0017201926,0.0016524099,0.007479799],"category_scores_gemma":[0.008959582,0.0005333783,0.0005951937,0.0010100055,0.0006173295,0.0021739223,0.0020348886,0.0015762944,0.0012263843],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004771427,0.00021561318,0.0012629749,0.0002874312,0.0002636689,0.0002316375,0.00019764694,0.14489943,0.019913306,0.24896279,0.016379425,0.5669089],"study_design_scores_gemma":[0.000023110786,0.00004658287,0.00037975385,0.000011335074,0.0000318551,0.00005444365,0.0000131310835,0.9170286,0.00333462,0.076904185,0.0021566793,0.000015676034],"about_ca_topic_score_codex":0.002416759,"about_ca_topic_score_gemma":0.0026557143,"teacher_disagreement_score":0.007479799,"about_ca_system_score_codex":0.00043466492,"about_ca_system_score_gemma":0.0007672543,"threshold_uncertainty_score":0.025022388},"labels":[],"label_agreement":null},{"id":"W4409261978","doi":"10.1109/wacv61041.2025.00722","title":"Learning Instance-Specific Parameters of Black-Box Models Using Differentiable Surrogates","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Black box; Differentiable function; Computer science; Artificial intelligence; Mathematics","score_opus":0.03640921672637185,"score_gpt":0.25468503080620547,"score_spread":0.21827581407983362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409261978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020764524,0.0004052696,0.97353894,0.0003523643,0.00005759517,0.00006232502,0.000173241,0.0016757379,0.002970107],"genre_scores_gemma":[0.5426726,0.0005151179,0.44651616,0.0007626668,0.00007450746,0.00042441415,0.0010874171,0.0011968149,0.006750374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951065,0.00017494525,0.000025062007,0.0001268319,0.000099814635,0.00006276503],"domain_scores_gemma":[0.99828464,0.0010200219,0.00015513501,0.0002364773,0.00021528466,0.00008841527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015399049,0.0015866864,0.0013694812,0.00049564574,0.0003607833,0.0016723744,0.0018590974,0.0023856426,0.0050426642],"category_scores_gemma":[0.0066211363,0.0008696636,0.0011243144,0.0004339896,0.0011930349,0.0018284685,0.0014304515,0.0033241084,0.0017310088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007214646,0.00004260666,0.0005189385,0.00009254817,0.00003244585,0.000055695513,0.00003151821,0.9659938,0.0015658509,0.0052530207,0.0016382926,0.02470319],"study_design_scores_gemma":[0.0000038158523,0.000008558487,0.000025908355,0.000008784185,0.0000020644036,0.000008191666,0.0000038409676,0.99715793,0.0003226807,0.0022086166,0.0002467643,0.0000029026708],"about_ca_topic_score_codex":0.0039417134,"about_ca_topic_score_gemma":0.005680623,"teacher_disagreement_score":0.0050426642,"about_ca_system_score_codex":0.0010946691,"about_ca_system_score_gemma":0.001336931,"threshold_uncertainty_score":0.016869426},"labels":[],"label_agreement":null},{"id":"W4409291012","doi":"10.1016/j.isci.2025.112399","title":"Burst firing optimizes invariant coding of natural communication signals by electrosensory neural populations","year":2025,"lang":"en","type":"article","venue":"iScience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Canada First Research Excellence Fund","keywords":"Coding (social sciences); Neural coding; Computer science; Artificial intelligence; Mathematics; Statistics","score_opus":0.02632096168848176,"score_gpt":0.28877421535203024,"score_spread":0.2624532536635485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409291012","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8188108,0.00009309486,0.17915061,0.00005455287,0.00001286128,0.000012584946,0.000038844777,0.00012410167,0.0017024647],"genre_scores_gemma":[0.9917806,0.000029494824,0.007898827,0.0000101101505,0.0000043060436,0.000009128134,0.000020482437,0.000020847852,0.00022611803],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999274,0.000014560591,0.0000067187625,0.000021721136,0.000013717887,0.000015916003],"domain_scores_gemma":[0.9997644,0.00008932817,0.00005883586,0.000032530817,0.000025276599,0.000029650564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020733419,0.00016792168,0.00019623578,0.00014954037,0.000114886585,0.00034602016,0.0002743202,0.00015425567,0.0004995843],"category_scores_gemma":[0.00087872334,0.000118167176,0.00016259805,0.00011299147,0.00034751077,0.0003891193,0.00040780552,0.00023374571,0.00007054892],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003134785,0.0000799192,0.0047857095,0.00007142368,0.00004868763,0.00016117249,0.00020513433,0.07748371,0.8459583,0.014695544,0.00024761038,0.05594932],"study_design_scores_gemma":[0.000026425922,0.00020070843,0.015035808,0.000010216355,0.00004057425,0.00021314766,0.00008609333,0.87122816,0.09099912,0.021430077,0.000705017,0.0000247264],"about_ca_topic_score_codex":0.00027982093,"about_ca_topic_score_gemma":0.00044400097,"teacher_disagreement_score":0.0004995843,"about_ca_system_score_codex":0.0002706551,"about_ca_system_score_gemma":0.0001512199,"threshold_uncertainty_score":0.0019637346},"labels":[],"label_agreement":null},{"id":"W4409299436","doi":"10.1137/24m1642548","title":"Ensemble Linear Interpolators: The Role of Ensembling","year":2025,"lang":"en","type":"article","venue":"SIAM Journal on Mathematics of Data Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science","score_opus":0.03539680150858747,"score_gpt":0.3314962504369784,"score_spread":0.2960994489283909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409299436","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029811963,0.00074136147,0.9666205,0.00024193167,0.00010739811,0.000027253858,0.000044301465,0.00039904055,0.00200626],"genre_scores_gemma":[0.6461675,0.00070999155,0.34826663,0.00023205625,0.00019368362,0.00010712467,0.00023827267,0.00027996398,0.0038048183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987614,0.00061525765,0.000063505104,0.00025247835,0.00023744625,0.000070007205],"domain_scores_gemma":[0.99177235,0.0051784255,0.00036865202,0.0013589062,0.0010775368,0.00024407984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005621409,0.0006733692,0.0016646509,0.0011146022,0.00083588716,0.0018054311,0.0017589092,0.0017245405,0.0026458097],"category_scores_gemma":[0.016386438,0.000634458,0.000553787,0.00085296977,0.0012948391,0.0043757795,0.002622672,0.002703902,0.0005854637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047015253,0.00020070872,0.0051112846,0.00020131448,0.00019294652,0.000115404204,0.000389999,0.50741726,0.005537807,0.17090982,0.0031331503,0.30632028],"study_design_scores_gemma":[0.0000075259886,0.00006860054,0.00019034327,0.000030233627,0.00001591674,0.0000370703,0.000017871625,0.9572952,0.0013267208,0.04019085,0.0008054178,0.000014310708],"about_ca_topic_score_codex":0.0009930733,"about_ca_topic_score_gemma":0.0017516462,"teacher_disagreement_score":0.005621409,"about_ca_system_score_codex":0.0004887207,"about_ca_system_score_gemma":0.0008944533,"threshold_uncertainty_score":0.029729187},"labels":[],"label_agreement":null},{"id":"W4409327148","doi":"10.1109/access.2025.3559794","title":"Vision Transformers Versus Convolutional Neural Networks: Comparing Robustness by Exploiting Varying Local Features","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Robustness (evolution); Convolutional neural network; Artificial intelligence; Transformer; Pattern recognition (psychology); Computer vision; Voltage; Engineering; Electrical engineering","score_opus":0.02814794017319139,"score_gpt":0.3123821569827348,"score_spread":0.2842342168095434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409327148","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8015877,0.017628962,0.15706775,0.001641439,0.0012292791,0.00047610962,0.0038527995,0.0064358595,0.010080212],"genre_scores_gemma":[0.9551555,0.002585047,0.034031797,0.000325723,0.0001485053,0.0001414547,0.004883125,0.0003170649,0.002411837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980793,0.0003767789,0.00022106807,0.00044030597,0.0006591048,0.00022336848],"domain_scores_gemma":[0.9970733,0.0011844625,0.00037011752,0.00066717545,0.0005677855,0.0001370922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034972446,0.0019742816,0.0009013055,0.0018560579,0.0003917063,0.0012493595,0.0011099032,0.0013054264,0.001344966],"category_scores_gemma":[0.010868099,0.00028834736,0.0011055693,0.0010466764,0.00081944384,0.00294732,0.0011269555,0.0013456014,0.00061478536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0041750977,0.0007285372,0.021472288,0.0015847263,0.0015147254,0.00043802537,0.00016415905,0.46286905,0.034301236,0.0034955714,0.011236582,0.45802003],"study_design_scores_gemma":[0.00014018307,0.0032757935,0.017521977,0.00020571901,0.00073617644,0.000531946,0.00020267649,0.9076538,0.059033837,0.0053465203,0.0052196155,0.00013191442],"about_ca_topic_score_codex":0.005855332,"about_ca_topic_score_gemma":0.0049789334,"teacher_disagreement_score":0.005855332,"about_ca_system_score_codex":0.0010062193,"about_ca_system_score_gemma":0.0006997919,"threshold_uncertainty_score":0.01849538},"labels":[],"label_agreement":null},{"id":"W4409340755","doi":"10.56367/oag-046-11966","title":"Programmable iontronic neural networks","year":2025,"lang":"en","type":"article","venue":"Open Access Government","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence","score_opus":0.02455733426975092,"score_gpt":0.3484049964644693,"score_spread":0.3238476621947184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409340755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09580408,0.01046496,0.6503838,0.0070037716,0.0031118658,0.00011615248,0.0005954608,0.002555797,0.22996424],"genre_scores_gemma":[0.8392465,0.005791921,0.0869971,0.0013841571,0.00033742533,0.00014968448,0.00035769635,0.000119017786,0.06561639],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999075,0.000014274703,0.0000042086144,0.000025512532,0.000032224238,0.000016320531],"domain_scores_gemma":[0.999907,0.000029618872,0.000011469821,0.0000130506305,0.000026943406,0.000011881363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014041382,0.00023318589,0.00020503739,0.00018671453,0.00028705515,0.0007745908,0.00067775947,0.0006061144,0.006017721],"category_scores_gemma":[0.00051762315,0.0001343074,0.00021544883,0.00022446603,0.00057565107,0.0009160548,0.0005214712,0.00068272796,0.0008452132],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026035972,0.0000776567,0.0006845632,0.0003428365,0.00006600382,0.00033754224,0.00013216744,0.13696966,0.06790831,0.5483957,0.031981196,0.21284398],"study_design_scores_gemma":[0.000059359754,0.0001431772,0.00038419827,0.00008222758,0.00003594341,0.0002399147,0.000058230627,0.66727257,0.03075562,0.18933627,0.11157583,0.000056580044],"about_ca_topic_score_codex":0.00066418905,"about_ca_topic_score_gemma":0.0011251604,"teacher_disagreement_score":0.006017721,"about_ca_system_score_codex":0.0005221539,"about_ca_system_score_gemma":0.0002595472,"threshold_uncertainty_score":0.02013129},"labels":[],"label_agreement":null},{"id":"W4409347793","doi":"10.1609/aaai.v39i18.34058","title":"Till the Layers Collapse: Compressing a Deep Neural Network Through the Lenses of Batch Normalization Layers.","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"HORIZON EUROPE Framework Programme; Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; China Scholarship Council; European Commission; Agence Nationale de la Recherche; Institute for Catastrophic Loss Reduction","keywords":"Normalization (sociology); Artificial neural network; Geology; Materials science; Computer science; Artificial intelligence","score_opus":0.05289706677164575,"score_gpt":0.2983619473187461,"score_spread":0.24546488054710036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409347793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018273149,0.0012803237,0.96551746,0.00084606593,0.00053075125,0.00017143875,0.0007442512,0.009125158,0.0035114205],"genre_scores_gemma":[0.20670943,0.0013273353,0.77032524,0.0014272601,0.00040122247,0.00042433388,0.0028963396,0.0022465189,0.014242328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915874,0.00012805198,0.000060786962,0.00019924113,0.00035168033,0.00010152743],"domain_scores_gemma":[0.9985422,0.00041421823,0.00015924063,0.0005081762,0.00027141426,0.00010473056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014058957,0.0021096757,0.0009933,0.0011093625,0.00086131133,0.0017960542,0.002677874,0.0012264192,0.0060628206],"category_scores_gemma":[0.007816244,0.0008950027,0.0013229651,0.0013667165,0.0014588209,0.005249199,0.0030430355,0.0040076277,0.0022309453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009954168,0.00021183342,0.0027812917,0.00041808357,0.0004362091,0.00050713465,0.0005133911,0.11195285,0.056930985,0.051087074,0.053888757,0.720277],"study_design_scores_gemma":[0.00007459832,0.00015187582,0.0008553336,0.00007290682,0.00012712252,0.00027802147,0.00012219671,0.8729135,0.06203528,0.038633753,0.024660528,0.00007490772],"about_ca_topic_score_codex":0.0123649435,"about_ca_topic_score_gemma":0.017180081,"teacher_disagreement_score":0.0123649435,"about_ca_system_score_codex":0.0015912615,"about_ca_system_score_gemma":0.0026095381,"threshold_uncertainty_score":0.024585962},"labels":[],"label_agreement":null},{"id":"W4409359924","doi":"10.1139/cgj-2024-0439","title":"Sand-specific interpretation of state via a modified relative dilatancy index","year":2025,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geotechnical engineering; Dilatant; Interpretation (philosophy); Geology; Index (typography); Forensic engineering; Engineering; Computer science","score_opus":0.010638257354398202,"score_gpt":0.23719603825280916,"score_spread":0.22655778089841097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409359924","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14361788,0.0007281989,0.82998216,0.00029091866,0.00016336325,0.00012535162,0.00083619455,0.0013590107,0.02289693],"genre_scores_gemma":[0.9089283,0.0004645041,0.08589824,0.00014445434,0.000080437065,0.00013158626,0.00046473672,0.00030847578,0.0035793034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991015,0.00013425568,0.00008922707,0.00030627975,0.00028862187,0.00008022127],"domain_scores_gemma":[0.9981804,0.00064733764,0.0004404854,0.00027727758,0.00038063218,0.00007387129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012952257,0.001132474,0.00057503633,0.0033208665,0.00056252384,0.002486655,0.0012735085,0.0008198348,0.0026829613],"category_scores_gemma":[0.0052640173,0.0004155926,0.00056741765,0.0015094581,0.002152149,0.003392881,0.001935599,0.001377957,0.0006620097],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031809922,0.00021341085,0.06478326,0.00077286764,0.00023016459,0.0015590513,0.0026247567,0.3321675,0.12114043,0.24455966,0.0041388515,0.22749197],"study_design_scores_gemma":[0.00002696265,0.0003599809,0.050740983,0.00022237464,0.00014649032,0.0008523687,0.0015172409,0.66717434,0.0646851,0.17982653,0.033945765,0.0005018383],"about_ca_topic_score_codex":0.0028537214,"about_ca_topic_score_gemma":0.0034764644,"teacher_disagreement_score":0.0033208665,"about_ca_system_score_codex":0.0009338453,"about_ca_system_score_gemma":0.0006906188,"threshold_uncertainty_score":0.008975387},"labels":[],"label_agreement":null},{"id":"W4409417890","doi":"10.2139/ssrn.5217220","title":"Local: Learning with Orientation Matrix to Infer Causal Structure From  Time Series Data","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Series (stratigraphy); Orientation (vector space); Time series; Matrix (chemical analysis); Computer science; Artificial intelligence; Econometrics; Mathematics; Machine learning; Geometry; Materials science; Geology","score_opus":0.008234114691474686,"score_gpt":0.26993294846026195,"score_spread":0.2616988337687873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409417890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0096241245,0.0003965267,0.983098,0.00019521534,0.00009350818,0.000048417627,0.0004294267,0.0057489187,0.00036593352],"genre_scores_gemma":[0.3247967,0.0008886796,0.6631653,0.00046208742,0.00038388945,0.0003526428,0.003049428,0.0014259503,0.0054754373],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99945825,0.00017186298,0.0000347533,0.00020146351,0.000087339875,0.000046341596],"domain_scores_gemma":[0.99777114,0.0012042442,0.000163605,0.0004971069,0.0002119311,0.00015198904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019661665,0.0014125576,0.0014864794,0.0015692426,0.00055084156,0.001441439,0.001738698,0.001529921,0.0052756956],"category_scores_gemma":[0.007835845,0.00093068613,0.0012935441,0.0017262807,0.0007952724,0.0026992464,0.0025259627,0.0022298023,0.0024387664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006554306,0.00035300315,0.00483791,0.00030265775,0.0004427913,0.00025183504,0.00016340337,0.1391339,0.010315015,0.013455074,0.017602192,0.81248677],"study_design_scores_gemma":[0.000078425444,0.00008884293,0.00075806375,0.000020093068,0.00005679601,0.000053892898,0.000029601586,0.9621093,0.0025138203,0.032433663,0.0018301755,0.000027223698],"about_ca_topic_score_codex":0.0056961253,"about_ca_topic_score_gemma":0.0095654735,"teacher_disagreement_score":0.0056961253,"about_ca_system_score_codex":0.00058062,"about_ca_system_score_gemma":0.0012417962,"threshold_uncertainty_score":0.017648935},"labels":[],"label_agreement":null},{"id":"W4409460565","doi":"10.21203/rs.3.rs-5815988/v1","title":"A Neural Symbolic Model for Space Physics","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Particle Physics","funders":"","keywords":"Space (punctuation); Artificial neural network; Physics; Computer science; Artificial intelligence","score_opus":0.13699686684799853,"score_gpt":0.4350451529984792,"score_spread":0.2980482861504806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409460565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038715873,0.0010759946,0.9296519,0.0018249225,0.00037627242,0.00003401564,0.0009805325,0.00071711326,0.026623372],"genre_scores_gemma":[0.8293445,0.001507443,0.13831817,0.00037197996,0.00042859648,0.00016552572,0.0009995601,0.00027080448,0.028593505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998983,0.000024174236,0.000006037206,0.00002073712,0.000037431404,0.000013279124],"domain_scores_gemma":[0.99974126,0.00012240303,0.000023994195,0.00003869061,0.000049533504,0.00002413032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000197659,0.00035427502,0.0007176084,0.0007159198,0.00061266555,0.0011147535,0.0013314056,0.0013022709,0.007862392],"category_scores_gemma":[0.0015066585,0.00028904757,0.0006268836,0.0010326097,0.0008214117,0.0023917241,0.0009855974,0.0013826459,0.0011801516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054183667,0.000040758827,0.00035275577,0.00011639403,0.00004412602,0.00010012677,0.000084878164,0.40838227,0.0023370453,0.53454554,0.0048862062,0.04905569],"study_design_scores_gemma":[0.000004963011,0.000004645948,0.000038547296,0.0000062451863,0.0000045556703,0.000011553023,0.0000064337714,0.81736434,0.00017015912,0.18095864,0.0014250979,0.0000048229936],"about_ca_topic_score_codex":0.004854528,"about_ca_topic_score_gemma":0.006421753,"teacher_disagreement_score":0.007862392,"about_ca_system_score_codex":0.00085275277,"about_ca_system_score_gemma":0.0009484577,"threshold_uncertainty_score":0.026302338},"labels":[],"label_agreement":null},{"id":"W4409595738","doi":"10.63485/hj8j8-fef64","title":"Strategies for populating IRs","year":2006,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business","score_opus":0.041791492722557916,"score_gpt":0.3016622993397435,"score_spread":0.25987080661718553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409595738","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02432865,0.0010971178,0.90226066,0.0018078162,0.00031987086,0.00085320626,0.0012496454,0.013399623,0.054683436],"genre_scores_gemma":[0.11953604,0.0009146479,0.84363884,0.0006102458,0.000091122834,0.0007991529,0.004017393,0.007459463,0.022933166],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98037875,0.007681962,0.0022859238,0.0024241256,0.005651627,0.0015775589],"domain_scores_gemma":[0.96809703,0.010615049,0.0013503114,0.012693722,0.0058880355,0.0013558576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015152701,0.0023881,0.0017280878,0.010736808,0.005733682,0.014820379,0.006197896,0.003406136,0.02099575],"category_scores_gemma":[0.06185025,0.001986752,0.0031319999,0.010259437,0.0047150985,0.015572402,0.01835778,0.003702506,0.0137144355],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032646046,0.00022385403,0.006683586,0.00085251767,0.00012995474,0.0016093008,0.021183351,0.015058196,0.012724905,0.29116356,0.049405765,0.6006386],"study_design_scores_gemma":[0.00008874026,0.00019787067,0.0016340577,0.00090013724,0.0002943373,0.0021843994,0.022945818,0.062948026,0.049526274,0.15979545,0.69910055,0.0003844506],"about_ca_topic_score_codex":0.004137857,"about_ca_topic_score_gemma":0.00678414,"teacher_disagreement_score":0.02099575,"about_ca_system_score_codex":0.002340688,"about_ca_system_score_gemma":0.004032181,"threshold_uncertainty_score":0.08013612},"labels":[],"label_agreement":null},{"id":"W4409663317","doi":"10.22214/ijraset.2025.69192","title":"Smart Learning Using Generative AI","year":2025,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Generative grammar; Computer science; Artificial intelligence","score_opus":0.056565270381461634,"score_gpt":0.41385272537357426,"score_spread":0.3572874549921126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409663317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055994134,0.0005693295,0.7912243,0.0012656013,0.00013130554,0.0002119818,0.00009040217,0.0026029595,0.1479101],"genre_scores_gemma":[0.7232758,0.00080156146,0.24446104,0.00044846165,0.000049777987,0.00035687027,0.00022245762,0.0002290045,0.030155046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996581,0.00013738914,0.000014214936,0.00006869598,0.00008864649,0.000032991637],"domain_scores_gemma":[0.99946994,0.0002962492,0.000023534445,0.00012911693,0.000036090438,0.000045036228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005608607,0.00041239397,0.00023384535,0.0004645127,0.00051346316,0.0022936817,0.00092229526,0.0007281934,0.0117421495],"category_scores_gemma":[0.0015240852,0.00022757956,0.00055272144,0.0003620314,0.0015725828,0.002040241,0.0031918485,0.0008612884,0.0018630303],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094362666,0.00035266922,0.00392107,0.00063392316,0.00008915088,0.00061083096,0.0040870532,0.08424286,0.031197896,0.51031566,0.006878825,0.35757568],"study_design_scores_gemma":[0.00006874565,0.00019818517,0.0021348854,0.00029130693,0.00005689749,0.00070893014,0.0010827548,0.39940584,0.01987416,0.42788473,0.14821897,0.000074690615],"about_ca_topic_score_codex":0.0005959683,"about_ca_topic_score_gemma":0.000884611,"teacher_disagreement_score":0.0117421495,"about_ca_system_score_codex":0.0005501807,"about_ca_system_score_gemma":0.00048758887,"threshold_uncertainty_score":0.03928137},"labels":[],"label_agreement":null},{"id":"W4409698250","doi":"10.2139/ssrn.5224437","title":"Neuroanatomical Correlates of Auditory and Visual Statistical Learning: Cortical and Subcortical Volume Predictors","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Neuroscience; Psychology; Brain size; Statistical learning; Auditory pathways; Audiology; Cognitive psychology; Medicine; Magnetic resonance imaging; Artificial intelligence; Computer science","score_opus":0.004671704854837481,"score_gpt":0.24583549931735765,"score_spread":0.24116379446252018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409698250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9810941,0.0006949004,0.01218252,0.00070108904,0.000037486905,0.000032531872,0.00062656554,0.00012926773,0.00450156],"genre_scores_gemma":[0.9976433,0.00015755912,0.0010826603,0.00003765732,0.000044917022,0.000008439505,0.00020894004,0.000039871124,0.00077664223],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99983835,0.000033349672,0.000010093424,0.000045917455,0.000039535596,0.00003266],"domain_scores_gemma":[0.9970372,0.0017430773,0.0005571361,0.00022021914,0.00023687858,0.00020563573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007520223,0.00031559996,0.00029424566,0.00079778134,0.0002325462,0.0014273347,0.0003550072,0.00036309042,0.0027461527],"category_scores_gemma":[0.007905795,0.00023157998,0.00026933633,0.00079669885,0.00068821217,0.00093944283,0.0006153815,0.00059902406,0.000357298],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004796143,0.0005797838,0.45735195,0.0003278335,0.0006378914,0.00088554004,0.0011750126,0.021195423,0.17771047,0.016066348,0.004413565,0.31486],"study_design_scores_gemma":[0.00007652749,0.00030892045,0.9134134,0.000027654873,0.00015764374,0.0010049122,0.00040617984,0.037006576,0.016793553,0.02975186,0.0010060811,0.000046653295],"about_ca_topic_score_codex":0.0019462513,"about_ca_topic_score_gemma":0.0025221596,"teacher_disagreement_score":0.0027461527,"about_ca_system_score_codex":0.00022112462,"about_ca_system_score_gemma":0.000490787,"threshold_uncertainty_score":0.009186864},"labels":[],"label_agreement":null},{"id":"W4409754266","doi":"10.63485/q74nt-gk193","title":"New software for OAI-compliant archives","year":2004,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Software; World Wide Web; Programming language","score_opus":0.037501516211235195,"score_gpt":0.2810433010337565,"score_spread":0.2435417848225213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409754266","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025909813,0.000605438,0.3199107,0.00042349705,0.00057106116,0.00050407456,0.02618699,0.6155706,0.03363661],"genre_scores_gemma":[0.026455209,0.0012575083,0.53909373,0.0005923832,0.00044338312,0.0021422019,0.15607588,0.2078813,0.06605846],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9940638,0.000530854,0.0011275781,0.000923309,0.002902687,0.00045181563],"domain_scores_gemma":[0.9837624,0.0017642503,0.0009715585,0.008346956,0.0042153164,0.0009395297],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0076694703,0.002230906,0.001889134,0.007910315,0.0023708246,0.009535397,0.005498286,0.0016432225,0.086572714],"category_scores_gemma":[0.022097131,0.002867727,0.002536625,0.008726852,0.0010568001,0.0106608365,0.009016662,0.0047915024,0.09016543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013267951,0.00022569209,0.002520784,0.0010918779,0.00028369966,0.0004708513,0.0011662809,0.001977047,0.012809023,0.027660213,0.5984999,0.35196784],"study_design_scores_gemma":[0.00028204714,0.00006267627,0.0014785071,0.00023782028,0.000087031825,0.000736963,0.00020319123,0.010242087,0.023931349,0.01649037,0.94602907,0.0002188965],"about_ca_topic_score_codex":0.0037636503,"about_ca_topic_score_gemma":0.0035674397,"teacher_disagreement_score":0.9904646,"about_ca_system_score_codex":0.002020673,"about_ca_system_score_gemma":0.0032863691,"threshold_uncertainty_score":0.28961456},"labels":[],"label_agreement":null},{"id":"W4409765337","doi":"10.7554/elife.96627.2.sa4","title":"eLife Assessment: Spatial periodicity in grid cell firing is explained by a neural sequence code of 2-D trajectories","year":2025,"lang":"en","type":"peer-review","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Sequence (biology); Code (set theory); Grid cell; Grid; Computer science; Artificial neural network; Algorithm; Artificial intelligence; Mathematics; Geometry; Set (abstract data type); Biology; Programming language","score_opus":0.0321299318256811,"score_gpt":0.32644872955152476,"score_spread":0.2943187977258437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409765337","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28617576,0.0004951744,0.6623611,0.0020915337,0.0006960651,0.00016494365,0.0042832177,0.014327827,0.029404324],"genre_scores_gemma":[0.8255108,0.00020996666,0.15608037,0.0003579486,0.000059943304,0.00016694119,0.0032399576,0.0020836806,0.0122904815],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990903,0.0001527102,0.000051295374,0.0002272673,0.00034953444,0.00012880057],"domain_scores_gemma":[0.99617875,0.00145161,0.00032031423,0.00052241684,0.0012681669,0.00025874263],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0020502391,0.00037905594,0.00067003595,0.00086603133,0.00063920545,0.0017945878,0.0017682271,0.0014765481,0.01515179],"category_scores_gemma":[0.016260145,0.0003819553,0.0005134497,0.0008950914,0.0006760806,0.0012076647,0.0013070337,0.001158649,0.004746067],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020801197,0.0002060668,0.06392383,0.0009520422,0.0004859709,0.0013874236,0.0010982838,0.14524479,0.083925396,0.066991754,0.12566926,0.5080351],"study_design_scores_gemma":[0.00013863794,0.0001094591,0.020962527,0.000082043414,0.000079692756,0.0005328113,0.00033915284,0.8596274,0.053738903,0.0342399,0.030067848,0.0000816318],"about_ca_topic_score_codex":0.004481678,"about_ca_topic_score_gemma":0.0063857995,"teacher_disagreement_score":0.9979498,"about_ca_system_score_codex":0.0007248893,"about_ca_system_score_gemma":0.0019976888,"threshold_uncertainty_score":0.05068779},"labels":[],"label_agreement":null},{"id":"W4409787614","doi":"10.61091/jcmcc127a-397","title":"Cross-domain Generalization Performance of a Neural Machine Translation System Incorporating Hierarchical Bayesian Models","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generalization; Computer science; Bayesian probability; Artificial intelligence; Machine translation; Translation (biology); Domain (mathematical analysis); Machine learning; Bayesian hierarchical modeling; Bayesian inference; Mathematics; Biology","score_opus":0.013802138578180103,"score_gpt":0.25763490681232026,"score_spread":0.24383276823414016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409787614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47537455,0.0014732525,0.50441515,0.00067602657,0.00024754647,0.00016145407,0.00023993601,0.005661864,0.011750298],"genre_scores_gemma":[0.9351431,0.00029577353,0.059059273,0.00022649989,0.00004951203,0.00007756468,0.0006740308,0.000120644625,0.004353572],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985051,0.00062984135,0.00012251755,0.00034918863,0.00029082896,0.000102658385],"domain_scores_gemma":[0.9982968,0.0007786588,0.00011259347,0.00030423066,0.00045372595,0.00005394072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027658686,0.00079281366,0.00084264437,0.0005178114,0.00053641776,0.00070867524,0.0007534246,0.0010049945,0.001805841],"category_scores_gemma":[0.005458185,0.00032424784,0.0007849804,0.0006446424,0.0004284802,0.0017773388,0.0008440989,0.0009619412,0.0010527926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058846234,0.00029362482,0.00323764,0.00020411189,0.00024468283,0.00023681411,0.00022223748,0.47780326,0.027877709,0.004608222,0.004388453,0.48029473],"study_design_scores_gemma":[0.000013552293,0.000096993776,0.00077326683,0.0000051281627,0.000033413104,0.000042205993,0.000014551641,0.99255216,0.004818049,0.0011386775,0.00049588236,0.00001616401],"about_ca_topic_score_codex":0.009696289,"about_ca_topic_score_gemma":0.0069727837,"teacher_disagreement_score":0.009696289,"about_ca_system_score_codex":0.0008260687,"about_ca_system_score_gemma":0.0012145049,"threshold_uncertainty_score":0.019279659},"labels":[],"label_agreement":null},{"id":"W4409791252","doi":"10.61091/jcmcc127a-448","title":"Deep Learning-based Classification and Prediction Modeling for Big Data","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Big data; Computer science; Deep learning; Machine learning; Data mining","score_opus":0.04799112065021306,"score_gpt":0.28720321679779676,"score_spread":0.2392120961475837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409791252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028150478,0.00092252117,0.96809083,0.00065778545,0.000093948394,0.000032080992,0.00020203274,0.0006429003,0.0012073888],"genre_scores_gemma":[0.8665884,0.0013769298,0.12670036,0.0003237103,0.00013911737,0.000191303,0.00083874824,0.00010483079,0.003736581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994899,0.00013679238,0.00003986014,0.0001282279,0.00012737181,0.00007778795],"domain_scores_gemma":[0.99913114,0.00045728803,0.0000974524,0.00009044456,0.00018526668,0.000038520735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011958281,0.00087380345,0.00095334597,0.0007155018,0.00042709286,0.0010450667,0.0013542979,0.0008919355,0.0010300443],"category_scores_gemma":[0.0028601533,0.00052329514,0.0008465387,0.0011505717,0.0006740318,0.001930182,0.0010339982,0.002404702,0.00030992247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005608514,0.00006854952,0.0019671416,0.000057845282,0.00007285758,0.00004666898,0.000043526834,0.9369405,0.0008207011,0.007966671,0.0015814647,0.050378058],"study_design_scores_gemma":[7.122258e-7,0.00000298047,0.00005872102,0.0000013222339,0.0000016917951,0.0000016074642,0.0000014461998,0.99812704,0.00008180987,0.0016433927,0.000077885205,0.0000013652055],"about_ca_topic_score_codex":0.015520746,"about_ca_topic_score_gemma":0.010799691,"teacher_disagreement_score":0.015520746,"about_ca_system_score_codex":0.0012583247,"about_ca_system_score_gemma":0.0012312586,"threshold_uncertainty_score":0.030860841},"labels":[],"label_agreement":null},{"id":"W4410391779","doi":"10.1177/02783649251336385","title":"Growable and interpretable neural control with online continual learning for autonomous lifelong locomotion learning machines","year":2025,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"King Saud University; H2020 Marie Skłodowska-Curie Actions; Vidyasirimedhi Institute of Science and Technology; Canadian Institute for Advanced Research","keywords":"Lifelong learning; Artificial intelligence; Computer science; Control (management); Artificial neural network; Machine learning; Online learning; Autonomous learning; Human–computer interaction; Engineering; Psychology; Multimedia","score_opus":0.026987420646617504,"score_gpt":0.3519711385473495,"score_spread":0.324983717900732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410391779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049388472,0.00043894904,0.94595844,0.00028280015,0.00004365488,0.00003511326,0.000052988198,0.0009992049,0.0028003543],"genre_scores_gemma":[0.88849306,0.00020061048,0.10873598,0.00013546478,0.000036138947,0.00012954288,0.00010521633,0.000067966976,0.0020959985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999785,0.000043445467,0.0000146589655,0.000073914365,0.00005260939,0.000030424091],"domain_scores_gemma":[0.9992642,0.00037405794,0.0000996411,0.00013787106,0.00009076493,0.0000335183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053378515,0.00057800196,0.00040805206,0.00023399366,0.0002035302,0.0005309954,0.0009828958,0.0006012927,0.0013730002],"category_scores_gemma":[0.0019705053,0.00024657845,0.00034463135,0.00019063895,0.00093693915,0.0011295059,0.0010936211,0.0013972996,0.00020871725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008825404,0.0001058036,0.0009904191,0.0001504633,0.00004121047,0.000198367,0.00023107821,0.7640484,0.019496424,0.028038746,0.0012432487,0.18536761],"study_design_scores_gemma":[0.0000047389603,0.00005224615,0.00014507836,0.000008234304,0.000004653487,0.000019145094,0.000008335858,0.9871292,0.001720909,0.010365669,0.0005355161,0.000006248237],"about_ca_topic_score_codex":0.0017001808,"about_ca_topic_score_gemma":0.0024355275,"teacher_disagreement_score":0.0017001808,"about_ca_system_score_codex":0.00060769037,"about_ca_system_score_gemma":0.0005147782,"threshold_uncertainty_score":0.004593134},"labels":[],"label_agreement":null},{"id":"W4410411432","doi":"10.2514/1.i011399","title":"Predicting Lateral Dynamics of CRJ700 Using Multilayer Perceptron and Support Vector Regression","year":2025,"lang":"en","type":"article","venue":"Journal of Aerospace Information Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Canada Research Chairs","keywords":"Support vector machine; Regression; Regression analysis; Dynamics (music); Computer science; Artificial intelligence; Statistics; Mathematics; Psychology","score_opus":0.012204376193651731,"score_gpt":0.2704177865399051,"score_spread":0.2582134103462534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410411432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96204233,0.00011842035,0.03457435,0.0001092198,0.00003222376,0.000024929272,0.00063247123,0.0005067878,0.0019592755],"genre_scores_gemma":[0.99130577,0.000043008906,0.007186694,0.000012921832,0.0000036913225,0.000013090893,0.00076566916,0.000020423842,0.00064875896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998541,0.000020143847,0.000006747202,0.00003313509,0.000061600964,0.00002422565],"domain_scores_gemma":[0.99972683,0.000106600746,0.000037775786,0.000027541479,0.000078557496,0.000022704315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035357845,0.00063977286,0.0003506162,0.0005064908,0.00024361162,0.00043568993,0.00043236327,0.00042553197,0.00084527524],"category_scores_gemma":[0.0009959043,0.00024376615,0.00047348387,0.0003202882,0.00017805368,0.0004054814,0.00026805722,0.0005488013,0.00024748687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020950152,0.00011950341,0.020278823,0.0000649424,0.000053182015,0.00015471515,0.000043268683,0.91716164,0.02153438,0.0003618478,0.00094085914,0.039077345],"study_design_scores_gemma":[0.0000029051191,0.00004004401,0.0049611237,0.000002197622,0.0000030228634,0.000008192066,0.000009086818,0.9933473,0.0014304899,0.000062329294,0.00012807082,0.000005219693],"about_ca_topic_score_codex":0.014778359,"about_ca_topic_score_gemma":0.015418608,"teacher_disagreement_score":0.014778359,"about_ca_system_score_codex":0.00039952263,"about_ca_system_score_gemma":0.0004604891,"threshold_uncertainty_score":0.029384673},"labels":[],"label_agreement":null},{"id":"W4410536703","doi":"10.1175/mwr-d-24-0189.1","title":"The Effect of Ensemble Size on the Mean Squared Error and Spread–Error Relationship","year":2025,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Mean squared error; Statistics; Mathematics; Ensemble average; Climatology; Geology","score_opus":0.021033186261198416,"score_gpt":0.2857139700948661,"score_spread":0.26468078383366767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410536703","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43622008,0.106159784,0.3882806,0.010689639,0.0023609768,0.00018600715,0.0020450077,0.0010172477,0.053040773],"genre_scores_gemma":[0.96156883,0.010544342,0.024014192,0.00047003006,0.00059092714,0.00008347173,0.00057440944,0.00013960246,0.0020142298],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9887804,0.0054104654,0.00064075494,0.0014006731,0.0035404025,0.00022735036],"domain_scores_gemma":[0.91715956,0.06432522,0.005554162,0.004641103,0.007893119,0.00042683305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01800286,0.00061599095,0.0009328587,0.0019860798,0.00030731238,0.0014729026,0.0009867653,0.0011883056,0.0017909943],"category_scores_gemma":[0.080148466,0.00024270164,0.0006827937,0.0016595001,0.0012675734,0.0028040747,0.0013207506,0.0017943846,0.0005039556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052547443,0.00012029418,0.119977355,0.0013751783,0.002472061,0.00022277591,0.0003838046,0.28499645,0.006491328,0.073854625,0.011975248,0.4976054],"study_design_scores_gemma":[0.000086194326,0.00068308995,0.34527174,0.0012964284,0.0011788893,0.00066074065,0.00047325308,0.44451934,0.017505966,0.14786056,0.040081702,0.00038208236],"about_ca_topic_score_codex":0.0044270726,"about_ca_topic_score_gemma":0.003066752,"teacher_disagreement_score":0.01800286,"about_ca_system_score_codex":0.0010847904,"about_ca_system_score_gemma":0.00086422317,"threshold_uncertainty_score":0.0952093},"labels":[],"label_agreement":null},{"id":"W4410756393","doi":"10.1007/978-981-96-6403-0","title":"Machine Learning and Soft Computing","year":2025,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universidad de Almería; Université Paris-Est Créteil Val-de-Marne; Jimei University; Nanjing University; Universiti Malaysia Kelantan; University of Surrey; Università degli Studi di Salerno; Jiangsu University; Simon Fraser University; Hunan University; University of Hong Kong; Jiangsu University of Science and Technology; Norges Teknisk-Naturvitenskapelige Universitet; Universiti Sains Malaysia; Universidad de Málaga; Universiti Tunku Abdul Rahman; Chongqing University of Technology; Chongqing University; Arkansas Tech University; Turun Yliopisto; City University of Hong Kong; Sun Yat-sen University; Iowa State University; Auburn University; Thammasat University; Mississippi State University","keywords":"Soft computing; Computer science; Artificial intelligence; Information retrieval; Artificial neural network","score_opus":0.021143988971010472,"score_gpt":0.29328640869955075,"score_spread":0.27214241972854025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410756393","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018430729,0.085271634,0.17363207,0.0050526406,0.006907184,0.00010344121,0.0006689318,0.0013336167,0.7251873],"genre_scores_gemma":[0.013244817,0.029847244,0.03460041,0.00132145,0.0030742686,0.00016165125,0.00048631994,0.0005771957,0.91668665],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995554,0.000072425115,0.000019451656,0.000060051003,0.00027058992,0.00002211442],"domain_scores_gemma":[0.9991905,0.00049744104,0.000040324685,0.00009535914,0.00012666291,0.000049672253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004078242,0.0015088796,0.0015955924,0.0020785925,0.0006396604,0.003167659,0.00080064,0.00097118743,0.07404717],"category_scores_gemma":[0.0019254789,0.0005705416,0.00051130395,0.0042337137,0.0012986832,0.0032255459,0.0015854253,0.002641094,0.04471687],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022363734,0.000052227293,0.000096738055,0.00059367705,0.000030405048,0.000047037276,0.000075275966,0.004509403,0.0009411853,0.16226493,0.31403497,0.5173317],"study_design_scores_gemma":[0.000008284609,0.00004030054,0.00033473995,0.0003978359,0.00001478413,0.00015854623,0.000044644465,0.01322309,0.00077446096,0.2820849,0.70289624,0.000022156135],"about_ca_topic_score_codex":0.0006611903,"about_ca_topic_score_gemma":0.0017231478,"teacher_disagreement_score":0.07404717,"about_ca_system_score_codex":0.0007950856,"about_ca_system_score_gemma":0.00090959965,"threshold_uncertainty_score":0.2477125},"labels":[],"label_agreement":null},{"id":"W4410809182","doi":"10.1109/les.2025.3574563","title":"A Memory Representation of Random Forests Optimized for Resource-Limited Embedded Devices","year":2025,"lang":"en","type":"article","venue":"IEEE Embedded Systems Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Mitacs","keywords":"Computer science; Representation (politics); Resource (disambiguation); Random access memory; Embedded system; Real-time computing; Computer architecture; Computer hardware; Computer network","score_opus":0.02047628820797066,"score_gpt":0.28564180675183604,"score_spread":0.2651655185438654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410809182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055389147,0.0006432396,0.93542415,0.00023026452,0.0001261369,0.00005963828,0.000544908,0.0042988835,0.003283575],"genre_scores_gemma":[0.5501296,0.000403062,0.44268948,0.00018864096,0.000059686157,0.0002522555,0.0015963274,0.0005824948,0.0040983846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998022,0.00003859045,0.0000130513645,0.000037996073,0.00006403274,0.000044135064],"domain_scores_gemma":[0.99966,0.00013476469,0.000025418798,0.000053579217,0.00011068888,0.000015526528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037302545,0.0005125458,0.00036087394,0.00041314808,0.00025219828,0.00060030835,0.0010965342,0.00054589054,0.0034639253],"category_scores_gemma":[0.001812023,0.00019874793,0.00046722763,0.0005106017,0.00016122418,0.0010288164,0.00037701218,0.0005322623,0.0010398375],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003127096,0.00013068623,0.0011654004,0.00016111362,0.00004386061,0.00015878788,0.000061039376,0.7280046,0.015939366,0.0075818575,0.008622385,0.23781827],"study_design_scores_gemma":[0.000013958777,0.000024866229,0.000138397,0.0000075285875,0.0000055194746,0.000030741732,0.000008289396,0.9940188,0.002568241,0.0020760016,0.0011012766,0.0000063322195],"about_ca_topic_score_codex":0.0030880268,"about_ca_topic_score_gemma":0.0055818544,"teacher_disagreement_score":0.0034639253,"about_ca_system_score_codex":0.00037164966,"about_ca_system_score_gemma":0.000531867,"threshold_uncertainty_score":0.011587977},"labels":[],"label_agreement":null},{"id":"W4410826381","doi":"10.37394/232018.2025.13.37","title":"Key Role Players in Artificial Neural Networks: An Overview","year":2025,"lang":"en","type":"article","venue":"WSEAS TRANSACTIONS ON COMPUTER RESEARCH","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Key (lock); Artificial neural network; Computer science; Artificial intelligence; Computer security","score_opus":0.10429333869852271,"score_gpt":0.39178784213020273,"score_spread":0.28749450343168004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410826381","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001516559,0.9089235,0.04009528,0.008745826,0.0022328123,0.0000373005,0.00006558402,0.00008752249,0.03829568],"genre_scores_gemma":[0.0290268,0.9298688,0.019338695,0.0022825517,0.006616648,0.00011831536,0.0001010008,0.000056541718,0.012590569],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993635,0.00021108676,0.000054186545,0.000110369365,0.00021351715,0.00004737489],"domain_scores_gemma":[0.9991886,0.0005156935,0.000042174634,0.000040335148,0.00015964061,0.000053603722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013091124,0.0011436903,0.0007019607,0.0017694616,0.000532122,0.0032292597,0.0011989116,0.0026377626,0.0030442425],"category_scores_gemma":[0.0018402166,0.00060761184,0.0004904231,0.0020804636,0.0021950817,0.0057348777,0.0012313519,0.0042580306,0.0020933363],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106578205,0.00015022856,0.00085852854,0.0032267917,0.000070159214,0.00021045428,0.00029195426,0.007001251,0.0010252239,0.5598182,0.03271477,0.39452586],"study_design_scores_gemma":[0.000013400793,0.0001398343,0.00070139946,0.00227004,0.000038072812,0.0003620118,0.00017840974,0.010831101,0.00066627096,0.42503023,0.5597266,0.00004264636],"about_ca_topic_score_codex":0.0010658335,"about_ca_topic_score_gemma":0.0008068533,"teacher_disagreement_score":0.0032292597,"about_ca_system_score_codex":0.0013612418,"about_ca_system_score_gemma":0.000770102,"threshold_uncertainty_score":0.01018405},"labels":[],"label_agreement":null},{"id":"W4410845774","doi":"10.1088/1361-6463/adde6d","title":"Asymmetrical response of NbO<sub>2</sub>-based neuristor for three-dimensional sound localization","year":2025,"lang":"en","type":"article","venue":"Journal of Physics D Applied Physics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Sound (geography); Acoustics; Computer science; Physics","score_opus":0.016729351892110552,"score_gpt":0.2547416223957808,"score_spread":0.23801227050367024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410845774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9008992,0.0006731331,0.08384915,0.00064685644,0.0005975926,0.00007778932,0.00036402213,0.0008862584,0.012006037],"genre_scores_gemma":[0.9886218,0.00013880215,0.008576512,0.00007150499,0.000013043776,0.000030440784,0.000048664046,0.000028633829,0.0024705844],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998691,0.000014534769,0.000008151572,0.000031329633,0.00005330417,0.000023662833],"domain_scores_gemma":[0.9998241,0.000026334454,0.00004191595,0.000017665217,0.000064440435,0.000025447083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016415211,0.0002876173,0.00022368792,0.00010773823,0.00016520733,0.00036756188,0.0006581123,0.0004130309,0.0013920645],"category_scores_gemma":[0.00043827886,0.00014476392,0.00016039985,0.000117398165,0.00029761065,0.0004986668,0.00052924384,0.00025895276,0.00064279296],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008711872,0.000017282959,0.00036978436,0.00009197289,0.0000064559467,0.00009656885,0.000060505055,0.0015045038,0.9907885,0.0012949962,0.00039020448,0.0052920287],"study_design_scores_gemma":[0.000028832008,0.00034981983,0.0012362777,0.000021948172,0.000028777153,0.000202771,0.00010555899,0.08901004,0.9003404,0.00088249246,0.007760474,0.000032563843],"about_ca_topic_score_codex":0.0004232115,"about_ca_topic_score_gemma":0.00065764616,"teacher_disagreement_score":0.0013920645,"about_ca_system_score_codex":0.00045874433,"about_ca_system_score_gemma":0.00021898244,"threshold_uncertainty_score":0.004656911},"labels":[],"label_agreement":null},{"id":"W4410863963","doi":"10.1007/978-3-031-84756-1_7","title":"Neural Networks—Introduction","year":2025,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on games and computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Sudbury; University of Alberta","funders":"","keywords":"Artificial neural network; Computer science; Neuroscience; Artificial intelligence; Psychology","score_opus":0.013514619198667125,"score_gpt":0.24183239600439269,"score_spread":0.22831777680572557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410863963","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001632444,0.21781152,0.15292343,0.0057202727,0.015954822,0.00011115683,0.001530328,0.0011942504,0.60312176],"genre_scores_gemma":[0.02369245,0.11688232,0.039907757,0.0026268717,0.010171418,0.00030772088,0.0018714479,0.00076933124,0.8037707],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99974257,0.000030869902,0.000012615283,0.00006944072,0.00012665412,0.000017832255],"domain_scores_gemma":[0.9997733,0.00007629992,0.000010113531,0.000033558284,0.00008756125,0.000019141731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027260542,0.0011747283,0.00082445424,0.0012509581,0.0003270086,0.0016364191,0.0010267841,0.0012599216,0.048481308],"category_scores_gemma":[0.0010644526,0.0005224135,0.0004362325,0.0016888517,0.0006835229,0.0021240332,0.0010511401,0.0017308383,0.025989791],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021345184,0.00006261535,0.00015217134,0.0008068371,0.000031606032,0.000049418635,0.0000609378,0.009307057,0.0010625405,0.14631298,0.30094817,0.5411843],"study_design_scores_gemma":[0.0000044435624,0.00001926714,0.00033603676,0.0003302087,0.000011139486,0.00011287128,0.00002064277,0.005780669,0.0004883702,0.12774816,0.86513215,0.000016085316],"about_ca_topic_score_codex":0.0016953441,"about_ca_topic_score_gemma":0.0026934491,"teacher_disagreement_score":0.048481308,"about_ca_system_score_codex":0.0010908867,"about_ca_system_score_gemma":0.00068279984,"threshold_uncertainty_score":0.16218615},"labels":[],"label_agreement":null},{"id":"W4410863974","doi":"10.1007/978-3-031-84756-1_9","title":"Grid-Based DNN PCGML","year":2025,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on games and computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Sudbury; University of Alberta","funders":"","keywords":"Computer science; Grid; Geography; Geodesy","score_opus":0.01785394981740896,"score_gpt":0.2522172997506062,"score_spread":0.23436334993319724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410863974","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00947931,0.0007494915,0.94284797,0.0003329352,0.00040673398,0.00011148519,0.0013809127,0.013453364,0.031237885],"genre_scores_gemma":[0.29415578,0.00097480183,0.63958436,0.00079660915,0.00017040363,0.00034174116,0.0050730957,0.0014299336,0.0574733],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998288,0.000022846474,0.000011333179,0.000065796055,0.000043975488,0.000027249334],"domain_scores_gemma":[0.9997693,0.00005080004,0.000008964175,0.000060965576,0.000091664646,0.000018406578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003101802,0.00055015204,0.00056478695,0.00042330773,0.00032204602,0.0009007032,0.0017239511,0.0007672182,0.025581684],"category_scores_gemma":[0.00074113184,0.00033790243,0.0005077682,0.00065574853,0.00026941928,0.0009862713,0.0008663388,0.001071592,0.008597532],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020710778,0.00009291327,0.00036783764,0.00020300163,0.00006550721,0.00010407261,0.00003380003,0.23752308,0.007614719,0.024976596,0.03837135,0.69044006],"study_design_scores_gemma":[0.000020722213,0.000028543085,0.00010292228,0.000017247692,0.000013143005,0.00002934029,0.000011774889,0.9707701,0.004172784,0.013851194,0.010975907,0.0000063083567],"about_ca_topic_score_codex":0.016786514,"about_ca_topic_score_gemma":0.029272655,"teacher_disagreement_score":0.025581684,"about_ca_system_score_codex":0.00091450475,"about_ca_system_score_gemma":0.0009279683,"threshold_uncertainty_score":0.085579276},"labels":[],"label_agreement":null},{"id":"W4411047062","doi":"10.1088/1402-4896/ade1b2","title":"Effect of weak measurement reversal on quantum correlations in a correlated amplitude damping channel, with a neural network perspective","year":2025,"lang":"en","type":"article","venue":"Physica Scripta","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Amplitude; Perspective (graphical); Physics; Weak measurement; Quantum; Channel (broadcasting); Quantum mechanics; Statistical physics; Quantum electrodynamics; Computer science; Telecommunications; Artificial intelligence","score_opus":0.01696617457986935,"score_gpt":0.26292738500904594,"score_spread":0.2459612104291766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411047062","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9514018,0.00012017854,0.04601144,0.00042523697,0.000027107342,0.000020679883,0.000028743474,0.000092609946,0.0018721779],"genre_scores_gemma":[0.9977107,0.000027650174,0.0019416749,0.000016187205,0.0000035302114,0.0000075739513,0.0000063389766,0.0000052014166,0.0002810185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996704,0.00015097867,0.000014292062,0.00004219143,0.00006762571,0.000054591328],"domain_scores_gemma":[0.99771,0.0014955031,0.00035191182,0.00017626664,0.00016808766,0.00009819907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011767773,0.00035573813,0.00038117726,0.00026625974,0.00032963222,0.0005374354,0.00046765603,0.00048595443,0.0007743568],"category_scores_gemma":[0.004593281,0.00017043343,0.00027437802,0.00019509513,0.0012200286,0.0008288327,0.0008059442,0.00075320096,0.000040271152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021114535,0.00010259007,0.0020444544,0.000056975685,0.000053055388,0.00021207063,0.000065221735,0.95874506,0.017788649,0.014317035,0.00015650014,0.0062471856],"study_design_scores_gemma":[0.0000034749978,0.000019966703,0.00017256071,0.0000012894146,0.0000041298786,0.0000048553347,0.0000039075444,0.99732673,0.0016458721,0.0007988678,0.000014851188,0.000003518241],"about_ca_topic_score_codex":0.0025930083,"about_ca_topic_score_gemma":0.0016036798,"teacher_disagreement_score":0.0025930083,"about_ca_system_score_codex":0.00045477762,"about_ca_system_score_gemma":0.00059966784,"threshold_uncertainty_score":0.00622344},"labels":[],"label_agreement":null},{"id":"W4411100345","doi":"10.1101/2025.06.05.657930","title":"Neural coding of spectrotemporal modulations in the auditory cortex supports speech and music categorization","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; International Laboratory for Brain, Music and Sound Research; Centre hospitalier universitaire de Québec; Université Laval; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Agence Nationale de la Recherche; Fonds de Recherche du Québec - Santé; Fondation Pour l'Audition; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec; Fondation Brain Canada; European Commission; Aix-Marseille Université","keywords":"Auditory cortex; Categorization; Speech recognition; Coding (social sciences); Computer science; Psychology; Communication; Neuroscience; Artificial intelligence; Mathematics","score_opus":0.014648177511911033,"score_gpt":0.22522154878206216,"score_spread":0.21057337127015113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411100345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9900255,0.00014612201,0.007449657,0.000113847695,0.000013415618,0.000007212695,0.00015634962,0.00006886719,0.002018914],"genre_scores_gemma":[0.9984067,0.000031088024,0.0012337085,0.000014495225,0.000006171413,0.00000468022,0.00006430234,0.000005623558,0.00023316972],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999454,0.00000803354,0.0000027738135,0.000021849008,0.000011933353,0.000010053769],"domain_scores_gemma":[0.999739,0.00008568942,0.00007208742,0.00003226591,0.000033345717,0.00003762915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016552236,0.0001113507,0.0001210786,0.00020053706,0.00007757531,0.0003498746,0.00011622715,0.00017453967,0.0012777074],"category_scores_gemma":[0.00086615846,0.00010179967,0.000127363,0.00010911821,0.00029922096,0.00021849731,0.000293048,0.0002214563,0.00017544923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022726317,0.000018823492,0.009796815,0.00005544395,0.000023383827,0.000067242196,0.00010718242,0.000551147,0.96897507,0.00035498122,0.00014779935,0.01967503],"study_design_scores_gemma":[0.000022443703,0.00018120316,0.8598828,0.00002425889,0.000055477227,0.00044191154,0.00024559515,0.01771136,0.116819724,0.0039739395,0.00062600314,0.000015239269],"about_ca_topic_score_codex":0.00061539625,"about_ca_topic_score_gemma":0.00085342786,"teacher_disagreement_score":0.0012777074,"about_ca_system_score_codex":0.00009937195,"about_ca_system_score_gemma":0.00011188223,"threshold_uncertainty_score":0.0042743683},"labels":[],"label_agreement":null},{"id":"W4411132236","doi":"10.54254/2755-2721/2025.tj23594","title":"Communication-Efficient Distributed Machine Learning: Techniques and Innovations","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Distributed learning; Artificial intelligence; Psychology; Pedagogy","score_opus":0.004919266137499274,"score_gpt":0.21226038561700714,"score_spread":0.20734111947950787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411132236","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010979628,0.002383267,0.98265356,0.0006112199,0.000109992776,0.00003639311,0.000029086486,0.0008131219,0.0023837604],"genre_scores_gemma":[0.58902276,0.0037142215,0.40175062,0.00030266537,0.00042828222,0.00021629383,0.00023527518,0.00022248909,0.0041073593],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915445,0.00022546586,0.00004943088,0.00018818051,0.0003144061,0.000067973495],"domain_scores_gemma":[0.99868566,0.00051863474,0.00009255226,0.00039351126,0.00026344598,0.00004623601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011833913,0.0006975756,0.00072761934,0.0006713725,0.00044171503,0.0009426806,0.0016675172,0.0006337669,0.0009879594],"category_scores_gemma":[0.0031634066,0.00032013067,0.00037315706,0.00133381,0.0007034111,0.0020622998,0.0011396001,0.0015434904,0.00044073962],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016843235,0.00013779652,0.0010920352,0.00020147066,0.00006480493,0.00013543887,0.00018525722,0.3316344,0.008067724,0.044290453,0.005677165,0.60834503],"study_design_scores_gemma":[0.00002195006,0.000051239367,0.00019384026,0.000013921976,0.000010419482,0.000059096743,0.000025640544,0.9697151,0.0036458902,0.02177305,0.0044797473,0.000010085041],"about_ca_topic_score_codex":0.0021834725,"about_ca_topic_score_gemma":0.0015915055,"teacher_disagreement_score":0.0021834725,"about_ca_system_score_codex":0.0007632119,"about_ca_system_score_gemma":0.00090360094,"threshold_uncertainty_score":0.006258428},"labels":[],"label_agreement":null},{"id":"W4411149127","doi":"10.1016/j.compchemeng.2025.109208","title":"On ReLU neural networks as piecewise linear surrogate models","year":2025,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Piecewise linear function; Surrogate model; Computer science; Mathematics; Algorithm; Artificial intelligence; Applied mathematics; Mathematical optimization; Mathematical analysis","score_opus":0.007792015854324048,"score_gpt":0.2153730808202059,"score_spread":0.20758106496588186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411149127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01674428,0.00077094976,0.9731756,0.00045857896,0.00011779376,0.000025215673,0.000083068015,0.0001969738,0.008427546],"genre_scores_gemma":[0.7253278,0.0014782642,0.24290797,0.0005004839,0.00018095144,0.0002466809,0.0004560821,0.0004762273,0.02842563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992009,0.00054943125,0.00002105425,0.00007126759,0.00011358018,0.000043687243],"domain_scores_gemma":[0.99767166,0.001717631,0.00014906657,0.0001768333,0.00021020554,0.000074730975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020032008,0.0008214583,0.00090680114,0.0005910021,0.0003311662,0.001023373,0.0010767663,0.0017793527,0.0036367814],"category_scores_gemma":[0.008515143,0.0007461967,0.0006230088,0.00088146806,0.001063091,0.0019627204,0.001972486,0.0021202485,0.0006354183],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004816262,0.000018006705,0.00010855808,0.00004299585,0.000014402244,0.000030382505,0.000035582623,0.9248437,0.0003604239,0.05963612,0.0007361895,0.014125567],"study_design_scores_gemma":[0.0000014558448,0.0000067017986,0.000011930954,0.0000050040203,0.0000013760833,0.0000041892094,0.0000027971778,0.9890154,0.0000584565,0.010650793,0.00023993073,0.0000018980136],"about_ca_topic_score_codex":0.0024209518,"about_ca_topic_score_gemma":0.0022504947,"teacher_disagreement_score":0.0036367814,"about_ca_system_score_codex":0.0006187426,"about_ca_system_score_gemma":0.00044799986,"threshold_uncertainty_score":0.012166262},"labels":[],"label_agreement":null},{"id":"W4411212255","doi":"10.2166/9781789065107_0069","title":"Strukturerad lagring av signaler samt deras metadata","year":2025,"lang":"sv","type":"book-chapter","venue":"IWA Publishing eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Metadata; Computer science; World Wide Web","score_opus":0.03983684470326744,"score_gpt":0.24763302812084337,"score_spread":0.20779618341757594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411212255","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057809833,0.016792944,0.41707352,0.0060754344,0.0030269048,0.0003687069,0.0034250407,0.008370439,0.539086],"genre_scores_gemma":[0.036805753,0.020622432,0.17777815,0.002247315,0.0015618436,0.00035693176,0.005231167,0.00399888,0.75139755],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99823797,0.0002435875,0.00012463283,0.00042245098,0.0008783977,0.00009297397],"domain_scores_gemma":[0.9972554,0.0011559997,0.00010672541,0.00065837015,0.0007094704,0.000113971175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018222965,0.0012002342,0.00068624207,0.0027038842,0.0011860919,0.008150708,0.0016381518,0.0018089897,0.071083635],"category_scores_gemma":[0.004576644,0.00096897664,0.0011834415,0.004529371,0.0017047753,0.010520619,0.002924793,0.002652304,0.049892187],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014191154,0.00006990012,0.0005942383,0.0017298639,0.000054661028,0.00038521443,0.0020305058,0.003276262,0.017982354,0.23151043,0.15950465,0.58272004],"study_design_scores_gemma":[0.000007395557,0.00003977631,0.00038062353,0.0003817206,0.00002704927,0.0005261277,0.0003449805,0.0019662357,0.006810843,0.03221678,0.9572568,0.000041783966],"about_ca_topic_score_codex":0.0028043534,"about_ca_topic_score_gemma":0.0032796378,"teacher_disagreement_score":0.071083635,"about_ca_system_score_codex":0.0018043366,"about_ca_system_score_gemma":0.0018089793,"threshold_uncertainty_score":0.23779845},"labels":[],"label_agreement":null},{"id":"W4411225583","doi":"10.1017/9781009504942.010","title":"Physics for deep learning","year":2025,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo; McMaster University","funders":"","keywords":"Mathematics education; Psychology","score_opus":0.020266301972648424,"score_gpt":0.20608473402131605,"score_spread":0.18581843204866763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411225583","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025686582,0.09484769,0.64637595,0.020358536,0.004397173,0.00005891069,0.00061027834,0.00105229,0.22973047],"genre_scores_gemma":[0.1783579,0.13495341,0.33699566,0.01334238,0.0072251316,0.0005534929,0.0015732474,0.0014511235,0.3255476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99975485,0.000061989806,0.000010568756,0.00004540499,0.000107532396,0.00001961009],"domain_scores_gemma":[0.99968386,0.00019091027,0.000011359786,0.000048874634,0.00004629679,0.000018787727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006305601,0.00074095,0.00046298563,0.0006128001,0.00043420086,0.0014905384,0.0007792361,0.0013591185,0.016001014],"category_scores_gemma":[0.0015529919,0.0003497339,0.0004371183,0.0008140198,0.0013772045,0.0027024318,0.0013151034,0.0037611073,0.005090785],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075802127,0.000016502006,0.000057445923,0.0002033745,0.000017299051,0.000030276613,0.00004510703,0.006156969,0.0005629508,0.8815592,0.04021714,0.07112615],"study_design_scores_gemma":[0.0000036892495,0.000006690901,0.000057212164,0.0001084503,0.000004027615,0.000050578237,0.000010372166,0.013310273,0.00033739008,0.8575511,0.12855293,0.000007303761],"about_ca_topic_score_codex":0.0010216272,"about_ca_topic_score_gemma":0.001330272,"teacher_disagreement_score":0.016001014,"about_ca_system_score_codex":0.0013612458,"about_ca_system_score_gemma":0.00075854716,"threshold_uncertainty_score":0.053528726},"labels":[],"label_agreement":null},{"id":"W4411233728","doi":"10.1109/i-coste63786.2024.11024754","title":"Improving Generalization in Convolutional Neural Networks with a Dynamic Attention Layer","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Generalization; Convolutional neural network; Layer (electronics); Artificial intelligence; Mathematics; Materials science","score_opus":0.008617628536668684,"score_gpt":0.23379742096717362,"score_spread":0.22517979243050495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411233728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2549144,0.0014717353,0.7363641,0.00075424236,0.00008228275,0.000055552722,0.00014824813,0.0029861776,0.0032231899],"genre_scores_gemma":[0.95732594,0.00024360456,0.040012248,0.0002467548,0.00003748741,0.00002958978,0.0001345357,0.00008393775,0.0018858712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958426,0.00008668683,0.000023638839,0.00012027987,0.000096514865,0.00008860909],"domain_scores_gemma":[0.9986461,0.0007357407,0.00014055577,0.00022345802,0.00020701818,0.000047163878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014911053,0.0009910899,0.00061660475,0.00053646887,0.00032870114,0.0006623385,0.00137999,0.0009230219,0.0009902999],"category_scores_gemma":[0.0050924444,0.00038539123,0.00048084307,0.0005781667,0.0007274134,0.0019325386,0.0017325389,0.0014194024,0.00034389566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023941112,0.00016080265,0.0050406433,0.00009042621,0.00011085329,0.00013447215,0.00013966685,0.66680515,0.02063482,0.0049485117,0.0020608734,0.29963443],"study_design_scores_gemma":[0.000006696667,0.000054462238,0.0005766054,0.0000063811494,0.0000127390185,0.000025121637,0.000011420358,0.9923767,0.0038702395,0.002684221,0.0003693788,0.0000059544914],"about_ca_topic_score_codex":0.009373058,"about_ca_topic_score_gemma":0.009974163,"teacher_disagreement_score":0.009373058,"about_ca_system_score_codex":0.0013216195,"about_ca_system_score_gemma":0.00085892144,"threshold_uncertainty_score":0.018637002},"labels":[],"label_agreement":null},{"id":"W4411238656","doi":"10.2139/ssrn.5287593","title":"Neural Correlates of Nominal Classification Rule Learning: The Role of Semantic Processing and Individual Differences","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Natural language processing; Computer science; Semantic memory; Psychology; Cognition; Neuroscience","score_opus":0.016426466902011844,"score_gpt":0.24873562572369382,"score_spread":0.23230915882168196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411238656","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9890731,0.00018132891,0.0046519116,0.00022577273,0.00005209066,0.000032854234,0.0004827029,0.000039148985,0.005261149],"genre_scores_gemma":[0.99730116,0.00007055412,0.0014756537,0.000053533597,0.000030526564,0.000018272902,0.00032014775,0.000050593124,0.00067953806],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993247,0.00017981733,0.000049387185,0.00024664504,0.0001566571,0.00004274969],"domain_scores_gemma":[0.98878866,0.007032659,0.0017039035,0.0014071737,0.0007342942,0.00033332314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016145777,0.00022372715,0.0004252191,0.00043171193,0.00017749431,0.00188751,0.00047346152,0.0007207951,0.0043726335],"category_scores_gemma":[0.018999781,0.00033235893,0.00021832094,0.00050524465,0.00070026354,0.0014353893,0.0005770917,0.0010568404,0.00054570247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0069532013,0.0016053906,0.4450342,0.0005235731,0.0011500623,0.0007446379,0.0050305477,0.0137004815,0.2483105,0.024872258,0.004787167,0.24728803],"study_design_scores_gemma":[0.000093087096,0.0003405001,0.9399794,0.00002437896,0.00012553361,0.00052936166,0.00040211232,0.017794022,0.006994642,0.032654863,0.0010017316,0.000060335165],"about_ca_topic_score_codex":0.0007783767,"about_ca_topic_score_gemma":0.00076752645,"teacher_disagreement_score":0.0043726335,"about_ca_system_score_codex":0.0002109305,"about_ca_system_score_gemma":0.00021563873,"threshold_uncertainty_score":0.014627874},"labels":[],"label_agreement":null},{"id":"W4411254892","doi":"10.1101/2025.06.07.658464","title":"On the relationship between neuronal heterogeneity and entropy","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Brain Institute; Krembil Foundation; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Entropy (arrow of time); Mathematics; Statistical physics; Physics; Thermodynamics","score_opus":0.03462313480790538,"score_gpt":0.2500747162230259,"score_spread":0.21545158141512052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411254892","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62454224,0.0092878565,0.32592332,0.0055977562,0.00028696022,0.000043025913,0.0006170915,0.00039150106,0.033310264],"genre_scores_gemma":[0.990022,0.00124813,0.0069418894,0.00020629184,0.00035823352,0.00003450584,0.000120090735,0.000069604546,0.0009992822],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987923,0.0004523938,0.00006856046,0.00027627917,0.00030187902,0.00010863293],"domain_scores_gemma":[0.96681476,0.029283367,0.0015433992,0.001220991,0.00067791605,0.00045964506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023645633,0.0003946234,0.00076420425,0.0019333132,0.00056595006,0.0014786449,0.0005989747,0.0006852167,0.0013246363],"category_scores_gemma":[0.025736649,0.0003079359,0.00040030014,0.0011556185,0.0034611481,0.0028603354,0.001571707,0.0011409628,0.00019386735],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027744338,0.000087451925,0.029247385,0.0003209381,0.00031261073,0.00059185125,0.0007209062,0.18594547,0.011902148,0.6987354,0.0035302385,0.06832815],"study_design_scores_gemma":[0.00001591143,0.000048980277,0.021810377,0.00005439687,0.000028738248,0.0002740687,0.000067582274,0.1949864,0.0013488783,0.78036994,0.0009244299,0.00007024202],"about_ca_topic_score_codex":0.00091891526,"about_ca_topic_score_gemma":0.00046651464,"teacher_disagreement_score":0.0023645633,"about_ca_system_score_codex":0.000806021,"about_ca_system_score_gemma":0.00032513594,"threshold_uncertainty_score":0.012505114},"labels":[],"label_agreement":null},{"id":"W4411264603","doi":"10.1088/1361-6633/ade453","title":"Quantum Fisher information and its dynamical nature","year":2025,"lang":"en","type":"review","venue":"Reports on Progress in Physics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute","funders":"QuantERA; European Research Council; Generalitat de Catalunya; Fundación Cellex; Austrian Science Fund; FUNDACIÓ Privada MIR-PUIG; UniCredit and Universities Foundation; Fundación Carmen y Severo Ochoa; HORIZON EUROPE Marie Sklodowska-Curie Actions; Scuola Normale Superiore","keywords":"Physics; Quantum; Quantum mechanics; Statistical physics; Theoretical physics; Mathematical physics","score_opus":0.01574718806824812,"score_gpt":0.3227085089536169,"score_spread":0.3069613208853688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411264603","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023276317,0.9496616,0.016049167,0.004066281,0.00056691386,0.000011378415,0.000082141836,0.000029759452,0.027205124],"genre_scores_gemma":[0.055050977,0.9347441,0.0040358743,0.00097334024,0.00093596784,0.00003518123,0.00008797619,0.000016587655,0.004119847],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999696,0.000088189365,0.000020514217,0.000054650445,0.00011717079,0.000023578774],"domain_scores_gemma":[0.9993864,0.0004330191,0.000045850895,0.000030348601,0.00008528449,0.000019044319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006852484,0.00050901505,0.00067479315,0.0018622129,0.00039296897,0.0013216666,0.00050721917,0.00087167666,0.0021564881],"category_scores_gemma":[0.0016071989,0.00024036929,0.00033591536,0.0016918627,0.002643743,0.0028360945,0.00077102793,0.0015890275,0.00042937257],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013195109,0.00001236015,0.00032451734,0.0032415146,0.000046235007,0.00013859813,0.00024298576,0.0015238825,0.001246187,0.7780126,0.010138751,0.20505925],"study_design_scores_gemma":[0.0000036163085,0.0000415838,0.0015078188,0.002336644,0.000040128358,0.0009295565,0.00018400692,0.0016303447,0.0009492324,0.5322344,0.46009701,0.00004569596],"about_ca_topic_score_codex":0.0015746444,"about_ca_topic_score_gemma":0.0015875009,"teacher_disagreement_score":0.0021564881,"about_ca_system_score_codex":0.0010885419,"about_ca_system_score_gemma":0.001190639,"threshold_uncertainty_score":0.007897973},"labels":[],"label_agreement":null},{"id":"W4411297226","doi":"10.52214/ow.v2i1.12700","title":"Polyrhythmic Cognition and Metric Spaces","year":2025,"lang":"en","type":"article","venue":"openwork","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Metric space; Metric (unit); Cognition; Mathematics; Pure mathematics; Psychology; Business","score_opus":0.008937071427612053,"score_gpt":0.2577977858364358,"score_spread":0.24886071440882376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411297226","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16532634,0.0040346864,0.7116638,0.0025283657,0.00022662502,0.00003204567,0.00012698311,0.00021921389,0.115842],"genre_scores_gemma":[0.9439788,0.0007446971,0.050301496,0.0001216305,0.00010093414,0.000039826813,0.0000638946,0.000027708753,0.0046210005],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99924904,0.0003060385,0.000036949932,0.0001996763,0.00015969352,0.000048542584],"domain_scores_gemma":[0.99906045,0.0003729324,0.00017365476,0.00016726005,0.00012540109,0.000100306985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000703381,0.000398674,0.00024473912,0.0009666907,0.0005189904,0.002409753,0.0005824649,0.00047080373,0.002941853],"category_scores_gemma":[0.002269461,0.00011986826,0.00037163604,0.0005545798,0.005517847,0.003510691,0.0018008326,0.0008186065,0.00023141818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018278908,0.000009158234,0.00044286263,0.000045793837,0.000016321475,0.000037878715,0.0009757462,0.0031028495,0.0011152381,0.9688956,0.00032562407,0.025014674],"study_design_scores_gemma":[0.000004279957,0.000015357375,0.00073885964,0.000010256777,0.0000032089285,0.000048380218,0.00032124374,0.008202336,0.00025713255,0.9843077,0.006081529,0.000009657559],"about_ca_topic_score_codex":0.0018182452,"about_ca_topic_score_gemma":0.0016309905,"teacher_disagreement_score":0.002941853,"about_ca_system_score_codex":0.0010879822,"about_ca_system_score_gemma":0.0003521543,"threshold_uncertainty_score":0.009841442},"labels":[],"label_agreement":null},{"id":"W4411300841","doi":"10.1016/j.engappai.2025.111164","title":"Dynamical multiple polynomial-based neural networks classifier realized with the aid of dropfilter and dual statistical selection","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Natural Science Foundation of China","keywords":"Computer science; Artificial neural network; Classifier (UML); Dual (grammatical number); Artificial intelligence; Selection (genetic algorithm); Machine learning; Pattern recognition (psychology)","score_opus":0.010146688308789455,"score_gpt":0.24639171596242257,"score_spread":0.23624502765363312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411300841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030756071,0.0003017137,0.96659464,0.00021539856,0.000084818996,0.00005459285,0.0000768376,0.0007650487,0.0011508643],"genre_scores_gemma":[0.73532206,0.00043846652,0.25764126,0.00028522432,0.00012607084,0.00021246646,0.0006615638,0.00013180875,0.0051811286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992865,0.0001268748,0.000039207098,0.00019749615,0.0002600285,0.00008990099],"domain_scores_gemma":[0.9991442,0.0002535663,0.00009911445,0.00009381561,0.00036592787,0.000043377313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012615534,0.0008246628,0.00091256935,0.0008281478,0.0005772389,0.0008481507,0.0016334591,0.00084676465,0.0011854423],"category_scores_gemma":[0.0030267455,0.00028963323,0.0009410035,0.00079066085,0.00045945088,0.0014696865,0.0010940441,0.0014162833,0.00046921906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036114486,0.00020933039,0.0043110587,0.000120218305,0.000116153344,0.00021211183,0.00010718646,0.41542903,0.021115083,0.014260715,0.0048167715,0.53894126],"study_design_scores_gemma":[0.0000031454517,0.000019694107,0.00016012929,0.0000018789228,0.0000057183292,0.00001246133,0.0000037914263,0.99718565,0.0015213276,0.0007803316,0.0003022385,0.000003583213],"about_ca_topic_score_codex":0.005859041,"about_ca_topic_score_gemma":0.006185839,"teacher_disagreement_score":0.005859041,"about_ca_system_score_codex":0.0009414931,"about_ca_system_score_gemma":0.0011237684,"threshold_uncertainty_score":0.011649907},"labels":[],"label_agreement":null},{"id":"W4411337774","doi":"10.1109/cain66642.2025.00027","title":"Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Debugging; Computer science; Artificial neural network; Programming language; Software bug; Software engineering; Artificial intelligence; Software","score_opus":0.007689746056186294,"score_gpt":0.25347563313315935,"score_spread":0.24578588707697305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411337774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80392975,0.000565562,0.17709234,0.00086461694,0.00010264907,0.000109206034,0.00068874,0.014879008,0.0017680946],"genre_scores_gemma":[0.9523958,0.00005378639,0.046376437,0.00008556732,0.000007871034,0.00004035097,0.00033869268,0.0002631595,0.00043835564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99810743,0.0006417277,0.00010026226,0.00048145783,0.00050842704,0.00016069331],"domain_scores_gemma":[0.9904953,0.006164314,0.0006419819,0.001581307,0.00090299855,0.00021400428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024100833,0.00093094254,0.00039105877,0.00063131086,0.00033826858,0.00065847585,0.0014336748,0.0008499099,0.00089188485],"category_scores_gemma":[0.015576721,0.00034233066,0.0005056397,0.0003681312,0.0009278017,0.0013826387,0.00090369774,0.001298193,0.00015280941],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010530936,0.00035184008,0.04070496,0.0005114385,0.00019666413,0.0027771608,0.001087835,0.7359927,0.028254442,0.010582361,0.007123091,0.17136435],"study_design_scores_gemma":[0.000019953106,0.00011328638,0.0018283556,0.000018681161,0.000017670156,0.00016526137,0.00007414546,0.9778415,0.015385702,0.003836347,0.0006872612,0.000011864144],"about_ca_topic_score_codex":0.005430921,"about_ca_topic_score_gemma":0.005814601,"teacher_disagreement_score":0.005430921,"about_ca_system_score_codex":0.0012888488,"about_ca_system_score_gemma":0.0009329853,"threshold_uncertainty_score":0.012745917},"labels":[],"label_agreement":null},{"id":"W4411505858","doi":"10.1504/ijict.2025.146834","title":"Recurrent neural network optimisation based on linearly constrained numerical methods","year":2025,"lang":"en","type":"article","venue":"International Journal of Information and Communication Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial neural network; Recurrent neural network; Artificial intelligence; Mathematical optimization","score_opus":0.012054222599626864,"score_gpt":0.3268048913773825,"score_spread":0.31475066877775565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411505858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0084740305,0.00027334594,0.98808265,0.00015212833,0.00003604354,0.000034285767,0.000039023867,0.00043234043,0.0024762067],"genre_scores_gemma":[0.5411753,0.00048793267,0.45019838,0.00027057185,0.000075987344,0.0004884681,0.00030185597,0.0005116717,0.006489756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942905,0.0002425426,0.000041791365,0.00011513826,0.00012434606,0.000047052446],"domain_scores_gemma":[0.9982424,0.0011979524,0.00016950787,0.0000883918,0.00026556145,0.00003620576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015337975,0.0010858492,0.0011745911,0.00059154484,0.00034702543,0.001000543,0.0014367172,0.0015132712,0.0028516785],"category_scores_gemma":[0.005786602,0.00058132346,0.0009160409,0.00064090797,0.000840905,0.0012964741,0.001137741,0.0014592939,0.00064549694],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020997562,0.000009788092,0.00012398462,0.000054961358,0.000019332721,0.000026524578,0.000025722726,0.9815993,0.0008444871,0.0038926578,0.00029265002,0.01308971],"study_design_scores_gemma":[0.0000015449474,0.0000026372961,0.000007644249,0.000001955949,0.0000010666956,0.0000017123384,9.478922e-7,0.999231,0.00009458254,0.0005883911,0.000067196204,0.0000013338515],"about_ca_topic_score_codex":0.0061888597,"about_ca_topic_score_gemma":0.005313037,"teacher_disagreement_score":0.0061888597,"about_ca_system_score_codex":0.00071152113,"about_ca_system_score_gemma":0.0011369296,"threshold_uncertainty_score":0.012305677},"labels":[],"label_agreement":null},{"id":"W4411638995","doi":"10.2139/ssrn.5320552","title":"Semi-Supervised Neural Network Model for Quadratic Multiparametric Programming","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial neural network; Multiparametric MRI; Computer science; Quadratic programming; Artificial intelligence; Quadratic equation; Machine learning; Mathematical optimization; Mathematics; Medicine","score_opus":0.025021557731753994,"score_gpt":0.2850355813184222,"score_spread":0.2600140235866682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411638995","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014349558,0.00022143956,0.9829313,0.0004458315,0.000035148372,0.00004700354,0.00029156802,0.0001747808,0.0015034544],"genre_scores_gemma":[0.81382227,0.0005377212,0.1572436,0.0004164475,0.00025237075,0.00058992446,0.0015434626,0.0002571687,0.025337102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861026,0.00076763827,0.000048719263,0.0003074777,0.00017021826,0.00009567649],"domain_scores_gemma":[0.99476886,0.0035946763,0.00045840073,0.00033893992,0.0006708698,0.00016827122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029600537,0.0009464858,0.0017875938,0.00054777967,0.0003827344,0.0014033401,0.0026099042,0.0021200073,0.0050161327],"category_scores_gemma":[0.009206147,0.001000429,0.0010058504,0.0007950724,0.0014109678,0.0021956342,0.0021648018,0.002833665,0.0008726902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013502047,0.0000902409,0.0006295833,0.00015931632,0.00008677431,0.00006499262,0.00007585405,0.9336796,0.0006913453,0.040183317,0.0022675258,0.021936426],"study_design_scores_gemma":[0.00000478395,0.000009597888,0.00007679743,0.0000036429929,0.0000031643356,0.0000058585447,0.000002043033,0.991956,0.000050825343,0.0077381236,0.00014492165,0.000004252677],"about_ca_topic_score_codex":0.004918366,"about_ca_topic_score_gemma":0.00590887,"teacher_disagreement_score":0.0050161327,"about_ca_system_score_codex":0.00119326,"about_ca_system_score_gemma":0.001533214,"threshold_uncertainty_score":0.016780615},"labels":[],"label_agreement":null},{"id":"W4411659371","doi":"10.1101/2025.06.23.661173","title":"Spatially and non-spatially tuned hippocampal neurons are linear perceptual and nonlinear memory encoders","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; York University; Université de Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada; Canada Foundation for Innovation; Compute Canada","keywords":"Hippocampal formation; Nonlinear system; Perception; Encoder; Neuroscience; Computer science; Physics; Psychology; Quantum mechanics","score_opus":0.012584413190702895,"score_gpt":0.22205507574480268,"score_spread":0.2094706625540998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411659371","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.896917,0.00031326766,0.09515656,0.0003514622,0.000033469212,0.000011530019,0.00012441973,0.00022050783,0.006871816],"genre_scores_gemma":[0.994753,0.000052999218,0.0040498297,0.000017444767,0.000005017513,0.0000033473254,0.000017117212,0.000010992271,0.0010902444],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999378,0.000009968483,0.000003657403,0.000020993259,0.000012801751,0.000014785032],"domain_scores_gemma":[0.9997272,0.00009056267,0.00007663075,0.000037416576,0.000038743612,0.000029457078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018528297,0.00015792159,0.00020053746,0.00017318378,0.00013071619,0.00078007905,0.00030351605,0.0002711135,0.0015490288],"category_scores_gemma":[0.0010297573,0.00019889105,0.00019287819,0.00015258678,0.0005632395,0.00079369615,0.00042750512,0.00033374014,0.00017033741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007133096,0.00012882224,0.02339178,0.0003268661,0.0001854209,0.0005888176,0.00074647344,0.1180031,0.6360092,0.11603455,0.0014644861,0.102407075],"study_design_scores_gemma":[0.00002582424,0.0001154452,0.026478605,0.0000327134,0.000056805933,0.00031637624,0.0002118029,0.8323014,0.05515097,0.08416006,0.0011078337,0.00004214905],"about_ca_topic_score_codex":0.0011884765,"about_ca_topic_score_gemma":0.0011559041,"teacher_disagreement_score":0.0015490288,"about_ca_system_score_codex":0.0002592136,"about_ca_system_score_gemma":0.00020504462,"threshold_uncertainty_score":0.005182028},"labels":[],"label_agreement":null},{"id":"W4411687279","doi":"10.1523/eneuro.0383-24.2025","title":"Spiking Neural Network Models of Interaural Time Difference Extraction via a Massively Collaborative Process","year":2025,"lang":"en","type":"article","venue":"eNeuro","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Ottawa","funders":"","keywords":"Computer science; Massively parallel; Artificial neural network; Extraction (chemistry); Process (computing); Spiking neural network; Artificial intelligence; Chemistry; Parallel computing; Chromatography","score_opus":0.011070909427378619,"score_gpt":0.2698035019395796,"score_spread":0.258732592512201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411687279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095250905,0.00042362057,0.89461005,0.0012394402,0.00010310368,0.00007847209,0.00044044634,0.000599616,0.0072542927],"genre_scores_gemma":[0.90889823,0.00035369516,0.078834385,0.00021080178,0.00007873993,0.00018942737,0.00038359832,0.00018210034,0.010869022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996952,0.00008696243,0.000015803147,0.00008711282,0.00007654416,0.000038459726],"domain_scores_gemma":[0.99879503,0.0006811316,0.00013477502,0.00012872543,0.00017456643,0.000085776854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013065303,0.00067324477,0.00089275505,0.0005179933,0.0004904755,0.0012494089,0.0025892437,0.001991269,0.003329041],"category_scores_gemma":[0.004625962,0.0007664158,0.0013869551,0.00080434256,0.0012935515,0.0021898178,0.0015628247,0.0018219668,0.00056066585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041975207,0.000016987391,0.0004075932,0.000023332284,0.00002055986,0.00006733351,0.000047563506,0.9808694,0.0007799499,0.013253896,0.00043372368,0.0040376675],"study_design_scores_gemma":[0.000002450805,0.0000015644233,0.000029063547,0.0000010482748,0.0000011687728,0.0000031879554,0.0000012603878,0.9974504,0.000049535698,0.0023998944,0.000058869828,0.0000015891488],"about_ca_topic_score_codex":0.014691598,"about_ca_topic_score_gemma":0.011785077,"teacher_disagreement_score":0.014691598,"about_ca_system_score_codex":0.0014797576,"about_ca_system_score_gemma":0.0010571626,"threshold_uncertainty_score":0.029212177},"labels":[],"label_agreement":null},{"id":"W4411725783","doi":"10.1109/iscas56072.2025.11044080","title":"MATSORT: Fast and Efficient Approximate Matrix Sorting Algorithm for DNN Applications","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Sorting; Algorithm; Matrix (chemical analysis); Sorting algorithm; Parallel computing; Algorithm design","score_opus":0.00837458434797329,"score_gpt":0.2776335451684636,"score_spread":0.26925896082049033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411725783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022265801,0.00087137055,0.9573384,0.0002996349,0.00022830896,0.0001580708,0.0004953653,0.011912131,0.006430861],"genre_scores_gemma":[0.10785361,0.0004901844,0.88318616,0.00024624102,0.000072339586,0.00023445133,0.0015579172,0.00061090634,0.005748232],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964225,0.000042696687,0.00003704582,0.00005401919,0.00018271059,0.00004127328],"domain_scores_gemma":[0.9995968,0.00011373696,0.000041810057,0.000058041427,0.00015861659,0.000031020027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041794937,0.0011040437,0.00052506384,0.0011075246,0.00062999903,0.00094210287,0.0015192052,0.0006563976,0.0066231308],"category_scores_gemma":[0.0015054531,0.0003892393,0.00050365785,0.0012226414,0.00035125588,0.0015897176,0.0009337034,0.0007988784,0.0023107764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005675613,0.00012749265,0.0012965009,0.0003675672,0.00011476621,0.00024100476,0.00016236759,0.1180403,0.031290013,0.020643197,0.031247595,0.7959016],"study_design_scores_gemma":[0.00008084963,0.00011935339,0.00025042953,0.00003102839,0.000021951326,0.0001605397,0.00006911268,0.9464846,0.022126382,0.012133455,0.01849369,0.0000286379],"about_ca_topic_score_codex":0.007072781,"about_ca_topic_score_gemma":0.017767629,"teacher_disagreement_score":0.007072781,"about_ca_system_score_codex":0.00087097095,"about_ca_system_score_gemma":0.0021040977,"threshold_uncertainty_score":0.022156537},"labels":[],"label_agreement":null},{"id":"W4411726178","doi":"10.1109/iscas56072.2025.11044077","title":"Enhancing Common Subexpression Elimination Algorithms","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Algorithm","score_opus":0.009388838050406426,"score_gpt":0.27926162389833675,"score_spread":0.26987278584793034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411726178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06923041,0.0007657442,0.92187005,0.00019952783,0.00010570802,0.00008887529,0.000078740486,0.0015984231,0.0060625575],"genre_scores_gemma":[0.29551432,0.000484042,0.6925266,0.0003211135,0.000084709514,0.000094434065,0.00035179596,0.0003180825,0.010304837],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989392,0.00016978238,0.000084838866,0.00017905302,0.00047083353,0.00015626369],"domain_scores_gemma":[0.99836355,0.0005731164,0.00012525101,0.00031460274,0.0005794221,0.00004406496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089972245,0.0007638966,0.00069433224,0.0014175936,0.00050727086,0.0010042378,0.0013116879,0.00054654625,0.004217426],"category_scores_gemma":[0.0032741823,0.000269602,0.0006658251,0.0013652672,0.000483625,0.0016292887,0.0010045718,0.0008789008,0.0014403756],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003294721,0.00015034253,0.001822039,0.00022964453,0.000067922614,0.00028535796,0.0002018855,0.04128002,0.059786994,0.03467973,0.0050248196,0.85614175],"study_design_scores_gemma":[0.00016237152,0.000495642,0.0017570844,0.00006601448,0.0001312265,0.0013146789,0.00014581898,0.7009168,0.2068669,0.039881214,0.04820358,0.000058709284],"about_ca_topic_score_codex":0.001531159,"about_ca_topic_score_gemma":0.0032834823,"teacher_disagreement_score":0.004217426,"about_ca_system_score_codex":0.00052054186,"about_ca_system_score_gemma":0.0014655914,"threshold_uncertainty_score":0.014108658},"labels":[],"label_agreement":null},{"id":"W4411771806","doi":"10.5539/ijsp.v14n2p21","title":"Bivariate-Uniform Representations and Copula Approximations","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Copula (linguistics); Bivariate analysis; Mathematics; Econometrics; Applied mathematics; Statistics; Mathematical economics","score_opus":0.015696599492976096,"score_gpt":0.30251397692665716,"score_spread":0.2868173774336811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411771806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008954886,0.00052987883,0.984654,0.00023982521,0.000049852544,0.000016461021,0.000098075325,0.000234916,0.0052220165],"genre_scores_gemma":[0.7042847,0.00289218,0.27951267,0.0005442676,0.0003487338,0.00020327265,0.0008238406,0.00048717303,0.0109030465],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99900526,0.000450635,0.000037346148,0.00013588385,0.00023375517,0.00013706445],"domain_scores_gemma":[0.99757,0.0013014057,0.00025686488,0.0003862907,0.0003746658,0.00011068143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002152362,0.0008078565,0.0006597718,0.0014583707,0.00039788315,0.0016916156,0.0014475832,0.0009548167,0.0050584883],"category_scores_gemma":[0.016121788,0.00039631294,0.00088757736,0.001931109,0.00091258675,0.0032070682,0.0012715777,0.0019038119,0.0009115474],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003665156,0.000029791994,0.0008672338,0.000060506056,0.000040276675,0.00009413065,0.000110674955,0.1490724,0.00043586674,0.80152774,0.0044043255,0.043320484],"study_design_scores_gemma":[0.000007549638,0.000014456312,0.0003355702,0.000032838383,0.00001221952,0.00008547419,0.00004546293,0.7475776,0.000266873,0.24825343,0.003350643,0.00001786891],"about_ca_topic_score_codex":0.004334456,"about_ca_topic_score_gemma":0.0028445013,"teacher_disagreement_score":0.0050584883,"about_ca_system_score_codex":0.0010860013,"about_ca_system_score_gemma":0.00079283706,"threshold_uncertainty_score":0.016922355},"labels":[],"label_agreement":null},{"id":"W4411793001","doi":"10.18280/ts.420310","title":"An Automated Mucilage Detection Model Using Deep Convolutional Neural Network: TuncerNeXt","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mucilage; Convolutional neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Deep learning; Artificial neural network; Biology; Botany","score_opus":0.0259592363801434,"score_gpt":0.2859583494479228,"score_spread":0.2599991130677794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411793001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2941898,0.002506838,0.67598486,0.0007505271,0.0004574646,0.0003146817,0.0033238232,0.011961639,0.010510298],"genre_scores_gemma":[0.821848,0.0009817707,0.1543797,0.00057465973,0.000082004786,0.00021182772,0.006162425,0.00033194115,0.015427601],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985254,0.0000068770014,0.0000056614545,0.000062718456,0.00003787307,0.000034334214],"domain_scores_gemma":[0.9998486,0.000025032614,0.000021951944,0.000022862096,0.00006581066,0.00001579864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028013138,0.0010639592,0.0005587331,0.0008322972,0.00024822867,0.000821344,0.0015980827,0.0007880357,0.0018675526],"category_scores_gemma":[0.0005906927,0.00048732475,0.00090768543,0.00045379004,0.00027265897,0.0010919226,0.000897065,0.00080768485,0.0007823458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008042497,0.00031276935,0.016137492,0.00033487356,0.00030008383,0.0005478296,0.00007985006,0.40637827,0.06687701,0.0036076554,0.01697841,0.48764148],"study_design_scores_gemma":[0.000011218691,0.00007026958,0.0014672119,0.000016729899,0.00003993136,0.000110447676,0.000012341079,0.9848636,0.010706825,0.00049588684,0.0021915846,0.0000140370785],"about_ca_topic_score_codex":0.016095208,"about_ca_topic_score_gemma":0.024728,"teacher_disagreement_score":0.016095208,"about_ca_system_score_codex":0.0011883242,"about_ca_system_score_gemma":0.0011476775,"threshold_uncertainty_score":0.032003045},"labels":[],"label_agreement":null},{"id":"W4411981123","doi":"10.1007/s11071-025-11533-1","title":"Type-3 fuzzy discrete-time modeling and synchronization of financial chaotic/hyperchaotic systems","year":2025,"lang":"en","type":"article","venue":"Nonlinear Dynamics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Synchronization (alternating current); Control theory (sociology); Discrete time and continuous time; Chaotic systems; Chaotic; Fuzzy logic; Computer science; Mathematics; Topology (electrical circuits); Control (management); Artificial intelligence; Statistics","score_opus":0.007426623346318635,"score_gpt":0.23526377921004443,"score_spread":0.2278371558637258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411981123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3395964,0.0003926617,0.6470494,0.00034681882,0.00013616902,0.000038666127,0.00012358265,0.00015326065,0.0121631045],"genre_scores_gemma":[0.9924884,0.0000808814,0.005733314,0.000011404653,0.000008905023,0.000012821544,0.000021641898,0.0000064939445,0.0016363066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99994326,0.000015391692,0.0000043463224,0.000012015788,0.000016962198,0.00000808464],"domain_scores_gemma":[0.9998814,0.000037495254,0.00002869914,0.0000119761835,0.000030298286,0.000010108183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027830544,0.00024110104,0.00036224473,0.00021677668,0.0002781744,0.0005967535,0.0004788408,0.00048345397,0.0010186917],"category_scores_gemma":[0.00046730577,0.00012177727,0.0004197371,0.00021230437,0.00034938063,0.0003626483,0.0002666517,0.00040666747,0.000107579115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088461595,0.00003098242,0.00124927,0.000045653185,0.000043258537,0.00015280029,0.000112830545,0.93077284,0.011155513,0.044070132,0.00031898808,0.011959305],"study_design_scores_gemma":[0.0000018061131,0.00000485248,0.000119774566,0.000001005427,0.0000024054618,0.0000064426868,0.0000037742382,0.9974003,0.00025967212,0.0021265158,0.0000711056,0.000002280338],"about_ca_topic_score_codex":0.0056059603,"about_ca_topic_score_gemma":0.0033826209,"teacher_disagreement_score":0.0056059603,"about_ca_system_score_codex":0.00047275572,"about_ca_system_score_gemma":0.00034592513,"threshold_uncertainty_score":0.011146665},"labels":[],"label_agreement":null},{"id":"W4411990890","doi":"10.1101/2025.07.02.662762","title":"Simultaneous cortical tracking of competing speech streams during attention switching","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"William Demant Fonden; Trinity College Dublin; Science Foundation Ireland","keywords":"STREAMS; Computer science; Tracking (education); Speech recognition; Psychology; Computer network","score_opus":0.01130518792789947,"score_gpt":0.23308589445968406,"score_spread":0.22178070653178458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411990890","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944992,0.00016537477,0.0044583664,0.000027571898,0.0000114939285,0.0000138751975,0.000065265136,0.000053495158,0.0007052448],"genre_scores_gemma":[0.99836165,0.000053011907,0.001303608,0.000018176986,0.000007205979,0.000011105171,0.000046786903,0.0000076807155,0.00019063104],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99988484,0.00001718138,0.000005801418,0.00003436186,0.00003659277,0.000021149408],"domain_scores_gemma":[0.9997303,0.00013958834,0.000044426255,0.000018239527,0.000035374433,0.00003205279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015776823,0.00020494514,0.00015479939,0.00021336204,0.000101168414,0.00031041127,0.00011997128,0.00020227578,0.00084983255],"category_scores_gemma":[0.0011416341,0.00010126503,0.00010604614,0.00013425542,0.00022819753,0.0002645875,0.0004013203,0.00027745,0.00013045035],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037611998,0.000027363089,0.0036697453,0.000057051933,0.000017658851,0.00018358458,0.00029215447,0.00019965183,0.97709936,0.00014455429,0.00009238769,0.017840339],"study_design_scores_gemma":[0.000061584215,0.00091751775,0.6558033,0.000046089535,0.00009695914,0.001337529,0.00073001854,0.011872843,0.32625487,0.0015351805,0.0013059735,0.000038130936],"about_ca_topic_score_codex":0.00054395036,"about_ca_topic_score_gemma":0.00067448424,"teacher_disagreement_score":0.00084983255,"about_ca_system_score_codex":0.00009359539,"about_ca_system_score_gemma":0.00009081825,"threshold_uncertainty_score":0.0028430223},"labels":[],"label_agreement":null},{"id":"W4412030861","doi":"10.1109/icphm65385.2025.11062060","title":"Query based learning via conformal uncertainty for RUL prediction","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Conformal map; Computer science; Artificial intelligence; Mathematics","score_opus":0.007863187623551995,"score_gpt":0.24503932853750726,"score_spread":0.23717614091395528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412030861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030883135,0.0004697281,0.9666784,0.00017477474,0.000031272484,0.000050675124,0.00015826171,0.00083391584,0.0007199348],"genre_scores_gemma":[0.9046545,0.00035442738,0.09284137,0.00019316509,0.000112570364,0.000094477604,0.000682152,0.00009340202,0.00097395625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902964,0.00019476768,0.00006234774,0.00028559862,0.00033552642,0.00009207906],"domain_scores_gemma":[0.9976146,0.0014924163,0.00020287657,0.00026149082,0.00035227693,0.00007631255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014189057,0.0008648237,0.0014963421,0.0009766364,0.0003848222,0.0010232267,0.0015997788,0.00079704094,0.0010891333],"category_scores_gemma":[0.0065934407,0.00037736853,0.00068385585,0.0012017905,0.00068557984,0.00166803,0.0013535467,0.0011687145,0.00027275673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020470585,0.000107039064,0.004267114,0.000097958145,0.00005752877,0.00013859666,0.00014037518,0.80149233,0.003846656,0.0076895533,0.0022384312,0.17971975],"study_design_scores_gemma":[0.0000037791463,0.000013531759,0.00014618202,0.0000020714926,0.0000036037454,0.000010368442,0.0000045024444,0.99658704,0.00057266897,0.0025026216,0.00015028822,0.0000032465318],"about_ca_topic_score_codex":0.0066975537,"about_ca_topic_score_gemma":0.004823716,"teacher_disagreement_score":0.0066975537,"about_ca_system_score_codex":0.00081144477,"about_ca_system_score_gemma":0.000865113,"threshold_uncertainty_score":0.013317168},"labels":[],"label_agreement":null},{"id":"W4412053871","doi":"10.1101/2025.07.04.663249","title":"Electrocorticographic Detection of Speech Networks in Glioma-infiltrated Cortex","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Glioma; Computer science; Neuroscience; Cortex (anatomy); Speech recognition; Psychology; Biology; Cancer research","score_opus":0.00836869084150014,"score_gpt":0.21522071372516477,"score_spread":0.20685202288366464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412053871","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9947413,0.00015448018,0.004095762,0.000039538005,0.0000048802085,0.00000809145,0.00037162413,0.00003534015,0.00054909184],"genre_scores_gemma":[0.998128,0.00010698303,0.0011719393,0.000012207567,0.0000060350408,0.000009149587,0.00032810727,0.000008597522,0.00022894201],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999571,0.000008611618,0.0000027795709,0.000013316175,0.000008321931,0.0000099507415],"domain_scores_gemma":[0.9999007,0.000039167535,0.000023065297,0.000009320288,0.000011671968,0.000015997415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000122053025,0.00016960358,0.00012321034,0.00034815422,0.00007401544,0.00026776094,0.00009726665,0.00013335222,0.0007244846],"category_scores_gemma":[0.00069063494,0.00006423477,0.00010846165,0.0002070195,0.00020330018,0.00014982384,0.00019916128,0.00012087604,0.0001218142],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011297268,0.00009487535,0.06522084,0.00016174896,0.00010738736,0.0008266874,0.00032745063,0.0034525094,0.8628983,0.0005395709,0.0007768187,0.064464055],"study_design_scores_gemma":[0.000031057793,0.00025539298,0.86506635,0.000023084967,0.00008351462,0.002086317,0.00041070662,0.022456393,0.10677674,0.001734239,0.0010520027,0.000024227289],"about_ca_topic_score_codex":0.00094212615,"about_ca_topic_score_gemma":0.0017259398,"teacher_disagreement_score":0.00094212615,"about_ca_system_score_codex":0.000108842716,"about_ca_system_score_gemma":0.00012457909,"threshold_uncertainty_score":0.0024237037},"labels":[],"label_agreement":null},{"id":"W4412056376","doi":"10.1016/j.robot.2025.105123","title":"Quantum neural network-based inverse kinematics of a six-jointed industrial robotic arm","year":2025,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robotic arm; Kinematics; Inverse kinematics; Artificial neural network; Quantum; Inverse; Artificial intelligence; Robot manipulator; Robot; Simulation; Physics; Mathematics; Classical mechanics; Geometry","score_opus":0.030101167926852277,"score_gpt":0.24570978002208985,"score_spread":0.21560861209523757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412056376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10221097,0.0003454239,0.88245505,0.00042474899,0.000116526266,0.000045376088,0.000053333843,0.000454949,0.013893692],"genre_scores_gemma":[0.9168091,0.00014480321,0.07900152,0.00006488265,0.000010434375,0.00004367933,0.000046332854,0.000021811056,0.0038574864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998878,0.000025631382,0.0000055051696,0.000026297279,0.000042476866,0.000012288105],"domain_scores_gemma":[0.9998673,0.00005343371,0.000020043548,0.000016426571,0.00003452543,0.000008178866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004043377,0.00025488998,0.00025866157,0.000207103,0.0002472093,0.00038010516,0.00050421146,0.0005608628,0.0017184828],"category_scores_gemma":[0.00073006004,0.00023031823,0.0003592404,0.00020883967,0.00047389383,0.00059145124,0.00037297976,0.0004921542,0.0001831803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038507453,0.000016637026,0.00033093072,0.000033404303,0.000013308619,0.00005267015,0.000038150658,0.970902,0.0043912944,0.007827189,0.00023577316,0.016120149],"study_design_scores_gemma":[0.0000016381686,0.000008337909,0.00009392178,0.0000020421235,0.0000017111793,0.000005898619,0.0000025136208,0.9983594,0.0004576531,0.00089839904,0.00016605134,0.0000025036948],"about_ca_topic_score_codex":0.0057370886,"about_ca_topic_score_gemma":0.004339942,"teacher_disagreement_score":0.0057370886,"about_ca_system_score_codex":0.00059002393,"about_ca_system_score_gemma":0.0007581919,"threshold_uncertainty_score":0.011407375},"labels":[],"label_agreement":null},{"id":"W4412202933","doi":"10.21105/joss.08101","title":"Lighter: Configuration-Driven Deep Learning","year":2025,"lang":"en","type":"article","venue":"The Journal of Open Source Software","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Python (programming language); Programming language","score_opus":0.013815487805606087,"score_gpt":0.2793738288749931,"score_spread":0.265558341069387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412202933","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076390104,0.0002760633,0.7501744,0.00036350655,0.00018442557,0.00033539566,0.0039399136,0.23122676,0.0058604153],"genre_scores_gemma":[0.21288057,0.00044083604,0.71299016,0.0019543073,0.00010744032,0.0018671291,0.019313613,0.03727082,0.0131751085],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983669,0.0003443168,0.000101887556,0.0005030186,0.0005230569,0.00016083],"domain_scores_gemma":[0.9971699,0.0010524064,0.00013729757,0.0010666458,0.00038578425,0.00018800261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027559535,0.0017188748,0.00074344815,0.0008879208,0.0004223088,0.0017847804,0.0048695244,0.0011725745,0.020025603],"category_scores_gemma":[0.009722529,0.0014516078,0.0012179143,0.000691031,0.0009036004,0.002873073,0.0042364225,0.0039124517,0.012142605],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001465355,0.0007147284,0.0068345475,0.0012494737,0.0005497957,0.00038428,0.00024695168,0.21470734,0.02669098,0.03889843,0.24254276,0.46571538],"study_design_scores_gemma":[0.00020093539,0.00013316555,0.0008209656,0.000067423905,0.00003944581,0.00009912808,0.000028454322,0.9039939,0.019872691,0.041456807,0.0332134,0.00007361369],"about_ca_topic_score_codex":0.0032744189,"about_ca_topic_score_gemma":0.007921125,"teacher_disagreement_score":0.020025603,"about_ca_system_score_codex":0.0011730895,"about_ca_system_score_gemma":0.0021485067,"threshold_uncertainty_score":0.06699234},"labels":[],"label_agreement":null},{"id":"W4412232194","doi":"","title":"Knowledge Distillation Techniques for Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Distillation; Computer science; Artificial intelligence; Machine learning; Chemistry; Chromatography","score_opus":0.022284404973831648,"score_gpt":0.30470696322284085,"score_spread":0.2824225582490092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412232194","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019671975,0.0009229166,0.9945614,0.00058103906,0.000042290096,0.000035449557,0.00007161779,0.00044357887,0.0013746053],"genre_scores_gemma":[0.19616358,0.002950792,0.7948804,0.00051572407,0.00033216202,0.000519937,0.00048475247,0.00032864086,0.0038240112],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996819,0.0015976091,0.0001942646,0.0006021814,0.00066847884,0.00011837923],"domain_scores_gemma":[0.9918949,0.0052733896,0.0004469786,0.0018902536,0.00039845853,0.00009598968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004590594,0.0015929032,0.001245549,0.001431642,0.00081659335,0.0020659105,0.0026592757,0.0020036432,0.0035505956],"category_scores_gemma":[0.018836517,0.000974839,0.0018693169,0.0022475873,0.003004216,0.0050514187,0.004185337,0.0068766847,0.0013169434],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010037465,0.0001000935,0.0006840252,0.00049659354,0.00021520266,0.00008483104,0.00020420438,0.31575495,0.0024439052,0.4097802,0.0043162922,0.26581928],"study_design_scores_gemma":[0.000013543019,0.000039837163,0.000110795634,0.000052709573,0.0000141558285,0.000023845161,0.000013990094,0.7303144,0.0016385766,0.26326692,0.004489206,0.000021995576],"about_ca_topic_score_codex":0.0024235821,"about_ca_topic_score_gemma":0.0028651538,"teacher_disagreement_score":0.004590594,"about_ca_system_score_codex":0.001947135,"about_ca_system_score_gemma":0.0016430957,"threshold_uncertainty_score":0.024277687},"labels":[],"label_agreement":null},{"id":"W4412338394","doi":"","title":"The Reach of Manifold Learning:Uniqueness bounds for latent representations","year":2023,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Uniqueness; Manifold (fluid mechanics); Mathematics; Computer science; Nonlinear dimensionality reduction; Artificial intelligence; Mathematical analysis; Engineering; Dimensionality reduction","score_opus":0.03437909879540475,"score_gpt":0.3080618020218879,"score_spread":0.27368270322648314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412338394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010076796,0.0011155851,0.9845744,0.00086004357,0.0000849531,0.000050706385,0.000112164504,0.00027296308,0.002852414],"genre_scores_gemma":[0.49848527,0.003072307,0.48484674,0.001546889,0.00086422195,0.0009063781,0.001164542,0.0016801067,0.0074336226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923643,0.0031831,0.00041492566,0.001719993,0.0018402359,0.00047749592],"domain_scores_gemma":[0.943646,0.0446615,0.002607553,0.004192418,0.0035100793,0.0013824982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012401847,0.0028326306,0.002348824,0.003069122,0.003010631,0.0038218666,0.0035856685,0.004467788,0.005756599],"category_scores_gemma":[0.086873345,0.0013045557,0.0027617025,0.0022624733,0.009955914,0.01813334,0.012275478,0.010674138,0.0017246922],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050928607,0.00013117248,0.0060195914,0.00070493965,0.00021360016,0.00038495797,0.0011710179,0.22179353,0.00549435,0.6489362,0.004742138,0.109899186],"study_design_scores_gemma":[0.000022505958,0.00028745795,0.0007449589,0.00020468287,0.00004263917,0.00018905831,0.00019040352,0.42789555,0.0055626924,0.56069744,0.0040678787,0.000094695446],"about_ca_topic_score_codex":0.0019873665,"about_ca_topic_score_gemma":0.0016309608,"teacher_disagreement_score":0.012401847,"about_ca_system_score_codex":0.0023154886,"about_ca_system_score_gemma":0.0016904596,"threshold_uncertainty_score":0.06558806},"labels":[],"label_agreement":null},{"id":"W4412375816","doi":"10.1109/tla.2025.11072499","title":"Attention Blocks Improve White Matter Hyperintensity Semantic Segmentation using U-Nets","year":2025,"lang":"en","type":"article","venue":"IEEE Latin America Transactions","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Calgary","funders":"","keywords":"Hyperintensity; Computer science; Segmentation; Artificial intelligence; White matter; Natural language processing; Pattern recognition (psychology); Magnetic resonance imaging; Medicine; Radiology","score_opus":0.01309702027091418,"score_gpt":0.2558730968913985,"score_spread":0.24277607662048428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412375816","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55023533,0.00850819,0.41275394,0.0018656186,0.0010250369,0.00036112455,0.0013566816,0.01337601,0.010518107],"genre_scores_gemma":[0.923547,0.00097104,0.06556249,0.0008918756,0.000203319,0.00014486292,0.0016768669,0.0004348211,0.0065676975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996345,0.00009356903,0.000021585645,0.00012489819,0.0000435958,0.000081848375],"domain_scores_gemma":[0.99916625,0.0004597536,0.00008350542,0.0000649358,0.00014650082,0.00007903345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011155822,0.0022379754,0.0011930907,0.0012029479,0.0005734343,0.00125116,0.0016006046,0.001974824,0.002803452],"category_scores_gemma":[0.0027018033,0.00047756376,0.001744193,0.00062615715,0.00057539163,0.0017230618,0.0013169083,0.0016468624,0.0010479764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017631407,0.0006381685,0.008079287,0.00029499567,0.00052534876,0.0003492056,0.0002004236,0.588984,0.013669123,0.0028328157,0.009966422,0.37269714],"study_design_scores_gemma":[0.00001981128,0.00011794849,0.00073447113,0.000020294829,0.000049985323,0.000027846318,0.000017884247,0.99442977,0.0026007658,0.0014284193,0.0005430655,0.000009773309],"about_ca_topic_score_codex":0.018128231,"about_ca_topic_score_gemma":0.015702596,"teacher_disagreement_score":0.018128231,"about_ca_system_score_codex":0.0014276175,"about_ca_system_score_gemma":0.0012267798,"threshold_uncertainty_score":0.036045372},"labels":[],"label_agreement":null},{"id":"W4412404435","doi":"10.1109/jstars.2025.3588154","title":"Unsupervised Global Difference Modeling for Image Change Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Change detection; Artificial intelligence; Image (mathematics); Computer vision; Pattern recognition (psychology); Remote sensing; Geology","score_opus":0.043551944298532534,"score_gpt":0.26641788156266194,"score_spread":0.2228659372641294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412404435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010798497,0.00013375124,0.9879352,0.00008375568,0.000026125446,0.00002124464,0.000054554577,0.0002833983,0.0006635099],"genre_scores_gemma":[0.7295214,0.0004412619,0.26315573,0.00024703192,0.00011071218,0.00015865656,0.00062298536,0.0002630918,0.005479195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959785,0.00007560982,0.000014725516,0.00014284492,0.00012807734,0.000040834093],"domain_scores_gemma":[0.99945813,0.00023151051,0.00008485266,0.00009777088,0.00010594422,0.000021820686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065020245,0.0006373986,0.00065330544,0.00065658556,0.00021344608,0.0005209814,0.0013903294,0.0006552275,0.0010114377],"category_scores_gemma":[0.0018618114,0.00034577283,0.0007902537,0.00058735005,0.000709827,0.0013553946,0.0008267628,0.001278629,0.00035625228],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013460922,0.00007332836,0.001978002,0.00009187801,0.00008409174,0.00010071799,0.00011640273,0.80223626,0.017035792,0.016329369,0.0018257276,0.15999392],"study_design_scores_gemma":[0.0000016019226,0.00001182627,0.00021394246,0.0000018429839,0.000004133933,0.000019068591,0.000003914418,0.99521166,0.0015334383,0.0025540676,0.00044064032,0.0000038779635],"about_ca_topic_score_codex":0.0024738794,"about_ca_topic_score_gemma":0.0028419003,"teacher_disagreement_score":0.0024738794,"about_ca_system_score_codex":0.0006606977,"about_ca_system_score_gemma":0.00038978626,"threshold_uncertainty_score":0.0049189925},"labels":[],"label_agreement":null},{"id":"W4412425485","doi":"10.17242/mvvk_18-19.05","title":"A magyar vízivad monitoring eredményei a 2008/2009-es idényben","year":2010,"lang":"en","type":"article","venue":"Magyar Vízivad Közlemények","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Political science","score_opus":0.013027365918961744,"score_gpt":0.2478538534112761,"score_spread":0.23482648749231436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412425485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9345133,0.0025391025,0.004464452,0.00032626232,0.00039905138,0.0001834825,0.028691655,0.00039415975,0.028488513],"genre_scores_gemma":[0.9059783,0.0019146288,0.012384683,0.00021520807,0.00010695493,0.00016730366,0.038039494,0.000095693,0.04109771],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998621,0.000016315356,0.000011341459,0.000048510894,0.000031545995,0.000030120553],"domain_scores_gemma":[0.9999106,0.000009531766,0.000023129534,0.000011087221,0.00003117469,0.00001437631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024682918,0.00030877552,0.00026726918,0.0009061452,0.00021299232,0.00034093193,0.00027088073,0.00025698903,0.004426892],"category_scores_gemma":[0.00018837542,0.00013166228,0.00023185281,0.0007227451,0.00010169507,0.00019902708,0.00049235055,0.00019505886,0.0010023075],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022794267,0.00045966113,0.5404825,0.0010567846,0.00041353784,0.0029730715,0.0009657445,0.0050434577,0.03358896,0.0012011598,0.03843175,0.37310395],"study_design_scores_gemma":[0.000054042343,0.00018221818,0.9338803,0.000077904064,0.00007643501,0.00038768604,0.00032369653,0.0019623751,0.0043241004,0.000090975685,0.058625314,0.000014859731],"about_ca_topic_score_codex":0.007868126,"about_ca_topic_score_gemma":0.016756548,"teacher_disagreement_score":0.007868126,"about_ca_system_score_codex":0.00043225926,"about_ca_system_score_gemma":0.0004565293,"threshold_uncertainty_score":0.01564461},"labels":[],"label_agreement":null},{"id":"W4412472891","doi":"10.1007/978-3-031-69769-2_22","title":"Correction to: Innovative Applications of Artificial Neural Networks to Data Analytics and Signal Processing","year":2025,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Analytics; Signal processing; Data science; Artificial intelligence; Data analysis; SIGNAL (programming language); Machine learning; Data mining; Digital signal processing","score_opus":0.129742225013474,"score_gpt":0.3851145378522375,"score_spread":0.2553723128387635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412472891","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012760227,0.0011806559,0.001455994,0.024931781,0.9676894,0.00003240747,0.00080081046,0.0007193327,0.0030619828],"genre_scores_gemma":[0.016416116,0.0081813885,0.010359769,0.05467796,0.54161257,0.0002816318,0.0030217147,0.0026522076,0.36279663],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99519485,0.0005290573,0.0008083753,0.000653833,0.0024051578,0.00040873198],"domain_scores_gemma":[0.9476069,0.008661022,0.0019520194,0.0037934664,0.035801403,0.0021851084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031116817,0.0024417357,0.0027992013,0.0058109947,0.0030344897,0.0049354974,0.0046742936,0.0071113547,0.15049472],"category_scores_gemma":[0.05735112,0.0011768697,0.0018028016,0.0043644393,0.0027726502,0.0033772127,0.0025872188,0.009499011,0.09697145],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038855953,0.0000080218215,0.000048492835,0.0001806893,0.000012723432,0.00012170373,0.000019656116,0.00008915405,0.00008440871,0.0010965613,0.98613054,0.012169213],"study_design_scores_gemma":[0.00003580537,0.000030362187,0.00064593035,0.00018180328,0.000018490637,0.000478728,0.00005561453,0.0005957446,0.0005940782,0.0026637402,0.9946602,0.00003945173],"about_ca_topic_score_codex":0.0073103835,"about_ca_topic_score_gemma":0.012119667,"teacher_disagreement_score":0.15049472,"about_ca_system_score_codex":0.00348841,"about_ca_system_score_gemma":0.003605946,"threshold_uncertainty_score":0.50345504},"labels":[],"label_agreement":null},{"id":"W4412505412","doi":"10.21203/rs.3.rs-6674875/v1","title":"Semantic Concept in Brain fMRI Spatio-Temporal Voxel Patterns","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Voxel; Computer science; Brain mapping; Artificial intelligence; Psychology; Neuroscience; Natural language processing; Cognitive psychology","score_opus":0.05905744963068927,"score_gpt":0.3930707724947613,"score_spread":0.33401332286407204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412505412","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88351786,0.0012432659,0.10504468,0.0012841499,0.00010792552,0.000041799267,0.0010297318,0.00018256716,0.0075479695],"genre_scores_gemma":[0.98782825,0.00035562032,0.0096080415,0.0000488946,0.000054063818,0.000016083566,0.00032693832,0.000035294855,0.0017268007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998714,0.00003532373,0.000006162845,0.000047557623,0.000023540419,0.00001607563],"domain_scores_gemma":[0.9991437,0.00052592024,0.00013218865,0.000061345694,0.00007976205,0.00005703213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008063291,0.00024921898,0.0002188547,0.00089290773,0.00019938093,0.0011108477,0.0003563012,0.0007037414,0.0034771846],"category_scores_gemma":[0.004713658,0.00032740072,0.00034311906,0.0011117518,0.0006459875,0.0021324118,0.00044619627,0.00069596234,0.0003142231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003140739,0.00056749425,0.099235944,0.00081965135,0.00051767734,0.00088222325,0.0017462033,0.033594903,0.35589793,0.1281065,0.008089885,0.36740085],"study_design_scores_gemma":[0.0001262166,0.0003363857,0.3827222,0.00009767972,0.00024000567,0.001412491,0.00071387406,0.24349822,0.023239765,0.3434989,0.0040403316,0.00007384278],"about_ca_topic_score_codex":0.0011751266,"about_ca_topic_score_gemma":0.0018919936,"teacher_disagreement_score":0.0034771846,"about_ca_system_score_codex":0.00027750872,"about_ca_system_score_gemma":0.00026443115,"threshold_uncertainty_score":0.011632323},"labels":[],"label_agreement":null},{"id":"W4412508600","doi":"10.21203/rs.3.rs-7082183/v1","title":"Feature similarity: a sensitive method to capture the functional interaction of brain regions and networks to support flexible behavior","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Institute of Psychology, Chinese Academy of Sciences; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Similarity (geometry); Feature (linguistics); Computer science; Functional connectivity; Artificial intelligence; Pattern recognition (psychology); Neuroscience; Psychology","score_opus":0.09520704085408563,"score_gpt":0.43741472361227035,"score_spread":0.3422076827581847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412508600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052765865,0.0002630141,0.9436553,0.00012132694,0.000068949535,0.00010473699,0.0006845271,0.0013172829,0.0010188997],"genre_scores_gemma":[0.7163699,0.00023845222,0.27814853,0.0001965201,0.00017989794,0.00027020305,0.0011877546,0.00048613411,0.0029225473],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921954,0.00014898721,0.00004658031,0.00023574516,0.00026702246,0.00008210188],"domain_scores_gemma":[0.9978434,0.0008407688,0.00037954902,0.00046823063,0.00030056745,0.00016747152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014372187,0.0007742359,0.0010657163,0.0023194652,0.0005171793,0.001148658,0.001267178,0.0013006626,0.0020441923],"category_scores_gemma":[0.00577141,0.00035355487,0.00090238947,0.0018606695,0.00082930905,0.0016730671,0.0018424751,0.00126014,0.00060147326],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013772475,0.00061876565,0.022434507,0.00048329984,0.00069536455,0.0005393281,0.0003657886,0.10064174,0.22450013,0.030282453,0.0119666355,0.6060948],"study_design_scores_gemma":[0.00005641671,0.00030444097,0.017249262,0.000027559943,0.00014272604,0.0005806871,0.000072440635,0.9117584,0.03401829,0.031801194,0.0038897104,0.0000987208],"about_ca_topic_score_codex":0.001781433,"about_ca_topic_score_gemma":0.0024524657,"teacher_disagreement_score":0.0023194652,"about_ca_system_score_codex":0.0005675226,"about_ca_system_score_gemma":0.0007102181,"threshold_uncertainty_score":0.007600844},"labels":[],"label_agreement":null},{"id":"W4412534222","doi":"10.1088/2632-2153/adf278","title":"Feature learning and generalization in deep networks with orthogonal weights","year":2025,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"High Energy Physics","keywords":"Generalization; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Feature learning; Computer science; Deep learning; Mathematics; Philosophy","score_opus":0.0023080742995250235,"score_gpt":0.21947355951038824,"score_spread":0.2171654852108632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412534222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48312768,0.00015110147,0.51290894,0.00026110964,0.000025777643,0.000024595509,0.000085834086,0.0010299584,0.0023850203],"genre_scores_gemma":[0.96936864,0.000036774098,0.029887997,0.00003741374,0.0000051889,0.000017186987,0.00006266143,0.000041345716,0.0005427404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99982256,0.000037742422,0.000009480343,0.000048784546,0.00004055083,0.000040854495],"domain_scores_gemma":[0.9990283,0.00042753175,0.00015842031,0.00019413541,0.00013319378,0.000058533456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088290963,0.00038166356,0.00030163035,0.0003210023,0.00019871589,0.00045078737,0.00074937294,0.0004476481,0.00092084426],"category_scores_gemma":[0.004325534,0.00032477133,0.0003079021,0.0002785566,0.0008059688,0.0014098657,0.0010246041,0.0009631715,0.00012079538],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000171762,0.00008884556,0.0029402734,0.000055524375,0.00004455369,0.00009281979,0.00012717149,0.860735,0.038186178,0.03836403,0.0007835428,0.05841033],"study_design_scores_gemma":[0.0000038114656,0.000023976485,0.0003285341,0.0000035502005,0.000002565959,0.000008843163,0.0000043323594,0.98869693,0.0037751852,0.0070414515,0.000107505475,0.0000032414684],"about_ca_topic_score_codex":0.0024575747,"about_ca_topic_score_gemma":0.0018095176,"teacher_disagreement_score":0.0024575747,"about_ca_system_score_codex":0.00079831027,"about_ca_system_score_gemma":0.0004882021,"threshold_uncertainty_score":0.0057922006},"labels":[],"label_agreement":null},{"id":"W4412536519","doi":"10.1109/tnnls.2025.3588327","title":"ODMTCNet: An Interpretable Multiview Deep Neural Network Architecture for Feature Representation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Feature (linguistics); Computer science; Representation (politics); Architecture; Deep neural networks; Pattern recognition (psychology); Artificial neural network; Computer vision; Geography","score_opus":0.012817373866126466,"score_gpt":0.2678928978562773,"score_spread":0.2550755239901508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412536519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076641333,0.00042141802,0.98837334,0.00012004875,0.000072476345,0.0000328736,0.0003153827,0.0016487754,0.0013516167],"genre_scores_gemma":[0.41651192,0.000942089,0.5728363,0.0003276322,0.0000960119,0.00021883313,0.0024821553,0.0003935932,0.0061914916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999808,0.000023480634,0.000011990949,0.00005848458,0.000071085204,0.000026970687],"domain_scores_gemma":[0.9998419,0.000035728634,0.00002572836,0.000028049746,0.00005351063,0.000015039054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004277819,0.0008839614,0.00055508775,0.00057317066,0.00023430109,0.0006703585,0.0019533825,0.00075136614,0.0025770797],"category_scores_gemma":[0.0011233783,0.00036425726,0.00069582404,0.0006382175,0.000355533,0.0013122575,0.00093776395,0.0011753198,0.0007359701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021597817,0.000088113535,0.0012782287,0.00016512863,0.00012675324,0.00015732764,0.00005828533,0.494977,0.021082576,0.026035462,0.011755359,0.44405982],"study_design_scores_gemma":[0.0000041253616,0.000015967233,0.00007953007,0.0000052909704,0.000006876925,0.000016754835,0.0000025239558,0.99359256,0.0016640296,0.0034244857,0.0011829453,0.0000048986008],"about_ca_topic_score_codex":0.008355336,"about_ca_topic_score_gemma":0.012620568,"teacher_disagreement_score":0.008355336,"about_ca_system_score_codex":0.0008903625,"about_ca_system_score_gemma":0.0010143709,"threshold_uncertainty_score":0.016613424},"labels":[],"label_agreement":null},{"id":"W4412597539","doi":"10.31234/osf.io/36xdz_v1","title":"Perceptual reality monitoring as higher-order inference on sensory precision","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Commission; UK Research and Innovation; HORIZON EUROPE Framework Programme; Government of the United Kingdom; Wellcome Trust; Canadian Institute for Advanced Research","keywords":"Perception; Inference; Sensory system; Order (exchange); Cognitive psychology; Psychology; Computer science; Artificial intelligence; Economics; Neuroscience","score_opus":0.08525968326460487,"score_gpt":0.36926870997350125,"score_spread":0.2840090267088964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412597539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14755633,0.00030595888,0.83113587,0.0014140103,0.0000544886,0.0000546082,0.00017620012,0.0003526971,0.018949924],"genre_scores_gemma":[0.93089885,0.00016475783,0.06749261,0.00008545274,0.000024030274,0.00004256627,0.00008255071,0.000052216194,0.0011568527],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985751,0.0004894735,0.00007772016,0.0003479935,0.00038082007,0.00012886326],"domain_scores_gemma":[0.9952904,0.0023923968,0.0009703799,0.00082720345,0.00030868914,0.00021091082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022051285,0.00041845648,0.0004945255,0.0007455713,0.00055028056,0.0032692137,0.0016831397,0.0010500872,0.002950797],"category_scores_gemma":[0.010843747,0.00044801226,0.00090742693,0.0005812118,0.0032440494,0.0068246853,0.0020045491,0.0018356504,0.00023047903],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013776979,0.000045053086,0.0021522024,0.00013226378,0.00008610156,0.00020924128,0.0007314725,0.1196959,0.0089938855,0.84119713,0.00052730896,0.026091581],"study_design_scores_gemma":[0.000025317771,0.000043311888,0.0017798624,0.000034968692,0.000025607515,0.000079041,0.00008234483,0.31685075,0.002070525,0.6780896,0.00088172033,0.000036955193],"about_ca_topic_score_codex":0.0018592611,"about_ca_topic_score_gemma":0.0016962368,"teacher_disagreement_score":0.0032692137,"about_ca_system_score_codex":0.0010613378,"about_ca_system_score_gemma":0.00067981664,"threshold_uncertainty_score":0.011662006},"labels":[],"label_agreement":null},{"id":"W4412659424","doi":"10.31234/osf.io/36xdz_v2","title":"Perceptual reality monitoring as higher-order inference on sensory precision","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"European Commission; UK Research and Innovation; HORIZON EUROPE Framework Programme; Government of the United Kingdom; Wellcome Trust; Canadian Institute for Advanced Research","keywords":"Perception; Inference; Sensory system; Order (exchange); Computer science; Psychology; Artificial intelligence; Cognitive psychology; Economics; Neuroscience","score_opus":0.08525968326460487,"score_gpt":0.36926870997350125,"score_spread":0.2840090267088964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412659424","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14755633,0.00030595888,0.83113587,0.0014140103,0.0000544886,0.0000546082,0.00017620012,0.0003526971,0.018949924],"genre_scores_gemma":[0.93089885,0.00016475783,0.06749261,0.00008545274,0.000024030274,0.00004256627,0.00008255071,0.000052216194,0.0011568527],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985751,0.0004894735,0.00007772016,0.0003479935,0.00038082007,0.00012886326],"domain_scores_gemma":[0.9952904,0.0023923968,0.0009703799,0.00082720345,0.00030868914,0.00021091082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022051285,0.00041845648,0.0004945255,0.0007455713,0.00055028056,0.0032692137,0.0016831397,0.0010500872,0.002950797],"category_scores_gemma":[0.010843747,0.00044801226,0.00090742693,0.0005812118,0.0032440494,0.0068246853,0.0020045491,0.0018356504,0.00023047903],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013776979,0.000045053086,0.0021522024,0.00013226378,0.00008610156,0.00020924128,0.0007314725,0.1196959,0.0089938855,0.84119713,0.00052730896,0.026091581],"study_design_scores_gemma":[0.000025317771,0.000043311888,0.0017798624,0.000034968692,0.000025607515,0.000079041,0.00008234483,0.31685075,0.002070525,0.6780896,0.00088172033,0.000036955193],"about_ca_topic_score_codex":0.0018592611,"about_ca_topic_score_gemma":0.0016962368,"teacher_disagreement_score":0.0032692137,"about_ca_system_score_codex":0.0010613378,"about_ca_system_score_gemma":0.00067981664,"threshold_uncertainty_score":0.011662006},"labels":[],"label_agreement":null},{"id":"W4412726145","doi":"10.1371/journal.pcbi.1013244","title":"Recurrent neural networks as neuro-computational models of human speech recognition","year":2025,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Agencia Estatal de Investigación; Eusko Jaurlaritza; National Science Foundation","keywords":"Recurrent neural network; Computer science; Speech recognition; Spectrogram; Artificial intelligence; Categorical variable; Frame (networking); Artificial neural network; Machine learning","score_opus":0.04288142269006092,"score_gpt":0.29873837699596645,"score_spread":0.25585695430590555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412726145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.118487425,0.0022765424,0.8600215,0.0014191848,0.00016459353,0.000041252155,0.00038544103,0.00094890536,0.016255092],"genre_scores_gemma":[0.9255014,0.0013057954,0.0652953,0.00014899066,0.00009912347,0.00009806212,0.00021120984,0.00011731944,0.007222915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998474,0.00006924564,0.000006883035,0.00003146309,0.000032441654,0.000012578138],"domain_scores_gemma":[0.9996723,0.00018692334,0.000042150892,0.00004160971,0.000038044655,0.000018965622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004610957,0.00040463114,0.0003497794,0.00030089048,0.00014678371,0.0008954356,0.0008125849,0.0006629125,0.0017046398],"category_scores_gemma":[0.0015960117,0.0002950713,0.0004016652,0.00027829857,0.0007231113,0.0013734434,0.00044382134,0.00082245097,0.00044006205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004657006,0.000015292726,0.00055263116,0.000058140948,0.000044804445,0.00009040464,0.00011817149,0.80566305,0.004437788,0.17703506,0.0007857177,0.011152402],"study_design_scores_gemma":[0.0000024241979,0.0000057986017,0.00010114412,0.00000466277,0.000004206701,0.000011111395,0.000005917939,0.95275366,0.00029392945,0.046336535,0.0004757148,0.0000050074864],"about_ca_topic_score_codex":0.002664301,"about_ca_topic_score_gemma":0.0028296744,"teacher_disagreement_score":0.002664301,"about_ca_system_score_codex":0.00059370167,"about_ca_system_score_gemma":0.00029441767,"threshold_uncertainty_score":0.005702555},"labels":[],"label_agreement":null},{"id":"W4412767561","doi":"10.23952/jano.7.2025.2.01","title":"Some notes about a relative order relation used in unsupervised deep learning","year":2025,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Relation (database); Order (exchange); Artificial intelligence; Unsupervised learning; Psychology; Computer science; Natural language processing; Cognitive psychology; Data mining; Economics","score_opus":0.007976941909530213,"score_gpt":0.23676345695101397,"score_spread":0.22878651504148376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412767561","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010551992,0.0048978887,0.92314696,0.011640514,0.0031918653,0.000054890486,0.0003490536,0.00026473746,0.0459021],"genre_scores_gemma":[0.3686118,0.008733404,0.5435752,0.007215207,0.008238591,0.00040417636,0.0006145745,0.0010421807,0.061564893],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974209,0.00087003096,0.00038167226,0.00046619723,0.00071849086,0.00014264451],"domain_scores_gemma":[0.9925613,0.0050332937,0.0003070625,0.0012589117,0.00067223574,0.00016730926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063419463,0.0008045791,0.001010219,0.0017804564,0.001960074,0.0029246649,0.0018242752,0.0020641063,0.007548945],"category_scores_gemma":[0.016636074,0.00073483156,0.001759637,0.0029433821,0.00753463,0.0121541405,0.0021462445,0.007403743,0.0013037025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000123945865,0.00000714049,0.00010779501,0.00004467947,0.000006684204,0.00003079388,0.00009651878,0.00083139236,0.00022060947,0.9895555,0.0017463964,0.007340088],"study_design_scores_gemma":[0.0000047661592,0.00002063135,0.000118117394,0.000026303176,0.000008854337,0.00005103202,0.000034030236,0.004538471,0.00062744215,0.9832086,0.011347201,0.000014652605],"about_ca_topic_score_codex":0.0027368092,"about_ca_topic_score_gemma":0.0027516899,"teacher_disagreement_score":0.007548945,"about_ca_system_score_codex":0.0020629584,"about_ca_system_score_gemma":0.00095127546,"threshold_uncertainty_score":0.03353983},"labels":[],"label_agreement":null},{"id":"W4412819461","doi":"10.1007/978-3-031-89033-8_17","title":"MPI Scaling and Recent Advanced Features","year":2025,"lang":"en","type":"book-chapter","venue":"CMS/CAIMS books in mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Scaling; Computer science; Mathematics; Geometry","score_opus":0.0180315742530979,"score_gpt":0.25770304033643626,"score_spread":0.23967146608333836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412819461","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021216653,0.106061146,0.13533585,0.030306224,0.01669511,0.00021422486,0.0020603936,0.038505923,0.6496045],"genre_scores_gemma":[0.3126955,0.07838996,0.21216656,0.009103327,0.019957136,0.0015654291,0.006524423,0.019013952,0.3405838],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99906486,0.00010566495,0.000033674358,0.00011875891,0.00053657097,0.00014052611],"domain_scores_gemma":[0.99750453,0.00063382747,0.00006413085,0.00073353853,0.00081552716,0.0002485727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016190606,0.0013508692,0.0013598411,0.0023444814,0.0018135856,0.0035614003,0.0031772372,0.0012001644,0.049647033],"category_scores_gemma":[0.0073900227,0.0008178365,0.0007139283,0.0050173467,0.0015427052,0.0067105126,0.0033041101,0.004041934,0.017947324],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022711304,0.00008091565,0.00045581628,0.0008174934,0.000038908263,0.00010210899,0.00017220907,0.0038427692,0.00314857,0.27095962,0.36737692,0.3527777],"study_design_scores_gemma":[0.000055458753,0.000054284363,0.00085187727,0.00018852443,0.00004400457,0.00018987479,0.000099853074,0.019476064,0.004237201,0.2142122,0.7605325,0.000058228346],"about_ca_topic_score_codex":0.0015835599,"about_ca_topic_score_gemma":0.0015428007,"teacher_disagreement_score":0.049647033,"about_ca_system_score_codex":0.0018913085,"about_ca_system_score_gemma":0.0010949261,"threshold_uncertainty_score":0.1660859},"labels":[],"label_agreement":null},{"id":"W4412819503","doi":"10.1007/978-3-031-89033-8_32","title":"Debugging and Profiling Compute Kernels","year":2025,"lang":"en","type":"book-chapter","venue":"CMS/CAIMS books in mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Debugging; Profiling (computer programming); Computer science; Programming language; Parallel computing","score_opus":0.02550926919699902,"score_gpt":0.2541912100895237,"score_spread":0.2286819408925247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412819503","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046809193,0.0013559693,0.6442084,0.0009087622,0.00066102063,0.00023069093,0.002421907,0.24783395,0.05557017],"genre_scores_gemma":[0.38924953,0.0010020626,0.47912267,0.0008061703,0.00016591552,0.00021154822,0.0060431673,0.053484347,0.06991461],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99767214,0.00049230765,0.00014656593,0.0005576018,0.0008746749,0.00025683705],"domain_scores_gemma":[0.993236,0.0022013185,0.00022416153,0.002977891,0.0011543366,0.00020637241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016164813,0.0020776452,0.00074568496,0.0014468471,0.00085100025,0.0032871002,0.002845697,0.0009607797,0.02139202],"category_scores_gemma":[0.011331647,0.0012968762,0.0009589567,0.0014131098,0.0010113303,0.0069653555,0.0020140344,0.0024323158,0.011865465],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011092031,0.00032272204,0.007171902,0.0006620746,0.0001297227,0.00032643075,0.0009706558,0.024233902,0.03252355,0.08362474,0.16250834,0.6864168],"study_design_scores_gemma":[0.0001372265,0.00023390412,0.0044123484,0.00031350376,0.0001767161,0.0006466552,0.0005738502,0.34641317,0.25827143,0.12421868,0.26439866,0.00020385472],"about_ca_topic_score_codex":0.0035835686,"about_ca_topic_score_gemma":0.0046959245,"teacher_disagreement_score":0.02139202,"about_ca_system_score_codex":0.001287018,"about_ca_system_score_gemma":0.0015496056,"threshold_uncertainty_score":0.07156342},"labels":[],"label_agreement":null},{"id":"W4412889036","doi":"10.18653/v1/2025.conll-1.35","title":"GCG-Based Artificial Languages for Evaluating Inductive Biases of Neural Language Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing; Artificial neural network; Inductive bias; Machine learning; Engineering; Multi-task learning; Systems engineering","score_opus":0.09848115993115467,"score_gpt":0.3950496185819756,"score_spread":0.29656845865082093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412889036","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7933062,0.00078298966,0.19377619,0.0005317912,0.00016013565,0.00038086966,0.002378001,0.0017314123,0.006952435],"genre_scores_gemma":[0.9349274,0.00014610135,0.062057078,0.00017582208,0.000027332402,0.0004921909,0.0016146639,0.00017780725,0.000381592],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957618,0.0025715635,0.00029980228,0.00054513564,0.00064360123,0.000178043],"domain_scores_gemma":[0.94510114,0.046842556,0.0017419319,0.0038976793,0.0019014132,0.00051532185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009784047,0.0012686822,0.0006521742,0.0024855281,0.00041715268,0.0018017053,0.0016232034,0.0021048097,0.002768254],"category_scores_gemma":[0.059593074,0.00037756,0.00081440696,0.00198666,0.0018847407,0.0028933412,0.002230701,0.0026595579,0.00040430782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088942685,0.0003469807,0.03045035,0.00068200834,0.0005575259,0.00020167512,0.00063860585,0.84713364,0.006595702,0.026903829,0.002371322,0.08322897],"study_design_scores_gemma":[0.00004000707,0.00019082447,0.0017977885,0.000034832145,0.000029529652,0.00006184042,0.00010608449,0.97586024,0.0026587383,0.018626232,0.0005698499,0.00002403126],"about_ca_topic_score_codex":0.002884007,"about_ca_topic_score_gemma":0.0028786915,"teacher_disagreement_score":0.009784047,"about_ca_system_score_codex":0.0019423336,"about_ca_system_score_gemma":0.0006778874,"threshold_uncertainty_score":0.051743567},"labels":[],"label_agreement":null},{"id":"W4412894805","doi":"10.1007/s40687-025-00535-5","title":"PDEs for machine learning","year":2025,"lang":"en","type":"article","venue":"Research in the Mathematical Sciences","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Algebra over a field; Artificial intelligence; Computer science; Pure mathematics","score_opus":0.2224180837462537,"score_gpt":0.48250870460933676,"score_spread":0.2600906208630831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412894805","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0081804395,0.044338424,0.85506713,0.017581768,0.004590942,0.000073171585,0.0010588339,0.00038642192,0.068722785],"genre_scores_gemma":[0.47641188,0.059994597,0.3090758,0.007459061,0.012829838,0.0008972555,0.0028246217,0.00079272204,0.1297142],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99941933,0.00021555444,0.00003631897,0.00013114909,0.00015419678,0.000043428376],"domain_scores_gemma":[0.9978484,0.001252366,0.000163271,0.00022827688,0.00035835744,0.00014925303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015315893,0.0011028185,0.0015993759,0.0016099066,0.0005955566,0.0021013361,0.0010838278,0.0018368835,0.0075487196],"category_scores_gemma":[0.0058068912,0.00040120154,0.0010944053,0.0014458011,0.0028850357,0.002940136,0.0016227121,0.005401302,0.0017221522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054928782,0.000008943523,0.00010955751,0.000077581215,0.000015471782,0.00001975411,0.000041610594,0.0046963445,0.00017243953,0.9826126,0.0047592493,0.007480906],"study_design_scores_gemma":[0.000007710094,0.0000070928804,0.0001141751,0.000034503733,0.000004873048,0.000021049871,0.000012350435,0.029481532,0.00007123606,0.95671946,0.01351843,0.000007548576],"about_ca_topic_score_codex":0.0037352142,"about_ca_topic_score_gemma":0.0020171192,"teacher_disagreement_score":0.0075487196,"about_ca_system_score_codex":0.0016000529,"about_ca_system_score_gemma":0.001112301,"threshold_uncertainty_score":0.025252998},"labels":[],"label_agreement":null},{"id":"W4412955598","doi":"10.1088/1742-5468/adefb1","title":"How feature learning can improve neural scaling laws<sup>*</sup>","year":2025,"lang":"en","type":"article","venue":"Journal of Statistical Mechanics Theory and Experiment","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Feature (linguistics); Scaling law; Scaling; Artificial neural network; Statistical physics; Artificial intelligence; Computer science; Law; Mathematics; Physics; Political science; Philosophy; Geometry","score_opus":0.007363415210228173,"score_gpt":0.25906248316942504,"score_spread":0.2516990679591969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412955598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27754363,0.0003117985,0.7115891,0.0013414642,0.00011350141,0.00006708344,0.00014992763,0.0016493911,0.007234186],"genre_scores_gemma":[0.94529176,0.00009798852,0.052969825,0.00013181259,0.00003876821,0.000062462444,0.00008944584,0.0001524478,0.0011655507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969566,0.00008482897,0.00001946203,0.00007161883,0.0000775472,0.000050927316],"domain_scores_gemma":[0.9968815,0.0016912358,0.0003091393,0.0006800893,0.00032215094,0.00011597766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001169698,0.00054798776,0.0004900984,0.0002752227,0.00027683275,0.00087554613,0.0010134692,0.0008014155,0.0026988625],"category_scores_gemma":[0.010785249,0.00025947965,0.00042461595,0.00026738617,0.00086414744,0.0026740183,0.00083868,0.0009837975,0.00063925993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003054084,0.00023773755,0.0034795129,0.00019951447,0.00005289068,0.00023111352,0.00025942177,0.72741836,0.03289728,0.102811664,0.005335886,0.12677127],"study_design_scores_gemma":[0.0000049722403,0.000032803273,0.00025236592,0.000004398889,0.0000034436514,0.000024165067,0.000008274524,0.9752075,0.00232387,0.021797707,0.00033536248,0.0000051337083],"about_ca_topic_score_codex":0.0011860941,"about_ca_topic_score_gemma":0.0007818647,"teacher_disagreement_score":0.0026988625,"about_ca_system_score_codex":0.0006778509,"about_ca_system_score_gemma":0.00044172342,"threshold_uncertainty_score":0.009028614},"labels":[],"label_agreement":null},{"id":"W4413097620","doi":"10.1016/j.ifacol.2025.07.040","title":"Transmission Neural Networks: Approximation and Optimal Control","year":2025,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Transmission (telecommunications); Computer science; Control (management); Optimal control; Artificial intelligence; Mathematical optimization; Telecommunications; Mathematics","score_opus":0.0059371483026605465,"score_gpt":0.23380743728358852,"score_spread":0.22787028898092798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413097620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011219127,0.003032117,0.97606736,0.00072430976,0.0001575917,0.000040585797,0.00007620796,0.00014949594,0.008533267],"genre_scores_gemma":[0.86768585,0.004651523,0.11477784,0.00024291835,0.0002747886,0.00038812353,0.00022997533,0.00007528583,0.0116735995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996427,0.00013845862,0.000020656744,0.00006975801,0.00008610064,0.000042306543],"domain_scores_gemma":[0.9989428,0.0007612136,0.000093647606,0.000038533093,0.00014178109,0.000022041237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010427937,0.0008870816,0.00095797505,0.00043746262,0.00034944725,0.0010921427,0.0008588749,0.0016023994,0.0020518408],"category_scores_gemma":[0.0041233613,0.0004933919,0.0005626581,0.0007547645,0.0009818826,0.00094783306,0.0009902196,0.0020158708,0.000246973],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016199343,0.000009429289,0.00016059162,0.000044947574,0.000013992971,0.00001861955,0.000016191576,0.96525097,0.00015933216,0.023864433,0.00043551906,0.010009882],"study_design_scores_gemma":[0.0000017367439,0.0000037555264,0.000018261124,0.0000046850028,0.0000016227185,0.0000028280963,0.0000017935976,0.9950341,0.000038491406,0.0046840073,0.00020726483,0.0000015137449],"about_ca_topic_score_codex":0.01261132,"about_ca_topic_score_gemma":0.0053234617,"teacher_disagreement_score":0.01261132,"about_ca_system_score_codex":0.0011939962,"about_ca_system_score_gemma":0.00092759,"threshold_uncertainty_score":0.025075853},"labels":[],"label_agreement":null},{"id":"W4413115115","doi":"10.18280/ts.420411","title":"Power Spectra Images for Mental Imagery Tasks EEG Classification","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mental image; Electroencephalography; Artificial intelligence; Pattern recognition (psychology); Computer science; Power (physics); Psychology; Neuroscience; Physics; Cognition","score_opus":0.01671218209979035,"score_gpt":0.27120389004699397,"score_spread":0.2544917079472036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413115115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68896407,0.0014184895,0.2902388,0.00055753684,0.0002700018,0.00040176505,0.003522498,0.0024271314,0.0121997865],"genre_scores_gemma":[0.9245802,0.00061647693,0.0700427,0.000055934936,0.000060042792,0.00015460429,0.001552798,0.00007566264,0.0028615207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988496,0.000015637934,0.000010574871,0.000026235984,0.00004559741,0.000016900432],"domain_scores_gemma":[0.99972624,0.00009723057,0.000042692373,0.000032410295,0.00008493583,0.000016506156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022315643,0.0005480954,0.00022231352,0.0011804259,0.00009227469,0.00048886286,0.00020431688,0.0002722174,0.00458352],"category_scores_gemma":[0.0018009954,0.000071153154,0.00027952789,0.00078424625,0.00016258394,0.00045224794,0.00022139485,0.00028985643,0.00097407075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008291397,0.000237632,0.009877913,0.0005597376,0.00007188897,0.00026361377,0.0001578656,0.0181625,0.15113407,0.0018372608,0.00692822,0.80994016],"study_design_scores_gemma":[0.000069127775,0.00075244054,0.15940274,0.00016729649,0.00015567715,0.0011039842,0.00050391053,0.686378,0.13273299,0.0056467224,0.012999597,0.00008751572],"about_ca_topic_score_codex":0.0013437202,"about_ca_topic_score_gemma":0.0011488397,"teacher_disagreement_score":0.00458352,"about_ca_system_score_codex":0.00015175871,"about_ca_system_score_gemma":0.00017720075,"threshold_uncertainty_score":0.0153333545},"labels":[],"label_agreement":null},{"id":"W4413125506","doi":"10.1109/tfuzz.2025.3596790","title":"Data Transformation-Driven Fuzzy Clustering Neural Network With Layerwise and End-to-End Training","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Cluster analysis; Artificial neural network; Artificial intelligence; Training (meteorology); Layer (electronics); End-to-end principle; Fuzzy logic; Fuzzy clustering; Pattern recognition (psychology); Data mining","score_opus":0.039299828434597825,"score_gpt":0.26748206419975995,"score_spread":0.2281822357651621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413125506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015364382,0.00037248048,0.9802626,0.00012294728,0.000075177675,0.00008597736,0.000049064536,0.0014913641,0.0021759607],"genre_scores_gemma":[0.57137465,0.0003197153,0.4212254,0.0002995122,0.000047028458,0.00034100065,0.00037605903,0.00013696226,0.0058797435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948823,0.000064606305,0.000035050147,0.00016841054,0.00017892622,0.00006468361],"domain_scores_gemma":[0.99946815,0.00012992701,0.000041218816,0.00006667652,0.0002712631,0.000022705066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088590494,0.0010551121,0.000835401,0.0004591164,0.0006341306,0.00070743734,0.002152992,0.0015440704,0.0018124068],"category_scores_gemma":[0.0017728641,0.00053500105,0.0006584546,0.0007504595,0.0005999099,0.0013594966,0.00090062385,0.0015949263,0.00072652946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017678697,0.00011161879,0.000886262,0.00011139015,0.00007371208,0.00008938475,0.00009933509,0.65380573,0.009363909,0.0047564013,0.0023642366,0.32816124],"study_design_scores_gemma":[0.0000045101033,0.000020295474,0.000082536724,0.000003768785,0.000005042124,0.000016102776,0.0000035624882,0.997092,0.0017763205,0.0006431391,0.00034640732,0.0000063077728],"about_ca_topic_score_codex":0.013121246,"about_ca_topic_score_gemma":0.013415823,"teacher_disagreement_score":0.013121246,"about_ca_system_score_codex":0.0010494835,"about_ca_system_score_gemma":0.0015761408,"threshold_uncertainty_score":0.026089728},"labels":[],"label_agreement":null},{"id":"W4413127311","doi":"10.1101/2025.08.08.669348","title":"Function aligns with geometry in locally connected neuronal networks","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Fonds de recherche du Québec – Nature et technologies; Alliance de recherche numérique du Canada; Université Laval","keywords":"Function (biology); Geometry; Computer science; Mathematics; Biology","score_opus":0.009702730656572898,"score_gpt":0.2020016739549706,"score_spread":0.1922989432983977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413127311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8891674,0.000054565608,0.10858598,0.00020723976,0.000009395468,0.00002094515,0.00007133154,0.00015765714,0.0017254053],"genre_scores_gemma":[0.9943521,0.000026042579,0.005339042,0.000015176094,0.0000028355016,0.000011978735,0.000022891325,0.000023882521,0.00020599186],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986327,0.00004777594,0.000004859126,0.000029924033,0.000030652012,0.000023487888],"domain_scores_gemma":[0.99952614,0.00019210043,0.00012141056,0.000059300426,0.000045575922,0.000055510154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038497298,0.00028084646,0.00026827815,0.0004687749,0.00024290655,0.00062275265,0.00034083726,0.00046069722,0.0008551344],"category_scores_gemma":[0.0021078135,0.0003638073,0.0002919518,0.00018635011,0.001075449,0.00064610416,0.0005594952,0.00036568588,0.00011992587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011674125,0.000045716264,0.008897067,0.000060578473,0.00004939577,0.00023347903,0.00024289395,0.85516423,0.090762,0.03794511,0.0003681793,0.0061145206],"study_design_scores_gemma":[0.000007611041,0.00002205184,0.0044754976,0.0000045232655,0.0000052101063,0.00004892092,0.000028105676,0.98027587,0.00340845,0.011582449,0.00013053059,0.000010791504],"about_ca_topic_score_codex":0.0023714437,"about_ca_topic_score_gemma":0.0020332423,"teacher_disagreement_score":0.0023714437,"about_ca_system_score_codex":0.0006794892,"about_ca_system_score_gemma":0.00037960522,"threshold_uncertainty_score":0.004930079},"labels":[],"label_agreement":null},{"id":"W4413127986","doi":"10.18280/ts.420434","title":"Determination of Inter-Channel Interactions in EEG Sub-Frequency Bands Based on Coherence Analysis and Neural Networks During the Audiological Test Process","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Akdeniz Üniversitesi","keywords":"Coherence (philosophical gambling strategy); Electroencephalography; Artificial neural network; Channel (broadcasting); Process (computing); Computer science; Test (biology); Speech recognition; Pattern recognition (psychology); Artificial intelligence; Acoustics; Psychology; Physics; Mathematics; Telecommunications; Neuroscience; Statistics; Geology","score_opus":0.013018783872681565,"score_gpt":0.26470481713190375,"score_spread":0.2516860332592222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413127986","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9679331,0.00059998187,0.029937284,0.000040218136,0.000010745716,0.00004492266,0.00016829981,0.000056211513,0.0012091382],"genre_scores_gemma":[0.9926857,0.00028871952,0.0065305056,0.000012000336,0.000014801489,0.00003370428,0.00011805712,0.0000072753173,0.00030916443],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998394,0.000036844027,0.000011403825,0.000039617695,0.00004543031,0.000027271695],"domain_scores_gemma":[0.9997162,0.0001468158,0.000053325555,0.000016103422,0.000047600242,0.000020038606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023878792,0.00021774882,0.00017416761,0.000654058,0.00009569848,0.0002956331,0.00008836121,0.00020743207,0.00075327564],"category_scores_gemma":[0.001209574,0.00008940184,0.00017629607,0.00044531922,0.00018346732,0.00030948105,0.00018952163,0.00013665284,0.00012718921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001408832,0.00020562124,0.14190309,0.0004183866,0.0003924412,0.0010403675,0.0011341798,0.005975528,0.5698549,0.0008145196,0.0005859638,0.27626616],"study_design_scores_gemma":[0.000016897768,0.00048196953,0.95005786,0.000022507216,0.00011743432,0.0006657262,0.00041041337,0.025690302,0.021131782,0.0008253288,0.0005530026,0.000026729],"about_ca_topic_score_codex":0.0007504956,"about_ca_topic_score_gemma":0.0009951594,"teacher_disagreement_score":0.00075327564,"about_ca_system_score_codex":0.000090656526,"about_ca_system_score_gemma":0.00008815173,"threshold_uncertainty_score":0.0025199652},"labels":[],"label_agreement":null},{"id":"W4413140657","doi":"10.1002/sim.70222","title":"An Efficient Two‐Dimensional Functional Mixed‐Effect Model Framework for Repeatedly Measured Functional Data","year":2025,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"City University of Hong Kong","keywords":"Computer science; Mixed model; Functional data analysis; Econometrics; Mathematics; Machine learning","score_opus":0.054495888216838366,"score_gpt":0.35451119008282744,"score_spread":0.30001530186598907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413140657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019983796,0.00030639247,0.9964646,0.0002668114,0.00004661764,0.00006446887,0.00042004042,0.00018213916,0.00025063634],"genre_scores_gemma":[0.15040714,0.0014044467,0.83606976,0.0004885535,0.0003277818,0.0024366542,0.0029974803,0.00029471246,0.0055735265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99258375,0.0054467996,0.00030325382,0.0009222398,0.0004635593,0.00028032865],"domain_scores_gemma":[0.98737514,0.010172973,0.0005904401,0.0007993479,0.0008582484,0.00020383146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016613856,0.0023297288,0.0026496085,0.0020141446,0.00072836864,0.0022779384,0.005237736,0.0028088787,0.006542246],"category_scores_gemma":[0.026816403,0.0017179316,0.0042230305,0.0027724192,0.0014632771,0.0022116988,0.0025884903,0.0040513757,0.0016022689],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002572959,0.000157885,0.0051433058,0.00047320512,0.0008860242,0.00046134755,0.00031420513,0.7002499,0.0012146985,0.2025516,0.004707107,0.08358341],"study_design_scores_gemma":[0.00003303413,0.00006835783,0.0005472119,0.000037377817,0.00008351608,0.00005880541,0.000027722477,0.9479403,0.0001630197,0.048322994,0.0026834696,0.00003420169],"about_ca_topic_score_codex":0.016390942,"about_ca_topic_score_gemma":0.019628398,"teacher_disagreement_score":0.016613856,"about_ca_system_score_codex":0.0016039326,"about_ca_system_score_gemma":0.0036356205,"threshold_uncertainty_score":0.087863505},"labels":[],"label_agreement":null},{"id":"W4413146138","doi":"10.1109/ecce-asia63110.2025.11112165","title":"Ultra-Efficient Three-Phase Integrated-Active-Filter Isolated Rectifier for AI Data Center Applications","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Active filter; Rectifier (neural networks); Center (category theory); Phase (matter); Electronic engineering; Electrical engineering; Artificial intelligence; Engineering; Physics; Voltage; Artificial neural network","score_opus":0.041605044059833535,"score_gpt":0.34083917508635525,"score_spread":0.29923413102652174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413146138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069352105,0.0007635824,0.9136887,0.00012279743,0.00007149894,0.000098646226,0.00009618747,0.00088527123,0.014921204],"genre_scores_gemma":[0.94333625,0.00029128656,0.05312374,0.000041245938,0.000022883967,0.000040359664,0.00009161342,0.000033703564,0.0030189792],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987364,0.000017218457,0.000008649301,0.000026322448,0.00005845909,0.000015707079],"domain_scores_gemma":[0.99989927,0.000024616851,0.000021313253,0.000010366745,0.000038424165,0.000005979944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018713035,0.0002934232,0.00032107547,0.00019037322,0.00020055716,0.0006037246,0.0007313214,0.00032804214,0.0030724297],"category_scores_gemma":[0.00021471448,0.00009085133,0.00025947668,0.00035432098,0.00012480989,0.0005785906,0.00017755169,0.00044473272,0.0008312497],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067346916,0.0003843941,0.0013606851,0.00084445055,0.000095250434,0.00028542132,0.0002458628,0.077901766,0.54510057,0.01827574,0.0043122065,0.35052028],"study_design_scores_gemma":[0.00011317639,0.0010052265,0.0013936803,0.00006996416,0.0000862142,0.00047774496,0.000099099816,0.8033987,0.16562662,0.0060545467,0.021646991,0.000028069202],"about_ca_topic_score_codex":0.0004642894,"about_ca_topic_score_gemma":0.0011598748,"teacher_disagreement_score":0.0030724297,"about_ca_system_score_codex":0.00029624646,"about_ca_system_score_gemma":0.00027502465,"threshold_uncertainty_score":0.010278344},"labels":[],"label_agreement":null},{"id":"W4413147183","doi":"10.1109/cvpr52734.2025.01412","title":"DEIM: DETR with Improved Matching for Fast Convergence","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":162,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"Hefei Normal University","keywords":"Convergence (economics); Matching (statistics); Computer science; Mathematics; Statistics","score_opus":0.007133822222443246,"score_gpt":0.24425384960375685,"score_spread":0.2371200273813136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413147183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013776404,0.00023032838,0.97284,0.00020702084,0.0001039953,0.000070526236,0.00021524812,0.009714839,0.0028416952],"genre_scores_gemma":[0.2351671,0.00020585355,0.7502605,0.00086831633,0.0000742909,0.0002814484,0.0022000796,0.0020056623,0.008936822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994067,0.00008579952,0.000028551452,0.00015053489,0.00023736955,0.00009107452],"domain_scores_gemma":[0.9992387,0.00022778584,0.00005448435,0.0002073379,0.00020812359,0.000063596264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013706328,0.0013601015,0.0008313749,0.000699088,0.00038758563,0.0012507983,0.0032095858,0.0012681736,0.006107679],"category_scores_gemma":[0.0052243616,0.0006466848,0.00076185673,0.00055223226,0.0005537909,0.0019715577,0.002721872,0.002447472,0.0036272132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045017953,0.00029223788,0.0034215713,0.00021402266,0.00013997611,0.00019316057,0.0001463452,0.3291708,0.028444622,0.014953988,0.027983729,0.5945894],"study_design_scores_gemma":[0.000017181832,0.000049447513,0.00014187847,0.000008143554,0.000006531745,0.0000631782,0.000011688844,0.9867286,0.0070988135,0.0027780733,0.0030875783,0.000008958148],"about_ca_topic_score_codex":0.003942966,"about_ca_topic_score_gemma":0.0078819515,"teacher_disagreement_score":0.006107679,"about_ca_system_score_codex":0.0007870653,"about_ca_system_score_gemma":0.0014777179,"threshold_uncertainty_score":0.020432234},"labels":[],"label_agreement":null},{"id":"W4413150654","doi":"10.1016/j.patcog.2025.112065","title":"Twin learning for domain agnostic time series analysis: A regime-switch approach","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Series (stratigraphy); Computer science; Time series; Domain (mathematical analysis); Artificial intelligence; Algorithm; Machine learning; Mathematics","score_opus":0.01610008411489124,"score_gpt":0.24577164308345492,"score_spread":0.22967155896856367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413150654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029487018,0.0005159361,0.96808827,0.00035571522,0.00004067049,0.000040141273,0.0001058403,0.00052025175,0.0008461004],"genre_scores_gemma":[0.84728426,0.0006380725,0.14727613,0.0004280053,0.00016798975,0.00019961361,0.0007225057,0.00019421734,0.0030892505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990583,0.0003173577,0.00006378021,0.00030808008,0.00015219499,0.000100174664],"domain_scores_gemma":[0.99651366,0.0023529194,0.0003297333,0.0003665278,0.00024781196,0.00018937176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034858007,0.00085293275,0.001312465,0.0015366721,0.0004909294,0.001308863,0.002153129,0.0016824355,0.0020014218],"category_scores_gemma":[0.0070260433,0.0006282371,0.0016053949,0.0011961425,0.0013241393,0.0022583234,0.0023602624,0.003086282,0.00052211306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027633016,0.00023594582,0.009534723,0.00017535791,0.00032368288,0.0002147381,0.0002669439,0.7924011,0.004534024,0.0332925,0.0021441777,0.1566005],"study_design_scores_gemma":[0.0000028180868,0.000009852632,0.00018916854,0.000003867069,0.000007035216,0.000010000813,0.0000043878513,0.992147,0.0001691019,0.0072971084,0.00015532691,0.00000430272],"about_ca_topic_score_codex":0.00293637,"about_ca_topic_score_gemma":0.0023060564,"teacher_disagreement_score":0.0034858007,"about_ca_system_score_codex":0.0009607207,"about_ca_system_score_gemma":0.0009465953,"threshold_uncertainty_score":0.018434882},"labels":[],"label_agreement":null},{"id":"W4413157759","doi":"10.1109/tit.2025.3598722","title":"An Information-Theoretic Framework for Out-of-Distribution Generalization With Applications to Stochastic Gradient Langevin Dynamics","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Langevin dynamics; Generalization; Computer science; Stochastic process; Statistical physics; Dynamics (music); Information theory; Stochastic dynamics; Applied mathematics; Mathematics; Statistics; Physics; Mathematical analysis","score_opus":0.006928188622609437,"score_gpt":0.2539832216293135,"score_spread":0.2470550330067041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413157759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012069278,0.0012315401,0.9775213,0.001373879,0.00009930152,0.000041978514,0.00009780302,0.00015997981,0.007404891],"genre_scores_gemma":[0.754908,0.0040202765,0.22848271,0.0013939478,0.00089598517,0.00051229855,0.00038292512,0.0004905255,0.008913356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974698,0.0008741648,0.00013744275,0.00055473647,0.0007153039,0.00024854956],"domain_scores_gemma":[0.9874887,0.008399798,0.0012573355,0.0014161209,0.0009341684,0.00050383696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068096956,0.0016294018,0.0017514935,0.0021476613,0.0015193485,0.0024670558,0.003800524,0.0026975973,0.0035864827],"category_scores_gemma":[0.02372841,0.00078179286,0.0023480533,0.0019782672,0.0051609743,0.008231433,0.006535118,0.006346651,0.0004935991],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030537634,0.000030123518,0.0005382236,0.00011720572,0.000034761517,0.000120328936,0.00016193355,0.19602646,0.0009907295,0.7878928,0.0012816585,0.012775186],"study_design_scores_gemma":[0.0000042523006,0.000022250437,0.00016423374,0.000027796214,0.000009297858,0.000051319035,0.0000176175,0.6658028,0.00040741893,0.3320999,0.0013695757,0.00002347467],"about_ca_topic_score_codex":0.0039805775,"about_ca_topic_score_gemma":0.0024490766,"teacher_disagreement_score":0.0068096956,"about_ca_system_score_codex":0.0046969764,"about_ca_system_score_gemma":0.0018695234,"threshold_uncertainty_score":0.036013544},"labels":[],"label_agreement":null},{"id":"W4413306624","doi":"10.23977/jnca.2025.100110","title":"An Adaptive Physics-Informed Neural Network by Sampling Alternately from Time and Space for Solving Spatiotemporal PDE","year":2025,"lang":"en","type":"article","venue":"Journal of Network Computing and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sampling (signal processing); Adaptive sampling; Artificial neural network; Space time; Spacetime; Space (punctuation); Computer science; Physics; Statistical physics; Artificial intelligence; Mathematics; Quantum mechanics; Optics; Statistics; Chemistry","score_opus":0.02009280907297405,"score_gpt":0.2946387713580662,"score_spread":0.27454596228509215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413306624","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019794973,0.0004073664,0.97756857,0.00016102474,0.00005672367,0.00002946349,0.00003599567,0.00025691124,0.0016888652],"genre_scores_gemma":[0.6251512,0.0007257041,0.36851287,0.0003561097,0.000106471365,0.00025074484,0.00025526373,0.00009781988,0.004543688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977547,0.00005131092,0.000012556881,0.000068040266,0.00006866226,0.000023990531],"domain_scores_gemma":[0.99979323,0.00008936602,0.000025242518,0.000021743132,0.000054566546,0.000015838194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052151596,0.0005344949,0.0005648106,0.00030393768,0.000299049,0.00040040835,0.0011577245,0.00068466447,0.00093902095],"category_scores_gemma":[0.0013727536,0.00034393667,0.00044284068,0.0004820454,0.00042721786,0.0010894134,0.0008621883,0.0008528564,0.00015297066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011562638,0.0000639438,0.00120815,0.00009420767,0.00005596379,0.000086298576,0.00007468463,0.86904806,0.009476373,0.0100973165,0.0015769942,0.10810243],"study_design_scores_gemma":[0.0000027426502,0.0000067030737,0.000034209726,0.0000014216339,0.000002652168,0.0000053468784,0.0000013099741,0.998892,0.00029810623,0.0005992991,0.00015440685,0.000001793036],"about_ca_topic_score_codex":0.0068841977,"about_ca_topic_score_gemma":0.00694617,"teacher_disagreement_score":0.0068841977,"about_ca_system_score_codex":0.0005375258,"about_ca_system_score_gemma":0.0007769402,"threshold_uncertainty_score":0.013688207},"labels":[],"label_agreement":null},{"id":"W4413318864","doi":"10.1109/mmm.2025.3556156","title":"Neuro-Transfer Function for Electromagnetic Modeling","year":2025,"lang":"en","type":"article","venue":"IEEE Microwave Magazine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Transfer function; Function (biology); Computer science; Electronic engineering; Electrical engineering; Engineering","score_opus":0.014039300972149388,"score_gpt":0.23778517179483047,"score_spread":0.22374587082268108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413318864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013978843,0.0016168421,0.9896259,0.000295568,0.000099421166,0.000013653479,0.00007631687,0.0003308841,0.0065436186],"genre_scores_gemma":[0.5308314,0.012907357,0.41246784,0.0007085662,0.0005233856,0.00049109204,0.0006772651,0.00039183735,0.04100131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998375,0.000055725963,0.0000097952625,0.000028327479,0.000056738307,0.000011888459],"domain_scores_gemma":[0.9998202,0.00008642313,0.00002060563,0.000020653004,0.00004701178,0.000005247227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039167178,0.0007135386,0.000483625,0.00045592373,0.00023999307,0.00089587545,0.0007447383,0.0011502928,0.0036907762],"category_scores_gemma":[0.000848956,0.00019908174,0.000688996,0.0006666655,0.0004394561,0.0007792235,0.0005368088,0.0015200893,0.0018177562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036644222,0.00002944884,0.00043769312,0.00022197746,0.00007048272,0.00015122656,0.00007420642,0.7606058,0.0054060775,0.10533128,0.0056473142,0.12198787],"study_design_scores_gemma":[0.000001423763,0.000010459442,0.00008554054,0.000018405834,0.0000052358164,0.000034308112,0.000007720065,0.9791955,0.0006176735,0.012996111,0.0070207915,0.0000069475313],"about_ca_topic_score_codex":0.0027187893,"about_ca_topic_score_gemma":0.0017565825,"teacher_disagreement_score":0.0036907762,"about_ca_system_score_codex":0.0005739535,"about_ca_system_score_gemma":0.0005355403,"threshold_uncertainty_score":0.012346864},"labels":[],"label_agreement":null},{"id":"W4413407902","doi":"10.1016/j.neucom.2025.131298","title":"Convergence of batch gradient descent with learnable smooth masks for pruning feedforward neural networks","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Convergence (economics); Gradient descent; Pruning; Artificial neural network; Feed forward; Computer science; Feedforward neural network; Stochastic gradient descent; Mathematics; Artificial intelligence; Algorithm; Mathematical optimization; Control theory (sociology); Control (management); Biology; Control engineering","score_opus":0.011125859370568546,"score_gpt":0.24056229334008594,"score_spread":0.2294364339695174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413407902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02099314,0.00046708083,0.9755224,0.000186707,0.00009533788,0.00006827528,0.000062520885,0.0007258255,0.0018785463],"genre_scores_gemma":[0.38345116,0.0002963137,0.6059059,0.00020776302,0.00008966175,0.00030266368,0.00032770893,0.00046193763,0.008956937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994892,0.00014626293,0.00003562109,0.00008680614,0.00017697326,0.00006519249],"domain_scores_gemma":[0.9968637,0.0020384062,0.00014692839,0.00021023872,0.00063236314,0.000108401866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024392793,0.0011801673,0.0012177008,0.00067428756,0.0004785937,0.0007681703,0.0019923523,0.0023255819,0.0039286353],"category_scores_gemma":[0.010538628,0.0008466783,0.00051636726,0.00039829564,0.0010229732,0.0014045468,0.0013496116,0.0018284736,0.0009525715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005738236,0.00014511734,0.00084721105,0.00028304136,0.00008468124,0.00013291543,0.0001394699,0.7532282,0.014867862,0.023319207,0.0054142657,0.20096414],"study_design_scores_gemma":[0.00000775585,0.000017438084,0.000052582178,0.0000071642303,0.0000035036085,0.000008058457,0.0000023440691,0.99717253,0.000995888,0.0015758557,0.00015462736,0.0000022890417],"about_ca_topic_score_codex":0.006259658,"about_ca_topic_score_gemma":0.0079988465,"teacher_disagreement_score":0.006259658,"about_ca_system_score_codex":0.0010064528,"about_ca_system_score_gemma":0.0015846933,"threshold_uncertainty_score":0.013142586},"labels":[],"label_agreement":null},{"id":"W4413470531","doi":"10.1007/978-3-031-84869-8_7","title":"From Interval-Valued Neurons to Convex-Polygon-Valued Neurons, with Sparsity and Entropy Criteria","year":2025,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Regular polygon; Mathematics; Polygon (computer graphics); Interval (graph theory); Combinatorics; Computer science; Geometry","score_opus":0.019321471274513752,"score_gpt":0.2608427138377692,"score_spread":0.24152124256325544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413470531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022692604,0.0014856639,0.9580353,0.0007162498,0.00014835183,0.00001957965,0.0001591299,0.00008400404,0.016659208],"genre_scores_gemma":[0.60809124,0.0029057104,0.36926195,0.00038026614,0.0005298458,0.00011775142,0.00036995794,0.00021262756,0.018130653],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99972373,0.00008833853,0.00001906856,0.000077535326,0.00006931212,0.000021970856],"domain_scores_gemma":[0.9988067,0.0007279762,0.00010653676,0.00009213668,0.0001966415,0.00006986551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006673221,0.000520482,0.0005665867,0.0005677341,0.000291591,0.0016938847,0.0011591817,0.00093801285,0.002935327],"category_scores_gemma":[0.004363892,0.0003259696,0.00047473612,0.00087555783,0.001613641,0.0032020723,0.0017335403,0.0019926974,0.0003752899],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007240234,0.000023861601,0.0005386623,0.00017933393,0.000030211677,0.00010541997,0.00017352823,0.0872594,0.0038905898,0.8214001,0.0031790615,0.083147325],"study_design_scores_gemma":[0.0000035612302,0.000026693875,0.00014811139,0.000025390831,0.000006325445,0.00006691303,0.00003457176,0.35412547,0.00054105336,0.6436057,0.0014082791,0.000007919931],"about_ca_topic_score_codex":0.001198238,"about_ca_topic_score_gemma":0.0012862135,"teacher_disagreement_score":0.002935327,"about_ca_system_score_codex":0.00068707805,"about_ca_system_score_gemma":0.0003999614,"threshold_uncertainty_score":0.009819627},"labels":[],"label_agreement":null},{"id":"W4413477185","doi":"10.1007/978-3-031-84869-8_28","title":"Parameter Estimation of Hodgkin-Huxley Model: A Comparative Study of Genetic Algorithm and Artificial Neural Network Approach","year":2025,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Artificial neural network; Hodgkin–Huxley model; Computer science; Estimation; Genetic algorithm; Artificial intelligence; Algorithm; Machine learning; Biology; Engineering; Neuroscience","score_opus":0.040832444342086036,"score_gpt":0.28748451257223895,"score_spread":0.2466520682301529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413477185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0825379,0.0063766204,0.9012494,0.00053720956,0.00007543689,0.000038968697,0.00007309447,0.00040328447,0.0087081045],"genre_scores_gemma":[0.85581213,0.0043798937,0.13584428,0.00009510994,0.000078820645,0.000054491393,0.00014234621,0.00019130278,0.0034015821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977,0.0001174482,0.000009016232,0.000034595363,0.000054976404,0.000013976618],"domain_scores_gemma":[0.99862623,0.0010956889,0.0000756778,0.000055737808,0.0001330536,0.0000136077415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010639613,0.0003215705,0.0007114883,0.00053339254,0.00024418507,0.00091988925,0.0009964909,0.00090715935,0.0010393258],"category_scores_gemma":[0.004282657,0.0002069591,0.0003730052,0.00086067227,0.00040068422,0.0015423548,0.00034283346,0.0006080171,0.00016105533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007725268,0.000028939683,0.0014211423,0.00015636203,0.0000607328,0.000056293124,0.00010292673,0.89711916,0.0014390087,0.031372946,0.00063658983,0.06752869],"study_design_scores_gemma":[0.0000022224535,0.000008534157,0.00023067145,0.000008300636,0.000008973536,0.000016666589,0.000014977497,0.9938188,0.00028865846,0.005352279,0.00024478274,0.0000050757812],"about_ca_topic_score_codex":0.012947526,"about_ca_topic_score_gemma":0.004614791,"teacher_disagreement_score":0.012947526,"about_ca_system_score_codex":0.000677222,"about_ca_system_score_gemma":0.00084579404,"threshold_uncertainty_score":0.025744319},"labels":[],"label_agreement":null},{"id":"W4413513735","doi":"10.1109/isie62713.2025.11124601","title":"Induction Motor Temperature Prediction via Transfer Learning on Physics-Based Synthetic Data Using Deep Neural Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Transfer of learning; Computer science; Deep learning; Artificial intelligence; Machine learning","score_opus":0.024821759661475287,"score_gpt":0.25898470768902154,"score_spread":0.23416294802754625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413513735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48982567,0.0003518729,0.50388265,0.0002901407,0.000081651204,0.000058822596,0.0007583585,0.0023906531,0.0023601966],"genre_scores_gemma":[0.97412306,0.00007359386,0.024308268,0.00002882436,0.000009009915,0.00004440591,0.00076319143,0.00004454839,0.00060505635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998894,0.000022914237,0.000006346751,0.00003977819,0.00002929722,0.000012322979],"domain_scores_gemma":[0.999564,0.00020797383,0.00005736039,0.00005868113,0.00009617112,0.00001590952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043614974,0.00055481563,0.00029322883,0.00036339153,0.00015209116,0.00034296975,0.00050968386,0.0004890798,0.0008261474],"category_scores_gemma":[0.0014973405,0.00025503535,0.00037338212,0.00030543687,0.0003815779,0.0007152215,0.00029417218,0.0007786118,0.00025057563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006349497,0.00005464872,0.0011660251,0.000035573932,0.000016195192,0.00002564191,0.000012272959,0.96894956,0.0055142823,0.0004406132,0.0004467112,0.023274932],"study_design_scores_gemma":[0.0000017406207,0.000009418803,0.00026397692,0.0000011830269,0.0000011387369,0.0000027223723,0.0000015122055,0.9980564,0.0013217577,0.00026625572,0.000072258175,0.0000017221181],"about_ca_topic_score_codex":0.0025697947,"about_ca_topic_score_gemma":0.0033900298,"teacher_disagreement_score":0.0025697947,"about_ca_system_score_codex":0.00050592964,"about_ca_system_score_gemma":0.00044445702,"threshold_uncertainty_score":0.005109668},"labels":[],"label_agreement":null},{"id":"W4413513958","doi":"10.1109/isie62713.2025.11124649","title":"Dynamic Voltage and Frequency Scaling (DVFS) Strategy for FPGA-Based Edge AI Inference","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Frequency scaling; Field-programmable gate array; Computer science; Inference; Enhanced Data Rates for GSM Evolution; Scaling; Parallel computing; Voltage; Computer architecture; Embedded system; Artificial intelligence; Electrical engineering; Engineering; Mathematics","score_opus":0.01886638391089451,"score_gpt":0.30988152703918126,"score_spread":0.29101514312828675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413513958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22468351,0.0015253505,0.7524888,0.0004218232,0.00021991182,0.00016720421,0.00027116382,0.0060704374,0.014151838],"genre_scores_gemma":[0.9480357,0.0001370756,0.04997922,0.000108090266,0.000023525912,0.000040213235,0.00009938236,0.00006949695,0.0015072422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998504,0.000015262614,0.000010357819,0.00004161442,0.00005036796,0.0000319603],"domain_scores_gemma":[0.99981195,0.00006099237,0.00002484128,0.000037434776,0.000048815305,0.000016024242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017335427,0.00056623743,0.0002092878,0.00044548395,0.00030769408,0.00040947905,0.00091911433,0.00021763635,0.0028322495],"category_scores_gemma":[0.000745312,0.00012749886,0.00014183417,0.0002392761,0.00019243472,0.0005599497,0.00038378098,0.0003645195,0.00030758526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045815183,0.0002682659,0.0041070324,0.0002238884,0.00004860172,0.000296638,0.00021120608,0.16384675,0.15941505,0.006811343,0.006712893,0.6576002],"study_design_scores_gemma":[0.0000256121,0.00018628093,0.0014443297,0.000017454498,0.000024188146,0.0001247836,0.000058358368,0.93867457,0.05108064,0.0027768526,0.0055690515,0.000017829896],"about_ca_topic_score_codex":0.0034360606,"about_ca_topic_score_gemma":0.007970613,"teacher_disagreement_score":0.0034360606,"about_ca_system_score_codex":0.00043078678,"about_ca_system_score_gemma":0.0005971526,"threshold_uncertainty_score":0.009474874},"labels":[],"label_agreement":null},{"id":"W4413554783","doi":"10.1109/jsen.2025.3600296","title":"Toward a Universal Sensor: Predictive Inference for Adaptive Sensing in Wildfires","year":2025,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"3v Geomatics (Canada); Cistel Technology (Canada); Carleton University; University of Waterloo; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Computer science; Remote sensing; Artificial intelligence; Machine learning; Geography","score_opus":0.030054909735059487,"score_gpt":0.2813926629656112,"score_spread":0.25133775323055174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413554783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10620358,0.0004571347,0.88921094,0.0004212893,0.00008344667,0.000026424159,0.000063053645,0.0012924838,0.0022415908],"genre_scores_gemma":[0.9399739,0.00016042324,0.059155367,0.000090110974,0.000023005166,0.000020917996,0.000040821233,0.000043115855,0.0004923852],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970335,0.00005612904,0.000013273724,0.00009432082,0.0000886795,0.00004427386],"domain_scores_gemma":[0.9992717,0.00036839073,0.000075785734,0.0001227786,0.00011525382,0.0000460725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085076306,0.00040260592,0.00053353974,0.00032399938,0.00042037337,0.00085220486,0.0014457165,0.00047476738,0.0006828038],"category_scores_gemma":[0.002994265,0.0003020545,0.0003331047,0.00037280223,0.0010402169,0.0016039931,0.0010690968,0.0010027235,0.00009451623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018796422,0.00011201208,0.0033333155,0.00009838693,0.00007798895,0.00011581576,0.0002467136,0.84352726,0.014724329,0.026980354,0.0013029845,0.10929281],"study_design_scores_gemma":[0.000004170475,0.00001944779,0.0002587683,0.000005076763,0.000009152294,0.000013479341,0.000018237095,0.9869437,0.0023999694,0.009842854,0.00047904794,0.0000060986727],"about_ca_topic_score_codex":0.008625087,"about_ca_topic_score_gemma":0.011045351,"teacher_disagreement_score":0.008625087,"about_ca_system_score_codex":0.00073087343,"about_ca_system_score_gemma":0.00097099773,"threshold_uncertainty_score":0.017149746},"labels":[],"label_agreement":null},{"id":"W4413556955","doi":"10.1109/isie62713.2025.11124778","title":"Adaptive Neural Fractional-Order Pseudo-Inverse Control for a Giant Magnetostrictive Actuator","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Magnetostriction; Actuator; Inverse; Control theory (sociology); Adaptive control; Computer science; Inverse problem; Order (exchange); Artificial neural network; Control (management); Mathematics; Physics; Mathematical analysis; Artificial intelligence; Magnetic field; Geometry","score_opus":0.012239835818914056,"score_gpt":0.2531900426593435,"score_spread":0.24095020684042942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413556955","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058941253,0.00074048643,0.9348535,0.0002669099,0.0001348724,0.000027029793,0.000017263124,0.00028073043,0.0047378954],"genre_scores_gemma":[0.9399957,0.00024193687,0.057227056,0.00006123975,0.000026753385,0.000036578112,0.000014627721,0.0000100853085,0.0023859886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993515,0.000011488427,0.0000039501792,0.000016252472,0.000026337453,0.000006794695],"domain_scores_gemma":[0.9999125,0.00002868389,0.000022569964,0.000007449559,0.000024119083,0.0000047260064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017762791,0.00027067316,0.00020905463,0.00010928972,0.00020880364,0.000237025,0.00044143258,0.0003839614,0.0005023717],"category_scores_gemma":[0.00028084457,0.00008738892,0.00021757015,0.00010139741,0.00028325967,0.00025789993,0.00020267656,0.00031948977,0.000067260975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017525782,0.00009662813,0.00060723565,0.00034867314,0.000056892517,0.00033835438,0.00020061282,0.61433125,0.21728091,0.018501855,0.0013284683,0.14673394],"study_design_scores_gemma":[0.0000039993683,0.000047178775,0.00010788284,0.000004073203,0.0000049397845,0.00003151488,0.000003938693,0.9942702,0.004304674,0.00052250864,0.0006942103,0.0000049851133],"about_ca_topic_score_codex":0.001693721,"about_ca_topic_score_gemma":0.0019805855,"teacher_disagreement_score":0.001693721,"about_ca_system_score_codex":0.00020724445,"about_ca_system_score_gemma":0.00022346515,"threshold_uncertainty_score":0.003367722},"labels":[],"label_agreement":null},{"id":"W4413574627","doi":"10.1190/geo2024-0383.1","title":"Modeling the thermal behavior of geothermal systems at Mount Meager, Southwestern Canada, using artificial neural networks and audio-magnetotellurics","year":2025,"lang":"en","type":"article","venue":"Geophysics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University; Geological Survey of Canada","funders":"Commission Géologique du Canada","keywords":"Magnetotellurics; Geothermal gradient; Mount; Artificial neural network; Geology; Geophysics; Computer science; Artificial intelligence; Engineering; Electrical engineering; Operating system","score_opus":0.017239612761165864,"score_gpt":0.22242339718434087,"score_spread":0.205183784423175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413574627","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9926708,0.00006283149,0.004876097,0.00009773123,0.000005546004,0.00001989655,0.00037841915,0.00015998729,0.0017286876],"genre_scores_gemma":[0.9977849,0.000024092888,0.0012635364,0.000006441976,0.000001434615,0.000007612563,0.00014906189,0.0000061833534,0.00075675803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999422,0.0000069244306,0.0000024710193,0.000018347242,0.000016612003,0.000013521004],"domain_scores_gemma":[0.99983835,0.00004809378,0.000025119714,0.000009139447,0.00006256063,0.000016653992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001348182,0.0005169597,0.00015820916,0.00037765017,0.0005226608,0.0005855994,0.00068199215,0.0003833698,0.00062958547],"category_scores_gemma":[0.0005999294,0.00021784249,0.00021082736,0.00046884263,0.000407963,0.00024391539,0.0002015296,0.00031459928,0.00009962053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004415672,0.000027724878,0.022587352,0.00001682741,0.000021540574,0.000055974127,0.000053783813,0.9675726,0.0022223222,0.00014446625,0.00030507505,0.006948159],"study_design_scores_gemma":[0.0000035797855,0.0000050138474,0.008852614,0.0000019818608,0.000003914946,0.0000032728974,0.000028281365,0.9905098,0.00044225538,0.000048750797,0.00009591581,0.0000046357954],"about_ca_topic_score_codex":0.8950955,"about_ca_topic_score_gemma":0.9157229,"teacher_disagreement_score":0.10490447,"about_ca_system_score_codex":0.006037487,"about_ca_system_score_gemma":0.0041353423,"threshold_uncertainty_score":0.21104449},"labels":[],"label_agreement":null},{"id":"W4413640093","doi":"10.1109/compsac65507.2025.00215","title":"Novel Norms for Pruning Convolutional Neural Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Convolutional neural network; Pruning; Computer science; Artificial intelligence; Pattern recognition (psychology); Natural language processing; Botany","score_opus":0.01745190554173133,"score_gpt":0.26436368746282946,"score_spread":0.24691178192109814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413640093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0446425,0.0014401432,0.9470727,0.0006045896,0.00022289268,0.00015064322,0.00031445138,0.0015515399,0.0040006023],"genre_scores_gemma":[0.30193967,0.0010662668,0.6906535,0.00054317206,0.00025138195,0.00047862544,0.0013098052,0.0009106454,0.0028468268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9888883,0.0028130466,0.0012621256,0.0011754441,0.0054537063,0.0004074712],"domain_scores_gemma":[0.964144,0.016285926,0.0038535125,0.0041452916,0.010771332,0.0007998859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011809492,0.00147716,0.0011076828,0.0020828359,0.00089744886,0.002705102,0.0031497679,0.0018177909,0.0012729464],"category_scores_gemma":[0.063121036,0.000594856,0.0009685285,0.0011693059,0.0022170541,0.0048133167,0.002523591,0.0027701384,0.0006116343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005246991,0.00029612752,0.008233028,0.00048117977,0.00017550163,0.00023822124,0.00048276922,0.48779133,0.012734246,0.12923342,0.010647299,0.34916222],"study_design_scores_gemma":[0.000045978202,0.00018691913,0.0005445546,0.00011974148,0.000038251455,0.00015970152,0.00007769221,0.9193402,0.012728807,0.06073027,0.005989967,0.00003783907],"about_ca_topic_score_codex":0.003956933,"about_ca_topic_score_gemma":0.006444337,"teacher_disagreement_score":0.011809492,"about_ca_system_score_codex":0.0021289282,"about_ca_system_score_gemma":0.0028954893,"threshold_uncertainty_score":0.062455356},"labels":[],"label_agreement":null},{"id":"W4413775399","doi":"10.1177/18758967251371265","title":"Fuzzy Relational Models: Convolution Techniques and Optimization","year":2025,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Convolution (computer science); Fuzzy logic; Relational model; Artificial intelligence; Data mining; Relational database; Artificial neural network","score_opus":0.027160508820775295,"score_gpt":0.2623669033736593,"score_spread":0.235206394552884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413775399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053917505,0.0007216711,0.99043405,0.0002750211,0.000022924445,0.000014528884,0.000037027603,0.00006593246,0.0030370106],"genre_scores_gemma":[0.47348735,0.0029800409,0.5127816,0.00023186347,0.00016709874,0.00015919331,0.00022777794,0.0001259307,0.009839144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994205,0.00023668564,0.000030908057,0.00010285799,0.00016551094,0.000043626686],"domain_scores_gemma":[0.9993129,0.00039032023,0.00008669057,0.00008749148,0.00009243762,0.000030090145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015730035,0.00065026444,0.0007774919,0.00069066853,0.00038760764,0.0016108289,0.0010230439,0.0010956147,0.0025872062],"category_scores_gemma":[0.0028191777,0.00043377225,0.0012004685,0.0010918498,0.0010635144,0.0024162922,0.0011341799,0.0013919339,0.00048944616],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027519423,0.00002675717,0.00034733707,0.00008005467,0.00005000037,0.00007272466,0.000093793715,0.5221553,0.0016241804,0.43263695,0.001326614,0.04155872],"study_design_scores_gemma":[0.0000021008075,0.0000072808843,0.000039484,0.000006674551,0.0000052621135,0.00002275119,0.0000070857104,0.9432393,0.00029079284,0.055544592,0.00082970335,0.0000048756815],"about_ca_topic_score_codex":0.0042301673,"about_ca_topic_score_gemma":0.0029860483,"teacher_disagreement_score":0.0042301673,"about_ca_system_score_codex":0.0012284238,"about_ca_system_score_gemma":0.00084070186,"threshold_uncertainty_score":0.008912861},"labels":[],"label_agreement":null},{"id":"W4413938989","doi":"10.2139/ssrn.5434594","title":"Neural extended generalized Pareto for rainfall time series stochastic generator","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Series (stratigraphy); Generator (circuit theory); Pareto principle; Computer science; Generalized Pareto distribution; Applied mathematics; Mathematics; Mathematical optimization; Statistical physics; Geology; Statistics; Physics; Paleontology; Extreme value theory; Power (physics)","score_opus":0.011146945117686737,"score_gpt":0.25957591479886694,"score_spread":0.2484289696811802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413938989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07344136,0.00061252865,0.9209534,0.0002725316,0.00008730344,0.000043236472,0.00015733227,0.00016049719,0.0042719245],"genre_scores_gemma":[0.92440975,0.0008500813,0.058975212,0.0001709874,0.000123057,0.0001241458,0.00034810396,0.000080053265,0.014918495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998171,0.000057624802,0.000009379802,0.000046986665,0.000038173086,0.00003074633],"domain_scores_gemma":[0.9994337,0.0003013021,0.00005598716,0.000037943984,0.00012746964,0.00004361091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010266184,0.000538347,0.0007540786,0.000495234,0.00035500617,0.00075527927,0.0012954028,0.0009690569,0.0033529752],"category_scores_gemma":[0.0023955433,0.00040298048,0.0007613458,0.00065516756,0.0005999085,0.00092253124,0.0009315951,0.0008326668,0.00032147116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004971411,0.000024529734,0.00074075616,0.00006575976,0.000053059823,0.00011844199,0.00004471231,0.9208909,0.0019353231,0.051545363,0.00085574697,0.02367568],"study_design_scores_gemma":[0.000002475966,0.000005044293,0.000082041064,0.0000023006462,0.0000029730923,0.000011671205,0.0000021646028,0.9936008,0.000060588845,0.00614976,0.000077341414,0.000002902659],"about_ca_topic_score_codex":0.0030088983,"about_ca_topic_score_gemma":0.0032395471,"teacher_disagreement_score":0.0033529752,"about_ca_system_score_codex":0.0006520157,"about_ca_system_score_gemma":0.00072162814,"threshold_uncertainty_score":0.011216819},"labels":[],"label_agreement":null},{"id":"W4413972371","doi":"10.3389/fnins.2025.1634652","title":"Mapping the computational similarity of individual neurons within large-scale ensemble recordings using the SIMNETS analysis framework","year":2025,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institutes of Health; Killam Trusts; U.S. Department of Veterans Affairs","keywords":"Similarity (geometry); Scale (ratio); Computer science; Artificial intelligence; Pattern recognition (psychology); Cartography; Geography; Image (mathematics)","score_opus":0.029999652611002584,"score_gpt":0.2787688058592512,"score_spread":0.2487691532482486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413972371","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094899334,0.00017951799,0.90052545,0.00016531427,0.000030708976,0.00007619153,0.0008197401,0.0018203754,0.0014833424],"genre_scores_gemma":[0.7063792,0.00030184543,0.28932527,0.00009459033,0.000056653043,0.0002886657,0.002006466,0.0004443675,0.0011029508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955267,0.00009886906,0.00003147677,0.00013866722,0.00013531877,0.000042987016],"domain_scores_gemma":[0.9988392,0.00051048433,0.0002464133,0.00017639495,0.00016196468,0.00006566496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011079119,0.0008234352,0.00057027926,0.0017790841,0.00045511304,0.0012623215,0.0010997279,0.0005702589,0.0017759011],"category_scores_gemma":[0.004691694,0.00031851477,0.0010507966,0.0011960035,0.0007749858,0.0015112264,0.001491217,0.00093795784,0.00038807554],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044409197,0.00018589121,0.022114996,0.00046075456,0.00061824726,0.0005910546,0.00058303686,0.63016564,0.08521392,0.049203157,0.0041355654,0.20628363],"study_design_scores_gemma":[0.000008851184,0.000051582218,0.005200769,0.000014681891,0.000027485316,0.000111641384,0.00008351272,0.9611011,0.007905371,0.0243367,0.0011379685,0.000020410629],"about_ca_topic_score_codex":0.0033950296,"about_ca_topic_score_gemma":0.0050801695,"teacher_disagreement_score":0.0033950296,"about_ca_system_score_codex":0.0007579839,"about_ca_system_score_gemma":0.00096119044,"threshold_uncertainty_score":0.006750524},"labels":[],"label_agreement":null},{"id":"W4413994637","doi":"10.1101/2025.09.02.672707","title":"Natural language processing captures memory content associated with shared neural patterns at encoding","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense","keywords":"Encoding (memory); Computer science; Natural (archaeology); Content (measure theory); Natural language processing; Artificial intelligence; Biology","score_opus":0.019057372079279388,"score_gpt":0.22572442398937817,"score_spread":0.2066670519100988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413994637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939917,0.00005532311,0.0053151287,0.000033166387,0.0000026345724,0.0000059812814,0.00008596178,0.000023169418,0.0004870189],"genre_scores_gemma":[0.99849916,0.000021478361,0.0012599642,0.000008904786,0.0000031348034,0.000004785825,0.00006208477,0.000004415565,0.00013600645],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988854,0.000029134619,0.0000060061707,0.00004751338,0.000015752303,0.000013076402],"domain_scores_gemma":[0.9992562,0.00029091423,0.00026923622,0.00008584496,0.00005841023,0.000039402697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030788954,0.000115555915,0.00012038601,0.000357505,0.000084908235,0.00047389587,0.0001662758,0.00022017663,0.0012605655],"category_scores_gemma":[0.0026672957,0.00011314846,0.00013404524,0.00022574047,0.00032128306,0.0006719816,0.00039527644,0.00022579795,0.00011002671],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017811548,0.00027916007,0.24658996,0.00036839044,0.00028354538,0.0007367826,0.005881694,0.0060862675,0.58151925,0.0038037107,0.0008489954,0.15182105],"study_design_scores_gemma":[0.000029488898,0.00041698109,0.924304,0.000030184267,0.00008280121,0.0007648726,0.0013167196,0.036770213,0.024813764,0.010762118,0.0006684658,0.00004029652],"about_ca_topic_score_codex":0.0006416432,"about_ca_topic_score_gemma":0.0010969118,"teacher_disagreement_score":0.0012605655,"about_ca_system_score_codex":0.0001629942,"about_ca_system_score_gemma":0.00007302565,"threshold_uncertainty_score":0.0042170286},"labels":[],"label_agreement":null},{"id":"W4414014554","doi":"10.1142/s0219477525400140","title":"Link Between Continuous and Discrete Descriptions of Noise in Nonlinear Resistive Electrical Components","year":2025,"lang":"en","type":"article","venue":"Fluctuation and Noise Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Resistive touchscreen; Noise (video); Nonlinear system; Statistical physics; Acoustics; Computer science; Electronic engineering; Physics; Electrical engineering; Quantum mechanics; Engineering; Artificial intelligence","score_opus":0.013727619401559948,"score_gpt":0.251107578914327,"score_spread":0.23737995951276705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414014554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1436955,0.0011927697,0.8390021,0.0011935277,0.00014914536,0.000034173703,0.00013294151,0.00015372426,0.014446113],"genre_scores_gemma":[0.9654199,0.00056059705,0.029237695,0.00022671996,0.0001602293,0.00006754115,0.0000753642,0.00006765075,0.0041843],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948823,0.00015274463,0.000025105723,0.000071629715,0.00020597933,0.000056169378],"domain_scores_gemma":[0.99909544,0.0004690062,0.00012428228,0.0001310776,0.00011265055,0.000067550216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080889364,0.00055294647,0.0007307192,0.0007766463,0.0004660366,0.0011882422,0.0017745628,0.001227201,0.0010966669],"category_scores_gemma":[0.0021004374,0.00032129377,0.00070006703,0.00034218136,0.0026651502,0.0029543457,0.0009086412,0.0014785716,0.00019386422],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029064291,0.000045025572,0.00045144995,0.00008082058,0.000019671097,0.00015732924,0.00023283175,0.10243326,0.0068782647,0.8867454,0.00026646777,0.0026604985],"study_design_scores_gemma":[0.000008799794,0.000030507528,0.00025068963,0.000019501073,0.000009032416,0.000087550245,0.000032006425,0.71245223,0.0010970966,0.28532228,0.00066481973,0.000025456422],"about_ca_topic_score_codex":0.0011557533,"about_ca_topic_score_gemma":0.00084087014,"teacher_disagreement_score":0.0017745628,"about_ca_system_score_codex":0.0009747243,"about_ca_system_score_gemma":0.00060012017,"threshold_uncertainty_score":0.0070722103},"labels":[],"label_agreement":null},{"id":"W4414092373","doi":"10.1561/3600000002","title":"Hypothesis Testing with E-values","year":2025,"lang":"en","type":"article","venue":"Foundations and Trends® in Statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Statistical hypothesis testing; Variety (cybernetics); Null hypothesis; Alternative hypothesis; Core (optical fiber); Test statistic; Value (mathematics)","score_opus":0.03986766164455913,"score_gpt":0.30508226178160325,"score_spread":0.2652146001370441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414092373","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001646893,0.010558506,0.9503572,0.008724477,0.004306812,0.00066428585,0.0013900498,0.0012726682,0.02107905],"genre_scores_gemma":[0.06834828,0.011835871,0.8886709,0.0086672725,0.005604375,0.007042452,0.0021139984,0.00086240965,0.0068545435],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.903146,0.07085274,0.006636626,0.0073374487,0.011312765,0.00071452383],"domain_scores_gemma":[0.63550967,0.33144358,0.007693088,0.017059945,0.007209214,0.0010846256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057537116,0.003019739,0.0041760164,0.005552365,0.0014256462,0.009090366,0.0049812347,0.0063092387,0.025326822],"category_scores_gemma":[0.22787772,0.0015228398,0.0031905246,0.007275864,0.012726296,0.012807557,0.007057464,0.013439979,0.011002534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004648608,0.00015572297,0.0020141818,0.004184741,0.00077388523,0.00071732845,0.0012421032,0.01167703,0.001093262,0.7024999,0.054753862,0.22042306],"study_design_scores_gemma":[0.00012148016,0.00024518868,0.000423988,0.0013466988,0.000094434596,0.00034236128,0.00028749017,0.015611041,0.0009657721,0.9002785,0.08018779,0.00009521829],"about_ca_topic_score_codex":0.00055228255,"about_ca_topic_score_gemma":0.00032835317,"teacher_disagreement_score":0.057537116,"about_ca_system_score_codex":0.00270673,"about_ca_system_score_gemma":0.0046026856,"threshold_uncertainty_score":0.30428904},"labels":[],"label_agreement":null},{"id":"W4414108176","doi":"10.36227/techrxiv.175753929.94587745/v1","title":"Towards Lightweight Adaptation of Massive Neural Network Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Artificial neural network; Adaptation (eye); Scale (ratio); Natural (archaeology); Deep neural networks; Key (lock)","score_opus":0.023104905914293403,"score_gpt":0.2513853742682449,"score_spread":0.22828046835395152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414108176","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013796931,0.00048814592,0.9818364,0.0003123443,0.00007326788,0.000045179862,0.00008540427,0.0013781664,0.0019840673],"genre_scores_gemma":[0.6659726,0.0011455123,0.32555482,0.00059585733,0.00021975616,0.00038018392,0.0006544421,0.00054250914,0.0049343035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99914944,0.00029646588,0.000050156126,0.00019490288,0.0002152828,0.00009385977],"domain_scores_gemma":[0.9970759,0.0015372349,0.00018680164,0.00071617536,0.0003724377,0.00011146098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023806975,0.0013516324,0.0016437365,0.0006087442,0.00047684257,0.0015209689,0.0028535216,0.0013872874,0.002170559],"category_scores_gemma":[0.010414337,0.0008685473,0.0009927361,0.0007165256,0.0011687913,0.002411637,0.003086546,0.0033382522,0.000924534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037446272,0.00003380828,0.0004561842,0.00006403831,0.000052466155,0.000042773227,0.00004468459,0.9508481,0.0010684367,0.009978248,0.0011824429,0.036191415],"study_design_scores_gemma":[0.0000024406247,0.0000061908922,0.00002888814,0.000004178494,0.000003094701,0.000005457559,0.0000027513004,0.9940112,0.00012657003,0.0056025637,0.00020463545,0.0000020256657],"about_ca_topic_score_codex":0.005327981,"about_ca_topic_score_gemma":0.007442421,"teacher_disagreement_score":0.005327981,"about_ca_system_score_codex":0.0010855425,"about_ca_system_score_gemma":0.0012558264,"threshold_uncertainty_score":0.012590468},"labels":[],"label_agreement":null},{"id":"W4414110305","doi":"10.1523/jneurosci.1093-25.2025","title":"Computational Properties of the Prefrontal Cortex","year":2025,"lang":"en","type":"article","venue":"Journal of Neuroscience","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Prefrontal cortex; Context (archaeology); Cognition; Neuropsychology; Working memory; Sensation","score_opus":0.022466443354924922,"score_gpt":0.251069406551136,"score_spread":0.2286029631962111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414110305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4261559,0.0031443876,0.48560435,0.006366545,0.00016833624,0.00004921279,0.0009657177,0.00044657424,0.07709896],"genre_scores_gemma":[0.98489136,0.00038103212,0.013099143,0.000073619674,0.000050836465,0.000022920456,0.0001312778,0.000028167273,0.0013216115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997298,0.00007519358,0.000014609925,0.00007638076,0.00006197909,0.000042028074],"domain_scores_gemma":[0.9988586,0.00069414894,0.0001146636,0.00014438231,0.00012176903,0.000066381566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005444418,0.000169807,0.00026595933,0.00048067071,0.00042597024,0.0015227703,0.0006346188,0.00040289233,0.00276101],"category_scores_gemma":[0.004338014,0.00023424569,0.0004761597,0.00026937743,0.0012119873,0.0016975245,0.00087681675,0.000652388,0.0002044529],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013462991,0.0000170676,0.0053313654,0.00012707652,0.000087907734,0.00022696484,0.00025487866,0.09887596,0.011203786,0.8506866,0.0019705934,0.031083161],"study_design_scores_gemma":[0.000018738856,0.000016665826,0.0042989887,0.000019700203,0.00002096368,0.00022294842,0.000056688805,0.29675376,0.0015460892,0.6941275,0.0028975022,0.000020535945],"about_ca_topic_score_codex":0.0026552218,"about_ca_topic_score_gemma":0.0016135481,"teacher_disagreement_score":0.00276101,"about_ca_system_score_codex":0.00094238075,"about_ca_system_score_gemma":0.0005725512,"threshold_uncertainty_score":0.009236515},"labels":[],"label_agreement":null},{"id":"W4414110700","doi":"10.1109/tsmc.2025.3605191","title":"A Unified Arbitrarily Predefined -Time Convergent Recurrent Neural Network for Motion Control of Redundant Robot Manipulators: A Unified Paradigm","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Key Research and Development Program of Hunan Province of China; National Natural Science Foundation of China","keywords":"Convergence (economics); Recurrent neural network; Piecewise; Robot; Stability (learning theory); Computation; Reduction (mathematics); Motion (physics); Redundancy (engineering)","score_opus":0.017498223818751688,"score_gpt":0.23325016952089883,"score_spread":0.21575194570214715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414110700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004289966,0.00026510266,0.99252194,0.00007638554,0.000030299372,0.000022826604,0.00001729841,0.0000937733,0.0026824232],"genre_scores_gemma":[0.5467087,0.0013719377,0.44078296,0.00032956598,0.00017308607,0.00045247487,0.00025816672,0.00014659285,0.009776465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955875,0.000100357014,0.000035873225,0.00012402394,0.0001410265,0.00003991066],"domain_scores_gemma":[0.9997583,0.000052773357,0.000038845934,0.000026288459,0.00010672719,0.000016946902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009300108,0.00084697676,0.0006821306,0.000369479,0.00032157908,0.0006274595,0.0016177747,0.0010100497,0.001356398],"category_scores_gemma":[0.0010845735,0.00026978456,0.0007704176,0.00041494536,0.0006696593,0.0012451367,0.0009791981,0.0009910449,0.00034414168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069600996,0.00004049466,0.00026122088,0.00019559012,0.000057178775,0.00019613479,0.00017733991,0.7132335,0.015439616,0.17727086,0.0021683148,0.09089019],"study_design_scores_gemma":[0.000003879618,0.00002885212,0.000019484081,0.0000045607344,0.0000057455104,0.000014646677,0.000004138316,0.99416745,0.0006561501,0.0043481397,0.0007416632,0.00000522486],"about_ca_topic_score_codex":0.0030816947,"about_ca_topic_score_gemma":0.0028567202,"teacher_disagreement_score":0.0030816947,"about_ca_system_score_codex":0.0006726813,"about_ca_system_score_gemma":0.0010433228,"threshold_uncertainty_score":0.0061275363},"labels":[],"label_agreement":null},{"id":"W4414114940","doi":"10.1007/s10586-025-05504-9","title":"Multiobjective pruning for sparse deep neural networks: leveraging $$L_0$$ regularization on batch normalization scaling factors","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Normalization (sociology); Regularization (linguistics); Pruning; Convolutional neural network; Deep neural networks; Computational complexity theory; Benchmark (surveying); Scaling","score_opus":0.018154779156090133,"score_gpt":0.26386150018222976,"score_spread":0.24570672102613964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414114940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010154381,0.00037460515,0.9862077,0.00029758603,0.00008109955,0.000056405024,0.000053714062,0.0006647654,0.0021096566],"genre_scores_gemma":[0.30337793,0.00039709316,0.6858889,0.0006384921,0.0002074866,0.00035203967,0.0004223684,0.000654157,0.008061438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988482,0.00043124787,0.000060170863,0.00014734955,0.00040420354,0.000108747015],"domain_scores_gemma":[0.99733907,0.0015435049,0.00017545826,0.00028866058,0.000536954,0.00011634403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00351275,0.0014503105,0.0014996999,0.00079424854,0.00059999747,0.0012089739,0.0025526942,0.0022232842,0.0038307197],"category_scores_gemma":[0.01036824,0.0006636186,0.0006923444,0.00086625415,0.0011532716,0.0016739029,0.0021479812,0.0025030035,0.00087299326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025706377,0.00028310285,0.0011086133,0.00021414178,0.00011219954,0.00016396395,0.00013517917,0.6040971,0.009931582,0.045150705,0.0129488725,0.32559744],"study_design_scores_gemma":[0.00000920172,0.000015867443,0.000056040302,0.000010014326,0.000005692571,0.000010488009,0.0000049089645,0.99405426,0.000771948,0.0046950374,0.00036318722,0.000003425611],"about_ca_topic_score_codex":0.004693578,"about_ca_topic_score_gemma":0.012915396,"teacher_disagreement_score":0.004693578,"about_ca_system_score_codex":0.0009976209,"about_ca_system_score_gemma":0.0018683653,"threshold_uncertainty_score":0.018577456},"labels":[],"label_agreement":null},{"id":"W4414240964","doi":"10.54254/2753-8818/2025.dl26846","title":"From RNNs to BERT: A Review of Neural Models for Sequence Learning","year":2025,"lang":"en","type":"review","venue":"Theoretical and Natural Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Recurrent neural network; Transformer; Sequence learning; Artificial neural network; Language model; Sequence (biology); Process (computing)","score_opus":0.0282848952368717,"score_gpt":0.35320163614975225,"score_spread":0.32491674091288053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414240964","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025922095,0.97589546,0.015760235,0.0010229324,0.0007476653,0.000018251843,0.0000716243,0.00010336843,0.006121332],"genre_scores_gemma":[0.0035544273,0.982219,0.009339148,0.00061288703,0.00080152944,0.00003938811,0.00012908978,0.00003920571,0.0032653674],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99963653,0.00007819977,0.000044732,0.00008305521,0.00013716597,0.000020331905],"domain_scores_gemma":[0.9992249,0.00045800096,0.00004415588,0.00003886707,0.0002080187,0.000026096703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010840513,0.001785168,0.0012614508,0.0015932263,0.0003251056,0.0012378844,0.0018717028,0.0017901396,0.004164282],"category_scores_gemma":[0.0022154893,0.0006731456,0.00056213443,0.0029971846,0.0009598566,0.003174149,0.0009091941,0.0022991616,0.0042972653],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050153467,0.000060756516,0.00023386058,0.007987052,0.000083919585,0.0001008949,0.00006996301,0.0069110044,0.000925248,0.04988674,0.034575034,0.89911526],"study_design_scores_gemma":[0.000011766192,0.0000954441,0.00032728707,0.0031725017,0.00007776003,0.00041492726,0.00004348875,0.006554793,0.0011492436,0.037466977,0.9506322,0.00005357289],"about_ca_topic_score_codex":0.0037527366,"about_ca_topic_score_gemma":0.0037819112,"teacher_disagreement_score":0.004164282,"about_ca_system_score_codex":0.0012135908,"about_ca_system_score_gemma":0.0014828205,"threshold_uncertainty_score":0.013930917},"labels":[],"label_agreement":null},{"id":"W4414245383","doi":"10.1115/1.4069829","title":"Can Graph Neural Networks Help Identify Promising Thermal Management System Architectures Among Vast Numbers of Possibilities?","year":2025,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Reduction (mathematics); Range (aeronautics); Graph; Computational complexity theory; Regression; Mean squared error; Network architecture; Computation","score_opus":0.014627026327764529,"score_gpt":0.2519016348591356,"score_spread":0.23727460853137106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414245383","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5729532,0.0010141769,0.41485962,0.0013213749,0.00010044129,0.00009130478,0.0004174216,0.0013399711,0.007902505],"genre_scores_gemma":[0.96540517,0.00014475068,0.033132862,0.00007917988,0.000013991136,0.00003819028,0.00019035304,0.000051658884,0.0009439708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998591,0.000046954327,0.0000060995626,0.000035762863,0.000027913862,0.000024249393],"domain_scores_gemma":[0.9992372,0.00046180486,0.00010244747,0.00005377699,0.00011528182,0.000029490073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005327033,0.00105921,0.0004827402,0.0008580368,0.00026713216,0.0006577232,0.0005122647,0.0006412929,0.0026382543],"category_scores_gemma":[0.0027677997,0.00034401997,0.00041879228,0.00039325698,0.00041515747,0.0011966638,0.00029589757,0.0006398744,0.00036281534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005393052,0.000041831678,0.0026190195,0.000040208455,0.000022963048,0.00002380503,0.00001155723,0.97348446,0.001415705,0.0010240204,0.00047566384,0.020786745],"study_design_scores_gemma":[0.000001680527,0.000015066037,0.0002547719,0.0000043682185,0.00000404913,0.000003408285,0.00000627686,0.99805343,0.00034006312,0.0012212723,0.000093493676,0.0000021971716],"about_ca_topic_score_codex":0.005088004,"about_ca_topic_score_gemma":0.009849457,"teacher_disagreement_score":0.005088004,"about_ca_system_score_codex":0.0005512494,"about_ca_system_score_gemma":0.0005840036,"threshold_uncertainty_score":0.010116756},"labels":[],"label_agreement":null},{"id":"W4414260689","doi":"10.1101/2025.09.09.675160","title":"netneurotools: a trainee-oriented approach to network neuroscience","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Workaround; Neuroinformatics; Software; Field (mathematics); Python (programming language); Pipeline (software); Heuristics","score_opus":0.02113911648639796,"score_gpt":0.23380843843244312,"score_spread":0.21266932194604515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414260689","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000603215,0.000069910966,0.9704873,0.001157279,0.00026163098,0.00017407835,0.00036688446,0.01980506,0.0070747067],"genre_scores_gemma":[0.0142363645,0.00048653173,0.94919837,0.0013016374,0.00029096808,0.0011794945,0.0020052572,0.009760939,0.0215404],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970385,0.0011037876,0.00017994178,0.0005686512,0.0008682856,0.0002409208],"domain_scores_gemma":[0.9930589,0.002449307,0.00027390456,0.0018458352,0.0011333242,0.0012387176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006744354,0.0014282165,0.0007296761,0.0024906336,0.0018199031,0.0064647817,0.0061609983,0.001966973,0.026332764],"category_scores_gemma":[0.014473513,0.0011972049,0.0015481478,0.0012801174,0.0027716747,0.007186657,0.009134348,0.0073145684,0.015166349],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023062769,0.00032824255,0.0013704689,0.00081904,0.00020414074,0.00044349337,0.0033908533,0.020683056,0.009384266,0.34459165,0.26446736,0.3540868],"study_design_scores_gemma":[0.00006787785,0.00006613754,0.00037996113,0.0002604277,0.000033501565,0.0002936462,0.00039698122,0.080522075,0.005534994,0.3701556,0.5421976,0.0000911508],"about_ca_topic_score_codex":0.0029007501,"about_ca_topic_score_gemma":0.007654196,"teacher_disagreement_score":0.026332764,"about_ca_system_score_codex":0.001496042,"about_ca_system_score_gemma":0.0051130163,"threshold_uncertainty_score":0.08809185},"labels":[],"label_agreement":null},{"id":"W4414269700","doi":"10.1142/s0129183126500361","title":"A minimal neural network at the edge of chaos","year":2025,"lang":"en","type":"article","venue":"International Journal of Modern Physics C","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ducks Unlimited Canada","funders":"","keywords":"Chaotic; Edge of chaos; Artificial neural network; Topology (electrical circuits); Simple (philosophy); SIGNAL (programming language); Biological neural network; Property (philosophy); Excitatory postsynaptic potential; Synchronization of chaos","score_opus":0.0166903305696617,"score_gpt":0.27803987849766026,"score_spread":0.2613495479279986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414269700","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6246209,0.0010221491,0.311852,0.001720331,0.000114911636,0.000052861244,0.00023329993,0.00040781658,0.059975654],"genre_scores_gemma":[0.98282236,0.00014845961,0.012174651,0.00008074088,0.000020299212,0.000033761648,0.00005924387,0.000016346583,0.004644215],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999892,0.000032617267,0.000004647896,0.000028454386,0.000023617631,0.000018682282],"domain_scores_gemma":[0.99984515,0.00005228977,0.000020567466,0.000013134863,0.000027009552,0.000041732914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016446826,0.00018732726,0.00041176094,0.00030174022,0.00046636412,0.0005659701,0.0005061752,0.0005977266,0.0024056355],"category_scores_gemma":[0.00084724114,0.00018600482,0.00022849077,0.00013335768,0.0007052748,0.0006838757,0.00080783025,0.0003757329,0.0002343243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024014401,0.0000499785,0.0013134038,0.00020159976,0.00007022991,0.00051061175,0.0002161226,0.31663665,0.038003333,0.62349755,0.0023717566,0.016888723],"study_design_scores_gemma":[0.000028032282,0.000060235147,0.0004761688,0.000012415843,0.0000125887855,0.00009986819,0.000018539626,0.84124583,0.0011617291,0.1546236,0.0022487282,0.000012281461],"about_ca_topic_score_codex":0.001336084,"about_ca_topic_score_gemma":0.0014966277,"teacher_disagreement_score":0.0024056355,"about_ca_system_score_codex":0.00052067183,"about_ca_system_score_gemma":0.00036897635,"threshold_uncertainty_score":0.0080477},"labels":[],"label_agreement":null},{"id":"W4414322228","doi":"10.1109/access.2025.3611390","title":"A Developer’s Guide to Compressing Pre-Trained Transformer Neural Networks Across Different Domains","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Transformer; Artificial neural network; Inference; Benchmark (surveying); Embedding; Key (lock); Deep learning","score_opus":0.021802131937250016,"score_gpt":0.36021066898077403,"score_spread":0.338408537043524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414322228","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017870185,0.004834054,0.814844,0.0013114574,0.0008741519,0.0007594586,0.021631274,0.11532806,0.03863056],"genre_scores_gemma":[0.01835443,0.008154931,0.8130478,0.0018894238,0.000231368,0.0019916345,0.036139254,0.048331276,0.07185992],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928313,0.00008044011,0.00009455563,0.00009391764,0.00040105722,0.000046816604],"domain_scores_gemma":[0.99639004,0.0015531689,0.00013404617,0.0005802799,0.0012217913,0.000120677636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011554231,0.0017394695,0.00075824896,0.0013564018,0.0003815057,0.0015159847,0.003160407,0.0014520861,0.11178515],"category_scores_gemma":[0.011809654,0.0015581643,0.001177123,0.002033457,0.00048982794,0.002498032,0.0015674446,0.003526879,0.071874134],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017470299,0.00010152281,0.0005647921,0.0018741608,0.000059952068,0.00028995032,0.00013750033,0.008928532,0.0069210893,0.013544229,0.5808716,0.38653207],"study_design_scores_gemma":[0.00012312518,0.000121186706,0.0010279721,0.00085795816,0.00003437725,0.0009816111,0.00010146379,0.052994307,0.014849467,0.020797295,0.90803283,0.00007839146],"about_ca_topic_score_codex":0.0036591508,"about_ca_topic_score_gemma":0.007757148,"teacher_disagreement_score":0.11178515,"about_ca_system_score_codex":0.00051963906,"about_ca_system_score_gemma":0.0018779329,"threshold_uncertainty_score":0.3739586},"labels":[],"label_agreement":null},{"id":"W4414359257","doi":"10.24963/ijcai.2025/634","title":"A Primal-dual Perspective for Distributed TD-learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Ode; Convergence (economics); Markov process; Perspective (graphical); Constant (computer programming); Markov chain; Ordinary differential equation; Subject (documents); Exponential distribution","score_opus":0.009777785938604141,"score_gpt":0.2853846315341374,"score_spread":0.2756068455955333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414359257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028820313,0.00024781207,0.99480695,0.00028446442,0.000044957458,0.000014624197,0.000016028058,0.000018950925,0.0016842673],"genre_scores_gemma":[0.70278424,0.001034253,0.28757152,0.0004077327,0.00026396854,0.00027145576,0.00009904551,0.000093871844,0.007473846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994493,0.00025331642,0.000022795282,0.000104736275,0.00012262078,0.000047304376],"domain_scores_gemma":[0.9979989,0.0013798275,0.0001497912,0.00009337372,0.0002867322,0.00009141389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024798703,0.0009294018,0.0010645782,0.00038058177,0.00034915074,0.0012373443,0.0013137546,0.0012614976,0.0028265268],"category_scores_gemma":[0.005266145,0.00039245066,0.0006628007,0.00053340575,0.0014854373,0.0013954154,0.001473708,0.002312348,0.0002611806],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004658671,0.000040395833,0.0003000166,0.00010681953,0.000030356123,0.000055404977,0.00004723941,0.8398879,0.0006421536,0.14727886,0.0006823731,0.010881966],"study_design_scores_gemma":[0.0000041258363,0.000012117087,0.0000137981515,0.000004524255,0.000002470449,0.0000075487746,0.000004302237,0.9816102,0.00010736469,0.017949846,0.00028154443,0.0000021932265],"about_ca_topic_score_codex":0.0018731332,"about_ca_topic_score_gemma":0.0012233951,"teacher_disagreement_score":0.0028265268,"about_ca_system_score_codex":0.001105783,"about_ca_system_score_gemma":0.0012636442,"threshold_uncertainty_score":0.013114929},"labels":[],"label_agreement":null},{"id":"W4414359488","doi":"10.24963/ijcai.2025/1245","title":"Toward Interpretable Time Series Modeling: A Kernel Representation Perspective","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Series (stratigraphy); Representation (politics); Kernel (algebra); Perspective (graphical); Time series; Kernel method; Key (lock)","score_opus":0.022420443200137318,"score_gpt":0.2903497513363312,"score_spread":0.26792930813619387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414359488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051985555,0.00042062046,0.99251723,0.00091838307,0.000038822327,0.000007892773,0.00008499461,0.00010108975,0.0007124415],"genre_scores_gemma":[0.67326146,0.0033470213,0.316332,0.00052746176,0.00059796247,0.00010775998,0.0006108064,0.00027831315,0.004937191],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993332,0.0002849138,0.000041439594,0.00014211232,0.0001478804,0.000050442824],"domain_scores_gemma":[0.99738723,0.0014920782,0.00032216718,0.0004472456,0.0002658707,0.00008535368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020248205,0.0007508887,0.00075325515,0.0008849904,0.00021281002,0.0018728044,0.0011873094,0.0010358864,0.0011351034],"category_scores_gemma":[0.007726894,0.00040556857,0.0009084135,0.0009397213,0.0013913513,0.003679949,0.0014187612,0.0038374476,0.00037970513],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053721313,0.000060860286,0.001335482,0.0001509053,0.00009472473,0.000117798285,0.0002758529,0.30547175,0.0033881513,0.62837386,0.0022046217,0.058472317],"study_design_scores_gemma":[0.0000035477128,0.000016105156,0.00018110045,0.000014977123,0.000008491032,0.000022647113,0.000018353734,0.8198555,0.00035104612,0.17787983,0.0016387962,0.000009534941],"about_ca_topic_score_codex":0.0017959696,"about_ca_topic_score_gemma":0.00101374,"teacher_disagreement_score":0.0020248205,"about_ca_system_score_codex":0.00080437254,"about_ca_system_score_gemma":0.00076229393,"threshold_uncertainty_score":0.010708392},"labels":[],"label_agreement":null},{"id":"W4414359880","doi":"10.24963/ijcai.2025/1230","title":"A Semantic Framework for Neurosymbolic Computation (Abstract Reprint)","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fraser Institute; Simon Fraser University","funders":"","keywords":"Semantics (computer science); Encoding (memory); Context (archaeology); Field (mathematics); Mathematical proof; Translation (biology)","score_opus":0.022202639930265514,"score_gpt":0.3224225983376349,"score_spread":0.3002199584073694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414359880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036497496,0.0011561305,0.94181406,0.0037508667,0.00048303648,0.00006156371,0.00030052054,0.00047290287,0.048311148],"genre_scores_gemma":[0.3380234,0.0017372816,0.63393295,0.0019024751,0.0007624807,0.00042979806,0.0007045142,0.00044643876,0.022060629],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988901,0.00043222823,0.000105799016,0.00020223422,0.00028529306,0.000084415515],"domain_scores_gemma":[0.9990736,0.00039828726,0.00007553191,0.00018234266,0.00020870073,0.00006158113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016162321,0.0006387216,0.00038962855,0.0014005502,0.0013401865,0.0029868165,0.0012860426,0.0012089763,0.010055225],"category_scores_gemma":[0.0032401,0.0003439757,0.0011914491,0.001458982,0.007433374,0.0068931365,0.0025817403,0.002564337,0.0016369062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027474484,0.000001808188,0.000010716854,0.000015308859,0.0000017577885,0.000011460035,0.000055535435,0.0005071273,0.00009097918,0.9961901,0.00062186917,0.0024905435],"study_design_scores_gemma":[0.0000035600465,0.000005116801,0.000020160402,0.000018541996,0.0000037707337,0.00003562796,0.0000360762,0.0048062783,0.0003108381,0.97694904,0.017806897,0.0000041530398],"about_ca_topic_score_codex":0.0018765356,"about_ca_topic_score_gemma":0.0022424133,"teacher_disagreement_score":0.010055225,"about_ca_system_score_codex":0.002238802,"about_ca_system_score_gemma":0.0011363773,"threshold_uncertainty_score":0.03363806},"labels":[],"label_agreement":null},{"id":"W4414360082","doi":"10.24963/ijcai.2025/103","title":"Dual-level Fuzzy Learning with Patch Guidance for Image Ordinal Regression","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Ordinal regression; Discriminative model; Fuzzy logic; Ambiguity; Ordinal data; Focus (optics); Regression; Pattern recognition (psychology)","score_opus":0.01929693882022971,"score_gpt":0.28714944370559514,"score_spread":0.26785250488536544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414360082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017999804,0.000105422696,0.9807669,0.00012648734,0.000012367992,0.000022712731,0.00003819075,0.00036773857,0.0005603177],"genre_scores_gemma":[0.5828317,0.00014953036,0.4143956,0.00022695938,0.000058461403,0.00009325763,0.0002427349,0.00011736079,0.0018844892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99906904,0.00020413156,0.00004712471,0.0003408358,0.0002510021,0.000087807945],"domain_scores_gemma":[0.9982717,0.00080334325,0.00020900984,0.0002514719,0.00036860796,0.00009592824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018481743,0.0006109975,0.0009784275,0.0008145277,0.0003557812,0.0011069255,0.0017427149,0.0011903709,0.0017328358],"category_scores_gemma":[0.0059887376,0.00034665904,0.0007491876,0.00059470435,0.0010992446,0.0016498113,0.001462449,0.0021004574,0.00053921615],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037744537,0.00020378084,0.005191183,0.00020438185,0.000102877806,0.00014245692,0.00028668507,0.4759076,0.029862387,0.023245625,0.002679078,0.4617965],"study_design_scores_gemma":[0.000007001982,0.000028691646,0.00022085139,0.00000603311,0.0000070966566,0.000014688174,0.000009416554,0.9923947,0.0019114389,0.00513334,0.000260565,0.000006319492],"about_ca_topic_score_codex":0.0028286802,"about_ca_topic_score_gemma":0.0027356122,"teacher_disagreement_score":0.0028286802,"about_ca_system_score_codex":0.0009251847,"about_ca_system_score_gemma":0.00070489454,"threshold_uncertainty_score":0.009774208},"labels":[],"label_agreement":null},{"id":"W4414360843","doi":"10.24963/ijcai.2024/1230","title":"A Semantic Framework for Neurosymbolic Computation (Abstract Reprint)","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fraser Institute; Simon Fraser University","funders":"","keywords":"Semantics (computer science); Encoding (memory); Context (archaeology); Field (mathematics); Mathematical proof; Translation (biology)","score_opus":0.0316473035647978,"score_gpt":0.3244545717442063,"score_spread":0.2928072681794085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414360843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003647975,0.001164184,0.9415581,0.003776466,0.00048783765,0.00006169955,0.00030226726,0.0004759583,0.04852541],"genre_scores_gemma":[0.33838266,0.0017465045,0.6333869,0.0019149494,0.0007703885,0.00043118463,0.0007089771,0.0004490115,0.022209428],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988906,0.00043159534,0.0001057565,0.00020230087,0.00028526303,0.00008449908],"domain_scores_gemma":[0.999074,0.00039803184,0.00007546354,0.00018216753,0.00020872927,0.00006153067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016137135,0.00063910126,0.00038938964,0.0014007537,0.0013401641,0.002987947,0.0012875416,0.0012096236,0.010080932],"category_scores_gemma":[0.003235657,0.0003439724,0.0011922211,0.0014612192,0.0074284566,0.006896349,0.002580142,0.0025687586,0.0016448159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027567448,0.0000018136419,0.0000107538135,0.00001538912,0.000001762566,0.000011509322,0.000055724937,0.00050818815,0.000091260204,0.99617296,0.0006287209,0.0024992055],"study_design_scores_gemma":[0.0000035643786,0.000005128771,0.000020215528,0.000018598372,0.0000037795485,0.000035722212,0.0000361606,0.0048075635,0.00031151983,0.9768226,0.017930944,0.0000041593075],"about_ca_topic_score_codex":0.0018809142,"about_ca_topic_score_gemma":0.0022460897,"teacher_disagreement_score":0.010080932,"about_ca_system_score_codex":0.0022405535,"about_ca_system_score_gemma":0.0011372599,"threshold_uncertainty_score":0.03372407},"labels":[],"label_agreement":null},{"id":"W4414360846","doi":"10.24963/ijcai.2024/1245","title":"Toward Interpretable Time Series Modeling: A Kernel Representation Perspective","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Series (stratigraphy); Representation (politics); Kernel (algebra); Perspective (graphical); Time series; Kernel method; Key (lock)","score_opus":0.03024328755111674,"score_gpt":0.2922221862334266,"score_spread":0.2619788986823099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414360846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052165897,0.00042409482,0.99248487,0.0009265105,0.00003904834,0.000007909424,0.0000854142,0.00010150116,0.00071411143],"genre_scores_gemma":[0.67355484,0.003359661,0.31601393,0.000528811,0.00060129707,0.00010779553,0.0006132765,0.00027905582,0.0049413545],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999331,0.0002856657,0.000041547257,0.00014270168,0.0001483349,0.00005072381],"domain_scores_gemma":[0.9973797,0.0014982952,0.00032183033,0.00044792393,0.0002666475,0.000085506035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020304266,0.0007513958,0.0007536673,0.00088883593,0.00021327764,0.001874008,0.0011909532,0.0010381329,0.0011363779],"category_scores_gemma":[0.007730847,0.0004061878,0.00090929976,0.0009420207,0.0013935223,0.0036896456,0.0014201691,0.00384344,0.00037944244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053917734,0.000061019233,0.0013415568,0.00015131902,0.00009505848,0.00011803363,0.00027648086,0.30515552,0.0033926032,0.62862015,0.002212868,0.058521517],"study_design_scores_gemma":[0.0000035613675,0.000016169257,0.000181763,0.000015028676,0.000008543123,0.000022751196,0.000018461964,0.81892025,0.00035221968,0.17880653,0.0016451227,0.000009557756],"about_ca_topic_score_codex":0.0018032453,"about_ca_topic_score_gemma":0.0010166212,"teacher_disagreement_score":0.0020304266,"about_ca_system_score_codex":0.0008065615,"about_ca_system_score_gemma":0.00076539733,"threshold_uncertainty_score":0.010738075},"labels":[],"label_agreement":null},{"id":"W4414404758","doi":"10.1109/tmc.2025.3612469","title":"Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-Play Neural Network Toolbox Enabling Edge Deployment","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of British Columbia","funders":"Beijing Union University; National Natural Science Foundation of China","keywords":"Object detection; Enhanced Data Rates for GSM Evolution; Convolutional neural network; Edge device; Adaptation (eye); Toolbox; Object (grammar); Edge detection","score_opus":0.01918597781620886,"score_gpt":0.27286429295063575,"score_spread":0.2536783151344269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414404758","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008245175,0.000068902315,0.9328745,0.00009792469,0.00006517234,0.000082667786,0.00026307872,0.053438503,0.0048640827],"genre_scores_gemma":[0.2154,0.00022290113,0.7522309,0.00054265413,0.00003994073,0.00042022215,0.0022200036,0.0073305136,0.021592796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997093,0.00003646064,0.0000143297275,0.00008613964,0.000105586296,0.000048131253],"domain_scores_gemma":[0.9995995,0.000103193604,0.000027185779,0.000105218576,0.00010391224,0.000061021594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063512585,0.0010976054,0.00037768527,0.0006598727,0.00021806525,0.00076551514,0.0031219681,0.0008826904,0.015484037],"category_scores_gemma":[0.0021258774,0.00054164807,0.00054871745,0.00032093498,0.00043274782,0.0015547882,0.0018503212,0.0015731443,0.0054071615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012827887,0.0005052731,0.002697333,0.0003193493,0.00014187414,0.00088844023,0.0002571379,0.17157109,0.07573505,0.031366847,0.07343248,0.6418023],"study_design_scores_gemma":[0.000047406476,0.000044269902,0.00026550368,0.000014143008,0.000011417129,0.00010803167,0.000009763018,0.9454097,0.03722564,0.0034210861,0.013418923,0.000024025096],"about_ca_topic_score_codex":0.0031692989,"about_ca_topic_score_gemma":0.004520275,"teacher_disagreement_score":0.015484037,"about_ca_system_score_codex":0.0008387311,"about_ca_system_score_gemma":0.00073671946,"threshold_uncertainty_score":0.051799238},"labels":[],"label_agreement":null},{"id":"W4414422418","doi":"","title":"POCO: Scalable Neural Forecasting through Population Conditioning.","year":2025,"lang":"en","type":"preprint","venue":"PubMed","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Interpretability; Context (archaeology); Artificial neural network; Scalability; Population; Key (lock); Session (web analytics)","score_opus":0.057619070653058435,"score_gpt":0.2619920832624037,"score_spread":0.20437301260934526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414422418","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023723401,0.0007363719,0.942473,0.00081973115,0.0004953973,0.00014564945,0.0023733964,0.021730596,0.0075023845],"genre_scores_gemma":[0.6014578,0.00075444544,0.3773865,0.0009061694,0.00032736023,0.0006784822,0.0058334097,0.002399304,0.0102565065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982053,0.000025193593,0.000009447327,0.00006443891,0.000051666542,0.000028892613],"domain_scores_gemma":[0.9991252,0.00038056631,0.00006432001,0.00022042751,0.00012492242,0.00008456117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086020527,0.0008806759,0.000695591,0.00036255133,0.00040309076,0.0008164303,0.0024556115,0.0011208775,0.008983891],"category_scores_gemma":[0.0048442692,0.00054296665,0.00059807993,0.000458005,0.0005465314,0.0016626219,0.0015684539,0.001946017,0.0021099348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035076428,0.00011598735,0.00345076,0.00034774494,0.00020293689,0.00015501311,0.00011982702,0.67022413,0.008082136,0.022043224,0.04353779,0.25136963],"study_design_scores_gemma":[0.000011972005,0.0000119285605,0.00013131439,0.000007689461,0.00000648207,0.000014021703,0.000005142926,0.9905612,0.0012413297,0.006662545,0.0013400302,0.0000063477114],"about_ca_topic_score_codex":0.008386288,"about_ca_topic_score_gemma":0.014033634,"teacher_disagreement_score":0.008983891,"about_ca_system_score_codex":0.00073889777,"about_ca_system_score_gemma":0.0016358942,"threshold_uncertainty_score":0.030054092},"labels":[],"label_agreement":null},{"id":"W4414422755","doi":"10.1016/j.compbiomed.2025.111102","title":"Dynamic topographies of intrinsic neural timescales: a key role for consciousness","year":2025,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Royal Ottawa Mental Health Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Beijing Municipality; Canadian Institutes of Health Research; China Scholarship Council; Science and Technology Commission of Shanghai Municipality; Ministero dell'Università e della Ricerca; National Natural Science Foundation of China; Social Sciences and Humanities Research Council of Canada; Horizon 2020 Framework Programme; Physicians' Services Incorporated Foundation; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Consciousness; Key (lock); Repertoire; Transition (genetics); Dynamics (music); Altered state; State (computer science); Cluster analysis; Dynamic functional connectivity","score_opus":0.007037726235448823,"score_gpt":0.29216719519441586,"score_spread":0.285129468958967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414422755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92202073,0.0011431718,0.07191648,0.00021942567,0.000026338801,0.000029643461,0.0018016143,0.0003180126,0.002524624],"genre_scores_gemma":[0.9943487,0.00018570428,0.004573011,0.000013473519,0.000016476,0.000013021388,0.000668173,0.000026678586,0.0001547701],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998684,0.000024545647,0.000010397236,0.000045999965,0.00002396219,0.00002667153],"domain_scores_gemma":[0.9994566,0.00017410543,0.00015329287,0.00009924808,0.00006112993,0.00005555104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024008917,0.00018771704,0.00017092966,0.0008086479,0.00018916414,0.0006365593,0.00018501993,0.00015116585,0.0007838431],"category_scores_gemma":[0.0022063325,0.00011025819,0.00024289427,0.0008279793,0.00041858014,0.00066515186,0.00048448506,0.00031981585,0.000120096156],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007625361,0.00011475501,0.30233744,0.000852004,0.00049641315,0.00096162583,0.003540366,0.029032895,0.3720122,0.019595452,0.0056604706,0.26463374],"study_design_scores_gemma":[0.000011522277,0.000117712756,0.8812951,0.00006686193,0.00009775338,0.00075798144,0.00069411297,0.07213013,0.015768504,0.024421437,0.004563564,0.00007532768],"about_ca_topic_score_codex":0.001926454,"about_ca_topic_score_gemma":0.0021813242,"teacher_disagreement_score":0.001926454,"about_ca_system_score_codex":0.00016572308,"about_ca_system_score_gemma":0.00021454197,"threshold_uncertainty_score":0.003830552},"labels":[],"label_agreement":null},{"id":"W4414451803","doi":"10.1101/2025.09.23.674728","title":"Exploring the impact of social relevance on the cortical tracking of speech: viability and temporal response characterisation","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"Research Ireland; Trinity College Dublin; University College Dublin","keywords":"Neurocomputational speech processing; Speech perception; Speech processing; Perception; Artificial neural network; Active listening; Relevance (law); Voice activity detection","score_opus":0.0572562850619966,"score_gpt":0.27581601983594567,"score_spread":0.21855973477394908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414451803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9794605,0.00026669164,0.015678653,0.00012153759,0.000028439757,0.0000335381,0.0001445005,0.00008496821,0.004181091],"genre_scores_gemma":[0.9980513,0.000058177175,0.0015183148,0.000021104604,0.0000141487335,0.000018909299,0.00004696129,0.000020181722,0.00025090884],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99974114,0.00009500971,0.000013312563,0.000058847007,0.00005537805,0.000036232395],"domain_scores_gemma":[0.99836916,0.0011081145,0.00015634098,0.00014208208,0.00014769718,0.000076588934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059031614,0.00028228827,0.00019150159,0.00034819622,0.00019177877,0.0007306245,0.0001810281,0.00032247955,0.0023670753],"category_scores_gemma":[0.0061461637,0.00014326698,0.00021778641,0.00017924354,0.0006819762,0.0005294908,0.00077447825,0.0004015999,0.00022168065],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083652884,0.0000599231,0.010305139,0.00025117004,0.000058965838,0.00030265388,0.0010069372,0.00203195,0.9400044,0.0012297983,0.00022718347,0.04368546],"study_design_scores_gemma":[0.000061055034,0.0011440726,0.73097163,0.00014715127,0.00023704919,0.001601034,0.0021766045,0.039060973,0.21204384,0.009635308,0.0028454221,0.000075871765],"about_ca_topic_score_codex":0.0006028872,"about_ca_topic_score_gemma":0.00072832487,"teacher_disagreement_score":0.0023670753,"about_ca_system_score_codex":0.00015252002,"about_ca_system_score_gemma":0.00014800095,"threshold_uncertainty_score":0.007918656},"labels":[],"label_agreement":null},{"id":"W4414486728","doi":"10.1007/s00426-026-02324-3","title":"Modality-Specific Consolidation Shapes Long-Term Retention in Statistical Learning","year":2025,"lang":"en","type":"preprint","venue":"Psychological Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Canadian Institutes of Health Research; Killam Trusts","keywords":"Consolidation (business); Statistical learning; Sensory system; Statistical hypothesis testing; Statistical analysis; Learning effect","score_opus":0.2962879667981741,"score_gpt":0.49967365021203386,"score_spread":0.20338568341385976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414486728","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9976743,0.00018470954,0.0016409867,0.000019276878,0.0000061832216,0.000009210206,0.000041838273,0.000034345714,0.00038929968],"genre_scores_gemma":[0.998847,0.000054903812,0.00062870653,0.000007057959,0.000004349701,0.000011334052,0.000065936874,0.000014208577,0.00036637837],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996264,0.00005515913,0.00003208487,0.00013144853,0.00009595294,0.000058965685],"domain_scores_gemma":[0.99768686,0.0006640106,0.0004939027,0.00047622505,0.00037783844,0.0003011064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008517121,0.00020704162,0.0004108942,0.0002983755,0.0001480567,0.00079851906,0.00036985875,0.00030791212,0.0018991206],"category_scores_gemma":[0.0056075766,0.00022244328,0.00021327112,0.00018757963,0.00045543423,0.0008980844,0.0008396631,0.0005753976,0.00042189573],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002704013,0.0007843686,0.13045904,0.0002955887,0.0001692798,0.00019737922,0.0022290691,0.0013726691,0.68800247,0.00063796545,0.0003761018,0.17277204],"study_design_scores_gemma":[0.00005123363,0.006866041,0.83950716,0.000047625283,0.00013881343,0.0005667091,0.0009488781,0.007677493,0.14047176,0.0024303815,0.0012448147,0.00004917495],"about_ca_topic_score_codex":0.00061128405,"about_ca_topic_score_gemma":0.0009153255,"teacher_disagreement_score":0.0018991206,"about_ca_system_score_codex":0.00019183676,"about_ca_system_score_gemma":0.00029971488,"threshold_uncertainty_score":0.0063531995},"labels":[],"label_agreement":null},{"id":"W4414492997","doi":"10.3390/jimaging11100333","title":"Effects of Biases in Geometric and Physics-Based Imaging Attributes on Classification Performance","year":2025,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Generalization; Focus (optics); Point (geometry); Sample (material); Selection (genetic algorithm); Process (computing); Data collection; Population; Sampling (signal processing)","score_opus":0.016292130097287444,"score_gpt":0.2683128066542441,"score_spread":0.2520206765569567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414492997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9448567,0.0019769745,0.043139953,0.00269809,0.00020530635,0.00020892305,0.00048296695,0.00040777473,0.006023305],"genre_scores_gemma":[0.98960274,0.00032313177,0.008172148,0.00047137917,0.00006541086,0.00009576,0.00040622716,0.000163147,0.0007000786],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9872665,0.0053430037,0.0013616448,0.0024177476,0.002654104,0.00095703686],"domain_scores_gemma":[0.8268669,0.13165359,0.010101444,0.020003304,0.008860321,0.0025144743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030859407,0.0010231459,0.0009704687,0.0007554704,0.0008325942,0.002727363,0.0013031543,0.0018743469,0.0018144161],"category_scores_gemma":[0.19282195,0.0006792199,0.00053694536,0.0007917009,0.0033563918,0.0046727387,0.0032527633,0.003135174,0.0007431365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01629551,0.0020227695,0.4204269,0.0010507685,0.001761578,0.0003366887,0.003156049,0.15700352,0.079208024,0.020123988,0.0059463647,0.29266796],"study_design_scores_gemma":[0.00083780097,0.004635219,0.38743687,0.0005278256,0.0011197734,0.0015156299,0.0025047935,0.3112698,0.14756577,0.13519655,0.006835064,0.0005549016],"about_ca_topic_score_codex":0.0026549997,"about_ca_topic_score_gemma":0.0017380451,"teacher_disagreement_score":0.030859407,"about_ca_system_score_codex":0.0016861943,"about_ca_system_score_gemma":0.001102064,"threshold_uncertainty_score":0.1632021},"labels":[],"label_agreement":null},{"id":"W4414497836","doi":"10.31234/osf.io/c73ws_v2","title":"Modality-Specific Consolidation Shapes Long-Term Retention in Statistical Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Canadian Institutes of Health Research; Killam Trusts","keywords":"Consolidation (business); Statistical learning; Sensory system; Statistical hypothesis testing; Statistical analysis; Learning effect","score_opus":0.02537903014380819,"score_gpt":0.29273382323925273,"score_spread":0.2673547930954445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414497836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983772,0.00023339779,0.000928754,0.000019610112,0.0000075379667,0.000007511838,0.00005330581,0.000026954827,0.00034575735],"genre_scores_gemma":[0.9987942,0.00008904748,0.0005357242,0.00001072014,0.0000048987276,0.000013503318,0.00008138682,0.000010867204,0.00045964462],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997079,0.00003421508,0.000028177617,0.000111436355,0.00006836294,0.000049951286],"domain_scores_gemma":[0.99861455,0.00029274143,0.0003721678,0.0002929783,0.00019632936,0.00023127472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005326949,0.00017356002,0.0003779165,0.00024386073,0.00011114543,0.00056341937,0.00037436606,0.0002710347,0.0017527045],"category_scores_gemma":[0.0028238317,0.00019545299,0.00020560199,0.00012273414,0.00041006264,0.00069340714,0.0007283866,0.0005489243,0.0002858971],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017739601,0.00046263848,0.051349696,0.00016120635,0.0000853607,0.00012925787,0.0007141686,0.00050156855,0.8673886,0.00026262773,0.00022494547,0.07694593],"study_design_scores_gemma":[0.00004779217,0.006701797,0.77462065,0.000043823653,0.00013698661,0.000547715,0.0005089985,0.003937216,0.21074462,0.0014047094,0.0012681514,0.000037589532],"about_ca_topic_score_codex":0.0005037325,"about_ca_topic_score_gemma":0.00089593447,"teacher_disagreement_score":0.0017527045,"about_ca_system_score_codex":0.00017370257,"about_ca_system_score_gemma":0.0002537731,"threshold_uncertainty_score":0.0058633685},"labels":[],"label_agreement":null},{"id":"W4414500038","doi":"10.1109/emcsipi52291.2025.11170270","title":"Differential Via Modeling Using Multilayer Perceptron-Sequential (MLP-SEQ) Neural Network","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Artificial neural network; Reliability (semiconductor); Differential (mechanical device); Feature (linguistics); Node (physics); Reproducibility","score_opus":0.03396665885586219,"score_gpt":0.2906468701733415,"score_spread":0.2566802113174793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414500038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025346188,0.000479451,0.97025675,0.00010845486,0.000061684375,0.000034905694,0.0001127494,0.0008619972,0.0027378008],"genre_scores_gemma":[0.8748012,0.00073833833,0.11646242,0.000099155084,0.00003596053,0.00013075459,0.00022694528,0.000114881914,0.0073903156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996252,0.00008091667,0.000019724674,0.00011007994,0.00012902202,0.00003492328],"domain_scores_gemma":[0.99964416,0.000119931196,0.0000662891,0.0000387299,0.000118687676,0.000012263186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063010055,0.0009036736,0.00062803447,0.0005904884,0.00029855873,0.0006929751,0.0014361284,0.00084708055,0.001813735],"category_scores_gemma":[0.0011406905,0.0005652032,0.0008056716,0.00050972344,0.0005287336,0.0018538445,0.0005005756,0.0007077759,0.0004653712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009584815,0.000039439597,0.0014731628,0.000117690666,0.000051683124,0.00015503984,0.000059882223,0.94300646,0.009602702,0.0071059964,0.00068564643,0.03760652],"study_design_scores_gemma":[0.0000012985914,0.000015839376,0.000089267916,0.0000028314255,0.0000070569636,0.000020047075,0.000002542099,0.99780935,0.0010911679,0.00069400336,0.00026309863,0.0000036386605],"about_ca_topic_score_codex":0.004009979,"about_ca_topic_score_gemma":0.0050188564,"teacher_disagreement_score":0.004009979,"about_ca_system_score_codex":0.0009841997,"about_ca_system_score_gemma":0.0005570509,"threshold_uncertainty_score":0.007973313},"labels":[],"label_agreement":null},{"id":"W4414552642","doi":"10.1101/2025.09.25.678554","title":"Modelling Discrete States and Long-Term Dynamics in Functional Brain Networks","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Dementias Platform UK; Medical Research Council; Royal Society; National Institute for Health and Care Research; Hertford College, University of Oxford; Wellcome Trust; National Science Foundation","keywords":"Interpretability; Categorical variable; Magnetoencephalography; Inference; Recurrent neural network; Network dynamics; Hidden Markov model; Artificial neural network; Dynamic functional connectivity; Dynamic Bayesian network","score_opus":0.013212933492062984,"score_gpt":0.22069775723709442,"score_spread":0.20748482374503144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414552642","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08204904,0.000345088,0.91541666,0.00036561943,0.00002517777,0.000021346019,0.00026025117,0.00041802862,0.0010987533],"genre_scores_gemma":[0.9169647,0.00050952844,0.080072835,0.00008693908,0.000030881172,0.00011655744,0.00029795623,0.00009097801,0.0018295723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996978,0.00011791229,0.000017152237,0.000100156416,0.00003947918,0.000027598258],"domain_scores_gemma":[0.99907684,0.0006302188,0.00012864367,0.00007940752,0.00005328566,0.000031592666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087125774,0.0004328627,0.00035761815,0.0004340769,0.00026125432,0.00096203294,0.0009758915,0.00095175137,0.000892218],"category_scores_gemma":[0.0036243675,0.0004791419,0.00064932683,0.00038667154,0.0011672965,0.0017935402,0.00075191515,0.0013253026,0.00019422204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021710943,0.000011583919,0.0010977229,0.00002501499,0.000020385567,0.00004931913,0.000061232255,0.97482467,0.0022044317,0.014596913,0.00018773091,0.006899273],"study_design_scores_gemma":[0.000001405735,0.0000043139976,0.00024467293,0.000002623175,0.00000233365,0.000009161554,0.0000046416985,0.98655266,0.00020044702,0.012849926,0.000124015,0.0000037280668],"about_ca_topic_score_codex":0.0074937665,"about_ca_topic_score_gemma":0.008385186,"teacher_disagreement_score":0.0074937665,"about_ca_system_score_codex":0.00094386557,"about_ca_system_score_gemma":0.0005253211,"threshold_uncertainty_score":0.014900267},"labels":[],"label_agreement":null},{"id":"W4414566528","doi":"","title":"Affine Feedforward Stochastic (AFS) Neural Network","year":2025,"lang":"en","type":"other","venue":"Toulouse Capitole Publications (University Toulouse 1 Capitole)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"","score_opus":0.00846523416840763,"score_gpt":0.2045148325148063,"score_spread":0.19604959834639865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414566528","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04494045,0.0046670525,0.8170407,0.0033520085,0.0018150683,0.00009658909,0.002312801,0.0060604545,0.11971491],"genre_scores_gemma":[0.57388043,0.0034458658,0.15047073,0.0007375371,0.0010850837,0.00013192507,0.0030047267,0.00043183015,0.2668118],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998115,0.00003187681,0.000009026355,0.0000624787,0.000054402826,0.000030829106],"domain_scores_gemma":[0.999595,0.00012587075,0.00002344563,0.00004294237,0.00017935451,0.000033380235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040726142,0.0006010652,0.0005963512,0.0004012635,0.00029311617,0.0008976878,0.0006565799,0.00097633974,0.027043683],"category_scores_gemma":[0.0013641929,0.00016307582,0.0003763296,0.0006080293,0.00029864415,0.0006254212,0.00061546394,0.0009962133,0.0060386946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002381247,0.00010747819,0.00052458455,0.0002341659,0.000049654234,0.0001351718,0.000025297612,0.26061603,0.0066688196,0.046449095,0.052590493,0.6323611],"study_design_scores_gemma":[0.000020204532,0.000075547265,0.0004691711,0.000033725984,0.000015252941,0.000051913215,0.000006833576,0.9587814,0.0033307674,0.016437445,0.020765131,0.000012527919],"about_ca_topic_score_codex":0.005368399,"about_ca_topic_score_gemma":0.0052410043,"teacher_disagreement_score":0.027043683,"about_ca_system_score_codex":0.00051740336,"about_ca_system_score_gemma":0.00056590344,"threshold_uncertainty_score":0.090470076},"labels":[],"label_agreement":null},{"id":"W4414576839","doi":"10.31234/osf.io/adq75_v1","title":"The Devil in the Details: Local Processing Sometimes Results in Suboptimal Category Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorization; Cognition; Priming (agriculture); Stimulus (psychology); Concept learning; Trait","score_opus":0.012838722153690163,"score_gpt":0.2658090804678758,"score_spread":0.2529703583141857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414576839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951493,0.00012696121,0.0027387855,0.00015122349,0.000010935492,0.000014401683,0.000038663566,0.00007065209,0.0016992019],"genre_scores_gemma":[0.9969093,0.000081497994,0.002343596,0.00013198791,0.000007014443,0.000015745378,0.000047022928,0.000058131893,0.00040576086],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929094,0.0001052968,0.00007404967,0.00024020574,0.00022476967,0.00006470021],"domain_scores_gemma":[0.9968406,0.0010592736,0.0008587325,0.00073872565,0.00019163088,0.00031103304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011345212,0.00026278218,0.0004979529,0.00066387065,0.00028119664,0.00065079634,0.0002896824,0.000521734,0.002243849],"category_scores_gemma":[0.0059466,0.00023421373,0.0002778052,0.00028617692,0.0008959716,0.00058049767,0.0009978119,0.0006572849,0.00037312863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011327998,0.00037732022,0.17377794,0.0004003388,0.00028824227,0.002432252,0.0027045945,0.0005415535,0.72342485,0.0027869293,0.0010604766,0.09107273],"study_design_scores_gemma":[0.00009117561,0.00085056806,0.90435034,0.00007967288,0.00017054586,0.0065633007,0.0012654063,0.0022014517,0.070732504,0.010848492,0.002785208,0.000061346815],"about_ca_topic_score_codex":0.00046971222,"about_ca_topic_score_gemma":0.0009883177,"teacher_disagreement_score":0.002243849,"about_ca_system_score_codex":0.00019352254,"about_ca_system_score_gemma":0.00017498943,"threshold_uncertainty_score":0.00750643},"labels":[],"label_agreement":null},{"id":"W4414581456","doi":"10.1101/2025.09.27.678958","title":"Intrinsic Neural Oscillations Predict Verbal Learning Performance and Encoding Strategy Use","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université de Montréal","funders":"","keywords":"Encoding (memory); Cluster analysis; Cognition; Encoding specificity principle; Dynamics (music); Electroencephalography; Neurophysiology; Verbal learning","score_opus":0.019047713621711718,"score_gpt":0.22137626192109244,"score_spread":0.20232854829938074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414581456","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99830735,0.000048134305,0.001011382,0.000018548228,0.0000021093529,0.000003446267,0.00007444076,0.000011600146,0.00052293635],"genre_scores_gemma":[0.998648,0.00004461963,0.00066213513,0.00000897084,0.0000032052594,0.0000062037766,0.00014322701,0.000008473,0.0004751858],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998841,0.00002215311,0.00001355014,0.00003811744,0.000022952456,0.000018965264],"domain_scores_gemma":[0.9984977,0.00067639834,0.00052808423,0.00013500912,0.000075088305,0.00008765327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004131841,0.00024868586,0.0001439783,0.0003006496,0.000057479447,0.0007765634,0.00011686661,0.0003097101,0.0015243697],"category_scores_gemma":[0.0039838287,0.00010215344,0.00009459693,0.00017255083,0.00018268841,0.00032729586,0.0002971219,0.00029526465,0.0002427822],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050419633,0.00041861672,0.83889765,0.00008816515,0.00025876827,0.0001221031,0.0007086732,0.0021888062,0.06573965,0.00035171813,0.00038758683,0.090334125],"study_design_scores_gemma":[0.0000067680726,0.00012650677,0.9927664,0.000007830299,0.00003246795,0.00009992776,0.00010621491,0.0034546265,0.0026728988,0.00062682043,0.0000910178,0.000008510278],"about_ca_topic_score_codex":0.00045440058,"about_ca_topic_score_gemma":0.0009009063,"teacher_disagreement_score":0.0015243697,"about_ca_system_score_codex":0.00007852224,"about_ca_system_score_gemma":0.00007553758,"threshold_uncertainty_score":0.0050994754},"labels":[],"label_agreement":null},{"id":"W4414695817","doi":"10.22541/au.175934542.29715691/v1","title":"Vectorizing Distinction: The Mathematical Logic of AI through a Bourdieusian Lens","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematical logic; Lens (geology); Window (computing); Through-the-lens metering; Calculus (dental)","score_opus":0.02281491753205638,"score_gpt":0.3048231305753375,"score_spread":0.2820082130432811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414695817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011773566,0.008116543,0.76848495,0.022471989,0.0008192337,0.00006035307,0.00024953295,0.00018598123,0.18783791],"genre_scores_gemma":[0.7407191,0.005528725,0.22852162,0.003716256,0.0016711043,0.00046553824,0.00022551185,0.0002781744,0.018874036],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953407,0.002879177,0.00017767772,0.0007351166,0.00063903956,0.0002283016],"domain_scores_gemma":[0.99494016,0.0037154302,0.0003102353,0.00045399682,0.00041726022,0.00016292173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006180849,0.0009122529,0.00082750607,0.0036604654,0.0030326406,0.008775694,0.0016705539,0.0023187601,0.0046854704],"category_scores_gemma":[0.009190601,0.00039969082,0.0012230607,0.0028811016,0.024612254,0.012223333,0.003595855,0.00515678,0.0011021994],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011661489,8.631836e-7,0.000018589186,0.0000052743744,0.0000011466885,0.0000038665585,0.00012795285,0.00011946633,0.000008873223,0.99884534,0.00017108633,0.0006964433],"study_design_scores_gemma":[0.0000022722252,0.0000020779967,0.000013608594,0.000009812227,9.711187e-7,0.000009471534,0.00005467453,0.00083405926,0.000024399851,0.9929811,0.006064948,0.0000026008122],"about_ca_topic_score_codex":0.003235571,"about_ca_topic_score_gemma":0.001390407,"teacher_disagreement_score":0.008775694,"about_ca_system_score_codex":0.0054523484,"about_ca_system_score_gemma":0.0020050795,"threshold_uncertainty_score":0.039559722},"labels":[],"label_agreement":null},{"id":"W4414751391","doi":"10.1039/d4sc07875f","title":"Pose ensemble graph neural networks to improve docking performances","year":2025,"lang":"en","type":"article","venue":"Chemical Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; University of Ottawa","keywords":"Closeness; Virtual screening; Artificial neural network; Graph; Docking (animal); Complement (music); Task (project management)","score_opus":0.007489392365018046,"score_gpt":0.2556514037562751,"score_spread":0.24816201139125704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414751391","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17797202,0.0020111618,0.79732376,0.0011362148,0.00038447714,0.00013589254,0.0018643617,0.010082221,0.009089862],"genre_scores_gemma":[0.897175,0.00044301106,0.09243016,0.00054672355,0.00011594669,0.00013570275,0.003584964,0.00049421244,0.0050741774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995759,0.00013530462,0.000014788649,0.00011771333,0.00010669642,0.000049635906],"domain_scores_gemma":[0.99912435,0.00044495452,0.000070870534,0.00014218279,0.00016479167,0.000052822044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008168618,0.001325124,0.0011043146,0.00080625183,0.00030624014,0.0007482026,0.001712789,0.0010742192,0.0037605641],"category_scores_gemma":[0.0037186581,0.000440121,0.00077917264,0.0011019307,0.00039017867,0.0016691755,0.0011238873,0.0018947382,0.0012180593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009670219,0.000088182795,0.0008420069,0.000038022266,0.000057120167,0.000029112178,0.000012982203,0.93516463,0.0010879899,0.0019622548,0.0037544155,0.05686655],"study_design_scores_gemma":[0.0000043224545,0.0000118469425,0.00007136565,0.0000021180138,0.00000477021,0.0000036647305,0.0000016176774,0.9981139,0.00028461093,0.0013426286,0.00015720722,0.0000020659159],"about_ca_topic_score_codex":0.008786864,"about_ca_topic_score_gemma":0.015592092,"teacher_disagreement_score":0.008786864,"about_ca_system_score_codex":0.0010456926,"about_ca_system_score_gemma":0.0008151896,"threshold_uncertainty_score":0.017471433},"labels":[],"label_agreement":null},{"id":"W4414763249","doi":"10.1101/2025.10.01.25337054","title":"The Brain Imaging and Neurophysiology Database: BINDing multimodal neural data into a large-scale repository","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Neuroimaging; Neurophysiology; Electroencephalography; Neuroinformatics; Medical imaging; Clinical neurophysiology; Brain mapping","score_opus":0.01913767410198091,"score_gpt":0.28878379532734516,"score_spread":0.26964612122536424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414763249","genre_codex":"dataset","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0118800355,0.0018533677,0.032476887,0.0015488393,0.00030087732,0.0006198468,0.92404366,0.023031807,0.0042446493],"genre_scores_gemma":[0.019586803,0.00064569316,0.024278846,0.0003205855,0.000084713225,0.0006691747,0.95264924,0.000934107,0.00083090656],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99758244,0.0004084922,0.0004808562,0.00069811207,0.0006388007,0.00019130933],"domain_scores_gemma":[0.9941354,0.0015840267,0.00055760215,0.002183942,0.0009263633,0.0006126074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037417891,0.0018390327,0.0013875233,0.0071377726,0.00082054257,0.0032993727,0.004032784,0.0020016474,0.010858917],"category_scores_gemma":[0.015186303,0.000743048,0.0011736657,0.005784962,0.0007589379,0.0035444652,0.0062021324,0.0021870055,0.012647182],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008512485,0.00033103678,0.014645752,0.0022332624,0.00047260386,0.0010977314,0.00041996874,0.005952622,0.0051776636,0.010778522,0.879029,0.079010546],"study_design_scores_gemma":[0.000928331,0.00035404766,0.043104384,0.0012275936,0.0004525088,0.0026250423,0.0009886273,0.045187727,0.015734488,0.04446247,0.8445215,0.00041320894],"about_ca_topic_score_codex":0.006577045,"about_ca_topic_score_gemma":0.008396303,"teacher_disagreement_score":0.010858917,"about_ca_system_score_codex":0.0011704302,"about_ca_system_score_gemma":0.003697932,"threshold_uncertainty_score":0.036326706},"labels":[],"label_agreement":null},{"id":"W4414774404","doi":"10.1016/j.oceaneng.2025.122946","title":"Reinforcement learning-based automated target motion analysis in underwater environments","year":2025,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"Defense Acquisition Program Administration; Korea Research Institute for Defense Technology Planning and Advancement","keywords":"Underwater; Motion analysis; Motion (physics); Reinforcement; Motion control","score_opus":0.0045183936214475235,"score_gpt":0.2030033865556241,"score_spread":0.1984849929341766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414774404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059538513,0.00012210441,0.93764395,0.00012864967,0.0000300672,0.000038296927,0.000029631245,0.0008487535,0.0016201093],"genre_scores_gemma":[0.93670404,0.000044122535,0.06179596,0.000050936254,0.000012627438,0.00004250027,0.00003795303,0.00004322714,0.0012685672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977916,0.000055853216,0.0000090062,0.00005978305,0.00005905895,0.000037078746],"domain_scores_gemma":[0.9993518,0.00032201738,0.00011841636,0.00004735663,0.00011296621,0.000047432157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061080244,0.00054886704,0.00051123917,0.00024275234,0.00020974525,0.00031146395,0.0007072776,0.0003878368,0.00072283845],"category_scores_gemma":[0.0019117603,0.00031145354,0.00023440385,0.0001405967,0.0005707929,0.00047482824,0.0006971542,0.00071787584,0.00017077863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048741545,0.00003377713,0.00080047577,0.000019883497,0.00001461407,0.000042021406,0.000031778312,0.9592067,0.003200154,0.0015909498,0.00031814442,0.034692857],"study_design_scores_gemma":[0.0000020866598,0.000009678283,0.000058699043,6.971621e-7,8.5571037e-7,0.00000253118,0.0000010708873,0.99922085,0.000287582,0.00035531723,0.000059611702,0.000001103393],"about_ca_topic_score_codex":0.0062038163,"about_ca_topic_score_gemma":0.004511773,"teacher_disagreement_score":0.0062038163,"about_ca_system_score_codex":0.0005189095,"about_ca_system_score_gemma":0.0010061418,"threshold_uncertainty_score":0.01233542},"labels":[],"label_agreement":null},{"id":"W4414971624","doi":"10.48550/arxiv.2510.04460","title":"Perspectives on Stochastic Localization","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Focus (optics); Stochastic process; Presentation (obstetrics); Probability distribution; Process (computing); Stochastic modelling; Key (lock)","score_opus":0.03487378707374568,"score_gpt":0.28688248175676984,"score_spread":0.25200869468302417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414971624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017833013,0.029887192,0.75721014,0.04227639,0.0023976006,0.000030073063,0.00030959156,0.0003007589,0.14975521],"genre_scores_gemma":[0.7599312,0.039686356,0.1429037,0.012700638,0.009643637,0.00025786133,0.00038259377,0.0004466614,0.03404727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99716216,0.0014629973,0.00010942516,0.00042777194,0.0006142162,0.00022334058],"domain_scores_gemma":[0.995862,0.0027402244,0.00027560504,0.00032814007,0.0005267192,0.00026723687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005348284,0.00088258326,0.0008564305,0.0028130303,0.0022396839,0.0033497023,0.0014526829,0.0026953327,0.009204855],"category_scores_gemma":[0.008305458,0.0003926842,0.001204945,0.002386184,0.0071243495,0.009890552,0.003272945,0.0046640425,0.0014488967],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020050963,0.0000022452673,0.00003211751,0.0000171982,0.0000013919872,0.000008709201,0.00004195338,0.0004574971,0.000029470462,0.9970283,0.00076548394,0.0016137544],"study_design_scores_gemma":[0.0000025295096,0.000008967608,0.000045843113,0.00003488818,0.0000022456168,0.000039058003,0.000043923563,0.0046472917,0.00006239291,0.9802791,0.014825546,0.000008189767],"about_ca_topic_score_codex":0.0019390566,"about_ca_topic_score_gemma":0.0013085689,"teacher_disagreement_score":0.009204855,"about_ca_system_score_codex":0.0033034207,"about_ca_system_score_gemma":0.0013918456,"threshold_uncertainty_score":0.030793369},"labels":[],"label_agreement":null},{"id":"W4414991883","doi":"","title":"Autoregressive Typical Thermal States","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Materials Research; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; National Science Foundation; Government of Canada; Ministero dello Sviluppo Economico; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Autoregressive model; Quantum; Artificial neural network; Unitary state; Thermal; STAR model; Quantum computer; Quantum state","score_opus":0.03362210792587448,"score_gpt":0.28859381439254794,"score_spread":0.25497170646667344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414991883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11075335,0.00018258658,0.88193566,0.0001871109,0.00004682959,0.000041106923,0.00006926366,0.00044392727,0.0063401423],"genre_scores_gemma":[0.9017805,0.0001333378,0.094187394,0.00008915785,0.000022968099,0.00007318526,0.000094839015,0.000111239526,0.0035074044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997756,0.00008373056,0.000010463491,0.00004643578,0.00005545786,0.00002830083],"domain_scores_gemma":[0.9993993,0.0003126855,0.000069034635,0.00009474537,0.00009081331,0.00003347948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070426735,0.00025144772,0.0003904384,0.00037867474,0.00039152487,0.0008263194,0.0009244957,0.0006564651,0.0024415012],"category_scores_gemma":[0.0026538873,0.00032912067,0.00044085603,0.00035131464,0.00082968525,0.0016937959,0.00060316705,0.0008337871,0.0002805633],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053035856,0.00003314373,0.0011104543,0.000041357773,0.000027275872,0.00009554314,0.00012327889,0.7434192,0.0048734783,0.23031303,0.00073570973,0.019174453],"study_design_scores_gemma":[0.0000012624388,0.000003669265,0.000052008254,0.0000020235757,0.0000014911965,0.0000073857364,0.0000043150603,0.98625004,0.0004444465,0.013075685,0.00015386863,0.0000038282074],"about_ca_topic_score_codex":0.0022346738,"about_ca_topic_score_gemma":0.0032459248,"teacher_disagreement_score":0.0024415012,"about_ca_system_score_codex":0.0006787902,"about_ca_system_score_gemma":0.00060553756,"threshold_uncertainty_score":0.0081676245},"labels":[],"label_agreement":null},{"id":"W4415028296","doi":"10.1007/978-981-95-1999-6_12","title":"NormSoftmax Attention: Improving Transformer Model Performance","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Adaptability; Transformer; Flexibility (engineering); Focus (optics); SIGNAL (programming language); Personalization","score_opus":0.005979224417218525,"score_gpt":0.19048117874730164,"score_spread":0.18450195433008312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415028296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024049748,0.0016485046,0.96107626,0.00056816626,0.0004835957,0.00005457465,0.0003284362,0.006461723,0.0053289896],"genre_scores_gemma":[0.57512766,0.0013343982,0.3903365,0.0008514758,0.00058824656,0.00020257023,0.0022431635,0.0028167765,0.026499186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868494,0.00044971964,0.000070996204,0.0002538511,0.00040279634,0.00013782835],"domain_scores_gemma":[0.9974884,0.001230035,0.000104403756,0.00046979697,0.00061632274,0.00009107039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002190017,0.0025976547,0.0013745223,0.00070347183,0.00049708434,0.001770148,0.0026764635,0.0017762909,0.011442796],"category_scores_gemma":[0.008310802,0.00068156916,0.0006415943,0.0007271597,0.0005526156,0.0036564257,0.002947771,0.0027600992,0.003358501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073342654,0.0002503293,0.0006994268,0.00016681061,0.00010616526,0.00007394302,0.00006760693,0.107168205,0.012134811,0.009988689,0.019242443,0.8493683],"study_design_scores_gemma":[0.000021014079,0.000058170714,0.00013589262,0.000008725004,0.000024297495,0.00002548406,0.0000123937425,0.9825053,0.007859186,0.007948534,0.0013928887,0.000008033052],"about_ca_topic_score_codex":0.0068888357,"about_ca_topic_score_gemma":0.011669256,"teacher_disagreement_score":0.011442796,"about_ca_system_score_codex":0.00096011173,"about_ca_system_score_gemma":0.0011699342,"threshold_uncertainty_score":0.03828001},"labels":[],"label_agreement":null},{"id":"W4415097369","doi":"10.48550/arxiv.2510.06840","title":"CNN-TFT explained by SHAP with multi-head attention weights for time series forecasting","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; Natural Sciences and Engineering Research Council of Canada","keywords":"Time series; Convolutional neural network; Salient; Pattern recognition (psychology); Multivariate statistics; Transformer; Feature extraction; Deep learning; Series (stratigraphy)","score_opus":0.05618668288287484,"score_gpt":0.2755162724223891,"score_spread":0.21932958953951423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415097369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07387017,0.0006146851,0.9192854,0.0005025902,0.00013257617,0.000031990556,0.00043096903,0.002501071,0.0026304843],"genre_scores_gemma":[0.9148816,0.00033743816,0.07928906,0.00018717803,0.00009601244,0.00004621137,0.00086572446,0.00013183153,0.0041650133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998524,0.000022914526,0.0000102595095,0.000055893863,0.000032983076,0.000025507745],"domain_scores_gemma":[0.9997008,0.00010058612,0.00004268697,0.00006220141,0.00006896753,0.000024798612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048467395,0.000822518,0.00051585905,0.00056659617,0.0002596789,0.0005980594,0.0009328369,0.00079976727,0.0024604427],"category_scores_gemma":[0.0018321396,0.00033390627,0.00088274095,0.0007306607,0.00035685903,0.0015614942,0.0008147536,0.0012014003,0.00042539687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014768499,0.00005879109,0.00265564,0.000060048602,0.00011879934,0.0001636405,0.00006799896,0.7727193,0.008228059,0.017106138,0.0047619017,0.19391194],"study_design_scores_gemma":[0.000002028749,0.0000075232238,0.00013394974,0.0000013759155,0.0000070850683,0.000009033363,0.00000192696,0.99532664,0.0005663503,0.003740781,0.00020094856,0.0000022634229],"about_ca_topic_score_codex":0.011548304,"about_ca_topic_score_gemma":0.011405783,"teacher_disagreement_score":0.011548304,"about_ca_system_score_codex":0.00088020024,"about_ca_system_score_gemma":0.0008606309,"threshold_uncertainty_score":0.022962213},"labels":[],"label_agreement":null},{"id":"W4415166449","doi":"10.54254/2755-2721/2025.ld27822","title":"A Comparative Study on Deep Learning-Based: Temperature Prediction Models: Performance Evaluation of CNN, Transformer and Random Forest","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Random forest; Artificial neural network; Preprocessor; Data pre-processing; Deep learning; Predictive modelling; Backpropagation; Transformer","score_opus":0.014893991779659247,"score_gpt":0.24203915184090366,"score_spread":0.2271451600612444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415166449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7654683,0.008702057,0.2108245,0.0009707851,0.00053735636,0.00017072876,0.0015887484,0.004321638,0.0074158087],"genre_scores_gemma":[0.9623136,0.0015797418,0.032793097,0.00011481796,0.00003901737,0.00006500281,0.0017419073,0.00008445984,0.0012683556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994506,0.00013208255,0.00004987071,0.00015034695,0.00012888563,0.000088069646],"domain_scores_gemma":[0.99866366,0.0006236482,0.000099157995,0.00012822163,0.00042350442,0.00006183876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002081666,0.0015032444,0.00088867964,0.0008628342,0.00027417013,0.0006313987,0.0011560417,0.00077988574,0.00077542645],"category_scores_gemma":[0.003876993,0.0002786275,0.0007461431,0.00095290097,0.00028034727,0.0017239446,0.0005088259,0.0008573568,0.00029962204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091706135,0.00031099605,0.016165765,0.00036891393,0.0002745562,0.000101155114,0.00005505628,0.7512295,0.0038433976,0.0013037676,0.0031700728,0.22225973],"study_design_scores_gemma":[0.00001009154,0.00012992957,0.001342599,0.000016105043,0.00003399324,0.000019342351,0.000015916943,0.99578214,0.0019924643,0.00035666293,0.00029052637,0.000010252905],"about_ca_topic_score_codex":0.027090367,"about_ca_topic_score_gemma":0.020376705,"teacher_disagreement_score":0.027090367,"about_ca_system_score_codex":0.0010144777,"about_ca_system_score_gemma":0.0012279787,"threshold_uncertainty_score":0.053865373},"labels":[],"label_agreement":null},{"id":"W4415174921","doi":"10.1088/1402-4896/ae132c","title":"Synchronization of BAM neural networks with discrete delays: a time-scale approach","year":2025,"lang":"en","type":"article","venue":"Physica Scripta","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Synchronization (alternating current); Bidirectional associative memory; Control theory (sociology); Artificial neural network; Discrete time and continuous time; Class (philosophy); Control (management)","score_opus":0.005419285341843996,"score_gpt":0.20862081126322662,"score_spread":0.20320152592138263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415174921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041728377,0.00032404164,0.9509476,0.00017991236,0.00007099353,0.000022500939,0.000034216446,0.00011707008,0.006575281],"genre_scores_gemma":[0.9574109,0.00030885058,0.039725356,0.000059250237,0.000044685887,0.00008216962,0.000023679864,0.000029463043,0.0023155985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985576,0.000038603048,0.000009342517,0.000032283042,0.000042759926,0.0000212734],"domain_scores_gemma":[0.9995648,0.0002048065,0.0001055799,0.00004093131,0.000059995527,0.000023954937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041537362,0.00035812514,0.0003345888,0.00034470216,0.00023139192,0.0005136406,0.0005814947,0.0004803675,0.0022224265],"category_scores_gemma":[0.0012538561,0.0001578939,0.00026158683,0.0001956788,0.0006164802,0.00082197547,0.0007584217,0.00047811287,0.00019956713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015298443,0.000050708037,0.0009368181,0.00023387474,0.000054218686,0.0004291701,0.00019973608,0.49243867,0.082722,0.39113566,0.000814429,0.030831728],"study_design_scores_gemma":[0.000007886624,0.000017590299,0.00014026155,0.0000058435844,0.000005741878,0.00001988852,0.000015924978,0.9810839,0.0021955103,0.015906954,0.00059577654,0.0000047475396],"about_ca_topic_score_codex":0.00080524985,"about_ca_topic_score_gemma":0.0007478003,"teacher_disagreement_score":0.0022224265,"about_ca_system_score_codex":0.00048118163,"about_ca_system_score_gemma":0.00030815555,"threshold_uncertainty_score":0.0074347258},"labels":[],"label_agreement":null},{"id":"W4415179518","doi":"10.23919/ecc65951.2025.11187175","title":"Least Squares Training of Quadratic Convolutional Neural Networks with Applications to System Theory","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Least-squares function approximation; Quadratic equation; System identification; Context (archaeology); Regularization (linguistics); Artificial neural network; Quadratic programming; Identification (biology); Global Positioning System","score_opus":0.014835912686424426,"score_gpt":0.2468272786475166,"score_spread":0.23199136596109218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415179518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005925457,0.00022642253,0.99237955,0.00012348653,0.000019141267,0.000012607377,0.000017609309,0.00018874464,0.0011069857],"genre_scores_gemma":[0.6250985,0.000572663,0.366283,0.00015549434,0.000068635294,0.00015540583,0.00016015784,0.0002068596,0.0072991904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996935,0.00011724819,0.00001754917,0.000059106464,0.00008110824,0.000031441086],"domain_scores_gemma":[0.9989594,0.0007218795,0.00006758445,0.000052989802,0.00017751876,0.00002051627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085604587,0.00071769283,0.0005174458,0.00025478163,0.0002425728,0.00048655426,0.00067513017,0.0008423112,0.0017907361],"category_scores_gemma":[0.0035303344,0.00050891464,0.00032610047,0.00050481234,0.0006352951,0.0006274239,0.00086708623,0.001211903,0.00031590488],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001989313,0.0000109335815,0.00016113998,0.000048347476,0.000016083208,0.000021263388,0.000036523317,0.9646301,0.0016849304,0.00729728,0.00033967086,0.02573377],"study_design_scores_gemma":[7.4267416e-7,0.00000432699,0.000019383608,0.0000016258308,7.108238e-7,0.0000023852401,0.0000010462543,0.99846303,0.00021438878,0.0011774605,0.000113820955,0.0000010997251],"about_ca_topic_score_codex":0.0071373465,"about_ca_topic_score_gemma":0.009365171,"teacher_disagreement_score":0.0071373465,"about_ca_system_score_codex":0.0008150758,"about_ca_system_score_gemma":0.0008850435,"threshold_uncertainty_score":0.0141916275},"labels":[],"label_agreement":null},{"id":"W4415233277","doi":"10.1101/2025.10.15.682672","title":"Using Information Geometry to Characterize Higher-Order Interactions in EEG","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Alberta Innovates","keywords":"Pattern recognition (psychology); Divergence (linguistics); Binary number; Discretization; Binary data; SIGNAL (programming language); Spike (software development); Electroencephalography; Mutual information; Signal processing","score_opus":0.028582675371987096,"score_gpt":0.26335831722278574,"score_spread":0.23477564185079863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415233277","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087300286,0.00013911512,0.9110795,0.00012710222,0.000016759446,0.000029886118,0.00016635566,0.00023071388,0.0009102283],"genre_scores_gemma":[0.8356719,0.00026491037,0.16277973,0.00005764202,0.000037848447,0.000076212236,0.0003438906,0.0001364533,0.0006313994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953175,0.00021074111,0.000027088212,0.000060151033,0.00013154892,0.000038781018],"domain_scores_gemma":[0.99707127,0.0019680848,0.00038230937,0.00031693745,0.00017424204,0.000087230226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001289561,0.0006285222,0.00036749337,0.0011836918,0.0002559525,0.00086212455,0.00047313306,0.00047536194,0.0007427255],"category_scores_gemma":[0.0049683163,0.00024798527,0.00047129273,0.0006918909,0.0012563923,0.0013522594,0.0008803566,0.00078762166,0.00018639577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037036993,0.00011288106,0.005917149,0.0002694802,0.00016104877,0.00032661879,0.00043524115,0.6787378,0.10002068,0.112300575,0.0013877122,0.0999605],"study_design_scores_gemma":[0.000004962788,0.000058139707,0.0028677788,0.000008818132,0.000007894329,0.00006201276,0.000023409453,0.96023905,0.010065704,0.026051268,0.0005880443,0.000023032613],"about_ca_topic_score_codex":0.0010816823,"about_ca_topic_score_gemma":0.00091552956,"teacher_disagreement_score":0.001289561,"about_ca_system_score_codex":0.00053985923,"about_ca_system_score_gemma":0.00046242346,"threshold_uncertainty_score":0.0068199635},"labels":[],"label_agreement":null},{"id":"W4415250911","doi":"10.1109/hpec67600.2025.11196591","title":"CRAMP: Categorizing Classifiers and Regressors for Scalable Parallelism on Distributed and Multicore Systems","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Adaptability; Multi-core processor; Scalability; MNIST database; Benchmarking; Multithreading; Parallelism (grammar); Support vector machine; Artificial neural network","score_opus":0.019309569032452786,"score_gpt":0.2685718665658487,"score_spread":0.2492622975333959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415250911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056626484,0.0025873599,0.9108555,0.0011534996,0.00029446685,0.0007336143,0.0014575227,0.018341767,0.007949919],"genre_scores_gemma":[0.3178705,0.0015545008,0.6704703,0.00035927095,0.00016283967,0.00093292416,0.004321749,0.001602768,0.0027251183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973117,0.00044417987,0.0001939577,0.0005371259,0.0012128047,0.00030033398],"domain_scores_gemma":[0.9955701,0.0010840362,0.00037415334,0.0013789171,0.0013528231,0.00023987617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035453795,0.0013275292,0.0008801256,0.0023939803,0.0009293008,0.0026055537,0.0032801381,0.00089559646,0.0019136145],"category_scores_gemma":[0.014048527,0.0006523283,0.0010032902,0.002975006,0.0008137794,0.0046595,0.0020712337,0.0026331837,0.00095506065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005311138,0.00045556467,0.01636816,0.0009500286,0.0002211956,0.00023712512,0.00054188666,0.2674373,0.011601588,0.09273273,0.043260466,0.5656628],"study_design_scores_gemma":[0.00007442609,0.00034728445,0.0031715573,0.00015073389,0.000057672678,0.00017838115,0.00019469357,0.8856924,0.012642563,0.059475966,0.03795633,0.000058006728],"about_ca_topic_score_codex":0.007432217,"about_ca_topic_score_gemma":0.0057385275,"teacher_disagreement_score":0.007432217,"about_ca_system_score_codex":0.0012843253,"about_ca_system_score_gemma":0.0037879038,"threshold_uncertainty_score":0.018749952},"labels":[],"label_agreement":null},{"id":"W4415315830","doi":"10.48550/arxiv.2509.12431","title":"Neural-Quantum-States Impurity Solver for Quantum Embedding Problems","year":2025,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Materials Research; Alliance de recherche numérique du Canada; National Science and Technology Council; National Center for Theoretical Sciences; Nvidia; National Science Foundation","keywords":"Embedding; Solver; Quantum; Degenerate energy levels; Benchmark (surveying); Observable; Artificial neural network; Scalability","score_opus":0.07320304638006003,"score_gpt":0.2264521935346517,"score_spread":0.15324914715459168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415315830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034435783,0.00021952232,0.9565195,0.0006450194,0.00006048073,0.000060792296,0.00010955185,0.0004825824,0.007466784],"genre_scores_gemma":[0.50649023,0.00015696776,0.4871253,0.00031143677,0.00006493373,0.00023146509,0.0002769355,0.00030509455,0.0050377077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961734,0.00017534576,0.000015918822,0.000040981737,0.00011744685,0.000032800373],"domain_scores_gemma":[0.9987148,0.00090066827,0.00006598847,0.000107498316,0.00015701994,0.00005409449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011572907,0.00044850472,0.00068117696,0.00036566862,0.0004191192,0.0007100496,0.0014585395,0.0012613396,0.0033847485],"category_scores_gemma":[0.004294361,0.00031021424,0.00035050357,0.00042305395,0.0009580321,0.0010907104,0.0013759814,0.0017143878,0.0004344449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065148655,0.00005947515,0.0005141457,0.000101233854,0.000029668017,0.00004570153,0.000045984438,0.8949566,0.0011944998,0.0773472,0.0014707452,0.024169518],"study_design_scores_gemma":[0.000003613383,0.000002567061,0.000010468861,0.0000017219253,6.637423e-7,0.0000018822656,0.0000026802031,0.9938398,0.00015134271,0.0058492,0.00013532184,8.0895416e-7],"about_ca_topic_score_codex":0.0038180088,"about_ca_topic_score_gemma":0.006359092,"teacher_disagreement_score":0.0038180088,"about_ca_system_score_codex":0.0009773932,"about_ca_system_score_gemma":0.001322841,"threshold_uncertainty_score":0.011323094},"labels":[],"label_agreement":null},{"id":"W4415378183","doi":"10.1109/ficloud66139.2025.00052","title":"Nitrous Oxide Emission Prediction by Combining Process-based Models and Neural Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Greenhouse gas; Artificial neural network; Nitrous oxide; Work (physics); Scale (ratio); Time horizon; Global-warming potential; Climate change","score_opus":0.01272854876836955,"score_gpt":0.2567317344325963,"score_spread":0.24400318566422674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415378183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5769064,0.00081705826,0.41037157,0.000819941,0.00016030356,0.00010327475,0.001022096,0.002014252,0.0077851648],"genre_scores_gemma":[0.96529025,0.00019529749,0.031777702,0.00006738097,0.000040053892,0.000060866263,0.00074075477,0.00003615801,0.0017915617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997161,0.000052765194,0.000019135929,0.000115966635,0.00005600181,0.000039967075],"domain_scores_gemma":[0.9992113,0.0004924762,0.0000833693,0.000034864905,0.00015554193,0.00002236712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007051455,0.0010105179,0.00062681217,0.000767879,0.00039497679,0.0009688206,0.0008564044,0.00087421114,0.00092865207],"category_scores_gemma":[0.0020761567,0.0006008584,0.0007125815,0.0008529433,0.00027634492,0.0010154292,0.0005027204,0.0009974968,0.00027207937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003458519,0.00007191323,0.002875302,0.000013528316,0.00003227484,0.00001601519,0.000009483394,0.983881,0.0004659924,0.00018655967,0.0001644469,0.012248881],"study_design_scores_gemma":[9.837767e-7,0.0000034768748,0.0001990453,6.285359e-7,0.0000020715522,6.639182e-7,0.0000013445324,0.9995484,0.00009672056,0.00011675475,0.000028414324,0.0000014128929],"about_ca_topic_score_codex":0.069640465,"about_ca_topic_score_gemma":0.059095964,"teacher_disagreement_score":0.069640465,"about_ca_system_score_codex":0.0012618545,"about_ca_system_score_gemma":0.0009882668,"threshold_uncertainty_score":0.13847023},"labels":[],"label_agreement":null},{"id":"W4415385739","doi":"10.1007/978-3-031-96015-4_7","title":"The Use of Neural Networks and PCA Dimensionality Reduction in the Imputation of Missing Fragments in High-Dimensional Time Series","year":2025,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Imputation (statistics); Dimensionality reduction; Artificial neural network; Missing data; Principal component analysis; Pattern recognition (psychology); Curse of dimensionality; Sequence (biology)","score_opus":0.020932822393903282,"score_gpt":0.245951410298817,"score_spread":0.22501858790491372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415385739","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005363163,0.0030812568,0.988643,0.00058675034,0.00020054767,0.000014617123,0.00014812213,0.00021429577,0.001748319],"genre_scores_gemma":[0.14738819,0.006310248,0.83466923,0.00028401395,0.0010181601,0.00011282856,0.0006553939,0.00018108064,0.009380769],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999065,0.00039694807,0.00006729675,0.00019753377,0.00022607176,0.000047201254],"domain_scores_gemma":[0.9963182,0.0026587148,0.00020088631,0.0004376598,0.000332688,0.000051898172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003138602,0.0008178494,0.0009929716,0.0010035139,0.00047942105,0.0015517961,0.0012275337,0.0013121708,0.0015855681],"category_scores_gemma":[0.009840039,0.0005945499,0.0012736735,0.0024500208,0.0010212823,0.0024071066,0.0011406118,0.0027078807,0.0005635744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018333303,0.00010525612,0.0023567586,0.0002448186,0.00028659287,0.0001997391,0.00016428821,0.24665713,0.0043376107,0.08082144,0.008409699,0.6562333],"study_design_scores_gemma":[0.000007099846,0.000027787135,0.0011510854,0.000044309047,0.000040165334,0.00008232915,0.00002100714,0.93370473,0.0017159409,0.06017259,0.0030016862,0.0000312935],"about_ca_topic_score_codex":0.003252431,"about_ca_topic_score_gemma":0.0047388556,"teacher_disagreement_score":0.003252431,"about_ca_system_score_codex":0.0005288036,"about_ca_system_score_gemma":0.00063727563,"threshold_uncertainty_score":0.016598701},"labels":[],"label_agreement":null},{"id":"W4415397854","doi":"10.1016/j.neunet.2026.109236","title":"Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks","year":2025,"lang":"en","type":"preprint","venue":"Neural Networks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normalization (sociology); Feature (linguistics); Embedding; Artificial neural network; Convergence (economics); Feature learning; Limiting; Consistency (knowledge bases); Key (lock)","score_opus":0.02511426141977303,"score_gpt":0.28243796856008874,"score_spread":0.2573237071403157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415397854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015342043,0.00044122676,0.9817125,0.0004189456,0.00009637182,0.000042762575,0.00012869167,0.0010412162,0.0007761119],"genre_scores_gemma":[0.5209241,0.0007663139,0.4685294,0.00091916684,0.00028584775,0.00027208103,0.000780419,0.0007223838,0.0068002488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991364,0.00033991935,0.00003395003,0.00021434785,0.00021241771,0.00006309868],"domain_scores_gemma":[0.9971584,0.0016134502,0.00022905316,0.00057869375,0.0003019718,0.0001185011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029109144,0.0014939102,0.0013455829,0.0004960931,0.0006677923,0.0009320101,0.0028791884,0.0023989426,0.0031435755],"category_scores_gemma":[0.016470995,0.0010532832,0.0007231433,0.0006607012,0.0011918605,0.003005283,0.004444167,0.0030367419,0.00067846524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000741861,0.00014304103,0.0016710254,0.00030441297,0.0002361288,0.00018016138,0.00021101904,0.6496799,0.010671826,0.03992041,0.007028372,0.28921178],"study_design_scores_gemma":[0.00002048379,0.000041333984,0.00016920915,0.000011263985,0.0000188542,0.000023001285,0.0000066674875,0.9752832,0.002651773,0.02100287,0.0007623738,0.0000089465375],"about_ca_topic_score_codex":0.003981706,"about_ca_topic_score_gemma":0.006755403,"teacher_disagreement_score":0.003981706,"about_ca_system_score_codex":0.0007871606,"about_ca_system_score_gemma":0.00191621,"threshold_uncertainty_score":0.015394568},"labels":[],"label_agreement":null},{"id":"W4415414490","doi":"10.2139/ssrn.5639673","title":"Neural and Emotional Dual Regulation by Music Tempo: An EEG Study of Alpha Waves, Functional Connectivity, and Dimensional Emotion Recognition","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Electroencephalography; Arousal; Valence (chemistry); Rhythm; Neurophysiology; Mechanism (biology); Brain activity and meditation; Neuropsychology; Alpha (finance)","score_opus":0.024941439773652537,"score_gpt":0.2516337524683681,"score_spread":0.22669231269471557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415414490","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997008,0.00027715322,0.0008889675,0.0000509589,0.00001302634,0.000024501047,0.00017175733,0.000006563849,0.0015589595],"genre_scores_gemma":[0.9978865,0.00018898811,0.00085268106,0.00005827134,0.000039080584,0.000033433673,0.00015462951,0.000011241208,0.0007751539],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9999099,0.000026778791,0.0000042501206,0.000029841496,0.000016705562,0.000012551338],"domain_scores_gemma":[0.99973935,0.000119084594,0.00006289655,0.000030757812,0.00001798056,0.000029840648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022538024,0.00021072045,0.00018734994,0.00031799532,0.0002151561,0.0005977585,0.00018156502,0.0002795756,0.002336784],"category_scores_gemma":[0.0011800139,0.0001772038,0.0001581873,0.0003506107,0.000398323,0.00030586202,0.00026483307,0.0003724436,0.0002227162],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008467013,0.0011345459,0.156054,0.00032266384,0.0005760273,0.00068682624,0.0023322722,0.00066577195,0.7434098,0.0017832298,0.0012720418,0.08329587],"study_design_scores_gemma":[0.00012714566,0.0005168568,0.9913606,0.00001258058,0.000094707655,0.00045527052,0.00029743777,0.0012758354,0.0044596633,0.0008541494,0.00053385686,0.000011867808],"about_ca_topic_score_codex":0.00071208493,"about_ca_topic_score_gemma":0.0017149615,"teacher_disagreement_score":0.002336784,"about_ca_system_score_codex":0.00008164676,"about_ca_system_score_gemma":0.00007928451,"threshold_uncertainty_score":0.007817268},"labels":[],"label_agreement":null},{"id":"W4415517558","doi":"10.1007/s11081-025-10044-7","title":"Inverse problems and machine learning","year":2025,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"","score_opus":0.008059836759119058,"score_gpt":0.18338180466018514,"score_spread":0.17532196790106608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415517558","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010719646,0.15701526,0.6741314,0.018313158,0.0051654894,0.000042979962,0.00019396999,0.0002875979,0.13413045],"genre_scores_gemma":[0.47565335,0.12494443,0.2197362,0.005520701,0.017946241,0.00031254685,0.0005820552,0.00042830317,0.15487628],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991679,0.0003398121,0.00004474584,0.000117603144,0.0002982082,0.000031637333],"domain_scores_gemma":[0.9985253,0.0010127316,0.00010257258,0.000166642,0.00015634278,0.000036457066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010865275,0.00086565147,0.0018120016,0.0012591076,0.0005810626,0.0026478563,0.0008157052,0.0022317413,0.007023246],"category_scores_gemma":[0.004741094,0.0004092394,0.0007353678,0.0014914059,0.00432346,0.0031755194,0.0016573683,0.0034139582,0.0017390224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009653941,0.000018756731,0.00014232965,0.00036961152,0.000033013595,0.000048478043,0.00011928003,0.00979601,0.00034055632,0.93490064,0.009434474,0.044787277],"study_design_scores_gemma":[0.0000037045118,0.0000075831936,0.00008449598,0.000051586765,0.000006493714,0.00007346628,0.000027043958,0.0097976485,0.00010640339,0.9694177,0.02041744,0.000006403118],"about_ca_topic_score_codex":0.0008915102,"about_ca_topic_score_gemma":0.0006224378,"teacher_disagreement_score":0.007023246,"about_ca_system_score_codex":0.0006951867,"about_ca_system_score_gemma":0.0006094992,"threshold_uncertainty_score":0.023495078},"labels":[],"label_agreement":null},{"id":"W4415589112","doi":"10.1145/3773034","title":"Synthesizing Efficient and Permissive Programmatic Runtime Shields for Neural Policies","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Software Engineering and Methodology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Medical Association","funders":"","keywords":"Shields; Overhead (engineering); Reduction (mathematics); Software; Control (management); Reliability (semiconductor); Safety standards; Runtime system","score_opus":0.0516818758031873,"score_gpt":0.3185185259233981,"score_spread":0.2668366501202108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415589112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062096994,0.0004412193,0.92267805,0.00029908965,0.00008241509,0.00018637023,0.00022193286,0.007832948,0.0061608697],"genre_scores_gemma":[0.59534514,0.00035345374,0.39941275,0.00025622887,0.000029897965,0.00032125966,0.00050693826,0.0012393415,0.0025350857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990644,0.0002198848,0.000058842794,0.00016997798,0.00033027164,0.0001566272],"domain_scores_gemma":[0.9978811,0.0013033128,0.00018976422,0.00032505218,0.00022472459,0.00007605667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011373542,0.0011240686,0.00058753655,0.000617406,0.0004250744,0.0008052084,0.0009090802,0.000806218,0.0040913112],"category_scores_gemma":[0.0054763732,0.0005283346,0.0010640961,0.00024061922,0.0013629015,0.0011828538,0.0014101537,0.0013995343,0.00068057806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025686985,0.0001230542,0.0030804537,0.00055428257,0.00006441872,0.000284458,0.00034283922,0.7486378,0.039152667,0.0331742,0.003239276,0.17108968],"study_design_scores_gemma":[0.00003236884,0.000095225725,0.00019720769,0.00003418319,0.000024860527,0.000056106877,0.000044281078,0.97191364,0.014017847,0.010189685,0.0033808379,0.000013715244],"about_ca_topic_score_codex":0.002903355,"about_ca_topic_score_gemma":0.004991282,"teacher_disagreement_score":0.0040913112,"about_ca_system_score_codex":0.0009396314,"about_ca_system_score_gemma":0.0025515968,"threshold_uncertainty_score":0.013686836},"labels":[],"label_agreement":null},{"id":"W4415608034","doi":"10.21105/joss.08261","title":"IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data","year":2025,"lang":"","type":"article","venue":"The Journal of Open Source Software","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Ottawa Mental Health Centre; University of Ottawa","funders":"","keywords":"R package; Open source; Software package; Python (programming language)","score_opus":0.02687650636405389,"score_gpt":0.3188395317402896,"score_spread":0.29196302537623575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415608034","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006745033,0.00091103197,0.41757894,0.0004695505,0.00071721047,0.00025747897,0.180692,0.3866772,0.0059515852],"genre_scores_gemma":[0.038583662,0.00076158234,0.5628513,0.00057719677,0.00024914363,0.0020296837,0.1537632,0.2288373,0.012346833],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993604,0.00011638953,0.0000833803,0.00016808479,0.00020548567,0.000066266686],"domain_scores_gemma":[0.9957014,0.0023976774,0.00037079767,0.0005283433,0.00085155753,0.0001502935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028322453,0.0018436823,0.0011429051,0.0020284478,0.00060557766,0.0024042318,0.0020627936,0.0009926566,0.07029614],"category_scores_gemma":[0.019521343,0.0016492405,0.0027050183,0.0015083789,0.00038622096,0.0025465386,0.002055525,0.002694574,0.04822468],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005701778,0.00009901621,0.0073866257,0.002265083,0.0007768274,0.0001973374,0.0006111188,0.009304477,0.008187126,0.008336721,0.7866223,0.17564315],"study_design_scores_gemma":[0.00063198153,0.00028096559,0.031361,0.0008099796,0.00067581487,0.00065577286,0.0003217593,0.21751794,0.021416007,0.05464444,0.6710343,0.00065012043],"about_ca_topic_score_codex":0.007911595,"about_ca_topic_score_gemma":0.013314952,"teacher_disagreement_score":0.07029614,"about_ca_system_score_codex":0.00062312995,"about_ca_system_score_gemma":0.0021858842,"threshold_uncertainty_score":0.23516399},"labels":[],"label_agreement":null},{"id":"W4416037419","doi":"10.18653/v1/2025.emnlp-demos.65","title":"Interactive Training: Feedback-Driven Neural Network Optimization","year":2025,"lang":"","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Artificial neural network; Process (computing); Feature (linguistics); Identification (biology); Noise (video)","score_opus":0.02363226653359277,"score_gpt":0.2756418337048446,"score_spread":0.2520095671712518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416037419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022846444,0.00021915323,0.96326846,0.00025800802,0.00006622207,0.00010892247,0.00009279169,0.0066120666,0.0065279515],"genre_scores_gemma":[0.7646573,0.00016964109,0.22935855,0.00037714638,0.000061228195,0.0004182181,0.00025025418,0.0010602054,0.0036474762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994771,0.00015813892,0.000020849142,0.00012715113,0.00014208032,0.000074809686],"domain_scores_gemma":[0.9981669,0.0012052942,0.00014128549,0.00019851881,0.00018665183,0.00010126873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012568795,0.0011220135,0.00060805853,0.0003521815,0.00043248883,0.00079719094,0.0023284866,0.001275404,0.005158123],"category_scores_gemma":[0.0057984074,0.00051137997,0.00035677315,0.00027817307,0.0009846443,0.0012835757,0.0020253544,0.0016646786,0.00080891105],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020865061,0.00015389745,0.0009601892,0.00009317773,0.00003957184,0.00012024539,0.00014875481,0.91932935,0.005350449,0.005677968,0.003646788,0.064270996],"study_design_scores_gemma":[0.000012842412,0.000019135266,0.000053893025,0.00000495635,0.0000024746855,0.000009651307,0.0000064775977,0.99647,0.001011964,0.001843159,0.0005612149,0.000004138331],"about_ca_topic_score_codex":0.0054315347,"about_ca_topic_score_gemma":0.007451599,"teacher_disagreement_score":0.0054315347,"about_ca_system_score_codex":0.00065687,"about_ca_system_score_gemma":0.0010251313,"threshold_uncertainty_score":0.017255664},"labels":[],"label_agreement":null},{"id":"W4416448090","doi":"10.1515/9783111436548-008","title":"179Injecting uniform chaotic sequences into an ANN’s learning fabric to reduce overfitting","year":2025,"lang":"","type":"book-chapter","venue":"Cybersecurity","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Overfitting; Chaotic; Pattern recognition (psychology); CHAOS (operating system); Chaotic map","score_opus":0.025551736043688583,"score_gpt":0.28948131065539195,"score_spread":0.26392957461170335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416448090","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16156802,0.00083157,0.78110707,0.00059154874,0.00066477276,0.00005459317,0.00012058143,0.0019525477,0.053109255],"genre_scores_gemma":[0.8226515,0.0006710543,0.13961037,0.00023378858,0.00010600612,0.000045782115,0.00019517902,0.00036409893,0.03612228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999074,0.000015121052,0.0000077208015,0.000021421794,0.000035042514,0.000013304063],"domain_scores_gemma":[0.99973685,0.00006940325,0.000026369877,0.000072003655,0.000081417114,0.000013986796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019603941,0.0005173568,0.00024257766,0.00028291636,0.0002688845,0.00058370276,0.00043411582,0.0003980713,0.0050742463],"category_scores_gemma":[0.0010421566,0.00025340728,0.00025514432,0.00026060056,0.00027433014,0.0006698725,0.0006090646,0.0006367025,0.0013238563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024443888,0.00015554114,0.0015188394,0.00024327003,0.000070586895,0.00030236781,0.0001389046,0.36683735,0.14960188,0.030527156,0.005392603,0.44496706],"study_design_scores_gemma":[0.0000064904248,0.00015794764,0.0008717994,0.000033995562,0.000021443124,0.00016708468,0.000025035193,0.9404532,0.039556567,0.008646015,0.010047301,0.000013161166],"about_ca_topic_score_codex":0.0006181782,"about_ca_topic_score_gemma":0.0009623065,"teacher_disagreement_score":0.0050742463,"about_ca_system_score_codex":0.00031516096,"about_ca_system_score_gemma":0.00021376889,"threshold_uncertainty_score":0.016974986},"labels":[],"label_agreement":null},{"id":"W4417228206","doi":"10.1088/1742-5468/ae23be","title":"Overlap gap and computational thresholds in the square wave perceptron","year":2025,"lang":"","type":"article","venue":"Journal of Statistical Mechanics Theory and Experiment","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Benchmark (surveying); Limit (mathematics); Perceptron; Constraint (computer-aided design); Feature (linguistics); Square (algebra); Artificial neural network; Function (biology); Space (punctuation)","score_opus":0.02214978398990659,"score_gpt":0.307473553843951,"score_spread":0.2853237698540444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417228206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.759369,0.0004313219,0.21497017,0.0041654413,0.00010352058,0.000030793133,0.00020251844,0.0005569276,0.020170247],"genre_scores_gemma":[0.9943605,0.000036727884,0.00431246,0.00009124824,0.00002649121,0.000020111287,0.000036424513,0.00003281223,0.0010832717],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985593,0.0004932699,0.000058895606,0.00022817605,0.00040359,0.00025679788],"domain_scores_gemma":[0.9886236,0.008338501,0.00082433666,0.00080044067,0.00045472773,0.00095841597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020171027,0.00035248027,0.00096955476,0.0007467723,0.00070866983,0.0022606517,0.0013999263,0.00165685,0.006658011],"category_scores_gemma":[0.01993249,0.0005237827,0.000546571,0.00046798843,0.0032112722,0.004491518,0.0032029033,0.0025953527,0.00038186336],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068167225,0.00013962676,0.0038273132,0.00017330772,0.000054230037,0.00036804014,0.00055351394,0.19033636,0.0045250915,0.7791928,0.0031927375,0.016955398],"study_design_scores_gemma":[0.00003629461,0.00006747769,0.00068769464,0.000016923403,0.0000066205653,0.00006333763,0.00007149515,0.5405871,0.0011637552,0.45684388,0.00043881798,0.000016608597],"about_ca_topic_score_codex":0.0010073248,"about_ca_topic_score_gemma":0.0007117531,"teacher_disagreement_score":0.006658011,"about_ca_system_score_codex":0.0010943344,"about_ca_system_score_gemma":0.0006720197,"threshold_uncertainty_score":0.022273302},"labels":[],"label_agreement":null},{"id":"W4714579","doi":"10.1136/jmg.10.2.129","title":"Autonomous perceptual feature extraction in a topology-constrained architecture","year":2008,"lang":"en","type":"article","venue":"Journal of Medical Genetics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Dimensionality reduction; Feature extraction; Feature (linguistics); Artificial intelligence; Curse of dimensionality; Perception; Topology (electrical circuits); Associative property; Pattern recognition (psychology); Theoretical computer science; Mathematics","score_opus":0.019507982861695784,"score_gpt":0.2876872465813401,"score_spread":0.2681792637196443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4714579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29685074,0.00028334756,0.6935407,0.0002572731,0.000048159094,0.000028817587,0.00008180154,0.0009681365,0.007941085],"genre_scores_gemma":[0.9334255,0.00009723144,0.06374403,0.00005015921,0.0000076270126,0.00003196436,0.00007061026,0.0000282702,0.0025446108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993956,0.0000112938515,0.0000028993452,0.000019057878,0.00001582508,0.000011319105],"domain_scores_gemma":[0.999869,0.000036829522,0.000016004691,0.00003388665,0.000027265403,0.000016980845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015998665,0.00016230327,0.00026751726,0.00015489949,0.0002498859,0.000507549,0.00088232505,0.00036829704,0.0014629443],"category_scores_gemma":[0.00059230055,0.0001696838,0.00022701609,0.00019983028,0.00035254008,0.0012085486,0.0005531268,0.00037806763,0.00032168883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037860117,0.00016338173,0.002282329,0.0002215362,0.00014515474,0.0005067188,0.0003018912,0.4127553,0.22110014,0.123601355,0.0022839454,0.23625958],"study_design_scores_gemma":[0.000014457161,0.000103922444,0.00070506503,0.0000066368334,0.000020089343,0.0001612849,0.000038312945,0.94085914,0.016414477,0.039753046,0.0019082353,0.0000153414],"about_ca_topic_score_codex":0.00088822376,"about_ca_topic_score_gemma":0.0010093128,"teacher_disagreement_score":0.0014629443,"about_ca_system_score_codex":0.0002368007,"about_ca_system_score_gemma":0.0002929128,"threshold_uncertainty_score":0.004894018},"labels":[],"label_agreement":null},{"id":"W52590268","doi":"10.1007/978-0-8176-4542-7_16","title":"Statistical Models and Artificial Neural Networks: Supervised Classification and Prediction Via Soft Trees","year":2007,"lang":"en","type":"book-chapter","venue":"Birkhäuser Boston eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Artificial neural network; Computer science; Classifier (UML); Feed forward; Tree (set theory); Machine learning; Hierarchy; Decision tree; Feedforward neural network; Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.06831789885246671,"score_gpt":0.2621282077697763,"score_spread":0.1938103089173096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W52590268","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004357359,0.016673457,0.9573711,0.0020535581,0.00043030264,0.00002873203,0.00015885073,0.00039429293,0.018532395],"genre_scores_gemma":[0.2948858,0.043037903,0.6068759,0.0015908093,0.0027000876,0.00030880698,0.0009558164,0.00040052863,0.049244337],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932563,0.00023701464,0.00002972071,0.00009481258,0.00028478506,0.000028096769],"domain_scores_gemma":[0.99894804,0.0007204898,0.00007418568,0.000121954035,0.00010638978,0.000029028735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011222669,0.00079983013,0.0007608214,0.00086359883,0.00023434129,0.0014759212,0.0011362268,0.0011669786,0.003005903],"category_scores_gemma":[0.0030655612,0.0004153534,0.00067997025,0.0016582608,0.0013185336,0.0027537697,0.0010286542,0.0022985744,0.0014948979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003970064,0.00005201394,0.0006802039,0.00048397438,0.000104964085,0.00011063729,0.00018093357,0.1444766,0.001461734,0.50539964,0.025218267,0.32179144],"study_design_scores_gemma":[0.000004681608,0.000022657687,0.00040600132,0.00010744474,0.000014218683,0.00009080439,0.000020952999,0.41442752,0.00045601395,0.5655444,0.018885856,0.000019486937],"about_ca_topic_score_codex":0.00082899735,"about_ca_topic_score_gemma":0.0011174051,"teacher_disagreement_score":0.003005903,"about_ca_system_score_codex":0.00059644587,"about_ca_system_score_gemma":0.0004597356,"threshold_uncertainty_score":0.010055721},"labels":[],"label_agreement":null},{"id":"W52787789","doi":"","title":"A Characterization of the Generalized Lah Matrix.","year":2007,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Characterization (materials science); Matrix (chemical analysis); Pure mathematics; Materials science; Composite material; Nanotechnology","score_opus":0.010083486272936069,"score_gpt":0.24532784251585396,"score_spread":0.2352443562429179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W52787789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15574723,0.0029592046,0.5746195,0.0036053208,0.0012327674,0.00015345337,0.0020499122,0.0009927533,0.2586399],"genre_scores_gemma":[0.86894447,0.0014650276,0.06084802,0.0017620742,0.0015014232,0.0001777181,0.0013108973,0.00038632195,0.063604124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99954236,0.00011168141,0.000021831664,0.00009552226,0.0001524409,0.00007631984],"domain_scores_gemma":[0.99878913,0.00031788796,0.00019518372,0.00018024261,0.00029071144,0.00022682425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004060018,0.0005462041,0.0004350098,0.0019366357,0.0007555445,0.0019547215,0.0007958562,0.00079294597,0.01849081],"category_scores_gemma":[0.0028154603,0.00021690279,0.00040616983,0.0010197612,0.0016893985,0.00192638,0.001192259,0.0016088332,0.005012417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025308396,0.000018077319,0.00024273978,0.00004938773,0.000008166406,0.00011599428,0.00009759643,0.0010204694,0.0027666816,0.9754193,0.005923965,0.014312358],"study_design_scores_gemma":[0.000011361326,0.000029824572,0.00043128923,0.000022235552,0.0000053992417,0.00035841385,0.00007815011,0.0144203855,0.00080760644,0.9695693,0.014238764,0.00002733231],"about_ca_topic_score_codex":0.0008743178,"about_ca_topic_score_gemma":0.001200209,"teacher_disagreement_score":0.01849081,"about_ca_system_score_codex":0.00052030024,"about_ca_system_score_gemma":0.0005422183,"threshold_uncertainty_score":0.06185794},"labels":[],"label_agreement":null},{"id":"W577779381","doi":"","title":"Bayesian inference and maximum entropy methods in science and engineering : 31st international workshop on bayesian inference and maximum entropy mthods in science and engineering , Waterloo, Ontario, Canada, 9-16 July 2011","year":2011,"lang":"en","type":"book","venue":"American Institute of Physics eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Principle of maximum entropy; Inference; Bayesian inference; Science and engineering; Bayesian probability; Probabilistic logic; Entropy (arrow of time); Computer science; Statistical inference; Graphical model; Data science; Artificial intelligence; Mathematics; Statistics; Engineering; Physics; Engineering ethics","score_opus":0.015722222507054313,"score_gpt":0.2591005200633257,"score_spread":0.24337829755627136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W577779381","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006245587,0.069748364,0.87865454,0.0038998418,0.002781303,0.0000602006,0.00063792686,0.00062755716,0.04296576],"genre_scores_gemma":[0.03245741,0.098821096,0.6825814,0.0025881096,0.006627551,0.00042036374,0.0022202341,0.0015183251,0.17276555],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99824893,0.0004483134,0.00009150352,0.00029305767,0.0008599252,0.00005827144],"domain_scores_gemma":[0.99731135,0.0018811098,0.000076516684,0.0002546888,0.00040449313,0.00007187378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035732228,0.0014835537,0.0015901135,0.0023927086,0.00062149914,0.0029885152,0.0014575744,0.002040403,0.018821608],"category_scores_gemma":[0.006228307,0.0013783961,0.0013461084,0.003106782,0.0023159294,0.0031461855,0.0016957419,0.0042539407,0.008344644],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005722239,0.00005391997,0.00030089632,0.001018598,0.00015912719,0.00013297629,0.00021353234,0.012373934,0.0020737082,0.25583202,0.2481907,0.47959343],"study_design_scores_gemma":[0.00002228478,0.000041728614,0.001053843,0.000566813,0.00005783154,0.00042801735,0.00004218886,0.039076656,0.0023748532,0.5003625,0.455881,0.00009217372],"about_ca_topic_score_codex":0.002248767,"about_ca_topic_score_gemma":0.004532647,"teacher_disagreement_score":0.99775124,"about_ca_system_score_codex":0.0017423963,"about_ca_system_score_gemma":0.0014922724,"threshold_uncertainty_score":0.0629645},"labels":[],"label_agreement":null},{"id":"W587610692","doi":"","title":"Apprentissage machine efficace: theorie et pratique","year":2012,"lang":"fr","type":"dissertation","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning; Curse of dimensionality; Artificial neural network; Computational learning theory; Graph; Deep learning; Algorithm; Theoretical computer science; Unsupervised learning","score_opus":0.01846195377935125,"score_gpt":0.2935381554376455,"score_spread":0.2750762016582943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W587610692","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010024963,0.031078136,0.7850564,0.05400084,0.001780756,0.00015100683,0.00034845682,0.0004877569,0.11707167],"genre_scores_gemma":[0.4338268,0.0559329,0.43349534,0.015592239,0.012341207,0.0018600053,0.0007109908,0.0011612468,0.045079302],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9926185,0.0030618988,0.00037146464,0.0014174011,0.0020543681,0.00047643273],"domain_scores_gemma":[0.95998013,0.03289587,0.0009575802,0.0034732579,0.0022440804,0.00044905243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013778548,0.0024581265,0.0024964297,0.003222331,0.0023654103,0.00915461,0.0038074956,0.005745091,0.009039224],"category_scores_gemma":[0.05292339,0.0017360259,0.002395843,0.0030737896,0.021765998,0.026683895,0.007018369,0.018861681,0.002941937],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009318665,0.000016653908,0.00014197262,0.00009374076,0.000015771093,0.0000384868,0.0001098171,0.0027636942,0.000059190214,0.9879579,0.0022400415,0.0065535596],"study_design_scores_gemma":[0.0000099272,0.000011663682,0.00004662711,0.00006683778,0.000008638953,0.00006786054,0.000025679872,0.013136996,0.00009671051,0.9771213,0.009397492,0.000010260371],"about_ca_topic_score_codex":0.002923172,"about_ca_topic_score_gemma":0.0009842236,"teacher_disagreement_score":0.013778548,"about_ca_system_score_codex":0.004830054,"about_ca_system_score_gemma":0.0023647726,"threshold_uncertainty_score":0.072868824},"labels":[],"label_agreement":null},{"id":"W607307917","doi":"10.1007/978-1-4612-0209-7","title":"Asymptotic Methods in Probability and Statistics with Applications","year":2001,"lang":"en","type":"book","venue":"Birkhäuser Boston eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Statistics; Probability and statistics; Econometrics; Mathematics; Computer science","score_opus":0.025570736922565875,"score_gpt":0.2985992193838099,"score_spread":0.273028482461244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W607307917","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000941708,0.072048865,0.8811838,0.003955699,0.0035094882,0.00007198571,0.0002646779,0.0011881444,0.0368356],"genre_scores_gemma":[0.11377355,0.106604576,0.6935413,0.0049226168,0.020670773,0.0013423852,0.001040145,0.0017106207,0.056394022],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99474794,0.0029003166,0.000283414,0.00042633803,0.0015009467,0.00014104629],"domain_scores_gemma":[0.9865139,0.010022584,0.0004478373,0.0014072688,0.001405603,0.00020283223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071433,0.002186116,0.0027272382,0.0038889144,0.000772916,0.0024437227,0.001584842,0.002088167,0.009961105],"category_scores_gemma":[0.021718675,0.00070795027,0.001487324,0.004534676,0.0037859487,0.0033897625,0.0022578936,0.0068483716,0.0070782285],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002751238,0.00006437996,0.0003988907,0.00068284763,0.00010872396,0.00014136745,0.0002537671,0.007330219,0.0005097673,0.7347854,0.07243293,0.18326417],"study_design_scores_gemma":[0.000019605037,0.000044447002,0.00034196244,0.00023423613,0.000024627425,0.00024882614,0.000042538457,0.019183164,0.00024280816,0.87684566,0.102746576,0.000025554333],"about_ca_topic_score_codex":0.0014401363,"about_ca_topic_score_gemma":0.0010059879,"teacher_disagreement_score":0.009961105,"about_ca_system_score_codex":0.0014614396,"about_ca_system_score_gemma":0.0020130016,"threshold_uncertainty_score":0.03777784},"labels":[],"label_agreement":null},{"id":"W612421975","doi":"10.1016/j.amc.2015.05.122","title":"Modeling of complex dynamic systems using differential neural networks with the incorporation of a priori knowledge","year":2015,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"A priori and a posteriori; Artificial neural network; Computer science; Differential equation; Differential (mechanical device); System dynamics; Artificial intelligence; Mathematics; Engineering; Mathematical analysis","score_opus":0.05449450763336155,"score_gpt":0.2752602071076151,"score_spread":0.22076569947425356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W612421975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010746432,0.00024189458,0.98712766,0.000074128,0.000022374139,0.00001342732,0.00003088211,0.00013941679,0.0016038782],"genre_scores_gemma":[0.7501882,0.00140674,0.24337415,0.00008855879,0.000060294307,0.00020319561,0.00017429375,0.000058180613,0.004446413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998524,0.00003429128,0.0000115066205,0.000033876233,0.000056583853,0.000011222268],"domain_scores_gemma":[0.99971956,0.0001638143,0.000040658055,0.000029744424,0.000037037837,0.00000910652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039333946,0.00048070925,0.0005577924,0.00041661502,0.00027089156,0.00078658434,0.00081855187,0.0007924572,0.00080316275],"category_scores_gemma":[0.001022285,0.0003755261,0.00052922545,0.00045099022,0.00063896365,0.0013071788,0.00063496095,0.00074903236,0.00019101685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001064795,0.000009200491,0.00021010934,0.000035186942,0.000011880312,0.000027028058,0.000022131957,0.9788986,0.0012475427,0.007398177,0.00008592341,0.012043527],"study_design_scores_gemma":[7.36107e-7,0.0000026107912,0.000028801302,0.000001824995,0.0000014201557,0.000004281348,0.0000013238266,0.9980856,0.00018467329,0.0015233674,0.00016384896,0.0000014597215],"about_ca_topic_score_codex":0.0042322767,"about_ca_topic_score_gemma":0.0043156384,"teacher_disagreement_score":0.0042322767,"about_ca_system_score_codex":0.00061143615,"about_ca_system_score_gemma":0.0005262616,"threshold_uncertainty_score":0.008415282},"labels":[],"label_agreement":null},{"id":"W62244799","doi":"10.1007/978-94-007-4792-0_48","title":"Autonomous Robot with Internal Topological Representation","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Representation (politics); Computer science; Artificial intelligence; Robot; Internal model; Fidelity; Perceptron; Artificial neural network; Multilayer perceptron; Control (management)","score_opus":0.030973713260883252,"score_gpt":0.2530353420685709,"score_spread":0.22206162880768768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W62244799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012329674,0.0023518943,0.89030945,0.000499328,0.0005356747,0.000037957157,0.00020735578,0.0015270769,0.09220148],"genre_scores_gemma":[0.3557587,0.007140152,0.5281711,0.00030269672,0.00041456087,0.0002833571,0.0011150696,0.0005807926,0.106233485],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999366,0.000006726081,0.0000024836522,0.000013412958,0.000035025096,0.0000057315674],"domain_scores_gemma":[0.99995553,0.000012402508,0.000005331658,0.000011489132,0.000010804596,0.0000044003323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007827493,0.0003890568,0.00030977651,0.00033867353,0.00020067325,0.00058501307,0.00058537954,0.00041412364,0.0043659685],"category_scores_gemma":[0.0002221705,0.00022545934,0.0003535444,0.0004118742,0.00072811596,0.00094548526,0.000768204,0.00066744164,0.0013943014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034121193,0.000021803207,0.00016543837,0.000373992,0.000029724897,0.000111175614,0.00012616314,0.09476707,0.020307979,0.5280895,0.018267253,0.33770573],"study_design_scores_gemma":[0.000015559268,0.00009469625,0.00048835645,0.00009768583,0.000029350696,0.0003867444,0.000060555565,0.35628614,0.007932696,0.488511,0.14604877,0.00004848618],"about_ca_topic_score_codex":0.0003604041,"about_ca_topic_score_gemma":0.00048558423,"teacher_disagreement_score":0.0043659685,"about_ca_system_score_codex":0.00025452086,"about_ca_system_score_gemma":0.0003136272,"threshold_uncertainty_score":0.014605641},"labels":[],"label_agreement":null},{"id":"W647045934","doi":"10.1007/0-387-29485-6","title":"Methods and Procedures for the Verification and Validation of Artificial Neural Networks","year":2006,"lang":"en","type":"book","venue":"Kluwer Academic Publishers eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Goddard Space Flight Center; McGill University; Ames Research Center; National Aeronautics and Space Administration","keywords":"Artificial neural network; Computer science; Artificial intelligence; Machine learning; Time delay neural network; Probabilistic neural network; Data mining","score_opus":0.030465735916866948,"score_gpt":0.30877584750876036,"score_spread":0.2783101115918934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W647045934","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00050082034,0.0006452931,0.9948992,0.000090374655,0.000100205296,0.00010701105,0.00009616654,0.0009843223,0.002576572],"genre_scores_gemma":[0.021008937,0.001452076,0.97146714,0.00014816082,0.0001536075,0.00059049664,0.0004917339,0.00070718705,0.0039806147],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9798172,0.0051963725,0.0023881416,0.0022602968,0.009826216,0.0005117459],"domain_scores_gemma":[0.9666595,0.020633135,0.0015396365,0.005972262,0.004969875,0.0002256773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012789448,0.0023433787,0.0019440425,0.004627998,0.001950164,0.004536644,0.0048702494,0.0030654066,0.0089002885],"category_scores_gemma":[0.057454042,0.001903591,0.0040103104,0.002652304,0.0066270945,0.0046790126,0.004301822,0.006269775,0.004708996],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018848217,0.0001318844,0.00090145576,0.0016742214,0.0002657781,0.00049400865,0.00064293505,0.048499137,0.015297655,0.4877114,0.010015164,0.43417782],"study_design_scores_gemma":[0.00010391431,0.00013947906,0.0008288933,0.00083672313,0.00014570085,0.00073041394,0.0001302868,0.22304784,0.034496874,0.65992033,0.07943277,0.00018673636],"about_ca_topic_score_codex":0.0021328786,"about_ca_topic_score_gemma":0.0015853272,"teacher_disagreement_score":0.012789448,"about_ca_system_score_codex":0.001989241,"about_ca_system_score_gemma":0.0032777556,"threshold_uncertainty_score":0.06763786},"labels":[],"label_agreement":null},{"id":"W6887729111","doi":"10.17605/osf.io/jyp2z","title":"ivado-medical-imaging","year":2019,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Identification (biology); Training (meteorology); Black box; Feature (linguistics)","score_opus":0.006849553727905188,"score_gpt":0.2258677170854937,"score_spread":0.2190181633575885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6887729111","genre_codex":"dataset","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037414553,0.0038584806,0.006155635,0.0009885079,0.00045598924,0.000499224,0.9680117,0.008388691,0.00790031],"genre_scores_gemma":[0.005705455,0.0008686846,0.0065908744,0.00027423672,0.0001093119,0.0003180058,0.98306966,0.0006695019,0.0023943386],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986511,0.00023321125,0.00016019144,0.00031534856,0.0004669127,0.00017318645],"domain_scores_gemma":[0.9970644,0.00061046553,0.00027440026,0.00095110375,0.0007502862,0.00034934204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018164193,0.0031111285,0.0028171088,0.0048204893,0.0008395344,0.0025599368,0.0060387864,0.004247565,0.039950076],"category_scores_gemma":[0.006899988,0.001052569,0.0016790895,0.004393638,0.00074188085,0.0009871724,0.0025515142,0.0024271046,0.050176386],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003439261,0.00016516462,0.0013371095,0.0015091517,0.00016626249,0.0002706423,0.00003264795,0.0020041051,0.0016849209,0.0010232743,0.97128624,0.020176606],"study_design_scores_gemma":[0.0009748768,0.0003067732,0.009736109,0.0012047337,0.00026776787,0.0036369748,0.00013930353,0.013584214,0.007347319,0.005372243,0.9572413,0.00018829119],"about_ca_topic_score_codex":0.024359966,"about_ca_topic_score_gemma":0.04868748,"teacher_disagreement_score":0.039950076,"about_ca_system_score_codex":0.0018262941,"about_ca_system_score_gemma":0.003866828,"threshold_uncertainty_score":0.13364631},"labels":[],"label_agreement":null},{"id":"W6893209086","doi":"10.5281/zenodo.14929706","title":"Clinocera abbreviata Sinclair & Plant, 2022, sp. nov.","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Food Inspection Agency","funders":"","keywords":"Summit; Chiang mai; Malaise; Population","score_opus":0.03574963081531999,"score_gpt":0.24078561941775606,"score_spread":0.20503598860243608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893209086","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21355371,0.06350825,0.026573025,0.004854081,0.009071589,0.004602625,0.07598435,0.002726059,0.5991263],"genre_scores_gemma":[0.7270688,0.034916315,0.04793469,0.00701066,0.0015868596,0.004462113,0.04922598,0.00053226436,0.1272623],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992557,0.00006748867,0.00011973856,0.00025165596,0.00020309328,0.000102334736],"domain_scores_gemma":[0.9989453,0.00017939888,0.00026344764,0.00013739125,0.00033501803,0.00013951732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005059468,0.0023346515,0.00105491,0.003058388,0.0031143762,0.0016508225,0.0013438546,0.0020447946,0.013929972],"category_scores_gemma":[0.0017108414,0.0005967641,0.00048190207,0.005303117,0.0011219617,0.0032978456,0.0012674937,0.0022280652,0.013557768],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005341545,0.00019410982,0.03402545,0.0018359635,0.0001700399,0.006518243,0.002007693,0.002719876,0.011604095,0.0026400005,0.14954889,0.7882015],"study_design_scores_gemma":[0.00028049754,0.00029668122,0.20743941,0.0023910098,0.0004096255,0.018952059,0.003946074,0.001965147,0.002843042,0.002022139,0.7592936,0.00016074089],"about_ca_topic_score_codex":0.03986503,"about_ca_topic_score_gemma":0.06904651,"teacher_disagreement_score":0.03986503,"about_ca_system_score_codex":0.0015644819,"about_ca_system_score_gemma":0.0015277389,"threshold_uncertainty_score":0.07926595},"labels":[],"label_agreement":null},{"id":"W6920963302","doi":"10.6084/m9.figshare.19802438.v1","title":"Additional file 5 of Data-sharing and re-analysis for main studies assessed by the European Medicines Agency—a cross-sectional study on European Public Assessment Reports","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Table (database); Risk assessment; European union; Public access; Public health; Conformity assessment","score_opus":0.23676884461420325,"score_gpt":0.38728936767920563,"score_spread":0.15052052306500238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920963302","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001354897,0.000043076136,0.0003405158,0.00010516094,0.000031299696,0.0002784534,0.9982015,0.00018848198,0.0006759233],"genre_scores_gemma":[0.014731573,0.00046937788,0.011277436,0.0015420006,0.00024304198,0.017502008,0.93635386,0.0014385863,0.016442062],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9939756,0.001523204,0.0021953194,0.0009872053,0.00082995236,0.0004887855],"domain_scores_gemma":[0.8416736,0.13156813,0.009158572,0.00715551,0.009180294,0.0012638932],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008640423,0.0015295292,0.0025569675,0.007875775,0.0011669692,0.002844451,0.0023680562,0.0018743424,0.87097514],"category_scores_gemma":[0.12793162,0.00089168054,0.0024430393,0.01081954,0.0005257832,0.0028058912,0.0021653168,0.0012954978,0.10347843],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089414965,0.00009507854,0.003051853,0.025663361,0.0005148492,0.000107939464,0.00013722015,0.00076311023,0.00017489605,0.002137556,0.9524091,0.01405076],"study_design_scores_gemma":[0.013642538,0.0004620315,0.03356512,0.024228146,0.0018162914,0.0006532108,0.0008060025,0.0025781922,0.0009859196,0.026549103,0.8943644,0.00034896232],"about_ca_topic_score_codex":0.007567727,"about_ca_topic_score_gemma":0.012430503,"teacher_disagreement_score":0.9913596,"about_ca_system_score_codex":0.002148116,"about_ca_system_score_gemma":0.0043600546,"threshold_uncertainty_score":0.18403822},"labels":[],"label_agreement":null},{"id":"W6926227889","doi":"10.22008/fk2/wp1tr3/uds13t","title":"wi20cnd.rtl","year":2021,"lang":"en","type":"dataset","venue":"Geological Survey of Denmark and Greenland (GEUS)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Apotex Pharmachem (Canada)","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","score_opus":0.034588214834550636,"score_gpt":0.2630440614428012,"score_spread":0.22845584660825055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926227889","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000119397024,0.000053353262,0.00006606986,0.000043892636,0.000036897596,0.000011818163,0.9982268,0.00067719474,0.0007646614],"genre_scores_gemma":[0.00022105285,0.00003057433,0.0001744516,0.000021191132,0.000008035464,0.000042160358,0.99876654,0.00006909113,0.00066695194],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999052,0.00015740015,0.000082633844,0.00030969214,0.00019341634,0.00020483813],"domain_scores_gemma":[0.99885154,0.00019046794,0.000097688426,0.00040566523,0.0002831192,0.00017155417],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009227193,0.0039942754,0.0023716153,0.004451629,0.0010191274,0.0027422456,0.005067544,0.002941074,0.11148946],"category_scores_gemma":[0.003732082,0.0009568202,0.0016960397,0.0067130392,0.00064984715,0.0017337417,0.0024030819,0.002052494,0.22004694],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026566957,0.000014966875,0.00017232413,0.00019109629,0.000011458485,0.000007939605,0.0000062630215,0.00014530921,0.00003701876,0.00014491496,0.9980646,0.0011775363],"study_design_scores_gemma":[0.00032320744,0.000029321123,0.0021687374,0.00021092774,0.000029037174,0.000058093097,0.000092785864,0.0010689069,0.00036221865,0.0011911164,0.9944348,0.00003088457],"about_ca_topic_score_codex":0.035738315,"about_ca_topic_score_gemma":0.07037298,"teacher_disagreement_score":0.8885105,"about_ca_system_score_codex":0.0013416039,"about_ca_system_score_gemma":0.0019123117,"threshold_uncertainty_score":0.3729694},"labels":[],"label_agreement":null},{"id":"W6929774190","doi":"10.48660/09050039","title":"Hamiltonian structure of isomonodromic deformations of rational connections on the Riemann sphere","year":2009,"lang":"en","type":"other","venue":"PIRSA","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Riemann sphere; Riemann surface; Hamiltonian system; Hamiltonian (control theory); Action (physics)","score_opus":0.010519001713384452,"score_gpt":0.22503193186060788,"score_spread":0.21451293014722342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929774190","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8592893,0.00045224538,0.06487582,0.00079277577,0.00017369378,0.00003936604,0.0001864511,0.00021905666,0.07397129],"genre_scores_gemma":[0.9827663,0.00015794,0.0044565233,0.000102978265,0.00013509413,0.0000207916,0.00010539841,0.0000735049,0.012181587],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997938,0.000046477697,0.000008167002,0.000045496745,0.00005925677,0.00004679619],"domain_scores_gemma":[0.99966645,0.000060246173,0.0000702136,0.00005328488,0.000047563117,0.0001023359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027596837,0.00044800158,0.0004775292,0.0011862484,0.0007782821,0.0014203766,0.0008707731,0.0009310339,0.0073634516],"category_scores_gemma":[0.00090862514,0.00026567394,0.0003752597,0.00045375197,0.0018624752,0.0016310324,0.0009372436,0.0010106142,0.00066460215],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028727385,0.000023184279,0.00023520974,0.000015806023,0.0000076370125,0.00008488897,0.000103176586,0.0042629726,0.001992635,0.9884016,0.0007771013,0.004067069],"study_design_scores_gemma":[0.000017442864,0.000026391774,0.00056122354,0.0000066386788,0.000003550089,0.00009910275,0.00007030433,0.03596114,0.00035166467,0.9618469,0.0010402646,0.00001553371],"about_ca_topic_score_codex":0.00088407466,"about_ca_topic_score_gemma":0.0012168185,"teacher_disagreement_score":0.0073634516,"about_ca_system_score_codex":0.00075584557,"about_ca_system_score_gemma":0.00041567345,"threshold_uncertainty_score":0.02463317},"labels":[],"label_agreement":null},{"id":"W6930245306","doi":"10.5281/zenodo.12258160","title":"junqueiras basic histology 15th edition pdf free download","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; TSG101; Dysgeusia; Hyporeflexia; Paraphernalia; Liquation","score_opus":0.023648017213054404,"score_gpt":0.23588199566124365,"score_spread":0.21223397844818925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930245306","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002566714,0.004850441,0.03446046,0.0021914837,0.0060701673,0.00059795094,0.02021189,0.026348613,0.9027023],"genre_scores_gemma":[0.004631631,0.0033623334,0.031165365,0.0008747543,0.0007633709,0.0003901383,0.015541951,0.014365725,0.92890465],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986028,0.00008876412,0.000109686334,0.00021721312,0.0008440898,0.00013748634],"domain_scores_gemma":[0.9973794,0.00021709956,0.00008444595,0.00054359867,0.0014657618,0.00030978877],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0018918954,0.0013786979,0.0012114196,0.004660013,0.002012089,0.0038270296,0.0020615198,0.0017686522,0.71915233],"category_scores_gemma":[0.002759506,0.0017553855,0.0014503435,0.0024944083,0.00083899446,0.0035156773,0.0025558078,0.003003138,0.6554456],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013629739,0.000048916012,0.0002974706,0.00093506207,0.000024299386,0.00030858105,0.000114469694,0.00015959326,0.028149264,0.004484195,0.8259134,0.13942835],"study_design_scores_gemma":[0.000011659553,0.000018056653,0.0005669094,0.00015002026,0.000011675285,0.00048729588,0.00004985431,0.00010173926,0.0042907028,0.0007726658,0.99352163,0.000017771625],"about_ca_topic_score_codex":0.0033375747,"about_ca_topic_score_gemma":0.009427753,"teacher_disagreement_score":0.28084767,"about_ca_system_score_codex":0.0013738889,"about_ca_system_score_gemma":0.0021420186,"threshold_uncertainty_score":0.40059507},"labels":[],"label_agreement":null},{"id":"W6930249818","doi":"10.5281/zenodo.12084585","title":"Google pixel 7 bedienungsanleitung deutsch pdf download","year":2024,"lang":"de","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Durvalumab; Dysgeusia; Hemopericardium; Subpoena; Hyporeflexia","score_opus":0.02931097598526616,"score_gpt":0.24916288453299923,"score_spread":0.21985190854773307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930249818","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003972408,0.0013416784,0.0017487148,0.0006581944,0.0012769719,0.00020340271,0.01659709,0.018460833,0.95931596],"genre_scores_gemma":[0.0022499969,0.001405868,0.0013035351,0.0007914442,0.0004654347,0.00013082109,0.014684595,0.010872158,0.9680962],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994319,0.000032582364,0.000025943627,0.00008088366,0.00031203884,0.000116572824],"domain_scores_gemma":[0.9985007,0.00019249189,0.00005706477,0.0002259514,0.0006682408,0.00035560742],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00046664343,0.001965895,0.0014862636,0.0030262102,0.0015589144,0.007929576,0.0018649829,0.0022555883,0.9198817],"category_scores_gemma":[0.0026426052,0.00087466935,0.0013357105,0.0034103345,0.0006470941,0.007918769,0.003836352,0.0018096992,0.91951144],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033870387,0.000013395582,0.000036649417,0.00019080905,0.00000236068,0.000041536718,0.000031080857,0.00003591448,0.00021899222,0.0009880693,0.97032577,0.028081553],"study_design_scores_gemma":[0.000011165309,0.000012218426,0.00017942744,0.00006489606,0.000003393698,0.0000646131,0.000054270025,0.00004978084,0.000273207,0.00058321323,0.9986939,0.000009978002],"about_ca_topic_score_codex":0.0047944095,"about_ca_topic_score_gemma":0.008193996,"teacher_disagreement_score":0.0801183,"about_ca_system_score_codex":0.0011585718,"about_ca_system_score_gemma":0.0011123811,"threshold_uncertainty_score":0.11427897},"labels":[],"label_agreement":null},{"id":"W6931138468","doi":"10.5281/zenodo.15792951","title":"Raw SEM and XRD Data Supporting the Use of Glass Fiber Filters for Particulate Phosphorus Measurements","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Scanning electron microscope; Glass fiber; Particulates; Fiber; Phosphorus; Analytical Chemistry (journal)","score_opus":0.15571432406536762,"score_gpt":0.3045600084735,"score_spread":0.1488456844081324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931138468","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014807442,0.00008953058,0.0021993993,0.000103826984,0.00014602029,0.00014504183,0.9874822,0.0009205797,0.007432589],"genre_scores_gemma":[0.0040003275,0.00045367904,0.007665216,0.00015846557,0.00003759374,0.0005904658,0.97895586,0.001451775,0.0066866633],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99732876,0.00012925136,0.0003952407,0.00048136883,0.0014547646,0.0002106626],"domain_scores_gemma":[0.9927166,0.0016201155,0.00045877797,0.0010837134,0.003922753,0.00019802072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022504688,0.0016755133,0.0016401315,0.0060907053,0.001861411,0.0020697974,0.0022189706,0.0015044312,0.2562625],"category_scores_gemma":[0.0075953924,0.0007612036,0.0012648875,0.010625112,0.00045017543,0.0019188921,0.0016943862,0.0018071652,0.10962769],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041842306,0.00009110683,0.004299262,0.0040588193,0.000078266086,0.00026499858,0.00016435976,0.0010237957,0.025210105,0.0012600807,0.9204638,0.042666964],"study_design_scores_gemma":[0.000061152336,0.000048023467,0.009693207,0.00034299656,0.000060855913,0.0002173255,0.00019808256,0.00039662689,0.018840136,0.0011326553,0.96895623,0.000052605115],"about_ca_topic_score_codex":0.0089060115,"about_ca_topic_score_gemma":0.02104128,"teacher_disagreement_score":0.2562625,"about_ca_system_score_codex":0.0015969968,"about_ca_system_score_gemma":0.004415164,"threshold_uncertainty_score":0.8572835},"labels":[],"label_agreement":null},{"id":"W6931151742","doi":"10.5281/zenodo.4385665","title":"Liogenys martinezi Cherman & Basílio & Mise & Frisch & Smith & Almeida 2020, new species","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Museum of Nature","funders":"","keywords":"Paratype; Holotype; Type (biology); Type (biology); Sequence (biology)","score_opus":0.04424341521597876,"score_gpt":0.23047133409403286,"score_spread":0.18622791887805412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931151742","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14780942,0.06365216,0.008968138,0.0026700709,0.0026634864,0.0013758456,0.0074220216,0.0025994543,0.76283944],"genre_scores_gemma":[0.76787144,0.034109328,0.023287741,0.0022760292,0.0024479493,0.0010849659,0.008879813,0.00027071842,0.15977192],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997968,0.00001645062,0.0000136139515,0.000051585554,0.000085195104,0.00003640104],"domain_scores_gemma":[0.99979836,0.000027707052,0.00006014505,0.000019827226,0.000066168766,0.000027766913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024670683,0.0008061251,0.0004910738,0.0018948916,0.0017102113,0.00057668675,0.0011764892,0.0005864624,0.017800245],"category_scores_gemma":[0.00040350747,0.0002590152,0.00023897857,0.0016578331,0.0010055553,0.0012696305,0.00072302,0.0008809415,0.004844065],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005460183,0.00008682797,0.017448341,0.0007600788,0.00006145557,0.0017928083,0.0016693021,0.0005437527,0.0058975774,0.008918534,0.08910864,0.87316674],"study_design_scores_gemma":[0.000097668395,0.0001697126,0.102152646,0.00059970474,0.0001461993,0.0056183697,0.0011950479,0.00078940886,0.0008362689,0.0020229006,0.88632274,0.000049344268],"about_ca_topic_score_codex":0.042113375,"about_ca_topic_score_gemma":0.1072874,"teacher_disagreement_score":0.042113375,"about_ca_system_score_codex":0.0018112487,"about_ca_system_score_gemma":0.0017300665,"threshold_uncertainty_score":0.08373648},"labels":[],"label_agreement":null},{"id":"W6931503201","doi":"10.5281/zenodo.5085228","title":"FIGURES 29–36 in Comparison of two new species of Mastogloia (Bacillariophyceae) with other small members of section Ellipticae","year":2013,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Museum of Nature","funders":"","keywords":"Raphe; Apex (geometry); Terminal (telecommunication); Section (typography); Row","score_opus":0.04008180053240891,"score_gpt":0.2496452318822773,"score_spread":0.20956343134986838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931503201","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8955567,0.007492888,0.005604375,0.00042049072,0.00053591013,0.00021190959,0.00535467,0.00045088454,0.0843722],"genre_scores_gemma":[0.97480667,0.0012714365,0.0075315502,0.00017864782,0.00008352096,0.000076706754,0.0041747084,0.00009724721,0.0117794555],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999504,0.0000048993775,0.0000042315587,0.00002279673,0.000008620394,0.0000091930815],"domain_scores_gemma":[0.9999317,0.000016134796,0.000021867929,0.0000067354204,0.000009331952,0.000014359785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000046445377,0.0002454563,0.00018942801,0.00096009084,0.0005221499,0.00029771824,0.00025164633,0.00037260944,0.009578163],"category_scores_gemma":[0.00018080143,0.00012430333,0.00023448165,0.0006108036,0.000275366,0.00035131653,0.000321795,0.00031544775,0.001473642],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024859963,0.00013326974,0.043642703,0.0020542983,0.00012828765,0.00615693,0.0033908598,0.0010651558,0.74709177,0.002698687,0.0072690966,0.18388294],"study_design_scores_gemma":[0.000105790365,0.00019014139,0.6519167,0.00018258952,0.00009984557,0.006507721,0.0016044871,0.000996568,0.014347961,0.0007848874,0.32322803,0.000035193825],"about_ca_topic_score_codex":0.0025482883,"about_ca_topic_score_gemma":0.004792078,"teacher_disagreement_score":0.009578163,"about_ca_system_score_codex":0.00034237566,"about_ca_system_score_gemma":0.00009570697,"threshold_uncertainty_score":0.032042146},"labels":[],"label_agreement":null},{"id":"W6931826788","doi":"10.5281/zenodo.4928859","title":"FIGURE 6 in Twenty years of Dipterology through the pages of Zootaxa","year":2021,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; Canadian Food Inspection Agency","funders":"","keywords":"Line drawings; Line (geometry); Linear relationship","score_opus":0.03508980983117838,"score_gpt":0.2536242117122183,"score_spread":0.2185344018810399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931826788","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007310285,0.08261085,0.007925811,0.021514751,0.02792601,0.00013707588,0.038961682,0.004641153,0.80897236],"genre_scores_gemma":[0.036175493,0.063378595,0.014350125,0.004129032,0.0033741163,0.0001224549,0.038179312,0.0019284135,0.83836246],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993574,0.00007166728,0.000050421153,0.00015769186,0.0002883653,0.00007448594],"domain_scores_gemma":[0.9984493,0.00015017799,0.00016782958,0.00013346996,0.0006987713,0.00040041964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009010989,0.00070271664,0.00031003891,0.0053671994,0.0009520407,0.0032481162,0.0004989129,0.00061575643,0.2384737],"category_scores_gemma":[0.0019905695,0.0002978673,0.0003871953,0.008279937,0.00064757845,0.0037671672,0.0017047176,0.0015702143,0.101459794],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057921912,0.000011830564,0.0017138848,0.00049113104,0.000014506755,0.000055362958,0.00015568576,0.00011493349,0.00074378494,0.012252777,0.79682994,0.18755819],"study_design_scores_gemma":[0.0000015103855,0.0000032223,0.0019159577,0.00007353888,0.0000024383278,0.00006202939,0.000051229596,0.000017470265,0.00005790703,0.0005115312,0.9973002,0.0000029436515],"about_ca_topic_score_codex":0.00640066,"about_ca_topic_score_gemma":0.018987015,"teacher_disagreement_score":0.2384737,"about_ca_system_score_codex":0.0011955178,"about_ca_system_score_gemma":0.0017182122,"threshold_uncertainty_score":0.797774},"labels":[],"label_agreement":null},{"id":"W6977272279","doi":"10.6084/m9.figshare.12875886","title":"Additional file 6 of The testosterone-dependent and independent transcriptional networks in the hypothalamus of Gpr54 and Kiss1 knockout male mice are not fully equivalent","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Coastal Health Research Institute; University of British Columbia; BC Cancer Agency","funders":"Biotechnology and Biological Sciences Research Council","keywords":"Gene; Set (abstract data type); Hypothalamus; Transcriptome; Table (database); Knockout mouse","score_opus":0.039458530447699224,"score_gpt":0.21203848023418526,"score_spread":0.17257994978648603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977272279","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025820878,0.000030863623,0.0008833575,0.00009697764,0.00006665277,0.000042022544,0.9962379,0.0014587449,0.00092535786],"genre_scores_gemma":[0.0089222,0.00021580842,0.007929675,0.0007156848,0.00012341942,0.0012485258,0.96424717,0.003878681,0.012718827],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995084,0.000054044005,0.000057059962,0.00013165266,0.0001702549,0.000078602745],"domain_scores_gemma":[0.9926246,0.005068722,0.0003528419,0.00056532555,0.001111349,0.00027717193],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009072298,0.0012093196,0.0015574256,0.0017380584,0.0009094173,0.0015142969,0.0019311468,0.001220232,0.8754311],"category_scores_gemma":[0.008781009,0.0007701556,0.001088315,0.0021798168,0.00031954705,0.0014724209,0.00089359423,0.0012855754,0.24097487],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004197524,0.00012785876,0.0018096257,0.0027141823,0.00006876655,0.00012244358,0.000068856454,0.00060295284,0.0015576289,0.0007417688,0.9795965,0.012169662],"study_design_scores_gemma":[0.0043810722,0.000541622,0.03871658,0.0031151418,0.00032414892,0.0011830671,0.00043118023,0.0048387744,0.009623368,0.01718278,0.9193571,0.0003051752],"about_ca_topic_score_codex":0.0051790494,"about_ca_topic_score_gemma":0.00932131,"teacher_disagreement_score":0.8754311,"about_ca_system_score_codex":0.00088658766,"about_ca_system_score_gemma":0.0013252251,"threshold_uncertainty_score":0.1776824},"labels":[],"label_agreement":null},{"id":"W6978007134","doi":"10.6084/m9.figshare.c.7090104.v2","title":"Supplementary material from \"Global indicators of the environmental impacts of invasive alien species and their information adequacy\"","year":2024,"lang":"en","type":"other","venue":"Figshare","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Biodiversity; Impact assessment; Warning system; Invasive species; Introduced species; Sustainability; Stewardship (theology); Risk assessment; Climate change","score_opus":0.009184845946380136,"score_gpt":0.2027113402855735,"score_spread":0.19352649433919336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6978007134","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015323012,0.00021712112,0.015503155,0.0014292925,0.00091161905,0.00021227889,0.89458686,0.0049944767,0.08061287],"genre_scores_gemma":[0.022229206,0.0007357622,0.038996115,0.0014134779,0.00035270237,0.0011209472,0.81557,0.0044130683,0.11516862],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99939847,0.00014220823,0.00007113989,0.0001049387,0.00022739658,0.000055770113],"domain_scores_gemma":[0.9932961,0.003774058,0.00028831357,0.0006201531,0.0017591619,0.00026211274],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012899749,0.0007047807,0.00040734315,0.002004676,0.0004957367,0.0015284583,0.0012403221,0.0008903506,0.7322595],"category_scores_gemma":[0.013889592,0.00030658225,0.00050194206,0.0032296358,0.0002454495,0.0016405352,0.0014647201,0.000617612,0.24500673],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004152665,0.000021466629,0.0004215971,0.00033771215,0.000009565377,0.000022096132,0.000022338374,0.00044403327,0.00012000056,0.0021268388,0.96559006,0.030842822],"study_design_scores_gemma":[0.00011228745,0.00004451072,0.005266623,0.00040827008,0.00002020934,0.00014209998,0.000092974755,0.0039586276,0.00096811634,0.013743466,0.97519094,0.000051829342],"about_ca_topic_score_codex":0.006701048,"about_ca_topic_score_gemma":0.014663702,"teacher_disagreement_score":0.7322595,"about_ca_system_score_codex":0.0009521246,"about_ca_system_score_gemma":0.0011944012,"threshold_uncertainty_score":0.3818993},"labels":[],"label_agreement":null},{"id":"W6979279185","doi":"","title":"A universal approximation theorem and its applications to vector lattice theory","year":2025,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Division of Mathematical Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Approximation theory; Function approximation; High frequency approximation; Artificial neural network; Approximation algorithm; Lattice (music)","score_opus":0.03214670854250632,"score_gpt":0.19211792776080877,"score_spread":0.15997121921830246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979279185","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05368774,0.0032231316,0.8871322,0.0034091074,0.00041624252,0.000036260313,0.000273441,0.0003437384,0.051478088],"genre_scores_gemma":[0.83442855,0.0030699365,0.1406469,0.0011965289,0.00090602174,0.00016635121,0.00046431573,0.00028019628,0.018841201],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986572,0.0004291285,0.00006199667,0.000224067,0.0004721726,0.00015541966],"domain_scores_gemma":[0.9977235,0.00131605,0.00016276399,0.0002910223,0.0003197465,0.00018701676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018846015,0.0005241363,0.0009766564,0.0016913096,0.0012201344,0.00205433,0.0017642456,0.0009610485,0.0044544274],"category_scores_gemma":[0.007323004,0.00038360798,0.001177262,0.0016502826,0.003626381,0.00410759,0.0038037912,0.0030984064,0.00069250574],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009222979,0.00000924243,0.000095547184,0.000022796768,0.000006648976,0.000016577473,0.000042803822,0.0028866748,0.00017530531,0.9915876,0.00062037073,0.004527241],"study_design_scores_gemma":[0.0000075514913,0.0000123732925,0.00006889458,0.000015704893,0.000004003623,0.000046955418,0.00002603393,0.054101985,0.00023155243,0.94321805,0.0022597131,0.000007170564],"about_ca_topic_score_codex":0.002675397,"about_ca_topic_score_gemma":0.001482558,"teacher_disagreement_score":0.0044544274,"about_ca_system_score_codex":0.0015849672,"about_ca_system_score_gemma":0.000745632,"threshold_uncertainty_score":0.014901578},"labels":[],"label_agreement":null},{"id":"W6987828393","doi":"","title":"Understanding Neural Networks Properties by Testing and Specification Mining","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial neural network; Feature (linguistics); Key (lock); Field (mathematics); Expert system","score_opus":0.08982339094331582,"score_gpt":0.24519479283783488,"score_spread":0.15537140189451906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987828393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10435053,0.0003744998,0.8820834,0.0022249692,0.00006412539,0.00012777532,0.0008584174,0.0028422056,0.007074053],"genre_scores_gemma":[0.744617,0.00048486932,0.24841289,0.0003665159,0.00007423175,0.00019745168,0.001966426,0.0005986538,0.0032819416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968907,0.0010816011,0.00022456706,0.00058585114,0.000998201,0.00021897788],"domain_scores_gemma":[0.9500767,0.040569905,0.0019459858,0.003912768,0.0031598427,0.00033475863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030421922,0.0006104684,0.0005093698,0.0010730987,0.00047498397,0.0020201767,0.0015463636,0.001029241,0.00554837],"category_scores_gemma":[0.04215538,0.00058193604,0.0011963437,0.00072092214,0.0015263791,0.0055646873,0.001094165,0.0021839133,0.0008640033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005124792,0.00047796473,0.03073814,0.00081356463,0.00022958951,0.0007230272,0.00069721165,0.2675677,0.016177425,0.25161347,0.014698003,0.41575143],"study_design_scores_gemma":[0.000027594011,0.000060424518,0.0020043927,0.0000704377,0.000044040302,0.00008536816,0.00012380938,0.8002218,0.010029637,0.18424895,0.003063542,0.000020121819],"about_ca_topic_score_codex":0.0049424646,"about_ca_topic_score_gemma":0.008898788,"teacher_disagreement_score":0.00554837,"about_ca_system_score_codex":0.0011284534,"about_ca_system_score_gemma":0.0014344212,"threshold_uncertainty_score":0.018561125},"labels":[],"label_agreement":null},{"id":"W6999201060","doi":"","title":"A comparison of the Kalman filter and recurrent neural networks for state estimation of dynamical systems","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Kalman filter; Estimator; Recurrent neural network; Linear dynamical system; Artificial neural network; Dynamical systems theory; State (computer science); Extended Kalman filter; Noise (video)","score_opus":0.020510297862870494,"score_gpt":0.2536936602393816,"score_spread":0.2331833623765111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6999201060","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016766487,0.0033557476,0.9742933,0.00033146222,0.00015175987,0.0000351789,0.000075001735,0.0004506869,0.0045402744],"genre_scores_gemma":[0.55272704,0.0068493406,0.43355855,0.00022846906,0.00028854597,0.0001590337,0.00037964951,0.0001851067,0.0056243245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871063,0.0005363582,0.000086077314,0.00022514552,0.00037378512,0.00006799477],"domain_scores_gemma":[0.9957337,0.0030924426,0.00018439296,0.00028361965,0.0006597509,0.000046249857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034767466,0.0006653753,0.00090865116,0.00061811425,0.00026993017,0.0010426973,0.0007815206,0.0009818176,0.0018640591],"category_scores_gemma":[0.012084196,0.00032530216,0.0005471423,0.00077958265,0.00040299317,0.0026163668,0.0005689269,0.000897044,0.00038564482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004539618,0.00007630979,0.0027563325,0.00034256003,0.00025186353,0.000057841662,0.00020157931,0.53723234,0.0027010182,0.06919463,0.0014167601,0.38531494],"study_design_scores_gemma":[0.000012602117,0.00005622511,0.00079588575,0.000031993754,0.000032897937,0.000016153946,0.000018026823,0.99017346,0.0009006659,0.0064927707,0.0014538505,0.000015447657],"about_ca_topic_score_codex":0.015390078,"about_ca_topic_score_gemma":0.012138802,"teacher_disagreement_score":0.015390078,"about_ca_system_score_codex":0.0009599238,"about_ca_system_score_gemma":0.0011134783,"threshold_uncertainty_score":0.030600965},"labels":[],"label_agreement":null},{"id":"W7000015469","doi":"","title":"D'une assise locale à un réseau régional : élites et institutions dans la région de Lanaudière (1825-1865)","year":2012,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Continuing education; Context (archaeology); Legislation; Control (management); Institution","score_opus":0.008756196650242975,"score_gpt":0.17830009138176295,"score_spread":0.16954389473151998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7000015469","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24189346,0.021524934,0.013351227,0.012033591,0.0016691639,0.00006852297,0.004754083,0.00045100792,0.704254],"genre_scores_gemma":[0.46195936,0.0052569117,0.00844876,0.00042884742,0.0003625787,0.000040096525,0.001717226,0.0002035884,0.52158266],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995697,0.00008243182,0.000016322629,0.0001223526,0.00010217592,0.000107033906],"domain_scores_gemma":[0.9997117,0.00006762245,0.00005909463,0.000032565047,0.000080406106,0.000048602036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044317642,0.00043615763,0.0003206319,0.0012644328,0.0035292243,0.0031779336,0.000510452,0.0010567216,0.030495541],"category_scores_gemma":[0.0012914046,0.00023296765,0.0004885936,0.002753825,0.0024256017,0.0018692146,0.00148356,0.0013721569,0.0045340885],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052694016,0.0000662364,0.03096552,0.00059468194,0.00010377596,0.0023132726,0.018707765,0.004549078,0.0030182155,0.54119444,0.12705141,0.27090874],"study_design_scores_gemma":[0.000018251958,0.000028289765,0.032581985,0.00015081695,0.000023053282,0.00061503745,0.0028199288,0.00069958536,0.0011713225,0.0072844783,0.95457953,0.000027771153],"about_ca_topic_score_codex":0.22006093,"about_ca_topic_score_gemma":0.426663,"teacher_disagreement_score":0.77993906,"about_ca_system_score_codex":0.0067911674,"about_ca_system_score_gemma":0.003729459,"threshold_uncertainty_score":0.43756008},"labels":[],"label_agreement":null},{"id":"W7007999199","doi":"","title":"ARTIFICIAL NEURAL NETWORK (ANN) APPLIED TO THE SRC DIAGNOSTIC TOOL","year":2002,"lang":"en","type":"dissertation","venue":"University Library (University of Saskatchewan)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial neural network; Set (abstract data type); Process (computing); Data set; Component (thermodynamics); Range (aeronautics); Time delay neural network","score_opus":0.008391436884397736,"score_gpt":0.166883435134598,"score_spread":0.15849199825020027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7007999199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2685364,0.00047228078,0.7050053,0.0007752842,0.00031648128,0.0008422829,0.00056058477,0.0024163567,0.021075133],"genre_scores_gemma":[0.6940624,0.00034280497,0.29913333,0.00016024023,0.000040896713,0.00041041416,0.0004695873,0.000094112824,0.005286194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671763,0.0016624033,0.00022771223,0.00041306228,0.0008401126,0.00013902708],"domain_scores_gemma":[0.9931938,0.0044189724,0.00047469707,0.0003019697,0.0015442773,0.00006631155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004164227,0.00093868515,0.0005434811,0.0017084358,0.00040110652,0.0013959236,0.0010345692,0.0009996664,0.0027922501],"category_scores_gemma":[0.019153366,0.00037885507,0.00064946385,0.0019430491,0.00035869319,0.0013581818,0.0008964438,0.0010138433,0.000568012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037778888,0.00031591117,0.019133372,0.00036327494,0.00016140942,0.00040853277,0.00033946196,0.719251,0.0032320467,0.011520307,0.0029406308,0.2419563],"study_design_scores_gemma":[0.000011498052,0.00010828395,0.0012399618,0.000027100798,0.000018103763,0.000036366837,0.00005114694,0.99514055,0.0010454081,0.0010733376,0.0012334379,0.000014817334],"about_ca_topic_score_codex":0.012481615,"about_ca_topic_score_gemma":0.009710186,"teacher_disagreement_score":0.012481615,"about_ca_system_score_codex":0.0017168755,"about_ca_system_score_gemma":0.0016842658,"threshold_uncertainty_score":0.024817944},"labels":[],"label_agreement":null},{"id":"W7008872295","doi":"","title":"Computational intelligence in rate-based control for data networking","year":2003,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Bibliographical Society of Canada","funders":"","keywords":"Emulation; Artificial neural network; Correctness; Fuzzy logic; Controller (irrigation); Fuzzy control system; Protocol (science); Software; Neuro-fuzzy","score_opus":0.033469545361449374,"score_gpt":0.24982116930132353,"score_spread":0.21635162393987417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008872295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007151842,0.014351931,0.9169453,0.004162006,0.0011880288,0.00008263425,0.000097225624,0.00032302146,0.055697992],"genre_scores_gemma":[0.5118036,0.021896312,0.4151015,0.0015160036,0.0021312714,0.00048136959,0.00024488443,0.00024908976,0.04657592],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991252,0.00025798474,0.00006481434,0.000156471,0.00034396385,0.000051624946],"domain_scores_gemma":[0.9986707,0.0008512277,0.00007814009,0.0001542764,0.00021375428,0.000031997264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016930769,0.00071191695,0.0007002866,0.00077460153,0.0004570934,0.0029566262,0.0010917863,0.0011030461,0.0033471833],"category_scores_gemma":[0.0039030118,0.0003024738,0.0007100651,0.00089002866,0.0022088839,0.0027211278,0.0010058341,0.002252501,0.00083855056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040438546,0.000027348811,0.00024154918,0.00020433727,0.00003867644,0.00007658848,0.00014084119,0.08036142,0.0018477595,0.84980375,0.0035726444,0.06364461],"study_design_scores_gemma":[0.000024554902,0.00006657587,0.0003309422,0.00013861556,0.00002533344,0.00008937166,0.00006513681,0.47202483,0.0019306982,0.47442353,0.050838333,0.00004206046],"about_ca_topic_score_codex":0.002342894,"about_ca_topic_score_gemma":0.0014664752,"teacher_disagreement_score":0.0033471833,"about_ca_system_score_codex":0.0015111702,"about_ca_system_score_gemma":0.00093857286,"threshold_uncertainty_score":0.011197448},"labels":[],"label_agreement":null},{"id":"W7015668578","doi":"","title":"Towards multiplier-less implementation of neural networks","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial neural network; Feature (linguistics); Set (abstract data type); Field (mathematics); Key (lock)","score_opus":0.019930780374519733,"score_gpt":0.2800100633313156,"score_spread":0.2600792829567959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7015668578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031232646,0.0010660854,0.93784803,0.00073764456,0.00038181423,0.000120622615,0.00017431004,0.0029858213,0.025453053],"genre_scores_gemma":[0.3471717,0.0009743023,0.63405937,0.0006406359,0.00012105163,0.00026352375,0.00039733324,0.00018808317,0.016184002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963653,0.0000759079,0.000027681335,0.00005544496,0.00015150402,0.00005296542],"domain_scores_gemma":[0.9996948,0.00008678601,0.00002449002,0.000058170037,0.00012122382,0.000014649133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003795629,0.00056767755,0.0002909004,0.00039268736,0.00033362527,0.001117329,0.0017140509,0.0006672124,0.008263079],"category_scores_gemma":[0.0015712319,0.0003156195,0.000366297,0.0004022982,0.00038606653,0.0015451772,0.0007076396,0.0013740165,0.0018766517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005438985,0.00025905613,0.0012952376,0.0008575698,0.00012730977,0.0004888155,0.0003289808,0.15812786,0.10575887,0.23222476,0.014564491,0.48542315],"study_design_scores_gemma":[0.000092464485,0.00024544794,0.00030571173,0.00015184311,0.00004510005,0.0002413614,0.00008081911,0.8457213,0.054419834,0.044803977,0.05385857,0.000033528202],"about_ca_topic_score_codex":0.0015717585,"about_ca_topic_score_gemma":0.003598542,"teacher_disagreement_score":0.008263079,"about_ca_system_score_codex":0.0006818908,"about_ca_system_score_gemma":0.00082004187,"threshold_uncertainty_score":0.027642787},"labels":[],"label_agreement":null},{"id":"W7018783610","doi":"","title":"Energy Efficiency Maximization for C-RANs: Discrete Monotonic Optimization, Penalty, and ℓ0-Approximation Methods","year":2019,"lang":"en","type":"article","venue":"Arrow@dit (Dublin Institute of Technology)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Science Foundation Ireland","keywords":"Maximization; Energy (signal processing); Efficient energy use; Monotonic function; Signal processing; SIGNAL (programming language)","score_opus":0.011459384174389842,"score_gpt":0.2736416599721067,"score_spread":0.26218227579771686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7018783610","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00157884,0.00025594374,0.99695265,0.0001749122,0.00004176664,0.000017516184,0.000024717237,0.00012423423,0.00082935614],"genre_scores_gemma":[0.0963017,0.000695502,0.8936784,0.00023668658,0.00016914937,0.00017869721,0.00028700542,0.00060723687,0.00784561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998863,0.0004790999,0.000057562676,0.0001829919,0.0003411551,0.00007624237],"domain_scores_gemma":[0.99787843,0.0011488668,0.00019754165,0.00026451898,0.00040732246,0.00010326803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003207466,0.0016407375,0.0016177539,0.0009003729,0.0005213567,0.0018360163,0.00206611,0.0025344784,0.0037729645],"category_scores_gemma":[0.0073026847,0.00086819567,0.001004813,0.0015385168,0.0013712536,0.0026640084,0.0024538415,0.0030782686,0.0015822297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030430613,0.00013902207,0.00046176053,0.00030440107,0.00009100116,0.00009191252,0.00011511022,0.69249266,0.005502668,0.056656808,0.010557156,0.23328315],"study_design_scores_gemma":[0.000003353059,0.000011588044,0.000024026194,0.000009213946,0.0000024711176,0.00001792836,0.000004952835,0.99571866,0.0005593222,0.0029612917,0.0006819172,0.0000052746286],"about_ca_topic_score_codex":0.0041227937,"about_ca_topic_score_gemma":0.004302116,"teacher_disagreement_score":0.0041227937,"about_ca_system_score_codex":0.00086110045,"about_ca_system_score_gemma":0.001581948,"threshold_uncertainty_score":0.016962886},"labels":[],"label_agreement":null},{"id":"W7024793281","doi":"","title":"Souveraineté, démocratie et gouvernance mondiale chez David Held : le problème de la démocratie au-delà de lâÃtat national","year":2011,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Corporate governance; Order (exchange); Politics","score_opus":0.005924293604948216,"score_gpt":0.16677558984615445,"score_spread":0.16085129624120623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024793281","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21826597,0.015568815,0.0038023521,0.07870929,0.0018054695,0.000024200435,0.0002228115,0.000047087367,0.68155396],"genre_scores_gemma":[0.80861115,0.0018830142,0.00046269185,0.002805093,0.0002091742,0.000015560297,0.000042838612,0.00005579131,0.18591474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985721,0.00042800498,0.000024258772,0.0003167,0.00021547072,0.0004433949],"domain_scores_gemma":[0.99960214,0.0001461463,0.00004206521,0.000034579334,0.00005341643,0.00012169814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000957288,0.000292579,0.0001749519,0.0005254959,0.0073684263,0.0061889123,0.0005928727,0.0015993668,0.008814749],"category_scores_gemma":[0.0013793907,0.00021014782,0.00023772048,0.00079936034,0.009603567,0.0030837785,0.0028767788,0.002911049,0.00083910045],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052974952,0.000009132704,0.0014690076,0.000045449982,0.0000054983857,0.00019371853,0.048301548,0.00009423402,0.00031821246,0.9213323,0.019858496,0.008319464],"study_design_scores_gemma":[0.000013569416,0.000032012464,0.005052451,0.00013357241,0.0000121156045,0.00019999722,0.030017229,0.00016159873,0.0005513717,0.04815324,0.915635,0.00003793755],"about_ca_topic_score_codex":0.0963622,"about_ca_topic_score_gemma":0.22703032,"teacher_disagreement_score":0.0963622,"about_ca_system_score_codex":0.01132037,"about_ca_system_score_gemma":0.0038335938,"threshold_uncertainty_score":0.19160265},"labels":[],"label_agreement":null},{"id":"W7025387331","doi":"","title":"Utilizing convolutional neural networks for data-driven modelling of stochastic processes with application to stem cell differentiation","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Process (computing); Stochastic process; Convolutional neural network; Artificial neural network; Stochastic modelling; Automatic differentiation","score_opus":0.038110199138189194,"score_gpt":0.25336622496049,"score_spread":0.2152560258223008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7025387331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04913345,0.00041365612,0.9486215,0.00024085586,0.00004380075,0.000031869367,0.00009816391,0.0004648994,0.00095179974],"genre_scores_gemma":[0.7865545,0.0010846663,0.2048108,0.00009269625,0.000050292543,0.00014352573,0.00043293549,0.00020701732,0.006623487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998884,0.000024471858,0.000009751618,0.00003249606,0.000028764664,0.000016063537],"domain_scores_gemma":[0.99918,0.00057532615,0.00006613447,0.00003066977,0.000118740434,0.000029084502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007957322,0.00055125385,0.00051451725,0.000336487,0.00028171402,0.00076295086,0.0005796203,0.00076156744,0.00090761285],"category_scores_gemma":[0.0021522401,0.00054312526,0.00070071314,0.00046730906,0.00036848846,0.0005978078,0.00063654117,0.0010430367,0.00024264635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021519769,0.000014765235,0.0003151709,0.00001958396,0.00001778176,0.000021412006,0.00001786159,0.97809875,0.0026706725,0.0029146308,0.00016711326,0.015720816],"study_design_scores_gemma":[4.0157215e-7,0.0000014386283,0.000025224994,6.3496174e-7,9.538907e-7,0.0000011544014,4.5110195e-7,0.99937075,0.0002417106,0.00031408752,0.000042492167,8.405974e-7],"about_ca_topic_score_codex":0.021885943,"about_ca_topic_score_gemma":0.021739358,"teacher_disagreement_score":0.021885943,"about_ca_system_score_codex":0.0011793245,"about_ca_system_score_gemma":0.00112326,"threshold_uncertainty_score":0.043517053},"labels":[],"label_agreement":null},{"id":"W7027939267","doi":"","title":"Direito e Identidade de Gênero, Estudo comparado entre Quebec e Brasil","year":2022,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Identity (music); Economic Justice; Family law; Context (archaeology)","score_opus":0.00828851647294799,"score_gpt":0.1970483245557207,"score_spread":0.1887598080827727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027939267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94917774,0.0054464233,0.00078373874,0.0031689443,0.0000675083,0.00016296247,0.0029249473,0.000014248821,0.03825346],"genre_scores_gemma":[0.99096686,0.0018607783,0.00034881462,0.0003588802,0.00001047288,0.00008282348,0.0005082849,0.000008086209,0.005854982],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9985688,0.00037993002,0.00006889916,0.00024709624,0.00036587234,0.00036930325],"domain_scores_gemma":[0.9970956,0.0007086893,0.0007045611,0.0002961862,0.0009669827,0.00022791461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002106972,0.0002468525,0.0005014378,0.0027440235,0.0046862443,0.00250517,0.00087093195,0.00045064022,0.007960805],"category_scores_gemma":[0.0063742013,0.0002823483,0.00023723551,0.004953588,0.0024383976,0.0013829187,0.001699189,0.0005808447,0.00043244573],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071870425,0.000047975067,0.8252388,0.00055710407,0.00015972652,0.0003803715,0.08047901,0.00012690258,0.00040312848,0.02387946,0.0061556255,0.062500015],"study_design_scores_gemma":[0.000007544392,0.000023500636,0.88468146,0.00083437253,0.00008844619,0.00018878329,0.083679266,0.0001923577,0.0002266839,0.00095741503,0.02909569,0.00002447593],"about_ca_topic_score_codex":0.8869955,"about_ca_topic_score_gemma":0.95965165,"teacher_disagreement_score":0.113004506,"about_ca_system_score_codex":0.010762461,"about_ca_system_score_gemma":0.014154144,"threshold_uncertainty_score":0.22733998},"labels":[],"label_agreement":null},{"id":"W7030256551","doi":"","title":"Newsbrief - WFTAO annual meeting represents turning point for federation","year":2000,"lang":"en","type":"article","venue":"NPARC","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Turning point; Point (geometry); Work (physics)","score_opus":0.013791494950248638,"score_gpt":0.25764151984911965,"score_spread":0.243850024898871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030256551","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012206013,0.0057003098,0.008480832,0.27049938,0.17630985,0.00059120456,0.0074492907,0.0019258758,0.5168373],"genre_scores_gemma":[0.037727173,0.0018380412,0.004806957,0.020736894,0.012368208,0.00040496996,0.006030767,0.0008987625,0.91518825],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99572515,0.0002983959,0.00019489323,0.00042467596,0.0016114463,0.0017453565],"domain_scores_gemma":[0.991184,0.0005153311,0.00035170992,0.00067134394,0.0028613827,0.004416135],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006575606,0.00084246084,0.00087526586,0.001530748,0.00554351,0.012629319,0.00168759,0.010567287,0.12867875],"category_scores_gemma":[0.00801423,0.00051194866,0.0014923206,0.0011619648,0.001022171,0.0065238527,0.0047149872,0.008833755,0.059651934],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014842575,0.000059065755,0.00042486444,0.000059596387,0.000008066063,0.00013648346,0.00008131842,0.00008685549,0.00043121117,0.011792154,0.9603976,0.026374443],"study_design_scores_gemma":[0.000013842101,0.000025833508,0.0013646996,0.00004261968,0.0000048344123,0.000042323634,0.00019603425,0.00010950677,0.00019265851,0.0022018256,0.9957926,0.000013269927],"about_ca_topic_score_codex":0.018282149,"about_ca_topic_score_gemma":0.029996064,"teacher_disagreement_score":0.87132126,"about_ca_system_score_codex":0.0027635691,"about_ca_system_score_gemma":0.012957261,"threshold_uncertainty_score":0.43047333},"labels":[],"label_agreement":null},{"id":"W7036309729","doi":"","title":"Around the World in Eight Hundred Days","year":2011,"lang":"en","type":"other","venue":"AUSpace (Athabasca University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Globe; Distance education; Object (grammar); Developing country; South asia","score_opus":0.01680661527315713,"score_gpt":0.20473717215780737,"score_spread":0.18793055688465024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036309729","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015067322,0.039443646,0.0022666797,0.05942174,0.058558043,0.00029953534,0.0077061043,0.0033627339,0.8138742],"genre_scores_gemma":[0.01170287,0.0050269617,0.0008767858,0.0071439804,0.0018682551,0.000051462954,0.0021602947,0.0002406874,0.9709288],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99945754,0.000074114294,0.000031305444,0.00010030916,0.00020928307,0.00012733125],"domain_scores_gemma":[0.9990121,0.00004695842,0.000060521113,0.00009543644,0.00023650411,0.0005484786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057375524,0.0008465734,0.00073171983,0.001147124,0.003297341,0.0043533966,0.0007048451,0.001282805,0.2568262],"category_scores_gemma":[0.001278604,0.00020135904,0.0003023876,0.0015492884,0.0008397211,0.0027107566,0.003705568,0.0020321994,0.1410238],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034292403,0.000037184596,0.00086130894,0.00018777869,0.000007932793,0.00023010989,0.00087719137,0.00002003989,0.0005489061,0.0035213882,0.8758962,0.117777705],"study_design_scores_gemma":[9.986692e-7,0.000006202093,0.0005166768,0.000041411437,6.874607e-7,0.000064003514,0.0004894,0.0000061912374,0.000027129032,0.00014666977,0.998698,0.0000025133827],"about_ca_topic_score_codex":0.003411623,"about_ca_topic_score_gemma":0.012129451,"teacher_disagreement_score":0.2568262,"about_ca_system_score_codex":0.0008465246,"about_ca_system_score_gemma":0.0010639705,"threshold_uncertainty_score":0.8591692},"labels":[],"label_agreement":null},{"id":"W7039797421","doi":"","title":"Maximum tolerance and maximum greatest tolerance","year":2009,"lang":"en","type":"article","venue":"UPCommons institutional repository (Universitat Politècnica de Catalunya)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Generalitat de Catalunya; European Regional Development Fund; Royal Canadian Geographical Society","keywords":"Separable space; Artificial neural network; Tolerance analysis; Control theory (sociology); Upper and lower bounds","score_opus":0.01022332702189286,"score_gpt":0.21885594496523755,"score_spread":0.2086326179433447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039797421","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053119104,0.0011627988,0.92392784,0.00087464915,0.0002123989,0.00006007584,0.00013780549,0.0005526772,0.019952603],"genre_scores_gemma":[0.77353156,0.0007679822,0.21798846,0.0005716228,0.00022539479,0.0002651735,0.00019477312,0.00046733048,0.0059876773],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99406946,0.0013630891,0.00062887685,0.0013810567,0.001882339,0.0006752416],"domain_scores_gemma":[0.98947984,0.004901338,0.0014044443,0.0021692207,0.0015166308,0.0005284779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050720563,0.0010648618,0.001515738,0.0011101648,0.0012821314,0.0029515966,0.0026164935,0.0018408062,0.0043604835],"category_scores_gemma":[0.0253626,0.000629773,0.0012603705,0.00086855475,0.0038944227,0.0058062193,0.0053910376,0.0030698443,0.001033209],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049117557,0.000105806204,0.0036049203,0.00092943694,0.00017227356,0.00046735385,0.0014345201,0.19085419,0.035039186,0.6435908,0.0028473716,0.1204629],"study_design_scores_gemma":[0.00005373078,0.00047425003,0.0027585875,0.00031559778,0.00007357931,0.00083787175,0.00053770636,0.155337,0.037658166,0.78748703,0.01431567,0.00015082181],"about_ca_topic_score_codex":0.0007592827,"about_ca_topic_score_gemma":0.000615483,"teacher_disagreement_score":0.0050720563,"about_ca_system_score_codex":0.0010500696,"about_ca_system_score_gemma":0.0010076314,"threshold_uncertainty_score":0.026823938},"labels":[],"label_agreement":null},{"id":"W7040051208","doi":"","title":"A Note on Decolonization, Poststructuralism, and Method in Indigenous Critical Theory","year":2022,"lang":"en","type":"article","venue":"Mspace (University of Manitoba)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pejorative; Subject (documents); Pretext; Temptation; Context (archaeology); Population","score_opus":0.01046542482504532,"score_gpt":0.2403644642628113,"score_spread":0.229899039437766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7040051208","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050348364,0.060586505,0.21771589,0.1771264,0.0036805985,0.00060721365,0.00015110338,0.00027068515,0.48951322],"genre_scores_gemma":[0.8796739,0.012784557,0.06393933,0.009390845,0.0015009297,0.0012577891,0.000052769024,0.00033444786,0.031065512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9761114,0.01943596,0.00048857275,0.0010276536,0.0019752027,0.00096109114],"domain_scores_gemma":[0.9770256,0.01937217,0.0008061098,0.0011052445,0.0012406354,0.0004502558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027766753,0.00084999896,0.0009448243,0.0055731535,0.012986439,0.0107983155,0.0031309773,0.0029819072,0.0033873632],"category_scores_gemma":[0.016943712,0.00046634368,0.00080911606,0.0053976052,0.13724181,0.013395458,0.006521547,0.0081112785,0.00038867665],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046296173,0.000006229409,0.0000930083,0.000060945567,0.000002430531,0.00003394983,0.03016435,0.00006901436,0.000036136305,0.9659647,0.00091438025,0.0026502563],"study_design_scores_gemma":[0.000013717039,0.000022680653,0.00038394795,0.0004942693,0.000009011266,0.00009565591,0.025185617,0.00088806014,0.00027484438,0.86075276,0.11184876,0.00003069049],"about_ca_topic_score_codex":0.06177861,"about_ca_topic_score_gemma":0.07448679,"teacher_disagreement_score":0.06177861,"about_ca_system_score_codex":0.030028455,"about_ca_system_score_gemma":0.018468432,"threshold_uncertainty_score":0.21787274},"labels":[],"label_agreement":null},{"id":"W7072069281","doi":"","title":"Utilization of neural networks to expand databases","year":2007,"lang":"en","type":"article","venue":"NPARC","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Aggregate (composite); Artificial neural network; Variety (cybernetics); Base (topology); Test data; Stress (linguistics)","score_opus":0.0518833048252512,"score_gpt":0.3108055296015322,"score_spread":0.25892222477628096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7072069281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036577705,0.0016391957,0.9491477,0.0009964037,0.00010682651,0.00023468159,0.00097444776,0.0038653766,0.006457666],"genre_scores_gemma":[0.30527997,0.0024355857,0.68547654,0.00043747717,0.00012409178,0.000423788,0.0030637523,0.00026984053,0.0024889845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965945,0.0010141016,0.0004902397,0.0006502095,0.0011213536,0.00012959928],"domain_scores_gemma":[0.9850425,0.008606979,0.00071347825,0.0032733618,0.0021683779,0.00019534086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059645027,0.000791188,0.0009544338,0.003159788,0.000523366,0.0027829926,0.0023902296,0.00088390324,0.002266673],"category_scores_gemma":[0.027382636,0.0006311547,0.0008358295,0.0031101021,0.00052715425,0.0057032327,0.0024639643,0.0012467051,0.0009876041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033891186,0.00023567757,0.0062117195,0.00040243653,0.00024061803,0.00037625336,0.0004386678,0.19789839,0.0048981356,0.03052452,0.005608384,0.75282633],"study_design_scores_gemma":[0.00003751114,0.000099047415,0.00080814556,0.00013415462,0.00010809572,0.00023571086,0.00014028639,0.9274754,0.0094097955,0.038584426,0.02292737,0.000039963088],"about_ca_topic_score_codex":0.0038003635,"about_ca_topic_score_gemma":0.0033935306,"teacher_disagreement_score":0.0059645027,"about_ca_system_score_codex":0.00076823216,"about_ca_system_score_gemma":0.00088760484,"threshold_uncertainty_score":0.031543672},"labels":[],"label_agreement":null},{"id":"W7097465294","doi":"","title":"Quanthed Position Measurements","year":2014,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Position (finance); Artificial neural network; Subject (documents); Derivative (finance)","score_opus":0.03818674101377739,"score_gpt":0.25816424491761986,"score_spread":0.21997750390384246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097465294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21928407,0.0027038995,0.3769565,0.0014441599,0.0049374816,0.000702454,0.032001197,0.010903839,0.35106644],"genre_scores_gemma":[0.6922363,0.0011924931,0.14163,0.00061813917,0.000237777,0.00032966526,0.011517597,0.0012477416,0.15099034],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986676,0.00006814192,0.0000457942,0.00030474793,0.00082386687,0.00008983023],"domain_scores_gemma":[0.99907887,0.000099649034,0.00007227166,0.00022259519,0.0004767306,0.00004977491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048767554,0.00072901475,0.0005257244,0.0021013261,0.00076489453,0.0013533186,0.0012280424,0.0012799364,0.050519858],"category_scores_gemma":[0.001801672,0.00043193653,0.00033605951,0.002127553,0.00033472088,0.0011695749,0.0010664638,0.0010351095,0.02110411],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010591536,0.0003277482,0.013498556,0.0010306226,0.00012246436,0.0006772642,0.000838605,0.010145993,0.43970573,0.013803756,0.05795172,0.46083844],"study_design_scores_gemma":[0.0001188495,0.0010810472,0.06474925,0.0002590633,0.0001415561,0.0014809063,0.0008791598,0.0949972,0.5466877,0.0045879288,0.28466007,0.0003572432],"about_ca_topic_score_codex":0.0017562681,"about_ca_topic_score_gemma":0.0043273806,"teacher_disagreement_score":0.050519858,"about_ca_system_score_codex":0.0007729855,"about_ca_system_score_gemma":0.0005724508,"threshold_uncertainty_score":0.16900575},"labels":[],"label_agreement":null},{"id":"W7097696519","doi":"","title":"Title: Dynamic Subset Selection Applied to Self-Organizing Maps","year":2004,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Permission; Selection (genetic algorithm); Signature (topology); Field (mathematics); Association (psychology)","score_opus":0.005723804881819575,"score_gpt":0.2165120014373019,"score_spread":0.2107881965554823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097696519","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013090604,0.003142647,0.9591862,0.0012343231,0.005751413,0.00034960982,0.0025689437,0.0056344606,0.009041759],"genre_scores_gemma":[0.16450499,0.004331541,0.76367825,0.0005581607,0.0044842353,0.0010549115,0.011905158,0.0023739187,0.04710883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960476,0.0000989676,0.000031316446,0.00009335804,0.00015477865,0.00001685818],"domain_scores_gemma":[0.99827576,0.00063615275,0.000062554784,0.00020710628,0.00072807056,0.00009027876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009745391,0.0006126845,0.0008105622,0.0010489803,0.0004998356,0.0014649107,0.0009589472,0.000677618,0.019206645],"category_scores_gemma":[0.004519762,0.0002908821,0.00041945273,0.0017315461,0.00031183654,0.0012553852,0.000719784,0.00063926936,0.0073995213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045365977,0.000057902438,0.00095452013,0.0008819782,0.000117879244,0.00016289271,0.00014575536,0.032034006,0.011514909,0.0103452075,0.13578859,0.80754274],"study_design_scores_gemma":[0.00016132285,0.00027930833,0.0032539836,0.00023330508,0.00013311858,0.00056750234,0.000120257566,0.74432033,0.038674086,0.031653788,0.18054445,0.000058549027],"about_ca_topic_score_codex":0.0006173827,"about_ca_topic_score_gemma":0.0007542673,"teacher_disagreement_score":0.019206645,"about_ca_system_score_codex":0.00024591683,"about_ca_system_score_gemma":0.00055088144,"threshold_uncertainty_score":0.064252615},"labels":[],"label_agreement":null},{"id":"W7097789493","doi":"","title":"PAR","year":2008,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Order (exchange); Work (physics); General interest; Control (management)","score_opus":0.02350358238747717,"score_gpt":0.21727880072210845,"score_spread":0.1937752183346313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097789493","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018999493,0.0054077283,0.47788784,0.011311474,0.0072967163,0.0008066856,0.044368125,0.02402046,0.40990153],"genre_scores_gemma":[0.2214224,0.004425538,0.11179396,0.004435892,0.001574223,0.001084611,0.052271146,0.0056988406,0.5972933],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998923,0.00018755168,0.000060271803,0.00046193402,0.00023867485,0.00012857371],"domain_scores_gemma":[0.998676,0.0003061607,0.00008892662,0.0003865582,0.00045332793,0.0000889351],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010811482,0.0013237055,0.00116022,0.001147933,0.0010102148,0.0050537824,0.0021430198,0.002415281,0.22839446],"category_scores_gemma":[0.0040744347,0.00066956005,0.0017281676,0.0013033375,0.00067839085,0.0038848014,0.0021909121,0.0023272708,0.1240054],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068100943,0.00018184219,0.0055656894,0.00088174944,0.00024208472,0.0005930655,0.00036979312,0.08293077,0.0041337903,0.14593732,0.41746283,0.34102008],"study_design_scores_gemma":[0.00015579836,0.00015066017,0.0031182566,0.0003141774,0.00009337482,0.00046446625,0.0003050559,0.27542263,0.0031456342,0.079423256,0.63727874,0.00012799514],"about_ca_topic_score_codex":0.01434698,"about_ca_topic_score_gemma":0.010818954,"teacher_disagreement_score":0.77160555,"about_ca_system_score_codex":0.0017492541,"about_ca_system_score_gemma":0.0020761518,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W71081281","doi":"10.1007/978-3-642-35289-8_27","title":"Training Deep and Recurrent Networks with Hessian-Free Optimization","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":206,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Implementation; Hessian matrix; Heuristic; Artificial intelligence; Training (meteorology); Artificial neural network; Machine learning; Deep neural networks; Computer engineering; Software engineering","score_opus":0.021763824428854932,"score_gpt":0.23382435180356947,"score_spread":0.21206052737471454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W71081281","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03393659,0.0010195201,0.9553301,0.00041602884,0.00025226446,0.00005098038,0.00016913928,0.0023169375,0.0065085245],"genre_scores_gemma":[0.44334987,0.00054642634,0.5321154,0.00039330465,0.00018017777,0.00023727959,0.0009945125,0.00091593136,0.021267157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997415,0.000060265527,0.000018661362,0.000075786236,0.00006372469,0.00003996771],"domain_scores_gemma":[0.9990972,0.00055949506,0.000047133908,0.00011721246,0.00013533699,0.00004360829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093347434,0.0012320852,0.0009649581,0.0003336345,0.000331362,0.0007889798,0.0017223352,0.0018940603,0.0067661707],"category_scores_gemma":[0.0033961234,0.0010685424,0.00074085966,0.0004627231,0.00060828635,0.001851403,0.0013516981,0.0025876937,0.0016053636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016356213,0.00010729286,0.00044051404,0.00013468118,0.000109829474,0.00008951702,0.000071154296,0.7320424,0.0060460595,0.016288925,0.0074041355,0.23710203],"study_design_scores_gemma":[0.0000063987877,0.00001384097,0.000028788161,0.0000044898725,0.0000052180576,0.0000058987757,0.0000028984591,0.9960939,0.00052171666,0.0030322876,0.00028187485,0.0000026087241],"about_ca_topic_score_codex":0.0048916107,"about_ca_topic_score_gemma":0.011717126,"teacher_disagreement_score":0.0067661707,"about_ca_system_score_codex":0.0006716084,"about_ca_system_score_gemma":0.00075651536,"threshold_uncertainty_score":0.022635102},"labels":[],"label_agreement":null},{"id":"W711100","doi":"10.1007/978-0-585-28000-4_2","title":"Neural Networks and Fuzzy Basis Functions for Functional Approximation","year":2007,"lang":"en","type":"book-chapter","venue":"International series in intelligent technologies","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Basis (linear algebra); Artificial neural network; Feedforward neural network; Basis function; Sigmoid function; Function approximation; Mathematics; Fuzzy logic; Radial basis function; Stochastic neural network; Types of artificial neural networks; Computer science; Artificial intelligence; Time delay neural network; Mathematical analysis","score_opus":0.040524539323966,"score_gpt":0.263875141372756,"score_spread":0.22335060204878998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W711100","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028200233,0.09764808,0.84057826,0.0013395705,0.0022319946,0.000040141163,0.00019106898,0.0002550524,0.054895755],"genre_scores_gemma":[0.13144419,0.10203473,0.6377096,0.0009037346,0.0040055183,0.0003142413,0.0006284251,0.0002907561,0.12266888],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997336,0.00007123926,0.000022384082,0.00004450893,0.00011698762,0.000011159029],"domain_scores_gemma":[0.99971896,0.00016166888,0.000017324197,0.000031722477,0.0000651611,0.000005180444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005930931,0.0010276071,0.0012124995,0.0009318055,0.00032059033,0.0011996694,0.001073335,0.0015241585,0.0071935663],"category_scores_gemma":[0.0016811825,0.00040512596,0.0006666817,0.0018481374,0.0010225261,0.0021178205,0.0005566879,0.0018687913,0.0021105674],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041790092,0.000025844975,0.00012852138,0.0009944446,0.00006835901,0.00010744161,0.0001430078,0.052777044,0.003101397,0.6203437,0.025520936,0.29674765],"study_design_scores_gemma":[0.000015512207,0.00004037017,0.0004409812,0.00029936372,0.000048028945,0.0002696312,0.000050998424,0.25884482,0.0017131472,0.62180173,0.11642263,0.000052640626],"about_ca_topic_score_codex":0.0016857658,"about_ca_topic_score_gemma":0.0020873623,"teacher_disagreement_score":0.0071935663,"about_ca_system_score_codex":0.00062945974,"about_ca_system_score_gemma":0.00036958398,"threshold_uncertainty_score":0.024064898},"labels":[],"label_agreement":null},{"id":"W7113065291","doi":"","title":"Systematic Exploration of Power-Augmented Feedforward Layers in Transformer Networks","year":2023,"lang":"en","type":"article","venue":"University Library (University of Saskatchewan)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Saskatchewan","keywords":"Perceptron; Feed forward; Artificial neural network; Transformer; Computation; Piecewise linear function; Stability (learning theory); Layer (electronics); Feedforward neural network","score_opus":0.010493980776392537,"score_gpt":0.17449849965592898,"score_spread":0.16400451887953643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7113065291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13010074,0.0007340114,0.8577647,0.00037396897,0.00004557439,0.00010472582,0.00011361454,0.0014465097,0.009316152],"genre_scores_gemma":[0.80684334,0.00045292295,0.19012831,0.00010996627,0.00001120155,0.00013214452,0.00013963487,0.00023706752,0.0019455309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995901,0.00017990422,0.000021352696,0.000057289122,0.00010743873,0.00004392053],"domain_scores_gemma":[0.99857676,0.0009834871,0.0000894027,0.00016698356,0.00014656654,0.000036783957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011574813,0.0010469377,0.00047738172,0.0003581651,0.00029272688,0.001150444,0.0011199436,0.0006375165,0.0025656621],"category_scores_gemma":[0.0050508245,0.0005552653,0.00047550676,0.00025483337,0.00078962144,0.0020711874,0.0013002179,0.0014241061,0.0004211959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017211428,0.000061531224,0.00092318485,0.00023011566,0.00003462967,0.0001836326,0.00012453044,0.87944037,0.008981113,0.021623245,0.0009826601,0.087242916],"study_design_scores_gemma":[0.000006887862,0.00003420883,0.00004897942,0.000015951899,0.0000064764617,0.000020799998,0.000012071317,0.99030274,0.0029113062,0.0060767056,0.0005608287,0.0000030941155],"about_ca_topic_score_codex":0.0021996011,"about_ca_topic_score_gemma":0.004021103,"teacher_disagreement_score":0.0025656621,"about_ca_system_score_codex":0.0008511372,"about_ca_system_score_gemma":0.00095573196,"threshold_uncertainty_score":0.00858295},"labels":[],"label_agreement":null},{"id":"W7121654","doi":"10.1159/000182630","title":"Comparing Techniques for Analyzing Electrochemical Noise Data from a Passive/Active System","year":2006,"lang":"en","type":"article","venue":"Nephron","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Electrochemical noise; Electrochemistry; Noise (video); Materials science; Computer science; Chemistry; Electrode; Artificial intelligence","score_opus":0.026470361186886452,"score_gpt":0.26676036158822547,"score_spread":0.24029000040133902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7121654","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65381324,0.0014067861,0.34200913,0.00014620947,0.000086265965,0.00015557892,0.00035996264,0.00077875046,0.0012441074],"genre_scores_gemma":[0.89342904,0.0005172802,0.10450234,0.00009163428,0.00007543448,0.00015463942,0.0004941294,0.000050150476,0.00068539276],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993191,0.00022931623,0.000049761726,0.00011625448,0.00023120097,0.00005431441],"domain_scores_gemma":[0.9984861,0.00083421805,0.00014655714,0.00007837699,0.00038752073,0.00006727585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090817036,0.00054777105,0.00060781115,0.0013720504,0.00017062598,0.0005692445,0.00046206592,0.000732347,0.0006804525],"category_scores_gemma":[0.0047320845,0.00018121081,0.00033216414,0.0009302908,0.00026423886,0.00038298187,0.00050452806,0.00033520247,0.00035260263],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038192899,0.00044103726,0.12872258,0.000963048,0.0005982436,0.0011850654,0.0008413897,0.008154154,0.3299275,0.0011908177,0.0010767112,0.52308017],"study_design_scores_gemma":[0.00054635026,0.0067452225,0.2952961,0.00016879509,0.0009413521,0.01905955,0.0015916623,0.3922806,0.264661,0.007828012,0.010548011,0.00033328982],"about_ca_topic_score_codex":0.00055938744,"about_ca_topic_score_gemma":0.0007792892,"teacher_disagreement_score":0.0013720504,"about_ca_system_score_codex":0.00016044329,"about_ca_system_score_gemma":0.00014787991,"threshold_uncertainty_score":0.0048028827},"labels":[],"label_agreement":null},{"id":"W7125936931","doi":"10.1145/3757377.3794966","title":"10.1145/3757377.3794966","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Session (web analytics); Representation (politics)","score_opus":0.0057958170990249245,"score_gpt":0.1770613221925657,"score_spread":0.17126550509354077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125936931","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032100093,0.011258412,0.017054353,0.0021823805,0.003437973,0.0005432727,0.036575213,0.021674575,0.90406376],"genre_scores_gemma":[0.00494261,0.003866973,0.0029795615,0.0009001406,0.00018403074,0.000207268,0.014277283,0.0020352958,0.9706068],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99935526,0.00004518619,0.000055344375,0.0001922831,0.00022550966,0.00012633236],"domain_scores_gemma":[0.99883646,0.00025888823,0.000057612273,0.0003819159,0.00029032733,0.0001748014],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015348983,0.0034543162,0.0023927449,0.0026188465,0.0018964155,0.0040794415,0.002833974,0.0046605016,0.9256212],"category_scores_gemma":[0.0027377126,0.001544649,0.0012137087,0.008800895,0.0014411364,0.008044325,0.004586371,0.0027643996,0.9472195],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024593386,0.00013239594,0.00040175117,0.0004853296,0.000039741215,0.00014850765,0.00005109442,0.0009964255,0.0009772251,0.0040161326,0.6829735,0.3095318],"study_design_scores_gemma":[0.000041055042,0.000030422292,0.00082799233,0.00026528255,0.000044164328,0.000117331416,0.000065043714,0.0015347162,0.0005422469,0.001937738,0.9945642,0.00002972037],"about_ca_topic_score_codex":0.034372576,"about_ca_topic_score_gemma":0.02929333,"teacher_disagreement_score":0.07437879,"about_ca_system_score_codex":0.0023455448,"about_ca_system_score_gemma":0.0012793922,"threshold_uncertainty_score":0.106092334},"labels":[],"label_agreement":null},{"id":"W7126400666","doi":"10.21428/594757db.0fbd2075","title":"Toward a Model of Associative Memory via Predictive Coding","year":2024,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Forgetting; Recall; Hopfield network; Bidirectional associative memory; Content-addressable memory; Coding (social sciences); Associative property; Memory model","score_opus":0.036884401020140484,"score_gpt":0.2650753181062644,"score_spread":0.2281909170861239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126400666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035348963,0.0010516692,0.9430804,0.0018716662,0.00014889677,0.000029773419,0.00026259632,0.000373683,0.017832333],"genre_scores_gemma":[0.8747335,0.0020146898,0.10110967,0.0004944745,0.00030108716,0.00017393762,0.0003439363,0.0001327078,0.020696],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998367,0.000048687587,0.000006649415,0.000038785973,0.000041180647,0.000027894821],"domain_scores_gemma":[0.99929523,0.00040570146,0.00006495118,0.000084579675,0.00010457501,0.000044962144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054672005,0.00039420906,0.00050562544,0.0005266008,0.00035799918,0.0013086525,0.0016297804,0.0011411667,0.0032980072],"category_scores_gemma":[0.0021800906,0.0003496832,0.0005858721,0.00069091306,0.0012800406,0.0023739336,0.0009860463,0.0016667347,0.0005904214],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048538466,0.000024144974,0.00045644553,0.000066341054,0.000028402455,0.00009416079,0.000106701,0.44704825,0.0013605688,0.5274885,0.0021564611,0.021121487],"study_design_scores_gemma":[0.000004125937,0.000008088437,0.000045751804,0.000007861983,0.0000044932117,0.000018055705,0.000005837184,0.83639693,0.00014568778,0.1625663,0.00079200574,0.0000048831157],"about_ca_topic_score_codex":0.004425293,"about_ca_topic_score_gemma":0.0031975447,"teacher_disagreement_score":0.004425293,"about_ca_system_score_codex":0.0007978521,"about_ca_system_score_gemma":0.00081951224,"threshold_uncertainty_score":0.011032939},"labels":[],"label_agreement":null},{"id":"W7133037975","doi":"","title":"An Overview of the Grokking Phenomenon in Neural Networks","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Phenomenon; Context (archaeology); Artificial neural network; Modular design; Field (mathematics); Prime (order theory)","score_opus":0.05654640006260653,"score_gpt":0.37598435827524723,"score_spread":0.3194379582126407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133037975","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004503741,0.7099493,0.20714928,0.0059478795,0.0018654531,0.00009370329,0.00031075694,0.0003664803,0.069813386],"genre_scores_gemma":[0.0884851,0.761999,0.11393356,0.0026468113,0.007274755,0.0003530342,0.0006981224,0.00026583465,0.02434382],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991321,0.00021153074,0.00009313679,0.0002306816,0.00026600406,0.00006656148],"domain_scores_gemma":[0.998747,0.000811461,0.00007730387,0.00009764793,0.00021148611,0.000055146884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012921687,0.0011140041,0.0008471406,0.0026027234,0.0007835153,0.0034040909,0.0011505729,0.0020645238,0.0038665247],"category_scores_gemma":[0.0025964219,0.0009244107,0.00083403144,0.0044213617,0.0018414538,0.0041893,0.0013702471,0.003731215,0.0017820022],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009646287,0.00015342273,0.0014582244,0.0058072153,0.00015009141,0.0004057838,0.00065857073,0.019888856,0.0032373539,0.48961535,0.030030338,0.44849828],"study_design_scores_gemma":[0.000014146249,0.00013891881,0.0027175115,0.0023945938,0.00006404476,0.00083892443,0.00019607232,0.039012026,0.0020444458,0.4753578,0.47711378,0.00010776581],"about_ca_topic_score_codex":0.00241646,"about_ca_topic_score_gemma":0.0019327089,"teacher_disagreement_score":0.0038665247,"about_ca_system_score_codex":0.0020939778,"about_ca_system_score_gemma":0.00095275935,"threshold_uncertainty_score":0.015192866},"labels":[],"label_agreement":null},{"id":"W7139934492","doi":"10.23919/cnc-usnc-ursi64444.2025.11419951","title":"Assessment of Neural Networks Localization Using RSSI &amp; CSI","year":2025,"lang":"","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Artificial neural network; Pattern recognition (psychology); Feature (linguistics); Noise (video); Deep neural networks","score_opus":0.03150656745065551,"score_gpt":0.3362739807355617,"score_spread":0.3047674132849062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7139934492","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83206975,0.0009138772,0.1581377,0.0005796801,0.00008694314,0.000069183516,0.0004901845,0.001106281,0.0065463227],"genre_scores_gemma":[0.99021626,0.0001257677,0.00894424,0.00002507228,0.0000071071463,0.000019156125,0.00014278734,0.000010100955,0.00050951034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943274,0.00015490234,0.000038617858,0.00009461408,0.00019951198,0.000079510995],"domain_scores_gemma":[0.9977367,0.0013596879,0.0001588635,0.00009122311,0.0006077109,0.000045776516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016195655,0.00095052534,0.0003999473,0.0008669602,0.00027676547,0.0008052215,0.00051355787,0.0010325994,0.00093521917],"category_scores_gemma":[0.007786215,0.00018115294,0.00022676883,0.0006317614,0.0003660802,0.0010059299,0.00055960583,0.0003991661,0.00022916275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005258138,0.00011656376,0.030220674,0.00019336287,0.00012878692,0.00018694514,0.000110621346,0.80398136,0.006549178,0.00072967773,0.00093531114,0.15632173],"study_design_scores_gemma":[0.00000576639,0.000137804,0.0076011065,0.000017795895,0.000024988765,0.000037458205,0.00006887398,0.9863289,0.0052150893,0.00035708986,0.00019265575,0.000012405943],"about_ca_topic_score_codex":0.011312543,"about_ca_topic_score_gemma":0.0104246475,"teacher_disagreement_score":0.011312543,"about_ca_system_score_codex":0.0008306836,"about_ca_system_score_gemma":0.00048652894,"threshold_uncertainty_score":0.022493422},"labels":[],"label_agreement":null},{"id":"W7144439471","doi":"","title":"Developing a Novel Recurrent Neural Network Architecture with Fewer Parameters and Good Learning Performance","year":2021,"lang":"en","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Ministry of Education, Culture, Sports, Science and Technology; Institute of Genetics; Japan Agency for Medical Research and Development","keywords":"Recurrent neural network; Artificial neural network; Deep learning; Feature (linguistics); Architecture","score_opus":0.021377047092759924,"score_gpt":0.2380540397666398,"score_spread":0.21667699267387988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7144439471","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041578073,0.0005771863,0.9511627,0.0003285694,0.00029118007,0.00008821648,0.00017872213,0.003324276,0.0024711462],"genre_scores_gemma":[0.52151215,0.0005535577,0.46198598,0.0005353413,0.00018035002,0.00025147886,0.0013773164,0.00027317955,0.013330733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996499,0.00005002891,0.000031921456,0.00012640783,0.00008855645,0.000053158885],"domain_scores_gemma":[0.9995503,0.00008178804,0.000031912215,0.000064079395,0.00024701405,0.000024901849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069309963,0.00080834044,0.0006551844,0.0003964531,0.00033465962,0.0006514864,0.0015841917,0.0012250791,0.0026992771],"category_scores_gemma":[0.0012992987,0.00044413496,0.00066515536,0.00046061428,0.00019053393,0.001521941,0.00070034224,0.0011899831,0.0018036463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030665204,0.0003436424,0.0022205857,0.00020344686,0.00030062522,0.00023472194,0.00007172426,0.23888688,0.061297767,0.00577231,0.010887624,0.67947406],"study_design_scores_gemma":[0.000012263524,0.000048810223,0.00016645872,0.0000054127295,0.0000401672,0.0000320877,0.000006184426,0.99145997,0.006702484,0.0006780239,0.00083923707,0.000008842145],"about_ca_topic_score_codex":0.008392667,"about_ca_topic_score_gemma":0.012243408,"teacher_disagreement_score":0.008392667,"about_ca_system_score_codex":0.00044108767,"about_ca_system_score_gemma":0.0009822703,"threshold_uncertainty_score":0.016687632},"labels":[],"label_agreement":null},{"id":"W7148541929","doi":"10.1007/978-3-032-07031-9_8","title":"Network of Neural Networks","year":2025,"lang":"en","type":"book-chapter","venue":"Static & dynamic game theory: foundations & applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Innovation and Economic Development Trois Rivières","funders":"","keywords":"Artificial neural network; Software; Evolutionary algorithm; Artificial Intelligence System; Evolutionary computation; Computational intelligence","score_opus":0.010168262165402809,"score_gpt":0.25793760340628935,"score_spread":0.24776934124088654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7148541929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017417809,0.0088742925,0.89776593,0.0015352592,0.0011819301,0.00007243386,0.0006776268,0.00077875046,0.07169589],"genre_scores_gemma":[0.5987894,0.009180315,0.26660055,0.0006813475,0.0010446353,0.00035182093,0.001669328,0.00018942573,0.121493086],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974686,0.000067123845,0.000012668674,0.00008893525,0.000059529375,0.000025011477],"domain_scores_gemma":[0.99961334,0.00015221232,0.000033463693,0.000059120153,0.00011436317,0.000027524802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038653295,0.00047920377,0.00053906435,0.0007163785,0.00035538457,0.0015423996,0.0011104017,0.0011492393,0.010324595],"category_scores_gemma":[0.002222782,0.00031222237,0.0003871359,0.000822667,0.0006237437,0.0014536766,0.0010573902,0.00093820365,0.0022477724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001411386,0.000044749053,0.00084785296,0.00027888705,0.00015532866,0.00012767456,0.000064195854,0.30451712,0.0041728015,0.44288245,0.019732509,0.2270353],"study_design_scores_gemma":[0.000016099828,0.00002908321,0.00039065097,0.000039061386,0.000030302901,0.00008929924,0.00001774044,0.7472711,0.001077573,0.23205677,0.01896196,0.000020335774],"about_ca_topic_score_codex":0.0020481513,"about_ca_topic_score_gemma":0.002083411,"teacher_disagreement_score":0.010324595,"about_ca_system_score_codex":0.0005306688,"about_ca_system_score_gemma":0.00046388904,"threshold_uncertainty_score":0.034539223},"labels":[],"label_agreement":null},{"id":"W784990704","doi":"10.1007/s11063-015-9430-9","title":"Stacked Autoencoders Using Low-Power Accelerated Architectures for Object Recognition in Autonomous Systems","year":2015,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; South Dakota Agricultural Experiment Station; Canadian Institute for Advanced Research","keywords":"Computational intelligence; Computer science; Artificial intelligence; Object (grammar); Cognitive neuroscience of visual object recognition; Power (physics); Complex system; Pattern recognition (psychology); Computer vision","score_opus":0.08358221029294056,"score_gpt":0.2972970668946654,"score_spread":0.21371485660172485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W784990704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07980203,0.0007720172,0.91192716,0.00026818184,0.00020706578,0.00002911435,0.00010564877,0.0019591735,0.0049296287],"genre_scores_gemma":[0.6501963,0.0004784791,0.3390176,0.00012552286,0.000099741585,0.000057493267,0.0002829509,0.000153012,0.009588877],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999093,0.000014944472,0.000006017986,0.000018470018,0.00003776546,0.000013551359],"domain_scores_gemma":[0.99983084,0.000052804375,0.0000093579865,0.000039828134,0.000059192487,0.000007893852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018661506,0.0004058114,0.00026987563,0.0001727077,0.00019469837,0.00043111216,0.0005364911,0.00040677702,0.0041843075],"category_scores_gemma":[0.0005871399,0.0002574172,0.0002698787,0.0002510412,0.00018046454,0.0007513966,0.00041547022,0.0008346941,0.0010181546],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028339075,0.00012718113,0.0012829145,0.0001277688,0.00010871058,0.0001411955,0.000094319854,0.23186438,0.089809015,0.016801063,0.00706938,0.65229064],"study_design_scores_gemma":[0.0000065372674,0.000040436484,0.00047036985,0.0000051419906,0.000014398738,0.000028979432,0.000013383344,0.97719646,0.015824988,0.00434587,0.0020465858,0.000006872533],"about_ca_topic_score_codex":0.002344002,"about_ca_topic_score_gemma":0.0074629355,"teacher_disagreement_score":0.0041843075,"about_ca_system_score_codex":0.0002522649,"about_ca_system_score_gemma":0.00046630524,"threshold_uncertainty_score":0.013997912},"labels":[],"label_agreement":null},{"id":"W789343280","doi":"10.1109/mnrc15848.2009.5338938","title":"Trigonometric function approximation neural network based coprocessor","year":2009,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Coprocessor; Interpolation (computer graphics); Computer science; Lookup table; Artificial neural network; Trigonometric functions; Function approximation; Trigonometry; Function (biology); Parallel computing; Algorithm; Computer hardware; Computational science; Artificial intelligence; Mathematics; Operating system","score_opus":0.016404453087664844,"score_gpt":0.2297679592082092,"score_spread":0.21336350612054436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W789343280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048080467,0.0006726454,0.91195905,0.0002698517,0.00037044927,0.00026076296,0.00035693974,0.010030831,0.02799907],"genre_scores_gemma":[0.53041387,0.0006681195,0.4268818,0.00034449948,0.00012221251,0.00034717395,0.00067215407,0.0003269678,0.040223215],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997507,0.000026763882,0.000016628694,0.000072299576,0.00010660898,0.000026935006],"domain_scores_gemma":[0.9997012,0.00004730962,0.00002610409,0.000057254252,0.00015288315,0.000015156463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019542167,0.0005045357,0.00043831498,0.00044379575,0.00027962492,0.0008418755,0.0010542836,0.00039025754,0.015753087],"category_scores_gemma":[0.00091950153,0.00016723412,0.0002092812,0.000773589,0.00019207524,0.0005084126,0.00023848146,0.0006397878,0.005007972],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062010007,0.00023526872,0.0014388006,0.0004677887,0.00008021473,0.00056811614,0.00015206615,0.0929716,0.1126586,0.021391585,0.02519814,0.74421775],"study_design_scores_gemma":[0.00006328663,0.00034057524,0.0015126865,0.00006195221,0.000052353604,0.0005662559,0.00003394002,0.87698215,0.087260425,0.0037607234,0.029315477,0.000050095176],"about_ca_topic_score_codex":0.0027041286,"about_ca_topic_score_gemma":0.0037884489,"teacher_disagreement_score":0.015753087,"about_ca_system_score_codex":0.00059193315,"about_ca_system_score_gemma":0.00071877707,"threshold_uncertainty_score":0.052699327},"labels":[],"label_agreement":null},{"id":"W822184926","doi":"10.71781/20750","title":"Bootstrapping high frequency data","year":2013,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Montréal","keywords":"Bootstrapping (finance); Data science; Computer science; Econometrics; Mathematics","score_opus":0.0902622999830452,"score_gpt":0.3458831475657818,"score_spread":0.2556208475827366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W822184926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01708417,0.0005216488,0.978679,0.00031296286,0.0002095458,0.00008021684,0.0005063786,0.0006492147,0.0019569574],"genre_scores_gemma":[0.3501526,0.0013330225,0.63608176,0.00045489514,0.000714387,0.000725848,0.0035069906,0.00063551747,0.006394915],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99479526,0.00249944,0.0003010362,0.0008301442,0.001351082,0.0002231307],"domain_scores_gemma":[0.9812986,0.012403769,0.00074596883,0.0036052165,0.001744711,0.00020160644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064666476,0.0008470156,0.0012890729,0.0025245743,0.0008288228,0.0023073447,0.0017926319,0.0015907828,0.0067084148],"category_scores_gemma":[0.05555947,0.00067993364,0.0017418291,0.0026106816,0.00092633103,0.0026989419,0.0019842745,0.0023954269,0.0033507675],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056136213,0.0001837001,0.014136236,0.0007963774,0.00047000955,0.0007847283,0.0010236173,0.18822865,0.010664989,0.13946265,0.013177561,0.6305101],"study_design_scores_gemma":[0.00008096805,0.00011827657,0.0058733383,0.00022469001,0.000071577495,0.00043840802,0.00026541538,0.8113317,0.005180804,0.14714502,0.02918731,0.000082618564],"about_ca_topic_score_codex":0.001894686,"about_ca_topic_score_gemma":0.00186731,"teacher_disagreement_score":0.0067084148,"about_ca_system_score_codex":0.0006055217,"about_ca_system_score_gemma":0.00087994366,"threshold_uncertainty_score":0.034199297},"labels":[],"label_agreement":null},{"id":"W82853341","doi":"10.1007/978-1-4614-5608-7_8","title":"Chaotic Neural Network and Multidimensional Data Analysis in Biometric Applications","year":2012,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Artificial neural network; Chaotic; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.05149273775705606,"score_gpt":0.2747798259903898,"score_spread":0.22328708823333374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W82853341","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071797944,0.13593154,0.69697154,0.002387891,0.0024616278,0.00008828855,0.0004305882,0.0008209601,0.15372778],"genre_scores_gemma":[0.11609372,0.13920549,0.29631963,0.0010342405,0.0026264184,0.0002483902,0.00074382155,0.0005237333,0.4432046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998392,0.000021359543,0.0000114922,0.000029247594,0.000090735775,0.000007975575],"domain_scores_gemma":[0.99981254,0.00009656454,0.000012300828,0.000027123198,0.0000456704,0.0000058606474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022834746,0.0005462919,0.0005821585,0.00090665603,0.00023093392,0.0010338564,0.0004777195,0.0008187672,0.009224554],"category_scores_gemma":[0.00074856565,0.0002795348,0.0002595561,0.0018614482,0.00055598293,0.0016394749,0.0005958064,0.0008358043,0.0037747247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003970667,0.000031085914,0.00034869878,0.0010141286,0.00003471127,0.0001623474,0.00018096242,0.015959261,0.009120766,0.14185928,0.051272493,0.77997655],"study_design_scores_gemma":[0.000009083231,0.00007528515,0.001815645,0.00044350873,0.000050723687,0.0013070476,0.0001246961,0.13372158,0.008765572,0.2412536,0.61235595,0.000077287616],"about_ca_topic_score_codex":0.0005667077,"about_ca_topic_score_gemma":0.00088085636,"teacher_disagreement_score":0.009224554,"about_ca_system_score_codex":0.00045503926,"about_ca_system_score_gemma":0.00031115703,"threshold_uncertainty_score":0.030859232},"labels":[],"label_agreement":null},{"id":"W836619539","doi":"10.1007/978-3-319-18305-3_2","title":"Computational Learning Theory","year":2015,"lang":"en","type":"book-chapter","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Cognitive science; Psychology","score_opus":0.036590412806971305,"score_gpt":0.25773278749733974,"score_spread":0.22114237469036843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W836619539","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012845384,0.051615693,0.15244885,0.0052908994,0.003335861,0.000052533283,0.00040397365,0.00055423356,0.78501344],"genre_scores_gemma":[0.04610079,0.04704165,0.04368335,0.0021263387,0.0035654905,0.00021402372,0.00090384,0.0006779211,0.8556865],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99964476,0.00006982225,0.000011443566,0.000063024076,0.00018830752,0.000022670905],"domain_scores_gemma":[0.9997547,0.000116675554,0.000010331849,0.000053561813,0.00005021159,0.000014495539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003741359,0.0011872421,0.0008458731,0.0011162662,0.0006279593,0.0021787393,0.0008734288,0.00086999475,0.03175141],"category_scores_gemma":[0.0012277804,0.00038006002,0.00040310842,0.0014783759,0.0017973781,0.002355222,0.0011618336,0.002389545,0.016295651],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064955516,0.000020620271,0.000074120544,0.00022098527,0.000016410935,0.000018988821,0.00009782661,0.0027219292,0.00027560585,0.58980495,0.19339113,0.21335104],"study_design_scores_gemma":[0.0000028850116,0.000005883397,0.00011800542,0.00015788745,0.000005588443,0.00004300358,0.000024659532,0.0030881718,0.00021062998,0.5631032,0.43323162,0.000008451755],"about_ca_topic_score_codex":0.0011622106,"about_ca_topic_score_gemma":0.0023914408,"teacher_disagreement_score":0.03175141,"about_ca_system_score_codex":0.0014615114,"about_ca_system_score_gemma":0.0011776502,"threshold_uncertainty_score":0.10621911},"labels":[],"label_agreement":null},{"id":"W94369806","doi":"10.3233/ica-2007-14204","title":"A genetic feature weighting scheme for pattern recognition","year":2007,"lang":"en","type":"article","venue":"Integrated Computer-Aided Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University","keywords":"Weighting; Pattern recognition (psychology); Scheme (mathematics); Feature (linguistics); Artificial intelligence; Computer science; Mathematics; Medicine","score_opus":0.012269318360034583,"score_gpt":0.21653867952178477,"score_spread":0.20426936116175018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W94369806","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055889958,0.00010313,0.9932626,0.00002747002,0.000034320794,0.000028662482,0.000022165428,0.00024998968,0.00068281294],"genre_scores_gemma":[0.14639236,0.00017150295,0.84930503,0.0000831064,0.000035735848,0.00011442747,0.00018869051,0.00006929147,0.0036398235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996026,0.000055912627,0.000025841447,0.000100613084,0.00019042692,0.000024540972],"domain_scores_gemma":[0.9997099,0.000057978727,0.00002640603,0.000069661655,0.00012199747,0.000013973727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042254958,0.0003585694,0.00043050334,0.0006172248,0.00025663106,0.000374231,0.00093010784,0.0005085774,0.0013223813],"category_scores_gemma":[0.0009799555,0.00014852673,0.00041239517,0.0007978027,0.00036130595,0.00069774874,0.00035240402,0.0004772377,0.00054617936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007506573,0.00006963897,0.0006644252,0.00009197772,0.00005591445,0.0000621538,0.000056796594,0.06287076,0.09395245,0.021075435,0.0020788105,0.81894666],"study_design_scores_gemma":[0.000028229637,0.0001807175,0.0013498088,0.000020017314,0.000050031525,0.00025678842,0.000014938355,0.92883664,0.038870312,0.0137676485,0.01657696,0.00004786158],"about_ca_topic_score_codex":0.002094207,"about_ca_topic_score_gemma":0.0019327722,"teacher_disagreement_score":0.002094207,"about_ca_system_score_codex":0.00040794548,"about_ca_system_score_gemma":0.00034318908,"threshold_uncertainty_score":0.0044238567},"labels":[],"label_agreement":null},{"id":"W951462336","doi":"","title":"Making Some Sense Out of the Near Flat ERG With the Continuous Wavelet Transform Approach","year":2008,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Erg; Continuous wavelet transform; Sense (electronics); Wavelet transform; Wavelet; Computer science; Mathematics; Discrete wavelet transform; Artificial intelligence; Optics; Physics; Engineering; Electrical engineering; Retina","score_opus":0.05849441077053362,"score_gpt":0.30650830367510945,"score_spread":0.24801389290457582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W951462336","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017047005,0.0075010867,0.9399356,0.017163906,0.0038739243,0.000054987748,0.00018121704,0.00059479586,0.01364748],"genre_scores_gemma":[0.37933922,0.01863813,0.56735784,0.0101561025,0.007678704,0.00017774194,0.00031540278,0.0010159827,0.015320935],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993808,0.00019148155,0.000056416833,0.00013106116,0.00019674197,0.00004343615],"domain_scores_gemma":[0.99825984,0.0008335678,0.00011662403,0.0004562246,0.00022169884,0.00011201642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027511036,0.0014765039,0.0014986835,0.0011733387,0.00083516823,0.0033454548,0.0016044528,0.0023741573,0.003224487],"category_scores_gemma":[0.0075401044,0.00053418626,0.0010237552,0.0006251262,0.004631773,0.011124651,0.003390629,0.0062767183,0.0010695257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041033575,0.000101691796,0.0017024286,0.00072727335,0.0002534395,0.0019992122,0.0011057897,0.011212041,0.035948128,0.59264916,0.016686527,0.33720407],"study_design_scores_gemma":[0.000042491996,0.000108495726,0.0011242441,0.00017172715,0.00006060956,0.0009986192,0.00040716297,0.041091885,0.0030304336,0.93542033,0.017458837,0.00008520599],"about_ca_topic_score_codex":0.0009130586,"about_ca_topic_score_gemma":0.00084716047,"teacher_disagreement_score":0.0033454548,"about_ca_system_score_codex":0.00035939747,"about_ca_system_score_gemma":0.0006604597,"threshold_uncertainty_score":0.014549375},"labels":[],"label_agreement":null},{"id":"W957392032","doi":"","title":"Corticotectal neurons in the primate prefrontal cortex carry rule-related signals for pro-saccades and anti-saccades","year":2005,"lang":"en","type":"article","venue":"Acta Neurobiologiae Experimentalis","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Neuroscience; Prefrontal cortex; Primate; Psychology; Carry (investment); Biology; Cognition","score_opus":0.02042867240788072,"score_gpt":0.2846842616947416,"score_spread":0.2642555892868609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W957392032","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98550355,0.00036167775,0.009694227,0.00021566158,0.000045119716,0.000010520058,0.0002722739,0.00016171433,0.003735305],"genre_scores_gemma":[0.99390197,0.0002056631,0.0036151533,0.000055775225,0.00001598935,0.000008068378,0.0001756575,0.000036973124,0.0019846782],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999895,0.0000073419765,0.000003184433,0.00002456843,0.00003427482,0.000035536425],"domain_scores_gemma":[0.9994778,0.00017203967,0.00009217371,0.000054100467,0.00013295995,0.00007095067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015757323,0.00018162199,0.0002307891,0.00026346004,0.00028417393,0.0006912325,0.00027492267,0.000479967,0.0013624959],"category_scores_gemma":[0.0017126137,0.0002384121,0.0002130303,0.00023103345,0.00029238325,0.00047440187,0.00029380247,0.00044909428,0.00029263648],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002525612,0.000023293082,0.0094178375,0.00007547793,0.000037819907,0.0001966943,0.00018981847,0.0008410241,0.93970203,0.0014422,0.00044601876,0.047375116],"study_design_scores_gemma":[0.000119748656,0.0003053921,0.7115562,0.000045161196,0.00015449703,0.0014685036,0.0005359408,0.0233354,0.24937108,0.009295685,0.0037529569,0.000059442074],"about_ca_topic_score_codex":0.0046252967,"about_ca_topic_score_gemma":0.008188301,"teacher_disagreement_score":0.0046252967,"about_ca_system_score_codex":0.0004252282,"about_ca_system_score_gemma":0.0003592642,"threshold_uncertainty_score":0.009196758},"labels":[],"label_agreement":null},{"id":"W95852415","doi":"10.1007/3-540-32390-2_1","title":"Neural Network-Based Chaotic Pattern Recognition — Part 2: Stability and Algorithmic Issues","year":2008,"lang":"en","type":"book-chapter","venue":"Advances in soft computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Chaotic; Eigenvalues and eigenvectors; Property (philosophy); Artificial neural network; Stability (learning theory); Computer science; CHAOS (operating system); Chaotic systems; Set (abstract data type); Control theory (sociology); Algorithm; Artificial intelligence; Control (management); Machine learning; Physics","score_opus":0.03146062181736194,"score_gpt":0.2641132686177948,"score_spread":0.23265264680043288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W95852415","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0553818,0.028027512,0.8503944,0.0029326377,0.00084513664,0.00008610579,0.00017213433,0.0003029337,0.061857313],"genre_scores_gemma":[0.8095842,0.018175824,0.1189212,0.00039985136,0.0014814347,0.00021102744,0.00031347602,0.00029466974,0.05061828],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985087,0.00002970263,0.000013876542,0.00003651849,0.000058967507,0.000010069965],"domain_scores_gemma":[0.9994597,0.00032915847,0.000056109686,0.000059218837,0.00008361778,0.000012169303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039863278,0.00040021696,0.0005562736,0.00043680985,0.0002920612,0.0013862316,0.0007257972,0.00079128373,0.0039826087],"category_scores_gemma":[0.0022986794,0.00033735635,0.00041301915,0.000771169,0.0012074491,0.0020860378,0.00062045985,0.00089976564,0.00063771824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001089882,0.000048787322,0.0011491842,0.0006408599,0.00009054364,0.00012951103,0.0002227794,0.21362028,0.01594448,0.50510615,0.008572295,0.25436622],"study_design_scores_gemma":[0.0000075128137,0.000039869537,0.0008391586,0.000051462528,0.000015044933,0.0001704495,0.000027906994,0.7119509,0.003618958,0.27568904,0.0075637233,0.000025967962],"about_ca_topic_score_codex":0.0005832073,"about_ca_topic_score_gemma":0.0004623113,"teacher_disagreement_score":0.0039826087,"about_ca_system_score_codex":0.0005994036,"about_ca_system_score_gemma":0.00026047835,"threshold_uncertainty_score":0.013323128},"labels":[],"label_agreement":null},{"id":"W963797112","doi":"10.1017/cbo9780511841453.014","title":"Signals and signal processing","year":2010,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; SIGNAL (programming language); Content (measure theory); Signal processing; Digital signal processing; Computer hardware; Mathematics","score_opus":0.02059616513601432,"score_gpt":0.2003266058077357,"score_spread":0.1797304406717214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W963797112","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001741011,0.19812652,0.28806424,0.0064366055,0.020308219,0.00021935135,0.0027138987,0.0049600718,0.47743014],"genre_scores_gemma":[0.016623594,0.13092384,0.05310988,0.0025833831,0.007934097,0.00021755099,0.003481144,0.0013100099,0.7838165],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951696,0.00005984374,0.000027424552,0.00009793752,0.00026832026,0.000029511753],"domain_scores_gemma":[0.9994733,0.00019689513,0.000020123312,0.00009825017,0.00017465214,0.000036844307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005762044,0.0012614959,0.0010536535,0.0016420573,0.00044237848,0.0031026895,0.00092924724,0.0016314348,0.15144809],"category_scores_gemma":[0.001452723,0.00040253298,0.0005591092,0.0021910248,0.0009180562,0.0022096396,0.00092080294,0.002188261,0.12692584],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050843522,0.00003404071,0.00011019833,0.0013041929,0.000049538292,0.00014656982,0.00010347062,0.0011698896,0.0057034115,0.044084463,0.4581638,0.48907965],"study_design_scores_gemma":[0.000005528962,0.000051137977,0.00033593026,0.00023267201,0.000012830994,0.00046473977,0.00003980131,0.0017376394,0.0013860244,0.02949841,0.9662184,0.000016701273],"about_ca_topic_score_codex":0.00062507857,"about_ca_topic_score_gemma":0.00073267194,"teacher_disagreement_score":0.15144809,"about_ca_system_score_codex":0.0005180654,"about_ca_system_score_gemma":0.0005881702,"threshold_uncertainty_score":0.5066443},"labels":[],"label_agreement":null},{"id":"W973008471","doi":"10.1007/978-3-642-24797-2_9","title":"Hierarchical Subsampling Networks","year":2012,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Toronto","funders":"","keywords":"Sequence (biology); Computer science; Set (abstract data type); Task (project management); Artificial neural network; Range (aeronautics); Function (biology); Artificial intelligence; Algorithm; Theoretical computer science; Engineering","score_opus":0.1371299284295775,"score_gpt":0.3634463853533317,"score_spread":0.22631645692375418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W973008471","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008945262,0.0055057188,0.95827925,0.0005276897,0.00031045103,0.000039623443,0.00016087842,0.0008401962,0.025390917],"genre_scores_gemma":[0.2524106,0.005986674,0.654539,0.0006961878,0.0010482789,0.00017748318,0.001363721,0.0007232993,0.083054855],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996517,0.000096567324,0.00001360341,0.000090185495,0.00012036889,0.000027640126],"domain_scores_gemma":[0.9993476,0.0003069843,0.00003097904,0.0001796367,0.000103238264,0.0000314578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008449567,0.0006308796,0.00088212924,0.00066228455,0.00041685693,0.0009979265,0.0010070427,0.0007710691,0.007248771],"category_scores_gemma":[0.0022784167,0.00044194327,0.0004924792,0.0009122624,0.00083035993,0.0014837824,0.0009877092,0.0013907546,0.001957532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009159669,0.000046707315,0.0004847036,0.0001457736,0.00008957149,0.00006532076,0.00011023389,0.08315254,0.0055873487,0.3402408,0.022733908,0.5472515],"study_design_scores_gemma":[0.000010278711,0.000042936914,0.0007679423,0.000032381286,0.000041646137,0.00011128144,0.00002222119,0.7049526,0.0036036368,0.2609703,0.029423222,0.000021601052],"about_ca_topic_score_codex":0.0021050621,"about_ca_topic_score_gemma":0.0037697342,"teacher_disagreement_score":0.007248771,"about_ca_system_score_codex":0.0006916664,"about_ca_system_score_gemma":0.00037734985,"threshold_uncertainty_score":0.024249494},"labels":[],"label_agreement":null},{"id":"W99440483","doi":"","title":"Types and forms of knowledge (patterns): neural networks","year":2002,"lang":"en","type":"book","venue":"Oxford University Press eBooks","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Scope (computer science); Artificial neural network; Context (archaeology); Computer science; Representation (politics); Artificial intelligence; Data science; Geography; Political science","score_opus":0.018468097660307676,"score_gpt":0.19950885922866576,"score_spread":0.18104076156835808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W99440483","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066127167,0.10555733,0.3974952,0.0109900385,0.0027735354,0.00017996298,0.0018553085,0.0014443618,0.4730915],"genre_scores_gemma":[0.18214989,0.16544162,0.3747894,0.0039878353,0.0032520455,0.00079652795,0.0042882035,0.0008037706,0.26449075],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99942386,0.00012951152,0.000055245553,0.00013514607,0.00022613269,0.000030154448],"domain_scores_gemma":[0.99931276,0.00038684453,0.000056396206,0.0001249969,0.00008964497,0.00002937993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005513287,0.00087683095,0.00053642795,0.0029234122,0.0005205751,0.0056609334,0.0013303738,0.0012452684,0.016703583],"category_scores_gemma":[0.0028316493,0.00055140123,0.0005152647,0.0072821314,0.002503123,0.011636755,0.001492747,0.0014410965,0.0057516885],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003874693,0.000018024222,0.00025550195,0.0006855351,0.000026252315,0.00013449897,0.00031325556,0.0019367252,0.00069352594,0.62935215,0.040111326,0.32643446],"study_design_scores_gemma":[0.000005587516,0.000009275049,0.00025523177,0.00038127508,0.000015268888,0.00048363884,0.00015114788,0.004612534,0.00059009244,0.7186036,0.27487698,0.000015298914],"about_ca_topic_score_codex":0.00089988403,"about_ca_topic_score_gemma":0.0010178166,"teacher_disagreement_score":0.016703583,"about_ca_system_score_codex":0.0011194109,"about_ca_system_score_gemma":0.0005210236,"threshold_uncertainty_score":0.055879056},"labels":[],"label_agreement":null}]}